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ISBN print: 978-3-0364-0997-9
ISBN eBook: 978-3-0364-1997-8
DOI book: 10.4028/b-DSI3dY
© 2026 Trans Tech Publications Ltd, All Rights Reserved

Additive Manufacturing

Edited by

Prof. Dermot Brabazon

Additive Manufacturing

Special topic volume with invited peer-reviewed papers only

Edited by
Prof. Dermot Brabazon

Scientific.Net

Copyright © 2026 Trans Tech Publications Ltd, Switzerland

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Volume 1047 of
Key Engineering Materials
ISSN print 1013-9826
ISSN web 1662-9795

Full text available online at https://www.scientific.net/book/additive-manufacturing/978-3-0364-1997-8

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp -1-0

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Accepted:
2026-04-08

Online:
2026-04-13

Preface

The original version of this paper is available on https://www.scientific.net/KEM.1047.-1.pdf

The aim of 'Additive Manufacturing' is to discuss the optimisation, capabilities and development within Additive Manufacturing.

The pre-process, in-situ process, and post process aspects of additive manufacturing are important to characterise and understand well how these impact on the final produced part properties.

Experimental, fundamental modelling and data analytic developments in relation to additive manufacturing are considered within this mini-symposium.

Characterisation of aspects of additive manufacturing materials engineering, production quality and control will be discussed within this issue.

Editor

The original version of this paper is available on https://www.scientific.net/KEM.1047.-1.pdf

Dermot Brabazon
Dublin City University

Co-Editors

The original version of this paper is available on https://www.scientific.net/KEM.1047.-1.pdf

Table of Contents

Preface

Rib Geometry in FDM of Light Alloys

Carlo Bruni

Machine Learning Topography Prediction and Optimization in Maskless Grayscale Laser Lithography

Adrian H.A. Lutey, David Kuhness, Seyyedhossein Mckee, Marco Negozio, Markus Postl, Barbara Stadlober

A Comparative Analysis of LPBF Process Maps for S2 and S6 Grades Tool Steel

Alessandra Segatto, Enrico Saggionetto, Dedry Olivier, Jérôme Tchoufang Tchuindjang, Anne Mertens

Laser Powder Bed Fusion of INCONEL® 625: Guidelines for Robust Parameter Selection toward Optimal Quality Parts

Gabriele Locatelli, Mariangela Quarto, Sara Bocchi, Cristian Cappellini, Gianluca D'Urso

Numerical Modelling of Directed Energy Deposition and Experimental Validation Based on Digital Image Correlation

Dejan Kovšca, Bojan Starman, Edvard Govekar, Miroslav Halilovič, Nikolaj Mole

Advanced Characterization of Flexible Auxetic Resin Lattice Structures Produced by Stereolithography

Stefano Belcuore, Stefano Pandini, Elisabetta Ceretti, Abdullah Al Maharbi, Mohammed M. Al-Hinaai, Paola Ginestra

Evaluation of SLA Biocompatible Resins Properties for Use as Dielectric Layers in 3D Printed Capacitive Force Sensors

Alessandro Epicoco, Tiziano Fapanni, Stefano Pandini, Paola Serena Ginestra, Elisabetta Ceretti, Emilio Sardini, Miriam Seiti

A Physics Informed Machine Learning Framework towards Density Prediction of Additively Manufactured Components

Abhilash Puthanveettil Madathil, Olga Bylya, Andrew Sherlock

Investigation of WAAM-PAW Fabricated 316L Steel/Invar 36 Nb Alloys for the Development of Functionally Graded Materials

Lucas Weber, Anis Hor, Leon Ratsifandrihana, Mathilde Alcaraz

Exploring the Feasibility of Incremental Forming Applied to 3D Printed PEEK Sheets

Ines Ferrer, María Luisa García-Romeu, Gabriel Centeno, Ana Rosa-Sainz, Joaquim de Ciurana

Numerical Study of the Flexural Behaviour of Additively Reinforced Blanks

Emanuele Fulco, Pasquale Guglielmi, Angela Cusanno, Donato Sorgente

Implementing Inkjet Printing to Manufacture Piezopolymer Films for Sensing Applications

Vasileios Stratiotou-Efstratiadis, Apostolos Argyros, Georgios Sarmas, Giannis Oikonomou, Dimitrios Dimitriou, Nikolaos Chrysochoidis, Dimitris Saravanos, Georgios Maliaris, Vasileios Mpinas, Nikolaos Michailidis

Effect of Hatch Distance and Scanning Speed Combinations at Constant Volumetric Energy Density on the Materials Properties of IN939 Fabricated by Powder Bed Fusion-Laser Beam

Merve Nur Dogu, Meris Meric Ikiz, Gavin Chapman, Sampreet Rangaswamy, Merve Yesim Yalcin, Seren Ozer, Kemal Davut, Dermot Brabazon

Integrating Topology Optimization and Lattice Infilling for Lightweight Aircraft Bracket Design via Additive Manufacturing

Numan Khan, Fatima Ghassan Alabtah, Marwan Khraisheh

Development and Characterization of 3D-Printed PETG Specimens Embedding Continuous Carbon Fiber Strain Sensors

Imi Ochana, François Ducobu, Thomas Rainchon, Anthonin Demarbaix

Development of 3D-Printed Prototype Moulds for Thermoforming

Jan Rösler, Joerg Hornig-Klamroth, Leon Dähn

Sustainable Electrochemical Machining of Additively Manufactured Nitinol with Deep Eutectic Solvents

Alessia Serena Perna, Fabio Scherillo

Effect of Deposition Strategy on Geometric Stability in Wire Arc Additive Manufacturing of Inconel 625

Eleonora Viola, Alessia Teresa Silvestri, Antonino Squillace

Volumetric Anomaly Detection in LPBF by Segmentation and Classification of Exposure Optical Tomography (EOT) Image Stacks

Karsten Scheibe, Johann Albers, Felix Jensch, Chukwuemeka Okolo, Sebastian Härtel

Thermal Measurement During High-Velocity Oxy-Fuel Coating Process for Single and Multiple Pass Rotational Spraying

George Diyoke, Yitong Yang, Christoph Schindelbacher, Sebastian Sperling, Jürgen Angerler, Sebastian Härtel

Optimal Design and Additive Manufacturing of Polymeric Metamaterials for Energy Absorption and Impact Mitigation

Victor Daniel Fachinotti, Erik Andreassen, Sylvain Gouttebroze

Feasibility of Direct 3D Printing Foam Polylactic Acid with MEX

Batagiannis Athanasios, Panagiotis Karmiris-Obratański, Ioannis T. Christodoulou, Emmanouil Lazaros Papazoglou, Angelos P. Markopoulos

Development of AMC Welding Wire and Inline Hot Rolling for Enhanced Properties of DED-Arc/Wire-Manufactured Parts

Tim Sebastian Tübbicke, Fabian Dittrich, Sebastian Härtel

Sensitivity Analysis and Calibration of the Heat Source in Additive Manufacturing of AINiCo Magnets

Victor Daniel Fachinotti, Sylvain Gouttebroze, Amin S. Azar

Coconut Fibre Reinforced Photopolymer Composites via Stereolithography: Feasibility and Mechanical Performance

Valeria Palomba, Antonio Fotia, Giulia Palomba, Pasqualino Corigliano, Mohamed Chairi, Guido Di Bella

Influence of Local Laser Power Distribution and Build-Plate Temperature on Porosity and Microstructure in Laser Powder Bed Fusion (LPBF)

Chukwuemeka Okolo, Sebastian Härtel

Influence of L-PBF Process Anisotropy on the Isotropic Behaviour of TPMS Structures

Anastasia Ciccarella, Veronica Colaiuda, Giuseppe Dell'Avvocato, Daniele Cortis, Donato Orlandi, Luca di Angelo, Edoardo Mancini

3D Printing of Continuous Carbon Fiber-Reinforced Polymer Tee Pipe: Strategy Development and Demonstrator Fabrication

Mattes Rix, Zsolt Kallai, Johann Kipping, Thorsten Schüppstuhl

Thermomechanical Modelling and Shape Prediction in 4D Printing Using FEA

Tiago Andrade, Mylene Cadete, João Dias-de-Oliveira

Advanced FE Modeling for Predicting Component Properties in Additive Manufacturing

Lennart Grüger, Tim Sebastian Tübbicke, Huan Po Huang, Sebastian Härtel

Measurement of Forces Generated during Robotized Additive Manufacturing Process Using Pellet Extruder

Salah Eddine Ouassil, Pascal Casari, Élodie Paquet, Guillaume Racineux

Anisotropy and Stress-State-Dependent Fracture in Additively Manufactured Metals

Ehsan Amini, Guijia Li, Sven Bossuyt, Junhe Lian

Effect of Preheating Temperature on the Optimal Processing Windows of Inconel 718 Processed by Laser Power Bed Fusion

Jérôme Tchoufang Tchuindjang, Edoardo Fassinato, Olivier Dedry, Anne Mertens

Effects of Current Density during Electrically Assisted Friction Stir Additive Manufacturing Hole Repair of AA 7075 on a Conventional Machine

Laura L. Catalano, Cordelia M. Norris, Elizabeth M. Mamros, John T. Roth

Comparison between 2D and 3D Thermal Finite Element Models of Directed Energy Deposition

Calogero Gallo, Samuel Van Hulle, Jérôme Tchoufang Tchuindjang, Anne Mertens, Laurent Duchene, Anne Marie Habraken

Additive Manufacturing of Automotive Metal Multi-Material Shunt Resistor: Cost and Carbon Footprint Analysis

Nikolaos D. Alexopoulos, Vasileios Zeimpekis, Evangelos Vasileiou, Theodoros Souxes, Ilona Lazaridou, Leonard Alberty, Ismail Uensal, Georg Schlick

Resistance to Fracture of Additively Manufactured Aluminium Alloy Alsi10Mg under Plane Stress Conditions

Ioannis Goulas, Alexis Kermanidis, Leonard Alberty, Ismail Ünsal, Georg Schlick, Sven Gründer, Dennis Lenhert, Florian Hengsbach, Nikolaos D. Alexopoulos

Effect of CuCr1Zr Cross-Contamination Level on Work Hardening Characteristics of AISi 10Mg Additive Manufactured Material

Ioanna Giavrouta, Max Horn, Leonard Alberty, Ismail Ünsal, Georg Schlick, Nikolaos D. Alexopoulos

Keywords Index

Authors Index

The original version of this eBook is available on https://www.scientific.net/book/additive-manufacturing/978-3-0364-1997-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 1-9

doi: 10.4028/p-Ye5u9o

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-11-11

Revised:
2026-02-11

Accepted:
2026-02-14

Online:
2026-04-13

Rib Geometry in FDM of Light Alloys

The original version of this paper is available on https://www.scientific.net/KEM.1047.1.pdf

Carlo Bruni 1,a*

¹DIISM - Università Politecnica delle Marche, via Brecce Bianche Ancona

a* c.bruni@univpm.it

Keywords: modeling, FDM, rib geometry.

Abstract

The present paper got the objective to propose and apply a methodology based on plastic behaviour modeling of a magnesium alloy AZ31 and on a Navier-Stokes approach to describe the rib geometry during printing by FDM (Fused deposition modeling). By the plastic modeling the rib section in terms of equivalent radius is obtained by the application of an already proposed constitutive equation under semisolid condition. The same information is obtained by the calculation of dynamic viscosity coefficient of the material under different conditions of nominal extruder nozzles that are 0.36 and 0.06 mm in radius with related extrusion velocity and internal pressure. The rib radius obtained by the plastic model is higher when the big nozzle is used compared with that given by the Navier- Stokes approach while an opposite behaviour is evidenced with the small nozzle where the apparent viscosity is higher. Increasing printing velocity similar rib dimensions are obtained in both cases.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.1.pdf

In the field of additive manufacturing the extrusion base techniques are very useful to realize the discretization of the material by which to produce complex shapes (double curvature surfaces, connections between curved and planar parts, multiple faceted surfaces, etc.) [1]. In fact high is the discretization, meaning low thickness values, high is the complexity of the geometry to be potentially generated. This is possible by obtaining very thin layers to be applied in any direction at working speed as high as possible comparable with that obtained during idle motion [1-4].

Such technologies known in the field of semisolid materials as FDM (Fused Deposition Modeling) can be applied successfully directly with metals when the material behaviour is known and correctly described [5-9] instead of more classic deposition modeling of bound particles and sintering [1]. The significant variable is in general represented by the temperature and by the pressure inside the extruder allowing the material flow [5-11]. Both of such variables can be tuned in order to represent a positive effect on the material deposition on the platform. Other variables are in general the geometry of the extruder and printing machine parameters [9-11].

Some authors report applications on titanium, magnesium and aluminium alloys in order to realize prismatic objects starting by the material knowledge under semisolid conditions [2-4,6,7,11]. They observed a overall decrease in internal stress with increasing temperature due to semisolid condition achieved. Similar results were obtained by the author in previous works on magnesium alloys [7,12] once approaching high strain rates leading to a faster achievement of semisolid condition due to heat generated. Even if the initial stress levels could appear naturally higher than those described.

This behaviour promotes high production rates also in prototyping phase in order to shorten the design stage by high printing velocities together with the discretization useful to manage very thin layers and complex surfaces. These objectives to be achieved at the same time in the field of light metal alloys need a modeling phase with which to describe the material also under the conditions of high pressure inside the extruder with micro-dimension diameters [9,11,13].

Many authors [8,9,11,14,15] approach the solution of the problem to describe FDM to get very thin layers by different methods based on analytics, on finite element simulation by different explicit/implicit algorithms, on discrete element methods, mesh less, discrete events, etc. Up today the analytical modeling represents the fastest and save costing way to approach the problem even if under defined hypotheses.

The framework of the reported field can be updated with the following proposed methodology based on analytical modeling in which two different approaches are considered. The one based on extrusion plastic modeling in which the constitutive equation of a AZ31 [12] magnesium alloy is adopted in order to describe high strain rate semisolid ribs obtained with deformation and friction heating contributions evaluated in previous works under similar or less heavy conditions [7] starting by a temperature of 400∘C. The other one based on Navier-Stokes fluid dynamic equation proposed in a form to get the dynamic viscosity coefficient under semisolid by which to calculate the rib thickness. The obtained results at under millimeter rib dimension are similar to each other when printing velocities increase.

Modeling Procedures

The original version of this paper is available on https://www.scientific.net/KEM.1047.1.pdf

The methodology reported in Fig. 1 consists in two modeling approaches based on flow behaviour knowledge of material updated to consider semisolid condition in one case and in a fluid dynamics approach based on Navier-Stokes equation to calculate the dynamic viscosity coefficient. The material bahaviour is described in order to get the thickness of the layer function of different variables.

Modeling by plastic behaviour of material is applied by an analytical model of extrusion considering the slab method when the semiconical angle approaches 90∘. In the described condition material flow promotes 45∘ angle under high temperatures at solid state. That is the conical part of extruding tool as reported in previous works by the author [7] where friction, deformation and distortion heating produce a rapid increase in temperature by the 400∘C initially tuned. The conditions are similar to those reported in the cited work, in which a initial radius of 0.6 is deformed to the nominal 0.3 , completed with a study concentrated on the initial radius of 0.36 mm deformed to 0.06 . The solid/liquid fraction is considered under similar hypotheses. The rib section dimensions obtained by the modeling of the plastic behaviour and the one by the fluid dynamics approach are compared to each other.

[picture]

Fig. 1. Modeling procedure.

Fig. 1. Modeling procedure.

When applying the first model the stress amount is calculated with the following equation:

σZ=2·σ¯·ln(R0Rf)R02Rf(2·R0−Rf)·exp(2μR0Δldef)(1)

where:
σz : tension at the stem of the container
σ : material flow
μ : friction coefficient
R0 : initial billet radius
Rf :final billet radius
Δl : travel covered by the stem
The total stress amount can be calculated considering the fraction of the solid by respect to one of completely solid. In the formulation the factors of 0.2 and 0.3 for friction and distortion phenomena are implemented [7].
The tension amount in the extrusion chamber is given by:

σextr=2·σ¯·ln(R0Rf)R02Rf(2·R0−Rf)(2)

The flow behaviour of material is reported by the power law based approach [7,12]:

σ¯=K·ε¯n·ε˙m(3)

In which K,n,m are material constants being ε=2·ln(R0/Rf) and ε˙=ε/Δt while 0.1 represents the part to be multiplied in order to model the semisolid state.
Once calculated the absolute stress amount the pressure interval evaluation inside the extruder is reported by the following:

Δσextrusionsolidfraction≈Δp(4)

The already reported formulas allow to determine the equilibrium equation given as follows:

ρ·V· g·Δ h+ρ·V·ΔvyΔtΔ h=σ·Δε·V(5)

Being the vy the velocity along the direction perpendicular to the platform and Δh the shortening of material in form of layer once arrived to the surface of that. The previous equation can be developed in:

ρ·g·Δh+ρ·ΔvyΔtΔh=K·(Δvyh0)m·(Δhh0)n·Δhh0(6)

Considering the density that can be represented as a function of temperature [16,17]

ρ=ρ0−0.23·( T−30)(7)

displacement of the upper surface of the layer at contact with the platform is evaluated in terms of:

Δh=ρ·(ΔvΔt+g)k·(Δv h0)m·(1 h0)n+1n(8)

The results obtained by the proposed analytical modeling are plotted in terms of the difference h=h0 Δh. They are compared with those obtained by the analytical modeling of the second approach. In particular, it is based on Navier-Stokes model described by the following applied equation:

ρΔvΔt+ρvΔvΔy=−ΔpΔy+k(ΔvΔx)2(9)

It can be used in the form of the following:

μ=k′=ρΔv(1Δt+v1Δy)+ΔpΔy(ΔvΔx)2ΔzΔt(10)

to calculate the dynamic viscosity coefficient μ.
In the same equation:
v= velocity along the y axis (parallel to the extruder nozzle)
Δv= velocity variation along the y axis
Δx,Δy,Δz= the dimensions of the volume considered
Δp= pressure variation inside/outside the extruder
By the above equation the shear stress normally reported in terms of τ=μ·γ˙=μ·Δl/(Δy·Δt) gives:

Δl=Δy·Δt·τμΔl′=Δl3

(11)(12)

The values obtained by the described equations allow to evaluate the thickness of the layer by the constant volume hypothesis and then the h=h0−Δh where h0 is described for both the models by the following:

h0=Drib=0.8·vextr·Dextr2·t L(13)

By that value the equivalent radius of the layer thickness is evaluated as r=(h0−Δh)/2 depending also on extruding velocity, nozzle diameter and printing velocity L/t.
The reduction in thickness due to shrinkage can be calculated considering the decrease in temperature until ambient by the following:

rf=α·r·Δ T(14)

With α dilatation coefficient and ΔT temperature variation.

Results

The original version of this paper is available on https://www.scientific.net/KEM.1047.1.pdf

The pressure inside the container in terms of stress can be estimated mainly with eqs. 1 to 3 through the material behaviour knowledge at the solid reported in [7]. It is adapted in order to consider the strain rates typical of the extruding ratios above described approximating hundreds of 1/s with coefficient 0.1 to describe semisolid. Fig. 2 reports the stress trend characterized by an increase at the exit as in more conventional extrusion operations. The obtained values to reach the filament radius of 0.06 mm are higher than those already reported in previous works where the strain rates and extruding ratios were lower.

Obviously when used for load sizing the eq. 1 must be applied at the end of displacement in which the stress values are calculated with eq. 2 at the maximum. Even if such evaluation is not the objective of the investigation being the value at the stem obtained by the value at the beginning of the extrusion chamber.

Instead, the values obtained in the forming chamber can be considered for Δp evaluation with eq. 4 inside the extruder on a length not higher than 0.3 mm . Following the values in terms of stress, Δp can be approximated to about 800 MPa being low the stress amount given by the constitutive equation at semisolid.

[picture]

Fig. 2. Stress behaviour inside the extrusion container for AZ31 magnesium alloy at nominal nozzle radius of 0.06 mm .

Fig. 2. Stress behaviour inside the extrusion container for AZ31 magnesium alloy at nominal nozzle radius of 0.06 mm .

Considering the equilibrium energy of eq. 5 in order to get information by eqs. 6 to 7 the Δh when material drops to the platform surface can be calculated by eq. 8 .

The other method based on viscosity evaluation at extruding velocity of 360 mm/s through an extruding radius of 0.06 mm allows the obtaining 3.60·104 Pa· s. To the purpose eqs. 9 and 10 are used with density of material of eq. 7.

By those the h amount is calculated by the knowledge of Δl being the material deformation under semisolid along the axis parallel to the platform evaluated by eq. 11 and eq. 12 as reported in the modeling procedure.

The results are reported initially in Fig. 3 in terms of section equivalent radius obtained by an half of the actual layer thickness h versus printing speed including the constat flow rate described by the eq. 13. It can be observed a decrease in equivalent radius mainly with printing speed. The effect of cooling in terms of geometry variation can be extremely negligible so that it can be overcome in subsequent threats.

In Fig. 4 the calculation of dynamic apparent viscosity using eq. 10 versus pressure is reported considering the working interval by 800 MPa revealed inside the extruder up to 1000 MPa when a quasi-hydrostatic condition needs to be realized without changes in any other variable. The results substantially agree with that found in [6].

[picture]

Fig. 3. Rib equivalent radius with semiplastic modeling function of printing velocity for nominal nozzle value of 0.06 mm .

Fig. 3. Rib equivalent radius with semiplastic modeling function of printing velocity for nominal nozzle value of 0.06 mm .

[picture]

Fig. 4. Viscosity values at different pressures.

Fig. 4. Viscosity values at different pressures.

Fig. 5 reports the rib equivalent radius vs. printing velocity with values of viscosity obtained by eq. 10 with layer thickness h calculated by the constant volume hypothesis. It is plotted vs. printing velocity considering the material flow dependent on the velocity outside the extruder.

The comparison between the rib behaviour obtained by the plastic model and that given by the Navier-Stoke equation based one is reported in Fig. 6 for nozzle radius of 0.06 mm . The high values
of viscosity obtained as reported in Fig. 3 determine low values of Δ h. In Fig. 7 the values are reported in the case of higher nominal nozzle radius 0.36 mm . The lower values of the rib equivalent radius vs. printing velocity curve obtained by the fluid dynamic model with respect to those of plastic model are due to different apparent viscosities obtained.

Similar behaviours in both extruding ratios are obtained by two models at high printing speed representing the significant variable of the metal FDM under investigation. This means the possibility to obtain high curvatures typical of those required for complex geometries as in conventional polymer additive manufacturing.

[picture]

Fig. 5. Equivalent radius obtained with fluid dynamic modeling function of printing velocity for nominal nozzle radius of 0.06 mm .

Fig. 5. Equivalent radius obtained with fluid dynamic modeling function of printing velocity for nominal nozzle radius of 0.06 mm .

[picture]

Fig. 6. Comparison between plastic and fluid dynamic models in describing rib equivalent radius with extruder nozzle at 0.06 mm .

Fig. 6. Comparison between plastic and fluid dynamic models in describing rib equivalent radius with extruder nozzle at 0.06 mm .

Fig. 6. Comparison between plastic and fluid dynamic models in describing rib equivalent radius with extruder nozzle at 0.06 mm .

Fig. 7. Comparison between plastic and fluid dynamic models in describing rib equivalent radius with extruder nozzle at 0.36 mm .

[picture]

Fig. 8. Comparison between layer radii obtained by semisolid plastic model and fluid dynamic model at extruder diameter of 0.06 mm .

Fig. 8. Comparison between layer radii obtained by semisolid plastic model and fluid dynamic model at extruder diameter of 0.06 mm .

Being the effect of temperature decrease negligible as reported previously due to the requirement to keep temperatures of the extruding system not higher than 600−630∘C in order to maintain the solid fraction of 0.1 according to the melting map [13], rib dimension is mainly dependent on the other variables according to that found in [6,10,18] under similar conditions.

To the purpose Fig. 8 reports synthetically the results of the rib layer in terms of the main variable represented by printing velocity when the extruding nozzle of 0.06 mm in radius is considered. Values by 0.134 mm at 40 mm/s to 0.026 mm at 1000 mm/s in the case of plastic model are obtained being 0.024 mm that given by the fluid dynamic model at the highest speed studied. The difference is about 0.84% in last case. At 0.36 mm nozzle radius it can be observed that for given printing velocities of 40,360 and 1000 mm/s the rib section in terms of equivalent radius tends to be similar for two modeling approaches in particular at high printing speeds. Some considerations could be done also at Δp=0 with similar results.

Summary

The original version of this paper is available on https://www.scientific.net/KEM.1047.1.pdf

Computing the layer thickness through the rib radius vs. nozzle dimension, extrusion velocity and printing velocity is the object of the investigation. Two approaches are proposed and followed. The one based on plastic modeling of the material considering semisolid at temperatures not higher than 600−630∘C in order to maintain the semisolid fraction of 0.1 . The other one starting by the Navier Stokes equation through viscosity evaluation in order to determine the elongation of the rib and subsequent layer thickness in terms of equivalent radius.

It is observed that layer thickness evaluated with plastic behaviour model descreases with increasing printing speed representing the main variable of the modeled FDM system where the effect of cooling appears negligible and that of the nozzle radius arises when the printing speed approaches the extrusion velocity. In particular, higher values are obtained by the fluid dynamic model compared with ones of plastic model for the extrusion nozzle of 0.06 mm . While lower values are given for the highest value of the nozzle. Such behaviour can be due to the increased value of relative apparent viscosity obtained being higher the pressure inside the extruder when the nominal 0.06 mm in radius is considered for analysis. The rib section described by both the models in terms of equivalent radius is similar when the printing speed is increased.

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The original version of this paper is available on https://www.scientific.net/KEM.1047.1.pdf

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Y. G. Mittal, G. Gote, Y. Patil, A. K. Mehta, P. Kamble, K.P. Karunakaran, Investigations on ironing parameters in screw extrusion additive manufacturing, Manufacturing Letters 41 (2024) 822-831. .

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 11-20

doi: 10.4028/p-OmNsl5

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-03

Revised:
2026-02-16

Accepted:
2026-02-25

Online:
2026-04-13

Machine Learning Topography Prediction and Optimization in Maskless Grayscale Laser Lithography

The original version of this paper is available on https://www.scientific.net/KEM.1047.11.pdf

Adrian H. A. Lutey 1,a*, David Kuhness 2, b, Seyyedhossein Mckee 3,c, Marco Negozio 1, d, Markus Post 2,e, Barbara Stadlober 2,f

¹Università degli studi di Parma, Dipartimento di Ingegneria dei Sistemi e delle Tecnologie Industriali, Parco Area delle Scienze 181/A, 43124 Parma, Italy

2 Joanneum Research Forschungsgesellschaft, Franz-Pichler-Straße 30, A-8160 Weiz, Austria

3 Università degli studi di Parma, Dipartimento di Ingegneria e Architettura, Parco Area delle Scienze 181/A, 43124 Parma, Italy

a* adrian.lutey@unipr.it, b David.Kuhness@joanneum.at, c seyyedhossein.mckee@unipr.it, d  marco.negozio@unipr.it, e  Markus.Postl@joanneum.at, f  barbara.stadlober@joanneum.at

Keywords: maskless grayscale laser lithography, machine learning, artificial neural networks, process optimization.

Abstract

A machine learning (ML) framework was developed for the prediction of surface topography obtained with maskless grayscale laser lithography based on the spatial distribution of the applied laser energy dose, or virtual photomask. Artificial neural networks (ANNs) were employed, with the virtual photomask and its radial averages selected as input variables and the surface elevation selected as the output variable. Training of ANNs was carried out with data acquired from the production of models comprising a wide range of representative geometries. Hyperparameter optimization was performed by assessing the accuracy of trained ANNs, with the final configuration comprising a single hidden layer with 15 neurons and a Sigmoid activation function. The trained ANN was then employed within an iterative optimization algorithm to determine the best virtual photomask for the production of new objects by updating the virtual photomask based on the predicted error, thus automatically compensating for proximity effects and sharp dose transitions. The developed approach achieved a reduction in average build error from 2.8μ m to 1.3−1.5μ m compared to standard experimental approaches in a single build, improving not only accuracy but also greatly reducing time requirements for optimization of the process.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.11.pdf

Maskless grayscale laser lithography is a key manufacturing process for the production of microscale components and features such as micro-electromechanical systems (MEMS), microoptical components and functional surfaces [1−3]. By locally modulating light intensity during exposure of a photoresist, continuous-relief structures can be produced with greater flexibility than traditional lithography, offering advantages in terms of versatility and potential applications [4,5]. Definition of the spatial distribution of the applied laser energy dose, or virtual photomask, is typically achieved by performing a series of single-line experiments, measuring the resulting elevation and defining an analytical contrast curve expressing the surface topography as a function of energy dose. This approach does not account for sharp dose transitions or proximity effects due to adjacent features, which can lead to significant deviations from the target geometry during the production of new objects [6]. Optimization of the virtual photomask therefore typically requires several iterations, requiring lengthy steps to be repeated due to difficulty in predicting outcomes within regions subject to sharp dose transitions or proximity effects [2].

Physics-driven models address these limitations by determining the equivalent absorbed laser energy dose and consequent changes in photoresist solubility, allowing fast and accurate correction of virtual photomasks for maskless grayscale laser lithography performed with established process setups and photoresists [7,8]. The applicability of such models, however, is limited by their complexity and the requirement for specialized software, as well as their sensitivity to calibration of
material properties and constants [7], particularly when changes to the process setup or photoresist are introduced. Machine learning (ML) offers a valid alternative to physics-driven modeling by automatically accounting for causal relationships between process parameters, material properties and process outcomes based on training data, allowing optimization of the virtual photomask required for a given object based on predicted outcomes [9,10]. ML models have seen application in traditional lithography for definition of optimized photomasks [11]. Artificial neural networks (ANNs) have been applied to maskless grayscale laser lithography for the prediction of optimal virtual photomasks based on U-Net convolutional neural networks [12]; however, this approach requires the design and training of complex deep neural networks while still not achieving accuracy up to the physical limits of the process.

Key issues surrounding the implementation of ML for topography prediction and optimization in maskless grayscale laser lithography include the development of standardized protocols for the acquisition of high-quality training datasets, selection of appropriate input variables and ML models, and development of optimization algorithms for the effective minimization of build error. The present work therefore addresses these issues by (i) employing tailored models for the acquisition of highquality training and testing datasets, (ii) defining and evaluating appropriate input variables based on physical principles, (iii) implementing grid hyperparameter optimization of ANNs over appropriate ranges, (iv) training a final ANN representation of the process and (v) developing an iterative algorithm for virtual photomask optimization based on the trained ANN. The developed methodology is shown to be effective at optimizing virtual photomasks, reducing the average build error from 2.8 μm to 1.3−1.5μ m compared to standard experimental approaches in a single build.

Methods and Materials

The original version of this paper is available on https://www.scientific.net/KEM.1047.11.pdf

Models were employed to generate training and testing data for the developed ML approach. The model presented in Figure 1a was employed to generate training data with a variety of features in terms of height, aspect ratio, slope and curvature. The model was originally developed by Minetola et al. for LPBF [13], requiring scaling to 165μ m(l)×165μ m(w)×20μ m( h) for production via maskless grayscale laser lithography in the present study. High aspect ratio features exceeded the limits of the employed experimental setup, thus providing information regarding process limits within the training dataset. The model presented in Figure 1b was instead employed to generate an independent testing dataset with simple geometric forms of various heights.

[picture]

Fig. 1. (a) Training [13] and (b) testing models employed within the study.

Fig. 1. (a) Training [13] and (b) testing models employed within the study.

Maskless grayscale laser lithography experiments were performed with a 405 nm continuous-wave diode laser, employing a series of filters to achieve a maximum laser power of 1 mW at the sample surface. Active control of laser power over the range 0−1 mW was achieved with an acousto-optic modulator (AOM) prior to focusing with a 10× objective to a spot size of approximately 2μ m. Samples were prepared by spin-coating 4" borosilicate glass wafers with mr-P 22G_XP positive photoresist (micro resist technology GmbH) for 20 s at 1000 RPM , prior to baking at 30∘C for 30 min,50∘C for 15 min,80∘C for 60 min and 90∘C for 360 min , and relaxation for 360 min . The final photoresist thickness was 50μ m. For each experiment, a virtual photomask was created by defining
the spatial distribution of the AOM voltage over the required build area. In order to determine the virtual photomask value at each point for a given target geometry, a series of single-line experiments was firstly performed to define an experimental contrast curve expressing the surface topography as a function of AOM voltage. Hatch spacing and scanning speed were held constant at 1μ m and 40 mm/s, respectively, for all experiments. After laser exposure, development of samples was performed for 30 min with TMAH-based ma-D 532/S (micro resist technology GmbH), followed by rinsing with deionized water and blow drying with nitrogen gas. Room temperature was 21±1∘C and humidity was 44±3% at all times. Prior to measurement, all samples were sputter coated with Au-Pt to a thickness of 30 nm . Acquisition of the surface topography was performed with a Keyence laser scanning confocal microscope (LSCM) equipped with a 50× objective.

To obtain a training dataset, the training model (Figure 1a) was employed in upright and inverted positions, creating virtual photomasks based on the aforementioned experimental contrast curve, as well as a linear contrast curve assigning AOM voltages proportional to the model height. Photomasks created with both contrast curves were employed to ensure adequate representation of the physical process, including at low laser power below the interaction threshold and at high laser power up to system limits. Further to providing training data, experimental outcomes obtained with the experimental contrast curve were used as a baseline for assessing improvements in build accuracy with the developed ML approach. For acquisition of an independent testing dataset, the testing model (Figure 1b) was employed in both upright and inverted positions, creating a virtual photomask based on the experimental contrast curve. Further to providing testing data, experimental outcomes were also used as a baseline to assess improvements in build accuracy obtained with the developed ML approach.

ANN inputs were defined as the virtual photomask value (AOM voltage), together with a series of radial averages representing the radial distribution function of the virtual photomask at each point. The ANN output was the surface elevation acquired via LSCM. Individual radial averages of the virtual photomask were considered over the ranges 0μ m<r≤2μ m,2μ m<r≤4μ m,4μ m<r≤8μm,8μ m<r≤16μ m,16μ m<r≤32μ m and 32μ m<r≤64μ m, where r is the radial distance from each point. Calculation of these input variables was achieved by performing two-dimensional convolutions of weighted Boolean masks and the virtual photomask. This approach allowed the absorbed energy dose within regions at and around each point to be accounted with a limited number of input variables, thus allowing simple ANNs to be employed. A flow diagram of the developed ML process is presented in Figure 2.

[picture]

Fig. 2. Flow diagram of machine learning process for maskless grayscale laser lithography.

Fig. 2. Flow diagram of machine learning process for maskless grayscale laser lithography.

Training of ANNs was carried out with the fitrnet function in MATLAB, employing the Broyden-Fletcher-Goldfarb-Shanno (LBFGS) algorithm and a mean squared error (MSE) loss function. Hyperparameter optimization was performed by evaluating the MSE for prediction of outcomes in the independent testing dataset with 1-2 hidden layers, 1-25 neurons per layer and ReLU, sigmoid and tanh activation functions. The simplest configuration achieving consistently low MSE for the
independent testing dataset consisted of 1 hidden layer with 15 neurons and a sigmoid activation function. A schematic of this structure is shown in Figure 2. Final training was performed by partitioning the training dataset into 80/20% training/testing datasets to allow direct comparison of actual and predicted heights following a standard approach. The permutation importance of each input variable was also evaluated to determine the appropriateness of each for the prediction of surface topography. Once the final ANN had been trained, optimization of photomasks for both the training and testing models was performed by iteratively updating the virtual photomask based on the predicted topography obtained with the ANN. At each new iteration, the AOM voltage was corrected based on the predicted error at each point and surrounding regions, limiting corrected values to the usable voltage range of 0−5 V. As a result, the optimization algorithm converged to an optimal virtual photomask achieving constant error corresponding to the physical limits of the process setup.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1047.11.pdf

Three-dimensional surface profiles acquired after fabrication of the training and testing models in upright and inverted positions with the experimental contrast curve are shown in Figure 3. The majority of features are relatively well-defined; however, vertical walls and regions of high aspect ratio in both models (Figure 1) are sloped due to sharp dose transitions. Tightly-packed features such as rectangular columns and pockets are poorly defined in some regions due to severe proximity effects. As noted previously, features with high aspect ratio, exceeding process limits, were deliberately included to provide a diverse and complete training dataset, as well as to assess the performance of the optimization algorithm up to process limits, providing benchmark data for future studies.

[picture]

Fig. 3. Three-dimensional surface profiles acquired via LSCM for the (a) training and (b) testing models.

Fig. 3. Three-dimensional surface profiles acquired via LSCM for the (a) training and (b) testing models.

During hyperparameter optimization, data obtained from production and analysis of the training model were employed to train ANNs with 1-2 hidden layers, 1-25 neurons per layer and ReLU, sigmoid and tanh activation functions. The performance of each ANN was evaluated in terms of the MSE achieved for prediction of outcomes in the independent testing dataset obtained from production and analysis of the testing model. The results of hyperparameter optimization are presented in Figure 4, where values of MSE generally decreased with increasing number of neurons before reaching constant values of approximately 1−1.5μ m2 with both one and two hidden layers. All activation functions performed similarly; however, sigmoid provided faster and more stable convergence to low values of MSE with both one and two hidden layers. In view of adopting the simplest configuration achieving acceptable results, the final ANN was selected as having 1 hidden layer with 15 neurons and a sigmoid activation function, yielding an MSE of 1.1μ m2 for the independent testing dataset.

[picture]

Fig. 4. ANN hyperparameter optimization: Mean squared error with independent testing dataset with 1-2 hidden layers, 1-25 neurons per hidden layer and ReLU, Sigmoid and tanh activation functions.

Fig. 4. ANN hyperparameter optimization: Mean squared error with independent testing dataset with 1-2 hidden layers, 1-25 neurons per hidden layer and ReLU, Sigmoid and tanh activation functions.

A comparison of the actual and predicted height in the testing dataset obtained by partitioning training data into 80/20% training/testing datasets is presented in Figure 5, together with the permutation importance of each input variable. It should be noted that negative height values were obtained due to the use of a positive photoresist. Good alignment between predicted and actual height values can be observed over the entire range, with only a limited number of points exhibiting large deviations. Values of mean permutation importance provided insight into the relative importance of each input variable, with greatest values observed for radial averages over the ranges 0μ m<r≤2μm,2μ m<r≤4μ m and 4μ m<r≤8, followed by the AOM voltage at each point ( r=0μ m ). Radial averages at distances greater than 8μ m were instead found to be of limited influence on the resulting surface topography. These outcomes nonetheless implied that the surface topography was greatly influenced by the incident laser energy dose within regions well beyond the focused laser spot of diameter of approximately 2μ m. The model therefore implicitly accounted for variations in converted initiator and photoresist solubility due to the absorbed energy dose [14,15]. This result confirms the importance of correctly accounting for sharp dose transitions and proximity effects during optimization of virtual photomasks for maskless grayscale laser lithography.

[picture]

Fig. 5. (a) Comparison of actual height and predicted height with final trained ANN using testing dataset obtained by partitioning training data into 80/20% training/testing datasets. (b) Permutation importance of ANN input parameters with the same dataset. RA: Radial average range.

Fig. 5. (a) Comparison of actual height and predicted height with final trained ANN using testing dataset obtained by partitioning training data into 80 / 20 % training/testing datasets. (b) Permutation importance of ANN input parameters with the same dataset. RA: Radial average range.

To gain physical insight into the effective performance of the trained ANN, Figure 6 presents virtual photomasks obtained for the training and testing models using the experimental contrast curve, together with the experimental and predicted surfaces in both cases. As noted previously, experimental surfaces exhibited clear deviations in topography from their respective models (Figure 1) due to sharp dose transitions at the edges of vertical elements and proximity effects taking place within closely-packed features such as rectangular columns and pockets in the upper region of the
training model. The surface topography predicted by the trained ANN generally reproduced the same effects accurately, with only minor differences between the experimental and predicted topography for both models at the center of complex features such as hexagonal prisms and truncated cones. Outcomes were nonetheless in line with physics-driven models [16] and considered acceptable at effectively accounting for the overall energy dose within all regions, including those subject to sharp dose transitions and proximity effects.

[picture]

Fig. 6. Photomask and comparison of experimental and predicted surfaces with final trained ANN for virtual photomasks employed to produce (a) training model and (b) testing model.

Fig. 6. Photomask and comparison of experimental and predicted surfaces with final trained ANN for virtual photomasks employed to produce (a) training model and (b) testing model.

Figure 7 provides a comparison of virtual photomasks obtained with the experimental contrast curve and the ML optimization algorithm for the training and testing models. Noting the differences in color scale, iterative correction of the virtual photomasks based on the predicted error led to profound changes in the spatial distribution of the AOM voltage for production of both objects. Large differences can be observed within base regions between features, particularly for upright structures on the righthand side where the AOM voltage increased from 2.7 V to approximately 4 V for optimized virtual photomasks, indicating macroscopic correction of the incident laser power. Furthermore, aggressive correction of sharp dose transitions and proximity effects can be observed at the edges of vertical elements and within closely-packed features. Sunken features, requiring higher laser power than adjacent base regions (due to the use of a positive photoresist), exhibited higher values around edges to compensate for the lower energy dose within adjacent base regions. The adjacent base regions, in turn, exhibit lower values to compensate for the higher energy dose at the edges of the features. For the same reason, raised features, requiring lower power than adjacent base regions, exhibit lower values around edges, while adjacent base regions exhibit higher values. Corrections were implemented up to process limits, exploiting the entire operating range of the AOM (0−5 V), suggesting that virtual photomask correction was implemented up the physical limits of the experimental setup. Conceptually, these outcomes are in line with proximity effect correction currently employed within physics-driven models [7].

[picture]

Fig. 7. Comparison of virtual photomasks obtained with experimental contrast curve and machine learning optimization algorithm for (a) training model and (b) testing model.

Fig. 7. Comparison of virtual photomasks obtained with experimental contrast curve and machine learning optimization algorithm for (a) training model and (b) testing model.

The surface topography of both training and testing models obtained with the experimental and optimized virtual photomasks are presented in Figure 8, together with the build error expressed in terms of the minimum Euclidean distance between the acquired and model surfaces. Results achieved with the experimental contrast curve exhibited evidence of proximity effects and sharp dose transitions due to non-uniform laser exposure and absorbed energy [6]. Clear differences in build accuracy can be observed between surfaces obtained with the experimental contrast curve and the optimization algorithm. In the former case, base regions between features exhibited constant error of up to 6-8 μm, while in the latter case, error was <2μ m within the same regions. This outcome suggests that the optimization algorithm successfully implemented macroscopic corrections within flat regions, automatically increasing the AOM voltage from 2.7 V to approximately 4 V within the same regions (Figure 7). While the same outcome could also be effectively achieved experimentally by correcting the contrast curve, this approach would require several trials, with additional time and resource consumption. Correction for sharp dose transitions and proximity effects was also observed at the edges of vertical elements and within rectangular columns and pockets on surfaces obtained with the optimization algorithm. Such a correction could not be achieved experimentally by correcting the contrast curve alone but would require adjustment of the virtual photomask within specific regions based on observed outcomes, with considerable time and resource consumption for each new build. Noting that some features exceeded process limits, residual error was nonetheless observed for surfaces produced with the optimization algorithm due to the physical limitations of the experimental setup, as well as LSCM measurement errors resulting from the higher aspect ratio obtained with the optimization algorithm. Such errors were most evident along the perimeter of upright models in each case. Use of the optimization algorithm led to a reduction in the mean Euclidean distance between the acquired and model surfaces from 2.8μ m to 1.3μ m for the training model and from 2.8μ m to 1.5μ m for the testing model compared to outcomes obtained with the experimental contrast curve, with maximum residuals generally <1μ m within central flat regions.

State-of-the-art physics-driven models and proximity effect correction techniques have been shown to reduce the root mean square (RMS) error from 0.629μ m to 0.189μ m for staircase structures [7], achieving maximum residuals of <0.5μ m within central flat regions [7]. While quantitative comparison with previous works is not possible due to differences in lithography setup, photoresist and target geometry, it should be noted that the step height of staircase structures in [7] was 2μ m and
the maximum overall height 10μ m, whereas the maximum step and overall heights in the present study were both 20μ m, leading to differences in terms of the physical limitations of the process itself. Few studies have proposed ML models for optimization of laser lithography, with quantitative comparison again not possible due to fundamental differences in experimental setup and measurement metrics. Nonetheless, state-of-the-art models and correction techniques have been shown to exhibit up to 10% deviation in surface elevation for relatively smooth structures without vertical walls [12]. Excluding such features, outcomes obtained in the present study generally provide similar or greater accuracy with a more straight-forward approach in terms of implementation.

[picture]

Fig. 8. Comparison of surface topography and error (minimum Euclidean distance) obtained with experimental contrast curve and ML optimization algorithm: (a) Training model, topography, (b) training model, error, (c) testing model, topography and (d) testing model, error.

Fig. 8. Comparison of surface topography and error (minimum Euclidean distance) obtained with experimental contrast curve and ML optimization algorithm: (a) Training model, topography, (b) training model, error, (c) testing model, topography and (d) testing model, error.

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.11.pdf

A machine learning framework for predicting and optimizing surface topography in maskless grayscale laser lithography was developed, achieving reductions in build error from 2.8μ m to 1.31.5μ m compared to standard experimental approaches for defining virtual photomasks based on single-line experiments and an experimental contrast curve. Key aspects of the developed approach were the use of training and testing benchmarks for the generation of diverse and complete datasets for training ANNs, as well as grid hyperparameter optimization over an appropriate range. Once training was complete, a simple iterative optimization algorithm was implemented to correct for sharp dose transitions and proximity effects along edges and within closely-packed features. The main limitations of the developed approach include difficulties in obtaining high-quality training data within critical regions due to surface acquisition artefacts within steep/vertical regions, as well as the ability to deal with changes in photoresist, process parameters and experimental setup. However, these are essentially the limitations as for physics-driven models, while machine learning has the potential to reduce time and cost requirements associated with training and calibrating models for new configurations. While the effectiveness of the approach was demonstrated for a single maskless grayscale laser lithography setup and photoresist with constant hatch spacing and scanning speed, it could potentially be applied to a greater variety of cases by enlarging the training dataset and introducing more input variables to account for additional process parameters. Further investigation is therefore required into expanding and improving machine-learning models and optimization algorithms for maskless grayscale laser lithography.

Funding Declaration

The original version of this paper is available on https://www.scientific.net/KEM.1047.11.pdf

The authors would like to kindly acknowledge financial support received from the OPTIMAL project (Horizon Europe, grant agreement No. 101057029).

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The original version of this paper is available on https://www.scientific.net/KEM.1047.11.pdf

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This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/additive-manufacturing/978-3-0364-1997-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 21-29

doi: 10.4028/p-94l5jS

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-04

Accepted:
2026-02-11

Online:
2026-04-13

A Comparative Analysis of LPBF Process Maps for S2 and S6 Grades Tool Steel

The original version of this paper is available on https://www.scientific.net/KEM.1047.21.pdf

Segatto Alessandra 1,a, Saggionetto Enrico 1, b, Dedry Olivier 1,c, Tchuindjang Jérôme Tchoufang 1, d and Mertens Anne 1,e*

¹Metallic Materials for Additive Manufacturing (MMS), Aerospace and Mechanical Engineering Dpt., University of Liège - Quartier POLYTECH1, Allée de la Découverte 9-4000 Liège (Belgium)

a alessandra.segatto@uliege.be, b enrico.saggionetto@liege.be, c olivier.dedry@uliege.be, dj.tchuindjang@uliege.be, e* anne.mertens@uliege.be

*corresponding author: anne.mertens@uliege.be

Keywords: LPBF, tool steel, processing map.

Abstract

Laser Powder Bed Fusion (LPBF) represents a significant advancement in metal additive manufacturing, facilitating the near-net-shape fabrication of intricate, high-performance tool-steel components. This process is accomplished through a layer-by-layer selective melting technique and extremely rapid solidification (ranging from 103 to 106 K/s ). The establishment of process maps-both empirical and predictive frameworks that correlate variables such as laser power, scan speed, and defect thresholds - is crucial for defining processing windows and optimising parameter selection. In tool steels, the influence of alloying elements is significant as they affect solidification behaviour, phase stability, and susceptibility to cracking. Carbide-forming additions can constrict the defect-free range, while stabilising elements can enhance toughness and dimensional accuracy. This study aims to develop a process map for S6 tool steel by varying laser scan speed and laser power. Small cubes are printed using various combinations of these parameters, followed by microstructural characterisation of the as-built material. This characterisation will include optical microscopy (OM) and porosity assessment. After establishing the process map for S6, the resulting microstructures are compared with those previously characterised for LPBF-processed S2 tool steel. This comparison provides valuable insight into the differences in the as-built microstructures of LPBF S2 and LPBF S6, particularly in relation to how the presence and relative amounts of alloying elements influence processability and microstructural development.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.21.pdf

Laser Powder Bed Fusion (LPBF) is a prevalent additive manufacturing (AM) technique known for its capability to produce high-performance metallic components characterised by complex geometries and near-net-shape accuracy. The layer-by-layer fabrication inherent to LPBF facilitates exceptional design freedom and efficient material utilisation, making it particularly advantageous for tool steels and high-strength alloys [1,2]. Nevertheless, the intrinsic characteristics associated with LPBF such as localised melting, rapid solidification, and the development of thermal gradients - present notable challenges in processing systems, particularly in relation to susceptibility to cracking and segregation [3]. Tool steels, in particular, are recognised as some of the most challenging materials to process successfully due to their high hardenability, substantial carbon content, and broad solidification temperature range [4].
Previous research has established that parameters including laser power, scanning speed, hatch spacing, and layer thickness are critical in determining melt pool stability, porosity, and microstructural outcomes [5,6]. Despite the advancements in understanding these parameters, certain grades of shock-resisting steels, such as S 2 and S 6 , have received relatively limited attention compared to other tool steel variants [7].
From a metallurgical standpoint, S2 and S6 tool steels are classified as shock-resisting steels, engineered to achieve a balance between moderate hardness and high toughness when subjected to dynamic loads. S2 features elevated carbon and silicon contents that contribute to a fine martensitic
matrix and enhanced compressive strength; however, these same qualities may lead to an increased vulnerability to cracking due to diminished thermal conductivity. Conversely, S6 incorporates higher levels of manganese and chromium, enhancing toughness and mitigating quench sensitivity (Table 1). It is crucial to comprehend these distinctions to establish process windows that promote consistent densification and microstructural homogeneity across both grades.
Recent advancements have seen the emergence of process maps (Figure 4) as an effective means to describe and visualise the processability of alloys in LPBF [8, 9]. These maps correlate critical process parameters with performance outcomes, such as relative density, surface roughness, and microstructural stability, facilitating the identification of stable processing windows. Initial works have suggested the development of process maps for various materials, including stainless steels and nickel-based superalloys, often supported by empirical optimisation and melt pool modelling [10, 11]. However, there remains a notable gap in the literature regarding process maps for tool steels, particularly for S6.
The integration of parameters for process mapping presents a dual benefit: it enables the identification of stable regions for achieving fully dense and defect-free consolidations while providing a physical explanation for divergences observed among alloys [11]. This approach is especially pertinent for tool steels, where processing windows are frequently narrow and sensitive to both thermal and metallurgical variables [12].
This study seeks to address the existing gap by undertaking a comparative investigation of the LPBF process maps for S2 and S6 tool steels. A systematic parameter study will be conducted to construct process maps that correlate laser power and scanning speed with relative density. The resulting samples will be examined using optical microscopy (OM), scanning electron microscopy (SEM), and surface profilometry to assess mechanisms of densification, defect formation, and melt track morphology. This research aims to establish a direct comparison between the LPBF processability of S2 and S6 tool steels, linking elemental differences to resultant microstructural and surface characteristics. The findings aspire to enhance the understanding of process-structure relationships in shock-resisting tool steels and serve as a foundation for optimising processing parameters across various alloy grades. Furthermore, the methodology proposed-integrating process mapping with microstructural evaluation-presents a transferable framework for evaluating similarly challenging alloys in the additive manufacturing landscape.

Materials and Methods

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Process map of S2 tool steel was previously studied [13]. Commercial powder of S6 tool steel were supplied by Sandvik Osprey LTD. The nominal composition of both steels is given in Table 1.

Table 1. Nominal composition of S6 and S2 tool steel powders.

%wt | C | Si | Mn | Cr | Mo | V | Fe
S6 | 0.31 | 2.1 | 1.1 | 1.7 | 0.4 | 0.37 | Bal.
S2 | 0.49 | 1.2 | 0.60 | - | 0.60 | - | Bal.

A total of 20 cubes, of 10×10×10 mm3 sizes, were produced using an Aconity MINI LPBF printer under Argon atmosphere. In comparison to the printer used in the study on S2 grade [13], this LPBF printer utilizes a reduced set of printing parameters. Table 2 shows the different values of laser powers (P) and scan velocities (Vs) used. Those parameters were selected based on the S2 samples results [13]. A laser spot of 80μ m with a gaussian distribution, a layer thickness (t) of 30μ m and a hatch spacing (h) of 80μ m were used for all samples. The scanning strategy was set as a 90∘ rotation for each layer, without contouring. The volumetric energy density (Ed) was calculated based on the processing parameters (Eq. 1).

Ed=Pt*h*vs(1)

The surface of the as-built samples was checked using an Alicona Infinite Focus G5 optical profilometer for roughness analysis. All the as-built samples were cut by Electro Discharge

Machining perpendicularly to the laser scan direction of the last layer, to obtain the vertical cross section. After cutting, samples were hot mounted with an electrically conductive resin, then ground and polished down to 1μ m. Samples were etched with 3% Nital to reveal specific microstructural features. Defects and porosity within the cross sections were examined using an optical microscope (OM - Olympus BX60). The porosity assessment through the optical microscope analysed the selected cross section and provided a percentage measurement of porosity.

Table 2. Parameters of printing S6 tool steel.

Sample | P [w] | Vs [mm/s] | Ed [J/mm³] | Print completed
1 | 125 | 200 | 260 | Yes
2 | 125 | 400 | 130 | Yes
3 | 125 | 600 | 87 | Yes
4 | 125 | 800 | 65 | Yes
5 | 150 | 400 | 156 | No
6 | 150 | 600 | 104 | Yes
7 | 150 | 800 | 78 | Yes
8 | 150 | 1000 | 63 | Yes
9 | 175 | 400 | 182 | No
10 | 175 | 600 | 122 | No
11 | 175 | 800 | 91 | Yes
12 | 175 | 1000 | 73 | Yes
13 | 175 | 1500 | 49 | Yes
14 | 200 | 400 | 208 | No
15 | 200 | 600 | 139 | No
16 | 200 | 800 | 104 | No
17 | 200 | 1000 | 83 | Yes
18 | 200 | 1200 | 69 | Yes
19 | 200 | 1500 | 56 | Yes
20 | 200 | 2000 | 42 | yes

Results and Discussion

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Profilometer analyses

The original version of this paper is available on https://www.scientific.net/KEM.1047.21.pdf

The printing was not completed for samples 5,9,10,14,15, and 16 due to excessive spatters and defects detected during printing (Table 2). Figure 1 presents an example of profilometer measurements (from sample 2). Figure 2 shows the results for the surface roughness ( Sa ); additionally, the largest difference between maximum peak height and maximum valley depth was recorded for sample 3(−181.07μ m). No relation between Sa and Ed was detected. After cutting, the samples were analysed with an optical microscope. Porosity measurements through OM are reported in Table 3. The best results of Sa are related to the best in terms of porosity, while the worst samples for Sa (i.e. samples 2, 3, 6) are related to the worst in terms of porosity.

[picture]

Fig. 1. Typical surface topography as obtained from profilometer measurement for sample 2.

Fig. 1. Typical surface topography as obtained from profilometer measurement for sample 2.

[picture]

Fig. 2. Surface roughness (Sa) results. In red, the three worst results; in green, the three best results.

Fig. 2. Surface roughness (Sa) results. In red, the three worst results; in green, the three best results.

Printing defects

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The porosity measurements and images are reported in Table 3, using the same red and green contours as in Figure 2.
The type of defects identified through porosity assessments and analysed in optical micrographs, as detailed in Table 4, are summarised in Table 3.

Table 3. Types of defects confronted and the specific samples in which they are present.

Type of defects | Sample present
Keyhole porosity | 1, 11, 12, 13
Cold cracks | 1, 7, 8
Lack of fusion | 2, 3, 4, 6, 7, 8, 19, 20

The defects outlined in Table 3 represent common issues observed in LPBF printed samples. Keyhole porosity has been previously documented [10,14], while cold cracks have been described [15,16]. Cold cracks result from high internal stresses and sharp thermal gradients that develop during the cooling and solidification processes. In contrast, keyhole porosities are associated with excessive energy input: when the laser power is excessively high or the scan speed is too low, intense metal evaporation can create a deep vapour cavity. If this keyhole becomes unstable, it may oscillate and collapse, trapping gas within the solidifying melt pool, leading to the formation of spherical or nearspherical pores. The evaporation of volatile alloying elements like manganese, silicon, or chromium in tool steels can further aggravate plume formation, thereby increasing keyhole instability.
Another persistent defect is a lack of fusion, which has already been analysed by [17]. This defect occurs when the laser energy is insufficient to fully melt both the powder and the underlying material.

Contributing factors may include high scanning speeds, low laser power, inadequate track overlap, or uneven powder distribution, all of which result in shallow melt pools. Lack-of-fusion defects are characterised by irregular and angular voids that indicate areas where melting did not occur.
In some instances, lack of fusion may coexist with cold cracking, while in others, keyhole porosities may be more prevalent. This emphasises the necessity for optimisation of process parameters to mitigate the occurrence of these defects.

[picture]

Fig. 3. Optical micrographs of the cross sections showing the porosity and highlighting the worst (red) and best (green) roughness measurements (Figure 1). The arrows show the building direction.

Fig. 3. Optical micrographs of the cross sections showing the porosity and highlighting the worst (red) and best (green) roughness measurements (Figure 1). The arrows show the building direction.

Influence of alloying elements on the processing map

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Figure 4 illustrates the processing maps for both tool steels S2 and S6, showing the regions associated with different defect as well as the zone for process stability. The printing parameters in S6 were observed to be lower than those in S2, which can be attributed to the differing characteristic of the two printers employed. Additionally, for S6 tool steel, the processing map exhibits a "not finished" window for some samples, in relations to interruptions of the printing process caused by excessive spatters. In both S2 and S6 grades, lack-of-fusion occurs at low laser powers and high scan speeds, while excessive energy input - particularly at high power and low scan speeds - leads to keyhole formation and spatter. However, the location and extent of these defect windows differ markedly between the two alloys.
Focusing on the optimal processing window, the area that enables the production of fully dense and defect-free samples is noticeably larger for S2 than for S6. In S2, a broad range of power-scan speed combinations results in stable melting behaviour with limited porosity or cracking. By contrast, the processing window for S6 is significantly more restricted, shifting toward higher power and scan speeds, particularly between 200 W and 100−1200 mm/s. At reduced power levels (e.g., 125 W ), the lack-of-fusion region widens considerably in S6, especially at higher scan speeds, indicating a much lower tolerance for variations in energy input.
The differences in window size and processability are closely linked to alloying content. S 6 contains substantially higher levels of Cr,V,Si, and Mn , which promote micro segregation, broaden the
solidification range, and encourage the formation of interdendritic carbide networks [18]. These features increase susceptibility to cracking, porosity, and melt-pool instability, ultimately limiting the feasible parameter space [5, 19]. In contrast, the lower alloying content in S2 reduces segregation and minimises the development of brittle eutectic or carbide-rich regions, resulting in a more stable melt pool and greater robustness against parameter fluctuations.
Overall, Figure 3 demonstrates that increasing alloying complexity in LPBF tool steels directly affects process stability and defect formation. Alloys such as S 6 therefore require tighter parameter control and potentially additional measures-such as in-situ heating-to mitigate cracking and expand the usable processing window, as suggested for highly alloyed steels by recent studies [19, 20].

[picture]

Fig. 4. Process map of LPBF S2 tool steel (up) and S6 tool steel (below).

Fig. 4. Process map of LPBF S2 tool steel (up) and S6 tool steel (below).

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.21.pdf

The present study investigates the laser powder bed fusion (LPBF) process applied to S6 tool steel cubes, with the objective of identifying the optimal processing parameters through systematic variation of key factors, particularly laser power and scanning speed.
Characterisation and classification of surface roughness and internal defects within the fabricated samples were conducted to evaluate the impact of these variables.
Furthermore, to elucidate the influence of alloying elements, the LPBF process window established for S6 tool steel was juxtaposed with that derived from a similar but distinct S2 tool steel.
The defects observed in the non-optimal processing zones were found to be analogous for both steel types. However, it is posited that the elevated concentrations of alloying elements in the S6 steel may either enhance micro-segregation and the formation of carbide networks or broaden the solidification range.
Ultimately, the findings indicate that the optimal processing window for S6 tool steel is more constrained in comparison to S2, with a shift towards higher laser powers and scanning speeds attributable to the differing alloy compositions. Future work will investigate the possible segregation and local variation of the microstructure.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/KEM.1047.21.pdf

This work was supported by the FNRS (Fonds National de la Recherche Scientifique of Belgium, PDR number 35298295). The authors also acknowledge the help of Sylvie Salieri for sample preparation.

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.21.pdf

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 31-40

doi: 10.4028/p-3Ka8Ao

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-15

Revised:
2026-02-20

Accepted:
2026-02-25

Online:
2026-04-13

Laser Powder Bed Fusion of INCONEL® 625: Guidelines for Robust Parameter Selection toward Optimal Quality Parts

The original version of this paper is available on https://www.scientific.net/KEM.1047.31.pdf

Gabriele Locatelli 1,a*, Mariangela Quarto 1, b, Sara Bocchi 1,c, Cristian Cappellini 1, d and Gianluca D'Urso 1,e

1 Department of Management, Information and Production Engineering, University of Bergamo, Via Pasubio 7/b, 24044 Dalmine, BG, Italy

a*gabriele.locatelli@unibg.it, b mariangela.quarto@unibg.it, c sara.bocchi@unibg.it, d  cristian.cappellini@unibg.it, e  gianluca.d-urso@unibg.it

*Corresponding author

Keywords: additive manufacturing, powder bed fusion, inconel 625, process optimization.

Abstract

Producing INCONEL® 625 (IN625) components by laser powder bed fusion (PBF-LB) demands a careful selection of process parameters to concurrently ensure high densification, stable microstructural features, and adequate surface integrity. Previous studies investigated the isolated effect of these parameters or narrow volumetric energy density (VED) ranges, albeit without offering indications on how to simultaneously optimize surface roughness, microhardness, and density. Furthermore, the validity of VED as an input for process optimization is still debated. The present study offers a systematic exploration of the laser power-scan speed ( P−v ) space over a wide VED interval ( 33−400 J/mm3 ) to identify stable and robust process regimes for PBF-LB of IN625. Cylindrical samples built according to dissimilar P−v combinations reveal an extended process window where the properties of interest remain well balanced. Within this region, surface roughness below 10μ m, microhardness near 300HV1, and relative density over 99.5% were consistently achieved. Furthermore, distinct P−v combinations sharing the same VED value were confirmed to produce markedly different results, underscoring the limitations of VED as a predictive descriptor. The findings allowed to establish quantitative guidelines for selecting robust P−v conditions, offering a practical foundation for future data-driven or physics-informed multi-objective process optimisation of PBF-LB IN625.

Introduction

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Nickel-based superalloys, and specifically INCONEL® 625 (IN625), are fundamental materials for high performance applications due to their excellent mechanical strength, good fatigue properties, hot corrosion resistance, and stability at temperatures approaching 1000∘C[1,2]. Owing to such properties, on one side, IN625 is widely used especially for critical, high-temperature and highpressure applications in harsh aerospace, marine, and chemical processing environments [3]. On the other side, the same characteristics make its processing through traditional technologies a continuous challenge both in terms of cost and feasibility. [4].

Metal additive manufacturing (MAM) thus emerges a compelling alternative to traditional manufacturing processes, as it offers high flexibility for complex part fabrication, as well as shorter production time and lower material waste [5]. Among MAM technologies, Laser Powder Bed Fusion (PBF-LB) emerges as a disruptive technology for processing IN625 enabling geometric complexity, refined microstructures and reduced material waste that were previously not possible [6]. However, translating these advantages into industrial-grade repeatability and process qualification remains highly challenging. PBF-LB process is inherently sensitive to the variation in laser power, scan speed and energy input. Small deviations may trigger the formation of lack-of-fusion (LOF) and keyhole pores, cracks, balling and poor interlayer consolidation, which negatively affect the quality of as-built parts in terms of density, surface finish, and mechanical properties [4]. For industries operating under strict safety and reliability requirements, such variability represents a critical barrier for the adoption.

The optimization of the PBF-LB process therefore relies primarily on tuning the laser power ( P ) and scanning speed ( v ) parameters, as they primarily determine the local energy input delivered to the powder bed. Hatch distance (h) and layer thickness (t) also govern the stability of the melting process by controlling melt track overlaps and heat accumulation. Volumetric energy density (VED), defined according to Eq. (1), is also adopted for PBF-LB process optimization. Indeed, VED represents a simplified, yet practical indicator for correlating process conditions with key as-built properties such as relative density, surface roughness and microhardness [7].

VED=P/(v·h·t).(1)

Despite significant advancements reported in the literature on PBF-LB of IN625, current knowledge does not yet provide a robust, generalizable, and multi-objective guidelines for ensuring consistent performance regardless of machines, operators, or production environment. Previous experimental studies are focused on isolated quality indicators and restrict the analysis to narrow energetic range, otherwise are relied on the definition of optimal VED as a synthetic descriptor of process behaviour, despite it is well-known its limitations in describing the physics of the melt pool. As summarized in Table 1, these constraints hinder the definition of an industry-relevant process window able of ensuring high density, controlled surface integrity, and stable mechanical response across a wide parameter space.

Table 1. Summary of previous methodologies and authors' proposal.

Focus and reference | Proposed methodology
Only one quality indicator at a time is analysed [8–11] | Concurrent evaluation of porosity, surface roughness and microhardness under controlled P–v conditions
Only narrow VED ranges are explored (~60–180 J/mm³) [12–16] | Systematic exploration of an extended VED window (33–400 J/mm³)
Parameter effects are studied individually, not as combined P–v interactions [9,14,17] | Quantitative analysis of P–v interaction to identify stable and reproducible operating domains
The predictive reliability of VED is not assessed [8–17] | Critical evaluation of VED as process descriptor

In this context there is an increasing industrial demand of reliable, transferable and physicsconsistent criteria able to support the process qualification, certification workflow, and the scale-up of AM processes with high value material. To meet the industrial needs, it is not enough to identify a specific set of optimal parameters; it is necessary to define a stable process domain that is clearly separated from the defect-inducing regions and supported by a strong methodology rigorous enough to be integrated in digital twin, predictive models, and monitoring systems. Only by this vision, it will be possible to push the technology beyond the mere laboratory optimization and bring it closer to a medium-high Technology Readiness Level (TRL), where robustness, repeatability and multiplatform applicability become essential requirements.

Establishing robust guidelines for process-parameter selection is therefore essential to ensure consistent part quality, reduce variability, and improve reproducibility in industrial settings. Therefore, the present study provides a comprehensive and systematic exploration of the P−v parameter space, aiming to the definition of a robust window for processing IN625. By investigating the combined influence of laser power and scan speed over an extended VED range, the study identifies the most stable and repeatable operating regimes to concurrently optimise surface roughness, microhardness, and densification. Furthermore, this work also offers a critical assessment of whether VED can adequately capture process behaviour. Overall, the present study proposes quantitative guidelines for a thoughtful selection of process parameters, establishing an empirical
foundation for future optimization frameworks and in-process monitoring tools supporting the production of IN625 components with superior density and improved metallurgical quality by PBFLB processing.

This study provides the first systematic mapping of the P−v space for IN625 across a wide energetic interval, delivering actionable guidelines and experimentally demonstrating the nonuniqueness of VED-based optimization.

Materials and Methods

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Commercially available gas-atomized IN625 powder was used in this study characterized by a chemical composition compliant with the ranges specified by the relevant ASTM/AMS standards for Ni-alloys. The particle size distribution (PSD) was assessed by means of laser diffraction analysis. The PSD curves and characteristic D10, D50, and D90 diameter percentiles are reported in Fig. 1.

PSD analysis

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[picture]

Fig. 1. PSD distribution and diameter quantiles of the IN625 powder.

Fig. 1. PSD distribution and diameter quantiles of the IN625 powder.

To systematically capture the influence of process parameters on part quality, a Design of Experiment (DoE) was defined by varying the laser power (P) and scan speed (v), as they directly affect melt pool stability and defect formation. Specifically, P was varied over five levels (100-150-200−250−300 W ), while v was varied over four levels ( 500−1000−1500−2000 mm/s ). To minimize disturbing factors, hatch distance and layer thickness were fixed to 50μ m and 30μ m, respectively. The DoE was thus designed as a parametric map spanning twenty (20) low to high VED inputs ranging from 33 to 400 J/mm3, aiming to include both under- and over-melting conditions.

As depicted in Fig. 2, cylindrical specimens ( 10 mm diameter and 6 mm thickness) were fabricated in a single build job using a laser powder bed fusion machine equipped with a 300 W laser (Print Genius 150 by Prima Additive). The build plate was preheated to 80∘C and 5 mm -width stripe scanning strategy with 67∘ inter-layer rotation was adopted. The PBF-LB process was carried out under Argon gas atmosphere, keeping the oxygen level below 0.1% to prevent oxidation.

The printed specimens were analysed in the as-built condition. The average surface roughness (Sa) was assessed through 3D optical profilometry (focus variation technique, 2×2 stitching with 20% overlapping) according to the ISO 25178 standard (S-Neox by Sensofar). For each specimen, three measurements were acquired on different portions of the top surface and results were averaged for accuracy. The samples were then separated from the build platform via W-EDM. After metallographic preparation with SiC emery paper up to 1200 grits, the top surface of each specimen was subjected to Vickers testing according to the ASTM E92-17 standard. A 1000 gf load was applied for 15 s and microhardness values ( HV1 ) were determined as the average of five indentations. The specimens were subsequently cut parallel to the build direction via W-EDM. The mounted samples were then grinded with SiC emery paper up to 4000 grit and polished with 1μ m diamond suspension.

High-resolution images of the polished cross-sections were captured at 100× magnification using a digital optical microscope. The optical micrographs (OM) where then analysed with the ImageJ software to determine the relative density ( ρrel ) and characterize the porosity of each specimen.

[picture]

Fig. 2. Layout of the as-built IN625 cylindrical specimens on the build platform.

Fig. 2. Layout of the as-built IN625 cylindrical specimens on the build platform.

Results and Discussion

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The combined influence of P and v on the surface texture, microhardness, and densification of IN625 processed by PBF-LB is illustrated by the graphs in Fig. 3.

Fig. 3a shows that high surface roughness is typically achieved by increasing the scanning speed for a selected laser power. Such an outcome is compatible with the formation of surface undulations rising due to unstable, irregular melt tracks and partially melted particles [18]. Interestingly, systematically higher Sa(12.4−17.4μ m) resulted from P=100 W processing compared to other laser power levels used in this study. On the contrary, the roughness appears to depend mostly on the choice of the scanning speed for medium-to-high laser power (150−300 W), with values of v(500−1000mm/s ) allowing to achieve Sa values lower than 10μ m. As illustrated in Fig. 3b, microhardness in the 280−320HV1 range was consistently achieved. Such values are consistent with the existing literature and can be attributed to the extremely fine dendritic and columnar microstructures developed by IN625 under PBF-LB process [19]. Only the 100 W−2000 mm/s combination resulted in markedly lower hardness (233HV1), which can be attributed to the development of LOF pores under the tested surface due to undermelting. It is interesting to note that medium-to-high laser power and scan speed over 1500 mm/s are required to stably achieve ~300HV1 microhardness. For relative density, Fig. 3c shows two distinct defect-prone regimes separated by a broad processing window where melting is more uniform and repeatable, as typically reported for PBF-LB processing [4]. In particular, a 100 W laser power leads to progressive density reduction as the scan speed is increased, a result that is consistent with LOF pores forming due to undermelting. Conversely, the combination of high power ( 250−300 W ) and low scan speed ( 500 mm/s ) is responsible for reduced densification due to the formation of unstable melt pools and keyhole porosity promoted by overmelting. A stable operating zone for achieving near-full density IN625 ( ρrel ~99.9% ) is observed at intermediate speeds (1000−1500 mm/s) and P>100 W, indicating balanced melt pool dynamics and robust material consolidation.

[picture]

Fig. 3. (a) Surface roughness, (b) microhardness, and (c) relative density of IN625 samples as a function of scan speed for different laser powers.

Fig. 3. (a) Surface roughness, (b) microhardness, and (c) relative density of IN625 samples as a function of scan speed for different laser powers.

While the analysis of the effect of P and v is the most rigorous method for understanding the physics of the process, analysing the effect of VED results to be a useful and synthetic parameter for identifying general trends of undermelting, keyholing and for comparing results with literature. Thus, surface texture, microhardness, and densification results as functions of the VED are summarized in Fig. 4. Sa exhibits a rapid decrease when moving from low to intermediate VED values, marking the transition to a stable melting regime (see Fig. 4a). However, further increases in VED beyond approximately 200−250 J/mm3 do not appear to improve surface roughness significatively. On the other hand, Fig. 4b shows that microhardness remains consistently around 300HV1 at in the 50-267 J/mm3. This behaviour can be associated with the refinement of dendritic structures at higher cooling rates, as reported in previous studies [20]. Finally, relative density exhibits an extended plateau for near-to-full densification between 67 and 167 J/mm3 (see Fig. 4c). Outside this interval, the lower ρrel  values stem from porosities characteristic of LOF and keyholing, as clearly illustrated by the OM in Fig. 5.

Although VED is widely employed as a synthetic parameter to study the PBF-LB process, its feasibility as a primary input variable for process optimisation presents intrinsic limitations. The comparison between P−v and VED clearly demonstrates that different combination of P and v with the same VED can generate dissimilar properties, highlighting the inherent limitations of VED as a primary optimization parameter and supporting the need for a direct evaluation of P−v parameters. As possible to observe in the area identified in Fig. 4 by a black dash-dotted ellipses, same VED value may lead to significantly different results in terms of surface roughness, microhardness, and densification due to being determined by different laser power-speed combinations. Such discrepancies arise due to the VED equation ignoring the complex physics of the melt pool such as recoil pressure, Marangoni flow, and melt-pool confinement [21].

[picture]

Fig. 4. (a) Surface roughness, (b) microhardness, and (c) relative density of IN625 samples as a function of volumetric energy density.

Fig. 4. (a) Surface roughness, (b) microhardness, and (c) relative density of IN625 samples as a function of volumetric energy density.

[picture]

Fig. 5. OM of polished cross-sections of IN625 samples showing different densification behaviours.

Fig. 5. OM of polished cross-sections of IN625 samples showing different densification behaviours.

The image-based porosity analysis may not reliably capture the onset of melt pool instabilities. For this reason, the cross-sections were etched by immersion in mixed acids ( 15 ml of HCl, 10 ml of HNO3 and 10 ml of acetic acid) to assess melt-pool geometry. The OMs of etched cross-sections in Fig. 6 clearly depict the evolution of melt pool geometry providing a microstructural validation of the trends observed in Fig. 3 and 4, linking the variability of the measured characteristics to a specific melting condition and demonstrating how melt-pool morphology affects the surface integrity, mechanical response, and the porosity distribution.

At low VED values, high roughness and low densification were recorded, the etched cross-sections show shallow, discontinuous melt pools characteristic of undermelting. These geometries reflect poor track overlap, which explain both the increased presence of LOF defects and the formation of surface undulations generated by unstable melt-track propagation. As the VED increases into the intermediate, more stable range, the process achieves the best balance between roughness, hardness, and density, and the melt pools evolve into deeper and more uniform conduction-mode profiles. The
regular shape promotes improved interlayer bonding and reduced porosity, which aligns with the near-full densification ( ~99.9% ) and the stable microhardness values around 300HV1. The refined and repeatable melt-pool geometry observed in Fig. 6 is thus a direct microstructural confirmation of the process stability inferred from the mechanical and surface measurements. At higher VED values, despite the persistence of acceptable hardness, the melt pools become progressively elongated and narrow, showing the beginning of keyhole-mode melting. These geometries are associated with vapor depression, enhanced recoil pressure, and localized instability. The keyhole morphologies correspond to the reduction in relative density observed at high power-low speed combinations, where keyhole porosity becomes more likely. This transition explains why improvements in surface roughness become stable despite the increment in the energy input: even though the melt becomes deeper, the instability of the keyhole morphology reduces the surface uniformity and compromises the densification. Furthermore, this finding confirms that keyhole formation can initiate also in a transition melting regime at high laser power-speed conditions [22], which can be bound approximatively into the 100−167 J/mm3 VED range for IN625. Considering the transition regime, the results prove to be consistent with the optimal VED range of 60−100 J/mm3 for IN625 densification by PBF-LB [10].

[picture]

Fig. 6. OM of etched cross-sections of IN625 samples showing the progressive evolution of melt pool morphology toward keyholing.

Fig. 6. OM of etched cross-sections of IN625 samples showing the progressive evolution of melt pool morphology toward keyholing.

To further clarify the comparison between the P−v and the VED-based approaches, Fig. 7 illustrates the laser power-speed domain with superimposed volumetric energy density contours. The yellow region defines the optimal P−v window for the robust and simultaneous optimization of surface roughness, microhardness, and relative density, corresponding to a narrow scan speed interval of approximately 1000 mm/s at laser powers exceeding 100 W . The blue band highlights the range of VED associated with the same multi-property optimization, approximately covering the 89-167 J/mm3 range. Notably, the limited overlap between the two areas indicates that relying only on VED would encompass P−v combinations associated with reduced process stability and non-optimal melt pool behavior. These findings are consistent with recent literature on PBF-LB, which has highlighted that identical VED values can correspond to markedly different melt pool morphologies and stability conditions owing to the distinct and non-linear influence of laser power and scan speed on melt pool dynamics and molten metal flow [23-25]. These observations confirm that while useful for trend identification and comparison with literature, VED is insufficient as a primary variable for PBF-LB process optimization. In contrast, investigating the P−v parameter space enables both effective process optimization and a clearer identification of the underlying melting regimes and defectformation mechanisms.

[picture]

Fig. 7. Process map in the P−v space with VED contours highlighting the difference between the two optimization approaches.

Fig. 7. Process map in the P − v space with VED contours highlighting the difference between the two optimization approaches.

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.31.pdf

Through the experimental exploration of a wide P−v process space for PBF-LB of IN625, this study provides a quantitative identification of the stability domains governing melt-pool behaviour and resulting as-built part quality in terms of surface roughness, microhardness, and density (summarized in Table 2). The study also confirms the intrinsic limitations of VED as a primary optimisation parameter for PBF-LB. While VED can still delineate energetic windows for post-process interpretation, it lacks the intrinsic capability to robustly define the stability process domain.

Table 2. Optimum ranges of P−v and VED for the properties of IN625 processed by PBF-LB.

Properties | Optimum P–v | Optimum VED | Remarks
Surface roughness | More than 100 W 500–1000 mm/s | More than 89 J/mm³ | Sa ≤ 10 μm Stable melt tracks
Microhardness | More than 100 W 500–2000 mm/s | 50–267 J/mm³ | ~300 HV₁ Microstructure refinement
Relative density | More than 100 W 1000–1500 mm/s | 67–167 J/mm³ | ρrel ≥ 99.5 % Transition regime included
Multi-objective | More than 100 W ~1000 mm/s | 89–167 J/mm³ | Narrow optimal window to achieve balanced properties Attention ought to be paid to transition regime

The main findings of the study can be summarised as follows:

Surface roughness is the most challenging part property to optimise in-process. Unlike density and microhardness, achieving significant reductions in Sa require operating closer to the transition and keyhole regimes.

Stable microhardness was achieved across a wide process space that extends near the onset of keyhole formation. This finding suggests that IN625 parts may also exhibit satisfactory tensile strength over a broad process interval.

Near full-densification was achieved in a broad stability zone where melt-pool consolidation is robust and consistent. Nonetheless, particular attention ought to be paid to the transition melting regime due to the increased risk of keyholing.
Overall, the present study offers new insights to guide future process optimization aimed to deepen the understanding of the trade-offs between surface integrity, densification stability, and mechanical performance in PBF-LB of IN625. Specifically, the structured mathematical formula mapping offers a valuable
empirical basis for developing data-driven or physics-informed models, especially for multi-objective optimization problems. Besides the need for synthetic energy input indicators that better capture meltpool dynamics, the findings further suggest the importance of future tensile and fatigue investigations across the identified process domain.

Funding

The original version of this paper is available on https://www.scientific.net/KEM.1047.31.pdf

This study was carried out within the MICS (Made in Italy - Circular and Sustainable) Extended Partnership and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) - MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.3 - D.D. 1551.11-10-2022, PE00000004). This manuscript reflects only the authors' views and opinions, neither the European Union nor the European Commission can be considered responsible for them.

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The original version of this paper is available on https://www.scientific.net/KEM.1047.31.pdf

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 41-51

doi: 10.4028/p-m2Tkvy

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-17

Revised:
2026-02-17

Accepted:
2026-02-25

Online:
2026-04-13

Numerical Modelling of Directed Energy Deposition and Experimental Validation Based on Digital Image Correlation

The original version of this paper is available on https://www.scientific.net/KEM.1047.41.pdf

Dejan Kovšca 1,a, Bojan Starman 1, b, Edvard Govekar 1,c, Miroslav Halilovič 1, d and Nikolaj Mole 1,e*

¹Faculty of Mechanical Engineering, University of Ljubljana, Ljubljana, Slovenia

a  dejan.kovsca@fs.uni-lj.si, b  bojan.starman@fs.uni-lj.si, c  edvard.govekar@fs.uni-lj.si, d  miroslav.halilovic@fs.uni-lj.si, enikolaj.mole@fs.uni-lj.si

*corresponding author

Keywords: directed energy deposition, laser cladding, finite element method, digital image correlation.

Abstract

This contribution presents a combined approach for in-situ experimental characterisation and numerical modelling of thermo-mechanical behaviour in directed energy deposition (DED). Fullfield temperature and substrate deformation are measured simultaneously using infrared (IR) thermography and stereo digital image correlation (DIC) during laser-beam powder deposition on a thin substrate. The experimental data are used to calibrate thermal boundary conditions and to validate a macroscopic finite-element model. The validated framework is then applied to compare different deposition strategies, demonstrating the capability of the coupled measurements and simulations to capture transient thermal fields, deformation evolution and toolpath-dependent effects relevant for process optimisation.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.41.pdf

Additive manufacturing (AM) is increasingly used to produce complex metal components, yet the steep thermal gradients and rapid solidification inherent to the process often lead to residual stresses, distortion, and reduced dimensional accuracy. Numerical simulations are therefore widely employed to predict thermal histories and mechanical responses and to support process optimisation, but their predictive capability depends strongly on high-quality experimental data for model calibration and validation [1].

Thermal measurements in metal AM traditionally rely on thermocouples, which offer robust but point-based observations [2]. Infrared (IR) thermography provides a contactless alternative with fullfield capability, though its accuracy is influenced by varying surface emissivity. Several studies have addressed these limitations through emissivity calibration or indirect viewing strategies, such as mirror-based observation of concealed surfaces [3],[4].

Similarly, deformation monitoring has progressed from single-point probes and displacement sensors [5] to optical full-field methods. Digital image correlation (DIC) has emerged as particularly suitable for AM because it enables non-contact measurement of full-field 3D displacements and strains by correlating sequential stereo images [6]-[8]. Despite its potential, in-situ DIC remains challenging due to illumination constraints, speckle-pattern degradation, and obstructions from the deposition process [9].

To address the need for reliable, continuous in-situ data, this study introduces an experimental methodology that combines IR thermography with stereo DIC for simultaneous full-field measurement of temperature and substrate deformation during directed energy deposition (DED). This coupled dataset is used to validate a macroscopic thermo-mechanical finite-element model. The paper describes the numerical framework, experimental setup, and in-situ measurement procedure, followed by thermal-boundary-condition calibration. The influence of deposition strategy on the thermal response, residual stresses, and deformation is then evaluated.

Experimental Study

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To assess the reliability of the numerical model, three representative toolpaths were deposited while monitoring both the thermal evolution and the resulting substrate deformation in-situ. For this purpose, an integrated measurement setup was developed that simultaneously records IR temperature data and out-of-plane displacement fields via a stereo DIC system.

Experimental setup. Material deposition was performed on a laboratory DED system equipped with an nLIGHT CMP-2500 continuous fibre laser with a maximum average power of 2.5 kW , a disc powder feeder and a custom annular cladding head with axial powder injection. Argon served as the carrier and shielding gas. Motion was provided by a CNC-controlled positioning table, allowing deposition within a 200×60×120 mm build volume.

The substrate was fixed to a water-cooled AA6061 mounting frame. The frame was elevated and contains a 100×100 mm opening, enabling direct optical access to the bottom surface of the substrate during processing, as shown in Fig. 1a.

[picture]

Fig. 1. (a) The experimental setup consists of the experimental DED system and an elevated mounting frame. The substrate is fixed using clamping brackets. IR camera and a pair of digital cameras for stereo DIC are positioned on the side of the mounting frame. Subfigure (b) shows the experimental setup during the deposition process, with the measuring equipment protected by a layer of aluminium foil.

Fig. 1. (a) The experimental setup consists of the experimental DED system and an elevated mounting frame. The substrate is fixed using clamping brackets. IR camera and a pair of digital cameras for stereo DIC are positioned on the side of the mounting frame. Subfigure (b) shows the experimental setup during the deposition process, with the measuring equipment protected by a layer of aluminium foil.

Surface deformation was measured using a stereo DIC system consisting of two Manta G-201B 5 Mpx digital cameras arranged to provide full coverage of the visible region. A high-contrast speckle pattern was applied using heat-resistant spray paint (thermally stable up to 800∘C ) on the bottom surface of the substrate, as shown in Fig. 2d, and the system was calibrated with a 5×5 mm target. Displacement fields were evaluated in Dantec Dynamics Istra 4D software from images acquired at 1 Hz . All optical equipment beneath the deposition area was shielded from spatter and radiation using a layer of thin aluminium foil, as seen in Fig. 1b.

The transient thermal field on the underside of the substrate was captured by an Optris PI640 IR camera, operated via the Optris PIX Connect software, positioned to view the surface through a firstsurface mirror integrated beneath the frame at a 45∘ angle. Measurements were performed in the temperature range of 150−900∘C at 10 Hz . Temperature evaluation was conducted directly on the DIC speckle coating. Consequently, the deposition of new beads on the opposite side of the substrate and any associated surface oxidation did not influence the IR temperature measurements. The emissivity of this painted surface was calibrated against a reference K-type thermocouple and determined to be 0.9 . No variation in thermal response or visible degradation of the coating was observed during the process; therefore, the emissivity was considered constant throughout the experiments.

Deposition process.

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The experiments were performed using AISI 316L powder deposited onto a hot-rolled AISI 304L substrate plate with dimensions of 120×120 mm and a thickness of 3 mm . The chemical compositions of both alloys, as provided by the manufacturers, are listed in Table 2. Key process parameters applied in all experiments are summarised in Table 1.

Table 1. Process parameters used in the experimental case studies.

Parameter | Value
Laser beam power [W] | 1000.0
Laser beam diameter [mm] | 3.4
Working move velocity [mm/min] | 300.0
Positioning velocity [mm/min] | 900.0
Powder mass flow rate [g/min] | 8.0
Shielding gas volume flow [L/min] | 8.0
Powder carrier gas volume flow [L/min] | 0.5
Cooling water temperature [K] | 292.0

Prior to deposition, the substrate was clamped to the water-cooled mounting frame using twelve uniformly preloaded M4 screws tightened to a torque of 0.8 Nm to ensure deterministic boundary conditions. Sufficient time was allowed for the assembly to reach thermal equilibrium before and after the deposition process.

[picture]

Fig. 2. Final deposits were manufactured using three distinct deposition strategies: (a) unidirectional, (b) bidirectional, and (c) inward-spiral. Subfigure (d) shows the speckle pattern applied to the underside of the substrate.

Fig. 2. Final deposits were manufactured using three distinct deposition strategies: (a) unidirectional, (b) bidirectional, and (c) inward-spiral. Subfigure (d) shows the speckle pattern applied to the underside of the substrate.

Three representative deposition-path strategies were investigated, each producing a deposition region of approximately 60×60 mm, as shown in Fig. 2a-c:

Unidirectional deposition path: 36 parallel beads were deposited in the same direction, with a centre-to-centre spacing of 1.68 mm . Each bead was deposited in approximately 11.7 s , followed by a 5.5 s dwell time while the cladding head repositioned to the starting point for the next path, resulting in a total processing time of 612 s .

Bidirectional deposition path: The same geometric pattern was used as in the unidirectional case, but the scan direction alternated after each bead. Interpass delays were kept identical to the unidirectional case, so the overall processing time remained unchanged.

Inward-spiral deposition path: 17 concentric perimeters were deposited from the outer edge inward, maintaining the same bead overlap as in the linear strategies. This approach reduced traveling motion, yielding a total processing time of 500 s .

Numerical Model Implementation

The original version of this paper is available on https://www.scientific.net/KEM.1047.41.pdf

The thermo-mechanical analysis follows a weakly coupled strategy in which the thermal and mechanical problems are solved sequentially, consistent with earlier work by Kovšca et al. [10]. A transient 3D thermal simulation is performed first using a calibrated heat-source model, and the resulting nodal temperatures are then applied as loads in a quasi-static mechanical analysis. The mechanical response is evaluated using a thermo-elasto-plastic material model, capturing temperature-dependent softening and plastic deformation induced by non-uniform thermal expansion.

Material deposition is represented through the incremental activation of finite elements following the hybrid inactive/quiet approach proposed by Michaleris [11]. Initially elements are added to the computational domain bead by bead, assigned with modified properties that prevent interaction with the active mesh. Upon activation, they are updated with the correct thermo-mechanical material properties. Activation times are precomputed using an in-house routine based on the toolpath and process parameters. To account for the evolving free surface, a boundary-detection algorithm from Kovšca et al. [10] is used to update heat-loss conditions as the build progresses. The simulation framework is implemented in Abaqus/Standard (2024), using implicit time integration with geometric nonlinearity enabled and customised subroutines for element activation and thermal boundarycondition control.

Material properties.

The original version of this paper is available on https://www.scientific.net/KEM.1047.41.pdf

Temperature-dependent material data for both the 316 L powder and the 304 L substrate, shown in Fig. 3, were generated using JMatPro (version 14.0), based on the certified chemical compositions presented in Table 2. The material model assumes homogeneous, isotropic behaviour. Melting and solidification effects are included through latent heat at the solidus and liquidus temperatures listed in Table 3. Temperature-dependent isotropic hardening follows the constitutive description of Muránsky et al. [12]. Although a mixed isotropic-kinematic hardening model would provide a more accurate representation of cyclic plasticity under repeated thermal loading and could improve residual stress and springback predictions, isotropic hardening was selected to limit computational cost. Given the dense mesh and full-process simulation of the deposition sequence, the use of a mixed hardening formulation would substantially increase solution time and was therefore not considered feasible within the available computational resources.

Table 2. Chemical composition [wt. %] of AISI 316L and AISI 304L material.

Material | C | Cr | Mn | Mo | N | Ni | P | S | Si | Fe
304L | 0.023 | 18.02 | 1.814 | - | 0.072 | 8.040 | 0.028 | 0.001 | 0.361 | bal.
316L | 0.020 | 17.30 | 1.740 | 2.660 | - | 13.10 | - | < 1.0 | 0.730 | bal.

Table 3. Phase change properties of AISI 316L and AISI 304L material.

Parameter | Symbol | AISI 304L | AISI 316L
Solidus temperature [K] | TS | 1676.1 | 1683.7
Liquidus temperature [K] | TL | 1733.6 | 1710.3
Latent heat of fusion [kJ/kg] | Lf | 174.9 | 180.2

[picture]

Fig. 3. Temperature-dependent material properties of AISI 316L and AISI 304L material.

Fig. 3. Temperature-dependent material properties of AISI 316L and AISI 304L material.

Finite element mesh.

The original version of this paper is available on https://www.scientific.net/KEM.1047.41.pdf

Accurate modelling of the bead profile is essential, as the deposited volume directly governs the heat input and thus the thermo-mechanical response of the substrate, as demonstrated by Santi et al. [13]. Although a realistic bead shape improves accuracy, it requires dense, case-specific meshing, making it impractical for studies on deposition-pattern or toolpath optimisation. For this reason, an area-equivalent rectangular bead representation is commonly adopted, offering simpler meshing and improved computational efficiency. In the present study, the bead width ( w ) is defined by the path offset distance (p) as 1.68 mm , while the bead height (h), determined from deposited volume conservation, equals 0.9186 mm .

This representation introduces sharp corners at the bead-substrate interface, which may locally increase thermal gradients and affect the local stress distribution compared to a parabolic bead profile. However, since the deposited area and heat input are preserved and the focus is on the global thermomechanical response of the substrate, particularly on its bottom surface, the simplification is considered acceptable within the scope of the study.

The numerical model replicates the real geometry of the substrate, brackets, and mounting frame used in the experiments. All deformable solid regions use eight-node hexahedral elements, with DC3D8 elements employed for the thermal analysis and C3D8 elements for the mechanical analysis. The mounting frame is modelled as a rigid body using R3D4 surface elements. The rectangular bead profile is represented by a 3×3 element layout, while the substrate mesh is gradually coarsened toward the edges and consists of four elements through the thickness, as shown in Fig. 4. The complete model contains 53,352 elements for the substrate and filler material, 2,112 elements for the mounting frame and 1,562 elements for each of the four clamping brackets.

Heat source model. During DED, molten filler material is continuously added along the toolpath, producing strong local heating and phase changes. In the simulation, this behaviour is captured by activating quiet elements once they reach the melting temperature Tmelt , meaning that the heat source accounts only for the excess energy transferred to the substrate. The volumetric power distribution follows the ellipsoidal model of Goldak et al. [14], as given by Eq. (1).

Q(x,y,z)=63Pηsysαsubfsubππabcexp(−3(x2a2+y2b2+z2c2)),(1)

where P is the nominal laser source power, whereas a,b and c define the semi-axes of the ellipsoid along the x,y and z directions, respectively. The heat-source efficiency combines system losses ηsys, substrate absorptivity αsub , and the fraction of power absorbed in substrate that does not contribute to melting of the filler material fsub. .

[picture]

Fig. 4. Finite element mesh of the substrate and beads (white), clamps (grey), and rigid mounting frame (green). Preload is applied to the clamps via spring elements at the reference points (RP). Hinge constraints (orange) are imposed on the clamps, the mounting frame is fully constrained, while the substrate is restrained only by contact interactions.

Fig. 4. Finite element mesh of the substrate and beads (white), clamps (grey), and rigid mounting frame (green). Preload is applied to the clamps via spring elements at the reference points (RP). Hinge constraints (orange) are imposed on the clamps, the mounting frame is fully constrained, while the substrate is restrained only by contact interactions.

For the present DED system, the heat-source semi-axes a,b, and c correspond to a four-sigma laser-spot diameter of 3.4 mm and a measured melt-pool penetration depth of 0.45 mm , with approximately 25% of the nominal laser power lost within the deposition head. The complete parameter set is listed in Table 4.

Table 4. Heat source parameters used in the numerical case studies.

Parameter | Symbol | Value
Nominal laser power [W] | P | 1000.0
Laser beam radius [mm] | a, b | 1.700
Laser beam penetration depth [mm] | c | 0.450
Cladding system efficiency [/] | ηsys | 0.750
Substrate absorptivity coefficient [/] | αsub | 0.360
Heat fraction transferred to substrate [/] | fsub | 0.693

Thermal boundary conditions.

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In the thermal model, only the substrate and deposited beads are represented explicitly, while the influence of the clamps and mounting frame is accounted for through an equivalent heat-loss boundary condition. The model also includes radiation and free convection on the substrate surface.

To determine the relevant heat-transfer coefficients, two cooldown experiments were performed: one with the substrate supported on tips to capture free convection, and one with the substrate clamped to the water-cooled mounting frame to characterise conductive heat loss. Emissivity values were identified by comparing thermal images with thermocouple measurements, resulting in 0.3 for the substrate surface and 0.9 for the speckle pattern. These surface emissivity values are also applied in the Stefan-Boltzmann radiation model.

Each cooldown experiment begins by uniformly heating the substrate and recording the subsequent cooling behaviour. The temperature field at the moment heating is stopped is processed and mapped onto a numerical mesh to initialise a steady-state analysis in Abaqus. Transient simulations are then
conducted over a range of convection and conduction coefficients, and the values that best reproduce the measured cooldown profiles are selected for subsequent DED simulations.

The calibration results, shown in Fig. 5, indicate that a surface film coefficient of 9 W/(m2 K) adequately represents free convection, while 500 W/(m2 K) corresponds to conductive heat transfer to the mounting frame. Thermal radiation is included in both simulations using the calibrated emissivity and an ambient temperature of 298 K . The calibrated boundary condition parameters, summarised in Table 5, are applied consistently throughout the subsequent numerical case studies.

[picture]

Fig. 5. Experimental (IR) and computed temperature histories at the substrate centre for (a) free convection boundary conditions and (b) conductive heat transfer to the mounting frame.

Fig. 5. Experimental (IR) and computed temperature histories at the substrate centre for (a) free convection boundary conditions and (b) conductive heat transfer to the mounting frame.

Table 5. Calibrated thermal boundary condition parameters used in the numerical case studies.

Parameter | Symbol | Value
Ambient temperature [K] | T∞ | 298.0
Cold water temperature [K] | Tbase | 292.0
Convective film coefficient [W/(m² K)] | hconv | 9.0
Contact film coefficient [W/(m² K)] | hcond | 500.0
Substrate surface emissivity [/] | εsub | 0.3
Speckle pattern surface emissivity [/] | εDIC | 0.9

Mechanical boundary conditions.

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In the mechanical analysis, the interaction between the substrate, clamping brackets, and mounting frame is captured through surface contact, combined with the compliance of the screw connections. Rather than prescribing fixed supports, the flexibility of each screwed joint is represented using axial springs with a stiffness of 5000 N/mm. These springs are attached to reference points (RP) associated with the washer regions beneath the screws, as shown in Fig. 4.

Contact interactions use hard pressure-overclosure behaviour and penalty friction with a coefficient of 0.3 . Gravity is included in all simulations. The mounting frame is fully fixed, while each clamping bracket is permitted to rotate about its outer edge, effectively acting as a hinge. As a result, the substrate is constrained only through these contact regions, with no additional boundary conditions applied.

Results and Discussion

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Thermal model validation.

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To evaluate the thermal model, numerically predicted temperature fields are compared with IRcamera measurements for the unidirectional deposition case. Three representative monitoring points on the underside of the substrate, located beneath the first, middle, and last cladding paths, are selected for direct comparison with corresponding numerical nodes, as shown in Fig. 6a-c.

[picture]

Fig. 6. Simulated (black) and measured (red) temperature histories on the bottom surface for the unidirectional deposition path beneath the: (a) first bead, (b) middle bead, and (c) last bead. The right-hand side shows corresponding bottom surface temperature fields during deposition obtained from: (d) IR measurements and (e) numerical simulation.

Fig. 6. Simulated (black) and measured (red) temperature histories on the bottom surface for the unidirectional deposition path beneath the: (a) first bead, (b) middle bead, and (c) last bead. The right-hand side shows corresponding bottom surface temperature fields during deposition obtained from: (d) IR measurements and (e) numerical simulation.

The model captures the overall thermal evolution with good agreement. The mean difference between measured and predicted temperatures is approximately 4.8 K , with the largest deviations occurring near peak temperatures due to IR-camera resolution limits and minor timing offsets. Spatial comparisons of full-field temperature distributions, shown in Fig. 6d-e, further confirm the consistency between simulation and experiment, supporting the validity of the thermal model for subsequent thermo-mechanical analyses.

Mechanical model validation.

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The mechanical model is assessed by comparing simulated vertical displacements for the unidirectional deposition case with in situ measurements obtained from the stereo DIC system. Fig. 7a-c shows the temporal evolution of out-of-plane deformation along a representative cross section during deposition, and Fig. 7d presents the final deformations after cooling and unclamping. Numerical predictions reproduce the main displacement patterns during deposition and match the experimental observations well within the DIC measurement region.

Larger discrepancies appear toward the end of the process, which can be attributed to the use of an isotropic hardening model and simplifications in the clamping representation. These effects cause the model to respond slightly too stiffly during deposition and to overestimate deformation after cooling and unclamping. Before unclamping, the maximum error between the numerical and experimental results reaches 0.328 mm , and after unclamping the release of residual stresses increases the discrepancy to a maximum of 0.520 mm . Despite these deviations, the simulated displacement evolution follows the experimental trends closely, and the comparison of full-field displacement field in Fig. 7e-f confirms that the thermo-mechanical model provides sufficiently accurate predictions for further analysis.

[picture]

Fig. 7. Simulated (black) and measured (red) substrate vertical displacement profiles for the unidirectional deposition path at the end of the: (a) third bead, (b) middle bead, and (c) last bead. Subfigure (d) shows the substrate displacement profile after cooldown and unclamping. The righthand side shows corresponding bottom surface displacement fields at the end of deposition before unclamping obtained from (e) stereo DIC measurements and (f) numerical simulation.

Fig. 7. Simulated (black) and measured (red) substrate vertical displacement profiles for the unidirectional deposition path at the end of the: (a) third bead, (b) middle bead, and (c) last bead. Subfigure (d) shows the substrate displacement profile after cooldown and unclamping. The righthand side shows corresponding bottom surface displacement fields at the end of deposition before unclamping obtained from (e) stereo DIC measurements and (f) numerical simulation.

Influence of deposition pattern on residual stresses and substrate deformation.

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The validated model is applied to compare three toolpath layouts: unidirectional, bidirectional and inward spiral. Fig. 8 presents the resulting vertical deformation of the substrate bottom surface before and after unclamping. The inward-spiral strategy leads to the lowest overall deformation and exhibits minimal springback after release, whereas both linear strategies show noticeably larger deformation and more pronounced springback.

[picture]

Fig. 8. Simulated (black) and measured (red) vertical displacement profiles of the substrate at room temperature before unclamping for the: (a) unidirectional, (b) bidirectional, and (c) inward spiral deposition paths.

Fig. 8. Simulated (black) and measured (red) vertical displacement profiles of the substrate at room temperature before unclamping for the: (a) unidirectional, (b) bidirectional, and (c) inward spiral deposition paths.

The evolution of the maximum vertical displacement during deposition is shown in Fig. 9, where the reported values represent the maximum within the DIC measurement area. The bidirectional strategy results in the largest displacement, reaching approximately 1.30 mm , followed by the unidirectional case with about 1.15 mm , while the inward-spiral path limits the displacement to 0.81 mm . The numerical model slightly overpredicts the final peak values by up to 0.166 mm(14.4%) for the unidirectional, 0.082 mm(6.3%) for the bidirectional and 0.065 mm(8.0%) for the spiral strategy.

[picture]

Fig. 9. Maximum substrate displacement history during unidirectional (black), bidirectional (red) and inward spiral (blue) cladding. Solid lines denote numerical simulation results, while dashed lines of the same colour indicate DIC measurements.

Fig. 9. Maximum substrate displacement history during unidirectional (black), bidirectional (red) and inward spiral (blue) cladding. Solid lines denote numerical simulation results, while dashed lines of the same colour indicate DIC measurements.

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.41.pdf

This study presents a thermo-mechanical FE framework for DED, together with an in-situ validation setup that combines IR thermography and stereo DIC, enabling continuous full-field monitoring of temperature and deformation on the underside of the substrate. The calibrated model reproduces the measured thermal and mechanical behaviour with good accuracy and highlights the importance of realistic boundary conditions. The simulation results show that the inward-spiral deposition strategy yields the smallest distortions, whereas linear paths produce larger deflections and more pronounced springback.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/KEM.1047.41.pdf

The authors acknowledge the financial support of the Slovenian Research Agency (research core funding No. P2-0263 and No. N2-0328).

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.41.pdf

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Z. Yan, W. Liu, Z. Tang, X. Liu, N. Zhang, M. Li, H. Zhang, Review on thermal analysis in laser-based additive manufacturing, Opt. Laser Technol 106 (2018), pp. 427-441.

M. Pratt, H. Mirmohammad, O. Kingstedt, T. Ameel, S. Rao, High-speed bottom-up surface temperature measurements of a flat plate: A thermal analog for laser metal additive manufacturing, J. Manuf. Process. 122 (2024), pp. 65-82.

P. Li, J. Liu, J. Zhou, A. Feng, Y. Gong, J. Lu, X. Meng, In-situ and off-line deformations of cylindrical walls manufactured by directed energy deposition with different dwell times, Measurement 198 (2022), 111402.

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B. Starman, T. Pepelnjak, A. Maček, M. Halilovič, S. Coppieters, Inverse calibration of out-ofplane shear anisotropy parameters of sheet metal, Int. J. Solids Struct. 313 (2025), 113313.

Š. Obid, B. Starman, J. Urevc, M. Halilovič, Extraction of uniaxial stress-strain curve from bending test using DIC measurements, Eur. J. Mech. A-Solid. 106 (2024), 105346.

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A. Santi, M. Bayat, V. K. Nadimpalli, A. Fabrizi, F. Bonollo, J. H. Hattel, Validation of an experimentally-based heat source for flash heating modeling of directed energy deposition: Systematic study of process and simulation parameters, J. Manuf. Process. 121 (2024), pp. 3550.

J. Goldak, A. Chakravarti, M. Bibby, A new finite element model for welding heat sources, Metall. Trans. B 15 (1984), pp. 299-305.

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 53-64

doi: 10.4028/p-AHYtF2

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-18

Revised:
2026-02-13

Accepted:
2026-02-14

Online:
2026-04-13

Advanced Characterization of Flexible Auxetic Resin Lattice Structures Produced by Stereolithography

The original version of this paper is available on https://www.scientific.net/KEM.1047.53.pdf

Stefano Belcuore 1,a, Stefano Pandini 1, b, Elisabetta Ceretti 1,c, Abdullah Al Maharbi 2, d, Mohammed M. Al-Hinaai 3,e and Paola Ginestra 1,f*

1 Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze 38, 25123, Brescia (BS), Italy

2 Department of Engineering, German University of Technology in Oman (GUtech), Oman

3 Department of Electrochemistry and Catalysis, College of Applied and Health Sciences, A 'Sharqiyah University, Oman

a s.belcuore@unibs.it, b stefano.pandini@unibs.it, c elisabetta.ceretti@unibs.it, d  22-0872@student.gutech.edu.om, e  Mohammed.alhinaai@asu.edu.om, f  paola.ginestra@unibs.it

Keywords: lattice structures, flexible resin, stereolithography.

Abstract

Lattice metamaterials with adjustable auxetic behavior are characterized by periodic configurations of interconnecting struts and nodes, allowing for precise control over their macroscopic mechanical properties. Different lattice configurations were examined, two-unit cell variants with varying void fractions were assembled into crystalline-inspired designs, specifically simple cubic and body-centered cubic. Using vat photopolymerization, fabrication was carried out using a transparent biomedical elastomeric resin that was chosen for its exceptional ductility and strain tolerance. The curing, crosslinking and thermal mechanical stability of the resin were examined using Fourier Transform Infrared Spectroscopy and Differential Scanning Calorimetry, before and after polymerization. In order to determine specific stiffness, specific yield strength, mechanical characterization involved quasi-static uniaxial compression testing. The effect of different aspects of the macroscopic structures was also observed, exploiting diverse possible applications. The combination of geometry and the behavior of the elastomeric material allowed the creation of lightweight structures that could support large reversible deformations that could be used in soft robotics and healthcare devices.

Introduction

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Lattice structures constitute a class of engineered cellular materials composed of periodically repeated unit cells, whose ordered networks of struts and nodes [ 1,2 ] allow control [ 3 ] over structural behavior. By tailoring geometry, these structures can be designed to exhibit a wide spectrum of mechanical responses ranging from bistability [4] and tunable Poisson's ratio [5] to deformation regimes [6]. Some configurations can deliver unconventional effects as auxetic structures [7-9], suitable for many applications, filtration devices [10], biomedical scaffolding[11], energy absorption and soft robotic systems [12]. In this study, the design process of the metamaterials originates from the base unit-cell. The main process is the transformation of a cube into a cuboctahedral [13,14] configuration by rotating each square face around its centroidal axis, generating a continuous topological reorganization while maintaining face geometry and edge lengths. On this framework, a secondary transformation is introduced by obtaining circular voids in the center of each face [15]. This modification triggers a torsional shift toward an octahedral arrangement and causes the narrow wall between the voids to elastically buckle [16,17]. These walls act as compliant hinges that guide the coordinated rotation of square and triangular surfaces. The structure ultimately undergoes a symmetric global contraction, reducing its volume. In elastomeric photopolymer, this deformation is fully reversible, and a specific subset of these materials consists of photocurable elastomeric formulations, which undergo photopolymerization reactions compatible with stereolithography (SLA) [18]. Developments in additive manufacturing (AM) have markedly enhanced the capability to produce lattice structures with intricate geometries and precise dimensional control. In vat-
photopolymerization processes such as SLA, a narrowly focused UV laser beam scans the resin, triggering localized photopolymerization. This solidification pathway enables fine feature resolution and smooth surface finishes, although it typically requires longer fabrication times relative to faster AM techniques. This study aims to demonstrate how SLA, used in combination with transparent, flexible, and biocompatible polymeric materials, enables the fabrication of geometrically complex structures. The investigation first focuses on the chemical composition and intrinsic properties of the material, followed by an analysis of the mechanical behavior and response of the cellular metamaterials produced across multiple aspect ratios.

Materials and Methods

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Design.

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The base unit cell was modeled in Autodesk Inventor Professional (version 2025, ©1982-2025 Autodesk) to parametrize the geometry through three independent design variables. These parameters, the inscribed outer diameter, the inner pore diameter, and the inscribed angle, define the global dimensions of the unit cell and its porosity. Two different cell geometries were examined in this study, distinguished only by their void fraction. Cell "A" exhibits a higher density, whereas cell "B" is characterized by increased porosity and consequently lower density. The unit cells were assembled into two distinct lattice configurations: simple cubic (SC) and body-centered cubic (BCC). In this framework, the SC arrangement is generated from cell "A", while the BCC lattice is composed of cell "B". To further investigate structures with reduced aspect ratios (i.e., thickness to width ratio), an additional group of samples was produced by adding the prefix " 1:4 " to the nomenclature, corresponding to a thickness/width ≈0.4. This procedure yielded the 1:4SC and 1:4BCC structures, which maintain the same spatial organization as SC and BCC but incorporate modified geometric proportions. The geometries SC,1:4SC,BCC,1:4BCC are illustrated in Fig. 1C (i, iii) and Fig. 1D (i, iii) while in Fig. 1A (i) and Fig. 1B (i) are presented the unit cells employed in the lattices. The three parametric variables are inscribed angle, inner pore diameter and inscribed outer diameter and are detailed in Fig. 1A (iii) and Fig. 1B (iii). BCC and SC structures consist of a 3×3×3 array of unit cells, while 1:4 BCC consists of a 5×5×3 array of unit cells and 1:4SC is made of 6×6×2, to ensure that the samples retain the same aspect ratio. The spatial configuration of the unit cells "A" and "B" is shown in Fig. 1C (ii) and Fig. 1D (ii).

Production.

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All samples were produced using a SLA 3D printing system (Form 3BL, © Formlabs 2025, Boston, MA, USA) in conjunction with the flexible photopolymer material (Biomed Elastic 50A V1), as illustrated in Fig. 2. The printing parameters were defined within the Preform slicing software (C) Formlabs 2025, Boston, MA, USA) and are reported in Table 1. The subsequent post-processing procedures of washing and curing are essential for achieving the required quality and functional performance of the printed components and are also summarized in Table 1.

Table 1. Process and post-process parameters for Formlabs 3BL Printer.

Layer

height mathematical formula

Washing

Time mathematical formula

Curing

Time mathematical formula

Drying

Temperature mathematical formula

Washing

Solution

0,100 | 15,0 | 30 | 70,0 | Isopropyl Alcohol

Chemical and thermal analysis.

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Chemical and thermal analysis characterization of the prepared specimens include density measurements, viscosity evaluation, and thermal and spectroscopic analyses. Thermal behavior was investigated by differential scanning calorimetry (DSC), while chemical functionalities were assessed by Fourier-Transform Infrared Spectroscopy (FT-IR). Rheological properties and density of the uncured resin were also quantified to evaluate its flow behavior, recoating performance, and optical/mechanical implications during the printing process. Viscosity of the uncured resin was evaluated to characterize flow properties relevant to recoating dynamics and fine feature replication
during stereolithographic printing. Measurements were carried out using a rotational viscometer (Ametek brookfield, Middleboro, Massachusetts, USA). Density of the uncured resin was determined to assess factors influencing light penetration depth, buoyancy effects during layer formation, and interlayer adhesion. Measurements were performed using pycnometry ( 25∘C ) and oscillating U-tube densitometer DMA 35 (Anton Paar, Graz, Austria). FT-IR spectra were acquired on the uncured resin using a Bruker FT-IR Spectrometer (Bruker, Billerica, Massachusetts, USA) to identify characteristic functional groups and verify the chemical composition prior to photopolymerization. DSC tests were performed on slices (about 10 mg ) sectioned from the printed specimens. Thermal scans were conducted using a DSC Q100 calorimeter (TA Instruments, New Castle, Delaware, USA). Each sample was subjected to a single heating cycle from −50∘C to 150∘C at a constant heating rate of 10∘C/min.

Dimensional accuracy Analysis.

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After fabrication, all specimens were examined using an optical microscope (Quick Scope QS250Z, ©Mitutoyo, Sakado, Japan) to assess dimensional accuracy. Five critical geometric parameters were measured: Total length, Right and Left wall thickness, and internal pore diameter along both the X and Y axes, as illustrated in Fig. 1A (ii) and Fig. 1B (ii). For BCC and SC samples, measurements were acquired from three cells located along the main diagonal on each of two mutually perpendicular faces. For the 1:4 BCC and 1:4 SC structures, one cell per row was evaluated on two orthogonal faces. For each of the five measured dimensions, the mean value and standard deviation were subsequently calculated.

[picture]

Fig. 1. Cell "A" and cell "B" are illustrated in A(i) and B(i). For both cell types, (ii) focus on the five critical parameters used to conduct the dimensional accuracy test, while (iii) display, highlighted in red, the three parametric values used to create the whole cell. C (i-iii) and D (i-iii) illustrate all the lattice structure and the spatial arrangement of the unit cells. E (i-ii) illustrates a schematic representation of the reference area used to calculate axial and transverse Poisson's ratio.

Fig. 1. Cell "A" and cell "B" are illustrated in A(i) and B(i). For both cell types, (ii) focus on the five critical parameters used to conduct the dimensional accuracy test, while (iii) display, highlighted in red, the three parametric values used to create the whole cell. C (i-iii) and D (i-iii) illustrate all the lattice structure and the spatial arrangement of the unit cells. E (i-ii) illustrates a schematic representation of the reference area used to calculate axial and transverse Poisson's ratio.

Compression test.

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An electromechanical testing machine (Instron 3366, ©Instron, Norwood, Massachusetts, USA) equipped with a 10 kN load cell was employed to perform uniaxial compression experiments. Quasi static assessments were conducted under displacement controlled conditions and continued to high compression levels until structural failure typically manifested as the rupture of one or more cell walls or, for specimens that did not fracture (e.g., 1:4 SC configurations), until reaching the maximum applied load of 9000 N . Owing to variations in specimen height, a constant crosshead displacement rate was imposed to ensure an identical nominal strain rate ( 5 min−1 ) across all tests. Throughout the uniaxial compression procedure, images were captured at fixed intervals of 10 seconds to document, frame by frame, the progression of deformation both along the loading axis and in the transverse direction, as well as the morphological evolution of the cellular architecture. The acquired images were subsequently processed using ImageJ (NIH, USA) to evaluate Poisson's ratio behavior of the structures. For this analysis, four tracking points were selected at the centers of the corner cells located along the structure's boundary, thereby defining a rectangular reference frame, as depicted in Fig. 1E (i, ii).

Results

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All generated structures are shown in Fig. 2. Distinct differences among the structures are clearly observed, arising from the different spatial arrangements of the two cells, as well as from variations in aspect ratio and overall dimensions.

[picture]

Fig. 2. Photographs of the fabricated samples produced using Biomed Elastic 50A resin and the Formlabs 3BL printer: (A) illustrate SC and 1:4 SC; (B) illustrate BCC and 1:4 BCC.

Fig. 2. Photographs of the fabricated samples produced using Biomed Elastic 50A resin and the Formlabs 3BL printer: (A) illustrate SC and 1:4 SC; (B) illustrate BCC and 1:4 BCC.

Chemical and thermal Analysis.

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The study of the printed structures begins with an analysis of the chemical physical properties of the material. Table 2 summarizes the density and viscosity of the Biomed Elastic 50A (BE50A) resin. The viscosity of the resin, measured is 1120mPa·s at 25∘C, plays a critical role in resin flow behavior and layer recoating during the printing process. A viscosity within the range of approximately 1000−1200mPa·s ensures effective resin leveling between successive layers, which is particularly advantageous in both top-down and bottom-up SLA systems. Excessively high viscosity could impede smooth recoating and promote the entrapment of air bubbles, potentially leading to incomplete curing or interlayer delamination. Conversely, low viscosity could cause excessive resin drainage, resulting in reduced dimensional accuracy and poor feature definition. The moderately viscous nature of BE50A can support high resolution printing, especially for lattice-based or geometrically intricate structures, while enhancing process stability by minimizing motion-induced defects in uncured layers and improving surface finish. The density of the BE50A resin is equal to 1.13 g/cm3 at 25∘C, influences both optical penetration and curing behavior. This density indicates a material composition that allows sufficient ultraviolet light transmission, which is essential for achieving uniform curing depths and accurate resolution along the build direction. Significantly higher densities could increase light absorption or scattering, thereby compromising complete layer polymerization. From a structural perspective, a density of 1.13 g/cm3 provides a favorable mass-tovolume ratio, contributing to adequate mechanical integrity while limiting material consumption.

Moreover, this property enhances dimensional stability during post-curing, reducing the risk of shrinkage or warping, which is particularly critical for complex geometries.

Table 2. Chemical properties data of the printed structures material BE50A.

Property

Measured Value

Unit
Method/ Instrumentation

Viscosity | 1120 | mathematical formula | Rotational Viscosimeter

Density
1,13
mathematical formula

Pycnometry and Oscillating

U-tube

The chemical characterization is further extended through FT-IR spectroscopy, which is a fundamental technique for confirming the chemical structure of the resin. FT-IR analysis enables the identification of characteristic vibrational modes, including carbonyl ( C=O ), ether/ester ( C−O−C ), and aliphatic C−H stretching regions. This analysis allows verification of the resin's primary polymer backbone, such as polylactic acid (PLA) based derivatives, assessment of functional groups responsible for UV-induced crosslinking, and detection of possible impurities or degradation products. The FT-IR spectrum, shown in Fig. 3, reveals a pronounced carbonyl stretching band around 1750 cm−1, indicative of ester functional groups. This feature is essential for photopolymer resins, as ester groups play a key role in UV-activated crosslinking. The sharp and intense nature of this peak suggests a high degree of purity and an effective formulation of reactive PLA-based monomers or oligomers. In the 1080 cm−1 region, the presence of strong C−O−C stretching vibrations confirms the ester linkages connecting the PLA monomer units. These bonds are particularly responsive to UV exposure, enabling rapid and efficient polymerization under SLA conditions. Notably, the absence of broad O−H stretching bands in the 3200−3600 cm−1 range indicates low moisture content and a lack of unreacted acids or alcohols. This characteristic is advantageous for maintaining resin stability, ensuring consistent UV reactivity, and improving the dimensional accuracy of the printed components. Additionally, the observed aliphatic hydrocarbon stretching and bending modes, associated with −CH3 and −CH2 groups around 2945 cm−1 and 1450 cm−1, reflect the flexible nature of the polymer chains. Such flexibility contributes to controlled shrinkage during curing and enhances the mechanical resilience of the final printed structures.

[picture]

Fig. 3. FT-IR analysis performed on the resin in the liquid state prior to the crosslinking (curing) process.

Fig. 3. FT-IR analysis performed on the resin in the liquid state prior to the crosslinking (curing) process.

Thermal analysis of the printed samples was carried out by differential scanning calorimetry (DSC), and the results are reported in Fig. 4. Specifically, Fig. 4A refers to a specimen extracted from the

BCC structure and a weak exothermic signal is observed during the first heating ramp, then it disappears during the second heating cycle. The same thermal behavior is consistently detected in all three specimens extracted from the SC structure, shown in Fig. 4B. This exothermic event can be attributed to the evaporation or release of residual solvents entrapped within the printed structures.

[picture]

Fig. 4. DSC analysis performed on samples extracted from the structures. (A) shows the analysis carried out on a single sample taken from the BCC structure, whereas (B) presents the analysis performed on three different samples extracted from the SC structure: the layer in contact with the printing platform, the core, and the top layer.

Fig. 4. DSC analysis performed on samples extracted from the structures. (A) shows the analysis carried out on a single sample taken from the BCC structure, whereas (B) presents the analysis performed on three different samples extracted from the SC structure: the layer in contact with the printing platform, the core, and the top layer.

Dimensional accuracy Analysis.

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For each of the four fabricated structures, three primary target dimensions were defined, and measurements were performed on two faces of each specimen, as summarized in Table 3. The dimensional measurement results are presented as histograms in Fig. 5 and Fig. 6. Specifically, Fig. 5 reports the data for the BCC and SC structures, whereas Fig. 6 shows the results for the 1:4 BCC and 1:4 SC configurations. In all histograms, the error bars represent the standard deviation of the measured values, providing an indication of dimensional variability, while the horizontal red line denotes the nominal target dimension specified in the CAD model. BCC and SC structures were designed using different repeating unit cells and consequently their corresponding reference CAD target values are different.

Table 3. Dimensional accuracy analysis values for each structure Diameter ( X and Y ), Thickness (LH and RH) and total Length (L) are reported in Fig. 1 A,B (iii).

SAMPLE | CELL | SIDE | DIAMETER [mm] | THICKNESS [mm] | TOTAL LENGTH [mm]
|  |  | CAD | X | Y | CAD | LH | RH | CAD | L
SC | SC cell cross-section | Face 1 | 3,46 | 3,50 ± 0,04 | 3,24 ± 0,08 | 2,47 | 2,49 ± 0,03 | 2,48 ± 0,06 | 8,40 | 8,45 ± 0,07
Face 2 | 3,48 ± 0,01 | 3,23 ± 0,07 | 2,50 ± 0,06 | 2,48 ± 0,02 | 8,45 ± 0,10
BCC | BCC cell cross-section | Face 1 | 4,18 | 4,32 ± 0,05 | 3,63 ± 0,29 | 2,11 | 2,00 ± 0,06 | 1,91 ± 0,11 | 8,40 | 8,20 ± 0,10
Face 2 | 4,36 ± 0,02 | 3,75 ± 0,33 | 1,93 ± 0,04 | 1,93 ± 0,04 | 8,20 ± 0,07
1:4 SC | 1:4 SC cell cross-section | Face 1 | 3,46 | 3,56 ± 0,06 | 3,15 ± 0,16 | 2,47 | 2,34 ± 0,11 | 2,38 ± 0,08 | 8,40 | 8,25 ± 0,09
Face 2 | 3,59 ± 0,05 | 3,13 ± 0,21 | 2,33 ± 0,20 | 2,39 ± 0,09 | 8,27 ± 0,15
1:4 BCC | 1:4 BCC cell cross-section | Face 1 | 4,18 | 4,49 ± 0,10 | 3,93 ± 0,11 | 2,11 | 1,85 ± 0,08 | 1,87 ± 0,05 | 8,40 | 8,21 ± 0,06
Face 2 | 4,43 ± 0,04 | 3,83 ± 0,40 | 1,89 ± 0,04 | 1,88 ± 0,07 | 8,19 ± 0,05

The graphs associated with the SC structure Fig. 5A are used as the reference case. Nonetheless, several relevant trends can be identified. As shown in Fig. 5A (i), the diameters measured along the horizontal (X) direction exhibit a higher mean value than those measured along the vertical (Y) direction. In addition, the X -axis measurements display a lower standard deviation and are in closer agreement with the nominal dimension defined in the CAD model. Notably, the results are consistent across the two analyzed faces, as both X and Y direction diameters show comparable values and trends. The largest deviation from the CAD reference is observed for the Y-axis diameters, with an average difference of -0.20 mm . From Fig. 5A (ii), it is evident that the left cell wall is systematically thicker than the right one. Among the two, the right wall thickness most closely matches the target value specified in the CAD model. The maximum deviation from the nominal dimension occurs for the left wall thickness, with a mean offset of +0.03 mm , indicating good dimensional stability and overall process consistency. The final graph Fig. 5A (iii) shows that the mean values are identical between the two measurement faces. However, this consistency is not fully reflected in the dispersion of the data, as the standard deviation is slightly higher for Face 2. In both cases, the largest deviation from the CAD reference corresponds to a mean offset of +0.05 mm . The dimensional analysis of the BCC metamaterial, reported in Fig. 5B, reveals trends that are shared with SC structure, while also highlighting distinct deviations associated with this specific structural configuration. As shown in Fig. 5B (i), the internal diameter measurements follow a pattern like SC structure; however, a systematic offset is observed along the horizontal (X) direction, where all measured values exceed the nominal dimension defined in the CAD model. This behavior results in the largest discrepancy with a maximum deviation of -0.50 mm relative to the target value. Fig. 5B (ii) presents the cell wall thickness measurements, which display relatively uniform mean values across the analyzed faces, despite noticeable differences in the associated standard deviations. In all cases, the measured thicknesses remain below the CAD reference, with a maximum deviation of -0.20 mm , indicating consistent under-sizing. The cell length results reported in Fig. 5B (iii) show identical mean values for both measurement faces, demonstrating good repeatability. Nevertheless, these mean values are significantly lower than the corresponding CAD specification when compared to SC structures. The maximum deviation from the nominal value in this case reaches 0.20 mm , confirming a systematic reduction in cell length.
Fig. 6A (i) exhibits the same behavior observed for the SC structure, with diameters measured along the horizontal ( X ) direction showing higher mean values than those measured along the vertical ( Y ) direction. Moreover, the X -axis measurements are characterized by a lower standard deviation and a closer agreement with the nominal dimensions defined in the CAD model. The results are consistent across the different analyzed faces, indicating good repeatability. Fig. 6A (ii) highlights a systematic trend in wall thickness measurements: the left-side thickness is consistently smaller than the right side thickness. Nevertheless, both values remain below the CAD reference, with a maximum deviation of -0.13 mm . Finally, Fig. 6A (iii) shows that the mean values measured on the two faces are very similar, demonstrating uniformity between measurement locations. However, both values deviate from the CAD specification, exhibiting an average offset of -0.15 mm . Fig. 6B (i), which reports the internal cell diameter, shows trends consistent with those observed in the previously analyzed structures. Conversely, Fig. 8B (ii) highlights a distinct behavior, as all mean values are closely clustered and exhibit a systematic deviation from the CAD reference of approximately -0.30 mm . Fig. 8B (iii) also presents tightly grouped average values, with a uniform offset of about -0.20 mm relative to the nominal dimensions defined in the CAD model.

[picture]

Fig. 5. Shows graphs related to the SC(A) and BCC(B) structures; each structure includes three analyses: internal cell diameter (i), cell thickness (ii), and cell length (iii). The columns represent the mean values, and error bars indicating the standard deviation. The horizontal red line denotes the CAD model reference value. Number " 1 " positioned on top of the columns refer to face 1 , while number " 2 " correspond to face 2.

Fig. 5. Shows graphs related to the S C ( A ) and B C C ( B ) structures; each structure includes three analyses: internal cell diameter (i), cell thickness (ii), and cell length (iii). The columns represent the mean values, and error bars indicating the standard deviation. The horizontal red line denotes the CAD model reference value. Number " 1 " positioned on top of the columns refer to face 1 , while number " 2 " correspond to face 2.

[picture]

Fig. 6. Shows graphs related to the 1:4SC(A) and 1:4BCC(B) structures; each structure includes three analyses: internal cell diameter (i), cell thickness (ii), and cell length (iii). The columns represent the mean values, and error bars indicating the standard deviation. The horizontal red line denotes the CAD model reference value. Number "1" positioned on top of the columns refer to face 1 , while number " 2 " correspond to face 2 .

Fig. 6. Shows graphs related to the 1 : 4 S C ( A ) and 1 : 4 B C C ( B ) structures; each structure includes three analyses: internal cell diameter (i), cell thickness (ii), and cell length (iii). The columns represent the mean values, and error bars indicating the standard deviation. The horizontal red line denotes the CAD model reference value. Number "1" positioned on top of the columns refer to face 1 , while number " 2 " correspond to face 2 .

Compression test.

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Fig. 7 presents the specific apparent stress versus apparent strain responses of the studied geometries, the term apparent is used to indicate that the stress was calculated as the applied force normalised by the total cross-sectional area, while the strain was evaluated as the ratio between the crosshead displacement and the initial specimen height, specific instead refers to normalization of the stress by the density of the lattice. A solid cube fabricated from Biomed Elastic 50A (BE50A) resin is included as a reference to represent the mechanical behavior of the bulk material and to emphasize the higher apparent compliance of the lattice-based samples. Curves reveals a progressive reduction in apparent stiffness, starting from 1:4SC to 1:4BCC, governed by both the relative density and the specific unit cell topology. The most important values are reported in Table 4, for each configuration, the density, specific apparent Young's modules, specific stress at a fixed apparent strain level ( 20% ), and Poisson's ratio. The highest stiffness is achieved by the solid cube, with a specific apparent Young's modulus of 2.3MPa/(g/cm3). Among the lattice structures, the 1:4SC and SC configuration exhibit the highest stiffness. The remaining systems show a more compliant mechanical response. Notably, these structures display a similar deformation behavior: an initial linear elastic region which ends one is reached a value of apparent strain occurring between 5% and 15%, then a pronounced stress plateau is observed which is subsequently followed by a rapidly specific apparent stress increase at higher deformations in the densification zone. The critical points highlighted in Fig. 7 correspond to the onset of structural collapse, key feature for energy absorption applications. In contrast, the SC based structures do not exhibit a defined plateau; instead, only an inflection point is observed in the stress strain curves, indicating a change in slope associated with barreling effects. Under compressive loading, the SC and 1:4 SC structures show some barreling phenomena, characterized by an increase in lateral dimensions. Conversely, the BCC and 1:4 BCC configurations exhibit minimal lateral expansion and deform predominantly through localized buckling of the cell struts. These local instabilities progressively evolve into global deformation modes, resulting in complex collapse mechanisms distinct from those observed in the SC architectures.
Uniaxial compression tests also enabled the evaluation of Poisson's ratio, characterizing the transverse to axial strain response under compressive loading. The SC-based structures exhibit a progressive increase in transverse deformation during the initial stages of axial compression, corresponding to a positive Poisson's ratio. In contrast, the BCC-based structures display transverse strains fluctuating around zero and a slightly negative transverse strain can be observed. These behaviors correspond, respectively, to a positive Poisson's ratio for the SC structures and a negative Poisson's ratio for the BCC structures, which indicating an auxetic mechanical response.

[picture]

Fig. 7. Specific stress-strain curves of all the samples along with the BE50A curve (in purple) representing the resin.

Fig. 7. Specific stress-strain curves of all the samples along with the BE50A curve (in purple) representing the resin.

Table 4. Mechanical properties data of the printed structures.

Structure | ρ [g/cm³] | EI [MPa/(g/cm³)] | σ0,2 [MPa/(g/cm³)] | ν0,2 [-]
BE50A | 1,01 | 2,30 | 0,584 | 0,44
SC | 0,58 | 1,12 | 0,225 | 0,14
BCC | 0,30 | 0,34 | 0,024 | -0,05
1:4 SC | 0,63 | 1,55 | 0,340 | 0,20
1:4 BCC | 0,36 | 0,24 | 0,032 | -0,02

Discussion

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Dimensional accuracy Analysis.

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Cell A, as designed, exhibits higher resistance to stress during the printing process compared to Cell B. This improved mechanical response can be attributed to the different level of porosity. The lower effective porosity of Cell A results enhances its ability to withstand process-induced stresses during fabrication. Furthermore, the spatial arrangement of the SC cells provides additional structural stability throughout the manufacturing process, particularly during the progressive formation of the lattice. This enhanced stability is associated with the significant reduction of unsupported surfaces relative to the BCC spatial configuration. In the SC arrangement, the geometric continuity and connectivity between adjacent struts limit the extent of overhanging features, thereby reducing deformation during layer-by-layer solidification. The aspect ratio also exerted a detrimental influence on dimensional accuracy. Specifically, structures characterized by a lower aspect ratio exhibited a higher number of cells affected by dimensional discrepancies. Collectively, these three factors cooperate to define the measured overall dimensional accuracy.

Compression test.

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The fundamental difference between structures characterized by an SC arrangement and those based on a BCC topology lies in their deformation mechanisms under compressive loading. SC structures exhibit an extended linear elastic region, beyond this regime, instabilities first develop locally in the form of cell barreling phenomena, where individual unit cells undergo progressive lateral expansion. As deformation proceeds, these localized instabilities evolve into a global mode involving the entire structure. In contrast, BCC structures display a shorter linear elastic region, followed by a distinct plateau phase in which deformation progresses at nearly constant stress. This stress plateau is associated with progressive cell collapse mechanisms. The deformation process culminates in a densification regime, where the collapsed cells compact and the stress rises rapidly due to material consolidation. The experimentally measured Poisson's ratio further highlights the different instability mechanisms between the two configurations. In BCC structures, a negative Poisson's ratio was observed during deformation, indicating auxetic behavior. This negative value implies localized densification mechanisms occurring during compression, which differ significantly from the lateral expansion typically observed in SC structures. The specific energy required for deformation, quantified as the area under the specific apparent stress versus apparent strain curves, is significantly lower for BCC structures compared to SC counterparts. Nevertheless, the ability of BCC architectures to undergo substantial deformation without a corresponding increase in stress, due to the extended plateau region, makes them particularly attractive for energy absorption applications.

Conclusion

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The study focused on both the mechanical analysis of the structures and the characterization of the material from which they are composed. Chemical and thermochemical analyses revealed excellent properties for the fabrication of complex geometries, additionally confirming that the manufacturing process was carried out correctly. Dimensional analysis showed that the highest dimensional precision and accuracy are achieved in structures with an aspect ratio greater than unity and without
unsupported elements or large overhangs. Nevertheless, the deviations from the threshold values set in the CAD model remain limited and do not affect the overall mechanical behavior of the structures. Mechanical analysis highlights the wide range of mechanical properties that these structures can provide, enabling potential applications where higher compliance is preferred over high stiffness. Image acquisition during mechanical testing allowed the calculation of Poisson's ratio for each structure, to verify the presence of auxetic behavior. BCC structures exhibit this behavior, although only weakly, likely due to the presence of planar elements that inhibit the free movement of the upper and lower rows of cells.

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 65-71

doi: 10.4028/p-lSElt0

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-18

Revised:
2026-02-18

Accepted:
2026-02-25

Online:
2026-04-13

Evaluation of SLA Biocompatible Resins Properties for Use as Dielectric Layers in 3D Printed Capacitive Force Sensors

The original version of this paper is available on https://www.scientific.net/KEM.1047.65.pdf

Alessandro Epicoco 1,a, Tiziano Fapanni 2, b, Stefano Pandini 1,c, Paola Serena Ginestra 1, d, Elisabetta Ceretti 1,e, Emilio Sardini 2,f, Miriam Seiti 1, g*

¹Department of Mechanical and Industrial Engineering, University of Brescia, IT

2 Department of Information Engineering, University of Brescia, IT

a  a.epicoco001@unibs.it, b  tiziano.fapanni@unibs.it, c  stefano.pandini@unibs.it, d paola.ginestra@unibs.it, e elisabetta.ceretti@unibs.it, f emilio.sardini@unibs.it, g* miriam.seiti@unibs.it

Keywords: 3D printed capacitive force sensor, SLA, flexible resin, electromechanical properties.

Abstract

Additive manufacturing (AM) technologies have enabled the fabrication of customizable, low-cost capacitive sensors for a wide range of applications, including robotics, automation, and bioelectronics. Although various AM techniques have been explored, structural inconsistencies often remain a challenge, limiting the performance and reproducibility of printed dielectric layers. Stereolithography (SLA), offers higher resolution and denser prints, yet the use of commercial photopolymer resins as dielectric materials remains underexplored. This study investigates two commercial SLA-compatible resins, a flexible medical-grade elastic resin and a dental-grade resin, as potential dielectric layers for capacitive force sensors. Both resins are biocompatible for short-term use or skin contact, making them suitable also for medical applications. The BioMed elastic 50A-V1 resin exhibited a Young's modulus of E=5.0±0.2MPa up to approximately 60% strain, whereas the Dental Clear V2 resin showed a significantly higher modulus of E=1020±80MPa under the same conditions. Therefore, the elastic resin was subsequently chosen as the dielectric material to fabricate a proof-of-concept capacitive force sensor, which exhibited a final capacitance of 1.13± 0.03 pF within a force range of 10 to 400 N . The findings serve as a preliminary step towards the development of fully 3D-printed capacitive force sensors for integration into soft robotic and smart biomedical systems.

Introduction

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The rapid advancement of additive manufacturing (AM) technologies has revolutionized the fabrication of electronic components, with 3D printed sensors emerging as a promising area of research. [1] These sensors can be implemented in different fields, such as robotics and clinical practice. [2-3] Recent developments in AM have indeed enabled the fabrication of low cost and easy processable capacitors, with customizable geometries and properties, offering advantages in miniaturization and integration with printed circuit boards. [4-8] In this frame, looking towards the integration of intelligence on everyday objects, [9] AM-based capacitors can be exploited to develop different type of sensors to fabricate smart objects. In a capacitor, the dielectric layer is a critical component determining sensor performance. In fact, the properties of a capacitor depends on its geometric properties and the type of dielectric used. [10] However, dielectric materials for capacitive sensing applications can vary widely and include substances like ceramics, thermoplastics, metal oxides, or even air. [11-12] In order to explore the capabilities offered by AM technologies, different works in the literature focus on various techniques, such as Fused Deposition Modelling (FDM) and stereolithography (SLA). [13-17] Although promising results, the performance of these techniques in producing dielectric printed layers remains a critical limitation, especially in processes like FDM, which inherently introduces structural inhomogeneities due to incomplete inter-line and inter-layer fusion. [12] This variability compromises the reliability and reproducibility of FDM-printed capacitors. On the other hand, Vat Photopolymerization (VPP) and in particular masked SLA, usually offers higher resolution and denser part structures than FDM, [18] leading to more consistent results.

However, while FDM materials have been explored as dielectric components, there remains a significant gap in the literature regarding the use of commercial SLA photopolymerizable resins. [1922] Therefore, this study evaluates the mechanical properties of two commercial SLA resins, a flexible medical-grade elastic resin and a dental-grade resin, for use as dielectric layers in SLAprinted capacitive sensors. Being biocompatible for short-term use or skin contact, these resins can also be applied in medical applications. A proof-of-concept capacitive force sensor is fabricated using the best performing resin to evaluate the feasibility of the material as dieletric. The results presented provide a preliminary investigation towards the development and fabrication of fully 3D-printed capacitive force sensors for soft robotics.

Materials and Methods

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Materials

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Two photocurable epoxy resins were chosen as the main dielectric layer materials: i) BioMed Elastic 50A-V1 and ii) Dental Clear V2 (Formlabs, USA). BioMed Elastic 50A-V1 is designed for soft tissue models and has a biocompatibility compliant with ISO 13485 standards, ensuring safety for longterm skin contact and short-term contact with mucous membranes. Dental Clear V2 (ISO 10993) dental exhibit reduced elasticity compared to 50 A−V1, but has significantly superior mechanical properties, making it a prevalent choice for the fabrication of resilient occlusal splints. Table 1 shows the mechanical properties of the BioMed Elastic 50A-V1 and Dental Clear V2 resins. Copper (Cu) tape was selected as conductive electrode material. Isopropanol (IPA, Sigma Aldrich, IT) and distilled water (DI) were used as cleaning solvents.

Manufacturing process

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A Prusa ©  SL1 Speed (CZ) SLA bottom-up system was used to produce the samples. SLA uses a highresolution LCD screen to project a mask of the intended layer onto a vat of liquid resin, where an ultra-violet (UV) light source solidifies the resin. The energy provided by the laser activates photo initiators present in the resin, initiating a chemical reaction that induces the polymerization of the monomers. Samples with different heights and three replicas for each resin were printed. Process parameters are reported in Table 2. Post-printing processes were performed using a Prusa Washing and Curing Machine for 10 and 15 minutes, respectively (CW1, Prusa (), CZ).

Materials Characterization

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Mechanical tests were performed using a dynamometer (Instron 3366, DE). The samples were mechanically tested by applying a force from 0 to 9000 N . Samples dimensions were 7×7 mm, with a variable thickness equal to 4.00 and 5.00 mm for both resins.

Table 1. Properties of selected SLA materials.

Property | BioMed Elastic 50A-V1 | Dental LT Clear V2
Density (g/cm³) | 1.1 | 1.09
Elastic Modulus (GPa) | 0.0016–0.0032 | 2.08
Poisson's Ratio | 0.3 | 0.35
Constitutive Model | Hyperelastic | Linear elastic

Table 2. SLA process parameters selected.

Process parameter | BioMed Elastic 50A-V1 & Dental LT Clear V2
Time 1st layer | 50 s
Time 2nd layer | 30 s
Thickness layer | 0.03 mm
Height (z) | 4.00, 5.00 mm
Dimensions (x,y) | 7.00 x 7.00 mm

The crosshead speeds were adopted to have a strain rate equal to 1 min−1. Three replicas were tested for each condition analysed.

Device Electrical Characterization

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To characterize the suitability of the selected material as force sensors, three 7×7×2 mm samples were prepared with conductive Cu tape to produce a set of capacitive devices. The experimental setup consists of an Instron test system (Model 3366), equipped with a 10 kN load cell. The uniaxial compression tests were performed at room temperature by using two planar compression platens with a 8 cm diameter to apply 10 load-unload cycles between two load targets of 10 and 400 N . Subsequently, 5 additional cycles with alternating maximum load peaks of 170 N and 270 N were carried out to assess the repeatability of the measurements after an appropriate preload phase.
Kapton foils were used as insulators during measurement acquisition process. The crosshead speed was specifically chosen to have a strain rate approximately equal to 1 min−1. For each test, the load vs crosshead displacement curve and the electrical capacitance signals were recorded over time. To measure the relevant electrical components, an MFIA 500 kHz Impedance Analyzer (Zurich Instruments) was interfaced with a laptop via a custom MATLAB script. The device was configured to sample its impedance at 10 kHz and to continuously monitor and output the corresponding capacitance over time.

Results and Discussion

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Process and Material Characterization

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Resin-based samples were successfully 3D SLA printed using the same set of print parameters. As illustrated in Fig. 1A, the resin 50A-V1 exhibits a non linear stress increase, with an initial elastic modulus equal to E=5.0±0.2MPa, up to about 60% strain (about 8 MPa ), followed by a steeper increase afterwards, due to strain hardening and the increase of transverse section.

[picture]

Fig. 1. Mechanical characterization results for the 3D SLA printed (a) 50A-V1 resin and the (b) Dental Clear V2 resin; (c) proof-of-concept force sensor, (d) electromechanical setup.

Fig. 1. Mechanical characterization results for the 3D SLA printed (a) 50A-V1 resin and the (b) Dental Clear V2 resin; (c) proof-of-concept force sensor, (d) electromechanical setup.

Contrarily, the Dental Clear V2 resin showed a significantly stiffer behaviour, with an elastic modulus of E=1020±80MPa under the same conditions (Fig. 1B), followed by yielding at a stress equal to 75±5MPa, a subsequent strain softening and a final increase in strain hardening regime. Due to its lower stiffness, 50 A−V1 was selected as the dielectric material for the capacitive force sensor, being significantly softer than the Dental Clear V2 resin. Its greater deformability indeed enables larger variations in electrode separation under applied force, which enhances the sensitivity of the sensor. Fig. 1C shows the proof-of-concept force sensor fabricated using the elastic resin 50A-V1.

Electrical Characterization Result

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Fig. 1D reports the electromechanical setup used in this study. Fig. 2A shows the force-displacement response of a representative sample, while Table 3 reports the stiffness values obtained by comparing the 1st  and the 10th  preload cycle for each sample. For all samples, a systematic increase in stiffness (Fig. 2B) is observed between the 1st  and the 10th  cycle, indicating a progressive mechanical stabilization of the polymeric dielectric under cyclic loading. The percentage variations associated with the two cycles and compared to the average value across all 10 cycles, remain limited, confirming the good repeatability of the mechanical response and the convergence of stiffness values after the initial cycles. Table 4 summarizes the main electromechanical parameters of the sensors.

[picture]

Fig. 2. Electromechanical results for 3D SLA printed samples: a) preload force vs displacement (Sample N∘1 ), b) preload capacitance vs force (Sample N∘1 ), c ) force vs displacement of 1st  and 10th  cycle, d) force and capacitance vs time in controlled cycles after preload.

Fig. 2. Electromechanical results for 3D SLA printed samples: a) preload force vs displacement (Sample N ∘ 1 ), b) preload capacitance vs force (Sample N ∘ 1 ), c ) force vs displacement of 1 st and 10 th cycle, d) force and capacitance vs time in controlled cycles after preload.

Table 3. Stiffness values for increasing preload cycle numbers for SLA 3D printed samples.

Sample N° | Stiffness [N/mm] | Δ%
| 1° cycle | 10° cycle | 1° cycle | 10° cycle
1 | 894.81 | 1059.83 | 1.37 | 0.87
2 | 908.65 | 1053.70 | 2.94 | 1.45
3 | 844.57 | 1093.98 | 4.32 | 2.32

Table 4. Main electromechanical parameters of SLA 3D printed samples.

Sample N° | F-D Hysteresis [%] | C-F Hysteresis [%] | Sensitivity [fF/N] | R²
1 | 14.5 | 12.8 | 3.08 ± 0.1 | 0.93
2 | 12.1 | 10.7 | 3.18 ± 0.1 | 0.92
3 | 12.0 | 20.7 | 2.69 ± 0.2 | 0.93
Sample N° | CMAX 1° | CMAX 10° | ΔC [pF] | CMAX 11° | CMAX 15° | ΔC [pF]
1 | 2.26 | 2.50 | 0.23 | 2.37 | 2.40 | 0.03
2 | 2.43 | 2.58 | 0.15 | 2.45 | 2.47 | 0.02
3 | 2.20 | 2.43 | 0.23 | 2.28 | 2.32 | 0.04

The observed hysteresis values are consistent with the viscoelastic behavior of the polymeric material and are reflected in the electrical response of the sensors. Despite the presence of hysteretic effects, all sensors exhibit an average sensitivity in the order of 3.0fF/N and good linearity ( R2>0.9 ), confirming that, once mechanically stabilized through the preload cycles, the system provides a reliable and repeatable electromechanical response within the investigated load range. The sensors show a final average capacitance variation of C=1.13±0.03pF.
Figure 2C shows the capacitance-force response of a representative sensor during the 10 preload cycles, highlighting the difference between the 1st  and 10th  cycle. Table 4 also display the maximum capacitance value measured during the 1st  and 10th  preload cycle, as well as the maximum values recorded during the subsequent five control cycles, together with the corresponding capacitance variation ΔC for each sample. For all samples, an increase in the maximum capacitance ( CMAX  ) is observed, with values ranging approximately between 0.20 and 0.25 pF . This increase is attributed to residual deformation of the polymeric dielectric induced by cyclic loading, which leads to a permanent reduction in the electrode separation and, consequently, to a shift in the absolute capacitance baseline. In contrast, the capacitance variation during the five subsequent cycles is much smaller. Therefore, these results demonstrate that, although the preload induces a baseline shift in the absolute capacitance, the useful capacitive response of the sensor is preserved and it is stable.

Conclusions and future perspectives

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This study demonstrates that SLA-compatible photocurable elastic resins can be effectively employed as dielectric layers in capacitive force sensors. In particular, the commercial BioMed Elastic 50A-V1 resin exhibited a stable and repeatable electromechanical behavior, providing an average capacitance variation of 1.13±0.03pF over a load range of 10−400 N, with an average sensitivity on the order of 3.0fF/N and good linearity ( R2>0.9 ). These results confirm that commercially available elastic resins can act as functional dielectrics for 3D-printed capacitive sensors, without the need for custom materials. The results further show that after 10 load-unload cycles the system reaches a mechanically and electromechanically stable condition, characterized by converging stiffness values, repeatable capacitive response, and limited hysteresis. Although the preload cycles induce a shift in the absolute capacitance value, with an increase in the maximum capacitance on the order of 0.20−0.25pF, the capacitance variation associated with the applied load ( ΔC ) remains essentially unchanged, ensuring reliable sensor operation in subsequent measurement phases. Overall, these findings confirm that elastic SLA resins can be used as reliable dielectrics for capacitive force sensors and that a preload phase limited to 10 cycles represents a sufficient mechanical conditioning step to achieve a stable and reproducible response. Future developments will focus on evaluating electromechanical stability under long-term cyclic loading, optimizing the readout electronics, and extending the approach to multi-sensor configurations, with the goal of realizing fully 3D-printed capacitive sensing systems for applications in soft robotics, biomedical devices, and intelligent monitoring systems.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/KEM.1047.65.pdf

The authors acknowledge MICS (Made in Italy - Circular and Sustainable) Extended Partnership (Next Generation EU (Italian PNRR - M4 C2, 1.3-D.D.1551.11-10-2022, PE00000004 CUP D73C22001250001).

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 73-82

doi: 10.4028/p-VVwf5P

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-18

Revised:
2026-02-16

Accepted:
2026-02-25

Online:
2026-04-13

A Physics Informed Machine Learning Framework towards Density Prediction of Additively Manufactured Components

The original version of this paper is available on https://www.scientific.net/KEM.1047.73.pdf

Abhilash Puthanveettil Madathil 1,a*, Olga Bylya 1, b and Andrew Sherlock 1,c

1 Applied Industrial Intelligence (AI 2 ), National Manufacturing Institute Scotland, University of Strathclyde, 3 Netherton Sq, Paisley, Renfrew, Glasgow, UK

a  abhilash.p-m@strath.ac.uk, b  olga.bylya@strath.ac.uk, c  a.sherlock@strath.ac.uk

*corresponding author

Keywords: laser powder bed fusion, additive manufacturing, physics-informed ML, density modelling.

Abstract

Laser powder bed fusion (LPBF) has become a key manufacturing route for high-value components, yet accurate prediction of part quality, particularly relative density, remains challenging due to complex interactions among process parameters, material properties, melt-pool physics and shielding environment. Traditional physics-based simulations offer mechanistic insight but suffer from model-form uncertainty and computational cost while purely data-driven machine learning (ML) models often lack interpretability, physical consistency and transferability across build conditions. To address these gaps, we propose a physics-informed machine learning (PIML) framework that integrates structured domain knowledge with symbolic regression to derive compact, interpretable analytical expressions for LPBF density. The framework constructs a knowledge base (KB) comprising dimensionless and normalized physics-informed process descriptors (PIPDs) that encode energy input, thermal diffusion and melt-track geometry; these descriptors form a physically consistent feature space for learning. The framework also serves as a foundation for the proposed future community-driven, knowledge-graph-based modelling of LPBF processes. The capability of the framework is demonstrated by modelling the relative density of additively manufactured maraging steel and evaluating cross-atmosphere transferability from Argon-shielded to Nitrogenshielded builds. The resulting symbolic models provide a transparent, extensible and physically meaningful alternative to black-box ML, achieving high predictive performance under the training regime ( R=0.964 ) and strong generalization across printing atmospheres ( R=0.896 ).

1. Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.73.pdf

Additive manufacturing (AM), and laser powder bed fusion (LPBF) in particular, is now used for safety-critical applications including aerospace, energy and biomedical sectors, where properties such as relative density, porosity and defect morphology directly govern fatigue life and reliability. Yet, despite extensive process development, accurate and efficient prediction of the 3D printed part properties, especially the density of laser powder-bed fusion (LPBF) built components remains challenging because outcomes depend on complex interactions among process parameters, machine settings, and material properties.
A wide range of approaches have been explored for modelling LPBF. Physics-based models including finite-element heat transfer, thermo-mechanical solvers and multi-physics computational fluid dynamics (CFD) provide valuable mechanistic insight and can resolve melt-pool dimensions, temperature fields and stress evolution (DebRoy et al., 2018; Sames et al., 2016). However, these models remain incomplete and often inaccurate for practical process-property prediction, as the underlying thermo-fluid-powder-metallurgical interactions are not fully understood or computationally tractable at industrial scales (Tang et al., 2018). Their throughput is limited because thousands of fully transient 3D simulations would be required to span realistic process windows (Bruna-Rosso et al., 2018). Model-form uncertainty including simplified boundary conditions, homogenized powder beds and uncertain absorptivity, introduces further deviation from experimental
behavior (Zakirov et al., 2020). Moreover, these simulations provide only indirect links to porosity formation and still rely on empirical rules to detect lack-of-fusion or keyhole defects.
These limitations have motivated the adoption of data-driven machine learning (ML), which has shown promise in modelling relationships between process parameters and properties such as density, porosity and microstructure (Chen et al., 2024). Supervised regression models, including gradient boosting, neural networks, Gaussian processes and random forests, have successfully correlated scalar parameters with density or porosity (Tapia & Elwany, 2015; Tapia et al., 2016; Garcia-Moreno et al., 2020). Recently, Khalad et al. (2024) proposed a generalised ML framework with gradient boosting with particle swarm optimisation (GB-PSO) for prediction, and Shapley additive explanations (SHAP) analysis to rank the importance of volumetric energy density and spot size. Similarly, Chen et al. (2025) developed a classification model that identifies high-density regimes during metal additive manufacturing.
While these methods are more accurate and computationally efficient, they tend to learn correlations rather than physics, with no built-in guarantee of physical plausibility or reliable extrapolation beyond the training regime (Maitra et al., 2024). Even with SHAP or local interpretable model-agnostic explanations (LIME) tools applied for post-hoc explanations (Khalad et al., 2024), their performance often degrades under domain shift, for example, when transitioning across alloys, machines or atmospheres (Barrionuevo et al., 2025). Furthermore, feature sets are often ad-hoc features and lack physical constraints, leading to unrealistic predictions or trends that contradict well-established LPBF behavior (Staszewska et al., 2024; Atwya & Panoutsos, 2024).
These limitations have motivated the development of hybrid approaches in which domain knowledge is systematically embedded into the learning process. Early demonstrations include physics-informed and hybrid ML for fused filament fabrication (Kapusuzoglu & Mahadevan, 2020) and the porosity classification framework of Liu et al. (2021), which maps machine settings to physically meaningful variables. While promising, such efforts remain fragmented, scenario-specific and lack generalization.
A key barrier to broader transferability is the absence of a unified, structured repository of LPBF process knowledge. Existing data, mechanistic insights and physical descriptors are distributed across publications, isolated datasets and model implementations, with no standardized mechanism for encoding relationships between process parameters, materials, machines and operating regimes. As a result, most ML models begin and end within narrow laboratory contexts, limiting their applicability across conditions.
These gaps motivate the physics-informed machine learning framework proposed in this work. The framework (i) constructs a structured and expandable knowledge base of LPBF process physics, (ii) defines a dimensionless and mechanistically meaningful feature space of physics-informed process descriptors (PIPDs), and (iii) learns compact symbolic expressions that capture the relationship between PIPDs and relative density. By combining the interpretability and physical grounding of physics-based modelling with the flexibility and efficiency of ML, this approach aims to deliver accurate, explainable and transferable models for LPBF density prediction. This work demonstrates that low-complexity, physics-informed symbolic models can accurately describe LPBF density while offering transparency and transferability.

2. Methodology

The original version of this paper is available on https://www.scientific.net/KEM.1047.73.pdf

2.1. Physics-informed ML framework

Most existing machine-learning models for LPBF operate as black boxes: although they often achieve high numerical accuracy, they offer limited physical insight into the mechanisms governing meltpool behavior, defect formation and density evolution. As a result, they provide little support for scientific understanding, process tuning or the development of generalisable process-propertyperformance relationships. A second limitation relates to deployment and reliability. Deep learning models can be difficult to calibrate, debug and validate in industrial environments, and their opaque
internal structure often undermines trust, an important concern for high-value manufacturing applications.
This study proposes an explainable and generalisable physics-informed machine-learning (PIML) framework that produces compact, physically grounded symbolic models for LPBF density and evaluates their transferability across variations in the built environment. The framework is composed of four layers that together embed domain knowledge directly into the learning process. The proposed framework consists of several layers.
First, a knowledge base (KB) acts as an extensible store of LPBF domain knowledge, capturing process variables, material properties, and physically meaningful (often dimensionless) process descriptors that form the building blocks of the model. Second, a data layer hosts the raw experimental conditions and measured performance indices (surface finish, porosity, relative densities), applies physically motivated pre-processing, and computes physics-informed process descriptors (PIPDs) from the raw inputs. Third, a symbolic regression engine searches over low-complexity analytical expressions that relate the PIPDs to relative density, while enforcing physical and dimensional constraints derived from the KB. Finally, a model interpretation and transferability assessment layer analyse and visualises the resulting expressions and evaluates their generalisation when applied to samples with previously unseen combinations of geometry and chamber atmosphere within same or different materials. The overall architecture of the framework is given in Fig. 1.

[picture]

Fig. 1. Schematic of the proposed PIML framework.

Fig. 1. Schematic of the proposed PIML framework.

2.2 Knowledge base and physics-informed process descriptors

The KB is designed as a generic, updateable repository of prior information on LPBF . It can be populated from published literature, online resources, expert experience and in-house experiments, and is not tied to any specific alloy or machine. The KB acts as the "physical backbone" of the PIML framework: it defines what constitutes a valid descriptor, how descriptors may interact, and which mechanisms are believed to dominate in typical LPBF regimes.
One of the key elements of KB is the normalised and dimensionless descriptors called physicsinformed process descriptors (PIPDs). They are constructed from the standard LPBF inputs such as laser power, scan speed, hatch spacing, layer thickness and beam diameter, together with relevant material and thermal properties where needed. Rather than working directly with the process parameters, these descriptors aggregate them into quantities that reflect the underlying physics of heat input, melt-pool stability and track overlap. Typical examples include normalised linear energy density (NLED), normalised areal energy density (NAED) and normalised volumetric energy density (NVED); ratios that compare hatch spacing to layer thickness or beam size; and dimensionless groups for effective beam power and scan velocity based on thermal conductivity and diffusivity. In the context of the framework, the precise choice and formulation of PIPDs can be adapted to the process and material under study, but the common objective is to span a feature space that is dimensionally consistent, mechanistically meaningful, and suitable for guiding the symbolic regression towards interpretable, physics-consistent expressions (Thomas et al., 2016).
Together, these descriptors encode energy input, spatial overlap of melt tracks, and characteristic thermal diffusion scales. They span both traditional energy-density metrics and dimensionless numbers emerging from first principles arguments and form the candidate input space for symbolic regression. Beyond numerical features, the KB can in principle store higher-level rules (e.g. "increasing NLED at fixed hatch and layer thickness should not reduce density within the conduction regime") that could later be used as additional constraints or regularisers. In this study, we focus on
descriptor-level and dimensional constraints and keep such behavioural rules as soft guidelines for post-hoc interpretation.

2.3 Concept of a hybrid KB

In the present work, the knowledge base is implemented in a lightweight manner using curated tables and Python routines that compute PIPDs and enforce basic physical constraints. This serves as a proof of concept for integrating explicit process physics into the learning pipeline. In the longer term, we envisage re-implementing the KB as a structured, graph-based database that can grow with the LPBF community. In such a graph-oriented KB, process parameters, materials, machines, build environments, PIPDs, physical mechanisms, and constraints are represented as nodes, while their dependencies, valid regimes, and provenance (e.g. specific experiments or publications) are encoded as typed edges. This design supports incremental extension: new descriptors, materials, alloys, process windows or mechanisms can be added without disturbing the existing structure, and researchers can issue rich queries such as "all dimensionless PIPDs involving laser power and thermal diffusivity for Ti-6Al-4V in conduction-mode regimes".
A complementary vector-based layer would index unstructured knowledge sources (journal articles, data descriptors, laboratory notes) using text embeddings. This layer provides a semantic retrieval interface that can surface relevant passages (for example, new correlations or revised parameter ranges) and propose them as candidate nodes and edges in the knowledge graph for expert curation. A collaborative web graphical user interface (GUI) could then act as the front end through which LPBF researchers, engineers and practitioners contribute new findings (e.g. additional PIPDs, updated mechanism maps, machine-specific corrections) directly into the KB, with versioning and attribution.
When building a physics-informed model for a particular use case, the PIML framework would query this evolving KB for the subset of descriptors, correlations and rules relevant to the chosen alloy, machine, build atmosphere and operating range. In this way, the same global, community-maintained knowledge graph can support many local modelling tasks, while each new study feeds back structured information, gradually strengthening the shared, physics-based understanding of LPBF. The overall conceptual framework is given in Fig. 2.

[picture]

Fig. 2. Conceptual framework of the community-driven knowledge base.

Fig. 2. Conceptual framework of the community-driven knowledge base.

2.4 Symbolic regressor

Symbolic regression is used to learn a closed-form mapping ρ^rel =f(x) from the vector of PIPDs ' x ' to the relative density. We employ the Qlattice algorithm, which treats candidate expressions as computation graphs composed of simple operations (addition, subtraction, multiplication, division, exponential, logarithm, hyperbolic tangent, etc.) and searches over this space in a probabilistic way.

2.4.1 Search procedure and objective

At a high level, the Qlattice-based symbolic regression proceeds through an iterative sample-fit-prune-update cycle. In each iteration, the algorithm first samples a population of candidate model structures, represented as computation graphs, from its current probability distribution over topologies. This sampling is biased by the knowledge base, which favours PIPDs and operator combinations with physical justification and excludes dimensionally inconsistent constructions. For every sampled structure, the numerical parameters (such as coefficients and exponents) are then optimised on the training data using gradient-based backpropagation, with the mean squared error between predicted and measured relative density as the loss. Once fitted, the candidate models are ranked using the Bayesian Information Criterion (BIC)
BIC=kln(n)−2ln(L)
where k is the number of free parameters, n is the number of training samples and L is the maximised likelihood; lower BIC values correspond to a better balance between accuracy and parsimony. Models that perform poorly or become unnecessarily complex are pruned away, while the best-scoring candidate is always retained. Finally, the Qlattice updates its internal sampling distribution to increase the probability of re-using structural motifs (specific PIPDs or operator patterns) found in highscoring models, while preserving some exploration to discover new structures. This process is repeated until a convergence criterion is met or a maximum number of iterations is reached, yielding a set of compact, physics-consistent symbolic models.

2.4.2 Complexity control and embedded feature selection

To keep the resulting analytical expressions interpretable, we cap the maximum graph complexity (measured as the number of edges or operations) at 10 , which implicitly limits how many PIPDs and interactions can appear in any single model. Because each candidate structure only uses a subset of the available descriptors and the BIC penalises additional parameters, the algorithm naturally performs embedded feature selection: descriptors that do not meaningfully improve the fit are discarded, and highly correlated PIPDs tend to compete such that only one representative typically remains in the final expression. To avoid converging onto a single, near-duplicate model, we retain a small ensemble of structurally distinct but high-performing candidates; comparing these allows us both to check the robustness of the discovered trends (for example, a monotonic dependence on an energy-density-type descriptor) and to recognise alternative but physically equivalent formulations (such as trading NVED for a combination of NLED and layer thickness).
After selecting a baseline symbolic model on the reference train/test split, we then examine its intramaterial transferability by applying it, without any re-training, to samples with different printed geometries (e.g. prismatic versus cylindrical specimens) and to samples produced under different shielding atmospheres (e.g. nitrogen versus argon), where such combinations exist in the data. Performance on these held-out subsets is compared against the in-distribution test performance, and any degradation in accuracy is interpreted in light of the underlying descriptors - for instance, whether additional geometry- or atmosphere-sensitive PIPDs are required, or whether the existing descriptors already encode the key changes in melt-pool behaviour. In this way, the methodology delivers not only a predictive model for relative density, but also an explicit, physics-based analytical expression that can act as a reusable baseline for future studies involving other clusters, materials, or the incorporation of in-process sensing data.

3. Case Study

The original version of this paper is available on https://www.scientific.net/KEM.1047.73.pdf

3.1 Source dataset

We use the open LPBF relative-density dataset compiled by Barrionuevo et al. (2025) which aggregates 1,579 experiments from 85 publications on commercial metallic alloys processed by LPBF. The dataset includes process parameters (laser power, scan speed, hatch spacing, layer thickness, spot size), powder characteristics (D50, median particle diameter)), a geometric factor, material type, shielding gas, printed geometry and the measured relative density.

In the original study, the authors performed extensive exploratory data analysis, calculated mutual information between each feature and relative density, and proposed a K-means clustering into four clusters to group experiments with similar process conditions and material characteristics. They further demonstrated that the dataset is suitable for training machine-learning regressors such as XGBoost and Random Forest. These models are however black box in nature and don't reveal the underlying physical mechanisms which influence the density and porosity of additively manufactured parts. For this study, we worked with a subset of the data (1 cluster, material-18Ni300) in order to demonstrate (a) obtaining a clean baseline physics-informed symbolic model, and (b) studying model transfer across atmosphere (atm 1-Argon, atm 2-Nitrogen) within the same material.

3.2 Preprocessing

The pre-processing pipeline begins with filtering and basic cleaning. Only entries with complete information for the required process parameters, material label, geometry and shielding atmosphere are retained. Any records with inconsistent combinations of process parameters are removed, guided by the parameter ranges reported in the original data descriptor.
From the cleaned subset, we construct a compact set of physics-informed process descriptors (PIPDs) that encode normalized energy input and geometric overlap between melt tracks. As a first step, the core LPBF inputs: laser power P, scan speed S, hatch spacing h, layer thickness t and spot size d, are individually normalized, yielding the dimensionless variables Pn,Sn,hn,tn and dn. On this basis, we define three energy-density-type descriptors: a linear energy density P/S, an areal energy density P/(Sh), and a volumetric energy density P/(Sht). Each of these is again normalized to produce the PIPDs NLED, NAED and NVED. In parallel, we compute a set of geometric and overlap descriptors: the hatch-to-layer ratio HT=h/t; the dimensionless hatch NH=h/d; the track overlap fraction, o=1−h/d; and a dimensionless layer thickness τt=t/d. Together, these twelve PIPDs form the primary feature space passed to the symbolic regression engine. The resulting modelling data frame therefore contains the PIPDs, the target relative density, and auxiliary columns for shielding atmosphere (Atm) and original row index, the latter being used only for stratification and transferability studies rather than as predictors in the baseline symbolic model.

4. Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1047.73.pdf

The proposed physics-informed symbolic modelling framework was first trained on the Argonshielded subset of the 18 Ni 300 LPBF dataset. According to the proposed PIML framework, input process parameter space is transformed to a PIPD space of 12 dimensionless physical process descriptors as described in section 2.2 and section 3.2.
Using a maximum model complexity of six, which restricts the expression to at most three physicsinformed process descriptors (PIPDs), the symbolic regression produced a compact analytical model that captured the dominant structure-property trends. The resulting expression

ρ^rel=−17.6e−2(0.27−11.2Sn)2−2e−2(1.34−2τt)2+17.6tanh(13.3NLED−0.6)+81.4(1)

showed excellent agreement with the experimental relative density under Argon conditions ( R2=0.9288;R=0.964 ). When this same model was transferred, without retraining, to the Nitrogenshielded dataset, it retained good predictive power ( R2=0.8015;R=0.896 ), with only moderate broadening of residuals (Fig. 3). This behaviour suggests that the model encodes atmosphereindependent physical relationships, although secondary effects related to shielding gas do introduce some performance degradation.

[picture]

Fig. 3. (a) Training performance of model 1 (with 3 PIPDs) in Argon print atmosphere (b) model transferability to Nitrogen atmosphere.

Fig. 3. (a) Training performance of model 1 (with 3 PIPDs) in Argon print atmosphere (b) model transferability to Nitrogen atmosphere.

To further investigate model compactness and interpretability, the symbolic regression was repeated with a stricter complexity limit of five, effectively restricting the solution to two PIPDs. Under this constraint, the symbolic regression produced a remarkably concise expression involving only the normalised linear energy density (NLED) and the normalised scan speed ( Sn ):

ρ^rel=18.6tanh(12.5NLED−0.5)+4.63tanh(15.9Sn−1.1)+76(2)

[picture]

Fig. 4. (a) Training performance of model 2 (with just 2 PIPDs) in Argon print atmosphere (b) model transferability to Nitrogen atmosphere.

Fig. 4. (a) Training performance of model 2 (with just 2 PIPDs) in Argon print atmosphere (b) model transferability to Nitrogen atmosphere.

Despite its simplicity, this two-term model preserved nearly the same predictive accuracy on the Argon dataset ( R2=0.9293;R=0.964 ), confirming that most of the variance in melt-pool quality and final density for 18 Ni 300 can be captured through two physically motivated descriptors. When evaluated on Nitrogen builds, the performance decreased moderately ( R2=0.6656;R=0.816 ), indicating that the omitted descriptor in the higher-complexity model likely captured subtle atmosphere-dependent behaviour such as variations in absorptivity or plume-driven instability (Fig. 4). Nonetheless, the two-descriptor formulation remains predictive and offers a highly interpretable baseline model.

4.1 Physical Insights

The symbolic model distilled by the PIML framework highlights two descriptors (NLED and Sn ) as the dominant physical drivers governing densification in 18 Ni 300 . Such compact relations enable rapid process-window estimation, reduce reliance on computational simulations, and support interpretable control strategies in production environments. The outcome is consistent with the fundamental LPBF physics linking melt-pool stability, thermal diffusion and defect formation. The plot (Fig. 5) depicts equation 2 (3d surface) embedded with experimental datapoints. Normalized linear energy density combines laser power and scan speed into a single physically interpretable measure of energy delivered per unit track length. Its influence in the model reflects the classical requirement for sufficient melt depth and track overlap to avoid lack-of-fusion. The saturating form of the NLED term corresponds to a well-known physical phenomenon: once the melt pool becomes large enough to reflow and remelt adjacent tracks, further increases in energy do not substantially improve densification due to thermal self-regulation and increased convective loss.

[picture]

Fig. 5. Trained symbolic model plotted as 3D surface with experimental datapoints embedded.

Fig. 5. Trained symbolic model plotted as 3D surface with experimental datapoints embedded.

While effective within the explored parameter space, the current descriptor set does not explicitly encode plume dynamics or absorptivity variation, which likely contributed to reduced performance under Nitrogen. Future work will incorporate these mechanisms into an expanded knowledge base and explore integration with in-process sensing modalities.

5. Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.73.pdf

This work introduced a novel physics-informed machine learning (PIML) framework that unifies explicit LPBF process physics with symbolic regression to produce compact, interpretable and physically consistent models for relative density. By embedding domain knowledge into a structured space of dimensionless and normalised physics-informed process descriptors (PIPDs), the framework constrains learning to mechanistically meaningful relationships rather than opaque correlations. This design allows symbolic regression to operate within a physically coherent hypothesis space, yielding analytical expressions that retain the transparency of traditional process maps while achieving predictive performance comparable to state-of-the-art ML.
Applied to LPBF of 18 Ni 300 , the framework generated concise symbolic models that captured the underlying density-process trends under Argon while demonstrating strong transferability to Nitrogen. The ability to recover accurate expressions using as few as two PIPDs highlights the framework's capacity to reveal minimal yet physically grounded forms that govern melt-pool behaviour. A key contribution of the framework is its extensibility: both the PIPD set, and the embedded physical rules can expand systematically as new scientific insights emerge. This opens the
path toward a community-driven, graph-based knowledge base, where researchers can contribute new descriptors, constraints, mechanistic hypotheses and experimental evidence. Such an evolving KB would allow future symbolic models to be trained on a richer, curated representation of LPBF physics, further improving interpretability, robustness and cross-condition generalization.

List of Acronyms

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Data Availability

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The study was carried out using an open-source dataset given in in the Harvard Dataverse repository https://doi.org/10.7910/DVN/VPBQK8.

Declaration of Competing Interests

The original version of this paper is available on https://www.scientific.net/KEM.1047.73.pdf

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this study.

Acknowledgement

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The authors would like to acknowledge the funding from Innovate UK D3M_CoLAB and D3MCoLab 2.

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.73.pdf

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 83-93

doi: 10.4028/p-5kyxnF

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-19

Accepted:
2026-02-11

Online:
2026-04-13

Investigation of WAAM-PAW Fabricated 316L Steel/Invar 36 Nb Alloys for the Development of Functionally Graded Materials

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Weber Lucas 1,2,a*, Hor Anis 1,b, Ratsifandrihana Léon 2,c and Alcaraz Mathilde 2,d

¹Clément Ader Institute, Toulouse University, CNRS, ISAE-SUPAERO, France

2 Segula Technologies, France

a* Lucas.WEBER@isae-supaero.fr, b Anis.HOR@isae-supaero.fr, c  Leon.RATSIFANDRIHANA@segula.fr, d  Mathilde.ALCARAZ@segula.fr

Keywords: wire arc additive manufacturing (WAAM), plasma arc welding (PAW), functionally graded materials (FGM), 316L stainless steel, invar 36 Nb , microhardness, mechanical properties, thermal expansion coefficient (CTE), corrosion resistance.

Abstract

Functionally graded materials represent a promising strategy for locally optimizing component properties while reducing both economic and environmental costs. To date, no study has addressed the development of a compositional gradient between 316L stainless steel and Invar 36 using the Wire Arc Additive Manufacturing (WAAM) process, despite the strong potential of this material combination. Indeed, such a gradient would combine the very low coefficient of thermal expansion (CTE) of Invar 36 with the low cost and excellent chemical resistance of 316L stainless steel. A particularly relevant application for this type of gradient is the storage of hydrogen or liquefied natural gas, where tanks are subjected to severe thermal stresses due to cryogenic operating temperatures. In addition, these structures must withstand aggressive environments and hydrogen exposure, which can induce material embrittlement, while maintaining sufficient mechanical properties to ensure structural integrity during service. Designing an optimal gradient therefore requires a detailed understanding of how mechanical, thermal, and chemical properties evolve with chemical composition. This study provides a preliminary assessment of these evolutions. The results show that the addition of 15-25 wt.% Invar 36 to 316L leads to a reduction in microhardness and ultimate tensile strength (UTS), associated with the disappearance of ferritic and σ phases, while significantly enhancing ductility. At higher Invar 36 contents, microhardness increases and ductility decreases due to carbide formation. From a thermal standpoint, the CTE does not follow a linear trend: it remains high up to approximately 75wt.% Invar 36 Nb , then decreases sharply as the ferromagnetic behavior characteristic of Invar becomes dominant. Corrosion resistance remains satisfactory for Invar 36 contents below 15wt.%, whereas higher contents lead to reduced chemical performance due to chromium dilution. Overall, these findings establish clear criteria for selecting optimal compositions in the design of a 316L-Invar 36 compositional gradient. They provide an essential foundation for the development, via WAAM, of robust and high-performance functionally graded materials suitable for applications requiring high dimensional stability, good chemical resistance, and controlled costs.

1. Introduction

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Technological development requires materials capable of withstanding multiple stresses (mechanical, chemical, thermal, etc.) that are becoming increasingly severe. High-performance metals, such as nickel-based or titanium alloys, meet these needs but remain costly and have a high environmental impact. One alternative is to design multi-material structures in which different conventional alloys are locally assembled to achieve optimal properties in specific areas. However, the interfaces between these materials are critical areas that can lead to premature failure, particularly in cases where there are sudden changes in microstructure or properties (i.e. thermal expansion coefficient) [1]. Additive manufacturing processes offer a solution by allowing the introduction of composition gradients and thus gradual transitions in properties [2]. Among these, the WAAM process stands out as one of the most promising for easily producing large gradient parts [3].

Different functional gradient materials (FGMs) have already been studied, particularly by combining stainless steels with Inconels to combine the high performance of these superalloys with the low cost of stainless steels. Rodrigues et al. manufactured an FGM using WAAM with 316L steel and Inconel 625 [4]. However, the appearance of brittle phases degraded the mechanical properties [4]. FGMs involving titanium alloys have also been explored for the same reasons. Chen et al. combined Ti-6Al-4V with 316L stainless steel [5]. This time, cracks appeared at the interface. It is therefore essential to develop and characterize intermediate alloys to design viable gradients. This includes optimizing manufacturing parameters and choosing chemical compositions to limit defects and the formation of undesirable phases.

Furthermore, FGMs using Invar 36 have been little studied, even though this material has a very low coefficient of thermal expansion up to its Curie temperature ( ~250∘C ), making it indispensable for cryogenic applications (liquid natural gas transport [6], transport of liquid hydrogen [7]) or applications requiring high dimensional stability (i.e. molds for polymers or composites [8], bonding composite panels on spacecraft [9] and tooling [10]). However, its high cost and poor chemical performance (i.e. corrosion and hydrogen resistance) justify its combination with stainless steel. Hofmann et al. manufactured an FGM by Laser Metal Deposition (LMD) by combining Invar 36 and 304L steel to reduce thermal expansion without loss of mechanical properties [9]. Arbogast et al. observed a 42% reduction in surface expansion after WAAM deposition of Invar 36 on steel [10]. Using the LPBF process, Guo et al. demonstrated that an appropriate choice of composition gradient between Invar 36 and 316L steel allows the properties to be controlled [11]. However, no study has yet investigated the development of an FGM combining stainless steel and Invar 36 using the WAAM process. This work, therefore, proposes to study different alloys resulting from the combination of 316L stainless steel and Invar 36 using a WAAM-Plasma process. The mechanical, thermal, and chemical properties of these alloys are evaluated to identify the most suitable compositions for obtaining an optimized FGM according to the application.

2. Materials and Methods

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The materials used are supplied in the form of 1.2 mm diameter wire spools. Two alloys are used: Invar 36 Nb and 316L austenitic stainless steel. Table 1 shows their chemical composition. Invar 36 is deliberately doped with niobium and carbon to reduce the risk of solidification cracking, which is common when welding conventional Invar 36 . Osuki et al. showed that sensitivity to these cracks decreases significantly when the composition contains approximately 0.2%C and 1.5%Nb [12]. In this study, several intermediate alloys were developed. These intermediate compositions were obtained by adjusting the feed rate of the two wires during the deposition process.

Table 1. Chemical composition in weight percent (wt.%) of the two alloys studied.

|  | Fe | Ci | Ni | Mo | Mn | Si | C | Nb | Ti
316L |  | Balance | 18.5 | 12.0 | 2.80 | 1.70 | 0.80 | 0.02 | / | /
Invar 36 Nb |  | Balance | / | 36 | / | 0.40 | 0.15 | 0.20 | 1.30 | 0.20

The deposition process used is illustrated in Figure 1. It is the WAAM-PAW (Plasma Arc Welding) process. At the center of the torch, there is a tungsten electrode, around which a plasma gas is injected. An electric arc is then established between the torch and the substrate, generating a very high-temperature plasma. This plasma allows the two filler filaments to melt and form the molten pool. A shielding gas, argon, is used to protect the molten pool from the ambient atmosphere. The torch and filler wires move along a programmed trajectory, allowing the part to be manufactured by successive deposition of beads. The entire process is carried out in an inert argon atmosphere to minimize oxidation, which occurs despite the presence of the shielding gas around the molten pool. Using this process, single-bead walls measuring 140×6×100 mm were manufactured for the various alloys studied, using the deposition parameters shown in Table 2.

Table 2. Manufacturing parameters.

Parameter | Value
Total wire feeding speed [mm/sec] | 40
Torch speed [mm/sec] | 5
Dwell time [sec] | 45
Plasma gas flow rate [L/min] | 1,5
Shielding gas flow rate [L/min] | 15
Oxygen [ppm] | 200 – 800
Intensity [A] | 230 – 155

[picture]

Fig. 1. Diagram of the WAAM-PAW process.

Fig. 1. Diagram of the WAAM-PAW process.

Microhardness measurements (HV5) are performed using an Innovatest Falcon 500G2 machine. A matrix of nine indents is made on a cube taken from the center of the walls. Tensile tests are performed at room temperature using a 50 kN INSTRON 5969 machine. The tensile test specimens are flat. They were taken vertically from the walls according to the geometry shown in Figure 2.

[picture]

Fig. 2. Geometry of tensile test specimens (thickness =3 mm ).

Fig. 2. Geometry of tensile test specimens (thickness = 3 m m ).

Thermal expansion measurements are performed using a Netzsch TMA 402 F1/F3 Hyperion device. These tests are carried out between 25∘C and 400∘C with a ramp rate of 5 K/min. The samples are cylinders of 6 mm in diameter and 10 mm in height. Potentiodynamic polarization tests are performed using an Origlaflex OGF+01A bench. The reference electrode is saturated calomel, the counter electrode has a platinum head, and a sample holder with a sample of the material under study constitutes the working electrode. The samples are 3 mm thick and 7,9 mm in diameter. The electrolyte solution is NaCl with a concentration of 3,5% by mass, or 0,6 mol/L. During the tests, the
free potential is recorded for 180 min . The measurement frequency is 1 Hz . The potentiodynamic polarization test is then performed over the range -300 mV to +1200 mV relative to the free potential. The scan rate is 0,1666mV/sec.

3. Results and Discussion

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Alloy Fabrication

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Manufacturing parameters directly influence the development of defects, which in turn can significantly degrade the properties of the material. As shown by Jafari et al., different types of defects can develop with the WAAM process: porosity, cracks, distortions, and humping [13]. It is therefore essential to identify the optimal parameters to limit these defects, especially since these parameters may depend on the chemical composition of the deposit. Manufacturing is mainly governed by three parameters: torch speed, wire feeding speed, and welding intensity. These parameters must be carefully adjusted. Insufficient heat input can cause lacks of fusion and insufficient metallurgical bonding. Conversely, too much heat input can cause the bead to collapse and increase residual stresses. However, the total wire feeding speed must remain constant because it determines the chemical composition of the alloys produced (Table 3). Similarly, the torch speed is kept constant to ensure that the same amount of material is deposited per bead. Thus, the intensity is the only parameter adjusted to optimize manufacturability and reduce the occurrence of defects.

Table 3. Wire feeding speed for different alloys.

Chemical composition | 316L wire feeding speed [mm/sec] | Invar wire feeding speed [mm/sec]
316L | 40 | 0
75% 316L - 25% Inv36 | 30 | 10
50% 316L - 50% Inv36 | 20 | 20
25% 316L - 75% Inv36 | 10 | 30
Invar 36 Nb | 0 | 40

The parametric optimization indicates that the optimal intensity depends directly on the wire feeding speed. Thus, 316L steel and Invar 36 Nb , both deposited at 40 mm/s, have similar optimal intensities. The parameters are therefore not adjusted according to chemical compositions, but rather according to wire feeding speeds. For the first layer, the optimal intensities are 230 A for 316L and Invar 36 Nb , and 220 A for the alloy composed of 50%316 L steel and 50% Invar 36 Nb . However, a gradual reduction in intensity is necessary to prevent the weld beads from collapsing. When the heat input has stabilized, the intensities are 180 A for 316 L and Invar 36 Nb , and 155 A for the alloy with 50%316 L steel and 50% Invar 36 Nb (Table 4). In the case of 316 L and Invar 36 Nb , a single wire at 40 mm/s is used. A high intensity is necessary to ensure complete melting. If the intensity is too low, the wire passes through the plasma without melting. For the alloy composed of 50% of each material, two wires, each with a feeding speed of 20 mm/s, are exposed to the plasma for a longer period of time. They therefore require a lower intensity.

Table 4. Stabilized intensity for different alloys.

Alloy
316 L

90% 316L

10% Invar

80% 316L

20% Invar

mathematical formula Invar

mathematical formula Invar

mathematical formula Invar

Invar 36

Intensity

180
175
170
165
160
155
180

When alloys require very different wire feeding speeds, a dripping phenomenon may occur. This is because the intensity has been optimized for the wire with the highest feeding speed. However, the wire with a lower feeding speed requires a lower intensity: if the intensity is too high for it, it melts periodically before reaching the molten pool, causing successive drops to form. To eliminate this phenomenon, the distance between the torch and the bead must be reduced so that the wire comes into direct contact with the previously deposited layer. In this configuration, the wire melts through radiation and conduction, and the surface tension is stabilized at the tip of the wire. It prevents the
formation of droplets. The chemical composition of the alloys obtained is then verified by EDS analyses, presented in Figure 3, and compared with the theoretical compositions. The trends observed are similar, indicating good chemical homogeneity of the deposits despite differences in wire feeding speed. The Marangoni effects and the successive remelting ensure effective mixing between 316L steel and Invar 36 Nb .

In summary, the different alloys are produced by modifying the wire feeding speeds to obtain the correct chemical composition and by adjusting the intensity to prevent the formation of defects. It is also necessary to optimize the process to prevent a drip effect and achieve a homogeneous chemical composition.

[picture]

Fig. 3. Change in the mass proportion of nickel and chromium as a function of the proportion of Invar 36 Nb .

Fig. 3. Change in the mass proportion of nickel and chromium as a function of the proportion of Invar 36 Nb .

Hardness Evolution

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To design an optimal gradient between 316 L steel and Invar 36 Nb , it is essential to carefully select the intermediate compositions. Indeed, certain contents may have undesirable properties and should be avoided. Furthermore, the evolution of properties is not necessarily linear: if the objective is to obtain a gradual and regular variation in performance, then suitable compositions must be chosen. An initial approach to identifying these optimal compositions is to determine the alloys with the best mechanical properties and those with the worst mechanical properties. To this end, Figure 4 shows the evolution of microhardness as a function of chemical composition and correlates it to microstructures.

The hardness measured for 316 L stainless steel is 165,8HV. This value is close to that obtained by Yu et al. with a WAAM-PAW process (165−170HV) [14], but remains lower than that obtained by other additive manufacturing processes such as WAAM-GMAW [15] or LPBF [11]. This relatively low hardness can be explained by the granular microstructure consisting of large columnar grains. When 15% Invar 36 Nb is added, the hardness drops significantly from 165,8HV to 124,1 HV , a relative reduction of about 25%. This decrease is explained by the disappearance of the ferrite and σ phases. In the initial 316L steel, the microstructure consists of austenite, ferrite, and the σ phase, the latter being very hard but brittle. With 5% Invar 36 Nb , the σ phase remains present, but in small quantities. At 10%, the threshold beyond which the formation of ferrite and σ becomes marginal is approached: only a few fractured islands remain. With 15%, ferrite and the σ phase disappear, which explains the decrease in hardness.

Above 15% of Invar 36 Nb , hardness increases gradually with Invar content. This rise is directly related to the increasing formation of carbides in the alloy. The presence of significant proportions of niobium and carbon promotes the formation of niobium carbides, to which are added, in smaller quantities, titanium and molybdenum carbides. These carbides, which are hard phases, reinforce the material by blocking the movement of dislocations, resulting in increased hardness. Pure Invar 36 Nb has a hardness of 147,8 HV, which is lower than the values reported by Iturrioz et al. with WAAM-

CMT ( 174 HV) [16] or by Veiga et al. with WAAM-PAW [17]. This difference may be due to different process parameters or to the location where the hardness tests are performed, as hardness varies within the same wall. Moreover, the trend observed in this study differs significantly from that reported by Guo et al. [11] or Hofmann et al. [9], as the chemical composition of the Invar used here is different.

[picture]

Fig. 4. Hardness and microstructure evolution as a function of the proportion of Invar 36 Nb .

Fig. 4. Hardness and microstructure evolution as a function of the proportion of Invar 36 Nb .

Mechanical Properties

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The mechanical properties of the different alloys are then evaluated by tensile testing. Figure 5a shows the evolution of ultimate tensile strength (UTS) and elongation at break (A) as a function of chemical composition. Pure 316L stainless steel achieves a maximum stress of 586 MPa with an elongation at break of approximately 23%. While the UTS obtained is consistent with the values reported in the literature, the elongation at break is lower: Wang et al., for example, report a UTS of 526,9MPa associated with an elongation of 46% with a WAAM-CMT process [18]. Saboori et al. also indicate that several DED processes lead to superior properties [19]. This reduced ductility can be attributed to the presence of the highly brittle σ phase, as well as to defects generated at the interlayer interfaces.

The addition of 25% Invar 36 Nb significantly alters the mechanical behavior: the UTS decreases while the elongation at break (A) increases sharply, from 23% to 44%. This improvement in ductility is consistent with microhardness observations and can be explained by the disappearance of ferritic and σ phases in the microstructure. When the Invar 36 Nb content reaches 50% or 75%, the UTS increases while the elongation decreases. This change is linked to the gradual increase in the fraction of carbides, which increases mechanical strength by blocking the movement of dislocations but reduce ductility by generating local stress concentrations favorable to crack initiation. However, pure Invar 36 Nb does not follow this reasoning: it has both a lower UTS and a lower elongation than compositions with 50% and 75% Invar 36 Nb . One might expect higher strength due to the high density of carbides. However, these hard particles create significant mechanical heterogeneities that promote premature failure. Ultimately, Invar 36 Nb has a UTS of 450 MPa and an elongation of approximately 29%. These results fall between those obtained by Veiga et al. ( 464MPa,49% ) [17] and Jiao et al. ( 569MPa,27% ) [20]. A similar trend of decreasing UTS is also observed in the work of Guo et al., although these authors report minimal elongation for intermediate Invar 36 contents (30−40%)[11]. This minimal elongation is not observed in this study.

Thermal Expansion of Alloys

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Invar 36 Nb was chosen for its very low coefficient of thermal expansion (CTE), a property that is essential to maintain in the gradient. Indeed, too much variation in CTE between two materials can lead to a significant accumulation of thermal stresses, which can cause premature failure. It is therefore essential to know precisely how CTE changes according to chemical composition to optimize the transition between 316L steel and Invar 36 Nb .

The evolution of the CTE between 25∘C and 100∘C as a function of chemical composition is shown in Figure 5b. The CTE of 316L steel is 15,3×10−6∘C−1, which corresponds to the expected values. With the addition of Invar 36 Nb , the CTE initially increases slightly, reaching 17,3×10−6∘C−1 for the alloy with 50% Invar 36 Nb . This increase is attributed to the disappearance of the ferrite and σ phases, whose thermal expansion is lower than that of austenite. Above 50% Invar, the CTE then begins to decrease, but this decrease only becomes significant above 75% Invar, where it falls from 13,4×10−6∘C−1 to only 2,4×10−6∘C−1 for pure Invar 36 Nb . This significant decrease can be explained by spontaneous magnetostriction: below the Curie temperature, the appearance of ferromagnetism causes the crystal lattice to expand, which compensates for thermal contraction [21]. The appearance of this ferromagnetic behavior is not linear with composition, because chromium-rich austenite disrupts magnetic coupling until a critical fraction of Invar 36 Nb is reached. Beyond that point, a coherent magnetic order is established, and the CTE drops sharply. The CTE obtained for pure Invar 36 Nb is lower than that reported by Aldalur et al. for a WAAM-PAW process (4.32×10−6∘C−1), but remains slightly higher than their reference value (2,29×10−6∘C−1) [22] and those obtained by laser powder bed fusion [23]. The non-linear trend of this evolution is consistent with the observations of Hofmann et al. [9]. On the contrary, Guo et al. report a significant decrease with 50% Invar [11], which is not observed here.

Corrosion Resistance

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316L stainless steel was chosen for its low cost and excellent chemical properties, including very good corrosion resistance. This corrosion resistance is therefore studied to validate the use of 316L steel and to characterize the influence of Invar content on chemical behavior. Different parameters can be determined from the intensity curves as a function of potential: the corrosion potential ( Ecorr  ), the corrosion current ( Icorr  ), the pitting potential ( Epit  ), and the pitting current ( Ipit  ). The corrosion current Icorr  characterizes the corrosion rate, but it is very sensitive to the surface condition and experimental conditions. To compare materials with each other, the difference ΔE=Epit −Ecorr  is therefore preferred. Ecorr  corresponds to the natural potential adopted by the metal in the environment studied, without the application of current, and is an indicator of generalized corrosion resistance. Epit  indicates the potential at which localized pitting corrosion begins, i.e., the voltage at which the protective passive film becomes locally unstable. Improved corrosion resistance results in a higher pitting potential. Thus, ΔE represents the potential range in which the material remains passive: the greater ΔE, the higher the corrosion resistance. Figure 5c shows the evolution of ΔE as a function of the proportion of Invar 36 Nb .

For 316L stainless steel, a ΔE of 635 mV is measured, close to the 620 mV reported by Chen et al. using a WAAM-GMAW process [24]. Chen et al. also showed that the presence of the σ phase degrades the corrosion resistance, as it creates areas depleted in chromium [24]. However, for stainless steels, the corrosion resistance is highly dependent on the chromium content, which forms a protective Cr2O3 film. Localized corrosion occurs when this oxide film is disturbed [25]. Wang et al. obtained a higher ΔE of 740 mV , as their 316L steel manufactured by WAAM-CMT did not contain any σ phase [26]. Then, as the proportion of Invar 36 Nb increases, the passivation range ΔE decreases. With the addition of 15% Invar 36 Nb , the corrosion resistance remains good because the chromium content is still high enough to have a stable passive film. Beyond the addition of 25% Invar 36 Nb , the chromium content falls below the threshold required for homogeneous passivation, and chromium-depleted areas appear, promoting pitting corrosion. Thus, ΔE is between 406 mV and 322 mV when the chromium content is between 25% and 85%.ΔE continues to decrease for pure Invar

36, which has a ΔE of only 230 mV . This material, therefore, exhibits low corrosion resistance, a result consistent with the literature. This decrease is mainly due to the reduction in chromium content as the proportion of Invar 36 Nb increases, thereby reducing the ability to form the protective Cr2O3 film.

[picture]

Fig. 5. Evolution of different properties of the multi-material (316L/Invar36Nb) as a function of Invar 36 Nb rate: (a) ultimate tensile strength (UTS) and elongation at break; (b) CTE coefficient; and (c) passivation layer.

Fig. 5. Evolution of different properties of the multi-material (316L/Invar36Nb) as a function of Invar 36 Nb rate: (a) ultimate tensile strength (UTS) and elongation at break; (b) CTE coefficient; and (c) passivation layer.

4. Conclusion

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Optimization of the WAAM-PAW process has enabled the successful production of a series of homogeneous 316L-Invar 36 Nb alloys free of defects. Their characterization (Table 5) highlights several conclusions:

316L stainless steel has the highest hardness (166HV) due to the presence of ferrite and σ phase. The addition of Invar 36 Nb eliminates these phases and leads to the formation of carbides, which lowers the hardness to a minimum of 124 HV for 15% Invar 36 Nb . Beyond that, the hardness gradually increases to 148 HV for pure Invar 36 Nb as the proportion of carbides increases.

The maximum UTS is obtained for 316 L stainless steel ( 586 MPa ). For other compositions, the UTS remains relatively stable between 450 and 496 MPa . The elongation at break is highest for the alloy containing 25% Invar 36 Nb , where the disappearance of the ferrite and σ phases and the low fraction of carbides promote better ductility.

The coefficient of thermal expansion (CTE) does not follow a linear trend. It remains high between 0% and 75% of Invar 36 Nb , then drops sharply from 13,4×10−6∘C−1 to 2,4×10−6∘C−1 between 75% and 100% of Invar. Indeed, with a high Invar 36 Nb content, the alloy becomes strongly ferromagnetic, and the "Invar" effect becomes clearly apparent.

Excellent corrosion resistance is observed for Invar 36 Nb contents below 15%, with a passivation range from 635mV(316 L) to 544mV(15% Invar ). Above this level, chromium depletion limits the formation of a stable passive film, which degrades pitting resistance.
These results show that, despite some variations, the mechanical properties of the intermediate alloys remain similar. The UTS is lowest for pure Invar 36 Nb , while the minimum elongation is observed for 316L steel. This means that within a functionally graded material, the intermediate compositions do not constitute an area of mechanical weakness, which is essential for ensuring the structural integrity of the gradient. Then, the CTE is the property that changes the most significantly. To avoid sudden variations in CTE that could generate thermal stresses and cause premature failure, the gradient will need to incorporate much finer composition transitions on the Invar 36 Nb -rich side. Conversely, corrosion resistance is only satisfactory for high 316L stainless steel contents. However, a change in chemical behavior does not lead to immediate mechanical failure. Therefore, it is not necessary to introduce several intermediate steps for this property, and a single composition rich in 316L can be incorporated into the gradient.

This analysis thus provides a rational basis for designing a gradient between 316L and Invar 36 Nb : smooth gradual transition to control thermal expansion, but direct transition for corrosion resistance, two key elements for developing a high-performance, durable gradient.

Table 5. Summary of the properties for the different characterized alloys.

Alloy (316L [%]/Invar [%]) | 100/0 | 85/15 | 75/25 | 50/50 | 25/75 | 15/85 | 0/100
Microhardness [HV 5] | 165,8 | 124,1 | 130,3 | 131,0 | 132,9 | 141,1 | 147,8
UTS [Mpa] | 586 | / | 453 | 496 | 469 | / | 450
A [%] | 22,7 | / | 44,4 | 30,4 | 36,1 | / | 28,8
CTE [10⁻⁶ °C ⁻¹] | 15,3 | / | 16,1 | 17,1 | 13,4 | / | 2,4
ΔE [mV] | 635,5 | 544,5 | 406 | 358 | 323 | 322,5 | 230,5

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 95-103

doi: 10.4028/p-6WLCZn

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-19

Revised:
2026-02-21

Accepted:
2026-02-25

Online:
2026-04-13

Exploring the Feasibility of Incremental Forming Applied to 3D Printed PEEK Sheets

The original version of this paper is available on https://www.scientific.net/KEM.1047.95.pdf

Inés Ferrer 1,a*, M. Luisa Garcia-Romeu 1, b, Gabriel Centeno 2,c, Ana Rosa-Sainz 2, d, Joaquim de Ciurana 1,e

1 Universitat de Girona, Departament d'Enginyeria Mecànica i de la Construcció Industrial, EPS Campus Montilivi, 17003 Girona, Spain

2 Department of Mechanical and Manufacturing Engineering, School of Engineering, University of Seville, 41092 Seville, Spain

a* ines.iferrer@udg.edu, b mluisa.gromeu@udg.edu, c gaceba@us.es, d arosa@us.es, e  quim.ciurana@udg.edu

*corresponding author

Keywords: single point incremental forming (SPIF), fused deposition modeling (FDM), polyether ether ketone (PEEK), manufacturing processes hybridization, polymer, biocompatibility.

Abstract

Incremental Sheet Forming (ISF) has been widely studied for metallic materials, demonstrating significant potential in flexible and low-cost sheet metal forming (aluminium, magnesium or titanium). Recently, attention has shifted toward polymeric materials due to their growing relevance in medical and customized applications (PCL, UHMWPE, PEEK). However, the availability of commercial sheets is limited to thicknesses, geometries, and material options. In this context, Fused Deposition Modeling (FDM) has emerged as a complementary technique to produce tailored polymeric sheets, enabling the integration of additive manufacturing with ISF processes to overcome limitations in available commercial sheets and expand design flexibility. Considering the success of this hybridization for forming PCL, this work investigates the feasibility of applying Single Point Incremental Forming (SPIF) to PEEK sheets produced via Fused Deposition Modeling (FDM). The study analyses the influence of printing parameters, forming conditions, and thermal treatment on part quality, porosity, forces, temperature, defects, and fracture behaviour.

Introduction

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Since the early developments of Incremental Sheet Forming (ISF), numerous research contributions have explored its suitability for manufacturing medical implants. Among the pioneering efforts, Ambrogio et al. [1] presented one of the first applications of Single Point Incremental Forming (SPIF) to produce a customized ankle support. The result demonstrated geometric deviations below 1 mm , highlighting the potential of SPIF for patient-specific components. Duflou et al. [2] were the first to explore the fabrication of a cranial prosthesis of AA3003-O aluminium using ISF, comparing it with hydroforming and significantly reducing the lead time (from 16.5 to less than 4 days). This approach was later extended to other body regions such as the maxillofacial area, denture/palate zones, and even orthopaedic sites like the clavicle, hip, and knee.

Although extensive research has been conducted on ISF applied to metallic materials, polymeric materials have also emerged as highly relevant in the medical field. In this regard, Fiorentino et al. [3] developed an innovative approach for the fabrication of plate prostheses using Incremental Sheet Forming (ISF) with Polycaprolactone (PCL), in combination with the Stereolithography (SLA) technique to produce molds or dies for the forming process. Later, Bagudanch et al. [4] confirmed the feasibility of ISF for manufacturing customized cranial implants made of UHMWPE, performing a comparative analysis between SPIF and TPIF processes. Likewise, Centeno et al. [5] produced similar cranial implants through SPIF using PVC and PC sheets. Yang et al. [6] proposed an experimental approach for thermally assisted SPIF of PEEK sheets ( 3 mm thickness), conducted at three different temperatures: 70, 100, and 150∘C. Later, Chen et al. [7] manufactured a PEEK cranial plate by thermal assisted SPIF showing its potentiality for medical applications. Rosa-Sainz, et al. [8]
presented an experimental investigation of Polyether ether ketone (PEEK) sheets deformed by SPIF at room temperature, determining the influence of the ISF process parameters on the material formability and revealing that the maximum strains achieved in two prosthesis geometries (cranial and cheekbone) remained well below the material's Fracture Forming Limit (FFL). However, the works dealing with PEEK and ISF are still scarce.

Overall, a broader application of SPIF to polymer sheets remains limited by the availability of commercial blanks, which offer restricted thicknesses and material type. Thus, having an alternative for producing customized blanks tailored to specific forming requirements becomes important. And in that sense, Fused Deposition Modeling (FDM) enables cost-effective fabrication of sheet-like structures with controlled geometry and composition, suitable for integration into SPIF processes. In this hybridization context, Rosa-Sainz, et al. [9] characterized the plastic formability and failure of PETG and PCL FDM and Garcia-Romeu et al. [10] proved its feasibility to deform them by ISF for the PCL sheets. In metal materials, Hasan and Akhtar [11] deposited material via AM on a preformed sheet that was subsequently formed, or Hafenecker et al. [12] and Ge et al. [13] analysed hybrid process chains that combined metal AM with forming processes and Ambrogio et al. [14] integrated Selective Laser Sintering and Single Point Incremental Forming to increase part complexity and functional performance.

This work investigates the feasibility of applying Single Point Incremental Forming (SPIF) to PEEK sheets manufactured via Fused Deposition Modeling (FDM). It will reveal how the printing parameters, the 3D printing machine and forming parameters can influence the forming results. Several sheets were manufactured using different printing strategies, with the aim of analysing their quality and porosity. Next, they were incrementally deformed using different SPIF process parameters with the aim of analyzing forces, temperature, defects, and breaks of the deformed parts. A comparison between sheets treated thermally and not is also presented, as well as the influence of using sheets printed with capabilities of different FDM machines.

Methods

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Material and 3D FFF Printer Machine.

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The material used was PEEK (Polyether Ether Ketone). PEEK is a high-performance, semicrystalline thermoplastic polymer belonging to the polyaryletherketone (PAEK) family. It is known for its exceptional mechanical strength, thermal stability, and chemical resistance, which make it suitable for demanding engineering environments (aerospace, automotive or medical).
In this research two fused filament fabrication (FFF) printing machines were used with their corresponding PEEK supplier. Roboze Plus PRO FFF printer was used to print filament ( ∅1.85 mm ) supplied by ROBOZE and the process parameters were adjusted by means of Simplify3D software. Whereas Innovatefil® PEEK filament ( ∅1.75 mm ) was used in Intamsys FUNMAT HT with the slicing Intamsuite NEO.

Experimental Approach

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The research is divided into three steps (Figure 1). The first two steps were carried out with specimens manufactured in our lab with Roboze Plus PRO FFF and the third step with a set of sheets printed provided by external supplier owner of a Intamsys FUNMAT HT printing machine. The aim of step 1 was to conduct an initial exploration of the feasibility of PEEK hybridization, using FDMprinted sheets and subsequently deforming them with IFS. Previous hybridization work developed by the research group revealed that a suitable compaction of the printed sheets is crucial to prevent breakage during deformation. Thus, in step 2, the printing parameters were adjusted to improve layer adhesion, and a more extensive exploration of the influence of ISF process parameters on part achievement was carried out. Finally, in step 3, using purchased printed sheets, a ISF process parameters were directly tested.

[picture]

Fig. 1. Experimental approach of the research.

Fig. 1. Experimental approach of the research.

3D Printing by FDM

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Previous hybridization studies showed that suitable compaction of the printed sheets is essential to prevent breakage during deformation, and therefore, adjusting the printing parameters is fundamental. In step 1, specimens were printed using the printing parameters provided by the filament manufacturer, and small variations in printing temperature were applied to analyze the compaction of the resulting parts (Table 1, varied process parameters). Subsequently, in step 2, the main printing parameters, such as printing temperature (PT), flow rate (FR), and printing speed (PS), were adjusted to improve layer adhesion, and a comprehensive exploration was conducted to determine their influence on porosity and thermal properties. In both cases, rectangular specimens measuring 30×20×6 mm were printed, three specimens in step 1 and one for each process parameter combination in step2. Table 1 shows a summary of the printing parameters varied in each step, as well as the fixed parameters used. Considering the high cost of PEEK material, some attempts were made to make smaller specimens to waste less material, but the material overheated too much and caused problems in obtaining the printed parts. The sheets printed by Intamsys, in step 3, were printed with constant printing parameters, as shown in Table 1. In that case, a visual analysis of the quality of the received boards was performed.

Table 1. Adjustment of the printing process parameters.

Varied process parameters
Step | # Spec. | Printing Temperature (PT) (°C) | Printing speed (PS) (mm/s) | Flow rate (FR) (%)
1 | PP 1.1 | 450 | 36,7 | 0,98
PP 1.2 | 440
PP 1.3 | 430
2 | PP 2.1 | 470 | 20 | 1
PP 2.4 | 470 | 36,7
PP 2.3 | 440 | 20
PP 2.2 | 440 | 36,7
Fixed process parameters
Machine | Roboze Plus PRO FFF | Intamsys FUNMAT HT
| Step 1 & Step 2 | Step 3
Infill (%) | 100 | 100
Layer height (mm) | 0,2mm | 0,2
Top/bottom layer | 0 | 0
Outline Perimeter layer | 0 | 0
Raster angle (°) | +/-45 | +/-45
Bed temperature (°C) | 150 | 145
Chamber Temperature (C) | 90 | 90
Drying conditions | 12h at 100 °C | 12h at 120 °C
Printing Temperature | Varied | 400°C
Printing speed | Varied | 30 mm/s

The printed specimens were analysed from two perspectives: a) assessment of qualitative compaction and b) porosity. In the step 1, a qualitative analysis of the filament union was performed to assess void formation at the filament interfaces by inspection visual. Cross-sectional images of printed PEEK samples were acquired using a Nikon SMZ745T stereomicroscope (Nikon, Japan), which provided detailed visualization of the internal structure. In the step 2, the selected specimens were cut in each section, embedded into 40 mm of Axson RSF816 epoxy resin, pre-polished by Struers Knuth Rotor-3 equipment and finished by a Buehler Ecomet sander polishing with alumina powder of 1, 3, and 9.5 microns. The observation of the section was done using a Leica DMR-XA optical microscope with 5x, lüx, and 20x Plan-Apochromat lenses. In the step 3, a visual inspection of the sheets was done.

ISF Process

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The incremental forming experiments were conducted using a Kondia TM  HS1000 three-axis CNC milling machine equipped with a fixed sheet-clamping system. The sheets were printed as square and circular plates, with side lengths of 60 mm and diameters of 50 mm , respectively. These dimensions were selected to fit the backing plates and clamping system of the ISF machine while maximizing material utilization. These dimensions were considered to deform incrementally a small conical and pyramidal frustum, using a tool diameter of 6 mm .

In the first step, the results obtained in previous research of ISF process parameters used for deforming PEEK commercial sheets of 1 and 2 mm thickness were considered [8], due to the success on the depth achieved (higher than 40 mm ). It is feed rate (FR) 200 mm/min, spindle speed (SS) 500 rpm and step down (SD) 0,1 mm (Table 2). Two geometries were tested: pyramidal (PF) and conical frustum (CF). Three sheets were printed using the printed process parameters obtained in this step 1 (PT 440∘C, PS 36,7 mm/s and FR 98%) but in one of them, a thermal treatment was applied to analyze its influence on forming capabilities. The material was heated from room temperature to 150∘C over a period of 2 hours, followed by a second heating stage from 150∘C to 200∘C over an additional 2 hours. After reaching 200∘C, the sheet was cooled down to room temperature using a controlled cooling rate of −50∘C per hour. In the second stage the spindle speed (SS) and step down (SD) were varied, being SS 500−1000rpm and SD 0,1−0.05 mm, respectively (Table 2). Four sheets were printed using the printing process parameters obtained in this step 2 (PT 470∘C, PS 20 mm/s and FR 100% ).

Distilled water was used on the top surface of each blank to reduce friction between the sheet and the forming tool. This choice was based on the findings of Rosa-Sainz et al. [15], who reported that certain industrial lubricants can negatively affect the polymer structure and its behaviour during forming.

During the forming process, two types of in-process data were recorded: forming forces and surface temperature. The forming forces were monitored using a Kistler ®  9257B dynamometric platform mounted on the CNC machine table. The force data was acquired through a DaqBoard ®505 interface and subsequently processed using DaqView ® 9.0.0 software. Simultaneously, thermal data were captured using an IRBIS ImageIR 13300 thermographic camera, which acquired infrared images at five-second intervals to monitor the temperature evolution.

Results

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3D Printing Results

The original version of this paper is available on https://www.scientific.net/KEM.1047.95.pdf

Fig. 2 shows the results of filaments compaction according to the printing temperature of the parts manufactured in step 1. The results reveal how the increase in temperature strongly overlaps the filaments with each other (P1.1), and at lower temperatures, inter-filament spaces are observed, which could induce potential porosity (P1.3).

[picture]

Fig. 2. Step 1, printed specimens.

Fig. 2. Step 1, printed specimens.

Fig. 3 shows the porosity results of the parts manufactured in step 2. Although few samples were explored, in general, good compaction of the filaments and very low porosity were observed, especially in the central part of the samples.

[picture]

Fig. 3. Step 2, printed specimens.

Fig. 3. Step 2, printed specimens.

Visual inspection of the printed sheets (Fig. 4) reveals clearly qualitative differences between the sheets produced by each machine. Those printed at a lower temperature (PT 400∘C, Intamsys) exhibit bubbles, warping, and delamination. In contrast, those printed at a higher temperature (PT 470∘C, Roboze) show almost no defects, other than the roughness generated by the support structure used during the printing process or inherent to the filament printing technology itself.

[picture]

Fig. 4. Comparison of printed sheets.

Fig. 4. Comparison of printed sheets.

ISF Process

The original version of this paper is available on https://www.scientific.net/KEM.1047.95.pdf

The results obtained from the parts deformed using Incremental Sheet Forming (ISF) are summarized in Table 2 and Fig 5. Step 1 and 2 results reveal that only minor variations are observed in the depth values, forming forces, and maximum temperatures achieved. The conical frustum geometry is formed more successfully than the pyramidal geometry, in which a higher incidence of material failure and fracture is observed (Fig. 5). Additionally, the results indicate that when the material was thermally treated, no significant plastic deformation occurs; instead, internal filament breakage was observed, leaving white regions visible along the tool path (Fig 5. and Fig. 6). Furthermore, sheets printed at lower temperatures exhibit filament separation during deformation, with failure occurring primarily at the interfaces between adjacent printed filaments (Fig 6). In contrast, sheets printed at higher temperatures show a more cohesive behavior, where failure is characterized by actual material fracture rather than inter-filament separation.

Table 2. ISF results.

Step | # Spec. | ISF geometry | Feed rate (mm/min) | Spindle (rpm) | Step down (mm) | Thermal Treat. | Fz max. (N) | Z max. (mm) | T°C max (°C)
1 | S 1.1 | PF | 200 | 500 | 0,1 | No | 535 | 4.2 | 55
S 1.3 | CF | 200 | 500 | 0,1 | No | 555 | 5.5 | 60
S 1.2 | CF | 200 | 500 | 0,1 | Yes | 400 | 0.5 | 24
2 | S 2.1 | CF | 200 | 500 | 0.1 | No | 525 | 5.2 |
2 2.1 | CF | 200 | 500 | 0.05 | No | 607 | 5.5 | 65
S 2.3 | CF | 200 | 1000 | 0.1 | No | 567 | 5 | 80
S 2.4 | CF | 200 | 1000 | 0.05 | No | 500 | 5.2 | 55
3 | PC1 | CF | 200 | 500 | 0.1 | No | 400 | 6 | 45
PC2 | CF | 200 | 1000 | 0.1 | No | 425 | 7 | 61
PC3 | CF | 200 | 2000 | 0.1 | No | 375 | 6 | 80
PC4 | CF | 200 | 3000 | 0.1 | No | 225 | 5 | 81

In step 3, the forming depth achieved is higher but with fully delamination on the layer in contact with the tool. As expected as higher the SS is, higher the temperature becomes amb lower the load is.

[picture]

Fig. 5. Examples of ISF specimens.

Fig. 5. Examples of ISF specimens.

[picture]

Fig. 6. Fracture analysis.

Fig. 6. Fracture analysis.

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.95.pdf

This study provides an initial assessment of the feasibility of deforming additively manufactured PEEK sheets by Incremental Sheet Forming (ISF) at room temperature. The results indicate that printed PEEK sheets exhibit significant limitations in formability compared to commercial PEEK sheets of equivalent thickness.
Although high printing temperatures enabled the production of sheets with a high degree of solidity, the printing parameters were found to be critical for achieving compact sheets, with the capabilities of the printing equipment playing a decisive role.
Future research should focus on the application of temperature during the forming process to enhance formability, as well as on evaluating the influence of temperature on PEEK degradation and mechanical properties. Furthermore, a more detailed analysis of porosity is required to better understand its effect on the forming behaviour of printed PEEK sheets.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/KEM.1047.95.pdf

The authors would like to acknowledge the Major Grants TED2021-131153B-C21/C22 funded by MICIU/AEI/10.13039/501100011033 and by the European Union under Next Generation EU/PRTR. The authors would also like to thank the work of Martí Fabrega during the research.

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The original version of this paper is available on https://www.scientific.net/KEM.1047.95.pdf

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A. Rosa-Sainz, M.L. García-Romeu, I. Ferrer, M.B. Silva, G. Centeno, On the effective peek application for customized cranio-maxillofacial prostheses: An experimental formability analysis, J. Manuf. Process. 86 (2023) 66-84. .

A. Rosa-Sainz, I. Ferrer, M.L. Garcia-Romeu, G. Centeno, Characterization of plastic formability and failure mechanisms in FDM additively manufactured PETG and PCL sheets, Polym Test 134 (2024).

M.L. Garcia-Romeu, I. Ferrer, C. Pasotti, J. Coma, A. Rosa-Sainz, G. Centeno, Preliminary study on the use of 3D printed biodegradable polymeric sheet for the manufacturing of medical prostheses by SPIF, in: Procedia CIRP, Elsevier B.V., 2022: pp. 76-81. .

Hasan, A. & Akhtar, S.S. Hybrid Manufacturing: A Critical Review on the Integration of Metal Additive Manufacturing and Forming 2025, Arabian J. Science and Engng. 1-30.

Hafenecker J, Bartels D, Kuball CM, Kreß M, Rothfelder R, Schmidt M, et al. Hybrid process chains combining metal additive manufacturing and forming - A review. CIRP J Manuf Sci Technol 2023;46:98-115.

Ge T., Li Y., Gao D., Yang C., Li F. Hybridizing additive manufacturing and sheet forming process to manufacture complex components with multi-features: A review. J Manuf Proc 2024;124:345-64.

Ambrogio G, Gagliardi F, Muzzupappa M, Filice L. Additive-incremental forming hybrid manufacturing technique to improve customised part performance. J Manuf Proc. 2019;37:38691.

Rosa-Sainz A, Centeno G, Silva MB, Vallellano C. Experimental investigation of polycarbonate sheets deformed by SPIF: formability, micro-mechanisms of failure and temperature analysis. J Mater Res Technol 2023;25:7546-65.

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 105-113

doi: 10.4028/p-Ms4I4K

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-22

Revised:
2026-02-16

Accepted:
2026-02-25

Online:
2026-04-13

Numerical Study of the Flexural Behaviour of Additively Reinforced Blanks

The original version of this paper is available on https://www.scientific.net/KEM.1047.105.pdf

Emanuele Fulco 1,a*, Pasquale Guglielmi 1, b, Angela Cusanno 2,c, Donato Sorgente 2, d

1 Department of Engineering, University of Basilicata, Potenza, Italy

2 Department of Mechanics, Mathematics and Management - Politecnico di Bari, Bari, Italy

a * emanuele.fulco@unibas.it, b pasquale.guglielmi@unibas.it, c angela.cusanno@poliba.it, d  donato.sorgente@poliba.it

*corresponding author: emanuele.fulco@unibas.it

Keywords: abaqus, bending, FEM, patchwork blanks, additive manufacturing.

Abstract

Tailored welded and patchwork blanks are commonly used in the automotive field to locally tailor the mechanical response of sheet metal components, but conventional manufacturing approaches often introduce structural discontinuities, corrosion-prone interfaces and limited formability. Additive deposition of local reinforcements offers a more flexible alternative, enabling material to be placed only where it provides the greatest structural benefit and reducing overall material usage and environmental impact. This work investigates the flexural behaviour of additively reinforced blanks through finite element simulations. A numerical model was developed in Abaqus to reproduce three-point bending tests on 22 MnB 5 sheets locally reinforced by the wire-laser additive deposition of a 316 L stainless steel. Metallographic cross-sections were used to define the reinforcement geometry and penetration depth, micro-hardness profiles to define the extent of the heat affected zone, and plastometric characterisation to obtain local mechanical properties. The simulations demonstrate that the proposed numerical model reliably reproduces the experimentally observed flexural behaviour of wire-laser additively reinforced blanks. The numerical forcedisplacement response is consistent with the experimental one, and within this agreement the increase in bending strength obtained with minimal added material is confirmed.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.105.pdf

Lightweight design is a key strategy for reducing emissions in the automotive field and supporting the wider transition toward low-carbon mobility. Reducing the mass of structural components directly lowers energy demand and CO2 emissions for both conventional and electrified vehicles [1−4], while offering immediate benefits that complement long-term decarbonisation efforts [5]. Given that sheetmetal manufacturing contributes significantly to the total energy and material footprint of vehicle production [6], improving material efficiency has become a central objective. Additive and hybrid manufacturing approaches have therefore gained increasing attention due to their potential to reduce waste, enhance resource efficiency and support the development of optimised lightweight structures [7,8]. A widely adopted solution for achieving lightweight construction involves tailoring the local properties of sheet-metal components. Traditional solutions such as tailored welded blanks, tailored rolled blanks, tailored heat treated blanks and patchwork blanks seek to reinforce only the most critical regions while avoiding unnecessary mass [9-12]. However, conventional joining-based approaches may introduce structural discontinuities, corrosion-susceptible interfaces and reduced formability during downstream processing, limiting their applicability in advanced automotive manufacturing. Additive deposition presents an appealing alternative, enabling the placement of reinforcement material with high spatial precision while reducing waste and supporting sustainability goals [6-8,13]. Recent contributions in hybrid manufacturing further highlight the potential of integrating additive strategies with established forming processes [14-16]. Experimental studies on wire-laser additively reinforced blanks have also shown that the additive deposition of metallic tracks can significantly increase bending strength while requiring only minimal additional material, thereby
improving the efficiency of patchwork-style reinforcements [17]. Despite this progress, the literature on additively reinforced sheet-metal blanks has primarily emphasised microstructural or material characterisation, whereas systematic evaluations of their mechanical performance, especially under bending loads relevant to automotive components, remain limited.

To support the industrial adoption of additively reinforced blanks, it is essential to rely on predictive tools that can relate the geometry of the reinforcement, its penetration into the substrate and the resulting material response to the overall structural behaviour of the component. Bending is a relevant loading condition for sheet-metal applications, as it is highly sensitive to local stiffness variations and therefore provides a direct measure of reinforcement effectiveness. In this context, the aim of the present work is to develop a numerical model capable of reproducing the flexural behaviour observed experimentally in additively reinforced blanks [17]. The model, implemented in Abaqus, incorporates reinforcement geometry and penetration depth obtained from optical microscopy, while the mechanical properties assigned to the base material, the heat affected region and the deposited metal reinforcement (fused zone) are derived from non-destructive Profilometry-Indentation Plastometry (PIP). By comparing numerical predictions with experimental measurements, the study investigates the reliability of the proposed model in capturing the flexural response of the investigated specimens.

Material and Methods

The original version of this paper is available on https://www.scientific.net/KEM.1047.105.pdf

Additive Deposition and Material Characterisation.

The original version of this paper is available on https://www.scientific.net/KEM.1047.105.pdf

To develop a finite element model with realistic and spatially differentiated material properties, an initial experimental characterisation campaign was carried out on physically manufactured specimens. Steel sheets of 22 MnB 5 were reinforced by the wire-additive deposition of two parallel 316L tracks, each 20 mm in length and spaced 1 mm apart. The deposition was performed using a Meltio M350 system, under constant processing conditions to ensure repeatability across samples. The system employs six 200 W diode lasers, angled at 25∘, with a 976 nm wavelength. Following deposition, the reinforced sheet was cut into specimens of 80×20 mm. A schematic representation of the wire-laser additive deposition setup used in this study is shown in Fig. 1.

[picture]

Fig. 1. Schematic representation of the wire-laser additive deposition setup, showing the clamped 22 MnB 5 sheet and the local reinforcement obtained by depositing parallel 316 L wire tracks.

Fig. 1. Schematic representation of the wire-laser additive deposition setup, showing the clamped 22 MnB 5 sheet and the local reinforcement obtained by depositing parallel 316 L wire tracks.

Each specimen was subsequently hot-mounted using ConduFast® resin at 180∘C and 250 bar, with a heating time of 3.5 minutes followed by 1.5 minutes of cooling. Then, they were polished and chemically etched to reveal the fused zone (FZ) and the surrounding heat affected zone (HAZ) in the cross-section. These cross-sectional analyses provided quantitative measurements of the reinforcement geometry, including bead height, width and penetration depth into the substrate. To determine the local mechanical response of the different regions, plastometric tests were carried out using a PLASTOMETREX PLX-Benchtop system, which implements the Profilometry-based Indentation Plastometry (PIP) technique. Indentations were performed in the three zones of interest, namely the Base Material (BM), the Heat Affected Zone (HAZ), and the Fused Zone (FZ)
corresponding to the deposited reinforcement. A spherical indenter with a diameter of 0.5 mm was employed, with an indentation velocity of 4μ m/s and a maximum indentation depth of approximately 90μ m, in order to probe the plastic response while maintaining the deformation in a predominantly localised regime. For each indentation, the applied load-displacement data and the three-dimensional imprint geometry, acquired by profilometry, were processed through the CORSICA software supplied with the PLX system. By means of an inverse analysis procedure, CORSICA identifies the constitutive parameters that best reproduce the measured response, providing the corresponding true stress-strain curve for the tested material point. In this work, the plastic portion of the response was approximated by a Voce-type hardening law, which was calibrated separately for the BM, HAZ, and FZ. The resulting geometric and constitutive data for the three regions were then used as input for the numerical model developed to simulate the bending test on the reinforced specimen.

Numerical Modelling.

The original version of this paper is available on https://www.scientific.net/KEM.1047.105.pdf

Finite element simulations were performed in Abaqus to replicate a three-point bending test on the reinforced sheet. For reference and comparison purposes, an additional simulation was also performed on a flat, unreinforced sheet, modelled entirely as BM. Three analytical rigid bodies were introduced to represent the punch and the two anvils, reproducing the experimental bending configuration. In order to ensure numerical robustness while limiting model complexity, the finite element model was constructed by introducing a set of simplifying assumptions, which are explicitly referenced throughout the following description. The specimen was represented as a deformable three-dimensional solid and, to reduce the computational cost, (i) the model was reduced to one quarter of the full geometry by assuming the reinforcement bead to be symmetric with respect to both the longitudinal and transverse directions. Appropriate symmetry boundary conditions were applied on the corresponding planes. The specimen was partitioned to distinguish the reinforcement (FZ) from the sheet metal substrate. (ii) The cross-section of the reinforcement was simplified with respect to the real deposited morphology and modelled as a portion of an ellipse, as shown in Fig. 2a and 2b, whose major axis corresponds to the average reinforcement width (3.66 mm) and whose height (0.81mm) matches the average reinforcement height measured at a deposition power of 1100 W , as reported in a previous study [17].

[picture]

Fig. 2. Experimental geometry of the reinforced sheet and corresponding numerical representation.
(a) Cross-section view showing the experimental reinforcement profile and its elliptical representation, together with the heat affected zone (HAZ) modelled with a straight-throughthickness simplified geometry. (b) Top view highlighting the simplified geometry adopted for the reinforcement and the HAZ. (c) Partitioned geometry adopted in the numerical model, showing the base material (BM), heat affected zone (HAZ) and fusion zone (FZ).

Fig. 2. Experimental geometry of the reinforced sheet and corresponding numerical representation. (a) Cross-section view showing the experimental reinforcement profile and its elliptical representation, together with the heat affected zone (HAZ) modelled with a straight-throughthickness simplified geometry. (b) Top view highlighting the simplified geometry adopted for the reinforcement and the HAZ. (c) Partitioned geometry adopted in the numerical model, showing the base material (BM), heat affected zone (HAZ) and fusion zone (FZ).

This representation was adopted to retain the dominant geometric features of the reinforcement while avoiding unnecessary geometric complexity. A second partition was introduced around the reinforcement to represent the heat affected zone (HAZ). (iii) The HAZ was modelled as a region extending through the sheet thickness with straight boundaries, as shown in Fig. 2a and 2b, meaning that the characteristic cup-shaped morphology observed experimentally in cross-section was not explicitly reproduced. The in-plane width and longitudinal extent of the HAZ were instead defined on the basis of experimental hardness maps, obtained using a Qness 10A+ tester by applying a load of 0.2 kg (HV0.2) in accordance with ASTM E-384, with a dwell time of 15 s . Accordingly, a uniform HAZ extension of 0.86 mm around the reinforcement was adopted. Within the numerical model, BM, HAZ and FZ were defined as distinct material regions, as shown in Fig. 2c. Each region (iv) was assumed to be mechanically homogeneous, and was therefore assigned a single constitutive description, derived from the corresponding plastometric characterisation discussed in the previous section. Although property gradients are known to develop within both the HAZ and the FZ in the real component, this simplification was adopted to preserve a clear mechanical distinction between regions while maintaining a manageable model complexity. All these assumptions also allowed to preserve a structured mesh in the discretization of the deformable body and to have a fast and reliable measurement strategy in the PIP tests. At the same time, these assumptions are not expected to significantly affect the global response of the specimen, in particular the macroscopic forcedisplacement behaviour under three-point bending. Contact interactions were defined between the punch, anvils and specimen, using a coefficient of friction equal to 0.20 . The three-point bending test was simulated by imposing a downward displacement of 12 mm to the punch under displacementcontrolled conditions, consistently with the experimental setup reported in a previous work [17].

The boundary conditions were defined consistently with the adopted symmetry assumptions and the experimental bending setup. The two anvils were fully constrained by applying an encastre condition at their respective reference points. During the bending step, the punch was allowed to move exclusively along the vertical direction, while all remaining degrees of freedom were constrained, ensuring a controlled kinematic loading. Symmetry boundary conditions were applied to the quarter model of the reinforced sheet to reproduce the full geometry behaviour. A symmetry constraint was imposed on the plane normal to the longitudinal direction ( Z -axis), and a second symmetry constraint was applied on the plane normal to the transverse direction ( X -axis). These constraints enforce zero displacement normal to the symmetry planes and prevent spurious rigid-body motion, ensuring that the deformation of the quarter model remains mechanically equivalent to that of the full specimen under symmetric loading conditions.

The punch and the anvils were modelled as discrete rigid bodies and discretised using four-node three-dimensional rigid elements (R3D4), with a uniform element size of 0.5 mm , as illustrated in Fig. 3a. The sheet was instead discretised as a deformable body using eight-node three-dimensional brick elements with reduced integration (C3D8R). As shown in Fig. 3b and 3c, a non-uniform mesh was adopted: an average element size of 0.5 mm was used in the BM region, a refined discretisation with an element size of 0.2 mm was applied within the HAZ , and a further refinement to 0.1 mm was introduced in the FZ beneath the punch, where the highest bending strains were expected.

[picture]

Fig. 3. Finite element discretisation of the numerical model: (a) mesh of the 3D discrete rigid punch and supports (R3D4); (b) mesh of the 3D deformable reinforced sheet (C3D8R), showing the HAZ and the local refinement in the FZ beneath the punch; (c) global view of the same mesh highlighting the coarsening towards the specimen ends.

Fig. 3. Finite element discretisation of the numerical model: (a) mesh of the 3D discrete rigid punch and supports (R3D4); (b) mesh of the 3D deformable reinforced sheet (C3D8R), showing the HAZ and the local refinement in the FZ beneath the punch; (c) global view of the same mesh highlighting the coarsening towards the specimen ends.

This meshing strategy allowed the local flexural response to be captured accurately while keeping the overall computational cost under control. All simulations were performed using Abaqus on a workstation equipped with an Intel Xeon W7-3465X processor, 256 GB of RAM. For the adopted mesh density and modelling assumptions, a complete three-point bending simulation required approximately 30 min of computation time.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1047.105.pdf

As reported in the previous sections, plastometry tests were used to characterise the mechanical response of the three material regions composing the reinforced sheet, namely BM, HAZ and the reinforcement (FZ). Fig. 4 shows the resulting true stress-true plastic strain curves of the three regions. The BM exhibits the lowest flow stress with values that are in agreement with those that can be found in literature for the as-received 22 MnB 5 steel [18]. The FZ displays a significantly higher strength level (up to about 1500 MPa ), while the HAZ shows the highest flow stress (up to about 1760 MPa ), particularly at low plastic strains, indicating a marked strengthening induced by the thermal cycle of the laser additive reinforcement process. The different hardening responses highlight the presence of strong local mechanical gradients across the sheet. These results support the modelling approach adopted in this work, in which BM, HAZ and FZ are treated as mechanically distinct regions for the numerical simulation of the three-point bending test.

[picture]

Fig. 4. True stress-true plastic strain curves obtained from plastometry for the base material (BM), the heat affected zone (HAZ) and the reinforcement (FZ) of the additively reinforced sheet.

Fig. 4. True stress-true plastic strain curves obtained from plastometry for the base material (BM), the heat affected zone (HAZ) and the reinforcement (FZ) of the additively reinforced sheet.

Fig. 5 illustrates the time evolution of the bending simulation in terms of equivalent plastic strain (PEEQ). The sequence clearly shows a through-thickness strain distribution, with tensile fibres developing at the outer surface (extrados) and compressive fibres at the inner surface (intrados), separated by a well-defined neutral line.

[picture]

Fig. 5. Time evolution of the bending simulation shown in terms of equivalent plastic strain (PEEQ) at selected time instants.

Fig. 5. Time evolution of the bending simulation shown in terms of equivalent plastic strain (PEEQ) at selected time instants.

The maximum PEEQ value ( 0.1658 ) occurs at the top of the reinforcement at the specimen midspan, where the bending moment is maximum. Plastic deformation remains mainly localised in the bending region throughout the process. In Fig. 6, a comparison between the experimental and numerical force-displacement curves is presented. To improve readability, the experimental repetitions for each condition are represented using a consistent colour scheme and condensed into a single legend label. The numerical force-displacement curves reproduce the trend of the experimentally obtained curves, both in terms of slope and force levels. For the 1100 W condition, the numerical curve slightly overestimates the experimental response in the mainly elastic region and slightly underestimates it in the plastic regime, where five out of the six experimental curves lie above the numerical prediction and only one falls below it (Fig. 6). These small deviations are probably related to the geometric simplifications adopted in the model, since the reinforcement profile observed experimentally is imperfect due to the deposition process and not completely symmetric (Fig. 2), which can reasonably explain the small shifts observed in the force-displacement curve. Furthermore, numerical parameters, such as mesh density and friction coefficient, are not expected to be the primary causes of these deviations, given that the same configuration applied to the unreinforced condition provided an excellent fit with the experimental data. Indeed, for both the base material and the reinforced condition, the numerical results lie within the range defined by the experimental curves throughout the bending process. In this context, in a previous work [17] the authors reported that, for the 1100 W condition, a mass increase of 1.6% for the specimen corresponds to an increase in maximum bending force of 82.2% with respect to the base material; this quantitative trend is consistently reflected by the positioning of the numerical force-displacement curve in Fig. 6. This outcome is particularly relevant for the reinforced specimens, where geometric and material simplifications were introduced in the numerical model, including symmetry assumptions, simplified representations of the reinforcement and the heat affected zone, and the definition of mechanically homogeneous material regions. Despite the absence of material property gradients within the HAZ and the FZ, the numerical response remains close to the experimental one, indicating that these assumptions do not significantly affect the predicted bending behaviour.

[picture]

Fig. 6. Comparison between experimental and numerical force-displacement curves for the base material (BM) and the reinforced condition (1100 W).

Fig. 6. Comparison between experimental and numerical force-displacement curves for the base material (BM) and the reinforced condition (1100 W).

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.105.pdf

A finite element model was developed to predict the flexural behaviour, under three-point bending, of wire-laser additively reinforced 22 MnB 5 blanks with a 316 L stainless steel reinforcement. The reinforcement geometry was defined from metallographic cross-sections, while the mechanical properties of the base material (BM), the heat affected zone (HAZ) and the deposited material (FZ) were obtained by Profilometry-based Indentation Plastometry (PIP). The numerical forcedisplacement curves obtained with the model fall within the experimental results for both reinforced specimens and unreinforced 22 MnB 5 sheets. This shows that, despite the adopted geometric and constitutive simplifications, the numerical approach reliably predicts the bending behaviour of the investigated configurations. Future work will focus on extending the model to incorporate property gradients within the HAZ and FZ, with the aim of eliminating the artificial local strain discontinuities observed in the current model and obtaining a more accurate estimation of the neutral-layer position during bending while also exploring multi-layer reinforcements.

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.105.pdf

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C. Gao, S. Wolff, S. Wang, Eco-friendly additive manufacturing of metals: Energy efficiency and life cycle analysis, J. Manuf. Syst. 60 (2021) 459-472. .

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 115-125

doi: 10.4028/p-mJr4B1

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2025-12-31

Revised:
2026-02-21

Accepted:
2026-02-25

Online:
2026-04-13

Vasileios Stratiotou-Efstratiadis 1,2,a, Apostolos Argyros 1,2, b, Giorgos Sarmas 3,c, Giannis Oikonomou 3, d, Dimitris Dimitriou 3,e, Nikolaos Chrysochoidis 3,f, Dimitris Saravanos 3,g, Georgios Maliaris 4,h, Vasileios Mpinas 5,i, Nikolaos Michailidis 1,2,j*

¹Physical Metallurgy Laboratory, School of Mechanical Engineering, Aristotle University of Thessaloniki. University Campus, 54124 Thessaloniki, Greece

2 Centre for Research & Development of Advanced Materials (CERDAM), Center for Interdisciplinary Research and Innovation (CIRI), Balkan Centre, Building B', 10th km ThessalonikiThermi Road, 57001, Thessaloniki, Greece

3 Department of Mechanical Engineering & Aeronautics, University of Patras, 26504 Rio-Patras, Greece

4 Additive Manufacturing Laboratory, Department of Chemistry, School of Science, Democritus University of Thrace, 65404 Kavala, Greece

5 Physical Chemistry Laboratory, Department of Chemistry, Aristotle University of Thessaloniki. University Campus, 54124 Thessaloniki, Greece

a vstratio@auth.gr, b aargyros@auth.gr, c sarmas.g@ac.upatras.gr, d  oikonomou.ioannis@ac.upatras.gr, e  d.dimitriou@ac.upatras.gr, f  nchr@mech.upatras.gr, g dsaravanos@upatras.gr, h gmaliari@duth.gr, i vbinas@chem.auth.gr, j nmichail@auth.gr

Keywords: piezoelectric, PVDF, poling, inkjet printing.

Abstract

The study explores the integration of additive manufacturing for the development of 4Dprinted piezopolymer metamaterials, aiming to create dynamic, multifunctional structures capable of distributed sensing and energy harvesting. The focus is on Polyvinylidene fluoride (PVDF), a partially fluorinated polymer renowned for its strong electromechanical coupling, specifically within its polar β-phase. To harness these properties, three distinct experimental strategies were evaluated for integrating PVDF with conductive electrodes necessary for electrical poling: direct 3D printing with manually applied silver paste, printing directly onto pre-integrated aluminium foil substrates, and a novel chemical solvent-based deposition using a DMF/acetone mixture. While high-precision inkjet printing was initially tested for electrode deposition, it demonstrated significant limitations in scalability, throughput, and durability, particularly suffering from structural degradation during the post-poling silicon oil removal process. Consequently, the study advocates for a robust, hybrid multimaterial extrusion platform. This approach will enable the simultaneous, monolithic deposition of structural PVDF thermoplastics and highly conductive thixotropic inks.

Implementing Inkjet Printing to Manufacture Piezopolymer Films for Sensing Applications

The original version of this paper is available on https://www.scientific.net/KEM.1047.115.pdf

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.115.pdf

Polyvinylidene fluoride (PVDF) is a type of fluoropolymer, a class of polymers that incorporate fluorine atoms within their molecular structures (polymerization of H2C=CF2 monomer) [1]. Fluoropolymers are generally classified into perfluoropolymers and partially fluorinated polymers. In perfluoropolymers, all hydrogen atoms in the polymer backbone are replaced by fluorine, whereas in partially fluorinated polymers, both hydrogen and fluorine atoms coexist in the polymer structure. PVDF falls into the latter category.

Fluoropolymers exhibit remarkable chemical resistance, excellent weatherability, low surface energy, and a high dielectric constant. These characteristics stem from the electronic properties of fluorine, the high stability of carbon-fluorine covalent bonds, and the inter and intramolecular interactions within the polymer chains [1].

PVDF demonstrates excellent resistance to a wide range of aggressive chemicals, along with enhanced thermal stability and high mechanical strength [2]. It can be found with several polymorphic phases and crystalline forms depending on its synthesis method. The main phases of PVDF are:

The alpha ( α ) phase is the most thermodynamically stable form of PVDF. It features an orthorhombic crystalline structure, a non-polar nature, and lacks both piezoelectric and ferroelectric properties.

The beta (β) phase is particularly significant for piezoelectric, pyroelectric, and ferroelectric applications due to its highly polar structure and strong electromechanical coupling.

The gamma ( γ ) phase serves as an intermediate state between the α and β phases, displaying partial dipole alignment. However, it is not as polar as the β phase.

The delta (δ) phase is essentially a polarized version of the α-phase, produced under an external electric field. It retains an orthorhombic structure but demonstrates some degree of polar behavior compared to the α− phase.

There is also epsilon ( ε ) phase, which is the least common and less understood phase. It is reported in high-pressure crystallization conditions.

The β-phase is the most sought-after due to its strong piezoelectric and ferroelectric behavior, while the α-phase is the most naturally occurring [3]. The β-phase, characterized by an all-trans chain conformation, aligns dipoles in the same direction, leading to stronger polarization and improved piezoelectric properties [4]. PVDF demonstrates resistance to strong acids. However, compared to other fluoropolymers, its overall chemical resistance is lower [5]. When exposed to strong polar aprotic solvents like dimethylacetamide (DMAc) or dimethylformamide (DMF), its crystalline structure, and strong fluorine-carbon bonds deteriorate. Ethanol and/or acetone can be used as cosolvents to adjust viscosity, drying rate, or film properties.

The aim of this study involves 3D printing of passive piezopolymer microstructures to evaluate material deposition quality, geometric precision, and mechanical performance. Building on these insights, the next step is the fabrication of functional piezopolymer configurations, incorporating active elements to assess their electromechanical response. To achieve this, the research explores optimization of additive manufacturing (AM) processes, ensuring precise material distribution, controlled porosity, and enhanced structural integrity. Material extrusion (MEX) AM is chosen for the 3D printing of PVDF films, and inkjet printing is used as the AM process of electrode deposition.

Materials & Methods

The original version of this paper is available on https://www.scientific.net/KEM.1047.115.pdf

PVDF filament used in the present study was the Adamant S1 PVDF, procured from add:north. The silver ink used, EM-Tec-C33, was procured from Micro to Nano Innovative Microscopy Supplies. PVDF films were printed using in a Voron Trident 300 3D-printer, optimizing the printing pattern, focusing on parameters such as print speed, overlapping of infill and perimeters, extrusion multiplier, to enhance print quality and structural integrity. As seen in Fig. 1. different infill printing patterns were tested. Concentric infill pattern was chosen as the prefered one for the manufacturing of the films as it offered better results regarding voids, gaps, overlapping and speed.

[picture]

Fig. 1. Printing patterns of PVDF film; concentric (left), aligned rectilinear (right).

Fig. 1. Printing patterns of PVDF film; concentric (left), aligned rectilinear (right).

To systematically determine the most effective method for integrating PVDF with conductive electrodes for subsequent electrical poling, three distinct experimental strategies were evaluated.

These approaches ranged from direct AM techniques to chemical solvent-based deposition, with each phase designed to address specific challenges related to material adhesion, surface morphology, and electromechanical compatibility.

The first phase of the study focused on the direct 3D printing of PVDF onto the printer bed to assess its processability, adhesion characteristics, and overall structural integrity after deposition. The printed samples were visually examined for uniformity, layer adhesion, and surface morphology, as these are critical factors influencing subsequent poling and electrode integration. To evaluate poling feasibility in a controlled manner, a conductive paste was manually applied to the printed PVDF surface to form the first electrode layer, as seen in Fig. 2. The initial application utilized a manual coating or painting method to ensure uniform coverage, allowing for the measurement of the coated electrode layer's resistance. This provided a preliminary assessment of electrode efficiency before transitioning to a fully integrated setup, where the deposition will eventually be automated using a pneumatic extruder.

[picture]

Fig. 2. PVDF films coated with silver paste.

Fig. 2. PVDF films coated with silver paste.

Building upon the direct printing method, the second approach involved printing PVDF directly onto aluminium foil, which serves as a pre-integrated electrode for the poling process. To ensure optimal adhesion and minimize sample warping, the printing was tested on both the rough and smooth sides of the foil. The electrical conductivity of the aluminium-PVDF interface will be characterized to determine the effectiveness of the aluminium as an electrode in this specific configuration. Additionally, adhesion tests will evaluate how well the PVDF bonds to each side of the foil, ultimately determining the mechanical and electrical suitability of aluminium foil both as a substrate and an electrode for PVDF poling applications. Fig. 3 shows the prints on the smooth (left) and rough (right) side of the foil.

[picture]

Fig. 3. Results of 3D printing PVDF onto the aluminium foil.

Fig. 3. Results of 3D printing PVDF onto the aluminium foil.

In the final approach, a chemical deposition method was explored by dissolving PVDF in a carefully formulated solvent mixture of DMF and acetone. Based on literature recommendations, these solvents were selected for their ability to dissolve PVDF effectively while maintaining the
appropriate viscosity and evaporation rate. The primary goal was to formulate a solution that enabled uniform deposition onto electrodes without introducing defects like incomplete wetting or adhesion irregularities. Key factors evaluated during this stage included the interfacial adhesion between the PVDF and the electrode, as well as the impact of deposition parameters on poling efficiency. Fig. 4 showcases the dilution of PVDF in DMF and acetone solution.

[picture]

Fig. 4. Stirring of PVDF in DMF and acetone solution.

Fig. 4. Stirring of PVDF in DMF and acetone solution.

Two specific experiments were conducted:

Experiment 1 (Temperature and Evaporation): Initially, 0.5 g of PVDF was added to a mixture of 5 g DMF and 5 g acetone at ambient temperature and mixed using a magnetic stirrer. After 20 minutes, most of the PVDF remained intact, prompting a temperature increase to 60∘C, which successfully diluted the polymer. The temperature was then raised to 120∘C to evaporate the solvents, leaving the PVDF securely adhered to the glass surface. Notably, the introduction of water to the mixture led to the formation of a foam-like structure, as seen in Fig. 5.

Experiment 2 (Saturation Limits): A second experiment was conducted to determine the maximum amount of PVDF that could be diluted to create a saturated solution. At ambient temperature, 0.5 g of PVDF was added to 6 g of DMF and stirred. After 20 minutes with the PVDF largely intact, the temperature was raised to 35∘C, resulting in almost immediate dilution. This step was repeated until a total of 1.5 g of PVDF ( 25%wt.) was successfully added. As the solution became highly viscous, 1 g of acetone was introduced before increasing the temperature to initiate solvent evaporation. Consistent with the first experiment, adding water resulted in a foam-like structure, again shown in Fig. 5.

[picture]

Fig. 5. Foam-like structure of PVDF during both experiments.

Fig. 5. Foam-like structure of PVDF during both experiments.

Building upon the insights gained from the isolated experiments, a hybrid approach was developed to leverage the strengths of both the integrated aluminium foil electrodes and the solventbased PVDF formulations. To optimize the electromechanical interface between the solid PVDF and the aluminium substrate, a targeted formulation of 1 g of PVDF, 5 g of acetone, and 5 g of DMF was magnetically stirred until the solvents began to evaporate. Rather than relying on conventional
commercial adhesives, this customized solution was applied as an active bonding layer to secure the 3D-printed PVDF film directly to the rough side of the aluminium foil. Observations indicated that applying a sufficient volume of this diluted PVDF is critical; inadequate application left the printed film highly susceptible to severe warping. Utilizing a diluted PVDF solution as the binding agent offered substantial advantages over traditional glues. The carefully balanced DMF and acetone mixture provided optimal viscosity and a controlled evaporation rate, facilitating uniform deposition without excessive residue or film defects. Furthermore, because the adhesive shared the exact chemical composition of the piezoelectric film, it ensured superior chemical compatibility, improved interfacial contact, and enhanced mechanical stability. Crucially for these applications, this homogeneous interface promoted better charge transfer and minimized interface electrical resistance, leading to a seamless integration that ultimately improved the efficiency and durability of the subsequent poling process. As a final variation of this combined approach, the 3D-printing step was bypassed entirely. Instead of adhering a pre-fabricated printed film to the substrate, a droplet of the diluted PVDF solution was deposited directly onto the shiny side of the aluminium foil electrode, as shown in Fig. 6. This direct-cast method allowed the solution to cure in place, successfully forming a thin, fully integrated piezoelectric film straight onto the conductive surface.

Differential scanning calorimetry (DSC) analysis was performed on PVDF filament to elucidate the influence of printing conditions on the material's microstructure and phase composition. Thermal profiling was conducted at a heating rate of 10∘C/ min under an inert nitrogen atmosphere (25 to 320 ∘C ) utilizing a DSC-25 apparatus (TA Instruments).

[picture]

Fig. 6. Combination of approaches 2 and 3 with diluted PVDF instead of glue is used.

Fig. 6. Combination of approaches 2 and 3 with diluted PVDF instead of glue is used.

Scanning electron microscopy (SEM) was performed to observe the morphology of samples. SEM uses a focused beam of high-energy electrons to generate signals at the surface of solid samples. The signals reveal information about the external morphology (texture), chemical composition, and internal crystalline structure of the sample. SEM analyses took place using Phenom ProX G5 from Thermo Fischer Scientific, using long lifetime thermionic source ( CeB6 ), with light optical magnification range: 20-134x and electron optical magnification range: 160-350,000x [6]. It is also equipped with an integrated energy-dispersive X-ray diffraction (EDX) detector, which makes elemental analysis of the sample possible.

Inkjet printing is a versatile, non-contact drop-on-demand (DoD) deposition AM method, widely used for the deposition of functional materials, such as conductive inks, onto polymeric substrates. In this process, precisely controlled droplets of ink are ejected through micron-scale
nozzles to form thin, patterned conductive traces. The method offers high spatial resolution, minimal material waste, and compatibility with flexible or temperature-sensitive substrates, such as PVDF [7]. Unlike conventional lithography or screen printing, the inkjet process does not require contact with the substrate surface, minimizing contamination and mechanical stress. This feature makes it highly suitable for delicate and flexible materials such as PVDF [7]. Using inkjet for the deposition of electrodes on the piezoelectric layer offers several advantages, such as: precise patterning, minimal invasive disruption of the mechanical integrity of the film, and seamless integration into the multimaterial printing workflow [8]. At its core, the system uses a piezo-driven print cartridge that houses an array of microscopic nozzles and an ink reservoir filled with the functional fluid. The working mechanism is initiated when a precise, electronically controlled voltage pulse (defined by a userprogrammed waveform) is applied to a piezoelectric element associated with an ink chamber. This voltage causes the piezoelectric material to rapidly deform (expand or contract), which in turn generates a pressure wave within the ink chamber [7], [9]. This transient pressure wave forces a tiny, uniform droplet of ink, to be ejected from the nozzle orifice and propelled toward the substrate. The printer utilizes a drop watcher camera system to observe and optimize the fluid's jetting characteristics (velocity, size, and trajectory), and a heated vacuum platen to precisely secure and thermally condition the PVDF substrate, ensuring accurate drop placement and print fidelity. The inkjet printer used in this study was Dimatix Materials Printer DMP-2850, by Fujifilm (Tokyo, Japan), which provided the necessary high-precision, non-contact, DoD piezoelectric technology for the accurate deposition of conductive ink.

Following the printing of the PVDF film and its integrated bottom electrode, electrical poling was required to activate the material's piezoelectric behavior. Electric poling involves the application of a strong electric field essential for aligning the molecular dipole of the electroactive β-phase of the PVDF, which are initially randomly oriented. The magnitude of the electric field should be both greater than the material's coercive electric field and below its dielectric breakdown threshold. To preserve the new molecular arrangement, the component is cooled down to room temperature before the voltage is removed. Among various poling techniques associated with PVDF films, contact and corona poling are widely utilized.

Corona poling is a non-contact poling technique where the air between the material and the high voltage point (usually needle) is ionized, and there is also a grounded electrode at the bottom of the material. On the other hand, in contact poling, the material is placed between two electrodes and exposed to an electric field at elevated temperatures to improve the mobility of the polymer's dipoles. The one electrode, typically the bottom, is grounded, and a high voltage is applied on the top electrode. This drives a highly robust orientation of the dipoles, which maximizes remanent polarization and significantly boosts the resulting electromechanical output [10]. Contact poling was favoured over the corona method due to its straightforward application and adaptability to non-planar, complex geometries. Also, the electrode pair that is used in contact poling can ensure a wider and more homogenized application of electric field to the material compared to the corona poling technique. Since only one electrode is printed and the contact poling requires two electrodes, another electrode is added using conductive epoxy with careful hand lay-up [11].

Results

The original version of this paper is available on https://www.scientific.net/KEM.1047.115.pdf

A multimeter was used to measure the resistance of the manually coated 3D printed samples and 3D printed PVDF on both sides of the aluminium foil. The resistance measurement of the first approach sample can be seen in Fig. 7. The resistance was equal to 0.73Ω, meaning that the material coated with the silver paste became conductive. Fig. 8 a presents the resistance equal to 0.53Ω of the rough side of the foil, which is in contact with the highly polished rollers during its manufacturing, presenting lower oxidation, hence lower resistance. However, Fig. 8 b presents the 3D printed sample of the second approach on the smooth side of the foil, resulting in a higher resistance of 15.05Ω.

[picture]

Fig. 7. Resistance measurement of approach 1 3D printed, silver coated sample.

Fig. 7. Resistance measurement of approach 1 3D printed, silver coated sample.

Approach 3 led to poor adhesion between the diluted PVDF and the aluminium foil and no samples remained functional, hence resistance could not be measured.

DSC measurements revealed the typical thermal response of PVDF under the examined conditions (Fig. 9). The thermograms highlighted a prominent endothermic signature (around 130∘C ) indicative of primary crystalline melting, complemented by a secondary, less intense peak associated with the breakdown of semi-ordered domains (around 45∘C ). No other thermal events were detected beyond these points, validating that PVDF matrix remained thermally stable throughout the required extrusion and post-print processing.

[picture]

Fig. 8. Resistance measurement of approach 2 printed sample on the (a) rough and (b) smooth side of the aluminium foil.

Fig. 8. Resistance measurement of approach 2 printed sample on the (a) rough and (b) smooth side of the aluminium foil.

[picture]

Fig. 9. DSC analysis of PVDF filament melts.

Fig. 9. DSC analysis of PVDF filament melts.

From the SEM analysis, as expected, pure PVDF 3D printed samples contain carbon and fluorine, as seen in Fig. 10. In the case of the 3D printed silver coated, approach 1, samples, a uniform
film of silver paste can be seen in Fig. 11. The point of element identification is fully coated, as no fluorine is visible. Finally, Fig. 12 presents a more zoomed-in image of the silver surface.

Fig. 10. SEM analysis of pure PVDF 3D printed film.

Fig. 10. SEM analysis of pure PVDF 3D printed film.

Element

Number

Element

Symbol

Element

Name

Atomic

Conc.

Weight

Conc.

47 | Ag | Silver | 56.91 | 86.99
6 | C | Carbon | 43.09 | 13.01

Fig. 11. SEM analysis of silver coated PVDF 3D printed approach 1 sample.

[picture]

Fig. 12. Zoomed-in image of silver coated PVDF 3D printed approach 1 sample.

Fig. 12. Zoomed-in image of silver coated PVDF 3D printed approach 1 sample.

As the aforementioned methods of manufacturing electrodes did not provide sufficient mechanical and electrical properties, inkjet printing was implemented to manufacture the electrodes. To enhance the surface energy and chemical reactivity of the PVDF substrate, a preliminary plasma pretreatment step was executed, using PiezoBrush PZ3, from Intertronics (Oxfordshire, UK). The process involved exposure of the polymer surface to low-pressure gas plasma, leading to physical etching (micro-roughening) and chemical functionalization (introducing polar groups like -OH and -

COOH ) [12]. The plasma treatment successfully increased the surface wettability, as seen in Fig. 13 (image captured from the inkjet printer). This crucial modification ensured the optimal spreading and adhesion of the printed silver ink, preventing the "beading up" effect and guaranteeing the formation of a uniform electrode. The ink used was JS-A211 Silver Nanoparticle Ink, from Novacentrix (Texas, USA).

[picture]

Fig. 13. Inkjet printing of silver ink onto PVDF substrates without (left) and with (right) plasma pre-treatment.

Fig. 13. Inkjet printing of silver ink onto PVDF substrates without (left) and with (right) plasma pre-treatment.

The printing of electrodes onto multiple PVDF substrates can be seen in Fig. 14 a, and a sample with 3 layers of silver ink can be seen in Fig. 14 b (image captured from the inkjet printer).

[picture]

Fig. 14. (a) Inkjet printing of silver ink onto plasma pretreated PVDF substrates, (b) 3 layers of ink printed on sample.

Fig. 14. (a) Inkjet printing of silver ink onto plasma pretreated PVDF substrates, (b) 3 layers of ink printed on sample.

Despite inkjet printing offering high-precision electrode fabrication using silver ink on PVDF, this technique suffered from several practical limitations that restrict its scalability for manufacturing large-format or structurally complex devices. First of all, during the poling process silicon oil was used to avoid electrical breakdown, provide uniform field distribution, and prevent arcing. After poling of inkjet-printed samples, it was observed that they exhibited structural degradation (the ink wore off) during the washing/ removal of the silicon oil, as seen in Fig. 15. The observed degradation of the films indicated insufficient mechanical and interlayer cohesion of the ink-based structures. The washing step induced significant wear and material loss, compromising structural integrity and functional reliability.

Moreover, high printing times were inherent to inkjet deposition, resulting in poor throughput for complex geometries. Furthermore, the use of specialized inks introduced frequent issues such as nozzle clogging due to particle agglomeration, viscosity shifts, and solvent evaporation, necessitating time-consuming maintenance [8]. Other challenges included the limited achievable film thickness per pass, requiring multiple layers to build up adequate conductivity, and the substantial time and energy required for the mandatory plasma pretreatment process to attain high wettability. Additionally, the
significant recurrent cost of specialized cartridges represented a substantial financial burden for research and development compared to the relatively inexpensive consumables used in MEX [9]. Consequently, for the large-scale and rapid fabrication of integrated devices, especially those demanding the simultaneous deposition of structural substrates and functional elements, a robust, high-speed technique like multi-material 3D printing will be chosen.

[picture]

Fig. 15. Inkjet printed sample, before and after the removal of the silicon oil.

Fig. 15. Inkjet printed sample, before and after the removal of the silicon oil.

Summary

The original version of this paper is available on https://www.scientific.net/KEM.1047.115.pdf

In this study, AM is utilized for 4D printing applications to develop piezopolymer metamaterials intended for distributed sensing and energy harvesting. Specifically, 3D printed PVDF films were manufactured and tested. Three distinct experimental integration strategies were evaluated: direct 3D printing of PVDF films with manually applied silver paste, printing directly onto aluminium foil substrates, and a novel chemical solvent-based deposition using a DMF and acetone mixture. 3D printed films were comprehensively characterized to prove the feasibility of the proposed approaches. DSC validated that the PVDF matrix remained thermally stable throughout the extrusion and post-print processing. Additionally, SEM and electrical resistance measurements confirmed the successful formation and structural composition of the conductive layers. While high-precision inkjet printing, aided by plasma pretreatment to increase surface wettability, was initially explored for electrode deposition, it demonstrated significant practical limitations. These included poor scalability, high printing times, nozzle clogging, and severe structural degradation (ink wear-off) during the postpoling silicon oil removal process. Consequently, this study advocates for a robust, high-speed multimaterial extrusion (MEX) technique for the simultaneous deposition of structural substrates and functional elements as future work. This multi-material hybrid MEX platform unlocks new possibilities for the design and application of functional piezopolymers. By upscaling the proposed manufacturing method, it becomes possible to transition from isolated, simple planar films to intricate, volumetrically complex designs, such as multi-layered piezoelectric stacks, spatially distributed sensor arrays, and architected metamaterial lattices. Future research will focus on fully exploiting the scalability of the multi-material AM setup to fabricate highly complex, 3Dpiezoelectric metamaterials, by optimizing the simultaneous printing of the thermoplastic matrix and the highly conductive thixotropic inks to further minimize interfacial resistance and improve overall structural integrity. A primary objective will be to transition from single-layer films and electrodes to large-scale architected structures, to evaluate their real-world performance in distributed structural health monitoring and energy harvesting applications.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/KEM.1047.115.pdf

Research project 4D-PIEZOUMME funded this work, which is implemented in the framework of H.F.R.I. call "Basic research Financing (Horizontal support of all Sciences)" under National Recovery and Resilience Plan "Greece 2.0" funded by the European Union - NextGenerationEU (H.F.R.I. Project No: 16580).

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.115.pdf

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 127-137

doi: 10.4028/p-C5YVCs

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-16

Revised:
2026-02-25

Accepted:
2026-02-25

Online:
2026-04-13

Effect of Hatch Distance and Scanning Speed Combinations at Constant Volumetric Energy Density on the Materials Properties of IN939 Fabricated by Powder Bed Fusion-Laser Beam

The original version of this paper is available on https://www.scientific.net/KEM.1047.127.pdf

Merve Nur Doğu 1,2,a*, Meris Meric Ikiz 1,2,b, Gavin Chapman 1,2,c, Sampreet Rangaswamy 1,2, d, Merve Yesim Yalcin 3,4,e, Seren Ozer 5,6,f, Kemal Davut 7,9 and Dermot Brabazon 1,2,h

1 I-Form Advanced Manufacturing Research Centre, Dublin City University, Dublin, Ireland

2 DCU Institute for Advanced Processing Technology, School of Mechanical & Manufacturing Engineering, Dublin City University, Dublin, Ireland

3 I-Form Advanced Manufacturing Research Centre, University College Dublin, Dublin, Ireland

4 School of Mechanical and Materials Engineering, University College Dublin, Dublin, Ireland

5 Department of Metallurgical and Materials Engineering, Atılım University, Ankara, Türkiye

6 Department of Metallurgical and Materials Engineering, Middle East Technical University, Ankara, Türkiye

7 Department of Materials Science and Engineering, Izmir Institute of Technology, 35430, Urla, Izmir, Türkiye

a* mervenur.dogu@dcu.ie, b meris.ikiz2@mail.dcu.ie, c gavin.chapman3@mail.dcu.ie, d sampreet.rangaswamy2@mail.dcu.ie, e merve.yalcin@ucdconnect.ie, f ozer.seren@metu.edu.tr, g  kemaldavut@iyte.edu.tr, h  dermot.brabazon@dcu.ie

*corresponding author: mervenur.dogu@dcu.ie

Keywords: powder bed fusion-laser beam, IN939, volumetric energy density, relative density, defect formation, microstructure, surface roughness, infra-red (IR).

Abstract

This study provides a comprehensive investigation into the effects of different scanning parameter combinations-specifically scanning speed and hatch distance-on the material properties of IN939 fabricated using the powder bed fusion-laser beam (PBF-LB) process under a constant volumetric energy density (VED). Despite the fixed VED, the fabricated samples experienced different thermal cycles, resulting in distinct microstructural features and corresponding variations in material performance. In-situ infrared monitoring indicated that the sample with the narrowest hatch distance and highest scanning speed (Sample 1) reached the highest normalized temperatures with intense heat accumulation, whereas wider hatch distances (Sample 3) promoted lower and more stable temperature distributions. The results revealed that the intermediate parameter set (Sample 2) achieved the highest relative density ( 99.29% ) and the lowest surface roughness. In contrast, both the narrowest and widest hatch spacing combinations promoted increased porosity, primarily consisting of lack-of-fusion (LoF) and gas pores. Electron backscatter diffraction (EBSD) analysis showed that the area-weighted average grain size increased from 29.5μ m to 36.7μ m as the hatch distance increased. Texture analysis indicated generally weak crystallographic texture development, with only slight intensification of <001>//BD and <111>//BD components, attributed to the 67∘ rotation strategy. Furthermore, the microhardness values demonstrated negligible variation across the samples, ranging from 356.7±14.3HV1 to 360.1±10.5HV1. This limited variation indicates that the strengthening behavior was predominantly governed by the combined influence of defect density and matrix-defect interactions, rather than being directly correlated with grain size.

1. Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.127.pdf

In PBF-LB, the components are produced by selectively melting powder particles in a layerwise manner, following digital geometries defined by computer-aided design (CAD) data and thereby allows reducing dependence on assembly operations and minimizing defect formation associated with secondary processing steps [1]. Additional advantages of PBF-LB include high dimensional accuracy, the potential for weight reduction through the fabrication of lattice or cellular structures, and a significant decrease in material waste and overall production costs [2].

IN939 is a precipitation-strengthened Ni-based superalloy in which mechanical strength is predominantly achieved through the formation of L 12 -ordered γ′(Ni3(Al,Ti)) precipitates [3]. IN939 is typically developed for service conditions up to approximately 850∘C, particularly in gas turbine applications, and is characterized by high mechanical strength together with excellent corrosion and oxidation resistance [4,5]. However, process-inherent defects-including gas pores, keyhole- and lack of fusion (LoF)-related pores, and particularly cracking-may arise during fabrication. These defects are primarily associated with the high Al and Ti contents, which impair weldability and are further intensified by the rapid solidification behaviour characteristic of the PBF-LB process [6-8].

The thermal behaviour characteristic of the PBF-LB process is governed by a highly coupled set of physical phenomena, including laser-powder interaction, high thermal gradients, localized melting and rapid solidification of powder particles. The resulting microstructural features and the quality of the produced components are strongly dependent on critical process variables such as laser power (P), scanning speed (v), hatch distance (h), and layer thickness (t). In combination, these parameters determine the VED (Eq. (1)), which represents the energy input per unit volume of the powder bed [9,10].

VED=Pv.h.t( J/mm3)(1)

It is known that the optimization of VED parameters is critical and must be systematically tailored to the material and application requirements in order to ensure complete densification, reduced surface roughness, and improved mechanical performance. Nevertheless, systematic investigations focusing on the optimization and influence of VED in PBF-LB-processed IN939 remain limited in the existing literature. In our previous study [11], the influence of various process parameters that govern the VED was systematically examined with respect to relative density, microstructural evolution, defect formation (i.e., porosity and cracking), and surface-related characteristics. The results revealed that, although arc-shaped melt pools were observed under all processing conditions, samples fabricated at high VED exhibited pronounced irregularly shaped pores and the presence of micro-cracks. Moreover, increasing the hatch spacing from 50μ m to 110μ m resulted in a reduction in the surface roughness of the produced samples. Marchese et al. [12] varied the VED from 30 to 320 J mm−3 to investigate its influence on the density and microstructural characteristics of PBF-LB-produced IN939. Their results demonstrated that both low VED (< 50 J mm−3 ) and excessively high VED ( >160 J mm−3 ) values promoted the formation of large-scale defects, with sizes reaching several hundred micrometers. These defects were attributed to the development of LoF defects under insufficient melting conditions and to keyhole-induced pores resulting from melt pool instability at elevated energy inputs. Dursun et al. [13] demonstrated that adjusting the VED within the range of 35−109 J mm−3 resulted in notable variations in melt pool morphology and relative density, with certain processing conditions also promoting the onset of the balling phenomenon.

Although several studies have been reported in literature, the majority primarily investigate the influence of varying VED values on material properties. In contrast, the effect of identical VED conditions has not yet been systematically explored, which is essential for isolating and clarifying the role of equivalent energy input. To address this gap, three sets of IN939 samples were fabricated using different combinations of scanning speed and hatch distance while maintaining the same VED. The samples were subsequently characterized in detail to assess surface roughness, relative density, and defect formation, along with microstructural features and microhardness.

2. Materials and Methodology

The original version of this paper is available on https://www.scientific.net/KEM.1047.127.pdf

2.1. IN939 fabrication by the PBF-LB process

Gas-atomized powder (Truform 939-N65, Praxair Surface Technologies) with a particle size range of 17.4-52 μm were utilized for the production of the samples by PBF-LB. The powder chemical composition is listed in Table 1, and a comprehensive characterization of the feedstock powder has been reported previously by the authors [14].

Table 1. The nominal chemical composition (wt.%) of the gas-atomized IN939 powder.

Elements | Al | Co | Cr | Nb | Ta | Ti | W | Zr | Ni | B | C | O | N
wt.% | 1.9 | 18.9 | 22.8 | 1.0 | 1.4 | 3.8 | 2.0 | 0.028 | Bal. | 0.004 | 0.16 | 0.014 | 0.009

IN939 samples were fabricated using an Aconity MINI (GmbH) laser-based metal additive manufacturing (AM) platform. All builds were performed under a protective argon atmosphere with a purity of 99.999%, while the oxygen level was controlled below 50 ppm to eliminate oxidation. CK45 steel served as the build plate material. Cubic specimens with dimensions of 10×10×10 mm3 were produced while maintaining a constant VED through systematic variation of scan speed and hatch distance. The PBF-LB processing conditions investigated in this study, together with the corresponding sample designations, are summarized in Table 2. The VED was calculated using Eq. (1).

The Aconity MINI has two coaxial infrared pyrometers (KLEIBER Infrared GmbH) for insitu infrared (IR) monitoring of melt pool thermal emission. Due to the absence of material-specific temperature calibration for IN939, the melt pool response output was in relative units (mV) rather than absolute temperature values. The acquired data, mapped to the ( x,y ) coordinates of the build plate, were processed using Python. Melt pool thermal distributions were visualized using normalized thermograms, with statistical mean layer values scaled between 0 and 1 to represent relative temperature variations.

[picture]

Fig. 1. Images of the build plate after fabrication of the as-built IN939 samples.

Fig. 1. Images of the build plate after fabrication of the as-built IN939 samples.

Table 2. PBF-LB process parameters used in the present study.

Sample | Hatch distance (μm) | Scanning speed (mm/s) | VED (J/mm³) | Power (W) | Layer thickness (μm) | Spot size (μm) | Scanning strategy

1
1A
40
1350
92.6
200
40
80
Alternating
bi-
directional
scan with
67° rotation
between the
adjacent
layers

1B
1C
2 | 2A | 60 | 900 |
2B
2C
3 | 3A | 80 | 675 |
3B
3C

2.2. Surface roughness measurements

Surface topography of the as-built samples was characterized using a Bruker ContourGT optical profilometer. Measurements were performed on the XY planes (perpendicular to the build direction) over an area of 5×5 mm2. In addition, surface roughness was evaluated on XZ planes (parallel to the build direction) at both the argon inlet and outlet sides, using a scan area of 3×3 mm2. Surface roughness was quantified using the areal parameter Sa , defined as the arithmetic mean height, while Sz was used to describe the sum of the maximum peak height and the maximum pit depth.

2.3. Relative density and porosity measurements

The relative density values of the as-built samples were determined using Archimedes' principle in accordance with ASTM B311-17 [15]. Measurements were performed with a Sartorius Entris II Essential BCE124I-1S analytical balance, offering an accuracy and repeatability of ±0.1mg. Ethanol (Lenox, 99.99% ) was employed as the immersion medium. For each sample, three independent measurements were performed, and the resulting values were averaged to determine the final relative density. A theoretical density of 8.15 g cm−3 was assumed for fully dense IN939 in the calculation of relative density.

Conventional optical microscopy was used to examine the spatial distribution of porosity in the as-built samples. Polished cross-sections from both the XZ and XY orientations were imaged using the stitching function of a Keyence 3D optical microscope to obtain large-area views. Porosity quantification was carried out by analyzing the stitched optical images with ImageJ software, comprising a minimum of 20 images for the XZ planes.

2.4. Microstructural characterization

The hot-mounted specimens were first subjected to automated grinding using conventional SiC abrasive papers up to 1200 grit, followed by polishing with progressively finer diamond suspensions (9, 3, and 1μ m ) using a Struers Tegramin-20 system. For EBSD analysis, an additional electropolishing step was applied using a Struers Lectropol-5 unit with Struers A2 electrolyte at 40 V , a flow rate of 16, and a polishing duration of 15 s . For microstructural observation, selected samples were electrochemically etched after electopolishing with Struers A2 electrolyte at 5 V , a flow rate of 15, and an etching time of 5 s . Microstructural characterization was performed using a Keyence VHX2000E 3D digital optical microscope and a Zeiss EVO LS-15 scanning electron microscope (SEM). Electron backscatter diffraction (EBSD) measurements were carried out with an Oxford Symmetry S3 detector operated at an accelerating voltage of 20 kV and a probe current of 7.0 nA . Orientation maps covering an area of 350×350μ m2 were acquired with a step size of 0.75μ m, and the raw EBSD data were post-processed using AZtecCrystal 3.3 software.

2.5. Microhardness

The Vickers microhardness of the as-built samples was evaluated on the XZ planes using a Zwick/Roell DuraScan G5 semi-automatic microhardness tester with an applied load of 1 kg , in accordance with ASTM E384 [16]. For each specimen, ten individual indentations were performed, and the reported hardness values represent the corresponding average.

3. Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1047.127.pdf

3.1. In-situ temperature monitoring

Fig. 2 shows the normalized melt pool temperature data used to assess the thermal history during the PBF-LB process, highlighting the strong influence of laser processing parameters on heat accumulation. Sample 1 showed the highest and most unstable thermal response throughout the build, which is mainly associated with the combination of high scan speed and narrow hatch spacing [17]. The reduced hatch distance limited inter-track cooling, promoting localized heat buildup and an unstable melt pool, as further evidenced by the corresponding 2D and 3D thermal maps. In contrast,

Samples 2 and 3, fabricated with wider hatch distances, exhibited lower and more stable temperature distributions, indicating more effective thermal dissipation during processing.

[picture]

Fig. 2. Thermal analysis of IN939 samples during the build process: (a) Normalized mean temperature evolution across build layers for selected samples ( 1 B,2 B, and 3 B ). (b) 2D thermogram of the second last layer, showing the temperature distribution across nine cubic samples. (c) 3D spatial visualization of the thermal data throughout the build height; the color bar indicates normalized temperature values.

Fig. 2. Thermal analysis of IN939 samples during the build process: (a) Normalized mean temperature evolution across build layers for selected samples ( 1 B , 2 B , and 3 B ). (b) 2D thermogram of the second last layer, showing the temperature distribution across nine cubic samples. (c) 3D spatial visualization of the thermal data throughout the build height; the color bar indicates normalized temperature values.

3.2. Surface roughness

The corresponding Sa and Sz values, categorized by the XY and XZ planes and by different measurement locations, are summarized in Table 3. Surface roughness profiles of selected samples, together with bar charts illustrating the associated Sa and Sz values, are presented in Fig. 3. The results indicated that hatch distance and scanning speed have a pronounced influence on the average surface roughness of the as-built samples. Sample 1 exhibited the highest surface roughness in both the XY and XZ planes ( Sa=22.72μ m and 8.51μ m, respectively), which is consistent with the thermal instabilities identified in the infrared monitoring data. The combination of high scanning speed and narrow hatch distance promoted excessive track overlap and localized heat accumulation, resulting in an elevated and highly fluctuating thermal response. This excessive thermal input destabilized the melt pool, hindered uniform track consolidation, and led to pronounced surface irregularities, as confirmed by the corresponding 3D surface topography. In contrast, Sample 2 showed the lowest surface roughness, with Sa values of 13.95μ m in the XY plane and 6.29μ m in the XZ plane. This improvement is attributed to the use of an intermediate hatch spacing (60μ m) and a moderate scanning speed ( 900 mm/s ), which promoted a more stable melting regime and reduced temperature fluctuations. These conditions resulted in improved surface uniformity and limited spatter-related surface defects. Although Sample 3 was processed at a lower scanning speed ( 675 mm/s ), which increased the longer laser-material interaction time, its larger hatch spacing ( 80μ m ) might have led to insufficient track overlap, resulting in poorer surface uniformity compared to Sample 2. Furthermore, the consistently higher roughness observed on XZ plane of the samples near the argon outlet suggests that gas flow behaviour and spatter accumulation further contribute to surface irregularities associated with thermal effects [18].

Table 3. Average surface roughness ( Sa ) and maximum height ( Sz ) values measured on the XY and XZ planes of the as-built IN939 samples.

Sample | Sa (μm)
| XY plane | XZ plane (argon inlet) | XZ plane (argon outlet)
1 | 1A | 22.07 | 22.72 ± 0.85 | 8.82 | 8.51 ± 0.60 | 14.63 | 15.08 ± 0.50
1B | 23.68 | 8.89 | 15.62
1C | 22.40 | 7.81 | 14.98
2 | 2A | 12.78 | 13.95 ± 1.10 | 6.71 | 6.29 ± 0.39 | 14.62 | 12.91 ± 1.60
2B | 14.96 | 6.20 | 12.66
2C | 14.10 | 5.95 | 11.45
3 | 3A | 15.18 | 17.19 ± 1.83 | 6.16 | 6.49 ± 0.60 | 11.86 | 12.45 ± 1.17
3B | 18.77 | 7.18 | 13.80
3C | 17.62 | 6.12 | 11.69
Sample | Sz (μm)
| XY plane | XZ plane (argon inlet) | XZ plane (argon outlet)
1 | 1A | 192.92 | 191.13 ± 1.56 | 175.61 | 155.13 ± 17.79 | 161.81 | 158.63 ± 6.38
1B | 190.42 | 146.18 | 162.80
1C | 190.05 | 143.59 | 151.28
2 | 2A | 153.88 | 167.62 ± 20.01 | 132.33 | 113.81 ± 17.03 | 150.67 | 163.67 ± 12.01
2B | 158.39 | 98.83 | 174.35
2C | 190.58 | 110.28 | 165.99
3 | 3A | 176.60 | 186.79 ± 10.04 | 140.53 | 145.37 ± 4.33 | 171.84 | 173.89 ± 4.91
3B | 196.67 | 146.73 | 179.50
3C | 187.10 | 148.86 | 170.34

[picture]

Fig. 3. (a-c) Surface roughness profiles of selected as-built IN939 samples ( 1 B,2 B, and 3B), and (d) bar charts summarizing the average Sa and Sz values for the samples.

Fig. 3. (a-c) Surface roughness profiles of selected as-built IN939 samples ( 1 B , 2 B , and 3B), and (d) bar charts summarizing the average Sa and Sz values for the samples.

3.3. Relative density and defect formation

The average relative density of the samples was determined using the Archimedes method, while the average porosity percentage (%), pore density (pores/ mm2 ), and average Feret size ( μm ) (as a quantitative measure of pore size) were quantified through ImageJ analysis. The corresponding results are summarized in Table 4. It should be noted that defect analysis was performed by accounting for all gas pores, LoF-related pores, and keyhole-induced pores identified on the as-polished surfaces, as also presented in Fig. 4. As given, the Sample 2 exhibited the highest average relative density of 99.29%, whereas the relative density values for Sample 1 and Sample 3 were comparable, measured as 98.65% and 98.75%, respectively. Consistent with the density measurements, Sample 2 exhibited the lowest average porosity percentage, pore density, and Feret size, measured as 0.05%, 5.09 pores /mm2, and 11.09μ m, respectively. It is well established that the integrity and performance of additively manufactured components are strongly influenced by defect population; therefore, achieving high relative density in conjunction with a low defect content is generally associated with superior mechanical performance. Interestingly, although the pore density of these two samples fell within a narrow range of 6.14-6.45 pores /mm2, Sample 3 exhibited a substantially lower average porosity percentage and Feret size compared to Sample 1, with values of 0.20% and 19.42μ m, respectively. Defect and microstructural analyses (Fig. 4) further revealed that Sample 1 contained very large, irregularly shaped pores with sizes reaching up to 1 mm , in agreement with its high average Feret diameter of 31.98μ m. In contrast, Sample 3 predominantly consisted of small, nearspherical pores with maximum sizes of approximately 60−70μ m, accompanied by only a limited number of large pores. It can be stated that the combination of a lower hatch distance ( 40μ m ) and a high scanning speed ( 1350 mm/s ) promoted the formation of a large number of defects, predominantly LoF-related pores, in Sample 1. Moreover, the use of a relatively large hatch distance (80μ m) during the fabrication of Sample 3 may have contributed to the development of some large LoF-related pores, due to insufficient inter-track overlap. However, most of the defects observed in Fig. 4(c) are small and spherical in shape, suggesting that gas porosity is the dominant defect type, while a smaller fraction may be associated with keyhole-related pore formation. Gas pore formation is primarily associated with entrapped gas, either within the feedstock powder particles introduced during the atomization process or trapped during the rapid solidification of the molten metal in the PBF-LB process [19]. On the other hand, keyhole-related porosity may arise under conditions of excessive energy input, wherein intense vaporization of the molten metal induces recoil pressure, resulting in keyhole instability and the entrapment of pores during solidification [19-21]. The use of a low scanning speed ( 675 mm/s ) during the fabrication of Sample 3 may have promoted the formation of these defects within the microstructure by increasing local energy input.

Table 4. Image J porosity analysis and Archimedes-based relative density results of as-built IN939 samples (error bars represent 95% CI).

Sample | Image J | Archimedes' principle
Average Porosity (%) | Average Pores/mm² | Average Feret Size (μm) | Relative Density (%)
1 | 1A | 0.99 | 0.93 ± 0.63 | 7.14 | 6.45 ± 1.50 | 31.07 | 31.98 ± 11.42 | 98.37 | 98.65 ± 0.15
1B | 1.15 | 6.02 | 36.96 | 98.58
1C | 0.65 | 6.20 | 27.90 | 99.00
2 | 2A | 0.03 | 0.05 ± 0.05 | 3.44 | 5.09 ± 6.79 | 9.97 | 11.09 ± 4.38 | 99.25 | 99.29 ± 0.20
2B | 0.05 | 8.24 | 10.17 | 99.29
2C | 0.07 | 3.58 | 13.12 | 99.33
3 | 3A | 0.12 | 0.20 ± 0.26 | 5.77 | 6.14 ± 2.11 | 16.08 | 19.42 ± 15.24 | 98.81 | 98.75 ± 0.19
3B | 0.32 | 5.54 | 26.50 | 98.67
3C | 0.15 | 7.11 | 15.68 | 98.76

[picture]

Fig. 4. The as-polished optical micrographs of as-built samples in the XZ plane (parallel to the build direction).

Fig. 4. The as-polished optical micrographs of as-built samples in the XZ plane (parallel to the build direction).

3.4. Microstructure and microhardness

Despite the use of a constant VED, the microstructure of the as-built samples was strongly influenced by the scanning parameters. Optical micrographs and EBSD maps revealed a predominantly columnar grain morphology, with arch-shaped melt pools aligned along the build direction in all samples. The area-weighted average grain sizes were 29.5±8.7μ m,31.9±9.4μ m, and 36.7±8.9μ m for Samples 1, 2, and 3, respectively. Sample 1 exhibited the highest normalized temperature (Fig. 2(a)) due to its narrow hatch distance, which promoted intense local heat accumulation; however, the combination of high temperature and high scanning speed led to melt pool instability and the formation of large, irregular lack-of-fusion pores [19]. In contrast, Sample 3 showed a lower overall temperature but a highly heterogeneous and non-uniform grain structure, as confirmed by EBSD maps, resulting from the combination of wide hatch distance and low scanning speed that caused uneven heat distribution and inconsistent melting. These observations, supported by 3D thermography, demonstrated that maintaining a constant VED was insufficient to ensure microstructural homogeneity, as process parameters governs the thermal history, melt pool stability, and grain evolution in IN939.

The microhardness values showed only a slight decrease from Sample 1 to Sample 3(360.1±10.5HV1,359.8±16.3HV1, and 356.7±14.3HV1, respectively), indicating that strengthening was not governed solely by grain size effects. Despite the progressive increase in average grain size, EBSD-derived parameters revealed a gradual reduction in grain orientation spread (GOS) and kernel average misorientation (KAM). Sample 1 exhibited the highest GOS and KAM values, indicating stronger intragranular lattice distortion caused by steep thermal gradients and unstable melt pool behavior. In contrast, Sample 3 showed lower average misorientation values, suggesting partial relaxation of internal strains through dislocation rearrangement and early-stage recovery, despite the presence of locally heterogeneous deformation [19,22].

The inverse pole figures (IPFs) with respect to the build direction (BD) obtained from the XZ planes in the as-built samples are displayed in Fig. 6. Although nickel-based superalloys typically promote preferential <001> growth due to their cubic crystal symmetry and alignment with the maximum thermal gradient, the IPFs of the as-built samples showed only weak texture development. Sample 1 exhibits a largely random orientation distribution, whereas a gradual decrease in scanning speed and increase in hatch distance in Samples 2C and 3C leads to a slight intensification of <001>//BD and <111>//BD components, as indicated by higher (multiples of random distribution) MRD intensities. This behavior is primarily attributed to the continuously varying thermal gradients generated during the PBF-LB process as a result of the 67∘ scan rotation. The frequent changes in heat flow direction prevent the establishment of a stable solidification gradient, thereby limiting strong texture development and promoting grain reorientation [23].

[picture]

Fig. 5. Optical micrographs of the as-built samples: (a) Sample 1C, (b) Sample 2C and (c) Sample 3C.

Fig. 5. Optical micrographs of the as-built samples: (a) Sample 1C, (b) Sample 2C and (c) Sample 3C.

[picture]

Fig. 6. EBSD maps and inverse pole figures (IPFs) of the XZ planes of the as-built samples: (a) Sample 1C, (b) Sample 2C and (c) Sample 3C.

Fig. 6. EBSD maps and inverse pole figures (IPFs) of the XZ planes of the as-built samples: (a) Sample 1C, (b) Sample 2C and (c) Sample 3C.

4. Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.127.pdf

This study systematically investigated the influence of scanning speed and hatch distance combinations at constant VED on the thermal behavior, surface quality, defect formation, and microstructural evolution of IN939 fabricated by the PBF-LB.

In-situ infrared monitoring revealed that the narrowest hatch distance combined with the highest scanning speed (Sample 1) resulted in the highest normalized temperatures and pronounced heat accumulation, whereas wider hatch distances promoted lower and more stable thermal distributions during the build.

The intermediate parameter set (Sample 2) exhibited the most favorable combination of properties, achieving the highest relative density ( 99.29% ) and the lowest surface roughness values, indicating a more stable melt pool and effective thermal dissipation.

Both the narrowest and widest hatch spacing conditions led to increased porosity, with Sample 1 dominated by large LoF defects and Sample 3 primarily containing gas pores and keyholerelated pores.

EBSD analysis showed a progressive increase in area-weighted average grain size from 29.5 ±8.7μ m (Sample 1) to 36.7±8.9μ m (Sample 3) with increasing hatch distance.

Crystallographic texture remained generally weak across all samples, with only a slight intensification of <001>//BD and <111>//BD components observed at lower scanning speeds and larger hatch distances.

Microhardness values showed minimal variation among the samples, ranging from 356.7± 14.3 HV 1 to 360.1±10.5HV1, suggesting that mechanical strengthening was governed primarily by defect formation and matrix characteristics rather than grain size effects alone.
Overall, the results demonstrated that VED alone is not sufficient for the PBF-LB process, as different parameter combinations can produce the same VED but result in significantly different material properties.

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The original version of this paper is available on https://www.scientific.net/KEM.1047.127.pdf

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The original version of this eBook is available on https://www.scientific.net/book/additive-manufacturing/978-3-0364-1997-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 139-149

doi: 10.4028/p-mQS6IQ

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-18

Revised:
2026-02-16

Accepted:
2026-02-25

Online:
2026-04-13

Integrating Topology Optimization and Lattice Infilling for Lightweight Aircraft Bracket Design via Additive Manufacturing

The original version of this paper is available on https://www.scientific.net/KEM.1047.139.pdf

Numan Khan 1,a, Fatima Ghassan Alabtah 1, b and Marwan Khraisheh 1,c*

¹College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar

a  nukhan@hbku.edu.qa, b  falabtah@hbku.edu.qa, c  mkhraisheh@hbku.edu.qa

*corresponding author: mkhraisheh@hbku.edu.qa

Keywords: additive manufacturing, topology optimization, lattice infilled structures, lightweighting, aircraft bracket.

Abstract

Integrating topology optimization (TO) with lattice infilling for additive manufacturing provides an effective route to lightweight, high-performance structures for aerospace applications. Reducing structural mass can deliver environmental and economic benefits by lowering fuel consumption and associated emissions. This study evaluates a computational workflow for weight reduction of an aircraft bearing bracket by combining topology optimization with stress guided lattice infilling. First, compliance minimizing TO is performed under additive manufacturing constraints to obtain an efficient global load-path layout. Next, lattice infill is introduced using both Triply Periodic Minimal Surface (TPMS) unit cells (gyroid) and strut-based unit cells (diamond). To avoid manual trial-and-error in selecting unit cell size, and thickness, an implicit modeling approach with Pythondriven iteration is used to systematically explore lattice parameters and identify feasible configurations. The proposed method uses the TO-derived stress field to tailor lattice parameters spatially, enabling graded cellular architectures aligned with local load demands. Compared with the baseline bracket, TO alone achieved a 44.42% mass reduction, while the stress-guided lattice designs achieved 70% (gyroid) and 68.6% (diamond) weight savings. Finite element analysis is used to compare the baseline, TO, and lattice-infilled brackets in terms of mass, maximum deflection, and von Mises stress, demonstrating that stress-guided lattice infill can improve structural efficiency beyond TO alone while maintaining AM oriented manufacturability through self-supporting cellular features. A key contribution is an automated, stress-guided ramp mapping for graded latticeparameter control, which is broadly applicable to other components, loading scenarios, and lattice families.

Graphical Abstract

1
Introduction

Additive manufacturing (AM) has emerged as a deriving force in modern manufacturing, transcending its origins in rapid prototyping to enable the production of end-use structural components across industries such as aerospace, automotive, and biomedical [1]. This shift is driven by AM's unparalleled design freedom, which allows for the fabrication of complex geometries that are unattainable through conventional methods. In aerospace applications, where lightweighting is critical to reducing fuel consumption and CO2 emissions, AM synergizes with advanced design strategies like topology optimization and lattice structures to achieve optimal mass reduction without compromising structural integrity. Topology optimization mathematically redistributes material within a design space to meet performance criteria under specified loads and constraints. Over decades, TO methodologies have evolved, including homogenization [2], evolutionary structural optimization (ESO) [3-5], level-set methods [6], and the widely adopted Solid Isotropic Material with Penalization (SIMP) [7, 8]. SIMP assigns a pseudo-density variable to each finite element, penalizing intermediate densities to steer designs toward binary (solid/void) solutions. Its computational efficiency has enabled landmark applications, such as a 64% mass reduction in Airbus A320 hinge brackets [9] and a 41.25% weight saving in RUAG's Sentinel satellite brackets [10]. Despite these successes, TO-generated designs often exhibit intricate geometries that challenge traditional manufacturing, underscoring AM's role in realizing such optimizations. Complementing TO, lattice structures offer a lightweighting alternative by replacing solid volumes with porous, repeating unit cells [11]. These structures excel in conditions requiring high strength-to-weight ratios, energy absorption, and thermal management [12, 13]. Among lattice architectures, TPMS designs such as the gyroid, and strut-based lattices such as the diamond, have gained prominence due to their biomimetic geometries, tunable stiffness, and self-supporting printability [14-16]. Comparative study by Al-Ketan et al. [17, 18] reveal that sheet-type lattices may outperform strut-based designs in compressive strength, attributed to their continuous, curvature-driven load transfer. Furthermore, the design of graded lattices enables spatially tailored mechanical responses, enhancing performance under applied loading [5, 19-21].
However, critical gaps remain in understanding the parameters that affect the mechanical properties of these lattice structures, which are vital for aerospace applications. While Alsalla et al. [22] reported orientation dependent fracture toughness in 316L gyroid lattices, and limited studies explore TPMS [23] and honeycomb [24] architectures, comprehensive data remain scarce. This is compounded by the suboptimal performance of uniform lattices compared to TO-driven designs [13], prompting interest in hybrid approaches that integrate TO with lattice infills. Such synergies aim to enhance mechanical efficiency, reduce support structures, and mitigate deformation [25]. For instance, embedding lattices within TO-optimized shells could balance global stiffness with local energy absorption. Yet, most research focuses on canonical problems (cantilever beams [11, 26]), leaving real-world industrial applications underexplored.
The core innovation of this work lies in its hybrid methodology, which integrates topology optimization with tailored lattice infill strategies to maximize weight reduction in aerospace components while adhering to additive manufacturing constraints. This integrated framework facilitates a systematic evaluation of trade-offs and benefits across diverse lightweight design approaches. Initially, traditional SIMP-based topology optimization was performed on an aerospace bracket, and resulting stress field from topology optimized structural FEA was used to guide the lattice infill process using nTop tool [27]. The stress map served as a path for selectively infilling the topology optimized structure with lattice architectures for additional weight savings. Python-based script was developed to automate the variation of lattice parameters, such as unit cell size and thickness, based on the stress distribution. To identify feasible configurations, the mechanical response of lattice infilled structures was rigorously analysed through structural FEA. The resulting hybrid designs were then evaluated against key performance metrics, including static mechanical behaviour, mass efficiency, support structure requirements, and overall manufacturability. The preceding Section 2 presents the integrated framework that combines topology optimization with lattice-parameter tailoring, along with the material specifications and finite element simulation
procedures. Section 3 assesses the weight savings and mechanical performance of the original, topology-optimized, and lattice-infilled brackets through comparative displacement and stress analyses. Finally, the conclusion summarizes the key findings and discusses their implications for spacecraft applications.

2 Materials and Methods

The original version of this paper is available on https://www.scientific.net/KEM.1047.139.pdf

2.1 Case Study

The case study is based on the GrabCAD Alcoa airplane bearing bracket challenge [28], which aimed to minimize bracket mass within a fixed design envelope while satisfying FEA-verified performance requirements and reducing the need for additive manufacturing support structures. The component studied is an additively manufactured aircraft bearing bracket constrained to this prescribed envelope and bolted to a high-stiffness mating plate using four #10-32 high-strength bolts (Fig. 1). The bracket is statically loaded through a stiff spherical bearing of 7.94 mm diameter, using load case of 5.56 kN horizontal. The specified material is 15-5PH stainless steel (AMS5862), modeled as linear elastic with E=200GPa,v=0.27, yield strength =1000MPa, and density =7833 kg/m3.

[picture]

Fig. 1. Baseline airplane bearing bracket and setup: (a) Schematic showing the bolted support region on a stiff plate and the applied load at the bearing interface, (b) 3D CAD model of reference bracket geometry.

Fig. 1. Baseline airplane bearing bracket and setup: (a) Schematic showing the bolted support region on a stiff plate and the applied load at the bearing interface, (b) 3D CAD model of reference bracket geometry.

2.2 Integrated TO-Lattice Design Framework

The proposed framework integrates topology optimization with lattice parameter tuning to achieve structurally efficient and lightweight designs as shown in Fig. 2a. Initially, a solid part undergoes topology optimization to determine the optimal material distribution under the applied load. Compliance minimization was used as the objective function with a prescribed volume fraction of 0.5 , while incorporating an additive manufacturing constraint that limits the maximum overhang angle to 45∘. The TO generated geometry is evaluated under applied loading conditions where the von misses stress field is exported as field to tailor lattice parameters such as unit cell thickness, and cell size. This variation is implemented using a ramp function that maps the exported stress field into lattice parameters. The mapping assigns smaller unit cell sizes and larger lattice thicknesses in high stress regions, and larger unit cell sizes with smaller lattice thicknesses in low stress regions. Let F(x,y,z) denote a given stress field. A lower stress bound a0 and an upper stress bound a1 are specified, with corresponding thickness limits b0 (minimum) and b1 (maximum):

Fmin=a0⇒tmin=b0,Fmax=a1⇒tmax=b1(1)

Using these bounds, a mapped field G(x,y,z) is constructed that converts stress values into thickness values. The mapping clamps stresses outside [ a0a1 ] to the thickness limits, and linearly interpolates within the interval:

G(x,y,z)={b0,F(x,y,z)≤a0,b1,F(x,y,z)≥a1,b0+F(x,y,z)−a0a1−a0(b1−b0),a0<F(x,y,z)<a1(2)

This transformation effectively rescales the original stress field F into a thickness field G, ensuring G varies smoothly between b0 and b1 as F transitions from a0 to a1. Within these bounds, linear interpolation provides a continuous gradation of lattice thickness throughout the part, as illustrated in Fig. 2b. A python script based design of experiments approach is employed in nTop tool [27] to design these lattice structures by meeting the global stiffness of the bracket. The objective function focuses on mass minimization, while constraints include allowable deflection limits. This TO-derived stress map ensures that denser lattice regions (areas of higher stress) for structural demand. Conversely, low stress regions are assigned more compliant lattice configurations to reduce weight without compromising performance. The design is refined iteratively by changing lattice parameters until the desired performance criteria are achieved. The resulting lattice structure is then evaluated through numerical simulations to validate its performance under the same applied loading conditions. Due to the geometric complexity of the lattice structure, it was discretized using tetrahedral elements. A mesh convergence study was conducted to determine best element size. A mesh size of 0.4 mm was selected, as further refinement did not significantly affect the results but substantially increased the simulation time. Finite element simulations were carried out in nTop on a dedicated HP Z8 G4 workstation featuring an Intel® Xeon ® Platinum 8260 M CPU @ 2.40 GHz ( 24 cores, 48 threads) and 128 GB of RAM. Each simulation required approximately 2 to 3 hours to complete, depending on model complexity.

[picture]

Fig. 2. (a) Integrated framework for topology optimization and lattice parameter tailoring using python-based scripting for lightweight design, (b) Stress-to-lattice parameters ramp function defining G(x,y,z) between b0 and b1 over the stress bounds a0 and a1.

Fig. 2. (a) Integrated framework for topology optimization and lattice parameter tailoring using python-based scripting for lightweight design, (b) Stress-to-lattice parameters ramp function defining G ( x , y , z ) between b 0 and b 1 over the stress bounds a 0 and a 1 .

3 Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1047.139.pdf

3.1 Weight saving of bracket designs

The lattice configurations that met the design requirements in this study converged to two distinct parameter sets: a gyroid TPMS lattice with an 8 mm unit cell size and a graded thickness of 0.5-1.5 mm , and a diamond strut-based lattice with a 5 mm unit cell size and a graded strut thickness of 0.82.0 mm . These feasible bounds provide a practical balance between manufacturability and structural performance by allowing thicker members in highly stressed regions and thinner members where stress is lower. Using these finalized parameters, the mass comparison across all designs shows a clear improvement in lightweighting as illustrated in Fig. 3. The original bracket weighed 860 gm , while topology optimization reduced the mass to 478 gm , corresponding to 44.42% weight saving. Introducing lattice infill on the TO-derived design space delivered a further step change: the gyroid infilled bracket achieved 258 gm ( 70% weight saving) and the diamond infilled bracket achieved 270 gm ( 68.60% weight saving). The small mass difference between the two lattice options ( 12 gm , around 1.4% of the original mass) indicates that both cellular strategies provide comparable lightweighting potential, with the gyroid offering a modest advantage in this study. This trend is consistent with prior aerospace bracket redesign studies that couple TO with lattice generation to achieve substantial weight reduction while still satisfying loading requirements; for example, William-Sobers et al [29]. reported a 52.5% reduction for an airplane bearing bracket. Furthermore, from a design standpoint, topology optimization provides the macroscopic layout by identifying the critical load paths, while lattice infilling refines the microscopic architecture to achieve fine tuned performance [2,30]. The TO stage primarily ensures global stiffness and structural continuity; importantly, it is carried out with an additive manufacturing overhang constraint to limit unsupported features and thereby minimize the need for support structures during fabrication [31]. Building on this, the lattice infilled designs introduce local tunability and improved manufacturability, since the selected lattice architectures are inherently self-supporting over practical build orientations, further reducing support requirements within internal volumes [32].
This integrated strategy offers substantial advantages for additive manufacturing. Both TO and lattice infilling generate organic, free-form geometries that align well with AM design freedom, but the inclusion of manufacturing aware constraints at the TO level and self-supporting cellular infill at the lattice level directly improves print feasibility. In addition, lattice parameters are spatially tailored to manage stress distribution and stiffness locally, while potentially reducing residual stress and improving thermal behavior during fabrication. By leveraging the synergy between TO and stress field driven lattice infill, the design space expands to enable co-optimization of external shape, internal material distribution, and cellular architecture. Such a holistic approach results in components that are not only lighter and structurally efficient, but also inherently AM print-ready, requiring fewer supports and less post-processing without compromising mechanical performance, consistent with similar findings reported in [33] which were validated through both finite element simulations and experimental testing. This makes the proposed strategy particularly suitable for load-bearing aerospace applications, where weight reduction, structural integrity, and manufacturing practicality must be achieved simultaneously. Ultimately, this study underscores the need to balance automated computational optimization with practical manufacturing constraints, showing that coupling overhang-constrained topology optimization with self-supporting lattice infill can deliver lightweight, high-performance, and production-ready designs.

[picture]

Fig. 3. (a) Design evolution of the bracket through a hybrid lattice design framework. The original bracket ( 860 gm ) undergoes topology optimization, reducing its mass to 478 gm . The TO-derived geometry FEA stress is then used to guide field-driven lattice infilling, further reducing the mass to 258 gm for gyroid and 270 gm for diamond base lattice infilling,
(b) Percentage mass saving compared to original bracket.

Fig. 3. (a) Design evolution of the bracket through a hybrid lattice design framework. The original bracket ( 860 gm ) undergoes topology optimization, reducing its mass to 478 gm . The TO-derived geometry FEA stress is then used to guide field-driven lattice infilling, further reducing the mass to 258 gm for gyroid and 270 gm for diamond base lattice infilling, (b) Percentage mass saving compared to original bracket.

3.2 Performance evaluation

The FEA results in Fig. 4 highlight how the different design strategies influence both stiffness (deflection) and strength (von Mises stress). Fig. 4a shows that the maximum deflection remains within a narrow band for all configurations, indicating that the lightweighting strategies did not severely compromise global stiffness. The basic bracket exhibits a peak deflection of 2.20 mm , while the topology optimized bracket shows the highest deflection at 2.30 mm , reflecting the expected stiffness penalty associated with aggressive material removal and a more slender load path. In contrast, the gyroid lattice infilled design achieves the lowest deflection ( 2.19 mm ), and the diamond lattice infilled design remains essentially at the baseline level ( 2.20 mm ). These trends are also evident in the deflection contour plots in Fig. 5, which compare the deformation distribution for (a) the original solid bracket, (b) the TO bracket, (c) the gyroid lattice infilled bracket, and (d) the diamond lattice infilled bracket under the applied load case. This suggests that the lattice infill, when guided by the TO-derived field, can recover (or slightly improve) stiffness by stabilizing the load-carrying regions and improving internal support, even while maintaining substantial mass reduction.

[picture]

Fig. 4. Finite element comparison of structural response across bracket configurations. Charts show (a) peak von Mises stress and (b) peak deflection for the basic bracket, topology-optimized design, gyroid lattice infilled, and diamond lattice infilled.

Fig. 4. Finite element comparison of structural response across bracket configurations. Charts show (a) peak von Mises stress and (b) peak deflection for the basic bracket, topology-optimized design, gyroid lattice infilled, and diamond lattice infilled.

[picture]

Fig. 5. Deflection contour plots for the bearing bracket under the applied load case, comparing (a) original solid bracket, (b) Topology-optimized bracket, (c) Gyroid lattice-infilled bracket, and (d) Diamond lattice-infilled bracket.

Fig. 5. Deflection contour plots for the bearing bracket under the applied load case, comparing (a) original solid bracket, (b) Topology-optimized bracket, (c) Gyroid lattice-infilled bracket, and (d) Diamond lattice-infilled bracket.

In addition, a more distinct contrast is evident in Fig. 4b for maximum von Mises stress, demonstrating how the different geometries redistribute loads and influence stress localization in the principal load paths. The TO only bracket exhibits the highest peak von Mises stress ( 775 MPa ) primarily because the optimization targeted compliance minimization rather than stress reduction; consequently, material removal concentrates load transfer into slender load paths and introduces abrupt geometric transitions near constrained and loaded regions, which amplifies local stress concentrations despite maintaining acceptable global stiffness. The basic bracket shows a lower peak stress (694MPa), benefiting from its more conservative, solid material distribution. Notably, both lattice infilled designs reduce peak stress relative to the basic and TO brackets: the gyroid lattice produces the lowest maximum stress (635MPa), followed closely by the diamond lattice (650MPa). This reduction indicates that the lattice architectures, tailored within the TO driven design space, promote more distributed load transfer and mitigate stress hotspots, particularly for the gyroid, where the continuous surface-based topology provides smoother stress flow compared to discrete strut junctions, consisting with the finding in [34]. This tendency is further visually supported in Fig. 6, which presents a comparative von Mises stress contour assessment for all bracket designs, including both isometric and cross-sectional views for lattice infilled designs, enabling direct evaluation of stress distribution and hotspot locations across the different configurations. Inclusive, these results demonstrate a favorable trade-off: TO alone achieves weight reduction but at the cost of higher stress and deflection, whereas lattice infill not only delivers larger weight savings but also reduces peak stress and maintains stiffness comparable to the baseline. Importantly, all designs remain below a

1000 MPa yield threshold, with implied safety margins of approximately 1.29 (TO) to 1.57 (gyroid), supporting the feasibility of the optimized, print-ready concepts for load-bearing applications where mass efficiency is critical.
Taken together, the comparative results show that mass reduction and mechanical response do not improve in the same way for all strategies. The TO only design achieves a clear reduction in weight, but this comes with the highest peak stress and largest deflection indicating that compliance driven TO solution can concentrate load into slender paths and amplify local stress levels. When lattice architectures are introduced within the TO derived design space, the outcome becomes more balanced: both lattice designs deliver substantially greater weight savings while lowering peak stress and keeping deflection essentially at the baseline level, a similar observation is presented in [35]. In other words, lattice infill acts not only as a lightweighting mechanism but also as a structural regularizer providing internal support that promotes smoother load transfer and reduces stress peaks most notably for the gyroid configuration, which exhibits the lowest peak stress among the studied designs [36]. However, these conclusions should be interpreted critically: peak stress metrics from linear static FEA do not capture potential fatigue performance, local buckling of thin features, node/strut stress amplification, or manufacturing induced defects, all of which can govern lattice based components in practice. Moreover, the apparent benefit of each lattice type is parameter dependent (cell size and thickness bounds), meaning the reported performance is specific to the selected feasible ranges that met the design constraints. In conclusion, the study supports the view that TO defines the global structural concept, but field guided lattice tailoring is required to achieve print-ready, high efficiency designs, provided that follow on validation (fatigue, manufacturability, and test correlation) is performed before aerospace deployment.

[picture]

Fig. 6. Comparison of von Mises stress distributions obtained from FEA for all bracket configurations. For each design, front and isometric views are presented: (a) Original, (b) Topology-optimized, (c) Gyroid lattice infilled, and (d) Diamond lattice infilled.

Fig. 6. Comparison of von Mises stress distributions obtained from FEA for all bracket configurations. For each design, front and isometric views are presented: (a) Original, (b) Topology-optimized, (c) Gyroid lattice infilled, and (d) Diamond lattice infilled.

4
Conclusion

This study demonstrates the importance of combining topology optimization with novel field guided lattice infill to achieve lightweight, AM ready bracket designs for aerospace applications. Relative to the baseline bracket, TO reduced mass by 44.42%, while the lattice infilled designs achieved substantially larger savings of 70% (gyroid) and 68.6% (diamond), showing that micro-architectural tailoring can extend lightweighting beyond TO alone. Importantly, the mechanical response remained well controlled: TO alone produced the highest peak stress ( 775 MPa ) and deflection ( 2.30 mm ), whereas the gyroid and diamond lattice designs reduced peak stress to 635−650MPa while maintaining deflection near the baseline (2.19-2.20 mm). These results indicate that compliance driven TO improves material efficiency but does not necessarily minimize stress peaks, and that lattice infill can mitigate stress hotspots while preserving global stiffness. Crucially, this was enabled by an automated, nTop-driven workflow in which the TO-derived stress field is mapped directly to spatially varying lattice parameters, allowing rapid, repeatable lattice tailoring without manual trial-and-error. Manufacturability was explicitly addressed through an overhang constraint during TO to reduce required supports, and through the use of self-supporting lattice architectures, yielding geometries that are more directly compatible with metal additive manufacturing and reduced postprocessing effort. To conclude, the proposed workflow provides a scalable route to co-optimize external topology and internal cellular architecture for mass-critical structures. While the conclusions are based on linear static FEA, the results motivate follow on validation under fatigue, damage tolerance, and manufacturing variability to confirm in service performance. Nonetheless, the demonstrated weight reductions with maintained stiffness and reduced peak stress show that integrating TO with lattice infill is a promising, practical strategy for next generation lightweight aerospace components.

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The original version of this paper is available on https://www.scientific.net/KEM.1047.139.pdf

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 151-160

doi: 10.4028/p-2GPesp

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-18

Revised:
2026-02-23

Accepted:
2026-02-25

Online:
2026-04-13

Development and Characterization of 3D-Printed PETG Specimens Embedding Continuous Carbon Fiber Strain Sensors

The original version of this paper is available on https://www.scientific.net/KEM.1047.151.pdf

Imi Ochana 1,2,a*, François Ducobu 2,b, Thomas Rainchon 1,c and Anthonin Demarbaix 1, d

1 Science and Technology Research Unit, Haute Ecole Provinciale de Hainaut Condorcet, Boulevard Solvay 31, 6000 Charleroi, Belgium

2 Machine Design and Production Engineering Lab, Research Institute for Science and Material Engineering, University of Mons, Mons, Belgium

a* imi.ochana@condorcet.be, b Francois.Ducobu@umons.ac.be, c thomas.rainchon@condorcet.be, d  anthonin.demarbaix@condorcet.be

Keywords: additive manufacturing, PETG composite, continuous carbon fiber, electrical resistivity, structural health monitoring.

Abstract

Continuous monitoring of additively manufactured structures is essential for understanding their mechanical behavior and durability. This study investigates the electromechanical behavior of additively manufactured PETG specimens reinforced with continuous carbon fiber, with a particular focus on the influence of reinforcement geometry on strain-sensing performance. Specimens were fabricated using Fused Filament Fabrication and designed with four different reinforcement configurations: a reference single-layer layout, an extended-length reinforced region, a wider reinforced region, and a double-layer reinforcement. A total of twelve specimens were experimentally characterized. Electrical resistivity measurements were conducted under unloaded conditions and during bending induced by a low applied load of approximately 1.6 N . The initial electrical resistivity was found to depend on reinforcement geometry, with average values of approximately 523Ω for the reference configuration, 888Ω for the extended-length reinforcement, 1066Ω for the wider reinforcement, and 285Ω for the double-layer configuration. Under mechanical loading, the relative resistance variation remained below 0.6% for all specimens, indicating that the induced strain was very small. To further quantify strain sensitivity, the gauge factor was calculated for each configuration. Low average gauge factor values were obtained for the reference ( K≈0.1 ), extendedlength ( K≈0.38 ), and wider ( K≈0.5 ) configurations. In contrast, the double-layer reinforcement exhibited a higher average gauge factor of approximately 2.24 . These results indicate that reinforcement architecture affects the electromechanical sensitivity under low applied loads and offer insights for the design of multifunctional additively manufactured composite structures.

Introduction

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Since the advent of additive manufacturing (AM), three-dimensional printing has transformed the way complex components are produced, offering new geometric freedom, material efficiency, and the possibility to rapidly iterate designs compared with traditional processes [1]. Among the AM techniques, Fused Filament Fabrication (FFF) is especially widespread due to its relative simplicity, cost-effectiveness, and compatibility with a variety of thermoplastics [2]. However, the layer-by-layer nature of FFF can introduce anisotropy, interlayer adhesion issues, and geometric imperfections, which challenge the mechanical robustness and dimensional stability of printed parts [3]. Beyond the mitigation of these process-related limitations, the incorporation of fiber reinforcements and composite architectures within FFF has emerged as a promising pathway to expand the mechanical capabilities of printed components, enable property tailoring, and meet the requirements of highperformance and load-bearing applications [4]. This approach reflects a broader trend toward multifunctional and application-driven material design, positioning reinforced FFF composites as viable candidates for advanced engineering environments.

In this context, embedding continuous carbon fiber reinforcement within FFF-printed thermoplastic matrices emerges as a promising route to produce lightweight, high-strength composite parts, while also exploiting the multifunctional potential of carbon fibers [5]. Unlike short fiber or particulate fillers, continuous carbon fibers can form percolating conductive pathways throughout the part, offering the dual benefit of enhancing mechanical performance and imparting electrical conductivity [6]. Such carbon fiber-reinforced polymers (CFRPs) fabricated via 3D printing combine the mechanical advantages of classical composites with the design flexibility and rapid manufacturing advantages of AM [7].

These considerations have sparked growing interest in integrating sensing capabilities directly into 3D-printed composite structures. The principle relies on the fact that deformation of a conductive reinforcement, like carbon fiber, produces a change in its electrical resistivity, which can be correlated to the applied mechanical strain [8]. Traditional strain gauges rely on this piezoresistive principle, but embedding continuous conductive fibers into the bulk of a printed part enables the realization of self-sensing structures in a seamless and fully integrated manner [9]. Prior research using conductive filaments has demonstrated the feasibility of in situ strain sensing via FFF printing [10].

Liu et al. [11] investigated the use of additively manufactured continuous carbon fiber reinforced polymers (CCFRPs) as self-sensing materials. By tailoring fiber placement and printing strategies, the authors demonstrated that sensing functionality could be selectively integrated into specific regions of printed components. Their results showed that 3D printed CCFRPs exhibit favorable mechanical behavior together with stable electrical responses, and that key printing parameters, such as extrusion temperature and printing speed, significantly affect these properties. The study further confirmed the capability of CCFRP structures to accurately detect strain and temperature variations, highlighting their potential for integrated sensing applications.

Shafighfard et al. [12] investigated additively manufactured continuous carbon fiber reinforced polymer (CCFRP) components integrating fiber Bragg grating sensors. Their work demonstrated that CCFRP-based sensing structures combine advantageous mechanical properties, such as high stiffness and low weight, with effective sensing capabilities. Experimental results showed high sensitivity and accuracy in applications including structural health monitoring and vibration detection, highlighting the strong potential of 3D printed CCFRP composites for advanced sensor development in engineering applications.

Lanzolla et al. [13]investigated the development of a low-cost strain sensor fabricated via material extrusion additive manufacturing using a conductive PLA-based composite filled with carbon black and carbon nanotubes. The study demonstrated that the additively manufactured sensor exhibits a clear and repeatable correlation between mechanical loading and electrical resistance variation. Although temperature effects and hysteresis were shown to influence repeatability, the sensor achieved high linearity and sensitivity in both force and displacement measurements. These results highlight the potential of conductive polymer-based 3D-printed sensors for low-cost, single-step integration into smart and multifunctional structures.

Chaudhry et al. [14] demonstrated the feasibility of manufacturing continuous carbon fiber reinforced thermoplastic composites using Fused Filament Fabrication. By optimizing process parameters such as the number of reinforced layers and interlayer characteristics, and by employing a modified dual-nozzle system to achieve controlled fiber placement, the authors reported significant mechanical enhancements. For polylactic acid (PLA) matrices, tensile strengths of up to 112 MPa and flexural strengths of 164 MPa were achieved, corresponding to nearly a threefold increase compared to unreinforced PLA. These results underscore the capability of FFF-based continuous fiber reinforcement to produce mechanically robust composite components suitable for industrial applications.

Beyond their contribution to mechanical reinforcement, continuous carbon fibers inherently provide electrical conductivity, which can be exploited to introduce additional functional capabilities into additively manufactured composites.

In this context, integrating continuous carbon fiber reinforcement within FFF-printed parts, and exploiting its electrical conductivity to monitor structural behavior in real time, represents a
compelling approach to design smart, multifunctional composites. In this work, we explore how the geometry of the reinforced zone, in terms of its length, width, and number of fiber layers, influences the electrical sensitivity under mechanical load, and we assess the reproducibility of the fabrication and measurement process. This investigation aims to establish a reliable method for self-sensing 3D-printed composites, capable of continuous structural health monitoring and extending the functionality of additive manufacturing beyond purely structural applications.

Materials and Methods

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Specimen Design and Fabrication

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Four types of PETG specimens reinforced with continuous carbon fiber were fabricated using FFF on an Anisoprint Composer A4 3D printer to investigate the influence of reinforcement geometry on strain sensing performance. All specimens had identical overall measurements of 190 mm×40 mm×2 mm, as illustrated in Fig. 1.

[picture]

Fig. 1. Measurements of specimens.

Fig. 1. Measurements of specimens.

The four reinforcement layouts are shown in Fig. 2. For all configurations, each continuous carbon fiber layer had a thickness of 0.4 mm . A reference specimen (1) containing a single continuous carbon fiber reinforcement layer was first produced. Three modified reinforcement configurations were then designed to evaluate the effect of geometric variations: (2) a longer reinforced area with increased length, (3) a wider reinforced area with increased width, and (4) a double-layer reinforced area consisting of two stacked continuous carbon fiber layers. For each configuration, three identical specimens were manufactured to assess fabrication repeatability and measurement reproducibility.

[picture]

Fig. 2. Four types of PETG specimens reinforced with continuous carbon fiber.

Fig. 2. Four types of PETG specimens reinforced with continuous carbon fiber.

The printing process was controlled to ensure accurate placement and continuity of the carbon fiber within the reinforced regions. Particular attention was paid to maintaining consistent fiber alignment with the PETG matrix across all specimens. This controlled fabrication approach minimized geometric and material variability, allowing the influence of reinforcement geometry on strain sensing behavior to be investigated reliably.

Electrical Measurement

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The electrical resistance of the embedded continuous carbon fiber was measured using a Keithley DMM6500 digital multimeter, both in the unloaded state and under applied mechanical loading. Because the carbon fiber reinforcement consists of a single continuous filament embedded within the PETG matrix, it behaves electrically as a one-dimensional conductive path. Consequently, the electrical measurement corresponds to the longitudinal resistance along the fiber axis, between its two exposed ends.
Electrical connections were established using clip cables attached directly to the exposed fiber extremities, ensuring that the measured resistance corresponded to the continuous carbon fiber, as shown in Fig. 3.

[picture]

Fig. 3. Electrical connection to the continuous carbon fiber.

Fig. 3. Electrical connection to the continuous carbon fiber.

For each measurement condition, an initial resistivity measurement was first performed with the specimen in its original orientation. The specimen was then rotated by 180∘ and the measurement was repeated. This rotation inverted the bending configuration, thereby switching the fiber from the tensile side to the compressive side of the specimen during bending. This procedure allowed evaluation of whether tensile or compressive strain influenced the electrical response of the continuous carbon fiber.

Mechanical Testing Protocol

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Mechanical testing was conducted using a simple bending configuration to evaluate the electromechanical response of the specimens under a controlled load case. Each specimen was first placed on two supports, forming a simply supported beam configuration, and electrically connected to the measurement system using a two-wire resistance measurement setup at the specimen connectors.

An initial electrical resistance measurement was recorded in the unloaded state to establish a reference. A calibrated mass of 170 g was then placed at the midpoint of the specimen, as shown in Fig. 4, to induce bending deformation. This mass corresponds to an applied force of approximately 1.6 N and defines the loading condition used throughout the study. Once the load was applied, a second electrical resistance measurement was taken under deformation. This mass was selected to ensure a repeatable and non-perturbing loading condition compatible with the experimental setup (stable placement, no fixture slippage, no contact degradation) while keeping the specimens well
within the linear elastic regime for screening the electromechanical response. The load was kept constant across all tests to enable fair comparison between reinforcement configurations.

[picture]

Fig. 4. Mechanical testing set.

Fig. 4. Mechanical testing set.

This loading-unloading sequence was repeated to ensure measurement repeatability in the initial orientation. The specimen was then disconnected, rotated by 180∘, and tested again using the same procedure. Because the specimens were designed to be geometrically symmetric, this additional step was performed to verify that orientation and the inversion of tensile and compressive regions during bending did not introduce measurable differences in the electrical response.

Results and Discussion

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A total of twelve specimens were analyzed, with three specimens per configuration. Graph on Fig. 5 shows the initial electrical resistivity values R0 measured for the four specimen configurations under top-face bending, bottom-face bending and their averaged value. In this study, the terms "top" and "bottom" are defined with respect to the FFF build direction: the bottom face corresponds to the surface formed by the first printed layer in contact with the build platform, while the top face corresponds to the surface formed by the last printed layer. Accordingly, "top-face bending" denotes the configuration where the last-layer surface is oriented on the loaded side, and "bottom-face bending" denotes the configuration where the first-layer surface is oriented on the loaded side.

[picture]

Fig. 5. initial electrical resistivity values Ro.

Fig. 5. initial electrical resistivity values Ro.

For the reference specimens, the measured resistivity values range approximately between 514Ω and 531Ω, with an average value close to 522Ω. The difference between the top and bottom bending orientations remains below 5%, indicating a very stable initial electrical behavior and limited sensitivity to specimen orientation for this configuration.

The extended-length reinforcement configuration (Double length) exhibits significantly higher resistivity values, ranging from approximately 885Ω to 890Ω, with an average close to 887Ω. Compared to the reference specimens, this corresponds to an increase in resistivity of approximately 60%, which is consistent with the increased effective fiber length contributing directly to higher electrical resistance.

An even higher resistivity is observed for the wider reinforcement configuration (Double width). In this case, R0 values are clustered between approximately 1058Ω and 1073Ω, yielding an average value around 1066Ω. This represents an increase of about 50% relative to the reference specimens and approximately 20% compared to the extended-length configuration. A likely explanation for this trend is that increasing the reinforcement width forces the continuous carbon fiber to follow a less direct path, introducing additional zigzag patterns and local deviations. These detours effectively lengthen the electrical conduction path and increase the number of fiber turns, which in turn raises the overall resistance despite the larger nominal width. This finding emphasizes the critical role of fiber trajectory in determining the electrical performance of composite reinforcements.

In contrast, the double-layer reinforcement configuration (Double layer) shows the lowest resistivity values among the modified specimens, with measurements ranging from approximately 280Ω to 290Ω and an average value close to 285Ω. This corresponds to a reduction in resistivity of roughly 45% compared to the reference specimens. This decrease is consistent with the presence of two stacked continuous carbon fiber layers, which effectively act as parallel conductive paths and reduce the overall electrical resistance.

Across all configurations, the difference between top and bottom faces bending orientations remains limited, generally below 5−7%. This low dispersion confirms good repeatability of the measurement protocol and indicates that the initial electrical resistivity R0 is weakly influenced by the bending direction prior to loading. Consequently, the averaged R0 values provide a reliable baseline for subsequent analysis of strain-induced resistivity variations.

The initial electrical resistivity ( R0 ) and the resistivity measured under mechanical loading ( R0+ΔR ) are compared in Fig. 6 for the four specimen configurations. The applied load corresponds to the calibrated mass of 170 g positioned at the specimen midpoint, inducing bending deformation. For each configuration, measurements were performed under top-surface bending, bottom-surface bending, and their averaged value. Notably, once the load was removed, the electrical resistance returned to its initial value within the measurement uncertainty, confirming that the specimens remained in the linear elastic regime.

[picture]

Fig. 6. Initial electrical resistivity ( R0 ) and resistivity measured under mechanical loading.

Fig. 6. Initial electrical resistivity ( R 0 ) and resistivity measured under mechanical loading.

For all configurations, the application of the load resulted in negligible changes in electrical resistivity. The averaged relative variation ΔR/R0 remained below 0.6% for all specimens, which is comparable to the measurement uncertainty of the experimental setup. For the reference specimens, the average resistivity changed from 522.53Ω to 522.38Ω, corresponding to a variation of −0.03%. Similarly small variations were observed for the extended-length ( +0.10% ), wider ( −0.09% ), and double-layer ( +0.06% ) reinforcement configurations.

These results indicate that, under the applied load level, the induced bending deformation is insufficient to generate a measurable electromechanical response in the embedded continuous carbon fiber. This suggests that the specimens remain within a very low strain regime, below the sensitivity threshold of the embedded continuous carbon fiber, in which variations in electrical resistance are not yet detectable. Identifying this threshold strain level constitutes an important perspective of the present work and will require experiments conducted under higher and progressively applied bending loads in order to induce larger deformation and activate the electromechanical response of the reinforcement.

Importantly, this behavior also demonstrates the electrical stability and repeatability of the embedded fiber under small mechanical perturbations. The absence of significant resistance variation confirms that the measurement protocol is reliable and that no spurious effects, such as contact instability or electrical noise, dominate the recorded signal.

To further quantify the strain-sensing capability of the embedded continuous carbon fiber, the gauge factor was calculated for all specimens. The gauge factor is defined as the relative change in electrical resistance normalized by the applied mechanical strain and is expressed in Eq. 1.

K=ΔR/R0ε(1)

where R0 is the initial electrical resistance, ΔR=R−R0 is the change in resistance under load, and ε is the mechanical strain induced by bending.

The strain used to compute the gauge factor was estimated analytically using composite beam theory. For a simply supported beam subjected to a concentrated load applied at mid-span, the axial strain at the location of the continuous carbon fiber, situated at a distance y from the neutral axis of the composite cross-section, is given in Eq. 2.

ε(y)=Mmax*y(EI)eq(2)

where (EI)eq  is the equivalent flexural rigidity of the composite cross-section, computed using the transformed section method with PETG selected as the reference material.

Under the same loading configuration, simply supported beam with a concentrated load applied at mid-span, the maximum bending moment occurs at the center of the span and is given by Eq. 3.

Mmax=PL4(3)

where P is the applied load and L is the span length between the supports.
Fig. 7 presents the calculated gauge factor values for the four specimen configurations under topface bending, bottom-face bending, and their averaged values. Despite the very small absolute changes in electrical resistance observed under the applied load, the gauge factor analysis reveals clear differences in strain sensitivity between the reinforcement geometries.

[picture]

Fig. 7. Gauge factor analysis.

Fig. 7. Gauge factor analysis.

For the reference specimens, very low gauge factor values were obtained, with an average K close to 0.10 , associated with a very limited relative resistance variation ( ΔR/Ro≈0.02% ). This indicates that, under the applied bending deformation, the embedded carbon fiber exhibits a weak electromechanical coupling and is only marginally affected by the induced strain. The electrical response therefore remains close to the measurement uncertainty, suggesting that the fiber barely perceives the imposed deformation in this configuration.

A slight improvement in strain sensitivity is observed for the extended-length reinforcement configuration, which exhibits an average gauge factor of approximately 0.38 and a relative resistance variation of about 0.10%. The increased specimen length likely promotes a more homogeneous strain distribution along the carbon fiber, resulting in a marginally enhanced transmission of deformation.

The wider reinforcement configuration shows a moderate increase in sensitivity, with an average gauge factor of around 0.50 and a relative resistance variation of approximately 0.12%. Increasing the specimen width alters the stress state within the specimen and appears to improve strain transfer from the polymer matrix to the embedded fiber compared to the reference configuration.

In contrast, the double-layer reinforcement configuration exhibits a markedly enhanced electromechanical response, with an average gauge factor of 2.24 and individual values reaching up to 2.44 , together with a significantly higher relative resistance variation ( ΔR/R0≈0.57% ). This corresponds to an increase of more than one order of magnitude in gauge factor compared to the reference specimens. The presence of two stacked carbon fiber layers likely intensifies strain localization and interfacial interactions, leading to a stronger resistance change under bending. Although the absolute resistance variations remain small, these results clearly demonstrate that the reinforcement architecture governs the strain sensitivity. In particular, an appropriate design of the carbon fiber layout can improve sensing performance, even under low applied deformation levels.

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.151.pdf

This study investigated the electromechanical behavior of PETG specimens reinforced with continuous carbon fiber manufactured by Fused Filament Fabrication using an Anisoprint Composer A4 printer. Four reinforcement geometries were analyzed, including a reference configuration, extended-length and wider reinforced regions, and a double-layer reinforcement, with a total of twelve specimens experimentally characterized.

Electrical resistivity measurements revealed that the initial resistance depends on the reinforcement geometry. Increasing the effective fiber length or width resulted in higher resistivity
values, while the double-layer configuration reduced the overall resistance due to the presence of parallel conductive paths. These trends were consistent across all specimens and confirmed the repeatability of the fabrication and measurement procedures.

Under a low bending load corresponding to a 170 g mass (~1.6 N), only negligible changes in electrical resistivity were observed for all configurations. The relative resistance variations remained below 0.6%, indicating that the applied load induced very small strain levels and that the electrical response of the embedded carbon fiber remained stable in this deformation regime. This behavior also confirms the reliability of the experimental setup and the absence of measurement artifacts related to contact or specimen orientation.

To further assess strain sensitivity, the gauge factor was calculated for all specimens. While low gauge factor values were obtained for the reference, extended-length, and wider reinforcement configurations, the double-layer reinforcement exhibited a higher gauge factor, with an average value of 2.24. This result demonstrates that reinforcement architecture plays a key role in governing electromechanical sensitivity, even under low applied loads, and highlights the potential of stacked continuous carbon fiber layouts for strain-sensing applications.

Overall, the results indicate that although low mechanical loading is insufficient to generate measurable resistance variations in single-layer configurations, appropriate reinforcement design can enhance strain sensitivity. These findings provide valuable insight into the design of multifunctional additively manufactured composite structures. Future research should pursue two complementary directions: first, the application of higher mechanical loads to promote larger strain levels and more effectively activate the strain-sensing behavior of continuous carbon fiber reinforcements; and second, the investigation of alternative mechanical loading modes, such as flexural testing, to better characterize and understand the electromechanical response and sensing potential of the reinforced architectures.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/KEM.1047.151.pdf

The authors would like to thank Région Wallonne for supporting this research as part of the SKYWIN ICOM2C3D research project under grant 8820.

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The original version of this paper is available on https://www.scientific.net/KEM.1047.151.pdf

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 161-174

doi: 10.4028/p-cumT94

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-18

Accepted:
2026-02-11

Online:
2026-04-13

Development of 3D-Printed Prototype Moulds for Thermoforming

The original version of this paper is available on https://www.scientific.net/KEM.1047.161.pdf

Jan Rösler 1,a*, Jörg Hornig-Klamroth 1, b and Leon Dähn 1,c

1 Berliner Hochschule für Technik, Luxemburger Straße 13, D-13353 Berlin, Germany

a* jan.roesler@bht-berlin.de, b  joerg.hornig@bht-berlin.de, cleonskiee@t-online.de 

*corresponding author

Keywords: thermoforming, prototyping, 3D-printed moulds, PLA, material reduction, cost reduction.

Abstract

The use of 3D-printing simplifies and accelerates the development of moulds for a thermoforming process. This article examines several aspects of the effective design of 3D-printed polymer moulds. The focus is on prototyping and applications in engineering education. Experiments are conducted on PLA mould to determine the actual temperature loads and permanent deformations. Measures to improve the durability of the moulds are discussed and approaches to material and cost optimization are investigated. Examples of the use of PLA-moulds in thermoforming are presented.

Introduction

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Thermoforming is an important forming process in plastics processing. It can be used to manufacture small and very large components at low cost. One important area of application is the packaging industry. Components can be manufactured using positive or negative moulds. However, producing the necessary moulds is a complex process. That is why there are activities to produce moulds from plastics using 3D printing, see e.g. [1]. Fused Deposition Modelling (FDM) is widely used in the field of 3D printing. PLA filaments are often used for this process. These filaments are easy to handle, enable detailed printing, and are less likely to warp when cooled. In addition to prototyping, these processes offer the opportunity in academic engineering education to carry out comprehensive, holistic projects covering the entire process chain, from the design of a thermoformed component to mould design and component manufacturing. The flexibility of 3D printing allows students to experimentally optimize mould design and test specific design features. This paper presents the results of several student projects, see Figure 1. In addition, investigations into the durability of PLA moulds are carried out and the influences of several design parameters are analysed in order to design and manufacture mould prototypes more efficiently.

[picture]

Fig. 1. Example of a thermoformed component produced during a student project (left), massive PLA-mould with 10 mm wall thickness (right), [9].

Fig. 1. Example of a thermoformed component produced during a student project (left), massive PLA-mould with 10 mm wall thickness (right), [9].

Vacuum Thermoforming Process

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Vacuum thermoforming is a widely used technology to shape plastic films or sheets. During this process the sheet is heated, pre-stretched, and formed, usually assisted with vacuum, cooled and finally demoulded, see [2]. The process is depicted in Figure 2. The demoulding (step 6) can be assisted by applying air pressure.

[picture]

Fig. 2. Vacuum thermoforming process.

Fig. 2. Vacuum thermoforming process.

For serial production of parts by means of thermoforming, the moulds are manufactured from steel or aluminum by precise 3D machining. This generates high costs, time expenditure and expenses. For prototypes 3D-printed polymer moulds can be an option.

[picture]

Fig. 3. Conventional aluminum vacuum thermoforming mould (left), Vacuum thermoforming machine Illig UA 100 ed (right).

Fig. 3. Conventional aluminum vacuum thermoforming mould (left), Vacuum thermoforming machine Illig UA 100 ed (right).

Investigation of the resistance to thermal and structural loading of 3D-printed PLA vacuum thermoforming moulds

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PLA is a popular material for widely used FDM printers. However, it has a relatively low operating temperature. The literature specifies a glass transition temperature ( Tg ) of approximately 55−60∘C [3] and [4]. The dynamic mechanical analysis (DMA) carried out in our laboratory shows a glass transition temperature Tg of 55∘C and a Tg onset of 51∘C. The measurements were carried out using PLA filament pearl-blue from Prusa and the device Netzsch DMA 242 (see Figure 4).

[picture]

Fig. 4. DMA Analysis of PLA filament - determination of glass transition temperature at a frequency of 1 Hz .

Fig. 4. DMA Analysis of PLA filament - determination of glass transition temperature at a frequency of 1 Hz .

Hence, PLA has a relatively low glass transition temperature and thus temperature resistance compared to other thermoplastics used in the FDM process. For comparison, reference is made to the values for glass transition temperatures given in the literature, e.g.:

ABS Tg=105∘C…125∘C according [5],

PET Tg=70∘C…80∘C according [6],

PETG Tg=85 according [7]

During the thermoforming process, the sheets are subjected to significantly higher temperatures. The Illig UA 100 Ed used here, see Figure 3 (right), has IR emitters with a heating capacity of 400−525∘C (bottom and top heating). The processing temperatures for the sheets are significantly lower, but with a temperature range of 110∘C to 190∘C for PETG sheets (see [2]) they are considerably above the glass transition temperature of PLA Tgg=55∘C.
The question arises as to what temperatures can occur in the mould during the thermoforming process? What design should a PLA mould have to function properly? What configurations, thickness, infill density and pattern, as well as structural design, are necessary for optimal results with low material costs and printing time? In the tests described below, PLA moulds are subjected to a challenging test program in order to help clarify these questions.

Design of analysed PLA thermoforming moulds

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Four different types of PLA-Thermoform moulds were investigated. The basic geometry is depicted in Figure 5.

[picture]

Fig. 5. General geometry of considered PLA-moulds.

Fig. 5. General geometry of considered PLA-moulds.

The distinguishing feature of the moulds are

wall thickness h=1.5 mm printed with 100% infill

wall thickness h=3.0 mm printed with 100% infill

wall thickness h=10 mm printed with 15% infill

wall thickness h=5 mm printed with 100% infill

While the moulds 1, 2 and 4 are monolithic, printed with 100% infill, the mould No. 4 has an internal honeycomb structure with 15% infill, see Figure 6. The moulds were attached to the wooden base plate of the thermoform machine by means of screws and sealed with aluminum tape (see Figure 8 c).

[picture]

Fig. 6. Printing mould No. 3 with 15% infill and honeycomb pattern.

Fig. 6. Printing mould No. 3 with 15 % infill and honeycomb pattern.

Since for prototyping the time expenditure is of great importance, the estimated printing times of all four moulds were compared. This is based on the output of the Bambu Lab Studio software. The CAD-models of the four moulds were sliced, yielding an estimation of time and filament mass estimated by the slicing software. Additionally, the required masses of PLA-filaments were analysed. Both, relative printing time and filament mass are depicted in Figure 7. The data are related to a mould design with 10 mm wall thickness and 100% infill, referenced as model 5 . Compared to this robust and heavy design, the proposed designs of mould 1 to mould 4 result in considerable reductions. However, these savings come with effects on durability, to be discussed next.

[picture]

Fig. 7. Comparison of designs - printing time and mass of used filament.

Fig. 7. Comparison of designs - printing time and mass of used filament.

Experimental design and measurement methods

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If PLA is to be used for thermoforming moulds, it is useful to determine the temperatures that can be expected during thermoforming in a polymere mould. For this reason, temperature measurements were taken on PLA-moulds during the thermoforming process inside the mould. During the series of measurements, temperatures in the inner of a mould T1 and T2 were recorded at two positions inside the mould (see Figure 8 a). The temperatures were measured using a Voltcraft K 202 thermometer with data logger and K-type thermocouples. The measuring device was placed inside the mould base plate of the thermoforming machine to continuously record the temperature.

Fig. 8. (a) Position of thermoelements T1 and T2, (b) measurement of deflection of the top plate with digital calliper, (c) mould installed in the thermoforming machine Illig UA 1100 ed.

In addition, the temperatures on the surface of the thermoformed sheet and on the top of the mould were measured using a Flir E4 infrared camera. This measurement was taken after completion of the thermoforming process, i.e. after cooling and demoulding of the sheet. In order to analyse the durability of the PLA mould, the deformations of the top plate of the mould were measured with a calliper depth gauge after each thermoforming cycle. The moulds were subjected to a 3-minute cycle. This includes the entire thermoforming process and a subsequent cooling phase, during which the measurements were taken and the next cycle was prepared.

Thermoforming process and test program

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The thermoforming processed with a vacuum thermoforming machine Illig UA 100 Ed using the parameters given in Table 1.

Table 1. Process parameters for thermoforming process.

Material | PETG sheet 540 mm × 400 mm, thickness 0.75–0.8 mm (see Figure 20)
Heater settings | upper heater: 3 zones at 360 °C | lower heater: 3 zones at 360 °C
Heating time | 22 s per cycle
Pre-blow | activation after 0 s | duration approx. 0.5 s
Vacuum delay and duration | vacuum applied with ca. 4 s delay (active during forming phase)
Cooling time | 8 s
Demoulding air | 1 s

In total this thermoforming process lasts approx. 45 s . In the course of the test series 3 -minutes cycles were run. Here, the detailed description of the test program for every of the four moulds will be given:

Mould No 1

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This mould was exposed to two cycles until its failure. The internal temperatures T1 and T2 were measured.

Mould No. 2

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It was exposed to 6 cycles. An additional cycle was interrupted due to failure. The internal temperatures T1 and T2 were recorded.

Mould No. 3

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Ten cycles were carried out with this mould.

Mould No. 4

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This mould was run 10 cycles of 3 minutes, except one 5 minutes interval between cycles 8 and 9 . The internal temperature T1 was measured.
The temperature at the top and the temperature of the processed PETG-sheet was measured direct after thermoforming for all four moulds. Furthermore, the permanent deflection in the centre of the top plate was determined.

Experimental analysis of thermal behaviour of PLA-moulds

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Mould No. 1

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With a wall thickness of 1.5 mm failed in the second cycle. After the first cycle, a temperature of T =58∘C was measured on the mould surface, which rose to 62∘C in the second cycle. The mould deformed significantly, meaning that the test series could not be continued.

Mould No. 2

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Temperatures inside the mould during thermoforming were examined for mould No. 2 with a wall thickness of 3 mm and 100% infill. The temperature curves T1 und T2 can be seen in Figure 9. Over the course of six cycles, a maximum temperature of 61∘C was measured inside the mould. This is significantly above the glass transition temperature.

[picture]

Fig. 9. Temperature in the inner of mould No. 2( h=3 mm).

Fig. 9. Temperature in the inner of mould No. 2 ( h = 3 m m ) .

The temperatures on the top of the mould and on the upper surface of the sheet were measured immediately after a thermoforming process by means of IR-camera (see Figure 10).

[picture]

Fig. 10. IR-Photograph of mould No. 2 bevor the 1st. and after the 6th cycle (top view).

Fig. 10. IR-Photograph of mould No. 2 bevor the 1st. and after the 6th cycle (top view).

An overview of these temperatures for all cycles is given in Figure 11. The top of the mould heats up to almost 70∘C.

[picture]

Fig. 11. Temperature of mould No 2( h=3 mm) top plate and the thermoformed sheet after finishing a cycle.

Fig. 11. Temperature of mould No 2 ( h = 3 m m ) top plate and the thermoformed sheet after finishing a cycle.

This significantly exceeds the glass transition temperature. After six cycles, the test series was discontinued due to excessive deformation of the mould. The associated structural mechanical behaviour is discussed below.

Mould No. 3

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Here too, the temperatures on the surface of the films and the mould were measured after each thermoforming cycle. Figure 12 shows the temperatures on the sheet and mould surface for 10 cycles. The temperatures on the mould surface reach up to 77∘C, which is 10∘C above the maximum temperature of mould No. 2. After three cycles, the surface temperature is 70∘C, compared to 61∘C after three cycles with mould No. 2. (see Figure 11). The cause of this is believed to be the internal structure of the mould, forming closed honeycomb cells. This structure reduces the thermal conductivity of this sandwich-like structure compared to the conductivity of the monolithic structure of mould No. 2.

[picture]

Fig. 12. Temperature of mould No. 3 Top plate and thermoformed sheet after finishing a cycle.
Mould No. 4

Fig. 12. Temperature of mould No. 3 Top plate and thermoformed sheet after finishing a cycle. Mould No. 4

The temperature T1 on the inside of the mould (Figure 13) reaches a maximum of 58∘C after 8 cycles and is therefore still lower than the maximum external surface temperature on the mould. Temperature T1 was not recorded for the remaining last two cycles.

[picture]

Fig. 13. Temperature in the inner of mould No. 4( h=5 mm).

Fig. 13. Temperature in the inner of mould No. 4 ( h = 5 m m ) .

Figure 14 shows the temperatures at the sheets and mould surface. The temperatures at the mould surface reach up to 66∘C, which is slightly higher than for mould No. 2. After three cycles, the surface temperature is 57∘C, compared to 61∘C after three cycles for mould No. 2 .

[picture]

Fig. 14. Temperature of mould No. 4( h=5 mm) Top plate and thermoformed sheet after finishing a cycle.

Fig. 14. Temperature of mould No. 4 ( h = 5 m m ) Top plate and thermoformed sheet after finishing a cycle.

Structural behaviour of PLA-Moulds and thermoformed PETG-sheets

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Mould No. 1

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In the tests the mould collapsed during the second cycle (see Figure 15 left). Significant deformation of the side walls and top plate is visible. The mould became unusable.

[picture]

Fig. 15. Mould No. 1 after collapse during the 2nd cycle (left), mould No. 2 after 6 cycles showing deformation of the top plate and of the sidewalls (right).

Fig. 15. Mould No. 1 after collapse during the 2nd cycle (left), mould No. 2 after 6 cycles showing deformation of the top plate and of the sidewalls (right).

Mould No. 2

The original version of this paper is available on https://www.scientific.net/KEM.1047.161.pdf

During the six test cycles of mould No. 2, an increasing plastic deflection of the top plate was observed. The development of this deflection is shown in Figure 16. The deformed state of the moulds top plate after 6 cycles is depicted in Figure 15 (right). Beside deflections of the top plate a deformation of sidewalls is visible. This deformation caused some problems during demoulding of the sheet occurring during cycle 4 and the following cycles.

[picture]

Fig. 16. Deflection of top plate of mould No. 2( h=3 mm).

Fig. 16. Deflection of top plate of mould No. 2 ( h = 3 m m ) .

Mould No. 3

The original version of this paper is available on https://www.scientific.net/KEM.1047.161.pdf

In tests with structural design, the structure proved to be only partially effective. Indentations appeared between the cell-like structural elements, depicted in Figure 17. As a result, the moulded components became stuck partially after six cycles. However, the basic shape of the mould remained intact. Because of the local deformation the permanent deflection of the top plate Δh was not analysed for this mould.

[picture]

Fig. 17. Mould No. 3 after 2 cycles outer skin with honeycomb pattern at surface (left), thermoformed sheet showing imprint of honeycomb pattern (right).

Fig. 17. Mould No. 3 after 2 cycles outer skin with honeycomb pattern at surface (left), thermoformed sheet showing imprint of honeycomb pattern (right).

Mould No. 4

The original version of this paper is available on https://www.scientific.net/KEM.1047.161.pdf

Tests with this design showed significantly higher resilience during thermoforming. Eight cycles could be carried out without any problems. After that, deformations appeared in the base, leading to difficulties in demoulding. This can be eliminated by simple reinforcements.

[picture]

Fig. 18. Deflection of top plate of mould No. 4( h=5 mm).

Fig. 18. Deflection of top plate of mould No. 4 ( h = 5 m m ) .

Figure 18 shows the deflection of the top plate of the mould. Compared with other designs, the deformations are small. However, the critical deformation occurred at the base of the mould, as shown in Figure 19.

[picture]

Fig. 19. Mould No. 4 after tests, deformation at the mould base.

Fig. 19. Mould No. 4 after tests, deformation at the mould base.

Deformation of the thermoformed PETG-sheets:

The original version of this paper is available on https://www.scientific.net/KEM.1047.161.pdf

To analyse the deformations, a grid with a 20 mm×20 mm grid was drawn on a sheet. After thermoforming, this grid is distorted. Figure 20 shows the undeformed (left) and deformed grid (middle).

[picture]

Fig. 20. Undeformed sheet 540 mm×400 mm with mesh (left), State of deformation of the thermoformed sheet (middle) and orientations in polarized light (right).

Fig. 20. Undeformed sheet 540 m m × 400 m m with mesh (left), State of deformation of the thermoformed sheet (middle) and orientations in polarized light (right).

For fields 1, 2 and 3 marked in Figure 20 (middle), the changes in length and thickness were measured and the resulting degrees of deformation were determined according [8].

φ=ln(x1x0)=ln(1+Δxx0)(1)

Here x0 and x1 are length in undeformed and deformed state respectively.

Table 2. Change of dimensions and degree of deformation for fields 1, 2 und 3.

| field No. 1 | field No. 2 | field No. 3
Δℓ [mm] | 7 | 0 | 6
φx [1] | 0,3 | 0 | 0,26
Δb [mm] | 0 | 6 | 0
φy [1] | 0 | 0,26 | 0
Δt [mm] | 0.11 | 0.08 | 0.09
φz [1] | -0.15 | -0.11 | -0.12

Results are given in Table 2. This analysis clearly shows significant local stretches of the sheets, thereby reducing the wall thickness. These deformations are accompanied by changes in the internal structure. The orientations can be made visible in a polariscope. Figure 20 (right) shows significant stretching of the material in the base area. Despite the symmetrical shape, the orientation is asymmetrical because the mould is not centred on the base plate of the thermoforming machine, i.e. there are different distances between the clamping points for the sheets in the clamping frame. At the same time, the heating effect of the IR radiators may vary in the centre and at the edge of the temperature field.

Applications - development of thermoformed products in academic engineering education

The original version of this paper is available on https://www.scientific.net/KEM.1047.161.pdf

Example A: One project involved developing thermoformed packaging and the corresponding thermoforming mould. Figure 21 [9] shows an example of this product development. The task required the design of clip connections for closing the packaging. Various geometries and dimensions of the clip connection were tested. 3D printing enabled the flexible production of thermoforming moulds for experimental parameter studies. This made it possible to easily produce various parts using thermoforming and determine the optimal geometry for the desired assembly and disassembly forces. There was no need for complex metal moulds or simulations.

[picture]

Fig. 21. Series for different geometries of clip connections (left), Example of packaging manufactured using thermoforming and 3D printed moulds (right).

Fig. 21. Series for different geometries of clip connections (left), Example of packaging manufactured using thermoforming and 3D printed moulds (right).

Example B: The aim of this project was to design buckets for wall paint that are easy to clean. This facilitates subsequent recycling and increases the quality of the recycled material. For this study, moulds were produced using 3D printing and used to manufacture scaled bucket parts by thermoforming (see Figure 22 [10]). These samples were used to test the suitability of the different designs for cleaning.

[picture]

Fig. 22. Printed mould (left) and thermoformed parts (right).

Fig. 22. Printed mould (left) and thermoformed parts (right).

Summary

The original version of this paper is available on https://www.scientific.net/KEM.1047.161.pdf

PLA moulds were successfully used in both projects. The flexibility of 3D printing enables detailed experimental investigations of the different designs. In the practical implementation of these projects, the cycle times were significantly less critical than in the tests carried out. The experiments showed that when using PLA moulds in thermoforming, the structure and process temperatures have a significant influence on their service life. The results obtained contribute to the efficient design of 3D-printed prototype moulds for thermoforming. The following guidelines for polymer mould design can be derived from this.

the outer skin of the mould should be solid or with dense infill to avoid local deformations and distortion of the outer mould shape.

to keep dimensional stability and improve structural integrity stiffeners as ribs or supporting structure inside the mould are highly recommended.

the inner structure of the mould should enable heat transfer, closed cells shall be avoided.

materials with high glass transition temperature are advisable.

A very simple method of reducing the temperature loads of the mould was to cool it briefly with a damp cloth. This led to a rapid drop in temperature.

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.161.pdf

N.N.: Schnelles Thermoformen in Kleinserie mit 3D-gedruckten Formwerkzeugen, formlab, [online], https://formlabs.com/de/white-papers/low-volume-rapid-thermoforming-with-3d-printed-molds/, 17th January 2026.

Illig, A. (ed.): Thermoformen in der Praxis, München, Wien, Hanser 1997.

Demiryurek, S.G: Evaluation of dynamic properties in ABS and PLA thermoplastics across frequency ranges. Sci Rep 15, 42629, 2025, .

Khouri N. G, Bahú J. O, Blanco-Llamero C, Severino P, Concha V. O. C: Polylactic acid (PLA): Properties, synthesis, and biomedical applications - A review of the literature, Journal of Molecular Structure Vol. 1309, 2024, .

Erhard, Gunter: Konstruieren mit Kunststoffen, 4. Auflage, Carl Hanser Verlag, 2008.

Ehrenstein, G W.: Mit Kunststoffen konstruieren, 3. Auflage, Carl Hanser Verlag, 2007.

N.N. PETG filament data sheet, BigRep GmbH, [online], , 17th January 2026.

Bender B, Göhlich D (eds.): Dubbel Taschenbuch für den Maschinenbau 2: Anwendungen Springer Vieweg Berlin, Heidelberg 2020, .

Kollmeier S, Wolf J, Schnellbach Dennis, Bernstein J: Konstruktive Aufgabe: Transportverpackung, Wintersemester 2024/25, Berliner Hochschule für Technik, 2025.

Hannes B. K, Heinrich J. P: Recyclinggerechte Werkstoffgestaltung eines Farbeimers, Sommersemester 2025, Berliner Hochschule für Technik, 2025.

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 175-181

doi: 10.4028/p-l1BCTO

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-18

Revised:
2026-02-12

Accepted:
2026-02-25

Online:
2026-04-13

Sustainable Electrochemical Machining of Additively Manufactured Nitinol with Deep Eutectic Solvents

The original version of this paper is available on https://www.scientific.net/KEM.1047.175.pdf

Perna Alessia Serena 1,a*, Scherillo Fabio 1, b

1 Department of Chemical, Materials and Production Engineering, University of Naples Federico II, Piazzale V. Tecchio 80, 80125 Napoli, Italy

a alessiaserena.perna@unina.it, b fabio.scherillo@unina.it

*corresponding author: alessiaserena.perna@unina.it

Keywords: additive manufacturing, nitinol, electrochemical machining, sustainability.

Abstract

Additively manufactured Nitinol components often exhibit rough surfaces and defects that affect functional performance. This study investigates the feasibility of electropolishing Nitinol in a deep eutectic solvent (ethaline). Linear sweep voltammetry was used to identify anodic potentials suitable for controlled dissolution, and electropolishing was performed at selected potentials. Surface evolution was analysed by SEM, EDX, optical microscopy, and confocal microscopy. Electropolishing in ethaline effectively reduced surface scratches and produced more homogeneous surfaces without altering alloy composition. Higher applied potentials ( 12.5 V ) resulted in complete removal of surface scratches and visually homogeneous surfaces, whereas lower potentials ( 6 V ) mainly reduced the visibility of surface scratches. Compared to conventional inorganic electrolytes, the process exhibits a lower dissolution rate, offering a safer and more controllable approach.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.175.pdf

Shape memory alloys are a class of functional metallic materials capable of recovering a predetermined geometry upon exposure to thermal or mechanical stimuli [1]. This characteristic response, known as the shape memory effect, together with superelasticity, has positioned these alloys as attractive materials for advanced applications. The first documented observations of shape recovery were reported in copper-based alloys in the early twentieth century, but the field gained significant momentum following the development of the nickel titanium alloy Nitinol in the 1960s [2]. This alloy demonstrated a unique combination of stable transformation behaviour, good corrosion resistance, biocompatibility, high recoverable strain, and favourable mechanical performance, which rapidly expanded interest in its scientific and technological potential [3]. These attributes supported the widespread adoption of Nitinol (NiTi) into aerospace components, electronic devices, safety mechanisms, and a wide range of biomedical devices. In particular, cardiovascular stents, orthodontic wires, orthopedic implants, and instruments for minimally invasive procedures have demonstrated the value of Nitinol due to its ability to undergo large reversible deformation while maintaining longterm functional reliability [4].

Despite these compelling advantages, the production and processing of Nitinol remain complex. The shape memory and superelastic responses are highly sensitive to the nickel-to-titanium ratio. Deviations as small as a few tenths of a percent can significantly alter transformation temperatures and mechanical performance [5]. During melting, the volatility of nickel and the high reactivity of titanium complicate control over the final alloy composition, while powder production introduces further challenges related to oxidation, impurity uptake and surface chemistry. Conventional processing routes such as rolling, drawing and tubing are therefore associated with considerable difficulty, and the machining of the alloy is hindered by its ductility, tendency to work harden and complex thermal behaviour [6].
In this scenario, additive manufacturing has emerged as a valuable method to overcome several of these limitations. In particular, Laser Powder Bed Fusion (LPBF) enables the fabrication of dense or porous structures with intricate geometries, internal channels and spatially controlled microstructures [7]. These capabilities have expanded the design space for nickel-titanium components and have
stimulated interest in the production of customised medical devices, actuators, and components for aerospace and robotics applications. Nevertheless, the sensitivity of the alloy to compositional changes and the presence of surface defects generated during melting or powder reuse highlight the importance of surface conditioning after fabrication. Moreover, poor as-built surface quality is a common characteristic of many additive manufacturing processes [8]. Layerwise building, stair stepping on inclined surfaces, partially melted or adhered powder particles, balling phenomena and localized lack of fusion commonly generate rough, irregular topographies and near surface defects across alloys and process variants. These process intrinsic features increase corrosion susceptibility, complicate fatigue and functional performance and therefore demand effective post processing and surface finishing strategies [6].

Surface finishing is essential for controlling corrosion behaviour, improving mechanical performance and managing nickel release, especially in applications involving contact with biological environments [9]. Electropolishing, anodising, chemical etching and hydrothermal treatments have therefore been the focus of extensive research. However, conventional electropolishing solutions, often based on highly concentrated mixtures of hydrofluoric and nitric acids or other strong corrosive agents, present several drawbacks [10]. These include operator safety concerns, stringent handling and disposal requirements, and strong oxidising power that may lead to uncontrolled surface attack. Such limitations have motivated the exploration of more sustainable and controllable electrolytes.

Deep eutectic solutions (DES) have emerged as promising candidates for more environmentally responsible metal finishing [11]. These liquids are formed by combining a hydrogen bond donor and a hydrogen bond acceptor, resulting in eutectic mixtures with reduced melting points, low volatility, low toxicity, and high electrochemical stability. DES-based electrolytes have been successfully employed for the electropolishing of metals such as nickel and stainless steels, producing smoother and more homogeneous surfaces while reducing environmental impact and extending electrolyte lifetime. Since Nitinol is composed of both nickel and titanium, for which DES-based electropolishing has already demonstrated effectiveness, applying DES electrolytes to NiTi is expected to combine the benefits observed for both constituents, enabling more uniform surface finishing, reduced surface defects, and improved corrosion stability, while avoiding the drawbacks of conventional acid-based electrolytes. For these reasons, the present work aims to assess the feasibility of electropolishing additively manufactured NiTi using the deep eutectic solvent ethaline, composed of choline chloride and ethylene glycol. Linear sweep voltammetry (LSV) was employed to identify suitable anodic potentials for controlled dissolution, followed by electropolishing experiments performed within the selected potential range. Surface morphology and topographical evolution were then evaluated using scanning electron microscopy, optical microscopy, and confocal microscopy.

Materials and Methods

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Additively manufactured nickel titanium samples were produced by laser powder bed fusion using pre-alloyed NiTi powder. The chemical composition of the alloy was selected to ensure nearequiatomic nickel-to-titanium ratio, in order to promote stable shape memory and superelastic behaviour. The fabricated specimens were sectioned to 10×0.5 mm cylinders, and mechanically polished with abrasive paper.
Electrochemical experiments were conducted using a conventional three-electrode cell configuration. The NiTi specimens acted as the working electrode, while a platinum electrode was employed as the counter electrode. An Ag/AgCl electrode containing 0.1 M KCl in acetonitrile was used as the reference electrode. The electrolyte consisted of the deep eutectic solution known as ethaline, prepared by mixing choline chloride and ethylene glycol at a molar ratio of 1:2. The mixture was heated under stirring until a homogeneous and transparent liquid was obtained.

Linear sweep voltammetry (LSV) was performed to investigate the anodic behaviour of the alloy and to identify potential regions associated with controlled material dissolution. Based on the voltammetric response, electropolishing experiments were carried out at selected anodic potentials within the diffusion-limited current regime. The treatment duration was fixed at 1 h for all conditions to allow a direct comparison of surface evolution as a function of the applied potential.

Surface morphology was examined by scanning electron microscopy. Optical surface inspection was performed using a Hirox digital microscope, while surface topography and roughness evolution were evaluated by confocal microscopy. Compositional analysis of the surface after treatment was carried out by energy dispersive spectroscopy to assess potential changes in alloy chemistry induced by the electrochemical process.

Results and Discussion

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In order to evaluate the electrochemical behaviour and, consequently, to assess the feasibility of an electrochemical material removal process, LSV tests were performed, as shown in Fig. 1.

[picture]

Fig. 1. LSV curve of additively manufactured NiTi in ethaline.

Fig. 1. LSV curve of additively manufactured NiTi in ethaline.

The potential range investigated spanned between -0.5 V and the potential of the AgCl 0.1 M KCl reference electrode in acetonitrile. It can be observed that, starting from 1.8 V , an initial increase in current occurs, culminating in a peak at around 2.1 V . Subsequently, the current exhibits a weakly increasing trend up to a second peak located at approximately 2.9 V . At higher potentials, around 5 V, a diffusion-limited current plateau is established. In this type of electrochemical measurement, the observed current peaks are generally associated with specific electrochemical reactions occurring at the electrode surface. One plausible interpretation is that these two peaks are associated with the dissolution of titanium, as reported in the literature [11]. The dissolution peak of nickel, expected around 0.5 V [12], is not observed, likely due to a surface enrichment in titanium on the NiTi alloy. The surface is covered by a native nanometric oxide layer, and between the two constituent metals, titanium is more readily oxidized, which dominates the electrochemical response.

Based on these results, the dissolution experiments were carried out at potentials where the diffusion-limited current regime is fully developed. Accordingly, the applied potentials were set to 6

V and 12.5 V. In future studies, supported by more detailed analyses, it will be possible to identify the specific chemical reactions corresponding to these peaks.

As shown in Fig. 2, at both selected dissolution potentials, a modification of the surface morphology is observed when compared to the initial sample treated only by abrasive paper polishing.

[picture]

Fig. 2. HIROX macrographs of additively manufactured NiTi surfaces: a) as-built condition; b) specimen after electrochemical dissolution at 6 V for 1 h ; c) specimen after electrochemical dissolution at 12.5 V for 1 h .

Fig. 2. HIROX macrographs of additively manufactured NiTi surfaces: a) as-built condition; b) specimen after electrochemical dissolution at 6 V for 1 h ; c) specimen after electrochemical dissolution at 12.5 V for 1 h .

In particular, a reduction in the visibility of surface scratches can be observed, while the porosity detected on the surface can be ascribed to pores originating from the additive manufacturing process, which were exposed as a result of material removal. The exposure of intrinsic additive manufacturing pores following electropolishing is a relevant outcome with potential implications for both mechanical and biological performance. From a fatigue perspective, surface-connected pores may act as stress concentrators and crack initiation sites, potentially reducing fatigue life compared to fully dense or sealed surfaces. However, compared to the as-built condition, electropolishing removes sharp surface asperities and partially melted particles, which are also known to promote early crack initiation. The net effect on fatigue performance is therefore expected to depend on the balance between pore exposure and surface smoothing. In terms of biocompatibility, surface-exposed porosity may have a dual role. While increased surface area and micro-scale porosity can promote cell adhesion and osseointegration in certain biomedical applications, open pores may also influence corrosion behaviour and nickel ion release. Consequently, the suitability of the electropolished surface state should be evaluated in relation to the specific functional requirements of the intended application.
A more detailed analysis by scanning electron microscopy (SEM), shown in Fig. 3a, indicates that after a treatment duration of 1 h at an applied potential of 6 V , a significant reduction in surface scratches is achieved, although they are not eliminated. By contrast, under the same treatment time, complete elimination of polishing-induced scratches and a markedly more uniform surface morphology are observed at an applied potential of 12.5 V .

[picture]

Fig. 3. a) SEM images of additively manufactured NiTi surfaces before and after electropolishing at 6 V and 12.5 V for 1 h ; b) EDX spectra of the electropolished NiTi surfaces.

Fig. 3. a) SEM images of additively manufactured NiTi surfaces before and after electropolishing at 6 V and 12.5 V for 1 h ; b) EDX spectra of the electropolished NiTi surfaces.

The dissolution process is homogeneous, and no dealloying phenomena are observed, as evidenced by the spectra in Fig. 3b, which indicate that the surface composition of the alloy remains unchanged after the treatment. It should also be noted that, when compared with electrochemical machining processes carried out in conventional inorganic electrolytes [9], the present treatment exhibits a markedly lower dissolution rate. From an industrial perspective, the lower dissolution rate observed in ethaline-based electropolishing may represent both a limitation and an opportunity. While longer processing times could reduce throughput compared to conventional inorganic electrolytes, the enhanced process controllability and reduced aggressiveness may be advantageous for components requiring precise dimensional tolerances.
With regard to the evolution of surface morphology, the results of the confocal microscopy analyses are shown in Fig. 4.

[picture]

Fig. 4. Confocal microscopy images of additively manufactured NiTi surfaces: a) as-built condition; b) after electrochemical dissolution at 6 V for 1 h ; c) after electrochemical dissolution at 12.5 V for 1 h .

Fig. 4. Confocal microscopy images of additively manufactured NiTi surfaces: a) as-built condition; b) after electrochemical dissolution at 6 V for 1 h ; c) after electrochemical dissolution at 12.5 V for 1 h .

After electrochemical treatment, the surface appears more homogeneous when compared to the as-built condition. For the dissolution performed at 6 V , pores associated with the additive manufacturing process become more evident than in the as-built sample. This behaviour can be attributed to the preferential erosion of surface asperities, since charge accumulation is enhanced at protruding features, leading to a more efficient removal of peaks. At this potential, a treatment duration of 1 h is insufficient for the dissolution process to progress towards a more pronounced surface homogenization.

As regards the surface roughness, similar values are measured before and after treatment. The asbuilt sample exhibits a Sa value of 2.88μ m, while values of 2.65μ m and 2.58μ m are measured after treatment at 6 V and 12.5 V , respectively. These results indicate that the treatment is particularly effective for scratch removal, while only a modest reduction in overall surface roughness is achieved.

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.175.pdf

In this study, the feasibility of electropolishing additively manufactured NiTi using a deep eutectic solution based on choline chloride and ethylene glycol was investigated. The effects of the applied potential on surface morphology and composition were systematically analysed. The main results of the work can be summarized as follows:

LSV highlighted distinct anodic features associated with the electrochemical reactions of the alloy constituents and enabled the identification of a diffusion limited current regime suitable for electropolishing.

Electropolishing in ethaline induced a clear modification of the surface morphology compared to the mechanically polished reference condition. Surface scratches originating from abrasive finishing were significantly reduced at 6 V and fully eliminated at 12.5 V after 1 h of treatment.

Porosity observed after treatment was attributed to intrinsic pores from the additive manufacturing process that became exposed as a result of material removal.

The dissolution process was homogeneous and no dealloying phenomena were detected, with surface composition remaining unchanged after treatment.

The dissolution rate was lower than that typically reported for electrochemical machining in conventional inorganic electrolytes, which has implications for process scalability and industrial application.

Confocal microscopy analysis confirmed enhanced surface homogeneity after electropolishing, while indicating that the treatment primarily promotes scratch removal rather than a significant reduction in areal surface roughness parameters.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/KEM.1047.175.pdf

This study was carried out within the MICS (Made in Italy - Circular and Sustainable) Extended Partnership and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) - MISSIONE 4 COMPONENTE 2.

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.175.pdf

K. Otsuka, C.M. Wayman, Shape memory materials, Cambridge university press, 1999.

T.W. Duerig, A.R. Pelton, Ti-Ni shape memory alloys, (n.d.).

K. Otsuka, X. Ren, Physical metallurgy of Ti-Ni-based shape memory alloys, Prog. Mater. Sci. 50 (2005) 511-678. .

T.C. Dzogbewu, D.J. de Beer, Additive manufacturing of NiTi shape memory alloy and its industrial applications, Heliyon 10 (2024) e23369. .

S. Mei, J. Wang, Z. Li, B. Ding, S. Li, X. Chen, W. Zhao, Y. Zhang, X. Zhang, Z. Cui, P. Fu, X. Pang, M. Liu, 4D printing of polyamide 1212 based shape memory thermoplastic polyamide elastomers by selective laser sintering, J. Manuf. Process. 92 (2023) 157-164. .

A. Jahadakbar, M. Nematollahi, K. Safaei, P. Bayati, G. Giri, H. Dabbaghi, D. Dean, M. Elahinia, Design, Modeling, Additive Manufacturing, and Polishing of Stiffness-Modulated Porous Nitinol Bone Fixation Plates Followed by Thermomechanical and Composition Analysis, Metals (Basel). 10 (2020) 151. .

S.L. Sing, J. An, W.Y. Yeong, F.E. Wiria, Laser and electron-beam powder-bed additive manufacturing of metallic implants: A review on processes, materials and designs, Journal of Orthopaedic Research 34 (2016) 369-385. .

U.S. Kim, J.W. Park, High-Quality Surface Finishing of Industrial Three-Dimensional Metal Additive Manufacturing Using Electrochemical Polishing, International Journal of Precision Engineering and Manufacturing-Green Technology 6 (2019) 11-21. .

G. Mani, D. Porter, K. Grove, S. Collins, A. Ornberg, R. Shulfer, Surface finishing of Nitinol for implantable medical devices: A review, J. Biomed. Mater. Res. B Appl. Biomater. 110 (2022) 2763-2778. .

A. Kityk, V. Pavlik, M. Hnatko, Green electropolishing using choline chloride-based deep eutectic solvents: A review, J. Mol. Liq. 392 (2023) 123519. .

A. Kityk, V. Protsenko, F. Danilov, V. Pavlik, M. Hnatko, J. Šoltýs, Enhancement of the surface characteristics of Ti-based biomedical alloy by electropolishing in environmentally friendly deep eutectic solvent (Ethaline), Colloids Surf. A Physicochem. Eng. Asp. 613 (2021) 126125. .

W.O. Karim, A.P. Abbott, S. Cihangir, K.S. Ryder, Electropolishing of nickel and cobalt in deep eutectic solvents, Transactions of the IMF 96 (2018) 200-205. .

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/additive-manufacturing/978-3-0364-1997-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 183-190

doi: 10.4028/p-iARZ2d

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-18

Accepted:
2026-02-11

Online:
2026-04-13

Effect of Deposition Strategy on Geometric Stability in Wire Arc Additive Manufacturing of Inconel 625

The original version of this paper is available on https://www.scientific.net/KEM.1047.183.pdf

Eleonora Viola 1,2,a*, Alessia Teresa Silvestri 3,b and Antonino Squillace 1,c

1 Department Of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, Piazzale Vincenzo Tecchio 80, 80125, Naples, Italy

2 Department of Management, Information and Production Engineering, University of Bergamo, Via A. Einstein 2, 24044, Dalmine, Italy

3 Department of Engineering and Sciences, Faculty of Technology and Innovation Sciences, Universitas Mercatorum, Piazza Mattei 10, 00186 Rome, Italy

a*eleonora.viola@unina.it, b alessiateresa.silvestri@unina.it, c squillac@unina.it

Keywords: wire arc additive manufacturing (WAAM), cold metal transfer (CMT), inconel 625, deposition strategy.

Abstract

Wire Arc Additive Manufacturing (WAAM) is a promising technology for producing large, high-performance metallic components, though process stability and geometric control remain critical challenges. The present work investigates the influence of deposition trajectory on the geometry, surface quality, and microhardness of wire arc additive manufactured Inconel 625 walls, produced by a Cold Metal Transfer (CMT) process. A conventional linear double-pass strategy is directly compared with a single-pass triangular weave trajectory under equivalent heat input per unit length, in order to isolate the effect of the torch path from other process variables. Single-layer and multi-layer walls were fabricated and characterized in terms of geometry, dimensional stability, surface waviness, and Vickers microhardness. The results show that the weave trajectory leads to improved geometric consistency, reduced variability, and significantly lower surface waviness compared to the linear strategy, while maintaining comparable mean wall width and microhardness. These findings demonstrate that appropriate trajectory design can enhance geometric stability and near-net-shape capability in WAAM-CMT without altering thermal input or material properties.

Introduction

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Additive Manufacturing (AM) has emerged as a revolutionary technology within the Industry 4.0 paradigm, representing a strategic departure from traditional subtractive manufacturing processes. AM offers significant advantages, including reduced material waste and shorter lead times [1]. These capabilities have established AM as a critical manufacturing option in high-value sectors such as the aerospace, marine, nuclear, biomedical, and petrochemical industries [2].
Metal AM processes can be categorized based on feedstock form into powder-based and wire-based methods [3]. Among wire-based techniques, Wire Arc Additive Manufacturing (WAAM) has garnered significant attention. WAAM uses an electric arc to melt wire feedstock and offers distinct advantages over powder-based methods, most notably a higher material deposition rate, resulting in shorter fabrication times and lower production costs [4]. In WAAM, the choice of heat source is critical, as it directly affects the deposition rate, thermal management, and the resulting metallurgical quality of the component [5,6]. Among the available options, Cold Metal Transfer (CMT), a specialized variant of the Gas Metal Arc Welding (GMAW), has emerged as a preferred approach due to its ability to provide significantly lower heat input, which is achieved through the digital coordination of the wire feeding movement and droplet transfer [7,8].
These characteristics make WAAM a highly promising technology for the production of large, highperformance metallic components, particularly when using strategically valuable and costly materials. Inconel 625, a premier nickel-based superalloy, is often chosen for such applications. It combines excellent tensile strength at elevated temperatures, superior resistance to corrosion and
oxidation, and outstanding fatigue performance, making it indispensable for components such as gas turbine cores, subsea controllers, and chemical plant hardware [3,9].
Despite these advantages, Inconel 625 presents specific challenges in WAAM, as the final properties of the manufactured component depend not only on the material itself but critically on the process parameters [10]. Different deposition strategies generate spatially varying thermal histories, which in turn produce microstructural features that directly influence mechanical performance [4,11]. Previous studies on deposition strategies have focused on geometrical accuracy, microstructural evolution, and final mechanical performance [11,12,13]. However, many of these works vary multiple parameters simultaneously, making it difficult to isolate the specific influence of the torch path.
In this study, the effect of deposition trajectory in WAAM-CMT of Inconel 625 is systematically investigated. A conventional linear double-pass strategy is directly compared with a single-pass weave strategy under equivalent heat input, ensuring that observed differences in wall geometry and surface quality can be primarily attributed to the deposition trajectory. This approach provides a clear link between trajectory design, layer stability, and final part quality, and demonstrates how trajectory control enhances process repeatability and enables a more robust understanding of geometry evolution in WAAM-CMT processes.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/KEM.1047.183.pdf

Experiments were carried out using the WAAM system shown in Fig. 1, which consists of a Fronius TransPuls Synergic CMT power source, a Delta robot integrated with the welding torch, a wire feeding system, and a gas shielding system. Inconel 625 (ERNiCrMo-3) wire with a diameter of 1.0 mm was used as the filler metal, while mild steel plates with dimensions of 250 mm×30 mm×20 mm served as the substrate. The average chemical composition of the wire is shown in Table 1. A constant flow of 16 L/min of shielding gas ( 98% Argon, 2%CO2 ) was used to prevent oxidation of the molten pool. The contact tip to work distance (CTWD) was maintained at 15 mm for all experiments.

[picture]

Fig. 1. WAAM setup used for deposition.

Fig. 1. WAAM setup used for deposition.

Table 1. Chemical composition of Inconel 625 wire ( wt% ).

Cr | Mo | Nb | Fe | Co | Si | Mn | Ti | Al | C | P | S | Ni
20.0 – 23.0 | 8.0 – 10.0 | 3.15 – 4.15 | ≤5.0 | ≤1.0 | ≤0.50 | ≤0.50 | ≤0.40 | ≤0.40 | ≤0.10 | ≤0.015 | ≤0.015 | Bal.

For the linear strategy, two parallel beads were deposited side by side with a step-over distance of 4.0 mm , which is defined as the distance between the centerlines of adjacent beads. Within each layer (intralayer strategy), deposition was unidirectional, meaning that the adjacent beads were deposited
with the same start and end points, as illustrated in Fig. 2a. Walls produced with the weaving strategy followed a triangular weave path, illustrated in Fig. 2b along with its defining parameters. Between successive layers (interlayer strategy), a bidirectional deposition sequence was adopted for both the linear and weaving strategies, alternating the start and end points of each layer to reduce the accumulation of excess material at arc start positions. All walls were 100 mm in length and were constructed by stacking five layers, incorporating a dwell period between layers to ensure adequate heat dissipation.
A wire feed speed (WFS) of 5.5 m/min was used in all experiments, which, through the built-in synergic control of the power source, corresponded to a welding current of 140 A and an arc voltage of 13.3 V .
For the linear case, the travel speed (TS) was 5 mm/s. The corresponding heat input was calculated using Eq. 1 and resulted in a value of 298 J/mm.

HI=ηVITS(1)

where

η= thermal efficiency (set to 0.8)[15]V=arc voltage [V]I= welding current [A]TS= travel speed along the deposition direction [mm/s]

To provide the same total heat input per unit length of deposited wall for both strategies, the single oscillating track was deposited with a heat input of 596 J/mm, corresponding to the cumulative energy of the two linear passes. Accordingly, the traverse speed, TSx, which is one of the two components of the weave motion and represents the longitudinal advancement of the torch along the wall, was adjusted to ensure this heat input and was therefore set to 2.5 mm/s, i.e., half of the 5 mm/s travel speed used in the linear case. Consequently, the actual travel speed along the triangular trajectory, TSw, was calculated using the relationship given in Eq. 2 [14] and resulted in 14.2 mm/s :

TSw=TSx1+(4Aλ)2(2)

where

TSx= weave traverse travel speed [mm/s]A= weave path amplitude [mm]λ= weave path wavelength [mm]

[picture]

Fig. 2. Schematic representation of the deposition trajectories: (a) linear path and (b) triangular weave path used for material deposition.

Fig. 2. Schematic representation of the deposition trajectories: (a) linear path and (b) triangular weave path used for material deposition.

As reported in the literature [13], a weaving frequency of f=1 Hz was found to maintain a roughly constant penetration depth along the deposition direction and a more uniform thermal field. This value was therefore adopted in the present work. The wavelength of the weave, λ, was determined as the ratio of the longitudinal traverse speed, TSx, to the weaving frequency, as depicted in Eq. 3, and was set at 2.5 mm :

λ=TSxf(3)

The weave amplitude, A=3.5 mm, was selected to replicate the effective width of two overlapping linear beads.
Table 2 summarizes the process parameters used for both the linear and weaving cases.

Table 2. Process parameters used for linear and weaving deposition strategies.

Deposition Strategy | WFS [m/min] | TS [mm/s] | Current [A] | Voltage [V] | CTWD [mm] | Wave parameters
|  |  |  |  |  | Amplitude [mm] | Wavelength [mm]
Wave | 5.5 | 14.2 | 140 | 13.3 | 15 | 3.5 | 2.5
Linear | 5.5 | 5.0 | 140 | 13.3 | 15 |  |

In a preliminary stage, single-layer depositions were produced and analyzed in terms of width and height in order to isolate the effect of the deposition trajectory on geometry and process stability, while minimizing the influence of layer stacking effects. Single-layer and multi-layer wall width and height were measured using a digital microscope at multiple locations along the deposition length to evaluate local geometric variations; the reported values represent the mean and standard deviation. Surface waviness of the multi-layer walls was evaluated using a confocal optical microscope (Leica DCM3D). Multiple surface profiles were acquired at different positions from both lateral surfaces of each wall. The waviness parameters analyzed were the arithmetic mean waviness deviation, Wa, defined as the average absolute deviation from the mean line, and the total waviness height, Wt, defined as the vertical distance between the highest peak and the deepest valley within the evaluation length.
Vickers microhardness measurements were performed on cross-sections of the wall samples using a CV Instruments 2000 tester. The specimens were metallographically prepared according to the ASTM E3-11 standard, following sequential steps of cutting, mounting in resin, and grinding and polishing. Tests were carried out with a 500 g load, a dwell time of 15 s , and indentations spaced 1 mm apart along the wall height; the reported values correspond to the mean and standard deviation.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1047.183.pdf

Fig. 3 presents the as-deposited walls examined in this study: (a) weave deposition and (b) linear deposition.

[picture]

Fig. 3. Top view of the multi-layer walls produced with the two deposition strategies: (a) weave trajectory and (b) linear double-pass.

Fig. 3. Top view of the multi-layer walls produced with the two deposition strategies: (a) weave trajectory and (b) linear double-pass.

The comparison between single-layer depositions highlights distinct differences in both geometric dimensions and deposition stability, as summarized in Fig. 4. The weave single-pass strategy results in a slightly larger mean bead width (8.81±0.07 mm) compared to the linear double-pass deposition (8.33±0.18 mm), accompanied by a reduced mean height (4.40±0.08 mm vs. 4.86±0.20 mm). This behavior suggests a lateral redistribution of the molten material induced by the oscillatory motion of
the weave trajectory, which promotes spreading in the transverse direction at the expense of vertical buildup. More importantly, the weave strategy significantly reduces geometric variability. Standard deviations in width and height decrease by approximately 61% and 60%, respectively, compared to the linear double-pass case. This indicates a more stable and reproducible deposition process, with fewer local inconsistencies caused by the interaction between adjacent tracks and their thermal overlap.

[picture]

Fig. 4. Comparison of single-layer geometry for the two deposition strategies: mean width and height with corresponding standard deviations.

Fig. 4. Comparison of single-layer geometry for the two deposition strategies: mean width and height with corresponding standard deviations.

These trends are further reflected and amplified at the multi-layer scale, as illustrated in Fig. 5a. The weave-deposited wall exhibits a lower mean height (13.7±0.1 mm) compared to the linear wall ( 15.7 ±0.3 mm ), bringing the final geometry closer to the nominal CAD target of 12 mm , while maintaining a comparable mean wall width ( 12.8±0.3 mm vs. 12.6±0.2 mm ). The reduced height overshoot and smaller standard deviation indicate improved layer stacking consistency when using the weave strategy.

[picture]

Fig. 5. Comparison of multi-layer walls geometry for the two deposition strategies: (a) mean width and height with standard deviations, and (b) mean waviness parameters ( Wa and Wt ) with standard deviations.

Fig. 5. Comparison of multi-layer walls geometry for the two deposition strategies: (a) mean width and height with standard deviations, and (b) mean waviness parameters ( W a and W t ) with standard deviations.

Surface quality is also markedly improved. As shown in Fig. 5b, both the average ( Wa ) and total ( Wt ) wall waviness are lower for the weave wall ( Wa=0.234±0.068 mm;Wt=1.052±0.298 mm ) than for the linear wall (Wa=0.289±0.108 mm;Wt=1.559±0.370 mm). Notably, the reduction in Wt(~33%) is more pronounced than that in Wa(~19%), indicating that the weave trajectory is particularly effective at suppressing extreme local deviations, which are often associated with transient process
instabilities or irregular layer stacking. The decrease in Wt is particularly significant, as it reduces the amount of material that must be removed during subsequent machining operations to achieve the desired final geometry. This highlights the practical advantage of the weave deposition strategy in producing near-net-shape parts and minimizing material waste.
Vickers microhardness measurements revealed subtle differences between the linear and weave walls (Fig. 6). The linear wall exhibited a mean microhardness of 235±11HV, while the weave-deposited wall showed a slightly lower mean value of 228±3HV. These hardness values are consistent with those reported in the literature for standard WAAM-CMT processes [10,16].

[picture]

Fig. 6. Comparison of Vickers microhardness of multi-layer walls for the two deposition strategies.

Fig. 6. Comparison of Vickers microhardness of multi-layer walls for the two deposition strategies.

The similarity in mean microhardness values suggests that the mechanical properties are primarily governed by the total heat input during deposition. Thus, under equivalent thermal conditions, the intrinsic microstructure of the material remains largely unaffected by the torch path. In contrast, the observed decrease in variability indicates that the deposition trajectory mainly affects the homogeneity of the material, mitigating local variability induced by thermal fluctuations and overlapping passes.

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.183.pdf

This study investigated the influence of deposition trajectory on the geometry, surface quality, and microhardness of multi-layer Inconel 625 walls produced by WAAM-CMT, comparing linear doublepass and triangular weave strategies under equivalent total heat input per unit length.
Walls produced with the weave trajectory exhibited lower height overshoot, comparable width, and significantly reduced surface waviness ( Wa and Wt ), indicating improved layer stacking consistency and more predictable wall geometry. Vickers microhardness measurements showed similar mean values for both strategies, confirming that mechanical properties are primarily governed by the total heat input, while the weave trajectory enhances material homogeneity by reducing local variability. These findings demonstrate the importance of torch path design in optimizing WAAM-CMT process stability and part quality. Future work will focus on optimizing weave trajectories as a means to increase the deposition rate, thereby reducing production time while maintaining dimensional accuracy and surface quality. Additional investigations could also consider larger or more complex geometries and examine the relationship between trajectory design, microstructural features, and overall performance.

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The original version of this paper is available on https://www.scientific.net/KEM.1047.183.pdf

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 191-204

doi: 10.4028/p-Jv9YEw

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-19

Accepted:
2026-02-11

Online:
2026-04-13

Volumetric Anomaly Detection in LPBF by Segmentation and Classification of Exposure Optical Tomography (EOT) Image Stacks

The original version of this paper is available on https://www.scientific.net/KEM.1047.191.pdf

Karsten Scheibe 1,a*, Johann Albers 1, b, Felix Jensch 1,c, Chukwuemeka Okolo 1, d, and Sebastian Härtel 1,e

Department of Hybrid Manufacturing 1, Brandenburg University of Technology Cottbus-Senftenberg, Konrad-Wachsmann-Allee 17, 03046 Cottbus, Germany

a  Karsten.Scheibe@b-tu.de, b  Johann.Albers@b-tu.de, c  Felix.Jensch@b-tu.de, d  okolo@b-tu.de, e  Sebastian.Haertel@b-tu.de

Keywords: LPBF, Exposure OT, Image Segmentation, Anomaly Detection, Machine Learning

Abstract

In Laser Powder Bed Fusion (LPBF), ensuring reproducible part quality remains a major challenge despite the availability of high-resolution in-situ monitoring systems, such as Exposure Optical Tomography (EOT). While EOT provides detailed layer-wise optical information, most existing approaches focus on single-layer analysis or real-time process control and do not exploit the full volumetric information contained in the acquired data.

This work presents a modular framework for the volumetric reconstruction and post-process analysis of EOT image data. Sequential EOT images are processed using volumetric component segmentation (VCS) and fused into a three-dimensional OT Image Cube, forming a central volumetric data structure called LPBF Cube. Each voxel encodes spatial and radiometric information and can optionally be augmented with additional process metadata.

Based on this representation, high-resolution two-dimensional slices are rendered along arbitrary orientations using profile-based slicing strategies for planar, cylindrical, and complex geometries. These slices enable intuitive, part-level inspection of laser exposure history and spatial process variations. The framework is validated using geometric and radiometric analyzes, demonstrating good agreement with nominal CAD geometry and a clear correlation between EOT-derived emission values, laser energy input, and local cross-sectional area.

The proposed approach extends the use of EOT data beyond layer-wise monitoring toward comprehensive, volumetric part inspection and provides a practical basis for geometry-aware quality assessment in LPBF, particularly for prototyping and post-build evaluation.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.191.pdf

Laser Powder Bed Fusion (LPBF) is one of the most mature and widely adopted technologies in metal Additive Manufacturing. Despite its industrial relevance, ensuring reproducible part quality remains a significant challenge. Process-induced variations-such as fluctuations in melt pool dynamics, laser-powder interactions, or insufficient fusion may lead to defects including porosity, lack of fusion (LoF), and local overheating zones [1]. To mitigate such deviations, the development of robust in-situ monitoring technologies has become increasingly important.

Exposure Optical Tomography (EOT) represents a promising sensing modality that captures highresolution transmission or emission maps for each processed layer during LPBF [2]. These images encode information about the optical behavior of the powder bed and the solidification process; local intensity variations often correlate with process anomalies and may reveal indicators of melt pool instability, powder distribution irregularities, or localized defects [3]. Consequently, numerous studies investigate signature-based EOT or OT to analyze individual layers or to support machine-learning-based defect detection [4-7]. Moreover, several studies have reported a strong correlation between EOT-derived features and defect indications obtained from X-ray micro-computed tomography ( μCT ), demonstrating the suitability of EOT data for defect assessment, for example, in [8].

However, the vast majority of existing research focuses on layer-wise monitoring aimed primarily at real-time process control, anomaly detection, or parameter optimization. A comprehensive perspective on the entire manufactured part-across all layers and the full build volume-has received much less attention. However, for prototyping, post-build evaluation, and the identification of design- or manufacturing induced weak points in critical regions that may result in LoF or keyhole formation, which are commonly identified as pores, a holistic part-level interpretation is crucial. In many cases, only minor adjustments to the part design or the build planning, particularly the scan or laser path strategy, are sufficient to achieve improved process stability and part quality while maintaining identical functional performance.

In contrast to conventional layer-centric approaches, this work introduces a volumetric methodology in which EOT images are not analyzed in isolation but fused into a unified three-dimensional data representation. Starting from the complete stack of EOT images, a 3D OT Image Cube is reconstructed, from which 3D Data (i.e., point clouds or meshed vertices) in standard industrial formats (e.g., STL or PLY) are derived. This geometric representation forms the basis for subsequent segmentation and classification tasks, enabling structural inspection of the printed component.

Furthermore, the volumetric representation allows the generation of arbitrary high-resolution 2D cross-sectional slices extracted from the 3D OT Cube. These renderings facilitate detailed analysis of the laser exposure history and the spatial distribution of potential process anomalies across multiple adjacent layers. As a result, the proposed approach provides a powerful tool for part-level quality assessment-particularly valuable in prototyping and design validation contexts.

Thesis Statement

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The volumetric reconstruction of a 3D OT Image Cube from sequential EOT layer images enables the generation of arbitrary high-resolution 2D cross-sections, significantly facilitating the identification of design-induced weak points as well as process-related anomalies throughout the entire part. This capability goes well beyond the limitations of purely layer-based in-situ monitoring approaches. By integrating all EOT layers into a unified three-dimensional voxel matrix, a central volumetric data struc-ture-the LPBF Cube-is formed. Each voxel stores not only its spatial coordinates and local emission value but may optionally contain additional process metadata such as timestamps, scan strategy, laser power, or acoustic process signatures. This enriched volume model establishes the foundation for a holistic, multi-perspective analysis of the manufactured component.

This article underscores the following key contributions:

Reconstruction of a 3D Model and development of various tools for comprehensive analysis of OT Image Data, including derivation of STL/PLY data and volumetric data structures for downstream evaluation.

High-Resolution 2D Image Rendering from the 3D OT Image Cube, enabling flexible visualization and targeted analysis of laser exposure history and spatial anomalies.

Methodology for Processing EOT Data

The original version of this paper is available on https://www.scientific.net/KEM.1047.191.pdf

The proposed framework comprises three main components:

data acquisition and preprocessing, including initial volumetric component segmentation;

volumetric reconstruction and 3D model generation; and

segmentation, classification, and analytical evaluation.

The overall architecture is designed to support both geometric reconstruction of the part and processoriented quality assessment.

[picture]

Fig. 1: Complementary views of the input dataset: a representative photograph of the finished part (left), the corresponding CAD model defining the intended geometry (center), and a typical EOT slice acquired during the LPBF process (right).

Fig. 1. Complementary views of the input dataset: a representative photograph of the finished part (left), the corresponding CAD model defining the intended geometry (center), and a typical EOT slice acquired during the LPBF process (right).

Acquisition and Pre-Processing of EOT Data

The original version of this paper is available on https://www.scientific.net/KEM.1047.191.pdf

The components were manufactured on an EOS M400-4 L-PBF system, which has a build volume of 400×400×400 mm3. It is equipped with four 400 W Yb fiber lasers, each operating at a wavelength of 1070 nm . Each laser covers a working area of 250×250 mm2 on the substrate plate, resulting in overlapping regions between the individual quadrants. All components were produced using identical parameters: a laser power of 370 W , an exposure speed of 1260 mm/s, a hatching distance of 0.18 mm , and a layer thickness of 80μ m. Nitrogen was used as a protective gas during fabrication, and the powder material was applied using a polymer lip. AlSil0Mg powder served as the base material for all manufactured components.

The component geometries were chosen to intentionally induce local overheating during production, in order to demonstrate the full potential of the method presented in this paper. The four components were fabricated once in each of the four laser working areas, with only one laser used per quadrant and slightly varied scan strategies and laser power between the quadrants. Additional parameter variations were investigated in separate builds for further studies but these results are outside the scope of the present work.

The L-PBF system is equipped with an exposure optical tomography (EOT) system, in which an sCMOS camera captures images in the near-infrared wavelength range across the entire build area. These images record radiation emissions originating from the laser-material interaction.

The EOT system provides two acquisition modes: a maximum gray-value mode and an integral gray-value mode. In this work, the integral gray-value mode is used, which accumulates the emitted radiation over the exposure time of each layer. While the recorded signal does not represent temperature directly, the temporal integration inherently captures thermal effects such as heat accumulation and heat flow within the material. Accordingly, the EOT signal reflects the effective energy input rather than instantaneous peak emissions.

Nevertheless, layer-integrated EOT signals have been shown to correlate strongly with the applied laser energy, local heat accumulation, and melt pool stability, making them highly suitable for component-level and process-consistent radiometric analyses. Accordingly, the EOT signal is interpreted in this work as a relative, process-dependent indicator of the effective thermal energy state rather than as an absolute physical quantity such as temperature or total energy.

Throughout the paper, and in particular in the radiometric validation section, a clear distinction is made between laser power applied per unit area and the effectively integrated energy input per layer, enabling a consistent interpretation of radiometric and process-related quantities.

The foundation of this work is a multi-modal dataset that combines conventional post-build inspection data, the corresponding CAD representation of the build job, and layer-wise EOT images acquired during fabrication. Figure 1 illustrates these three complementary perspectives: the left im-

[picture]

Fig. 2: Integration of the SAM2 architecture into the proposed framework for prompt-based volumetric component segmentation of memory-driven sequential EOT data in LPBF.

Fig. 2. Integration of the SAM2 architecture into the proposed framework for prompt-based volumetric component segmentation of memory-driven sequential EOT data in LPBF.

age shows a representative photograph of the completed part obtained after removal of the construction site and surface inspection; the center image depicts the underlying CAD model that defines the intended geometry of the component; and the right image provides a typical EOT slice captured during the LPBF process, representing the spatial distribution of optically emitted radiation for a single processed layer. For each manufactured layer, a corresponding EOT image is recorded, typically resulting in several thousand images per build (commonly between 1,000 and 5,000 layers per part). Before volumetric reconstruction, raw images undergo a standardized preprocessing pipeline that includes grayvalue normalization, noise reduction, and geometric alignment to ensure spatial consistency across the stack. Additionally, the layer index is synchronized with the build-height information, enabling an unambiguous mapping of each image to its physical position within the final volumetric model.

Image Segmentation and Classification

The original version of this paper is available on https://www.scientific.net/KEM.1047.191.pdf

Segment Anything Model 2 (SAM2) is the second generation of Meta's Segment Anything framework, designed to provide robust, promptable segmentation across a wide variety of visual domains. SAM2 introduces significant architectural and performance improvements over the original SAM, including a more efficient image encoder, a persistent memory mechanism for handling video or sequential imagery, and stronger generalization capabilities, particularly for non-natural or highly technical image data. As a prompt-driven model, SAM2 can generate high-quality object masks based on points, boxes, or coarse masks, making it well-suited for segmenting complex LPBF and EOT imagery where clear object boundaries are difficult to obtain with classical image-processing pipelines. Its ability to handle challenging illumination conditions, texture-poor regions, and irregular geometries makes SAM2 an effective tool for segmentation of volumetric components in additive manufacturing applications.

Figure 2 illustrates the SAM2 architecture and highlights its integration into the proposed framework, showing how prompt-based interaction and sequential EOT data are combined to perform volumetric component segmentation in the context of LPBF process monitoring [9].
Volumetric Component Segmentation (VCS). A central challenge in processing EOT data is the reliable isolation of the additively manufactured component from background structures such as the powder bed, platform boundaries, optical artifacts, or process noise. Traditional image-processing tech-niques-such as edge detection, threshold-based binarization, morphological operations (e.g., erosion or closing), or region-growing methods-have been applied in earlier studies to extract component contours. However, these approaches often suffer from sensitivity to local contrast variations, powder accumulation, spatter-induced artifacts, and layer-to-layer fluctuations, which can lead to fragmented or incomplete segmentation masks. To robustly address these limitations, the present work employs Volumetric Component Segmentation (VCS) based on modern deep-learning-driven foundation models such as SAM2. This model enables highly stable, geometry-aware separation of the component from its surroundings, even in the presence of significant noise or non-uniform illumination condi-

[picture]

Fig. 3: Representative EOT slices of the curved wall example from Fig. 1, illustrating typical segmentation challenges.

Fig. 3. Representative EOT slices of the curved wall example from Fig. 1, illustrating typical segmentation challenges.

tions typical for EOT imagery. SAM2 provides consistent per-slice masks that accurately delineate the part boundaries, thereby enabling the generation of a clean, binary segmentation volume.

Figure 3 illustrates these challenges by comparing classical image-processing results with representative examples of EOT slices. From top to bottom, the figure presents three characteristic cases. In each row, the left column shows the raw EOT input, the center column displays the corresponding mask obtained using classical image-processing techniques, and the right column shows the segmentation generated by SAM2, which yields robust and high-quality results across all scenarios.

The top row represents a comparatively clean and simple example, where both classical processing and SAM2 perform well. However, the classical pipeline exhibits slight instability due to pronounced patterning effects, whereas SAM2 can be explicitly prompted to recognize and classify these patterns as distinct regions if required (green patches detected by SAM2; two are disabled for improved visual clarity). It is important to note that such patterns should not be interpreted as defects as long as they originate from optical emission effects rather than from actual process irregularities. These patterning effects are primarily caused by process-related optical phenomena. For example, process emissions such as smoke or plume dynamics can disturb the optical signal, and incident laser power may be partially absorbed and re-emitted at different wavelengths (e.g., Raman scattering). To mitigate these influences (which cannot be entirely eliminated), a narrow near-infrared bandpass filter (center wavelength 900 nm , bandwidth 25 nm ) is used in the EOT system, effectively suppressing plasma emissions and back-reflected laser light at 1060 nm . The observed patterns typically consist of two stripe-like patterns corresponding to antiparallel laser scan directions ( 180∘ apart).

The middle row highlights typical challenges already described above-most notably acquisition artifacts (e.g., spatter-induced particle) and the "corona" around the edges of the part (e.g., very small particles and condensate), where sharp boundary separation is often difficult for classical methods. Due to the high energy density, the melt pool reaches the evaporation point of certain alloying elements and as a consequence, the vaporized metal is ruptured out of the melt pool. Vaporized metal is cooled down very quickly and condensates, forming particles with very small diameters.

The bottom row shows the discussed odd-even pattern, for which simple threshold-based approaches may fail, while SAM2 still yields an accurate segmentation.

The resulting component mask is subsequently used for voxel-space reconstruction, where either the entire segmented region or only its convex hull can be considered, depending on the desired level of geometric fidelity. This mask defines the volumetric envelope of the reconstructed LPBF Cube and ensures that downstream analyzes operate exclusively within the relevant part volume.
Optional Segmentation and Process-State Classification. Beyond component-level segmentation, additional segmentation and classification of internal regions may be performed as an extended step in the analysis pipeline. Modern methods such as SAM2-based region segmentation, U-Net architec-
tures, self-organizing maps (SOM)[6], or clustering-based techniques can be applied to partition the EOT data into semantically meaningful subregions. Each segmented region can then be assigned to a predefined process class, for example: Normal Consolidation, LoF, Overheating, or other conditionspecific categories reported in prior work. When applied voxel-wise across the entire 3D OT Image Cube, this classification produces a fully annotated volumetric representation of the component, capturing both geometric and process-state information.

This optional phase enables a richer interpretation of spatial defect patterns and their relationship to part geometry, but is conceptually distinct from the primary VCS step, which focuses solely on isolating the component volume. In addition, this extended segmentation stage is particularly beneficial for arbitrary 2D slice rendering, where class-aware slice extraction provides clearer, context-sensitive visualization of internal structures and defect regions.

Construction of a 3D OT Image Cube

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In previous work, the construction and inspection of volumetric optical-tomography data has often relied on generic visualization tools such as the ImageJ 3D Viewer plugin. While these tools provide a convenient means for rendering stacked EOT slices, they quickly reach their limits even for small to medium-sized parts due to out-of-memory constraints. Moreover, such approaches typically operate on the entire EOT acquisition without explicitly separating the part from the surrounding measurement domain. As a result, the generated volume represents a global EOT cube rather than a part-specific reconstruction, causing relevant structural details to be obscured by non-informative data and remaining identifiable-if at all-only through manual slicing. Although sufficient for basic visualization, such volumes lack semantic or structural linkage between rendered pixels and therefore do not represent a coherent, component-aware data structure.

By contrast, once individual components-or even sub-components-are separated using the described machine-learning-based segmentation methods, additional volumetric processing steps become straightforward to implement. In particular, volumetric region growth algorithms can be applied directly within the segmented voxel space, enabling the extraction of connected regions and the quantitative evaluation of geometric properties such as shape, size, and volume. These metrics can subsequently be compared against nominal CAD data to assess deviations in geometry and structural integrity and to derive indicators of manufacturing effects such as distortion or warping.

Figure 4 is organized into two rows and three columns. The top-left panel schematically illustrates the generation of component masks from a stack of approximately 2,000 sequential EOT images using volumetric component segmentation. In the center column, example segmentation results are shown for two representative layers: Layer 0 (top) and Layer 1500 (bottom). Here, individual components are visualized as color-coded classes, with the curved wall shown in red, the cylindrical lattice structure in green, the benchmark tree element in blue, and a sloped structure in yellow. The dotted profiles shown in Layer 1500 are not required for the three-dimensional reconstruction but are included here for illustration purposes and will be explained in detail in the context of high-resolution 2D slice rendering.

The top-right panel presents an aggregated view of the four individual components, displayed according to their segmentation-class masks. The bottom-left panel highlights the lattice cylinder as an example of a single sub-component. In the bottom-right panel, the same four components are shown, but the color corresponds to the original EOT emission values rather than the class-based segmentation.

In the bottom-left and bottom-right panels, the components are rendered using different color schemes to emphasize distinct aspects of the EOT data. For the lattice cylinder shown in the bottomleft panel, the rendering represents the integrated emission values of the original EOT images (without applying a jet color-map), providing a direct visualization of the raw layer-wise signal distribution.

Both panels exhibit slightly bluish shading along the boundary of the components. As shown in the zoomed view of the top-left panel (red arrows), these regions correspond to low-emission con-

[picture]

Fig. 4: Illustration of volumetric component segmentation and reconstruction from EOT image stacks, showing class-based masks at different layers and component-specific volumetric representations.

Fig. 4. Illustration of volumetric component segmentation and reconstruction from EOT image stacks, showing class-based masks at different layers and component-specific volumetric representations.

tour scans, which are intentionally exposed with reduced energy and therefore appear radiometrically "cold". These effects must not be confused with process-induced LoF defects. In EOT data, however, both phenomena may exhibit very similar low-intensity signatures. In addition, previously described corona-related artifacts may occur in the vicinity of component edges; these arise from optical effects such as scattering, condensate, or stripe-border effects and represent a further independent source of low-signal regions.

To avoid false-positive LoF detection at the component boundaries, the VCS-derived segmentation mask is first eroded by a small number of pixels. This purely geometric preprocessing step removes the contour region from the subsequent analysis. Only after this erosion step is a radiometric threshold applied to identify low-emission regions within the remaining interior of the component. This sequential combination of geometric mask erosion and radiometric thresholding ensures that intentionally low-energy contour exposures and corona-related edge artifacts are excluded, while potential LoF-related low-emission regions inside the bulk material are preserved. As a result, LoF detection becomes more robust and less sensitive to boundary-related artifacts, without sacrificing sensitivity to process-induced defects in the component interior.

Building on the volumetric reconstruction and VCS-based component segmentation, the following example demonstrates how radiometric information can be exploited for intuitive part-level analysis. Figure 5 illustrates the potential to identify overheated regions within the volume of reconstructed components. For narrow-band EOT images, such regions can often be highlighted without the need for elaborate algorithms. The three views on the left show a benchmark lattice cylinder rendered as a VCS-derived point cloud, where the base geometry is displayed in medium gray and regions exceeding specified emission thresholds are emphasized. In the right lattice cylinder view, the remaining gray points therefore represent regions well below the mean energy level. Regions far below the mean could alternatively be highlighted using a contrasting color (e.g., blue), which is not shown here.

The rightmost panel of Fig. 5 demonstrates the same concept applied to a different component geometry. In contrast to the lattice cylinder shown in the left panels, this example represents a fully solid component, whereas the lattice cylinder consists primarily of a thin, hollow lattice ring. In this case, the VCS-derived segmentation masks are exploited not only for radiometric thresholding but also for efficient geometric processing.

[picture]

Fig. 5: VCS-based renderings with additional thresholding of EOT emission relative to its mean. Left: three point-cloud views of a lattice cylinder. Right: an alternative transparent surface rendering (AR), derived from VCS masks using erosion and convex hull extraction of the benchmark tree.

Fig. 5. VCS-based renderings with additional thresholding of EOT emission relative to its mean. Left: three point-cloud views of a lattice cylinder. Right: an alternative transparent surface rendering (AR), derived from VCS masks using erosion and convex hull extraction of the benchmark tree.

The component boundary is determined through mask erosion, after which a convex hull is computed directly on the segmented volume. Compared to operating in the full point cloud, this approach significantly simplifies hull generation and subsequent meshing. The resulting surface mesh is rendered using a transparent, glass-like material, while regions exceeding a defined emission threshold are overlaid as red points within the enclosed volume. This combined visualization allows simultaneous inspection of the internal high-energy regions and the geometry of the enclosed component.

Together, these examples demonstrate that even simple threshold-based analyzes applied to segmented volumetric data can reveal meaningful spatial patterns of elevated energy input. When combined with mask-based geometric processing, the approach enables intuitive and efficient visualization of both radiometric and structural information, complementing the segmentation- and cube-based volumetric analyzes described above.

Finally, this volumetric representation enables a wide range of downstream analyzes that go beyond purely visual inspection. In addition to geometric validation and qualitative evaluation, the reconstructed volume provides a consistent basis for the quantitative evaluation of structural features, defect-prone regions, and process-dependent variations within the component. Because volumetric data are derived directly from the layer-resolved EOT acquisition and are constrained by the VCS segmentation, all subsequent analyzes remain inherently component-aware and spatially consistent across the entire build volume. The reconstructed model can be exported in standard industrial formats such as STL or PLY, enabling direct interoperability with established analysis and inspection tools, including MeshLab, CloudCompare, and numerical simulation environments. This allows, for example, comparison with nominal CAD geometries, mesh-based deviation analysis, or integration of the reconstructed component into downstream simulation workflows. At the voxel level, the LPBF Cube acts as a unified container that combines geometric, radiometric, and process-related information. Each voxel in the reconstructed volume stores at least the following information:

spatial coordinates (x,y,z),

EOT emission values derived from the optical tomography images, and

optionally, additional process metadata such as timestamp, scan strategy, laser power, or acoustic signals.

By aggregating this information into a single volumetric data structure, the resulting 3D representation forms the central volumetric LPBF Cube. It provides a flexible and extensible foundation for component-level inspection, multi-modal data fusion, and future process-aware analysis approaches in LPBF.

High-Resolution 2D Slice Rendering

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While the construction of the 3D OT Image Cube primarily enables volumetric visualization and component-level inspection, many process-related phenomena and defect patterns remain difficult to interpret in purely three-dimensional renderings. For detailed analysis, engineers and process experts, therefore, rely on two-dimensional cross-sectional views that provide direct insight into internal structures and local variations of energy input. Building upon the volumetric representation introduced in the previous section, high-resolution 2D slices are generated by performing virtual cuts through the volume along arbitrary orientations, including standard Cartesian planes ( xy,yz,xz ) as well as freely defined slicing directions. These slices enable detailed analysis of laser energy distributions, facilitate the identification of vertically extended defect structures, and provide intuitive cross-sectional representations suitable for engineering assessment. All slicing operations described in the following are performed on the VCS-derived masks (cf. dotted lines in Fig. 4), ensuring that all extracted views are strictly constrained to the segmented component geometry. However, the rendering itself is performed directly on the original EOT images rather than on the reconstructed point clouds, which may suffer from resolution loss due to point reduction or meshing operations.
Profile-Based Slicing for simple component geometries, linear slicing profiles are sufficient to generate planar cross-sections that are well aligned with the part geometry. While comparable slicing functionality is available in most common CAD environments, these basic slicing operations are implemented as lightweight "gadgets" within the proposed framework to enable direct interaction with the EOT-based volumetric data. Such profile-based slicing is particularly suitable for components whose cross-sectional shape remains invariant along the build direction with respect to the slicing profile. In these cases, the profile can be defined in various ways, including freehand drawing, splinesupported curves, or polylines. A representative example is the blue benchmark tree shown in Figure 4: although the structure exhibits gradual tapering along the build direction, the underlying slicing profile remains unchanged across layers, allowing consistent extraction of 2D cross-sections throughout the component height directly in the VCS mask.

Figure 6 shows, on the left, the rendered benchmark tree at an approximately isotropic resolution. This isotropy is achieved by exploiting the inherently higher physical resolution in the build direction (80μ m), while the in-plane ( xy ) resolution is enhanced using high-quality interpolation based on the SciPy library (mode='reflect', anti_aliasing=True, preserve_range=True). The left rendering preserves the original radiometric values of the EOT data, whereas the right rendering applies median smoothing to mitigate layer-wise pattern variations caused by alternating laser scan directions along the build axis. As a result, the latter representation emphasizes the cumulative laser energy distribution across the entire component within the selected slice. Optionally, individual slices can either be analyzed independently or aggregated across multiple layers and processed using the algorithms introduced earlier, which is particularly beneficial for identifying vertically extended phenomena such as keyhole-related features.
Cylindrical and Revolved Profiles for rotationally symmetric structures, such as lattice cylinders, cylindrical slicing profiles are employed. These profiles can be unwrapped into two-dimensional representations, enabling continuous inspection of circumferential features within a single image. To facilitate this process, a lightweight three-point selection gadget is implemented within the proposed framework, allowing the slicing profile to be interactively defined directly on the VCS-derived masks and subsequently rendered as a high-resolution 2D slice.

The primary advantage of this representation lies in the ability to visualize the entire unwrapped surface of the component. In contrast to conventional planar cuts in CAD environments-where only a narrow set of pixels intersecting the cutting plane, or at best all pixels behind the plane, are dis-played-the unwrapped view provides complete circumferential coverage. Figure 6 illustrates this approach in the center panel, showing an unwrapped lattice cylinder in which all lattice intersection points are fully represented. The coordinate system of the unwrapped image is defined consistently

[picture]

Fig. 6: High-resolution 2D slice renderings obtained from VCS-derived profiles, illustrating planar slicing of a benchmark tree (left), cylindrical unwrapping of a lattice structure (center), and skeletonbased slicing of a curved wall (right).

Fig. 6. High-resolution 2D slice renderings obtained from VCS-derived profiles, illustrating planar slicing of a benchmark tree (left), cylindrical unwrapping of a lattice structure (center), and skeletonbased slicing of a curved wall (right).

with the physical build geometry. Along the vertical axis ( y -axis), the coordinate corresponds directly to the build direction and is metrically scaled according to the thickness of the layer (80μ m). Along the horizontal axis ( x -axis), the coordinates are expressed in an angular space, preserving the angular fidelity in polar coordinates. Consequently, a constant arc length is maintained along the slicing profile. The angular resolution in x is adapted to achieve an approximate isotropy with the higher resolution in the build direction and corresponds to approximately 0.25∘ per pixel, which is equivalent to an arc length of 80μ m in the original rendered images.
Skeleton-Based Slicing for Complex Geometries such as curved walls, skeleton-based slicing strategies are applied [10,11]. This approach relies on the extraction of the medial axis of the segmented component, which provides a compact representation of its topology and central geometry. Given a VCS binary segmentation mask M(x,y), the skeleton S⊂M :

S={p∈M∣∃q1,q2∈∂M,q1≠q2,d(p,q1)=d(p,q2)=minq∈∂Md(p,q)}

is defined as the set of all points p inside the segmentation mask M for which there exist at least two distinct boundary points q1 and q2 on ∂M such that the distance from p to each of these boundary points is equal and corresponds to the minimum distance from p to the boundary. This formulation yields a one-pixel-wide centerline that captures the essential shape of the component and serves as a curved slicing path. Here, ∂M denotes the boundary of the segmentation mask M, i.e., the set of all pixels in M that are adjacent to the background and thus form the interface between the segmented component and its surrounding domain.

Based on the medial axis extracted from the VCS-derived masks, a curved slicing profile is generated and dynamically adapted across the build layers to account for geometric evolution along the build direction. In such cases, the geometry of the components may change significantly with increasing build height, for example, due to gradual deformation or flaring (outward bulging) toward the top layers (see Fig. 4, center column, comparing the red wall in Layer 0 and Layer 1500). Figure 6 illustrates the resulting cut of the curved wall in the rightmost panel, shown as an orthogonal projection along the derived curved profile. As with the unwrapped cylindrical representations, this approach offers the key advantage that all pixels associated with the slicing profile are projected into a single 2D image, rather than only those intersecting a planar cutting surface. This enables continuous inspection of complex, non-planar structures either by following a single centerline-based slicing profile or by aggregating all profile samples between the minimum and maximum extents across successive build layers into a single two-dimensional representation, while preserving the full spatial context of the component.

[picture]

Fig. 7: Geometrical validation of the reconstructed 3D OT-Cube against the nominal CAD geometry.

Fig. 7. Geometrical validation of the reconstructed 3D OT-Cube against the nominal CAD geometry.

Validation

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To assess the reliability and applicability of the proposed framework, a validation is performed with respect to both geometric accuracy and radiometric consistency. The validation focuses on evaluating how well the reconstructed volumetric representations derived from EOT data correspond to the nominal part geometry and whether the preserved optical information enables meaningful interpretation of process-related effects. Accordingly, the validation is divided into a geometric and a radiometric analysis.
Geometrically Validation aims to quantify the deviation between the reconstructed 3D OT-Cube and the nominal CAD geometry of the manufactured components. For this purpose, the volumetric reconstruction obtained from the EOT data is converted into a surface representation and spatially aligned with the corresponding CAD model. A deviation analysis is then performed by computing point-wise distances between the reconstructed geometry and the reference CAD surface.

Figure 7 illustrates the geometric validation workflow. The left side shows the input data, consisting of the original CAD model and the reconstructed 3D OT-Cube. On the right, a color-coded deviation map visualizes the local geometric differences between both models, with the deviation magnitude expressed in millimeters. This comparison allows systematic evaluation of dimensional fidelity and highlights regions where process-induced distortions or reconstruction inaccuracies may occur.
Radiometrical Validation analyzes the relationship between the optical emission information captured by the EOT system and the applied accumulated laser energy during the LPBF process. To this end, the benchmark tree geometry is evaluated using aggregated profile-based slicing, in which all slicing profiles spanning the local component cross-section are accumulated rather than relying on a single, arbitrarily chosen centerline. This approach enables a truly volumetric analysis by capturing the full radiometric response of the component across successive build layers while simultaneously reducing local scan-pattern-induced artifacts. By integrating all profile samples, the resulting two-dimensional representations reflect the cumulative energy input within the component and allow systematic investigation of both global trends along the build direction and geometry-dependent variations in laser exposure. As a result, correlations between radiometric emission, applied laser energy, and segmented cross-sectional area can be analyzed in a robust and physically meaningful manner.

Figure 8 illustrates the results of this analysis. On the left, high-resolution 2D slice renderings of the benchmark tree are shown, obtained by accumulating all slicing profiles across the local component cross-section, as described in Section High-Resolution 2D Slice Rendering. In contrast to centerlinebased approaches, this representation integrates the radiometric contribution of the entire profile region and thus captures the full volumetric emission response associated with each individual build layer.

[picture]

Fig. 8: Radiometric validation based on aggregated profile slicing of the benchmark tree, showing high-resolution 2D renderings and slice-wise emission statistics along the build direction.

Fig. 8. Radiometric validation based on aggregated profile slicing of the benchmark tree, showing high-resolution 2D renderings and slice-wise emission statistics along the build direction.

Two complementary radiometric representations are shown. The first representation normalizes the accumulated emission by the effective profile extent, yielding a geometry-aware measure of the mean radiometric emission per unit area within each layer. Although evaluated on a per-layer basis, this quantity represents an area-normalized radiometric response rather than the total energy deposited within the layer. The second representation depicts the accumulated radiometric energy per layer without normalization by the number of contributing samples, thereby emphasizing the total effective energy input into the component cross-section. This accumulated view amplifies geometryinduced effects and highlights regions of increased thermal loading that are not apparent in purely area-normalized representations. On the right, the slice-wise radiometric measures are plotted as a function of the axial build position z. Both radiometric representations reveal a gradual increase in emission intensity along the build height, indicating a systematic process-related trend over the entire component. This build-height-dependent increase is observed consistently in this analysis and is also evident in Fig. 5 (Tree), where an increasing number of regions with elevated emission levels are detected toward higher build layers. With increasing build height, such regions become more frequent and spatially extended, which is consistent with an increased likelihood of local overheating phenomena that may manifest as keyhole-related defects.

In addition, the segmented cross-sectional area derived from the VCS masks is shown for each slice, revealing a clear correlation between local geometry and radiometric response. Layers with increased segmented area consistently coincide with elevated radiometric values, reflecting the higher effective energy input and enhanced thermal accumulation required to process larger contiguous crosssections. Although one of the radiometric measures is evaluated in a normalized manner, the resulting mean emission value is not invariant with respect to the segmented cross-sectional area. This behavior does not arise from image discretizations effects but instead reflects the underlying physics of energy accumulation in contiguous material regions. For larger connected cross-sections, heat dissipation through component boundaries becomes less effective, while repeated laser exposure within the same layer leads to increased thermal superposition. Consequently, the integrated EOT emission represents the effective, thermally accumulated energy input per unit area rather than the nominal laser power density alone.

The radiometric trends observed in this analysis show very good agreement with results obtained from the EOS Melt Pool Monitoring (MPM) system [12]. In particular, regions of elevated and reduced energy input identified in the EOT-based evaluation correspond closely to the melt pool signatures reported by the MPM data. This consistency confirms that the proposed volumetric EOT analysis captures physically meaningful information related to the applied laser input and overall process stability. Based on the observed correlation between VCS-derived geometric information and radiometric emission values, the results further indicate that an inverse relationship between component geometry and required laser power could, in principle, be exploited to derive corrective laser power profiles.

A detailed discussion of such inverse correction strategies is beyond the scope of this work and is therefore addressed as an outlook in the summary.

Summary

The original version of this paper is available on https://www.scientific.net/KEM.1047.191.pdf

This work introduced a volumetric framework for processing and analyzing Exposure Optical Tomography data acquired during the LPBF process. By reconstructing sequential EOT images into a component-aware 3D OT Image Cube, the proposed approach enables holistic, part-level inspection that extends beyond conventional layer-wise monitoring. The integration of volumetric component segmentation ensures that all subsequent analyzes are constrained to the actual component geometry, while high-resolution 2D slice rendering allows flexible visualization of internal structures along arbitrary slicing directions.

The presented slicing strategies-ranging from planar and cylindrical profiles to skeleton-based slicing for complex geometries-provide intuitive and information-dense representations that are well suited for engineering analysis. Geometric and radiometric validation demonstrate that the reconstructed volume preserves both dimensional fidelity and meaningful radiometric trends correlated with laser energy input and part geometry. As a result, the framework offers a practical foundation for post-process quality assurance, defect localization, and design-related weakness analysis in LPBF, with particular relevance for prototyping workflows.

Beyond the presented validation, the demonstrated correlation between volumetric component segmentation and radiometric emission data opens promising perspectives for future work. In particular, the inverse relationship between component geometry and laser energy input suggests that geometryaware laser power correction profiles could be derived directly from sliced CAD data. By mapping local cross-sectional properties of connected components to expected radiometric responses, it becomes conceivable to predict EOT-like emission distributions prior to manufacturing and to compute mean correction factors for individual components or connected regions, but this has to be evaluated first.

Such an approach could enable geometry-adaptive process parameterization and complement existing monitoring systems by providing predictive, model-based laser power adjustments. While this work focuses on post-process analysis and validation, the presented framework establishes the necessary foundation for future investigations toward closed-loop or pre-compensated LPBF process control.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/KEM.1047.191.pdf

The author gratefully acknowledges EOS for the excellent calibration of the sCMOS camera used in the Exposure Optical Tomography (EOT) system, which significantly facilitated the quantitative evaluation of the acquired data. The resulting consistency between recorded optical signals and actual process conditions-summarized by the principle "What you CAM is what you get"-formed a reliable foundation for the analyzes presented in this work.

Special thanks are extended to the Additive Minds Academy of EOS for providing fundamental training in Laser Powder Bed Fusion and for the comprehensive documentation and knowledge transfer related to the EOSTATE MeltPool Monitoring (MPM) system, which supported the interpretation and validation of radiometric process data.

In addition, this work substantially benefited from the availability of open-source and public software. In particular, the Python ecosystem and its extensive libraries-including OpenCV, NumPy, SciPy, Matplotlib, and Open3D-provided essential tools for image processing, numerical analysis, and volumetric data handling. The author acknowledges the contributions of the open-source community and the developers who make these tools freely available for research and development.

The use of open AI-based tools is also gratefully acknowledged, especially the Segment Anything Model 2 (SAM2) developed by Meta and distributed through the broader research community, including platforms such as Hugging Face. In addition, ChatGPT was used as a supportive tool for translating assistance in refining the text during manuscript preparation.

Finally, the author acknowledges the availability of free software tools such as MeshLab, ImageJ, and GIMP, which were used for visualization, inspection, and figure preparation throughout this work.

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.191.pdf

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This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/additive-manufacturing/978-3-0364-1997-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 205-212

doi: 10.4028/p-8J71lD

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-19

Revised:
2026-02-13

Accepted:
2026-02-14

Online:
2026-04-13

Thermal Measurement During High-Velocity Oxy-Fuel Coating Process for Single and Multiple Pass Rotational Spraying

The original version of this paper is available on https://www.scientific.net/KEM.1047.205.pdf

Diyoke George 1,a*, Yang Yitong 2,b, Schindelbacher Christoph 3,c, Sperling Sebastian 3, d, Angerler Jürgen 3,e and Härtel Sebastian 1,f

1 Chair of Hybrid Manufacturing, Brandenburg University of Technology, Cottbus, Germany

2 Chair of Metallurgy and Material Engineering, Brandenburg University of Technology, Cottbus, Germany

3 Voith Austria GmbH, VPRW Betriebsstätte, Wimpassing, Austria

a* diyoke@b-tu.de, b yang@b-tu.de, c christoph.schindelbacher@voith.com, d  sebastian.sperling@voith.com, ejuergen.angerler@voith.com, f  sebastian.Haertel@b-tu.de

Keywords: high-velocity oxy-fuel, thermal measurements, heat source, coating process.

Abstract

High-velocity oxy-fuel spraying is a widely used thermal spray technology for producing dense and wear-resistant coatings. The thermal input during spraying strongly influences coating microstructure, residual stress state, and substrate integrity. In this work, in situ thermal measurements were performed on S235 substrates during High-velocity oxy-fuel deposition of 316L coatings. Two spraying strategies were compared: (i) single-pass rotation and (ii) multi-pass rotation. Thermocouples embedded at 1.8 mm depth captured transient temperature responses, revealing significant thermal cycling effects. Single-pass operations produced no significant heating-cooling cycles, while multi-pass strategies led to thermal accumulation and overlapping cycles. The results provide reference data for the calibration of finite element heat source models and support the development of process-structure-property relationships in High-velocity oxy-fuel coatings.

Introduction

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Austenitic stainless steel, particularly 316L, is well known for its excellent corrosion resistance and mechanical reliability, which makes it an attractive candidate for use as a protective coating material [1].

[picture]

Fig. 1. Process set up (a) HVOF spraying setup showing the combustion-driven jet impinging on a rotating cylindrical substrate; (b) schematic of the HVOF gun, illustrating kerosene/oxygen combustion, particle acceleration, and deposition on the substrate [2].

Fig. 1. Process set up (a) HVOF spraying setup showing the combustion-driven jet impinging on a rotating cylindrical substrate; (b) schematic of the HVOF gun, illustrating kerosene/oxygen combustion, particle acceleration, and deposition on the substrate [2].

High-velocity oxy-fuel (HVOF) spraying offers an effective deposition route by accelerating semimolten particles toward the substrate, promoting dense, well-bonded coatings with reduced porosity and limited oxide formation compared to atmospheric plasma spraying (APS). As illustrated in Fig. 1(a), HVOF produces a distinct thermal footprint, while Fig. 1(b) shows how kerosene-oxygen combustion drives particles toward the substrate at high velocity. Upon impact, these particles plastically deform into splats, consolidating the coating and enhancing densification. Although HVOF substantially improves coating density and minimizes oxidation, thin oxide films, localized
porosity, and subtle compositional shifts may still form during deposition and can influence the passive-film stability of 316L stainless steel in aggressive environments [3]. Understanding how these deposition dynamics affect microstructure, residual stresses, and coating performance requires investigation at both the splat and coating process scales.

[picture]

Fig. 2. Schematic illustration of the splat formation during coating with respect to temperature and time history. This captures the: process of particle spreading, solidification, recalescence, and final cooling. Undercooling is represented in dashed line [4].

Fig. 2. Schematic illustration of the splat formation during coating with respect to temperature and time history. This captures the: process of particle spreading, solidification, recalescence, and final cooling. Undercooling is represented in dashed line [4].

During thermal spraying such as HVOF, coating build-up occurs through the successive splat formation which involves: (1) impact and spreading (2) solidification (with release of latent heat) and (3) cooling of individual semi-molten particles [4, 5, 6]. As shown schematically in Fig. 2, splat formation is an ultrafast, highly transient process characterized by extreme cooling and solidification rates [4]. Upon impact with the substrate, a molten particle spreads radially within a few microseconds, during which rapid heat extraction leads to significant undercooling below the equilibrium melting temperature. Solidification initiates only after the completion of spreading and is accompanied by recalescence due to latent heat release following droplet impact and rapid heat extraction to the substrate [7, 8]. Because this phase transformation occurs under strong thermal gradients with constrained shrinkage against the substrate, and transformation strains are generated and locked into the splat. Subsequent cooling of the fully solidified splat toward the substrate temperature further contributes to stress development through thermal mismatch effects [9]. These coupled thermo-mechanical phenomena at the splat scale are decisive in residual stress evolution in thermal spray coatings.
At the coating-substrate scale, the fundamental mechanisms of splat formation and adherence are strongly influenced by the cumulative residual stresses generated during successive particle impacts [10]. The thermal and mechanical response of an HVOF coating is ultimately controlled by the combined thermal history imposed by particle deposition and torch motion. Capturing this highly transient heat transfer exceeds the resolution of experimental techniques and requires finite element modeling. Moving heat source models, similar to those used in weld simulations, allow simulation of local transient heating, cooling rates, and thermal gradients induced by the moving energy source [11, 12, 13]. The predictive capability of such models depends critically on accurate heat source descriptions, realistic thermal boundary conditions, and proper accounting of spatial and temporal variations in heat input due to spray kinematics, pass strategy, and coating build-up. This approach enables reliable correlations between process parameters, resulting microstructure, and coating properties.

In this study, in situ thermal measurements were conducted during HVOF deposition of 316L stainless steel on S235 structural steel, comparing single-pass and multi-pass rotational spraying. The thermal histories across the spray footprint were correlated with coating thickness, microstructure evolution, and hardness distribution. A finite element-based heat source model, incorporating appropriate boundary conditions, was developed to capture the thermal behavior of the process. This analysis provides new insights into the process-microstructure-property relationships of HVOFsprayed 316L coatings.

Material and Methods

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S235 structural-steel plate in Fig. 1(a) of dimension 𝐋100×𝐖100×𝐇10 mm3 is used as substrates and roughened through sand blasting to Rz ~40μ m. The experimental arrangement for mounting and processing the samples is illustrated in Fig. 2(b). The coating material is a gas-atomized 316L stainless-steel powder with a particle-size distribution of nominal (−53μ m/+20μ m).

[picture]

Fig. 3. Experimental set up of the process for thermal measurement; (a) the location of the thermocouple (TC) at the centre of the substrate and approximately 1.8 mm away from the substrate surface; and (b) the sample mounted on rotating roller with a carrier a carrier which protects the thermal measurement device.

Fig. 3. Experimental set up of the process for thermal measurement; (a) the location of the thermocouple (TC) at the centre of the substrate and approximately 1.8 mm away from the substrate surface; and (b) the sample mounted on rotating roller with a carrier a carrier which protects the thermal measurement device.

Coatings were deposited using a HVOF torch mounted on a programmable robotic system. As illustrated in Fig. 1(a), the substrates were mounted on a carrier and fixed onto a rotating cylindrical roller of 310 mm diameter, rotating at 100 rpm . Two spraying strategies were evaluated: (1) Single-pass rotation, in which the cylinder completed one full rotation while the torch kept fixed at a specific distance; and (2) Multi-pass rotation, consisting of 20 and 40 consecutive passes. Other process parameters used for the experiment are detailed in Table 1.

Table 1. Parameters for high velocity oxy fuel process.

Powder

particles

Kerosene

flow rate

Oxygen flow

rate/ superscript number

Nitrogen

flow rate/

Rotational

speed of the

roller/ mathematical formula

Maximum

thickness mathematical formula

Spray

distance mathematical formula

316L | superscript number | mathematical formula | superscript number | 100 | 400 | superscript number

The thermocouple location is depicted in Fig. 2(a). Type-K thermocouples were embedded 1.8 mm below the substrate surface at the spray-center position. Temperature data were sampled at 10 Hz using a data logger, and thermal histories are recorded for both single- and multi-pass conditions. Cross-sections were prepared using standard metallographic procedures. Microstructural characterization is carried out using a Keyence VHX-7000 digital microscope. Electron backscatter diffraction (EBSD) measurements were performed on mechanically polished cross-sections using a step size of 0.02μ m. Hardness measurements is performed on polished sample surfaces using a ZwickRoell Durascan 70 G5 (EMCO-TEST) equipped with a Vickers indenter, applying a 200 g load with a spacing of 1.0 mm between indentations.

Process Model

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To infer the temperature evolution within the coating layer, a 3D finite element model incorporating a moving heat source is developed using Simufact numerical tool. As a first approximation, the heat source was represented as a cylindrical volumetric heat source with a radius of 50 mm , introducing thermal energy exclusively into the coating layer. The substrate heating occurred solely through heat transfer from the hot coating layer to the colder substrate. The basic model setup is illustrated in Fig. 4, where a finer mesh is used in the coating and adjacent substrate to capture the thermal history, and a coarser mesh is applied to the remaining substrate.

[picture]

Fig. 4. Schematic illustration of the process set showing moving cylindrical heat source.

Fig. 4. Schematic illustration of the process set showing moving cylindrical heat source.

The cylindrical heat source represents a spatially averaged approximation of the effective thermal footprint of the HVOF jet.

Table 2. Simulation Input Parameters.

Category | Parameter | Symbol | Value
Geometry | Substrate dimensions | – | 100 × 100 × 10 mm³
Geometry | Coating dimensions | – | 100 × 40 × 0.4 mm³
Process control | Velocity | v | 1620 mm/s
Heat source | radii | r | 50 mm
Heat source | depth | d | 0.15mm
Heat source | Average energy | E | 580 J/cm
Boundary condition | Convection coefficient to air | h | 300 W·m⁻²·K⁻¹

The heat-source power was determined using an inverse calibration approach. Specifically, the heat input was iteratively adjusted until the calculated temperature at the measurement location ( 1.8 mm below the substrate surface) matched the experimentally measured temperature. This procedure resulted in an average energy input per unit length of 580 J/cm, which was subsequently used for all simulations. Other simulation input parameters are listed in Table 2.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1047.205.pdf

[picture]

Fig. 5. Substrate maximum temperature and coating thickness of single-pass, multi-pass and validation of multi-pass during HVOF spraying.

Fig. 5. Substrate maximum temperature and coating thickness of single-pass, multi-pass and validation of multi-pass during HVOF spraying.

Fig. 5 illustrates the maximum substrate temperature and corresponding coating thickness achieved for single- and multi-pass HVOF spraying as well as numerical validation. For the single-pass deposition, the temperature in the substrate remains low, with a corresponding low thickness achieved. In contrast, multi-pass rotation results in a significant increase in maximum substrate temperature, reaching approximately 50−55∘C after 20 passes and further rising to about 90∘C for 40 passes. The trend indicates accumulation of heat in successive passes during coating. The results also show good agreement with simulated temperature and thickness predictions for 40 passes. Thermal accumulation during multi-pass rotation may play a key role in determining the final microstructural and mechanical properties of the deposited layer, which will be discussed next.
The thermal and geometric evolution of the HVOF coating during the two-stage deposition process offers critical insight into how particle flux distribution, heat accumulation, and impact dynamics govern the resulting microstructure. In Fig. 6(a) no significant deposit is observed after single pass spraying which align with the absence of thermal cycle recorded and room temperature reported in Fig. 5. In contrast, as shown in Fig. 6(b), repeated spraying produces a pronounced convex profile at the center of the spray track. After approximately 20−40 passes, the coating reaches a maximum thickness of about 400μ m at the center, while the edges accumulate substantially less material due to the divergent expansion of the particle jet as it exits the nozzle.

[picture]

Fig. 6. Coating thickness profiles and representative cross section micrographs and grain properties. (a) Coating thickness profile after single pass; (b) coating thickness profile after 20−40 passes showing the pronounced convex geometry and maximum centre thickness of 400μ m; (c) GOS map, and (d) IPF map (both c and d are magnifications from the center region in red box in b).

Fig. 6. Coating thickness profiles and representative cross section micrographs and grain properties. (a) Coating thickness profile after single pass; (b) coating thickness profile after 20 − 40 passes showing the pronounced convex geometry and maximum centre thickness of 400 μ m ; (c) GOS map, and (d) IPF map (both c and d are magnifications from the center region in red box in b).

Fig. 6(c) shows the grain orientation spread (GOS), with most grains exhibiting values above 10∘, which is significantly higher than the values typically associated with dynamic recrystallization (<3∘). Furthermore, Fig. 6(d) shows the distribution of grain boundary angles, with a predominance of lowangle boundaries 92.8% of the boundaries have angles below 15∘ which contrasts with the microstructural indication of continuous dynamic recrystallization, generally characterized by the progressive transformation of low-angle to high-angle boundaries. Together, these observations indicate that the microstructure reflects an absence of dynamic recrystallization rather than its occurrence. At the macroscopic scale, the coating geometry formed during multi-pass rotation reflects the spatial distribution of particle and heat flux inherent to the HVOF jet.
The convex coating morphology observed for the multi-pass is consistent with the Gaussian-type particle flux distribution characteristic of HVOF jets. The observed deposition profile aligns with the Gaussian heat source model widely used in literature for simulation. The fundamental expression that relates heat flux with the coating width reads [9]:

q(r)=q0exp(−r22σ2)(1)

where q0 is the peak heat flux at the centerline and σ defines the coating width. In the present study, the convective heating pattern and coating build-up strongly mirror this Gaussian behavior: the maximum temperature rise ( ~90∘C ) and the thickest coating ( ~400μ m ) occur at the same center region where particle flux and heat flux are theoretically highest. Toward the edges (~10 mm away from the center), reduced heat input predicted by the Gaussian curve is consistent with the reduced particle flattening and lower coating thickness observed. Fig. 7 shows the temperature field under quasi-steady-state process conditions; the numerically determined maximum temperature reached in the layer is approximately 850∘C.

[picture]

Fig. 7. Simulated spatial temperature distribution of the coated layer.

Fig. 7. Simulated spatial temperature distribution of the coated layer.

The microstructural, geometric, and thermal variations are reflected in the spatial distribution of hardness across the coating.

[picture]

Fig. 8. Hardness distribution across the coating and substrate width.

Fig. 8. Hardness distribution across the coating and substrate width.

Hardness profiles measured across the 10 mm -wide coated centreline (Fig. 8) further support the microstructural trends observed in Fig. 6(b). The coating exhibits substantially higher hardness values of approximately 250−340HV at the centre. The highest hardness occurs at the region of maximum coating thickness and particle flux, consistent with intensified impact energy and enhanced local densification. The S235 substrate maintains a hardness of 185−215HV for both single-pass and multipass conditions, reflecting its limited thermal exposure; even after 20−40 passes. Overall, the combined effects of progressive heat accumulation and plastic deformation during successive passes explain the localized microstructural changes in the thickest region, corresponding to the observed increase in hardness.

Conclusion

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The single- and multi-pass thermal evolution exhibits a systematic increase with coating thickness. In multi-pass deposition, the coating thickness is highest at the center and decreases toward the periphery, resulting in a convex, Gaussian-like profile. This geometry, together with the localized hardness peak at the track center, is associated with regions of higher particle flux and cumulative thermal input, while no evidence of dynamic recrystallization was observed. Overall, these results establish a clear quantitative and qualitative relationship between process parameters, microstructure, and mechanical response in the HVOF process. The FE heat source model, calibrated using in situ temperature measurements, successfully reproduces the experimentally observed thermal accumulation during multi-pass rotation and supports interpretation of the measured trends. Future work will include repeated experimental trials to quantify statistical variability and further strengthen the reliability of the derived thermal trends and model calibration.

Funding

The original version of this paper is available on https://www.scientific.net/KEM.1047.205.pdf

The authors acknowledge financial support from the Deutsche Forschungsgemeinschaft (DFG) under Project HeatCoatRoll, Grant No. 539450784.

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.205.pdf

Y.B. Lei, Z.B. Wang, B. Zhang, Z.P. Luo, J. Lu, K. Lu, Enhanced mechanical properties and corrosion resistance of 316L stainless steel by pre-forming a gradient nanostructured surface layer and annealing, Acta Mater. 208 (2021) 116773. https://doi.org/10.1016/j.actamat.2021.116773.

S. Kumar, D.C. Prasad, H. Hanumanthappa, Role of Thermal Spray Coatings on Erosion, Corrosion, and Oxidation in Various Applications: A Review, J. Bio-Tribo-Corros. 10 (2024) 22. .

S. Kuroda, T. Fukushima, M. Sasaki, T. Kodama, Microstructure and Corrosion Resistance of HVOF Sprayed 316L Stainless Steel and Hastelloy C Coatings, Mater. Trans. 43 (2002) 31773183. .

A. Valarezo, K. Shinoda, S. Sampath, Effect of deposition rate and deposition temperature on residual stress of HVOF-sprayed coatings, J. Therm. Spray Technol. 29 (6) (2020) 1322-1338. .

S. Sampath, X. Jiang, J. Matejicek, A. Leger, A. Vardelle, Substrate temperature effects on splat formation, microstructure development and properties of plasma sprayed coatings Part I: Case study for partially stabilized zirconia, Mater. Sci. Eng. A 272 (1) (1999) 181-188.

K. Shinoda, Y. Kojima, T. Yoshida, In situ measurement system for deformation and solidification phenomena of yttria-stabilized zirconia droplets impinging on quartz glass substrate under plasma-spraying conditions, J. Therm. Spray Technol. 46 (2005) 98.

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S. Chandra, P. Fauchais, Formation of solid splats during thermal spray deposition, J. Therm. Spray Technol. 18 (2009) 148-180. .

A. Fardan, R. Ahmed, Modeling the evolution of residual stresses in thermally sprayed YSZ coating on stainless steel substrate, J. Therm. Spray Technol. 28 (4) (2019) 717-736. .

P. Bansal, P.H. Shipway, S.B. Leen, Residual stresses in high-velocity oxy-fuel thermally sprayed coatings - Modelling the effect of particle velocity and temperature during the spraying process, Acta Mater. 55 (15) (2007) 5089-5101.

Goldak, J., Chakravarti, A., & Bibby, M. (1984). A new finite element model for welding heat sources. Metallurgical Transactions B, 15(2), 299-305.

Zhang, X., Li, S., & Kovacevic, R. (2005). Finite element modeling of heat transfer in laserbased surface engineering processes. International Journal of Machine Tools and Manufacture, 45(10), 1135-1145.

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The original version of this eBook is available on https://www.scientific.net/book/additive-manufacturing/978-3-0364-1997-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 213-221

doi: 10.4028/p-krUYO9

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-19

Revised:
2026-02-16

Accepted:
2026-02-25

Online:
2026-04-13

Optimal Design and Additive Manufacturing of Polymeric Metamaterials for Energy Absorption and Impact Mitigation

The original version of this paper is available on https://www.scientific.net/KEM.1047.213.pdf

Fachinotti Victor D. 1,a*, Andreassen Erik 2, b and Gouttebroze Sylvain 1,c

¹Metal Production and Processing, SINTEF Industry, Oslo, Norway

2 Materials and Nanotechnology, SINTEF Industry, Oslo, Norway

a* victor.fachinotti@sintef.no, b erik.andreassen@sintef.no, c sylvain.gouttebroze@sintef.no

*corresponding author: victor.fachinotti@sintef.no

Keywords: optimal design, metamaterial, energy absorption, impact mitigation, FFF, TPU.

Abstract

Architected metamaterials fabricated by additive manufacturing offer deterministic geometries and tunable mechanical properties that can outperform conventional foams in energy absorption and impact mitigation. In this work, origami honeycomb and plate-lattice metamaterial concepts are unified within a common, quantitatively characterised metamaterial. An optimizationbased design approach is employed to maximise absorbed energy while keeping the peak stress below a predefined threshold, using metamaterial geometric parameters as design variables. The objective function is evaluated through post-processing of Abaqus compression simulations on automatically generated designs. Owing to the high computational cost, the optimisation is performed using an evolutionary algorithm with a limited number of evaluations, yielding a best-performing design rather than a global optimum. Despite this limitation, the results elucidate the critical roles of buckling in limiting initial peak stress and of contact in enhancing post-peak energy absorption, and they highlight the significant potential for further performance gains through expanded design space exploration.

Introduction

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Recent advances in architected ("meta") materials have enabled cellular structures with deterministic geometries and tunable mechanical properties that outperform conventional stochastic foams in energy absorption and impact mitigation. Townsend et al. [1] developed origami honeycombs with fold geometries designed to tailor stress-strain responses and energy absorption. These metamaterials, fabricated from thermoplastic polyurethane (TPU) via fused filament fabrication (FFF), combine the elastic recoverability of TPU with the geometric freedom of additive manufacturing. More recently, Smith et al. [2] introduced tunable plate-lattice metamaterials for impact mitigation, also manufactured by FFF using TPU, achieving up to an order-of-magnitude higher energy absorption than conventional foams at comparable peak stresses. These studies form the basis of the optimal design approach proposed here.

In this work, we assimilate Townsend et al.'s origami honeycombs and Smith et al.'s plate-lattice metamaterials into a common, quantitatively characterised material framework. We then apply an optimization-based metamaterial design approach [3], treating the parameters that characterise the metamaterial as design variables. The objective is to maximise absorbed energy while constraining the peak stress below an admissible threshold for impact mitigation. Objective evaluations are obtained by post-processing Abaqus compression simulations of automatically generated designs.

The optimisation problem is solved using an evolutionary algorithm, with the number of objective function evaluations limited to a few tens due to the high licensing cost of Abaqus. Consequently, we report a best design-rather than a true optimum, given the limited exploration of the design space. Despite this limitation, comparison among evaluated designs clearly reveals the role of buckling in limiting the initial peak stress and the contribution of contact to enhancing post-peak energy absorption. The results further indicate that substantial improvements in energy absorption and impact mitigation may be achieved by exploring a richer design space once the licensing constraint is removed.

Parametrisation

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The current work aims to apply the optimization-based metamaterial design (OMD) approach to optimize the performance of metamaterials for energy absorption and impact mitigation. In OMD, the objective function characterises the performance to be optimised, and the design variables define the metamaterial. The first step is the parametrisation of the considered metamaterials, in order to define the design space.

The currently studied metamaterials are based on the origami honeycombs proposed by Townsend et al. [1] and the place-lattice metamaterials proposed by Smith et al [2]. Figure 1 shows an origami honeycomb [1], which is characterised by the unit cell side b, the wall thickness t, the number nz of cells in the z (loading) direction, the number nx of cells in the x and y directions, and the aspect ratio a, that equals 1 when the unit cell is a prismatic tube, and reduces as the fold angle increases. Ref. [1] evaluated the energy absorption performance of these origami honeycombs for several values of nz and a; which played the role of design variables for energy absorption optimization in their work.

[picture]

Fig. 1. Origami honeycomb and its topological parameters.

Fig. 1. Origami honeycomb and its topological parameters.

Figure 2 shows a plate-lattice metamaterial [2], which is defined by the size w and height h of the unit cell, the wall thickness t, and the geometric prebuckling parameter p, which is equal to zero when the unit cell is a prismatic tube and increase with the folding angle, and the number of cells in the loading direction and across it, nz and nx, as before. Ref. [2] evaluated the energy absorption efficiency for several values of p and the plate-lattice density ρ. Since they kept w,h,t,nz and nx fixed, p is the only variable in the definition of ρ; so p can be seen as the only design variable in their work. Note that they allowed p to change in the loading direction, so p is not one scalar but a set of nz scalar design variables.

[picture]

Fig. 2. Plate-lattice metamaterial and its topological parameters.

Fig. 2. Plate-lattice metamaterial and its topological parameters.

However, beyond their particular denominations, Townsend et al's origami honeycombs [1] and Smith et al's plate-lattice metamaterials [2] just define different unit cells of the same periodic metamaterial, as shown to the right in Figure 2. Their geometrical parameters related by:

p=1−a1+abh,w=21+ab(1)

Therefore, we adopt the unit cell as defined by Smith et al [2], since it is easier to build using the drawing and meshing tools provided by Abaqus and Salome_Meca, which are the finite element packages used in this work. Therefore, any sample of the current metamaterial with given dimensions W×W×H is characterized by the following design variables: 1) the prebuckling parameter p,2 ) the number nz of cells or folds in z direction, 3 ) the number nx of cells in the x and y directions, 4) the wall thickness t.

This geometrical parametrisation was implemented via two alternative Python scripts, one called XtoGeoAbaqus.py made for running under the Abaqus shell, the other called XtoGeoSM.py that runs under the Salome_Meca shell. Each script takes the geometrical design variables as input and generates the geometry of the corresponding design in STL (stereolithography) format, which is a common file type for slicer software for 3D printing. Using either of these scripts, a large series of designs can be automatically generated in the search of the optimal design. Figure 3 shows some of the generated designs.

[picture]

Fig. 3. Parameterised metamaterial samples.

Fig. 3. Parameterised metamaterial samples.

Fused filament fabrication (FFF) using thermoplastic polyurethane (TPU) is a common technique for the additive manufacturing of these metamaterials. Townsend et al [1] and Smith et al [2] modelled the TPU material as a Mooney-Rivlin hyperelastic material and an Ogden-Prony viscoelastic material, respectively. Smith et al [2] based their choice of TPU grade on the need of a material being soft enough to enable relatively low-force characterization experiments, and stiff enough to allow robust, repeatable fabrication of many samples with high yield. The choice of material, in particular its hardness, affects the metamaterial's response. Hence, a new design variable, say m, can be added to define the material. In the current approach, the material properties are assumed to be uniform and independent of the additive manufacturing conditions.

In summary, any design of the current metamaterial is fully described by the following set of design variables:

X=[p,t,nx,nz,m](2)

where p and t are real number variables, nx and nz are integer variables and m is a categorical variable. The set X belongs to the mixed-variable (discrete-continuous) design space D, where each variable is properly bounded. Further, Smith et al [2] allowed p to vary in the z direction, making p not one but a set of parameters. Given the freedom offered by additive manufacturing, not only
prebuckling but also thickness and cell size could be variables inside the sample, enriching the design space.

Characterisation of the energy absorption and impact mitigation performance

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We characterise the mechanical response to energy absorption and impact mitigation by the relation between the stress σ and the strain ε under compression loading. We consider bulk measures of σ and ε, defining ε as the ratio between the magnitude ΔH of the applied compressive displacement and the initial height H of the sample, and σ as the ratio between the magnitude R of the resultant of reactions at z=0 and the initial area W2 of the cross section. Note that both σ and ε are magnitudes, i.e., σ>0 and ε>0.

The energy absorbed per unit volume by the sample when it is compressed up to the strain εend  is:

A(εend)=∫0εendσ(ε)dε(3)

Townsend et al [1] and Smith et al [2] characterised the performance of the current metamaterials using the energy absorption efficiency:

E*(εend)=∫0εendσ(ε)dεP(εend)(4)

where P is the peak stress:

P(εend )=maxε≤εend σ(ε)(5)

Figure 4 shows the σ(ε) curve for a metamaterial sample with W=38.4 mm,H=38.4 mm, prebuckling p=0.4,nx=4 cells in x and y directions, nz=6 cells in z direction, wall thickness t=0.6 mm, compressed at 36 mm/s (i.e., 0.75/s strain rate) in z direction, as obtained using Abaqus explicit module, adopting the Ogden hyperelastic material model as calibrated by Smith et al [2]. Let us note Smith's material model also include viscous terms, which are neglected in the current study. Smith et al [2] performed repeated loading-unloading tests, allowing the samples to recover a minimum of 1000 s such that viscoelastic effects are relevant [2]. Then, they used a Prony series model to capture these time-dependent effects. On the other hand, Townsend et al [1] allowed a much shorter recovery between tests, neglecting viscoelastic effects. Being the current study restricted to only one loading cycle, we neglect viscoelastic effects following Townsend et al [1].

[picture]

Fig. 4. Stress-strain curve under compression determined using Abaqus' explicit dynamic finite element analysis for the metamaterial sample on the right, having prebuckling p=0.4 and wall thickness t=0.6mm.

Fig. 4. Stress-strain curve under compression determined using Abaqus' explicit dynamic finite element analysis for the metamaterial sample on the right, having prebuckling p = 0 . 4 and wall thickness t = 0 . 6 m m .

This curve exhibits common features of the response of the current energy-absorbing metamaterials. The initial stress peak P1, corresponding to the onset of structural instability, is subsequently attenuated by buckling. After this peak, the load-bearing capacity decreases as buckling progresses until the onset of contact between different faces or even self-contact in a largely deformed face. Beyond this point, the bearing capacity increases again due to progressive compaction, reaching the so-called densification strain εdens , after which there is a sharp increase in the slope of the σ(ε) curve. Townsend et al. [1] reported εdens  to vary between 0.55 to 0.60 for origami honeycombs with aspect ratio a varying between 0.6 and 0.9 . Let us note that there is a large dispersion in the definition of the densification strain [7]; unfortunately, Townsend et al. [1] did not specify which one they used.

The densification strain εdens  is usually considered to be the upper limit of the useful energy absorption range, named εend , as it is the case for the metamaterials of Townsend et al [1] and Smith et al [2]. However, for impact mitigation problems, the upper limit of the useful energy absorption range should actually be defined by the injury threshold level σthr  [6]. To adopt εend =εdens  may lead to either an underestimation of the energy absorption efficiency (if P(εdens )<σthr  ) or inadmissible responses (if P(εdens )>σthr  ). To avoid these cases, and the uncertainty in the definition of εdens  as well [7], we define the upper strain limit εend  by the following relation

P(εend)=σthr(6)

Optimisation of the energy absorption and impact mitigation performance

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The application of the optimization-based metamaterial design (OMD) approach [3] to the metamaterials and objectives considered by Townsend et al [1] and Smith et al [2] gives rise to the following mathematical optimization problem:

maxX∈D∫0εend σ(ε)dεP(εend )(7)

having the energy absorption efficiency E* as the objective function. If εend  is not defined by Eq. (6) but prescribed, the following non-linear inequality constraint must be considered:

P(εend)≤σthr(8)

In this work, having defined εend  by Eq. (6), we prefer to solve a simpler optimization problem

maxX∈D∫0εend σ(ε)dε(9)

i.e., considering the energy absorption per unit volume A(εend ) as the objective function.
The objective function depends on the design variables X via the σ(ε) relationship. Embodying the Python script XtoGeo.py that takes the geometrical design variables and builds the domain of analysis (discussed in the previous section), we coded the Python script XtoFE.py that takes as input all the design variables and gives as output the finite element (FE) model for Abaqus. Note that only Abaqus is kept henceforth, since Salome_Meca solutions diverge as soon as the contact onset is attained.

Running the FE model with Abaqus gives the σ(ε) curve as result. A new Python script called Fitness.py takes the σ(ε) curve as input, computes εend  for the given σthr , and finally the objective function A(εend ).

Gradient-based algorithms were preferred in the previous OMD applications developed by Fachinotti and collaborators, e.g. [3]. Here, without an explicit analytical relationship between the objective function and the design variables, we must compute the gradient numerically, which is prohibitively expensive; for instance, the determination of the σ(ε) curve in Figure 4 took more than 12 hours. Furthermore, the nature of Abaqus' explicit dynamic analysis with automatic time stepping
introduces discontinuities in the dependence of the objective function on the design variables. Therefore, we solve the current optimisation problem, Eq. (9), using evolutionary algorithms [13]. Figure 5 shows the integrated computational workflow for the optimization-based design of these energy-absorbing metamaterials. This is a straightforward adaptation of the workflow developed by the authors for the optimization of the microstructure in 3D printed duplex steels [9].

[picture]

Fig. 5. Workflow for energy absorption optimisation in metamaterials.

Fig. 5. Workflow for energy absorption optimisation in metamaterials.

Results

The original version of this paper is available on https://www.scientific.net/KEM.1047.213.pdf

The stress-strain response of the current metamaterials is obtained using the commercial finite element software Abaqus. The material behaviour is modelled using the Ogden hyperelastic law calibrated by Smith et al [2]. Regardless of the loading rate, the use of Abaqus' explicit dynamic solver is mandatory, since, unlike the static solver, it includes the general contact model. This contact model does not require to explicitly define every possible contacting pair, which is impractical (even impossible) in presence of folding, buckling, and large deformation problems where parts may touch unexpectedly and self-contact occur. Given its robustness, it has been chosen by Townsend et al [1] and Smith et al [2]. Following these authors, we also assume a Coulomb-type friction between all contact surfaces. We adopt a sliding coefficient of friction equal to 0.75 , value proposed by Smith et al [2] to imitate real-world conditions of contact between plastics, other elastomers, or flesh.

The lack of such a robust contact solver in Salome_Meca precludes the use of this free and opensource software for the current purposes. On the other hand, the high cost of the Abaqus license for the current, highly time-consuming problems, obliges us to limit the number of iterations when solving the optimisation solution problem to 20 , less than those that are typically needed to satisfy a convergence criterion.

Other simplifications for limiting the computational cost concern the design variables. We will consider only one material. Hence, only the geometrical design variables are kept. Further, the categorical design variables are allowed to take just a few levels: two for the wall thickness t ( 0.6 and 0.8 mm ), and two for the number of folds nz (6 and 8). A larger freedom is allowed for the prebuckling parameter (0≤p≤0.8), but a unique value is adopted to the whole structure instead of changing across the loading direction, as proposed by Smith et al [2].

We assume σthr =1MPa, which gives a reasonable security margin to keep the peak transmitted stress below 1.5 MPa as required for head health applications [6].

Figure 6 shows some of the designs evaluated in the optimisation process. The best design has prebuckling p=0.6, wall thickness t=0.6 mm, and nz=6 folds in the compression direction.

[picture]

Fig. 6. Absorbed energy per unit volume under compression up to the stress threshold ( 1 MPa ) for different values of prebuckling (p), wall thickness (t), and folds in the loading direction (nz).

Fig. 6. Absorbed energy per unit volume under compression up to the stress threshold ( 1 MPa ) for different values of prebuckling (p), wall thickness (t), and folds in the loading direction (nz).

Figure 7 shows the σ(ε) curves for some designs, highlighting the crucial role of the design variables in the energy absorption performance. Low prebuckling may lead to an excessive stress peak, above the prescribed threshold σthr, considerably shortening the useful energy absorption range. Large prebuckling can make the metamaterial excessively soft, lowering the energy absorption even if the useful range is large. Increasing the wall thickness makes the metamaterial stiffer, showing a first stress peak that may exceed σthr. Compared to the best design, a design having the same prebuckling ( 0.6 ) and wall thickness ( 0.6 mm ) but more folds in the loading direction ( 8 instead of 6 ) was stiffer, showing a first stress peak that exceeds σthr. More designs with varying nz are needed to explain the cause of this difference.

[picture]

Fig. 7. Stress-strain curves compression for different values of prebuckling (p), wall thickness (t), and folds in the loading direction (nz).

Fig. 7. Stress-strain curves compression for different values of prebuckling (p), wall thickness (t), and folds in the loading direction (nz).

Finally, some of the designs were fabricated and tested for validation purposes. We used the fuse filament fabrication (FFF) method, with Prusament TPU 95A (having Shore A hardness 95) as the base material (filament). Figure 8 shows the stress-strain curve under compression for the best sample ( p=0.6,t=0.6 mm, and nz=6 ), as determined using Abaqus and experimentally. Beyond the uncertainties in the numerical model (material, boundary conditions, contact properties) to reproduce the experiment, numerical and experimental results show a satisfactory agreement. The absorbed energy is noteworthy well predicted: 0.225 MPa from the Abaqus' results, 0.220 MPa from the experiment.

[picture]

Fig. 8. Experimental and numerical stress-strain curves and absorbed energy per unit volume (A) below 1 MPa stress threshold for the metamaterial on the right.

Fig. 8. Experimental and numerical stress-strain curves and absorbed energy per unit volume (A) below 1 MPa stress threshold for the metamaterial on the right.

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.213.pdf

In this work, we define a unique parameterised metamaterial that unifies recently proposed origami honeycombs [1] and plate-lattice metamaterials [2], both shown to outperform conventional foams in energy absorption and impact mitigation.

An evolutionary optimisation is performed to maximise energy absorption while constraining the peak stress below a prescribed threshold, using the metamaterial parameters as design variables. For each candidate design, the objective function-defined as the absorbed energy per unit volume over the strain interval from zero up to the strain at which the peak stress reaches the threshold-is computed by integrating the stress-strain response obtained from finite element simulations of compression tests. These simulations are conducted using the commercial software Abaqus, selected for the robustness of its general contact formulation. However, the high licensing cost of Abaqus obliged us to adopt an over-constrained design space containing a small number of design variables (pre-buckling, wall thickness, and number of folds), few discrete levels for each, and to allow a low total number of objective function evaluations in the optimization process. That's why we prefer to call "best" instead of "optimal" the design resulting from the current optimization process.

Despite these limitations, the results clarify the central roles of buckling and contact: the former limits the initial stress peak, while the latter enhances post-buckling energy absorption. The findings further indicate substantial potential for achieving truly optimal metamaterial designs by exploiting the full geometric freedom afforded by additive manufacturing to define a richer design space. But this richer design space should be adequately explored, which requires to avoid licensing constraints. So, future work will focus on identifying free software with robust contact algorithms capable of handling large deformations.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/KEM.1047.213.pdf

This work has been done under the internal project SEP-25 BU #102032862: Optimised design and production of metamaterials, funded by SINTEF.

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.213.pdf

S. Townsend, R. Adams, M. Robinson, B. Hanna, P. Theobald. 3D printed origami honeycombs with tailored out-of-plane energy absorption behavior, Materials and Design 195 (2020) 108930. https://doi.org/10.1016/j.matdes.2020.108930.

L. Smith, B. Hayes, K. Ford, E. Smith, D. Flores, R. MacCurdy. Tunable metamaterials for impact mitigation, Adv. Mat. Technol. 9 (2024) 2301668. .

V. Fachinotti, I. Peralta, A. Albanesi, Optimization-based design of an elastostatic cloaking device, Sci. Rep. 8 (2018) 9857. .

Y. Xiao, J. Yin, X. Zhang, X. An, Y. Xiong, Y. Sun. Mechanical performance and cushioning energy absorption characteristics of rigid polyurethane foam at low and high strain rates, Polym. Test. 109 (2022) 107531. .

J. Miltz, G. Gruenbaum, Evaluation of cushioning properties of plastic foams from compressive measurements. Polym. Eng. Sci. 21 (1981) 1010-1014. .

B.J. Ramirez, V. Gupta, Evaluation of novel temperature-stable viscoelastic polyurea foams as helmet liner materials, Materials and Design 137 (2018) 298-304. .

M. Shinde, I.E. Ramirez-Chavez, A. Potts, D. Bhate, A critical assessment of the onset strain of densification in the evaluation of energy. Manufacturing Letters 41 (2024) 708-719. .

Z. Michalewicz, M. Schoenauer. Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Computation 4 (1996) 1-32. .

V.D. Fachinotti, S. Gouttebrozze, S. Dumoulin, X. Ren. Integrated computational framework for the optimization of the microstructure in additive manufacturing of metals. Computational Materials Science 256 (2025) 113944. .

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 223-230

doi: 10.4028/p-3AelE3

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-19

Revised:
2026-02-18

Accepted:
2026-02-25

Online:
2026-04-13

Feasibility of Direct 3D Printing Foam Polylactic Acid with MEX

The original version of this paper is available on https://www.scientific.net/KEM.1047.223.pdf

Batagiannis Athanasios 1,a, Panagiotis Karmiris-Obratański 2, b, loannis T. Christodoulou 1,c, Emmanouil L. Papazoglou 1, d, Angelos P. Markopoulos 1,e*

1 School of Mechanical Engineering - Laboratory of Manufacturing Technology, National Technical University of Athens, Athens, Greece

2 Advanced Manufacturing Laboratory, Department of Manufacturing Systems, Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, Krakow, Poland

ªbatagiannis.a@gmail.com, b karmiris@agh.edu.pl, cichristodoulou@mail.ntua.gr, d  mlpapazoglou@gmail.com, e  amark@mail.ntua.gr

Keywords: material extrusion (MEX), PLA - foam PLA, different temperatures - flow rates, tensile properties.

Abstract

Additive manufacturing of polymeric foams via Material Extrusion (MEX) is an attractive route to lightweight components with tunable mechanical response. However, reproducible performance remains challenging because foam expansion, cell stabilization, and inter-layer bonding are strongly governed by the thermal-processing window. This study evaluates the feasibility of directly printing a commercial PLA foaming filament and quantifies the influence of nozzle temperature (and the associated flow-rate adjustment) on density and tensile behavior. ISO 527-2 tensile specimens were printed under three printing-condition combinations, a nominal PLA setting (190∘C,100% flow) and two foaming-window settings ( 250∘C,55% flow and 270∘C,50% flow). Tensile tests were conducted, and the tensile properties were assessed via Young's Modulus, Yield and Ultimate properties (stress/strain), and elastic and total absorbed energy up to fracture. In addition to absolute values, all relevant metrics were normalized by relative density to enable robust comparisons across foaming levels. Finally, the DIC maps at the Yield and Fracture point were used to support the derived results and conclusions.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.223.pdf

Additive manufacturing (AM) of polymeric foams is attracting increasing interest due to its ability to merge layer-wise fabrication with cellular material architectures [1,2]. This integration gives the ability to develop lightweight components with tunable mechanical performance [3]. AM allows precise control over part morphology and internal porosity, which is difficult to achieve using conventional foam manufacturing routes due to its geometric freedom [4].
Among the available AM techniques, material extrusion (MEX)-widely known as fused filament fabrication (FFF)-is one of the most established and commercially prevalent technologies [5]. In MEX, three-dimensional components are fabricated directly from computer-aided design (CAD) data through the sequential deposition of molten thermoplastic material. Compared with traditional manufacturing techniques, MEX offers superior design flexibility, cost-effective fabrication of complex geometries, rapid production cycles, and straightforward customization using relatively lowcost materials and equipment [6].
Despite its technological maturity, the application of MEX to polymeric foams remains comparatively underexplored, particularly with regard to the controlled generation of cellular structures during the printing process [7]. The introduction of foaming agents and mechanisms within the extrusion stage enables the fabrication of lightweight components with reduced material consumption and enhanced functional performance [8]. However, the resulting foam morphology and mechanical behavior are strongly influenced by the type and size of the foaming agent, as well as the printing parameters and thermal history, which govern gas expansion, cell stabilization, and interlayer adhesion [9]. Damanpack, Sousa and Bodaghi [10] investigated the influence of printing temperature and flow rate
on the tensile mechanical properties and porosity of the printed samples. The authors developed an analytical model that can correlate the mechanical properties with the porosity of PLA foams. In the study by Abu Hassan et al. [11] investigated the influence of the foaming agent and the printing temperature on the properties of PLA/kenaf composites. The findings show that lower extrusion temperatures promote finer and more uniform cell structures, although this was accompanied by a reduction in mechanical properties.
Based on the abovementioned, a systematic investigation of the relationship between processing conditions and foam characteristics is essential to ensure reproducibility and performance consistency in additively manufactured polymeric foams, given that even small variations in printing temperature can lead to measurable changes in material properties. The present study examines the influence of nozzle temperature on the resulting density and tensile performance of a PLA foaming filament. Three printing temperatures were selected: one close to the nominal processing temperature of conventional PLA and two within the material's foaming window, in conjunction with appropriate adjustments of the flow rate. The tensile response was characterized in terms of Young's Modulus (YM), Yield Stress (YS) and the corresponding Yield Strain (YStr), Ultimate Tensile Strength (UTS) and the corresponding Ultimate Tensile Strain (UTStr), as well as the Total absorbed Energy up to fracture (TE) and the Elastic Energy (EE). In addition to the absolute values, density-normalized properties were calculated using the relative density, enabling more robust comparisons across foaming conditions. Finally strain data obtained via digital image correlation (DIC) were also used to support the assessment of the material's tensile behavior.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/KEM.1047.223.pdf

The specimen geometry followed BS EN ISO 527-2:2012 and the parts were fabricated via material extrusion (MEX) on a Flashforge Creator 3 using 1.75 mm PLA foaming filament. The "Standard" slicing profile was employed with a 0.8 mm nozzle. Specifically, a fixed layer-height strategy was adopted with a nominal layer height of 0.30 mm and an increased first-layer height of 0.40 mm . The perimeter configuration employed three shells (nominal shell thickness ≈2.40 mm ) with 10% perimeter overlap, and seam placement was controlled via start-point selection ("closest to specific location"). Full-density infill ( 100% ) was used with a line pattern (start angle 45∘, cross angle 90∘ ), combined with 4 top solid layers and 3 bottom solid layers and a 15% infill-perimeter overlap, whilst the infill combining option was disabled (maximum solid and sparse combine =1 ). The base print speed was 60 mm/s with a travel speed of 100 mm/s, a minimum speed of 20 mm/s, and the first layer printing speed was limited to 40 mm/s. Finally, the exterior wall speed was capped at 40 mm/s and interior wall maximum speeds were set to 60 mm/s. Keeping in mind that the aim of the present work is to investigate the effect of printing temperature on the tensile properties of 3D-printed PLA foaming material, three nozzle temperature - flow rate combinations were examined. The tested conditions spanned from the typical PLA processing window ( 190∘C at 100% flow rate) up to a hightemperature, reduced-flow regime ( 250∘C at 55% flow rate and 270∘C at 50% ).
The tensile properties were quantified in terms of YM, YS and the corresponding YStr, UTS and the corresponding UTStr, as well as the TE up to fracture and the EE. Since specimens produced at elevated nozzle temperatures exhibit significant foaming, and thus reduced density, direct comparison of the as-measured tensile properties based solely on absolute values is not meaningful. Therefore, in addition to reporting the absolute properties, all values (YM, YS, UTS, EE and TE) were normalized with respect to the relative density of the specimens for each printing condition. The relative density was defined as the ratio of the specimen nominal density to the nominal density of solid PLA ( 1.24 g/cm3[12]). The specimen nominal density was calculated by dividing the experimentally measured mass by the CAD-based specimen volume ( 8.55 cm3 ). Any deviations between the manufactured specimen dimensions and the CAD geometry were considered minor and are not expected to materially affect the results or the conclusions drawn.
Finally, all tensile tests were performed using a Shimadzu universal testing machine equipped with a 20 kN load cell and an optical extensiometer for precise strain acquisition, utilizing a 16 MP CCD camera operating at 5 fps . The tensile loading rate was set to 2 mm/min. Additionally, Digital Image

Correlation (DIC) software (xsight) was employed to obtain full-field strain maps and to validate the strain measurements acquired by the optical extensiometer, enabling a detailed analysis of strain localization and deformation evolution during loading. For this purpose, each specimen was prepared by applying a uniform white matte primer coating (plastic skin), followed by the deposition of a random black speckle pattern on the gauge section to ensure adequate image contrast and reliable DIC correlation throughout the test.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1047.223.pdf

Table 1 summarizes the measured tensile metrics and the corresponding relative-density-normalized values for all printing conditions.

Table 1. Tensile properties and the respective reduced to relative density values.

| Rel. Density | YM (MPa) | YS (MPa) | TYStr | UTS (MPa) | UTStr | EE (MJ/m³) | PE (MJ/m³) | TE (MJ/m³)
T190 | 80% | 543 | 16.11 | 2.23% | 16.876 | 2.85% | 0.22 | 1.77 | 2.00
T250 | 40% | 173 | 5.45 | 1.61% | 5.951 | 2.33% | 0.06 | 1.85 | 1.92
T270 | 41% | 213 | 4.95 | 2.31% | 5.426 | 24.90% | 0.06 | 1.23 | 1.29
Reduced to relative density tensile properties
T190 | 80% | 677 | 20.09 | 2.23% | 21.05 | 2.85% | 0.28 | 2.21 | 2.49
T250 | 40% | 431 | 13.54 | 1.61% | 14.79 | 2.33% | 0.16 | 4.61 | 4.77
T270 | 41% | 524 | 12.17 | 2.31% | 13.35 | 24.90% | 0.16 | 3.02 | 3.18

In Fig. 1 the dominant role of foaming-induced density reduction on stiffness is presented, while also indicating an additional temperature-dependent microstructural effect beyond density alone. The relative density decreases markedly from ~0.80 at T190 to ~0.40−0.41 at T250-T270, consistent with strong in situ cell nucleation and expansion at elevated nozzle temperatures and reduced flow rate. This density drop is reflected in the absolute Young's modulus, which falls from 543 MPa (T190) to 173 MPa (T250) and 213 MPa (T270). However, after normalization by relative density, the reduced YM remains lower for the foamed conditions ( 677 MPa at T190 versus 431 and 524 MPa at T250 and T270 respectively), implying that the stiffness loss cannot be attributed solely to reduced solid fraction. This behavior may be explained by three main reasons. The transition from a predominantly solid "skin-core" structure at T190 to a more compliant cellular core at higher temperatures, the increased inter-layer compliance due to gas expansion during deposition and reduced effective interlayer adhesion area, and possible changes in crystallinity/thermal history that alter the intrinsic stiffness of the polymer matrix. Notably, T250 and T270 exhibit nearly identical relative density but different modulus values, suggesting that the foam morphology (cell size distribution, cell-wall thickness, and degree of coalescence) along with the interlayer bonding quality vary with nozzle temperature even at similar bulk density, thereby modulating stiffness.

[picture]

Fig. 1. a) Young's Modulus (bar charts), reduced Young's Modulus (line) and b) Relative density for the different printing temperatures.

Fig. 1. a) Young's Modulus (bar charts), reduced Young's Modulus (line) and b) Relative density for the different printing temperatures.

Based on the data presented in Fig. 2, yielding is highly sensitive to both density reduction and the thermo-foaming regime, since strength is degrading more than can be explained by density alone. The YS decreases from 16.11 MPa (T190) to 5.45 MPa (T250) and 4.95 MPa (T270), consistent with reduced load-bearing cross-section and stress concentrations introduced by pores. Density normalization partially collapses this trend but does not eliminate it since the reduced YS drops from 20.09 MPa (T190) to 13.54 MPa (T250) and 12.17 MPa (T270), indicating that the effective yield mechanism is additionally governed by microstructural integrity like the cell-wall continuity, the interlayer bonding quality and the void morphology.

[picture]

Fig. 2. a) Yield Strength (bar charts), reduced Yield Strength (line) and b) Yields Strain for the different printing temperatures.

Fig. 2. a) Yield Strength (bar charts), reduced Yield Strength (line) and b) Yields Strain for the different printing temperatures.

The YStr behavior is also interesting since T250 exhibits the lowest YStr ( 1.61% ), implying earlier onset of irreversible deformation, possibly due to weaker bonding and more heterogeneous porosity, whereas T270 shows a higher YStr ( 2.31% ), suggesting a shift toward more stable pre-yield deformation and delayed yielding due to improved chain mobility and interdiffusion at the highest nozzle temperature partially counteracting the foaming-induced heterogeneity. Overall, the combined trends support the interpretation that higher-temperature printing generates a more compliant, porosity-dominated structure with reduced yield capacity, while the yield strain reflects the balance between foam morphology and thermal crystallinity effects that promote a more uniform deformation. In Fig. 3, a significant reduction in tensile strength with foaming is presented, accompanied by a substantial change in post-yield deformation capacity, especially at the highest nozzle temperature. The UTS drops from 16.876 MPa (T190) to 5.951 MPa (T250) and 5.426 MPa (T270), consistent with the lower effective load-bearing area and premature damage initiation at pores and inter-road defects. Normalization by relative density again reduces, but does not remove, the differences ( 21.05 MPa at T190 versus 14.79 MPa at T250 and 13.35 MPa at T270), reinforcing that the strength reduction is not purely a density effect and likely reflects reduced interlayer bonding efficiency, larger and less uniform cells, and earlier microcrack nucleation at cell-wall junctions. In contrast, the ultimate strain shows a qualitatively different response. More specifically, while T190 and T250 exhibit low UTStr ( ~2−3% ), T270 shows a notably high UTStr ( 24.90% ), suggesting a transition from relatively brittle and limited-strain failure to a highly ductile, stable deformation regime. Mechanistically, this response can arise from a more extensible foam network in which deformation proceeds through progressive cell-wall stretching and reorientation. In this regime, the UTS shifts away from the initial elastic region and occurs at higher strains, closer to the final fracture strain, indicating that the material exhibits limited strain hardening up to fracture.

[picture]

Fig. 3. a) UTS (bar charts), reduced UTS (line) and b) UTStr for the different printing temperatures.

Fig. 3. a) UTS (bar charts), reduced UTS (line) and b) UTStr for the different printing temperatures.

Finally, Fig. 4 consolidates the mechanical implications of foaming by separating stiffness-dominated energy storage from the overall energy absorption capacity up to fracture, and it reveals that density normalization can invert the interpretation of "performance". The EE is highest for the dense material (0.22MJ/m3 at T190) and drops to 0.06MJ/m3 for both foamed conditions, which is expected because EE scales strongly with YM and YS / YStr, which have both been reduced by porosity. The absolute TE remains comparable between T190 ( 2.00MJ/m3 ) and T250 ( 1.92MJ/m3 ) but decreases at T270 (1.29MJ/m3), indicating that excessive foaming and/or microstructural degradation at the highest temperature reduces the material's ability to sustain load over large deformations before fracture. However, once normalized by relative density, TE increases substantially for the foamed conditions ( 2.49MJ/m3 at T190 versus 4.77MJ/m3 at T250 and 3.18MJ/m3 at T270), implying that, per unit solid material, the foamed architectures can dissipate more energy, a conclusion consistent with progressive damage mechanisms that are efficient energy sinks in cellular polymers. The fact that T250 provides the highest density-normalized TE suggests it offers the most favorable compromise between creating a cellular structure (enhancing energy dissipation per unit mass) and preserving sufficient integrity to avoid premature catastrophic failure, whereas T270, limits absolute and normalized energy absorption despite increased ductility indicators.

[picture]

Fig. 4. a) Elastic absorbed energy (bar charts), reduced Elastic absorbed energy (line) and b) Total absorbed energy (bar charts), reduced Total absorbed energy (line) for the different printing temperatures.

Fig. 4. a) Elastic absorbed energy (bar charts), reduced Elastic absorbed energy (line) and b) Total absorbed energy (bar charts), reduced Total absorbed energy (line) for the different printing temperatures.

[picture]

Fig. 5. DIC maps for the different printing temperatures at the (a), (c) and (e) Yield Point and (b), (d) and (f) at the fracture point.

Fig. 5. DIC maps for the different printing temperatures at the (a), (c) and (e) Yield Point and (b), (d) and (f) at the fracture point.

The DIC maps of axial strain e1 in Fig. 5 provide direct evidence that the deformation mode transitions from largely uniform straining at the nominal PLA temperature (T190) to increasingly heterogeneous and localized deformation as the printing temperature enters the foaming window. At the Yield Point, the low-temperature condition of T190 exhibits a comparatively homogeneous strain field across the gauge section, which is consistent with stable load transfer and limited early strain localization. In contrast, the foamed conditions (T250 and T270) show clear strain gradients and banding and local "hot spots," indicating that yielding initiates preferentially in regions of locally reduced stiffness and strength (e.g., pore-rich zones, thinner cell walls, and weaker inter-layer adhesion). At the fracture point, the high-temperature foamed specimens (T250 and T270) develop substantially larger strains prior to rupture, with a broad region of elevated strain followed by the emergence of a dominant necking failure band, behavior that is consistent with progressive cell-wall stretching, bending, and reorientation and with gradual damage accumulation rather than abrupt catastrophic cracking. Conversely, the low temperature specimen (T190) fractures shortly after yielding, a result in agreement with the small yield-to-fracture strain gap reported in Table 1, whilst the final map is dominated by a narrow high-strain zone at the crack path. This pattern is typical of rapid localization and limited plastic redistribution, while the very high strain values at the immediate fracture line should be interpreted cautiously as they may be amplified by DIC decorrelation during crack initiation and opening.
To close the Results and Discussion section, it is worth noting a few limitations and directions for future work. First, the foaming response was evaluated mainly through bulk density and mechanical/DIC data, without direct microstructural quantification. Measurements of cell-size distribution, porosity fraction, cell-wall thickness, and possible skin-core gradients would strengthen the process-morphology correlation. Second, inter-layer bonding was assessed indirectly, while fracture-surface analysis and interfacial characterization could clarify bonding mechanisms in directly printed MEX foams. Finally, only three nozzle-temperature conditions (with corresponding flow-rate adjustments) and a single material were examined, so the findings should be interpreted within this parameter window and may not directly generalize to other systems. Future work should therefore combine systematic process mapping with quantitative morphology and bonding assessment to further reinforce the process-structure-property link.

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.223.pdf

In the current study, the effect of nozzle temperature, together with the corresponding flow-rate adjustment, on the foaming intensity and tensile response of material-extruded PLA foaming filament was investigated. ISO 527-2 tensile specimens were printed at one nominal PLA condition and two conditions within the foaming window. The tensile behavior was quantified via YM, YS, YStr, UTS, UTStr, EE and TE, supported by full-field DIC strain mapping. To enable meaningful comparisons across markedly different foam contents, the measured properties were additionally normalized by the relative density derived from specimen mass and CAD volume. Overall, nozzle temperature was shown to be an effective process lever to tune density and mechanical performance, while the results also demonstrate that small thermal shifts within the foaming window can lead to substantial changes in deformation mode and failure characteristics. More specifically, the main conclusions were:

Increasing nozzle temperature from the nominal PLA setting into the foaming window produced a pronounced reduction in relative density, confirming strong process sensitivity of cellular expansion. This density shift is the primary driver for the reduction of stiffness and strength in absolute terms.

Density normalization reduced, but did not eliminate, differences in YM and strength between the processing conditions. This indicates that foam morphology and inter-layer bonding quality influence the effective load-bearing efficiency in addition to bulk solid fraction.

The highest-temperature condition (T270) promoted substantially larger tensile strains prior to fracture, consistent with a more extensible cellular network and progressive cell-wall deformation. DIC maps corroborated this by showing a transition from comparatively uniform straining at nominal temperature to stronger strain localization and band formation in the foamed conditions.

In absolute terms, EE decreased strongly with foaming due to the reduction in stiffness, while TE depended on the balance between ductility and premature damage. When normalized by relative density, the foamed conditions exhibited improved energy absorption per unit solid material, highlighting the potential of controlled foaming for lightweight, energy-dissipative applications.

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The original version of this paper is available on https://www.scientific.net/KEM.1047.223.pdf

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 231-241

doi: 10.4028/p-W0OiLu

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-19

Revised:
2026-02-26

Accepted:
2026-02-26

Online:
2026-04-13

Development of AMC Welding Wire and Inline Hot Rolling for Enhanced Properties of DED-Arc/Wire-Manufactured Parts

The original version of this paper is available on https://www.scientific.net/KEM.1047.231.pdf

Tim Sebastian Tübbicke 1,a*, Fabian Dittrich 1, b and Sebastian Härtel 1,c

1 Department Hybrid Manufacturing BTU Cottbus Senftenberg, Konrad-Wachsmann-Allee 17, 03046 Cottbus, Germany

a*Tim.Tuebbicke@b-tu.de, b  Fabian.Dittrich@b-tu.de, c  Sebastian.Haertel@b-tu.de 

Keywords: additive manufacturing, AMC, inline hot rolling.

Abstract

The wire arc directed energy deposition (DED-Arc/wire) process offers great potential for the additive manufacturing of large-volume components thanks to high deposition rates and costeffective plant technology. Aluminium matrix composites (AMCs) are a high-performance alternative to conventional aluminium alloys, but their use in additive manufacturing has been limited so far due to porosity, restricted mechanical properties and a lack of semi-finished wire products. This paper presents a resource-efficient inline process chain for the additive manufacturing of silicon carbide-reinforced (SiC) AMCs (AMC-SiC). Since commercial AMC-SiC welding wires are not available, an AMC-SiC wire was produced from an aluminium tube (AW-6060) filled with AMCSiC chips by means of multi-stage rotary swaging to a diameter of 1.6 mm . The DED-Arc/wire welding tests carried out demonstrate the basic processability of the developed wire, but with pronounced porosity in the weld seam. By integrating an inline hot rolling process, the weld bead was significantly deformed and the porosity was significantly reduced, thereby expanding the application potential of additively manufactured AMC-SiCs.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.231.pdf

Additive manufacturing is a continuously growing manufacturing process that offers economic and ecological advantages over other manufacturing processes due to the possibility of saving material during component production [1-3]. In particular, the wire arc direct energy deposition (DEDArc/wire) process offers the possibility of efficiently manufacturing larger components thanks to high deposition rates and cost-effective plant technology [2].
Conventional aluminium (Al) alloys often reach their limits in certain areas of application. These limitations can be reduced by using Al matrix composites (AMC), as these have excellent specific strength values, higher wear and temperature resistance, and lower thermal expansion [4]. The challenges in additive manufacturing, on the other hand, are reduced mechanical properties and an accumulation of pores in the resulting structure [5]. The production of an AMC-SiC welding wire and the inline hot rolling of the applied weld seam are intended to create a homogenised welding microstructure due to dynamic recrystallisation and closed pores [6]. This would significantly improve and expand the usability and range of applications of AMCs in the future.
The aim is to develop a resource-efficient inline process chain, which is primarily used for the targeted adjustment of the properties of silicon carbide-reinforced AMCs in AM. The AMC-SiC welding wire was manufactured in-house, as there is currently no AMC-SiC wire available on the market. AMC-SiC chip waste was used for this purpose. This has the advantage that it can be used directly and does not require energy-intensive melting to create the welding wire. In addition to this ecological advantage, process control and costs are significantly improved without the need for remelting. The AMC-SiC chips, measuring approximately 500 to 710μ m, were filled into a tube (AW-6060, dimensions 10×2×300 mm3 ) and then reduced to the required minimum diameter of 1.6 mm through rotary swaging in several stages.

Material und Methodology

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Materials

The original version of this paper is available on https://www.scientific.net/KEM.1047.231.pdf

As there is currently no commercially available AMC−SiC welding wire, the required wire was produced independently by the Department Hybrid Manufacturing (DHM) at the Brandenburg University of Technology Cottbus-Senftenberg (BTU). The starting point for production is silicon carbide-reinforced (SiC) AMC chips, which were provided by CMMC GmbH in Chemnitz, Germany. The AMC chips used consists of an AlSi9Mg alloy as a matrix and is reinforced with SiC with a grain size of F320 (29μ m) and a reinforcement content of approx. 20% by volume. The initial structure of the AMC-SiC alloy used is shown in Figure 1.

[picture]

Fig. 1. Initial microstructure of the AMC alloy used, magnified 100 times.

Fig. 1. Initial microstructure of the AMC alloy used, magnified 100 times.

These AMC-SiC chips are waste products from the machining of AMC-SiC semi-finished products. This approach aims to use the waste products from semi-finished products to produce welding wires without significant additional energy expenditure, which can then be used for the maintenance or repair of AMC components. The AMC-SiC chips used were not subjected to any pre-treatment, such as drying or similar. The reason for this was to be able to better examine the subsequent inline postprocessing. The purpose is to produce a weldable AMC-SiC wire with a diameter of at least 1.6 mm from these AMC-SiC semi-finished products. In addition to the AMC-SiC material, an aluminium tube made of AW-6060 is also used to manufacture the wire, and all subsequent welding tests are carried out on a substrate plate made of AlMgSi0.5.

Methodology

The original version of this paper is available on https://www.scientific.net/KEM.1047.231.pdf

The FR 25 - VU rotary swaging machine from Felss Systems GmbH was used to manufacture the AMC-SiC welding wire. This machine makes it possible to gradually reduce the maximum diameter of 10 millimetres through forming. The forming steps are carried out in such a way that the diameter of the semi-finished product is reduced by approximately 9% in each step.
The production of AMC-SiC welding wire based on the DFHs previous experience with selfmanufactured filler wires for additive manufacturing [7] and is shown schematically in Figure 2.

[picture]

Fig. 2. Schematic illustration of the structure of AMC welding wire.

Fig. 2. Schematic illustration of the structure of AMC welding wire.

The recycled AMC-SiC chips were filled into an aluminium tube (AW-6060) measuring 10×2×300 mm3 and sealed at both ends. The size of the AMC-SiC chips ranged between 500 and 710μ m. The forming took place in several steps up to a wire diameter of approximately 1.6 mm .

Additive Manufacturing

The original version of this paper is available on https://www.scientific.net/KEM.1047.231.pdf

The welding tests were carried out using a mobile pentapod from the company Metrom GmbH as the guide machine and connection point for the welding torch. Figure 3 shows the equipment used, including the pentapod.

[picture]

Fig. 3. Equipment used for additive manufacturing.

Fig. 3. Equipment used for additive manufacturing.

The welding machine used was a Fronius tps400i. The welding tests with the manufactured AMCSiC welding wire were carried out on a 10 mm thick aluminium substrate plate ( AlMgSi 0.5 ). An AlSi 5 variant was used as the characteristic curve, as there is no explicit AMC−SiC characteristic curve available within the Fronius welding machine. The general welding parameters can be found in Table 1.

Table 1. Process parameters for additive manufacturing with AMC-SiC wire.

Welding parameters | Values | Units
Welding current | 187 | A
Welding voltage | 15.3 | V
Wire feed speed | 5 | m/min
Power | 3.40 | kW
Stickout | 15 and 20 | mm
Welding speed | 400 and 600 | mm/min
Shielding gas | 100 % argon |
Welding wire diameter | 1.6 | mm

Inline Hot Rolling

The original version of this paper is available on https://www.scientific.net/KEM.1047.231.pdf

Inline hot rolling within the process chain is implemented using a specially manufactured rolling tool from the company Ecoroll AG. The rolling tool and its connection to the guide machine are shown in Figure 4.

[picture]

Fig. 4. Illustration of the rolling tool (left) and the inline process (right).

Fig. 4. Illustration of the rolling tool (left) and the inline process (right).

The rolling tool moves together with the welding torch and reshapes the resulting weld seam approximately 30 mm after application. Rolling forces of 4.5 to 11 kN can be set. The rolling force is generated via the spring deflection of the built-in disc springs and can be controlled via the stickout or the spacer plates between the tool and the system.
The inline reconsolidation process shown was carried out prior to welding with AMC-SiC welding wire using commercially available aluminium welding wire ( AlSi 10 Mg ). This was necessary due to the lack of AMC-SiC material. The preliminary tests are also considered in the results section.

Material Characterization

The original version of this paper is available on https://www.scientific.net/KEM.1047.231.pdf

The samples were ground (SiC grit #1200, #2000, and #4000) and polished ( 3μ m diamond suspension and 0,025μ m OPS solution) in multiple steps using a Struers Tegramin 30. The samples were investigated without etching using a Keyence VHX-7000 digital microscope and a Thermofisher Scientific Phenom XL Generation 2 tabletop SEM.

Results

The original version of this paper is available on https://www.scientific.net/KEM.1047.231.pdf

AMC Welding Wire Production

The original version of this paper is available on https://www.scientific.net/KEM.1047.231.pdf

The AMC-SiC welding wire could be manufactured without any restrictions. The minimum target diameter of 1.6 mm was achieved. A further reduction of the diameter to 1.2 mm was not pursued due to previous experience with other wires. The optical microscope analysis of the embedded AMCSiC welding wire, shown in Figure 5, shows the inclusion of the AMC chips, including the SiC, in the manufactured wire.

[picture]

Fig. 5. Optical microscope examination of the manufactured AMC welding wire (left) and SEM analysis of the AMC material from inside the wire.

Fig. 5. Optical microscope examination of the manufactured AMC welding wire (left) and SEM analysis of the AMC material from inside the wire.

The SiC particles are clearly visible under the optical microscope. In general, the size of the particles has decreased slightly compared to the size in the AMC-SiC chips. The SiC particle size in the AMC chips was 20 to 30μ m. The average particle size is smaller, but certain particles still reach the original size. The wire core surface area is roughly 25% of the Al -mantle surface area, resulting in an approximately 8% particle content in a successful AMC-SiC weld seam. This theoretically achievable strengthening phase content could be increased either by decreasing the mantle thickness and/or increasing the SiC-content in the base material chips.
A total of five AMC-SiC welding wires were produced in two different batches, as only enough AMC-SiC chips for two wires were available during the first wire production. Accordingly, the first batch was first welded completely and then the second batch was produced with new starting material. The total length of the welding wire available at the end was approximately 26 m .

AMC Welding Test Series 1

The original version of this paper is available on https://www.scientific.net/KEM.1047.231.pdf

The first welding tests with the self-produced AMC-SiC wire demonstrated the basic workability of the material combination. The general DED-Arc/wire process could be repeated without any process interruptions. During the process, there was significant spatter formation and a marked increase in smoke development. This indicates unstable molten pool control and potential gas release during the melting process. The resulting weld seam, which is shown in Figure 6, have irregular geometries and high porosity.

[picture]

Fig. 6. Cross-section of the first AMC weld seam under the optical microscope.

Fig. 6. Cross-section of the first AMC weld seam under the optical microscope.

A hydrogen analysis of the AMC welding wire showed a significantly increased hydrogen content compared to commercially available welding consumables such as AlSi 10 Mg , which was used in preliminary tests for inline hot rolling. This fact was initially accepted in order to better investigate the subsequent inline hot rolling process with the rolling tool. Extensive welding tests to optimize the welding parameters and thereby improve the weld seam results were not possible due to the limited amount of available material.

AMC Welding Test Series 2

The original version of this paper is available on https://www.scientific.net/KEM.1047.231.pdf

In the second batch of AMC-SiC welding wire, three wires with a diameter of approx. 1.6 mm were produced. Hydrogen analysis of the wires shows a significant reduction in hydrogen content. This value was only three times higher than that of commercially available wire and thus only one tenth of the value of the other batch. This value was deliberately maintained in order to better investigate the expected improvement in the weld seam through inline hot rolling. Further hydrogen reduction through cleaning and drying of the recycled AMC-SiC chips would be possible and ultimately desirable. The reduced hydrogen content led to more stable process behaviour with fewer spatters and less smoke development during the welding test. The resulting seam, shown in Figure 7, has a slightly more uniform surface structure and is also slightly better in terms of shape than the comparison seams from batch 1 .

[picture]

Fig. 7. Cross-section of the AMC weld seam produced with wire from batch 2 under an optical microscope.

Fig. 7. Cross-section of the AMC weld seam produced with wire from batch 2 under an optical microscope.

The improved seam quality is related to the reduced hydrogen content of the wire, which indicates that the gas content in the starting material has a significant influence on process stability and seam formation. The observed result can be further improved with better pre-treatment of the starting material. The seam examined still has a hydrogen content three times higher than the reference seam. Careful variation of the process parameters through various welding tests with sufficient AMC-SiC welding wire would also help to further improve the welding result.

Inline Hot Rolling Pre-Tests

The original version of this paper is available on https://www.scientific.net/KEM.1047.231.pdf

Due to the limited availability of AMC welding wire, preliminary tests for inline hot rolling were carried out using a welding wire made from the Al alloy AlSi 10 Mg . This material is very similar to the matrix material of the AMC and is therefore well suited for preliminary tests.
Using the inline post-compaction method shown, the AlSil0Mg weld seams were formed immediately after application by the rolling tool. A comparison of the unrolled and rolled seams is shown in Figure 8.

[picture]

Fig. 8. Comparison of CT scans of the normal weld seam with the inline recompacted seam, including colored pore analysis.

Fig. 8. Comparison of CT scans of the normal weld seam with the inline recompacted seam, including colored pore analysis.

The CT images clearly show that the porosity of the applied weld seam has decreased significantly, especially in the upper area of the seam. The porosity across the entire weld seam, of 50 mm , was reduced from approx. 5% to around 1% through inline hot rolling. This effect can also be seen in the fundamental simulation of inline hot rolling process in Figure 9.

[picture]

Fig. 9. Simulation result regarding the effective plastic strain of an comparable 50 mm aluminium weld seam.

Fig. 9. Simulation result regarding the effective plastic strain of an comparable 50 mm aluminium weld seam.

Plastic deformation is at its highest directly below the surface. This leads to the pores close to the surface being closed in particular.

Inline Hot Rolling with AMC Welding Wire

The original version of this paper is available on https://www.scientific.net/KEM.1047.231.pdf

Inline hot rolling with AMC-SiC welding wire also worked. As can be seen in Figure 10, the percentage reduction in the height of the applied weld seam is significantly higher than in the preliminary test.

[picture]

Fig. 10. Cross-section from optical microscope analysis - comparison of normal AMC weld seam (left) with inline recompacted weld seam (right).

Fig. 10. Cross-section from optical microscope analysis - comparison of normal AMC weld seam (left) with inline recompacted weld seam (right).

This is mainly due to the porosity of the AMC-SiC weld seam. The rolling tool presses the seam more strongly through these larger pores, as there is less material present. Unfortunately, it was not possible to perform a comparative CT analysis of the porosity, as was done for the AlSil0Mg seam, because the evaluation software for porosity analysis was unable to examine the AMC-SiC seams. Nevertheless, the comparative image of the microscopic analysis of the cross-section shows that the porosity in the AMC-SiC weld seam is significantly reduced. Here, too, it can be seen that the compression is more pronounced in the upper part of the seam.
Even in the unetched state, distinct microstructural features were visible in all samples. Figure 11 illustrates needle-like features identified as a non-SiC phase throughout the matrix of the deposited material. The needles vary in length from 10μ m up to 100μ m, with a width of up to 10μ m for the bigger needles. They are distributed mostly homogenously through the matrix. Only in some samples, localized areas with fewer or no needles were observed.

[picture]

Fig. 11. Optical microscope image of the cross-section of an AMC weld seam.

Fig. 11. Optical microscope image of the cross-section of an AMC weld seam.

Investigating these features with the SEM revealed the plate-like characteristic of the needle-shaped features in the matrix, see Fig. 12. The pore surfaces were covered with these plates, exhibiting various shapes, including dendrites and hexagons. EDS measurements on the needles/plates were not successful in identifying the phase. From a point measurement on a singular plate (indicated in Fig. 13 by a red arrow), it can just be assumed that the phase is a complex aluminum ( 30.6at.% ) silicon ( 2.3at.% ) carbon ( 25.9at.% ) oxygen ( 41.2at.% ) phase. Due to the small size of the plate, only low counts were registered in the detector over the 60 -second measuring time, resulting in a rough estimate of the true composition of this phase only.

[picture]

Fig. 12. SEM image of the cross-section of an AMC weld seam.

Fig. 12. SEM image of the cross-section of an AMC weld seam.

Moreover, next to no SiC particles were found in any of the samples. Only singular particles were found with sizes smaller than 10μ m (compared to the 20−30μ m measured in the base material/wire). Fig. 13 shows a former SiC particle likely during its dissolution during the process. Note that the former particle edge is lined with what appears to be the needle-like phase in the rest of the matrix.

[picture]

Fig. 13. Disintegrating SiC particle in an AMC welded seam.

Fig. 13. Disintegrating SiC particle in an AMC welded seam.

The observed microstructure of the sample does not meet the expectation of a welded layer with an aluminum matrix reinforced with SiC particles. This finding is consistent with those of Xi et al. [8]. Their team investigated the processability of SiC reinforced AlSi10Mg via PBF-LB. They also report the disappearance of SiC after processing their base material. However, they did not discuss the reason for this observation. For this investigation, the dissolution of the SiC is likely a result of the process itself. Arc temperatures are reported to be higher than 6500 K [9]. Naturally, the processed material does not reach these temperatures, as that would mean that the material is starting to evaporate. However, melt pool temperatures exceeding 3000 K for short periods are realistic. Fig. 14 supports the idea that temperatures at least reach the decomposition temperature of SiC(3100 K[10]). The free silicon will readily dissolve in the aluminum matrix, while the free carbon has the potential to react with various partners, such as aluminum (forming aluminum carbide, Al4C3 or Al4SiC4 ), and aluminum oxide (forming Al4O4C ). Liu et al. break down possible chemical reactions in their study [11]. Assuming these interactions are applicable to this investigation, the disappearance of the SiC particles, as well as the appearance of another phase, could be explained.

Summary and Forecast

The original version of this paper is available on https://www.scientific.net/KEM.1047.231.pdf

The aim of this paper was to demonstrate the production and weldability of a SiC -reinforced AMC material. To this end, the manufacturing process for AMC-SiC welding wire was described, and a variant was developed that enables the production of weldable AMC-SiC welding wire with a diameter of 1.6 mm . The welding process with the AMC−SiC material was also successfully carried out in preliminary tests. The wires produced from the two different AMC-SiC welding wire batches had an increased hydrogen content, which result in unstable weld pool control and potential gas release. This circumstance was particularly pronounced in the first AMC-SiC welds and was already improved in the second batch, showing the importance of proper weld material preparation and handling. In order to improve the production and processability of AMC-SiC materials as welding consumables in the future, it is essential to optimize the properties of the raw materials used in the manufacturing process and then carry out larger-scale welding tests with the AMC-SiC wire in order to tailor the parameters of the DED-Arc/wire process to the material as closely as possible.
Future work will involve identifying the unknown phase using XRD. Furthermore, laser-based additive manufacturing methods may enable a process window where the temperature of the melt pool can be maintained below critical temperatures, thereby preventing the dissolution/decomposition of the SiC particles.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/KEM.1047.231.pdf

The author gratefully acknowledges the support of the German Research Foundation e.V. (DFG e.V.), which enabled participation at ESAFORM 2026. The author also thanks colleagues and mentors for their valuable feedback and guidance during the preparation of this work.

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.231.pdf

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 243-249

doi: 10.4028/p-YF4lIB

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-19

Revised:
2026-02-16

Accepted:
2026-02-25

Online:
2026-04-13

Sensitivity Analysis and Calibration of the Heat Source in Additive Manufacturing of AINiCo Magnets

The original version of this paper is available on https://www.scientific.net/KEM.1047.243.pdf

Fachinotti Victor D. 1,a*, Gouttebroze Sylvain 1,b, Azar Amin S. 2,c

¹Metal Production and Processing, SINTEF Industry, Oslo, Norway

2 3D-Components AS, Oslo, Norway

a* victor.fachinotti@sintef.no, b sylvain.gouttebroze@sintef.no, c amin.azar@3d-components.co

*corresponding author: victor.fachinotti@sintef.no

Keywords: sensitivity analysis, calibration, AM, magnetic material, AINiCo alloy

Abstract

This paper presents an initial investigation into the numerical modeling of additive manufacturing processes for AlNiCo magnets. The research concentrated on calibrating the heat source parameters by utilizing previously published experimental results. The influence of laser power and scanning speed on the laser fusion of AlNiCo5 on SS 304 substrates was investigated through single track experiments. The geometries of the melt pools were measured and utilized as the foundation for model calibration. A two-step calibration methodology was adopted: (1) a simplified 2D model implemented in Octave was used for sensitivity analysis and parameter fitting; and (2) validation was performed using a 3D model within the commercial software Simufact Welding software. Parameters calibrated through 2D simulations could be directly transferred to the 3D context. However, while the calibration procedure enabled accurate fitting for individual tracks, it resulted in globally non-optimal parameters, suggesting that process parameters influence laser penetration depth.

Introduction

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Permanent magnets are key components in modern technologies such as electric motors, windturbine generators, sensors, and actuators. The strongest commercial magnets rely on rare-earth elements, whose supply chains face major geopolitical and sustainability risks. Among rare-earthfree alternatives, AlNiCo alloys stand out for their high temperature magnetic stability and reasonable performance. AlNiCo magnets are typically manufactured by employing slow and carefully controlled directional solidification before undergoing magnetic treatment.

Additive manufacturing (AM) offers a transformative route to produce permanent magnets with complex geometries, controlled microstructures, and minimal material waste. Unlike conventional casting or powder metallurgy, AM allows localized control of solidification and phase evolution, that can potentially enable tailored magnetic anisotropy and optimized internal architectures. Numerical modelling is crucial for exploring this multidimensional design space, although significant uncertainties persist regarding material properties and their behavior under AM conditions.

AlNiCo5 is known to be brittle, and initial testing has revealed that it often develops cracks easily. In addition, the magnetic performance is strongly affected by the material texture. The material's full potential is realized only when the grains are well aligned, a condition presently attained through the directional solidification process. Numerical modelling is a powerful tool to explore different process parameters and their consequences on key parameters such as the temperature gradient and the cooling rate at the melt pool surface which are commonly used to determine the processability window and the columnar to equiaxed transition. In addition, numerical modelling is essential to test and design laser patterns that enable the reduction of residual stress and crack formation.

The model parameters are often calibrated based on dedicated experiments. In this work, singletrack LPBF experiments on various substrates provided melt pool measurements that are used to calibrate the numerical parameters required for process simulation and optimization. The initial section provides a concise overview of the material data and experimental procedures. Subsequently,
the chosen modelling software and heat source models are detailed. Lastly, the results of the calibration process are examined and discussed.

Material and Experimental Results

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This study focuses on AlNiCo5 magnetic material and its deposition on different substrates. The details of the experimental work have already been published [1], so only a quick summary is provided here. Gas-atomized AlNiCo5 metallic powder was used as the feedstock. The single-track experiments were performed using an LPBF machine equipped with a 500 W ytterbium-fiber laser operating at 1064 nm and a laser spot size of 80μ m by Gargalis and his co-authors [1]. Material samples were placed in slots.

After the single scan trials were carried out, a cross section of the samples was analyzed with optical microscope to extract information on the shape of the melting zone as presented in Figure 1.

[picture]

Fig. 1. Slot and material substrate (left) and measurement of the melting zone dimensions from experiments from [1].

Fig. 1. Slot and material substrate (left) and measurement of the melting zone dimensions from experiments from [1].

Table 1. Design of experiment and single scan track processing parameters on AlNiCo5 and SS304 substrates.

High laser power

Track
#
Weld
pool
width
(μm)
Weld
pool
depth
(μm)
Power
(W)
Scan
speed
(mm/s)

1 | 145.65 | 89.76 | 230 | 600
2 | 154.06 | 74.93 | 250 | 600
3 | 143.92 | 70.47 | 270 | 600
4 | 144.16 | 89.27 | 290 | 600
5 | 139.47 | 74.93 | 230 | 700
6 | 140.21 | 78.88 | 250 | 700
7 | 149.6 | 79.38 | 270 | 700
8 | 159.5 | 81.85 | 290 | 700
9 | 122.9 | 43.27 | 230 | 800
10 | 125.96 | 64.74 | 250 | 800
11 | 129.33 | 72.7 | 270 | 800

Process Modelling

The original version of this paper is available on https://www.scientific.net/KEM.1047.243.pdf

Simufact offers two software for additive manufacturing: Simufact Additive dedicated to a production perspective to predict and optimize building components, and Simufact Welding that allows for advanced heat source and melt pool simulations. Simufact Welding, version 2024.4, was selected for the following simulations as the melt pool is properly described. Single scan tracks of AlNiCo5 powder deposited on AlNiCo5 and AISI304 substrates via laser powder bed fusion (LPBF) are modelled. The powder bed is not explicitly represented in the simulation, only the added material
is present. The simulations are reproducing the experimental work done in the MagNEO project and summarized in the previous section and published [1] as seen in Figure 2. We focus on the AISI304 substrate due to available material property data (X5CrNi18-10_sw in Simufact's database). The properties of AlNiCo5 materials are subject to considerable uncertainty, which will not be addressed in this work. To numerically study this point, a proper sensitivity analysis should be performed considering variations of key material properties (e.g. variations of a factor 4 for the conductivity, or +/- 100 K for melting point between different sources or CALPHAD simulations).

[picture]

Fig. 2. Experiment [1] and simulation results from the high laser power deposition on AISI 304 substrate.

Fig. 2. Experiment [1] and simulation results from the high laser power deposition on AISI 304 substrate.

The heat source was defined using either a conical or a Goldak double-ellipsoidal model [2] as presented in Figure 3. The mesh refinement was activated around the heat source to properly capture the shape of the melting pool (minimum of 6 elements in the width).

[picture]

Fig. 3. Heat source models in Simufact Welding.

Fig. 3. Heat source models in Simufact Welding.

Results

The original version of this paper is available on https://www.scientific.net/KEM.1047.243.pdf

From a qualitative standpoint, comparison of the simulated and measured melt pool shapes (refer to Figure 4) demonstrates that the Goldak model more accurately captures the weld pool geometry observed in experiments. Although the conical model is commonly used for laser heat sources and has been recently identified as the most appropriate for LPBF [3], subsequent sections will focus exclusively on results obtained using the Goldak model.

[picture]

Fig. 4. Left: Observed weld pool in track 6, high laser power. Right: Corresponding weld pool estimation using Simufact Welding model with Goldak heat source.

Fig. 4. Left: Observed weld pool in track 6, high laser power. Right: Corresponding weld pool estimation using Simufact Welding model with Goldak heat source.

As it can be seen on the right of Figure 3, the Goldak heat source is defined by five parameters: the dimensions b (half-width), d (depth), af (front length), and ar (rear length) of the ellipsoids, and the Gaussian parameter M, which governs the distribution of heat within the ellipsoids. Following the original work of Goldak et al [2], we adopt M=3 corresponding to a Gaussian distribution, and af=b/2 and ar=2 b, as they suggested in cases like these where there are no measures of the weld pool in the welding direction.

The error in weld pool prediction is assessed using Eq. (1):

ε=max(|wmeas −wmod |wmeas ,|dmeas −dmod |dmeas )(1)

where wmeas  and dmeas  represent the measured width and depth of the weld pool, while wmod  and dmod  denote the corresponding values estimated using a finite element model. Then, we evaluate the sensitivity of this error to the remaining Goldak parameters ( b and d ), as well as four other parameters with uncertain values: welding heat source efficiency η, the distance from the heat source center to the surface y0, the heat convection coefficient with the environment h, and the ambient temperature T∞.

Constructing a sufficiently large sample for sensitivity analysis and calibration using the Simufact 3D finite element model is not feasible due to prohibitive computational time requirements. Consequently, we developed a simplified two-dimensional heat conduction model of the crosssection capable of estimating weld pool extension for any specified set of heat source parameters within seconds. This model was adapted for compatibility with Octave [4], a free open-source platform, based on the Matlab code previously utilized by Fachinotti and colleagues for welding [5] and additive manufacturing [6] applications. The accuracy of this two-dimensional cross-sectional approximation relative to the full 3D model is discussed in Ref. [5].

A sample containing 600 normalized sets of parameters [η,b,d,y0, h, T∞] is built using the Octave's function stk_sampling_maximinlhs within the package STK [7], that implements the maximin Latin Hypercube method [8]. For each set, the weld pool is predicted for 24 cases with measured weld pool under different scan speeds and heat power, including all the cases listed in Table 1.

A classical regression-based sensitivity analysis commonly used in engineering design and uncertainty studies [9] is performed by taking the previous results as input. The linear and quadratic sensitivities of the error in predicting the weld pool, Eq. (1), to each parameter is shown in Figure 5. Three parameters are clearly the most important in the numerical prediction of the weld tool, and will be kept for calibration: η,b and d.

[picture]

Fig. 5. Linear and quadratic sensitivities of the error in predicting the weld pool with respect to heat source efficiency (η), Goldak's parameters (b: half-width, and d:depth), distance from the heat source center to the surface ( y0 ), heat convection coefficient to the environment ( h ) and environment temperature ( T∞ ).

Fig. 5. Linear and quadratic sensitivities of the error in predicting the weld pool with respect to heat source efficiency ( η ) , Goldak's parameters (b: half-width, and d:depth), distance from the heat source center to the surface ( y 0 ), heat convection coefficient to the environment ( h ) and environment temperature ( T ∞ ).

Calibration is performed using a new maximin Latin Hypercube sample containing 600 normalized sets of parameters [η,b,d], generated once again with Octave's function stk_sampling_maximinlhs. First, in an attempt to determine a unique heat source, the weld pool was predicted for all the highpower tracks listed in Table 1 and for each set [η,b,d]. Unfortunately, not one single set works satisfactorily well for all the tracks, so we decided to perform calibration for each individual track. Now, the numerical results obtained using the simplified 2D cross sectional model match the experimental observations satisfactorily (see Figure 6). Let us remark that similar melt pool dimensions were obtained when running the full 3D model in Simufact Welding as illustrated in Figure 7 for track 6.

[picture]

Fig. 6. Measured weld pool width and depth compared to results obtained using Simufact Welding with Goldak heat source for the tracks 1 to 6 deposited with high laser power.

Fig. 6. Measured weld pool width and depth compared to results obtained using Simufact Welding with Goldak heat source for the tracks 1 to 6 deposited with high laser power.

[picture]

Fig. 7. Comparison of Octave melt pool shape and Simufact cross-section for high power track 6.

Fig. 7. Comparison of Octave melt pool shape and Simufact cross-section for high power track 6.

Table 2 lists the sets of calibrated Goldak's parameters for each track, and the agreement of numerical predictions with experimental observations, quantified by the error defined by Eq. (1(1). The values of the parameters vary significantly and without a clear trend.

Table 2. Results of individual and global calibration for high laser power.

Track # | af [μm] | ar [μm] | b [μm] | d [μm] | Error
1 | 28.4 | 113.61 | 56.8 | 93.7 | 0.1099
2 | 36.5 | 146.0 | 73.0 | 66.8 | 0.0155
3 | 33.1 | 132.4 | 66.2 | 62.0 | 0.0116
4 | 28.1 | 112.4 | 56.2 | 97.5 | 0.0041
5 | 30.5 | 122.2 | 61.1 | 73.4 | 0.0800
6 | 31.5 | 126.2 | 63.1 | 83.0 | 0.0674

Some intrinsic variability is expected in such process, but the process study requires the prediction of the source parameters based on the process parameters. Therefore, a response surface should be developed to define the Goldak source as a function of both its intrinsic parameters and process variables such as scan speed and laser power, factors often missing in calibration studies reported in the literature (e.g., [10]).

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.243.pdf

In this work, a two-step modelling approach was implemented to decouple heavy 3D modelling from the heat source calibration. This strategy enabled a successful calibration of the model parameters at the level of individual laser tracks, demonstrating the capability of the approach to reproduce the experimentally measured melt pool for one testing substrate. Nevertheless, the global calibration does not generate a unique set of parameters that can be used in the power and scanning speed design domain. While the results confirm the relevance and robustness of the proposed methodology for single-track analysis, further work is required to predict the heat source parameters toward truly process-dependent quantities. One solution could be to obtain implicit relations between process parameters and source parameters using a machine learning algorithm. Additional data covering not only single tracks, but also multiple layers deposition needs to be included in future study to correlate the residual stress calculation with the crack occurrence to analyze the effect of the different patterns. Measurements of the melt pool dimensions during a multi-layer deposition will also enable the extension of the calibration of the source in a future work.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/KEM.1047.243.pdf

The authors would like to thank the European Union for the funding received for the HEU MagNEO project (grant agreement ID: 101130095).

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.243.pdf

L. Gargalis, L. Karavias, A. Argyrou, E.K. Karaxi, E.P. Koumoulos, Additive Manufacturing of AlNiCo5 Hard Magnetic Alloy Through Laser Powder Bed Fusion: A Single Scan Track Study of Microstructure and Nanomechanical Integrity, Appl. Sci. 15(23) (2025), doi: 10.3390/app152312522.

J. Goldak, A. Chakravarti, M. Bibby, A new finite element model for welding heat sources, Metallurgical Transactions B, Vol. 15B, 299-305, 1984.

M. Kusano, M. Watanabe, Heat Source Model Development for Thermal Analysis of Laser Powder Bed Fusion Using Bayesian Optimization and Machine Learning, Integrating Materials and Manufacturing Innovation (2024) 13:288-304, doi: 10.1007/s40192-023-00334-2.

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V.D. Fachinotti, A.A Anca, A. Cardona, Analytical solutions of the thermal field induced by moving double-ellipsoidal and double-elliptical heat sources in a semi-infinite body. International Journal for Numerical Methods in Biomedical Engineering 27 (2011) 595-607.

V.D. Fachinotti, A. Cardona, B. Baufeld, O. Van der Biest. Finite-element modelling of heat transfer in shaped metal deposition and experimental validation. Acta Materialia 60 (2012) 6621-6630.

J. Bect, E. Vazquez. STK: a Small (Matlab/Octave) Toolbox for Kriging. Release 2.8.1 (2023). .

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D.C. Montgomery. Design and Analysis of Experiments. 9th Edition, Wiley (2020).

G. Ertugrul, A. Hälsig, R. Rimpl, J. Hensel, S. Härtel, Artificial neural network based calibration of Goldak heat source parameters in tandem plasma transferred arc process using finite element analysis, Int. J. Adv. Manuf. Tech. 139 (2025), 2349-2363, doi: 10.1007/s00170-025-15843-x.

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 251-258

doi: 10.4028/p-Brv8Ec

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-19

Revised:
2026-02-20

Accepted:
2026-02-25

Online:
2026-04-13

Coconut Fibre Reinforced Photopolymer Composites via Stereolithography: Feasibility and Mechanical Performance

The original version of this paper is available on https://www.scientific.net/KEM.1047.251.pdf

Valeria Palomba 1,a, Antonio Fotia 1, b, Giulia Palomba 1,2,c*, Pasqualino Corigliano 1,2, d, Mohamed Chairi 1,e and Guido Di Bella 1,2,f

1 CNR ITAE, Salita S. Lucia sopra Contesse 5, 98126 Messina, Italy

2 Department of Engineering, University of Messina, Contrada di Dio, 98166 Messina, Italy

a  valeria.palomba@cnr.it, b  antonio.fotia@cnr.it, c * giulia.palomba@unime.it, d pasqualino.corigliano@unime.it, e mohamedchairi@cnr.it, f guido.dibella@unime.it

*corresponding author: giulia.palomba@unime.it

Keywords: additive manufacturing, bio-composites, natural fiber reinforcement.

Abstract

The growing demand for sustainable materials has driven interest in bio-based composites for additive manufacturing (AM). This study explores the feasibility of incorporating untreated coconut fibres into commercial photopolymer resins for stereolithography (SLA). Coconut fibres were extracted, processed, and integrated at varying concentrations into resin formulations, followed by fabrication of ASTM D638 Type IV specimens using a desktop SLA printer and UV post-curing. Mechanical characterization included tensile testing to assess Young's modulus, tensile strength, and elongation at break, complemented by microscopy of fracture surfaces to evaluate fibre dispersion and failure mechanisms. Results indicate good compatibility between coconut fibres and photopolymer resin, with mechanical performance strongly influenced by fibre content. These findings highlight the potential of coconut fibre-reinforced photopolymer composites as sustainable alternatives for AM applications.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.251.pdf

The global pursuit of sustainable materials and circular economy principles has intensified research into bio-based composites [1] and their integration in additive manufacturing (AM) [2]. AM offers the potential for sustainable and customizable fabrication with minimal material waste, and within this realm, stereolithography (SLA) is distinguished by its high resolution, surface finish capabilities and dimensional accuracy. This technology is based on the sequential solidification of liquid photopolymeric resins through localized exposure to a focused ultraviolet light source, enabling the fabrication of three-dimensional structures. However, the incorporation of natural fibres into SLA resins remains underexplored due to challenges associated with fibres-resins compatibility, resin rheology, light scattering, cure kinetics, and interfacial adhesion [3].

The integration of lignocellulosic fibres into AM composites offers promising mechanical and environmental benefits. However, numerous challenges still need to be addressed [4]. Among others, Moorthy and Chandran [5], emphasised the critical role of fibre surface treatment and fibre-resin interface chemistry in stereolithography-based natural fibre composites. Prior studies have reported enhanced toughness and biodegradability of composites reinforced with flax, jute or kenaf [6], produced via traditional methodologies. Some promising investigations were performed using natural fibres in different forms in SLA-compatible formulations for AM: it is the case of Rahman et al. [7] who incorporated long-fibres of jute in the middle plane of 3D printed specimens or Wu et al. [8] who used micro-scale bamboo fibres and a vinyl palm oil-based resin to obtain a bio-composite. A recent study from Muller et al. [9] indicated that incorporating short natural fibre fillers or particulates, into photocurable SLA resins can enhance mechanical performance, but results depend on type of loading and treatment. Moderate additions of cotton flakes, miscanthus, walnut, spruce tree, wheat or eggshells improved tensile strength (up to ~22% ) while demonstrating excellent matrixfiller interfacial bonding.

However, few have examined in literature coarse fibres such as coconut (Cocos nucifera), which are abundant, cost-effective, with favourable aspect ratio and high lignin content, for AM composites. Some preliminary results of their potential in terms of thermal and dimensional stability improvements on SLA components are provided by Pereyra et al. [10]. Excellent thermal stability, enhanced resistance to degradation and mechanical performance were also reported in Ref. [11], for composites manufactured by traditional techniques with polypropylene and coconut shell particles as filler. A critical parameter in AM bio-composites using coconut fibres is their quantity, as observed by Mosi et al. [12]. They investigated composites produced via fused filament fabrication process using a custom polypropylene filament incorporating coconut fibres. They noted that tensile and flexural strengths reached a peak at ~2wt% fibre, whereas higher fibre content reduced strength due to poor fibre-resin adhesion, as a result of high lignin content in coir fibres which impedes bonding. To boost compatibility, fibres pre-treatments can be performed, as suggested by Rujnić Havstad et al. [13] in a study on moulded composites integrating coconut coir fibres into epoxy resin. They found an optimal combination of alkaline treatments (e.i. NaHCO3 soaking) concentration and duration to achieve significantly improved coir/epoxy composite stiffness and strength. Statistical analyses (ANOVA) confirmed these trends, showing fibre content and treatment have significant effects on composite tensile and impact properties.

The current study investigates the feasibility of incorporating untreated and sieved coconut fibres into commercial resins to produce composites via vat photopolymerisation. The aim is to understand the photopolymer-fibre interaction, dispersion challenges, and mechanical performance of printed composite specimens. Through mechanical testing and failure mode analysis, this work contributes new insights into the feasibility of coconut fibre integration in SLA 3D printing and expands the material alternatives for sustainable additive manufacturing.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/KEM.1047.251.pdf

The composites developed and analysed in the present study use a commercial photosensitive resin as matrix and coconut chopped fibres as reinforcement. The materials examined in this study fall under the category of polymer composites incorporating discontinuous reinforcements, such as particulate matter or short fiber fillers. These types of composites are suited for applications demanding straightforward manufacturing processes along with adequate mechanical strength [9]. The fillers employed in this work were used in their untreated, natural state. They were only extracted, washed in water, dried in an oven and milled before being integrated into a Formlabs White Resin v5, through mechanical stirring to promote homogeneity. In order to investigate the effect of reinforcements/matrix ratio, four different volume concentrations of fibre/resin mixtures were prepared, i.e. 5, 10, 15 and 20%.

Test specimens conforming to ASTM D638 Type IV were fabricated using a commercial desktop SLA printer, i.e. Formlabs 4. Printing was performed with LPU irradiance equal to 13 mW/cm2, model infill exposure of 8 mJ/cm2, exposure of model perimeter and support equal to 19 mJ/cm2 and 0.5 s of post-exposure wait time. The printing process was followed by UV post-curing for 5 minutes at 35∘C. Printed specimens and relative geometrical features according to the applied standard are displayed in Figure 1.

[picture]

Fig. 1. Printed specimens at different fibre volume fraction (vol.%) and geometrical features.

Fig. 1. Printed specimens at different fibre volume fraction (vol.%) and geometrical features.

Unreinforced resin specimens, identified as 0% fibres in the following, served as controls. All samples were subjected to tensile testing to evaluate Young's modulus, tensile strength, and elongation at break. Three specimens for each fibre percentage were subjected to tensile tests with a Zwick Roell electromechanical universal testing machine equipped with a 2.5 kN load cell, with a rate of 1 mm/min.

Furthermore, microscopy observations were performed on fracture surfaces to investigate fibre dispersion, resin-fibre adhesion, and crack propagation pathways. The role of fibre content on mechanical performance and fracture modes was systematically compared.

One-way ANOVA and Kruskal-Wallis tests were applied to the tensile test results to assess statistical significance.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1047.251.pdf

Figure 2 shows typical tensile stress-strain curves of SLA-printed specimens produced with neat photopolymer resin ( 0% coconut fibre) and with increasing coconut fibre contents (5,10,15 and 20% volume).

[picture]

Fig. 2. Stress-strain curves of SLA-printed specimens with different coconut fibre volume fractions (vol.%) subjected to tensile tests.

Fig. 2. Stress-strain curves of SLA-printed specimens with different coconut fibre volume fractions (vol.%) subjected to tensile tests.

The unreinforced resin exhibits the highest tensile strength and strain at break, reaching a maximum stress of approximately 27 MPa and showing a pronounced nonlinear deformation prior to failure, indicative of post-yield plasticity. This "ductile" response refers specifically to the neat photopolymer resin.

The introduction of coconut fibres significantly modifies the tensile response of the material. The initial slope of the stress-strain curves does not show a clear monotonic change; however, a clear reduction in tensile strength and elongation at break compared to the neat resin is detected. The curves show earlier deviation from linearity and premature failure, in comparison to pure resin. Overall, the fibre-filled specimens exhibit a more predominantly linear-elastic response up to failure, followed by a more abrupt stress drop, consistent with a brittle-like fracture behaviour. The observed response, typical of more brittle materials, suggests the onset of stress concentrations and limited load transfer efficiency at the fibre-matrix interface. This phenomenon may stem from several contributing factors, including fibre agglomeration, increased resin viscosity during printing, and light scattering effects during photopolymerization, which may lead to incomplete curing and the formation of microvoids.

Additionally, the absence of fibre surface treatment likely limits interfacial adhesion, promoting fibre pull-out and crack initiation under tensile loading.

The fracture surfaces of tensile-tested specimens, captured via optical microscopy, are shown in Figure 3 for each fibre content. All samples exhibit characteristics of brittle fracture, as evidenced by clean, planar break surfaces and the absence of significant plastic deformation. This is consistent with the reinforced specimens, where early damage initiation at the fibre-matrix interfaces (e.g., debonding and microcracking) can limit stable plastic deformation of the matrix and promote abrupt failure. In fibre-reinforced specimens (b-e), multiple crack propagation paths aligned with the printing direction are visible, particularly originating from fibre extremities. These discontinuities likely act as stress concentrators and preferential crack initiation points, contributing to the early failure observed in mechanical tests. The pure resin (Figure 3a) shows a smooth and homogeneous fracture surface, while the composite samples display increasing roughness and heterogeneity with fibre content. In samples with higher fibre loadings ( 15% and 20%, Figure 3 d-e), fibre pull-out and interfacial debonding are evident, suggesting poor fibre-matrix adhesion. This is consistent with the untreated nature of the coconut fibres and may explain the reduced tensile strength observed at higher reinforcement levels. The orientation of visible fibres appears predominantly random, with limited alignment along the stress direction. This may reduce the reinforcing efficiency of the fibres, especially under uniaxial tensile loading.

Considering the printing direction indicated in Figure 3, a noticeable geometrical distortion is observed, likely associated with excessive resin adhesion to the build platform and/or local overcuring during the initial layers. Moreover, the fracture surfaces show a higher apparent fibre density in the uppermost printed regions, which is plausibly attributable to phase segregation during the build (e.g., fibre sedimentation/flotation in the vat and/or insufficient homogenisation over time). This evidence supports the hypothesis of a non-uniform fibre distribution throughout the process. The progressive increase in surface roughness and irregularity with fibre volume fraction further suggests process limitations related to increased suspension viscosity and hindered resin recoating, as well as enhanced light scattering/attenuation that can reduce effective UV penetration and curing uniformity. Overall, these observations highlight the need for targeted optimisation of printing parameters (and mixing/homogenisation strategies) in future work.

[picture]

Fig. 3. Fracture surfaces of tested specimens at a) 0%, b) 5%, c) 10%, d) 15%, e) 20% coconut fibres volume content.

Fig. 3. Fracture surfaces of tested specimens at a) 0 % , b) 5 % , c) 10 % , d) 15 % , e) 20 % coconut fibres volume content.

Among reinforced specimens, the 10% fibre content shows the best performance in terms of ultimate tensile strength ( ~17MPa ). At higher fibre loadings ( 15% and 20% ), tensile strength is further reduced, with the lowest values observed within these high-fibre formulations. The 5% content yields intermediate results. The slight slope changes and stress drops may indicate localized fibre pull-out or microcrack initiation under tensile loading. Overall, the results suggest that while small amounts of fibre may be tolerable, excessive fibre loading compromises mechanical integrity in SLAprinted composites, underscoring the importance of optimizing fibre content and interfacial properties for sustainable photopolymer-based composites.

Figure 4 summarises the tensile properties of neat photopolymer resin and coconut fibre-reinforced composites in terms of maximum strain, Young's modulus, and maximum tensile stress as a function of fibre content, reporting the error bars for each volume content.

[picture]

Fig. 4. Results of tensile tests in terms of a) maximum strain b) Young's modulus and c) maximum tensile stress.

Fig. 4. Results of tensile tests in terms of a) maximum strain b) Young's modulus and c) maximum tensile stress.

The maximum strain at break (Figure 4a) shows a marked decrease with the introduction of coconut. The unreinforced resin exhibits the highest ductility, with a maximum strain of approximately 0.06 mm/mm. Even at the lowest fibre content (5%), the strain at break drops by more than 50%, indicating a strong embrittlement effect induced by the fibres. Further increases in fibre content led to a progressive but less pronounced reduction in ductility, reaching minimum values at 20%. This trend reflects the restriction of polymer chain mobility caused by rigid lignocellulosic fibres and the increased likelihood of stress concentrations at the fibre-matrix interface.

The Young's modulus values (Figure 4b) exhibit a non-monotonic trend with fibre content. Compared to the neat resin, a slight reduction in stiffness is observed at 5% volume content, followed by a recovery and even a marginal increase at 10%. At higher fibre contents ( 15 and 20% ), the modulus decreases again, although remaining within a comparable range. This behaviour suggests a competing effect between the intrinsic stiffness of coconut fibres and microstructural defects such as fibre agglomeration, void formation, and incomplete curing associated with higher filler loadings. The absence of fibre surface treatment likely limits efficient stress transfer, preventing a consistent stiffening effect across all compositions.

The maximum tensile stress (Figure 4c) decreases significantly upon fibre addition. While the neat resin reaches values close to 27−28MPa, fibre-reinforced specimens show reduced strength, with the lowest values observed at 15% volume fraction. A partial recovery at 10% and 20% may be attributed to improved fibre dispersion at intermediate loadings or statistical variability; however, the overall trend confirms that fibre reinforcement does not enhance tensile strength under the investigated conditions. This reduction is consistent with premature crack initiation at fibre ends, weak interfacial adhesion, and defects introduced during the SLA process.

Overall, the combined results indicate that untreated coconut fibres primarily act as stiffness modifiers rather than effective load-bearing reinforcements in SLA-printed photopolymer composites. The strong reduction in ductility and tensile strength highlights the need for optimised fibre surface treatments, controlled fibre size distribution, and tailored photopolymer formulations to fully exploit the reinforcing potential of natural fibres in vat photopolymerization processes.

To evaluate the statistical significance of the observed mechanical trends, a one-way ANOVA was performed on the tensile test results after verifying normality of the data. The analysis showed that fibre volume fraction has a statistically significant effect on the maximum tensile stress ( F=29.82,p<0.001 ), indicating that coconut fibre content strongly influences tensile strength under the investigated conditions. The model explains a large portion of the variance (R2=92.27%,R2(adj)=89.17%,R2( pred )=82.60% ), with a residual standard deviation S=1.85MPa.

To further corroborate this outcome without relying on parametric assumptions, a Kruskal-Wallis test was also performed on max Stress. The test confirmed statistically significant differences among groups ( H=11.70,DF=4,p=0.020 ). The average rank values indicate that the 0vol.% group tends to exhibit the highest tensile strength (Ave Rank =14.0 ), followed by 10vol.%(10.7) and 20vol.% (7.0), while 5vol.%(5.7) and especially 15vol.%(2.7) show the lowest values. Given the small sample size ( N=3 per group), these results should be interpreted with caution; nevertheless, both tests consistently support a statistically significant effect of fibre loading on tensile strength, in agreement with the trends observed in the stress-strain curves.

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.251.pdf

The current study presents one of the first experimental investigations into the integration of untreated coconut fibres into photopolymer resins for stereolithography (SLA) 3D printing. The results demonstrate the feasibility of producing fibre-reinforced composites via vat photopolymerization using an abundant, low-cost, and renewable bio-based filler. Although fibre addition led to reduced ductility and tensile strength due to limited interfacial bonding and fibre agglomeration, the modulus was moderately influenced by fibre content, with 10% fibre specimens showing relatively better mechanical balance. Microscopic analysis of the fracture surfaces further confirmed the brittle failure mode and highlighted key microstructural issues such as fibre pull-out, interfacial debonding, and irregular fibre distribution. These phenomena were particularly evident at higher fibre contents, where
poor resin-fibre adhesion and potential sedimentation during printing led to crack initiation and compromised mechanical integrity. Such findings underscore the need for optimized fibre dispersion techniques and surface modification strategies to improve interfacial bonding and ensure uniform reinforcement throughout the printed volume.

These findings offer valuable insights into the use of coarse natural fibres in high-resolution AM processes, expanding the feasible choices of sustainable materials for applications such as ecofriendly prototypes, lightweight non-structural components, or biodegradable consumer products. The work was addressed at laying the groundwork for further developments including fibre surface treatments, resin formulation optimisation, and hybrid reinforcement strategies aimed at improving fibre dispersion and interfacial adhesion. Future research will also explore the impact of fibre orientation, print parameters, and environmental durability, moving toward fully optimized, biodegradable SLA composites for industrial and circular-economy-oriented manufacturing.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/KEM.1047.251.pdf

The present work is partially funded by European Commission under Grant Agreement n. 101130021 (4TunaTES).

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M. N. Ahmad and M. N. Puasa, "Mechanical and Thermal Properties of Coconut (Cocos nucifera)-Reinforced Polypropylene Composite," Eng, vol. 6, no. 11, Nov. 2025, doi: 10.3390/eng6110299.

G. Mosi, B. W. Ikua, S. K. Kabini, and J. W. Mwangi, "Characterization and Modeling of Mechanical Properties of Additively Manufactured Coconut Fiber-Reinforced Polypropylene Composites," Advances in Materials Physics and Chemistry, vol. 14, no. 06, pp. 95-112, 2024, doi: 10.4236/ampc.2024.146008.

M. Rujnić Havstad, I. Tucman, B. Krajačić, and A. Pilipović, "Influence of Coir Fibre Preparation on Mechanical Properties of Coir Fibre/Epoxy Resin Composites," Journal of Manufacturing and Materials Processing, vol. 8, no. 6, Dec. 2024, doi: 10.3390/jmmp8060291.

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 259-265

doi: 10.4028/p-7mVx9y

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-26

Revised:
2026-02-16

Accepted:
2026-02-25

Online:
2026-04-13

Influence of Local Laser Power Distribution and Build-Plate Temperature on Porosity and Microstructure in Laser Powder Bed Fusion (LPBF)

The original version of this paper is available on https://www.scientific.net/KEM.1047.259.pdf

Chukwuemeka Okolo 1,a*, Sebastian Härtel 1, b

¹Chair of Hybrid Manufacturing (FHF), Brandenburg University of Technology, 03046 Cottbus, Germany

a*Okolo@b-tu.de, b  haertel@b-tu.de

Keywords: laser powder bed fusion (LPBF), porosity, build-plate temperature.

Abstract

Laser powder bed fusion (LPBF) parts are commonly fabricated using nominally uniform process parameters; however, local variations in thermal boundary conditions can significantly influence part quality. In this study, the spatial distribution of build-plate temperature during LPBF of Inconel 718 was experimentally characterized using a thermocouple grid, and its influence on porosity, microstructure and hardness was investigated. Despite a nominal build-plate set temperature of 180∘C, measured temperatures ranged from approximately 101∘C to 120∘C and exhibited a pronounced radial gradient from the center toward the edges of the build-plate. Cubic samples fabricated at locations corresponding to the highest and lowest local temperatures showed distinct microstructural differences, with higher temperatures promoting slightly coarser cellular-dendritic morphologies and lower hardness values. Although bulk volumetric porosity showed identical values for both locations ( ≈0.01vol.% ), the pore populations differed: the hotter location contained fewer but locally larger voids while the cooler location exhibited a higher number density of smaller pores, as shown by equivalent-diameter histograms and cumulative distributions. Samples produced at cooler locations exhibited finer microstructures and higher hardness. These results demonstrate that spatial non-uniformity in build-plate temperature can lead to local variations in microstructure and mechanical properties within a single LPBF build, highlighting the importance of characterizing local thermal conditions when establishing reliable process-structure property relationships.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.259.pdf

Laser powder bed fusion (LPBF) is a leading additive manufacturing method for producing complex, high-performance metal components because it enables near-net-shape fabrication with high geometrical freedom and rapid production rates [1], [2]. The quality of LPBF parts is dependent on local thermal conditions and the delivered laser energy, and small variations in these inputs can result in large differences in pore formation, solidification morphology, and resulting mechanical properties [3]. Standard experimental studies commonly report nominal process parameters such as laser power, scan speed, hatch spacing and a global build-plate preheat temperature, but these studies frequently assume that energy delivery and build-plate temperature are spatially uniform across the build-plate [4], [5], [6]. In real production environments, the build-plate temperature often shows substantial spatial variation caused by heater zoning, non-uniform contact to fixtures, thermal losses at plate edges, and localized differences in gas flow or convection [7]. Such build-plate temperature nonuniformity modifies the local thermal boundary condition and therefore changes heat extraction during melting and solidification at the molten track [8]. For nickel-base superalloys like Inconel 718, changes in cooling rate, driven by build-plate temperature shifts can alter cell or dendrite spacing and the kinetics of precipitation, and these microstructural changes influence hardness and local mechanical response [9]. Because the thermal boundary condition controls the solidification rate, spatial differences in build-plate temperature can change whether adjacent tracks are fully remelted or whether lack-of-fusion defects form, even when laser settings are unchanged [10].

Local deviations in delivered laser energy at the work plane, for example due to beam profile imperfections, variable spot size, or optics misalignment, also change the local melt-pool energy density and can move a location between conduction, lack-of-fusion, or keyhole regimes [11]. Meltpool fluid flow and vapor depression behavior respond to both instantaneous energy input and the substrate boundary condition, so the joint state of local laser energy and build-plate temperature determines the instantaneous thermal history of a powder layer [12].
Despite the separate acknowledgement of beam quality and preheat importance, few studies have systematically mapped both local laser-energy distribution and plate thermal fields across the build area and then directly correlated those spatial maps with position-resolved porosity and microstructure. Prior work has established process maps for defect regimes and demonstrated the sensitivity of melt-pool geometry to local specific energy, but most investigations assume spatial uniformity or report only single-point measurements [11]. Similarly, preheat studies typically report mean platen temperature effects on porosity or microstructure [7], [8] without quantifying intra-plate gradients or their interaction with local beam heterogeneity. This leaves a critical gap: how do spatial overlaps between low (or high) local specific energy and cold (or hot) platen spots jointly influence the formation, type and connectivity of pores, and the resultant microstructural heterogeneity within a single build.
The present study investigates the influence of build-plate temperature on porosity, microstructure and hardness by measuring build-plate temperature with a thermocouple grid, and placing test samples at mapped locations so that each sample is associated with a documented local thermal history.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/KEM.1047.259.pdf

In this study, the spatial distribution of laser energy delivered across the build platform was not measured due to time constraints and the unavailability of a mobile power-measurement instrument for short-term irradiation with solid-state lasers.
To measure the temperature of the build-plate, holes of diameter 1.5 mm , and depth of 4 mm were drilled on the plate in a grid arrangement as shown in Figure 1. Thermocouples were placed in these holes to measure the temperature at different locations on the platform. The build-plate was heated up to 180∘C and maintained for two hours before taking temperature measurement. The recorded temperatures were interpolated and plotted as a colour-coded grid. Geometries were printed on specified locations on the build-plate and characterized through microscopic analysis for grain morphology and texture. Hardness testing was carried out on these samples for mechanical properties. Cubic specimens ( 10×10×14 mm ) were fabricated at these locations. Fabrication was performed on an SLM280 system operating in an argon atmosphere. The x−y surface areas were ground (P220-P2500) and polished using diamond suspension of 6μ m,3μ m, and 1μ m successively. The morphology of the microstructure was observed using a Phenom XL Generation 2 scanning electron microscope (SEM) (FEI Deutschland GmbH / Thermofisher Scientific). The porosity was determined using X-ray micro-computed tomography ( μ CT) on a TESCAN CoreTOM system (Tescan, Brno, Czech Republic) with a voxel size of 10μ m. The sizes of three-dimensional pores, expressed as equivalent sphere diameters, were determined using Volume Graphics VGSTUDIO MAX 2021 software. HV0.5 Vickers microhardness tests were carried out using the ZwickRoell DuraScan 70 hardness testing machine. Indentations were positioned at the center of the polished cross-sections, with a distance of 1.5 mm between each one.

[picture]

Fig. 1. Placement of thermocouple on the build platform to measure spatial build-plate temperature distribution.

Fig. 1. Placement of thermocouple on the build platform to measure spatial build-plate temperature distribution.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1047.259.pdf

Build-plate temperature

The original version of this paper is available on https://www.scientific.net/KEM.1047.259.pdf

Figure 2 shows the temperature map obtained for the In718 build-plate. Although the nominal set build-plate temperature for In718 was 180∘C, the measured temperatures were significantly lower and exhibited pronounced spatial variations across the plate. Measured temperatures ranged from approximately 101∘C to 120∘C, indicating a maximum temperature difference of 19∘C across the build-plate. The highest temperatures were consistently observed in the central region of the plate (position G), while lower temperatures were recorded toward the edges and corners (positions A, K, and M). This temperature gradient reflects non-uniform heat transfer conditions, likely governed by the build-plate heating mechanism, thermal contact resistance, and enhanced heat losses at the plate boundaries. The observed temperature field demonstrates a radially decreasing trend from the center toward the edges. Furthermore, the measured temperatures show a significant deviation from the nominal set build-plate temperature, indicating that the programmed value does not directly reflect the local thermal state experienced by the material during fabrication. This highlights the importance of experimentally characterizing local build-plate temperatures when evaluating spatial variations in microstructure and mechanical properties.

[picture]

Fig. 2. Spatial distribution of the build-plate temperature across the build surface.

Fig. 2. Spatial distribution of the build-plate temperature across the build surface.

Porosity and relative density

The original version of this paper is available on https://www.scientific.net/KEM.1047.259.pdf

Volumetric porosity quantified by μCT showed approximately the same value of 0.01%. for both positions on the build-plate. Despite equal global porosity, the pore populations differ markedly in morphology and size distribution. The sample at position G (hotter location) contains fewer pores but a small population of larger voids concentrated locally within the scanned volume. By contrast, the sample at position K (colder location) contains a greater number density of smaller pores distributed more uniformly throughout the volume. These contrasting morphologies are reflected in the equivalent-diameter histograms and cumulative curves shown in Figure 3. The distribution of position G is dominated by a narrow peak with a tail of large pores, whereas Position K shows a broader distribution with a heavier count of small features. The observation of equal total porosity but different defect-size architectures indicates that local build-plate temperature controls the defect formation pathway rather than only changing net void volume.

[picture]

Fig. 3. X-ray micro-computed tomography image and histogram of the pore size distribution.

Fig. 3. X-ray micro-computed tomography image and histogram of the pore size distribution.

Microscopy

The original version of this paper is available on https://www.scientific.net/KEM.1047.259.pdf

Figure 4 shows the SEM micrographs of the printed coupons at the highest and lowest build-plate temperature locations. Both samples exhibit the characteristic LPBF microstructure composed of melt-pool related features and a fine cellular-dendritic substructure. However, differences are observed between the two locations. The sample fabricated at position G, corresponding to the higher local build-plate temperature, shows a slightly coarser cellular morphology, suggesting enhanced thermal exposure and reduced cooling rates during solidification. In contrast, the microstructure at position K, which experienced a lower build-plate temperature, appears finer with more pronounced cellular contrast, indicative of higher cooling rates and increased thermal gradients. These microstructural differences are consistent with the measured hardness values, where the finer substructure at position K correlates with higher hardness, while the comparatively coarser features at position G are associated with a slight reduction in hardness.

[picture]

Fig. 4. SEM micrographs of the printed coupons at different build-plate temperature.

Fig. 4. SEM micrographs of the printed coupons at different build-plate temperature.

Hardness Results

The original version of this paper is available on https://www.scientific.net/KEM.1047.259.pdf

The average hardness result of the printed samples is shown in Figure 5. Six hardness measurements were performed on each sample. The average hardness at position G was 274 HV0.5, whereas position K showed a higher average hardness of 280 HV 0.5 . The increase in hardness at position K can be attributed to the lower local build-plate temperature, which promotes higher cooling rates and results in a finer microstructural scale. In contrast, the elevated temperature at position G likely led to partial thermal softening or microstructural coarsening, reducing hardness.

[picture]

Fig. 5. Hardness results of samples printed at highest and lowest build-plate temperature.

Fig. 5. Hardness results of samples printed at highest and lowest build-plate temperature.

Limitation

The original version of this paper is available on https://www.scientific.net/KEM.1047.259.pdf

A limitation of the present study is that the spatial distribution of delivered laser energy across the build platform was not directly quantified. While build-plate temperature was experimentally mapped
and correlated with variations in microstructure and hardness, potential spatial variations in laser power density were not assessed within the scope of this work. Consequently, the individual contributions of laser-energy heterogeneity and build-plate temperature to the observed thermal and mechanical responses cannot be fully decoupled. Future investigations should therefore integrate spatially resolved laser-power monitoring together with thermal mapping to enable a more comprehensive evaluation of coupled process-thermal effects in LPBF.

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.259.pdf

This study investigated the influence of spatial variations in build-plate temperature on the porosity, microstructure, and hardness of Inconel 718 fabricated by laser powder bed fusion. Thermocouplebased measurements revealed pronounced non-uniformity in the build-plate temperature, with measured values ranging from approximately 101∘C to 120∘C despite a nominal set temperature of 180∘C. A clear radial temperature gradient was observed, with higher temperatures at the center of the build-plate and progressively lower temperatures toward the edges and corners, highlighting that the programmed build-plate temperature does not directly represent the local thermal boundary condition experienced during fabrication.

Volumetric porosity measured by micro-CT was essentially equal for the two sampled positions, however, the pore populations differed in architecture. The sample from the hotter location contained fewer but locally larger voids, whereas the colder location exhibited a higher number density of smaller pores distributed throughout the volume. This indicates that build-plate temperature influences defect-formation pathways even when net void volume is unchanged.
Microstructural characterization showed that samples fabricated at higher local build-plate temperatures exhibited a slightly coarser cellular-dendritic substructure, while samples printed at cooler locations displayed finer microstructural features consistent with higher cooling rates. These microstructural differences were reflected in the mechanical response, as samples produced at lower build-plate temperatures exhibited higher hardness values compared to those fabricated at warmer locations. The results demonstrate that spatial variations in build-plate temperature alone, even under nominally identical process parameters, can lead to measurable differences in microstructure and hardness within a single LPBF build.
Overall, the findings emphasize the importance of accounting for spatially varying thermal boundary conditions when evaluating process-structure-property relationships in LPBF. Local deviations in build-plate temperature can introduce unintended heterogeneity in material properties, which is particularly critical for components requiring high reliability and uniform performance. The results underscore the need for improved thermal management and localized process monitoring in LPBF systems to ensure consistent part quality across the build platform.

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.259.pdf

T. DebRoy u. a., "Additive manufacturing of metallic components - Process, structure and properties", Progress in Materials Science, Bd. 92, S. 112-224, März 2018, doi: 10.1016/j.pmatsci.2017.10.001.

M. McGregor, S. Patel, K. Zhang, A. Yu, M. Vlasea, und S. McLachlin, „A Manufacturability Evaluation of Complex Architectures by Laser Powder Bed Fusion Additive Manufacturing", Journal of Manufacturing Science and Engineering, Bd. 146, Nr. 6, S. 061007, Juni 2024, doi: 10.1115/1.4065315.

A. Ur Rehman, F. Pitir, und M. U. Salamci, „Laser Powder Bed Fusion (LPBF) of In718 and the Impact of Pre-Heating at 500 and : Operando Study", Materials, Bd. 14, Nr. 21, S. 6683, Nov. 2021, doi: 10.3390/ma14216683.

S. Chowdhury u. a., „Laser powder bed fusion: a state-of-the-art review of the technology, materials, properties & defects, and numerical modelling", Journal of Materials Research and Technology, Bd. 20, S. 2109-2172, Sep. 2022, doi: 10.1016/j.jmrt.2022.07.121.

Q. Chen u. a., "Elucidating the effect of preheating temperature on melt pool morphology variation in Inconel 718 laser powder bed fusion via simulation and experiment", Additive Manufacturing, Bd. 37, S. 101642, Jan. 2021, doi: 10.1016/j.addma.2020.101642.

J.-H. Park u. a., „Effect of Preheating Temperature on Microstructural and Mechanical Properties of Inconel 718 Fabricated by Selective Laser Melting", Met. Mater. Int., Bd. 28, Nr. 11, S. 2836-2848, Nov. 2022, doi: 10.1007/s12540-022-01169-w.

N. Baldi, A. Giorgetti, M. Palladino, I. Giovannetti, G. Arcidiacono, und P. Citti, ,,Study on the Effect of Preheating Temperatures on Melt Pool Stability in Inconel 718 Components Processed by Laser Powder Bed Fusion", Metals, Bd. 13, Nr. 10, S. 1792, Okt. 2023, doi: 10.3390/met13101792.

D. Bayoumy, T. Boll, A. S. Karapuzha, X. Wu, Y. Zhu, und A. Huang, „Effective Platform Heating for Laser Powder Bed Fusion of an Al-Mn-Sc-Based Alloy", Materials, Bd. 16, Nr. 24, S. 7586, Dez. 2023, doi: 10.3390/ma16247586.

E. Duong, L. Masseling, C. Knaak, P. Dionne, und M. Megahed, „Scan path resolved thermal modelling of LPBF", Additive Manufacturing Letters, Bd. 3, S. 100047, Dez. 2022, doi: 10.1016/j.addlet.2022.100047.

M. Tang, P. C. Pistorius, und J. L. Beuth, "Prediction of lack-of-fusion porosity for powder bed fusion", Additive Manufacturing, Bd. 14, S. 39-48, März 2017, doi: 10.1016/j.addma.2016.12.001.

J. S. Weaver, J. C. Heigel, und B. M. Lane, „Laser spot size and scaling laws for laser beam additive manufacturing", Journal of Manufacturing Processes, Bd. 73, S. 26-39, Jan. 2022, doi: 10.1016/j.jmapro.2021.10.053.

S. A. Khairallah, A. T. Anderson, A. Rubenchik, und W. E. King, "Laser powder-bed fusion additive manufacturing: Physics of complex melt flow and formation mechanisms of pores, spatter, and denudation zones", Acta Materialia, Bd. 108, S. 36-45, Apr. 2016, doi: 10.1016/j.actamat.2016.02.014.

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/additive-manufacturing/978-3-0364-1997-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 267-276

doi: 10.4028/p-AmWdb0

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-19

Revised:
2026-02-19

Accepted:
2026-02-25

Online:
2026-04-13

Influence of L-PBF Process Anisotropy on the Isotropic Behaviour of TPMS Structures

The original version of this paper is available on https://www.scientific.net/KEM.1047.267.pdf

Anastasia Ciccarella 1,a*, Veronica Colaiuda 1, b, Giuseppe Dell'Avvocato 1,c, Daniele Cortis 2, d, Donato Orlandi 2,e, Luca di Angelo 1,f and Edoardo Mancini 1, g

¹Università degli Studi dell'Aquila, DIIIE, Piazzale Ernesto Pontieri I, 67100 L'Aquila, Italy

2 Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali del Gran Sasso, via G. Acitelli 22, 67100 L'Aquila, Italia

a anastasia.ciccarella@graduate.univaq.it, b veronica.colaiuda1@student.univaq.it, c  giuseppe.dellavvocato@univaq.it, daniele.cortis@Ings.infn.it, e  donato.orlandi@Ings.infn.it, f luca.diangelo@univaq.it, 9 edoardo.mancini@univaq.it

Keywords: TPMS, gyroid, L-PBF, 316 stainless steel, anisotropy, zener ratio.

Abstract

Triply Periodic Minimal Surfaces (TPMS) structures, such as the Gyroid, can exhibit nearly isotropic mechanical behaviour over specific relative density ranges, as predicted by the Zener anisotropy ratio. In contrast, the Laser Powder Bed Fusion (L-PBF) process may induce anisotropy in the material due to thermal gradients and residual stresses, potentially influencing the overall structural response. This work investigates how the anisotropy generated by the L-PBF process interacts with the inherent isotropy of Gyroid architecture. Gyroid lattices in 316L stainless steel were produced with varying unit cell sizes and wall thickness. Quasi-static compression tests were performed along directions parallel and perpendicular to the build axis to evaluate orientation effects. Numerical simulations, using both isotropic and anisotropic material properties, were employed to estimate the effective elastic response and the Zener anisotropy ratio. The combined experimental and numerical study aims to assess whether and to what extent the Gyroid architecture partially mitigates the transmission of process-induced anisotropy to the effective elastic response, contributing to a better understanding of the mechanical behaviour of additively manufactured metallic lattices. In particular, the study clarifies whether the theoretically isotropic Gyroid architecture is able to attenuate or transfer L-PBF-induced material anisotropy at the lattice scale.

1. Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.267.pdf

Triply Periodic Minimal Surfaces (TPMS) geometries have emerged as a promising class of metallic architected cellular structures for lightweight mechanical applications [1], including aerospace, energy and biomedical engineering, where highly porous topologies are desirable to reduce stiffness mismatch, promote osseointegration, and enhance biological fixation [2]. Among these, the Gyroid achieves the best compromise between structural stiffness and lightweight design. Additive manufacturing (AM) technologies, specifically Laser Powder Bed Fusion (L-PBF), capable of fabricating geometries in almost any shape, have given a significant boost to this type of metallic structure [3, 4]. The Gyroid is known to exhibit almost isotropic mechanical behaviour over a specific range of relative densities, as predicted by the Zener anisotropy ratio [5]. This isotropic behaviour, however, is typically derived assuming homogeneous and isotropic base material properties. However, the L-PBF process can introduce anisotropic behaviour due to thermal gradients and residual stresses, thereby affecting the macroscopic behaviour of these structures [6]. Understanding how L-PBF-induced anisotropy interacts with the intrinsic isotropy of the Gyroid lattice is essential for optimising the design and mechanical performance of additively manufactured metallic lattices. While the isotropic elastic behaviour of Gyroid TPMS at selected relative densities is well established, and the anisotropic response of L-PBF materials has been extensively reported, the interaction between these two effects has not yet been systematically investigated. The novelty of the present work lies in the combined experimental-numerical investigation of this interaction, explicitly
quantifying how process-induced material anisotropy propagates to the effective elastic response of Gyroid TPMS lattices.
In particular, it remains unclear whether the intrinsic geometrical isotropy of the Gyroid architecture can mitigate, transfer, or amplify process-induced material anisotropy at the lattice scale. In this work, this issue is addressed through a combined experimental and numerical investigation on 316L Gyroid lattices produced by L-PBF. The base material anisotropy was quantified through uniaxial tensile and torsion tests on bulk specimens in different building orientations, and the effective elastic response of Gyroid unit cells was evaluated via numerical homogenisation using both isotropic and experimentally derived anisotropic material properties. Zener anisotropy ratios were computed from the resulting stiffness matrices to assess effective isotropy. Multi-scale finite element analyses were used to validate the homogenised meso- and macroscale models, and experimental compression tests provided reference data for comparison. This combined experimental and numerical strategy aims to clarify whether the Gyroid architecture mitigates or transfers process-induced anisotropy at the structural level, with implications for the design of reliable additively manufactured lattices for engineering applications.

2. Materials and Methods

The original version of this paper is available on https://www.scientific.net/KEM.1047.267.pdf

2.1 Materials.

Bulk and lattice specimens (Figure 1) were additively manufactured in AISI 316L stainless steel using L-PBF technology. Two orientations, parallel and perpendicular to the building directions (Z), were adopted to assess the influence of process-induced anisotropy. The bulk set included uniaxial dog bone and cylindrical tensile specimens and torsion specimens, while the lattice set consisted of compression specimens based on Gyroid unit cells.

[picture]

Fig. 1. (a) (b) Tensile specimens, (c) Torsion specimens, and (d) (e) Lattice specimens produced via Additive Manufacturing with different orientations to the building direction (z).

Fig. 1. (a) (b) Tensile specimens, (c) Torsion specimens, and (d) (e) Lattice specimens produced via Additive Manufacturing with different orientations to the building direction (z).

Gyroid lattices were produced with varying unit cell sizes and wall thicknesses to achieve a relative density theoretically associated with isotropic elastic behaviour for this architecture. The nominal features of the cells are explained in Table 1.

Table 1. Details of Gyroid Specimens for compression tests.

Cell Size [mm³] | Wall thickness [mm] | Relative Density | Printing direction | Loading direction | Designation
3.5×3.5×3.5 | 0.4 | 0.36 | Z | Z | G 3.5 T0.4 Z
5×5×5 | 0.6 | 0.36 | Z | Z | G 5 T0.6 Z
3.5×3.5×3.5 | 0.4 | 0.36 | Z | X | G 3.5 T0.4 X
5×5×5 | 0.6 | 0.36 | Z | X | G 5 T0.6 X

All specimens were manufactured using a PRIMA Print Sharp 150 system. The corresponding processing parameters are reported in Table 2.

Table 2. L-PBF process parameters.

v

P

h

L

VED

1200 | 140 | 80 | 30 | 48.6

2.2 Equipment.

Quasi-static mechanical tests were conducted on both bulk and lattice specimens using a servohydraulic universal testing machine (Figure 2). Uniaxial tensile and torsion tests were performed on the bulk specimens to determine the mechanical properties of the base material. In contrast, quasistatic compression tests were conducted on the lattice specimens to evaluate the structural behaviour of the Gyroid architecture. In both cases, tests were conducted by applying a load either parallel or perpendicular to the building direction to quantify the influence of process-induced anisotropy on mechanical performance.

[picture]

Fig. 2. Equipment for (a) tensile and compression tests available at the University of L'Aquila and (b) torsion tests available at Sapienza University of Rome.

Fig. 2. Equipment for (a) tensile and compression tests available at the University of L'Aquila and (b) torsion tests available at Sapienza University of Rome.

2.3 Finite Element Model.

Finite element simulations were performed to evaluate the mechanical behaviour of the Gyroid unit cell and to determine whether the anisotropy induced in the base material by the L-PBF process affects the effective elastic response of the lattice geometry. Gyroid TPMS lattices are theoretically expected to behave isotropically at the selected relative density; therefore, numerical analyses were used to verify whether this condition is preserved when material anisotropy is introduced. A numerical homogenization approach was applied to the selected Gyroid unit cells. For each cell geometry, representative volume elements (RVEs, Figure 3) were analysed under six loading conditions to compute the elastic stiffness matrix, as described in literature [7, 8].

[picture]

Fig. 3. Gyroid RVEs with (a) Cell size 3.5 mm and thickness 0.4 mm and (b) Cell size 5 mm and thickness 0.6 mm .

Fig. 3. Gyroid RVEs with (a) Cell size 3.5 mm and thickness 0.4 mm and (b) Cell size 5 mm and thickness 0.6 mm .

In parallel, two material properties were assigned to the same RVEs: the theoretical isotropic elastic properties of AISI 316L and the experimentally derived anisotropic elastic properties from bulk testing. The experimentally derived elastic constants suggest a transversely isotropic behaviour with symmetry around the build direction. This assumption was adopted in the numerical modelling as a reasonable approximation of the L-PBF-induced anisotropy. More complex anisotropic descriptions, including local heterogeneities or spatially varying properties, were not considered at this stage and are left for future investigations. Comparing the homogenised stiffness matrices allowed evaluation of how base-material anisotropy influences the cell's effective elastic behaviour. From these matrices, the Zener ratio was calculated to quantify anisotropy at the cell level: values close to 1 indicate isotropic behaviour, whereas deviations indicate elastic anisotropy [9].
To validate the procedure and to assess the effect of the number of cells under loading, meso- and macroscale analyses were performed on a Gyroid cubic scaffold (10×10×10 mm3). In the mesoscale model (Figure 3(a)), the scaffold geometry was modelled explicitly and assigned the base material properties. In the macroscale model (Figure 3 (b)), the homogenised elastic response obtained from the unit cell was assigned to a solid cube with the exact external dimensions as the scaffold. The two models were subjected to equivalent loading conditions (Figure 4), and their stiffness responses were compared to verify consistency.

[picture]

Fig. 4. Geometry and boundary condition of (a) mesoscale simulation and (b) macroscale simulation.

Fig. 4. Geometry and boundary condition of (a) mesoscale simulation and (b) macroscale simulation.

Finally, quasi-static numerical compression simulations were carried out on Gyroid cells to support the experimental campaign. A fixed support was applied to the bottom surface, and a displacementcontrolled load was applied to the top. The analyses were performed in the elastic range to isolate the effects of geometry and material anisotropy and to compare numerical and experimental stiffness responses. The models were discretised using 4-node linear tetrahedral elements with an average element size of 0.1 mm . The mesh size was selected based on a sensitivity analysis reported in [9]. The cubic cell was discretised using linear tetrahedral elements with an average size of 0.6 mm .

3. Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1047.267.pdf

3.1 Experimental results.

The bulk experimental results are summarised in Table 3, where 1≡X,2≡Y,3≡Z. The mechanical behaviour on plane XY is consistent. The differential ΔQS(E3E1)≈15GPa corresponds to an approximate 13% increase in Young's modulus along the build direction, indicating a moderate but systematic elastic anisotropy induced by the L-PBF process [9].

Table 3. Bulk experimental quasi-static results.

E₁ = E₂ = E | 113000 MPa
E₃ | 128000 MPa
v₃₁ | 0.32
v₂₃ = v₁₃ | 0.28
v₁₂ | 0.29
G₁₂ | 32000 MPa
G₂₃ = G₁₃ = G | 35000 MPa

This directional dependence could be attributed to the layer-wise fabrication strategy and is associated with thermal gradients that promote preferential grain growth and microstructural texture along the building direction. As a result, the base material exhibits a transversely isotropic mechanical behaviour, with isotropy in the XY plane and distinct properties along the Z axis [10-12].
The lattice experimental results are shown in Figure 5 as stress-strain engineering curves.

[picture]

Fig. 5. Stress-strain engineering curves of lattice experimental results.

Fig. 5. Stress-strain engineering curves of lattice experimental results.

The analysis focuses on the elastic regime of the stress-strain response, and the effective Young's modules are summarised in Table 4.

Table 4. Lattice experimental quasi-static results.

EG3.5T0.4X | 11750 ± 750 MPa
EG3.5T0.4Z | 13000 ± 1000 MPa
EG5T0.6X | 7000 ± 1000 MPa
EG5T0.6Z | 5500 ± 750 MPa

The experimentally observed stiffness differences between loading directions correspond to variations of approximately 10−15% in effective Young's modulus at the lattice level. The lattice results reveal a difference in Young's modulus between the Z and X loading directions, confirming the presence of anisotropic mechanical behaviour. In both cases, the measured difference is approximately 1300 MPa . The smaller unit cell exhibits higher stiffness when loaded parallel to the building direction, whereas the larger unit cell shows superior mechanical performance when loaded perpendicular to the building direction. This trend requires further investigation, including computed tomography analyses of the specimens to assess the influence of internal porosity and manufacturinginduced defects. Overall, the Gyroid lattices reduce the effective stiffness compared to the bulk material, as reported in the literature [13,10]. At the same relative density, the unit cell size and wall thickness influence the measured modulus, with smaller cells generally being stiffer than larger ones [13,14].

3.2 Numerical results.

In accordance with the methodology described in the Materials and Methods section, the numerical homogenisation results are reported as the homogenised elastic stiffness matrix of the Gyroid unit cell. For each investigated unit cell size, two homogenised elastic matrices were obtained: one derived from simulations assuming isotropic base material properties and one obtained by assigning the experimentally measured anisotropic material properties. For each homogenised elastic matrix, the Zener anisotropy ratio (Zr) was calculated [15].

[picture]

Fig. 6. Homogenised Elastic Stiffness Matrix and Zener Ratio of Gyroid (a) cell size 3.5 and thickness 0.4 mm with isotropic material properties, (b) cell size 3.5 mm and thickness 0.4 mm with experimental anisotropic material properties, (c) cell size 5 and thickness 0.6 mm with isotropic material properties, (b) cell size 5 mm and thickness 0.6 mm with experimental anisotropic material properties.

Fig. 6. Homogenised Elastic Stiffness Matrix and Zener Ratio of Gyroid (a) cell size 3.5 and thickness 0.4 mm with isotropic material properties, (b) cell size 3.5 mm and thickness 0.4 mm with experimental anisotropic material properties, (c) cell size 5 and thickness 0.6 mm with isotropic material properties, (b) cell size 5 mm and thickness 0.6 mm with experimental anisotropic material properties.

By analysing the numerical results, it is possible to observe that, when isotropic material properties are assigned, the Gyroid unit cell exhibits isotropic elastic behaviour, in agreement with the literature [5] for the selected relative density. Conversely, when anisotropic material properties are considered, the Zener ratio deviates from unity, indicating that the effective elastic response of the cell is no longer perfectly isotropic due to process-induced anisotropy. The homogenisation approach was further validated through meso- and macroscale finite-element simulations of a Gyroid scaffold. The macroscale model, in which the homogenised elastic response of the unit cell was assigned to a solid cube with the same external dimensions as the scaffold, exhibited a stiffness of 145kN/mm. The mesoscale model, in which the scaffold geometry was explicitly modelled and assigned base material properties, showed a stiffness of 147kN/mm. The close agreement between the two results confirms the validity of the homogenisation procedure and indicates that the effective elastic response is independent of the number of cells considered.

Following the procedure, the anisotropic material properties were applied to simulate the quasi-static compression of the Gyroid lattices investigated experimentally. The analysis was limited to the elastic field, and the numerical Young's modules are summarised in Table 5.

Table 5. Lattice numerical quasi-static results.

EG3.5T0.4X | 18000 MPa
EG3.5T0.4Z | 19600 MPa
EG5T0.6X | 17200 MPa
EG5T0.6Z | 18750 MPa

Also in this case, a difference ( 1500−1600MPa ) in stiffness between the X and Z loading directions is observed. However, the numerical simulations predict a higher stiffness compared to the experimental results. The systematic overestimation of stiffness observed in the numerical simulations can reasonably be attributed to manufacturing-related effects not explicitly accounted for in the model, such as internal porosity, surface roughness, local wall thickness variations, and residual stresses. Among these factors, deviations between nominal and effective load-bearing wall thickness are expected to play a primary role, followed by internal porosity and surface roughness. This discrepancy suggests that additional process-related effects, not captured by the numerical model, may influence the actual mechanical response and reduce the effective stiffness of the printed structures.

3.3 Discussion.

The combined experimental and numerical results consistently indicate that process-induced anisotropy in L-PBF 316L is partially mitigated, but not eliminated, by the Gyroid TPMS architecture. The experimental results confirmed that the L-PBF process induces moderate anisotropy in 316L stainless steel, with higher stiffness in the build direction (Z) than in the XY plane [10-12]. Gyroid TPMS lattices reduce overall stiffness relative to the bulk material, and their architecture partially mitigates the transmission of material anisotropy to the lattice level, consistent with previous studies on TPMS structures [6,10,13,14]. The unit cell size and wall thickness influence the measured modulus, with smaller cells generally stiffer than larger ones at the same relative density [ 10,13,14 ]. Numerical homogenisation captured these trends and provided quantitative estimates of the elastic matrix and Zener ratio, confirming that the lattice geometry limits, but does not eliminate, the effect of process-induced anisotropy [6,10,13].

4. Conclusion

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A combined experimental numerical investigation was conducted to analyse the interaction between process-induced material anisotropy and the mechanical behaviour of Gyroid TPMS lattices manufactured in AISI 316L by Laser Powder Bed Fusion. Experimental characterisation confirmed that the L-PBF process induces anisotropic elastic properties in the base material, which in turn affect the theoretically isotropic response of the Gyroid architecture. The results indicate that the Gyroid TPMS architecture partially mitigates the transmission of process-induced anisotropy from the base material to the lattice level, particularly within the elastic regime and at the investigated relative densities. Numerical homogenisation enabled the computation of the effective elastic stiffness matrix and Zener anisotropy ratio of the unit cell, showing that the Gyroid geometry partially mitigates the transmission of anisotropy from the material to the lattice level. Meso- and macroscale simulations validated the homogenisation approach and demonstrated its independence from the number of unit
cells. Numerical compression simulations captured the general trends observed experimentally. However, an overestimation of stiffness was observed, suggesting that additional manufacturingrelated effects are not fully accounted for in the current study. Overall, the Gyroid TPMS architecture emerges as a mechanically robust lattice design capable of partially filtering process-induced anisotropy, while future work must explicitly incorporate manufacturing-related effects to achieve fully predictive numerical models.

Funding

The original version of this paper is available on https://www.scientific.net/KEM.1047.267.pdf

Funded by the European Union - Next Generation EU. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them. PNRR MUR M4 C2 Inv. 1.5 CUP B83C22003920001 - ORTO ATTIVE.

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.267.pdf

Al-Ketan, O., & Abu Al-Rub, R. K. (2019). Multifunctional mechanical metamaterials based on triply periodic minimal surface lattices. Advanced Engineering Materials, 21(10), 1900524.

Sun, J., Chen, C., Zhang, B., Yao, C., & Zhang, Y. (2025). Advances in 3D-printed scaffold technologies for bone defect repair: materials, biomechanics, and clinical prospects. BioMedical Engineering OnLine, 24(1), 51.

Nazir, A. Abate, K. M., Kumar, A. Jeng, J. Y. A state-of-the-art review on types, design, optimization, and additive manufacturing of cellular structures. International Journal of Advanced Manufacturing Technology, 2019, 104, 3489-3510.

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Chatzigeorgiou, C., Piotrowski, B., Chemisky, Y., Laheurte, P., & Meraghni, F. (2022). Numerical investigation of the effective mechanical properties and local stress distributions of TPMS-based and strut-based lattices for biomedical applications. Journal of the mechanical behaviour of biomedical materials, 126, 105025.

Mukherjee, M. (2019). Effect of build geometry and orientation on microstructure and properties of additively manufactured 316L stainless steel by laser metal deposition. Materialia, 7, 100359.

Z. Xia, Y. Zhang, and F. Ellyin, "A unified periodical boundary conditions for representative volume elements of composites and applications," Int J Solids Struct, vol. 40, no. 8, pp. 19071921, 2003.

Ciccarella, A., Dell'Avvocato, G., Cortis, G., & Mancini, E. (2025). Meso-and macroscale modelling strategies for biomimetic structures produced using L-PBF technology. Material Forming: ESAFORM 2025, 313.

E. Mancini, M. Utzeri, E. Farotti, A. Lattanzi, and M. Sasso, "DLP printed 3D gyroid structure: Mechanical response at meso and macro scale," Mechanics of Materials, vol. 192, May 2024.

Hitzler, L., Hirsch, J., Heine, B., Merkel, M., Hall, W., & Öchsner, A. (2017). On the anisotropic mechanical properties of selective laser-melted stainless steel. Materials, 10(10), 1136.

Deev, A. A., Kuznetcov, P. A., & Petrov, S. N. (2016). Anisotropy of mechanical properties and its correlation with the structure of the stainless steel 316L produced by the SLM method. Physics Procedia, 83, 789-796.

Kok, Y., Tan, X. P., Wang, P., Nai, M. L. S., Loh, N. H., Liu, E., & Tor, S. B. (2018). Anisotropy and heterogeneity of microstructure and mechanical properties in metal additive manufacturing: A critical review. Materials & Design, 139, 565-586.

Maconachie, Tobias, et al. SLM lattice structures: Properties, performance, applications and challenges. Materials & Design, 2019, 183: 108137.

Maconachie, T., Leary, M., Zhang, J., Medvedev, A., Sarker, A., Ruan, D., ... & Brandt, M. (2020). Effect of build orientation on the quasi-static and dynamic response of SLM AlSil0Mg. Materials Science and Engineering: A, 788, 139445.

C.M. Zener, S. Siegel, Elasticity and Anelasticity of Metals, J. Phys. Colloid Chem. 53 (1949) 1468-1468.

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 277-286

doi: 10.4028/p-2Jvb9B

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-19

Revised:
2026-02-25

Accepted:
2026-02-26

Online:
2026-04-13

3D Printing of Continuous Carbon Fiber-Reinforced Polymer Tee Pipe: Strategy Development and Demonstrator Fabrication

The original version of this paper is available on https://www.scientific.net/KEM.1047.277.pdf

Rix Mattes 1,a, Kállai Zsolt 1, b*, Kipping Johann 1,c and Schüppstuhl Thorsten 1, d

1 Hamburg University of Technology, Institute of Aircraft Production Technology, Denickestr. 17, 21073 Hamburg, Germany

a  mattes.rix@tuhh.de, b  zsolt.kallai@tuhh.de, c johann.kipping@tuhh.de, d  schueppstuhl@tuhh.de

*corresponding author: zsolt.kallai@tuhh.de

Keywords: 3D printing, continuous carbon fiber-reinforced polymers, carbon fiber pipes, tee pipe.

Abstract

Conventional manufacturing techniques for continuous carbon fiber-reinforced polymers (CFRP) rely on costly, geometry-specific molds, which substantially limit design flexibility. To overcome these constraints, this paper proposes a robot-based, multi-axis Fused Filament Fabrication (FFF) approach for the production of CFRP components of complex geometries. The setup enables advanced material placement so that support structures and internal cores can be avoided, thereby reducing process preparation effort, post-processing requirements, and overall manufacturing cost. As a case study, a tee pipe geometry is investigated. A dedicated slicing strategy is developed in which the main pipe and the branching section are fabricated sequentially. This approach requires precise cutting and controlled re-adhesion of the CFRP material, a critical capability for extending conventional neat-polymer FFF processes to the additive manufacturing of CFRP. Experimental validation demonstrated the feasibility of the process, highlighting the critical role of an innovative technique called nozzle ironing for surface preparation, as well as the challenges associated with fiber cutting mechanisms. While the final component achieved structural coherence, leakage testing revealed porosity at specific interface regions, suggesting directions for future hardware refinement and process optimization.

Introduction

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Carbon fiber-reinforced polymers (CFRP) have become indispensable in modern engineering due to their exceptional properties, specifically their high strength-to-weight ratio. These materials play a decisive role in sectors such as aerospace, where mass reduction directly correlates with increased efficiency and reduced fuel consumption [1]. However, a major disadvantage is the reliance on costly, restrictive molds that make automation of complex geometries highly demanding. In addition, conventional processes still used for complex-shaped parts, such as autoclave molding or hand layup, produce material waste, often require additional finishing to achieve high quality, and are generally not economically viable for small-series production [2].

To address these limitations, research has increasingly focused on Additive Manufacturing (AM), specifically Fused Filament Fabrication (FFF), also known as Fused Deposition Modeling (FDM), adapted to print with continuous CFRP filament [3]. While commercial 3-axis systems exist, they are often limited by planar layer deposition. Multi-axis systems, like robotic platforms, allow the print head and/or build platform to move, rotate and reorient with additional degrees of freedom, potentially eliminating support structures and allowing for load-path optimized fiber placement [4].

This paper addresses an alternative approach to manufacturing a tee pipe, such as those used in aircraft Environmental Control System (ECS) ducting, where components must be extremely lightweight and are produced in low quantities. Currently, these parts are primarily manufactured using bladder molding or bonded joint assembly, both of which are mold-dependent, limit design variability, require manual labor and are economically viable only at higher production volumes.

This paper focuses on the manufacturing of a tee pipe using continuous CFRP filament on a multiaxis robotic FFF system. First, the hardware setup is introduced, followed by material, process and hardware constraints, which define design and strategy requirements. Then a suitable printing strategy
is developed that aims for good geometric fidelity and structural integrity without the use of support structures or sacrificial cores. Finally, the practical validation of the process through the production of a physical prototype is presented. The part is evaluated through a leak test and dimensional accuracy measurements. In the "Conclusion and Outlook" chapter, process limitations are discussed and possible work is outlined to improve process robustness, part quality and the applicability of multi-axis CFRP 3D printing as an alternative manufacturing route for complex, low-volume composite components.

State of the Art

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CFRP Manufacturing.

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Carbon fiber-reinforced polymers are composite materials consisting of carbon fibers embedded in a polymer matrix, which together yield exceptional mechanical properties at low weight. This unique combination has driven extensive research into their applications across industries, such as aerospace and automotive [2,5].

For tee pipes specifically, methods such as filament winding and bladder molding are dominant. Filament winding creates rotationally symmetric parts but requires complex kinematics and/or lost cores to produce tee pipes. Moreover, it requires complex trajectory planning and usually results in non-uniform wall thicknesses [6]. Bladder molding allows for complex hollow structures like whole bicycle frames, but requires expensive, geometry-specific metal molds and rubber bladders [7, 8]. Alternatively, complex geometries are often assembled by joining simpler sub-components using adhesives or a co-curing process, which introduces potential weak points at the bond lines [9]. When using a thermoplastic matrix, welding is also a feasible method to join CFRP parts without the need for additional materials [10].

Additive Manufacturing with Continuous Fibers.

The original version of this paper is available on https://www.scientific.net/KEM.1047.277.pdf

FFF is the most widespread layer-by-layer extrusion process, typically using thermoplastics in the form of filament. Conventionally used for rapid prototyping, there is an increasing trend to manufacturing functional parts. To improve the mechanical performance of printed parts, fibers are incorporated into the polymer matrix, with continuous fibers offering substantially greater reinforcement than short fibers [11]. Commercial printers from manufacturers like Markforged and Anisoprint successfully print with continuous CFRP [12, 13]. However, they are generally restricted to three-axis Cartesian systems with planar layer deposition which often requires support structures for overhangs. This reduces layer compaction as the compaction force partially acts towards the generally sparse infill support material, which is removed after printing and leaves behind a rather rough surface. Furthermore, they are limited to partly adding carbon fibers into a print part, instead of fully printing with towpregs (pre-impregnated CFRP filament). All these technologies for continuous fiber extrusion require a cutting unit to cut the fiber when needed to separate the deposited layer from the remaining fiber on the spool [14].

Robotic FFF and Slicing.

The original version of this paper is available on https://www.scientific.net/KEM.1047.277.pdf

Standard 3-axis slicing generates planar layers, resulting in the staircase effect on curved upper surfaces and weak interlayer bonding always in the z-direction. Multi-axis robotic systems open the possibility for printing non-planar sliced layers, where layer orientation can change dynamically or follow the curvature of the part (tangential printing), thereby aligning fibers with load paths and reducing support requirements [4, 15]. This in contrast often requires complex and demanding programming, because conventional slicing software is mostly limited to 3-axis Cartesian systems.

Most publications focus on material properties, print parameter characterization and printing hollow pipes. However, there is a scarcity of literature describing the printing of technical, hollow components with additional geometries like branches, such as tee pipes, using continuous fibers on multi-axis systems. Particularly regarding the complex process of cutting and re-starting print paths
with fiber-reinforced filament, which is crucial for the transition from neat polymer to continuous fiber printing of equivalent geometries and similar slicing effort when using towpregs.

Methodology

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The Robot-Based Experimental Setup.

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The experiments were conducted on a dual-robot setup. This system consists of two opposing 6-axis industrial robots (ABB IRB 2600-20/1.65) mounted on a common platform (Fig. 1a). Robot 1 carries a dual print head, while robot 2 manipulates the heated build platform ( ∅400 mm ) and is hanging upside down on a cantilever beam. This robotic configuration enables greater flexibility in orienting the part, such as aligning the print direction with the gravity vector, achieved through motion of either the print head or the platform. Further info about the setup can be found in [16].

The printing system features two print heads mounted next to each other. The print head for printing conventional filament extrudes neat thermoplastics, while the other is equipped with a commercial Markforged fiber nozzle. This nozzle has an inner diameter of 1.3 mm , characterized by a relatively rounded inner edge at the nozzle exit and extrudes a continuous carbon fiber towpreg [17]. The extruder used to extrude the towpreg is a Bondtech BMG extruder, modified such that it enables selective activation or deactivation of active filament feeding through a mechanical clutch mechanism. If active, this clutch mechanism engages gears, which then grab the filament to feed it to the nozzle. Another crucial component is the integrated cutting mechanism, which uses a servo-driven rotating blade to cut the continuous fiber strand. This unit is located between the nozzle and the extruder.

Program development employs RobotStudio, an ABB-specific offline programming environment enabling virtual system simulation and program verification prior to execution on the physical system. Robot code is written in RAPID, the ABB proprietary programming language. Each robot receives independent code that synchronizes with the other through dedicated commands. The geometry is not sliced using commercial software adapted for multi-axis systems; rather, toolpaths are generated through direct RAPID code programming, as current commercial slicing software is not optimized for robotic systems with this configuration. This approach provides geometric control but requires substantially greater programming effort compared to conventional 3D printer workflows.

Requirements and Constraints.

The original version of this paper is available on https://www.scientific.net/KEM.1047.277.pdf

The robotic setup and the material properties of the carbon fiber filament impose strict constraints that need to be considered:

Material.

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The towpreg with a nominal diameter of 0.375 mm is also from Markforged. It has a Polyamidebased matrix and roughly 34.5% fiber content [18]. The filament is brittle and it is prone to breakage at room temperature. For initial test prints, a Polylactide (PLA) filament from BASF Ultrafuse with a nominal diameter of 1.75 mm is applied.

Process.

The original version of this paper is available on https://www.scientific.net/KEM.1047.277.pdf

The towpreg is mostly not actively extruded, as this could break the fibers because it is challenging to synchronize material placement with the nozzle-speed of the robot. Instead, passive extrusion is used, where the filament is pulled from the spool through the extruder and out the nozzle just by the movement of the print head. Active extrusion in contrast is when the mechanical clutch mechanism is engaged, and the gears grab the filament and feed it from the spool to the nozzle. The passive extrusion relies on the adhesion to the previous layer, for which the material needs to be firmly pressed onto it. Unlike neat polymers, the printing process cannot move the nozzle away from the print part by simply stopping the extrusion. The fiber must first be mechanically cut to prevent damage to the print part due to pulling on the fibers. After it is cut, the section between cutter and nozzle's orifice cannot be moved by the extruder, it needs to be removed by pulling it out. This requires a specific "run-out length" to be accounted for in the toolpath, which measures 44 mm . From previous projects
it is known that a layer thickness of 0.125 mm must be kept to ensure sufficient bonding to the underlying layer.

Hardware.

The original version of this paper is available on https://www.scientific.net/KEM.1047.277.pdf

The bulky print head and the large build platform create significant collision risks when printing complex geometries like the tee pipe. This requires collision detection prior to printing. Further constraints arise from axis range limitations and kinematic singularities encountered during build platform tilting, both of which are characteristic of robotic systems.

[picture]

Fig. 1. a: Digital rendering of the robotic printing setup used. b: Tee pipe segmented into the main pipe A and the branch B, where the connecting surface is referred to as the opening.

Fig. 1. a: Digital rendering of the robotic printing setup used. b: Tee pipe segmented into the main pipe A and the branch B , where the connecting surface is referred to as the opening.

Development of the Printing Strategy.

The original version of this paper is available on https://www.scientific.net/KEM.1047.277.pdf

The selected component is a standard tee pipe with a perpendicular branch of the same diameter as the base pipe. The mean diameter is chosen to be 40 mm . The tee pipe was segmented into two phases, as seen in Fig. 1b, to enable printing without the use of support structures. First, the main pipe with the opening was printed on a horizontally positioned heated build platform. Utilizing the multi-axis capability, the print bed with the finished main pipe was tilted by 90∘ to enable material deposition in the direction of the gravity vector to eliminate a potential negative influence on the fiber placement and to allow similar printing conditions as when printing the main pipe. The printing movement is done by the print head (robot 1). Following the hoop stress for internal pressure, circumferential placement of the fibers is chosen to meet the requirements for ECS ducting. The wall thickness is selected to be a single path width, to produce the lightest possible part.

Phase 1 (Main Pipe).

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The main pipe is printed vertically on the build platform. A circular opening is left on the side wall to accommodate the branch. Above and below the opening there is a run-in and run-out zone which makes the opening rather elliptic, see Fig. 2c. Without this feature, material at the overhang (as seen in Fig. 2b) cannot be properly compacted, likely resulting in an unsuccessful print. This is due to insufficient layer overlapping because there is no support to take the re-adhering force for proper
layer bonding. Before the opening starts, the main pipe needs to reach a height of 45 mm to avoid collision when printing the branch pipe. The opening requires to cut the fiber and re-adhere it at every layer within the opening section. To do this successfully, filament is actively extruded until it sticks out the nozzle by approximately 2 mm , which has been established through preliminary experimentation. Then the nozzle approaches the desired contact point of the next layer via a linear ramp movement and, upon reaching it, pinches the material between the nozzle and the previously printed layer. The ramp is parameterized with a start point that has a 5 mm horizontal and 2 mm vertical offset from the contact point. Pushing with the right force and extruding actively re-adheres the material after approximately 5 mm of printing so the extruder can be switched to passive extrusion again. This is one of the two most crucial steps in manufacturing this tee pipe. The filament is cut by the run-out length before the end of the layer. At the cutting point, the print head stops its movement, closes the mechanical clutch mechanism of the extruder (active extrusion mode) to prevent the filament from slipping backward out of the extruder, and then resumes the movement to deposit the remaining material before the cut.

Phase 2 (Branch Pipe).

The original version of this paper is available on https://www.scientific.net/KEM.1047.277.pdf

After Phase 1 is finished, the build platform is rotated by 90∘. The branch pipe is then printed directly onto the elliptical side opening of the main pipe. As the cutting procedure separates the fibers, the fiber ends are not always even and their length can also vary depending on whether the re-adhesion took place slightly earlier or later than in the previous layer. To address this uneven surface created by cutting the fibers at the opening of the main pipe, a novel "nozzle ironing" strategy was developed. Before printing the branch, the hot nozzle slowly moves over the interface area without extruding material to smooth out irregularities and fuse loose fiber ends (Fig. 2a). This is done in several rounds, usually 3 to 4 rounds, depending on how uneven the surface is. Each time lowering the nozzle by roughly 0.1 mm until an even surface line is created. After this is done, the branch pipe can be printed directly on that surface which is partly only as wide as a single path of the main pipe wall.

[picture]

Fig. 2. a: Working principle of the nozzle ironing technique. b: Unrealizable overhang at main pipe without geometric compensation. c: Optimized main pipe with run-in and run-out zones and brim.

Fig. 2. a: Working principle of the nozzle ironing technique. b: Unrealizable overhang at main pipe without geometric compensation. c: Optimized main pipe with run-in and run-out zones and brim.

Manufacturing / Experiments.

The original version of this paper is available on https://www.scientific.net/KEM.1047.277.pdf

The actual realization of the prototype was done in three steps: strategy tests with PLA, preliminary tests with CFRP filament and final printing of the tee pipe.

PLA Tests.

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Initial tests were conducted using PLA, printed at 210∘C, to verify robotic path planning and the absence of collisions and kinematic singularities during execution. No active part cooling is used throughout all prints. Print bed adhesion is known from previous projects to be best at 60∘C bed temperature with a 5 mm brim and a glue stick coating on the bed prior to the print start. By applying these parameters, print bed adhesion is ensured for all prints with both materials. During these PLA test prints, some minor issues in the robot code were corrected. Furthermore, these tests highlighted issues with absolute positioning accuracy when rotating the print bed after Phase 1, necessitating manual calibration of offsets. After the offset was compensated in the robot code, the experiment could be resumed and no further calibration was needed during the PLA tests.

Preliminary Tests with CFRP Filament.

The original version of this paper is available on https://www.scientific.net/KEM.1047.277.pdf

Once the full tee pipe was printed with PLA, material-related parameters like layer height and nozzle temperature were adjusted to those of the CFRP filament. The nozzle temperature was set to 265∘C for all CFRP prints. The tests with CFRP filament focused on the reliability of the cutting and re-adhering technique. Here, the specific ramping motion for pressing the severed fiber end onto the existing layer to ensure adhesion was tested repeatedly. For this purpose, only the opening section of the main pipe was printed, as it provided sufficient geometry for multiple iterations. This step was also used to further adjust parameters and proved that it is generally possible to successfully re-adhere the fibers automatically with minimal overhang. Fig. 3b shows a corresponding test print.

Printing the CFRP Tee Pipe.

The original version of this paper is available on https://www.scientific.net/KEM.1047.277.pdf

The final step involved printing the full tee pipe with CFRP filament using the developed strategy. The printing speed was conservatively set to a maximum of 20 mm/s and varied depending on the complexity of the print step. The most critical part, the re-adhering, was printed with only 2.5 to 5 mm/s to permit adequate fiber cooling and substrate adhesion. The nozzle ironing was performed at a speed of approximately 1 mm/s, while maintaining a printing temperature of 265∘C, in order to promote effective heat transfer into the ironed material. This step is slow when relying solely on the nozzle's thermal energy.

[picture]

Fig. 3. a: Side view of the opening with nozzle ironing applied in the marked area and direction (showing an intermediate test part). b: Preliminary CFRP print to test the re-adhesion (left edge), featuring minimal overhang on the left edge and a uniform edge on the right, based on the cuts made in-process. c: Final result of the CFRP tee pipe printed with the described strategy.

Fig. 3. a: Side view of the opening with nozzle ironing applied in the marked area and direction (showing an intermediate test part). b: Preliminary CFRP print to test the re-adhesion (left edge), featuring minimal overhang on the left edge and a uniform edge on the right, based on the cuts made in-process. c: Final result of the CFRP tee pipe printed with the described strategy.

Results

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The tee pipe was successfully fabricated with CFRP without the use of support structures (Fig. 3c). The multi-axis strategy allowed for the reorientation of the part, ensuring that fibers were deposited as intended.

Main Pipe and Opening.

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The main pipe was printed successfully. However, the filament feeding mechanism proved unreliable for the thin towpreg, requiring manual intervention to assist the feed after the cutting procedure. Filament was pushed out the nozzle and held with tweezers until the material bonded to the underlying layer. This resulted in long overhanging fiber material pieces at the re-adhering side of the opening. Holding the filament manually was also needed to avoid it sometimes slipping sideways rather than being pressed firmly against the already printed wall. The reason for this behavior is the large difference in diameter between the nozzle ( 1.3 mm ) and the filament ( 0.375 mm ), which allows for a relatively large movement of the filament within the nozzle. The resulting fiber ends protruding from the re-adhering points on the main pipe were manually trimmed using a fine saw to prevent high mechanical forces and to ensure a flush surface, even though preliminary CFRP filament tests demonstrated that this is ideally unnecessary.

Nozzle Ironing.

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The nozzle ironing technique was then executed to smoothen the fitting surface, significantly improving the interface quality and ensuring successful branch attachment. An example with sideways bent layers can be seen in Fig. 3a. Moreover, it was found that fiber material ends as long as 1 mm can be ironed down to achieve a good mating surface.

Branch Printing.

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The branch was printed onto the ironed surface, starting with an elliptical shape defined by the main pipe opening and ending with the desired planar, circular shape. During the print of the first layer, it could be seen that it did not perfectly bond to some sections of the mating surface below, because of some slight deviations from the desired geometry.

Geometric Accuracy.

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The mean diameter of the printed pipes was 39.5 mm , deviating by 0.5 mm from the nominal 40 mm . This is likely due to calibration offsets of the robots, but it was not further investigated.

[picture]

Fig. 4. a: Gap at bridging area, created by material that got pulled into the main pipe. b: Broken inter-layer connection due to manually cutting the fiber ends. c: Some during the process automatically cut fibers did not fully reach the nozzle ironing surface. d: Leakage test reveals some leaking areas, after filling the tee pipe with water (water level can be seen on the tape).

Fig. 4. a: Gap at bridging area, created by material that got pulled into the main pipe. b: Broken inter-layer connection due to manually cutting the fiber ends. c: Some during the process automatically cut fibers did not fully reach the nozzle ironing surface. d: Leakage test reveals some leaking areas, after filling the tee pipe with water (water level can be seen on the tape).

Defect Analysis.

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Visual inspection by a backlight test to search for defects revealed three primary defect types. All defect types occur only at the joining area between the main pipe and the branch:

At the top of the branch connection, where bridging in the main pipe was necessary, the initial layers were slightly pulled to the center axis of the pipe. As a result, small gaps were created at the start and end area of the bridging, as can be seen in Fig. 4a.

On the side where the fibers were manually cut, small gaps remained (Fig. 4b). The cutting force led some inter-layer connections to break. Thus, this issue can be traced back to the unreliable feeding mechanism.

In some areas on the opposite side of the opening, the automatically cut fibers do not fully reach the surface of the nozzle ironing (Fig. 4c). This issue arises because the fibers are cut slightly too early. Adjusting the cutting locations in the robot code would resolve this issue.

Validation.

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A leakage test was performed to assess the functional integrity of the printed part. The bottom and branch openings were sealed with adhesive tape, and the tee pipe was filled with water. It held the bulk of the water, demonstrating structural coherence. However, leaks were observed at the specific defect points identified above. Fig. 4d shows a stage of the leakage test where the water level had already dropped. With the exception of two narrow leakage traces extending downward on the left and right sides of the branch, the outer surface of the tee pipe remains dry, indicating the absence of additional leak paths.

Furthermore, a manual pulling test was performed at the branch to qualitatively test the bonding strength between the main pipe and the branch. It showed sufficient adhesion between the two subparts of the tee pipe.

Conclusion and Outlook

The original version of this paper is available on https://www.scientific.net/KEM.1047.277.pdf

This paper presents a printing method for robotic printing of a continuous CFRP tee pipe. A slicing strategy involving part segmentation and build platform rotation was successfully implemented and executed. The main pipe was printed without issues and fiber cutting functioned as expected. The developed nozzle ironing technique was successfully utilized and proved to be valuable for creating a uniformly smooth surface to print the branch on. The branch pipe adhered to the base pipe and created a complete CFRP tee pipe. While the final part has slight deviations from the planned geometry, its fidelity remains acceptable. Functional validation revealed gaps at the cut and readhesion seams.

Multi-axis additive manufacturing has the potential to emerge as a viable alternative process for small-lot production and the fabrication of individualized components featuring complex CFRP geometries, such as the tee pipe considered in this study. The primary advantage demonstrated lies in the complete elimination of both support structures and dedicated molds, thereby enabling a high degree of geometric flexibility while significantly reducing lead time. However, the process stability is currently limited by hardware constraints. The reliability of the active feeding mechanism for the delicate fiber towpreg is the main bottleneck.

To establish multi-axis CFRP printing as a viable industrial alternative, not only these hardware challenges but also material development, particularly increasing the carbon fiber content, has to be addressed. Before its wide application, further optimization is needed; future work must therefore focus on:

Improving the extruder grip on thin fibers to reliably extrude desired distances of filament and thereby eliminating manual intervention. An auxiliary heating system for nozzle ironing could be investigated to increase the speed at this process step. Addressing the deviation from the nominal size, a new nozzle for precise deposition shall be developed or path compensating strategies need to be applied. This can reduce geometric deviations and prevent the filament from slipping sideways at the re-adhering step. Developing automated path-planning software
that can handle various multi-axis systems and non-planar slicing natively to reduce the manual coding effort.

Conduct further tests to quantify the strength and structural integrity of the printed tee pipe compared to traditional laminates. Destructive testing can address the strength of the connection between the main pipe and the branch, while structural integrity should especially be assessed at the fiber ends and the zones in which the nozzle ironing was performed.
By addressing these challenges, robotic CFRP printing has the potential to become a powerful tool for the flexible manufacturing of high-performance (non-critical), lightweight components.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/KEM.1047.277.pdf

This research was funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) through the Federal Aviation Research Program (LuFo VI).

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.277.pdf

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Kállai, Z., Kipping, J., Bremer, J., Schüppstuhl, T.: A Preliminary study: Support-free manufacturing of rotationally symmetric pipes from continuous carbon fiber reinforced polymers with multi-axis 3D printing. Conference: Material Forming, 344-353 (2025). DOI: 10.21741/9781644903599-38.

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The original version of this eBook is available on https://www.scientific.net/book/additive-manufacturing/978-3-0364-1997-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 287-297

doi: 10.4028/p-li38bJ

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-19

Revised:
2026-02-18

Accepted:
2026-02-25

Online:
2026-04-13

Thermomechanical Modelling and Shape Prediction in 4D Printing Using FEA

The original version of this paper is available on https://www.scientific.net/KEM.1047.287.pdf

Tiago Andrade 1,a, Mylene Cadete 1, b and João Dias-de-Oliveira 1,c*

1 TEMA - Center for Mechanical Technology and Automation, Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal

a  tiago.s.andrade@ua.pt, b  mylene@ua.pt, c*jalex@ua.pt

Keywords: numerical modelling, FDM, finite element analysis, shape morphing, 4D printing, shape prediction.

Abstract

In today's competitive manufacturing landscape, balancing cost and performance is crucial. Additive Manufacturing (AM) offers a path to efficient, functional designs, with FourDimensional (4D) printing emerging as a key innovation. By using materials that are responsive to external stimuli, 4D printing enables objects to change shape over time, making them active and opening new possibilities in adaptive design. Building on this, research into the shape morphing behaviour of 4D-printed objects was conducted through simulation. Based on the literature, this process can be effectively approached as a thermomechanical problem. This work first simulates the shape morphing of two-layer structures. Multiple parameters are varied through Finite Element Analysis (FEA) to assess both their independent influence and the feasibility of the proposed method. The study then analysed the use of orthotropic properties to evaluate control over deformation directions. Finally, insights from these phases were applied to more complex geometries. It is concluded that the morphing process can be computationally planned using a thermomechanical approximation, paving the way for the incorporation of the influence of printing parameters, pattern design and the strategic division into active/passive regions. This study provides foundational work in 4D printing regarding the shape prediction of printed objects.

Introduction

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Modern manufacturing is shaped by the ongoing pursuit of greater efficiency, functionality, and costeffectiveness. As industries evolve to meet complex design demands, Additive Manufacturing (AM) (commonly referred to as 3D printing) has become a transformative technology. It allows for the direct fabrication of intricate, customized components from digital models, reducing material waste and streamlining production processes. Extending the capabilities of AM, Four-Dimensional (4D) printing introduces the dimension of time by incorporating smart materials that react to external stimuli such as temperature, light or magnetic fields (Figure 1). These responsive materials enable printed objects to dynamically alter their shape, behaviour or function over time, paving the way for innovative, adaptive applications in fields such as aerospace, biomedicine, defence and construction [1-10].

[picture]

Fig. 1. Representation of the morphing process from a variation of the ratio of CTE in a bilayer beam.

Fig. 1. Representation of the morphing process from a variation of the ratio of CTE in a bilayer beam.

Despite the rapid advancement and growing range of applications of 4D printing, one of the main challenges impeding its widespread adoption is the difficulty in predicting and designing the final shape and behaviour of printed parts. Notably, the morphing process involves complex interactions among multiple variables, namely material composition, geometry, stimulus conditions and printing parameters. Accurate prediction models are essential for controlling and programming these transformations effectively remain underdeveloped [11-18].

Recent studies such as Zeng et al. [13], Bodaghi et al. [14], Peng et al. [15], Tholking et al. [16] and Song et al. [18] have made strides toward addressing this challenge, demonstrating how anisotropic properties can be embedded into a structure via Fused Deposition Modelling (FDM) and other Material Extrusion (MEX) technologies, given its material compatibility, operational accessibility and relevance to the development of 4D printing, enabling controlled deformation under thermal stimuli without post-processing. These approaches demonstrated how anisotropic material properties can be strategically exploited to program deformation directionality under thermal activation. In these cases, emphasis was placed on material orientation and interlayer stiffness mismatches in order to induce shape morphing behaviour.

Remarkably, several works [8,11,16,19-25] demonstrated that 4D morphing can be effectively simulated using thermomechanical models, where mismatches in properties such as the coefficient of thermal expansion (CTE) and Young's modulus drive shape transformation. These works underscore the relevance of simulating 4D printing behaviour as a thermomechanical problem, particularly for bilayer structures, and highlight the potential of tailoring deformation by adjusting parameters such as material orientation, activation temperature, and dimensions. Nevertheless, it can be noted that previous contributions remain mostly application-specific, often relying on experimental calibration or narrowly scoped simulations. There is a need for a generalisable, computationally efficient modelling framework that captures the essential mechanics of 4D shape transformation, particularly relevant during the early stages of design, where physical prototyping may not be feasible. Moreover, existing works don't often explore the programming potential of orthotropic properties in simulation environments, despite their practical relevance in FDM-based 4D printing.

The present work aims to contribute to shape prediction strategies in 4D printing through a computational approach. In this case, the main objective is to develop and validate a numerical simulation models based on Finite Element Method (FEM) that accurately represent the shapemorphing behaviour of 4D-printed objects under thermal activation. The study begins with simple bilayer configurations to isolate and examine the influence of key parameters, then advances toward complex geometries incorporating directional control and strategic material distribution. By analysing these effects in a controlled digital environment, this work provides a conceptual framework for guiding the design and programming of 4D-printed systems.

A key aspect of the adopted strategy is its exploratory and conceptual nature. Rather than replicating every physical detail of the printing process, the method explored the potential of a simplified approach to the problem by modelling 4D shape change as a purely thermomechanical phenomenon, following literature guidelines. This treats deformation as the result of internal stress imbalances generated by mismatches in thermal expansion and stiffness between material regions when subjected to a uniform thermal load. Although this approach does not fully capture timedependent effects or complex multi-physics interactions, it offers an effective approximation for studying and programming shape transformation. This balance between apparent simplicity and predictive power contributes to a systematic framework for shape prediction, offering researchers a scalable tool to guide the development of programmable, stimuli-responsive systems in 4D printing.

Thermal stimuli simulation

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An initial analysis investigates the thermomechanical behaviour of 4D-printed bilayer structures under thermal stimuli, aiming to clarify how key material and geometric parameters govern shape morphing. A simulation-based approach using FEA was adopted, treating morphing as a mechanically driven deformation problem induced by uniform heating. Controlled parametric studies
were conducted to assess how variations in the CTE, Young's modulus, activation temperature, thickness, and length influence the structure's response.

The reference model was a bilayer beam, 80×8×1.6 mm, with bonded layers (Figure 2). The top layer remained fixed as Poly(lactic acid) (PLA), while the bottom layer's properties were varied to study their effects - such as the CTE, Young's modulus, activation temperature, layer thickness and beam length. A thermal variation of 125∘C was applied to activate the system and a minimal constraint method was applied to allow free thermomechanical deformation (Figure 3). To assess solver behaviour and geometric nonlinearity, four numerical formulations were compared: linear and quadratic elements under both small and large deflection assumptions.

[picture]

Fig. 2. 3D representation of the used geometry and the constraints applied to the model.

Fig. 2. 3D representation of the used geometry and the constraints applied to the model.

CTE emerged as a dominant driver of morphing abilities. The layer with the higher CTE consistently dictated the bending direction, with the structure curling away from it due to thermal strain mismatch. As the CTE ratio increased, curvature intensified, though nonlinearly. When only one material's CTE was altered, low values yielded minimal deformation, revealing an asymmetry in behaviour. In fact, it was noted that at very low CTE values, the ability of that layer to contribute to deformation diminished, nullifying its morphing role. These trends aligned with thermomechanical theory, where expansion produces internal moments that induce curvature. Notably, the small deflection solver failed to capture the expected curling, producing unrealistic stretching due to its static stiffness assumption. In contrast, the large deflection solver accurately represented the geometry and stress redistribution during deformation. This validated the need for nonlinear formulations when simulating thermally driven shape changes. The results from the CTE influence are presented in Figure 3.

[picture]

Fig. 3. Data acquired from the CTE study: displacement in the Y direction (left) and elastic strain (right).

Fig. 3. Data acquired from the CTE study: displacement in the Y direction (left) and elastic strain (right).

The role of Young's modulus showed a complementary yet distinct effect. Maximum displacement occurred when both layers had similar stiffness, allowing cooperative bending. As the modulus ratio increased, deformation decreased and internal stress rose, where energy was stored instead of released via curvature. These results reinforced that thermal and mechanical strains act together, and that higher stiffness limits deformation while amplifying stress. Again, nonlinear solvers were necessary to correctly capture these interactions under large deformations. Figure 4 presents the obtained results from the Young's modulus variation.

[picture]

Fig. 4. Results attained from the Young's modulus study: equivalent stress (left) and total displacement (right).

Fig. 4. Results attained from the Young's modulus study: equivalent stress (left) and total displacement (right).

Together, CTE and Young's modulus studies revealed that bilayer deformation can be precisely guided by tuning either thermal expansion or stiffness. The direction and degree of morphing are dominated by the CTE ratio, while the modulus ratio influences the balance between deformation and stress. This justifies a modelling focus on one of these two parameters for predictive and controllable design in a computational environment.

Further studies explored geometric influences. Higher activation temperatures increased deformation, particularly under nonlinear solvers, confirming the link between thermal input and strain. Increasing thickness reduced displacement, as expected from bending theory, but internal stress profiles remained relatively unchanged, highlighting continued mechanical response. Length scaling showed greater deformation with size, though nonlinear effects again necessitated large deflection solvers. At larger scales, convergence difficulties emerged, pointing to computational limits in simulating extensive morphing. Figure 5 further illustrates these remaining studies.

[picture]

Fig, 5. Data acquired from the remaining studies: (a) equivalent stress, (b, c) directional displacement, (d) simulation time.

Figure 5. Fig, 5. Data acquired from the remaining studies: (a) equivalent stress, (b, c) directional displacement, (d) simulation time.

In summary, this phase established a clear methodology for simulating thermomechanical shape change in bilayer structures. It confirmed that nonlinear geometric formulations are essential, and that CTE and Young's modulus are the most effective levers for design control. These insights provide a foundation for more complex morphing simulations in later development stages.

Orthotropic properties evaluation

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To assess directional control in 4D-printed structures, a second phase of simulations was carried out using orthotropic material properties, representing a more realistic approximation of printed parts. Two geometries, a cylindrical model and a square model (Figure 6), were analysed to evaluate how anisotropic thermal expansion affects deformation behaviour under thermal loading. Both retained the PLA top layer while the bottom layer's material was assigned orthotropic properties with varying
principal directions (Figure 7). This allowed the simulation of directionally dependent expansion akin to that observed in printed filament paths.

A sequential methodology of five parametric studies per geometry was employed: initial studies replicated the isotropic behaviour for comparison, followed by cases varying directional CTEs along specific axes, and culminating with a sweep of orientation angles to map out morphing directionality.

[picture]

Fig. 6. 3D illustration of the analysed geometries.

Fig. 6. 3D illustration of the analysed geometries.

[picture]

Fig. 7. 3D representation of the assignment of different material directions.

Fig. 7. 3D representation of the assignment of different material directions.

Results from the cylindrical model showed radially uniform deformation, confirming that, under uniform geometry and symmetric boundary conditions, shape morphing direction is unaffected by orthotropic property assignment when expansion is equal in all directions (Figures 8 and 9). Conversely, in the square model, the presence of geometric asymmetries led to highly directional deformation patterns. Varying the orientation of the orthotropic properties caused a shift in the morphing direction, with corners exhibiting amplified deformation due to their structural isolation (Figure 10).

These findings confirm that directional material assignment effectively controls the orientation of shape morphing without significantly affecting stress, strain, or displacement magnitudes, provided that the material distribution remains uniform. Together with previous results, this underscores that the deformation direction in 4D-printed bilayers can be governed by both the CTE ratio between layers and the spatial orientation of material properties, enabling tailored actuation through anisotropic design.

[picture]

Fig. 8. Equivalent elastic strain data from a cylindrical model: 3D results for an isotropic model (left); data acquired from an isotropic model and orthotropic models for validation.

Fig. 8. Equivalent elastic strain data from a cylindrical model: 3D results for an isotropic model (left); data acquired from an isotropic model and orthotropic models for validation.

[picture]

Fig. 9. Results achieved by the cylindrical model, varying the bottom body's CTE in only one direction, with a principal material direction in the X axis (left) and Z axis (right).

Fig. 9. Results achieved by the cylindrical model, varying the bottom body's CTE in only one direction, with a principal material direction in the X axis (left) and Z axis (right).

[picture]

Fig. 10. Results obtained from the square model analysis: isotropic model (left) and orthotropic model (right).

Fig. 10. Results obtained from the square model analysis: isotropic model (left) and orthotropic model (right).

Shape prediction tests

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Building on previous results that highlighted the influence of key parameters, such as Young's modulus and CTE ratio between layers, structural dimensions, material orientation, and stimulation temperature, an integrated computational methodology was developed to predict 2D-to-3D shape transformations.

The strategy began with the design of a 2D two-layer pattern programmed to morph into a cube, selected for its straightforward uniaxial deformation behaviour and its suitability as a benchmark. Inspired by origami principles, the model featured hinge-like active regions at the edges and passive regions on the faces. Material properties were strategically assigned: the top layer remained passive, while the bottom layer included directional active segments ( 0∘ or 90∘ ), driving localized bending at critical edges. Figure 11 further illustrates this methodology. The cube's faces were modelled as individual bodies with bonded contacts and minimal constraints to enable free deformation. Uniform thermal loads triggered the transformation, and a series of calibrations explored how stimulation temperature, CTE ratios, structural thickness, and critical region length influenced the outcome.

[picture]

Fig. 11. 3D illustration of the cube model, representing the division in multiple regions.

Fig. 11. 3D illustration of the cube model, representing the division in multiple regions.

The initial tests revealed that increasing stimulation temperature or CTE ratios enhanced deformation but led to unfeasibly high stresses, especially near face junctions, questioning their realworld applicability. Subsequent calibrations refined the morphing behavior by introducing independent control over the top face, and by adjusting the offsets of active regions from shared vertices, reducing stress concentrations. A final set of tests shifted lateral edge starting points to tune morphing symmetry. Figure 12 provides a better understanding of the obtained results. Overall,
results showed that complex shape programming requires not only correct directional assignment and CTE control, but also geometric tuning to mitigate mechanical incompatibilities.

[picture]

Fig. 12. Exemplification of the cube's morphing process.

Fig. 12. Exemplification of the cube's morphing process.

After validating the methodology with the cube, the approach was applied to more complex geometries to demonstrate its versatility. A chair-like model explored multi-axial deformation, with active regions placed at the leg-seat and seat-back interfaces (Figures 13 and 14). Programming material activity layer-wise induced downward leg folding and upward backrest motion. Calibration showed that the degree of morphing was primarily influenced by the length of active regions along the deformation axis, with negligible sensitivity in the perpendicular direction.

[picture]

Fig. 13. 3D illustration of the chair model, representing the division in multiple regions.

Fig. 13. 3D illustration of the chair model, representing the division in multiple regions.

[picture]

Fig. 14. Illustration of the chair's morphing process.

Fig. 14. Illustration of the chair's morphing process.

A flower model introduced opposing petal deformations, requiring more intricate material orientations ( 0∘,45∘,90∘,135∘ ) and active region control. Though geometrically more complex, the same core methodology enabled differentiated petal movements, validating the directional and regional assignment strategy, as it can be seen in Figures 15 and 16.

[picture]

Fig. 15. 3D illustration of the flower model, representing the division in multiple regions.

Fig. 15. 3D illustration of the flower model, representing the division in multiple regions.

[picture]

Fig. 16. Exemplification of the flower's morphing process.

Fig. 16. Exemplification of the flower's morphing process.

Finally, a serpent-inspired model incorporated torsional behaviour. Its segmented body used alternating material orientations and layer dominance to generate slithering motion, while the head and hood were independently programmed. A four-step calibration confirmed that torsion, bending, and complex sequencing could be achieved through controlled parameter variation, again reinforcing the modular and scalable nature of the computational strategy. Figures 17 and 18 further illustrates the obtained results.

[picture]

Fig. 17. 3D illustration of the snake model, representing the division in multiple regions.

Fig. 17. 3D illustration of the snake model, representing the division in multiple regions.

[picture]

Fig. 18. Exemplification of the snake model's morphing process.

Fig. 18. Exemplification of the snake model's morphing process.

This sequence of models, increasing in complexity, demonstrated that programmable deformation can be achieved through careful region subdivision, strategic material orientation, and layered property assignment. While real-world implementation still requires refinement, particularly regarding the physical definition of active/passive regions, the results offer a structured path toward predictive shape control in 4D printed systems.

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.287.pdf

This work presented a simulation-based methodology for predicting shape morphing behaviour in temperature-responsive 4D-printed structures. By treating the process as a thermomechanical deformation problem, it was possible to evaluate the impact of key design parameters, including CTE, Young's modulus, activation temperature, thickness, and geometry, on the performance of bilayer systems. Finite Element Analysis showed that shape transformation can be effectively controlled by tuning either thermal expansion or stiffness, with CTE emerging as the dominant factor in defining deformation direction.

The methodology was further extended using orthotropic properties to program deformation orientation, confirming that directional control can be achieved through anisotropic material assignment. This was validated in increasingly complex geometries, where programmable segmentation enabled the prediction of 3D shapes from 2D patterns.

Notably, this research contributes a flexible and scalable numerical strategy for the design of adaptive 4D-printed components. Future developments should focus on improving the definition of active and passive regions in printed structures, further incorporating variable printing parameters, such as speed, pattern, and thickness, into the simulation environment. Nevertheless, an experimental component would be crucial for a proper validation of this research field. Overall, these refinements would help close the gap between digital prediction and physical performance, supporting more accurate and reliable shape programming in practical applications.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/KEM.1047.287.pdf

Thanks are due to the University of Aveiro, FCT/MCTES for the financial support of TEMA research unit (FCT Ref. UIDB/00481/2020 & UIDP/00481/2020) and CICECO Aveiro Institute of Materials (FCT Ref. UIDB/50011/2020 & UIDP/50011/2020), and CENTRO01-0145-FEDER-022083 Regional Operational Program of the Center (Centro2020), within the scope of the Portugal 2020 Partnership Agreement, through the European Regional Development Fund. The authors also acknowledge FCT - Fundação para a Ciência e a Tecnologia, I.P. for the Individual Call to Scientific Employment Stimulus (IG, ref. CEECIND/00430/2017) and the PhD grants ref. 2020.04681.BD (MSC).

Declaration of Generative AI and AI-Assisted Technologies in the Writing Process

The original version of this paper is available on https://www.scientific.net/KEM.1047.287.pdf

The authors acknowledge the use of ChatGPT (OpenAI, https://chat.openai.com) as a copilot for brainstorming topics related to this work and for assisting with English language corrections, particularly in identifying improvements in writing style.

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This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/additive-manufacturing/978-3-0364-1997-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 299-311

doi: 10.4028/p-PV3T4o

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-19

Revised:
2026-02-16

Accepted:
2026-02-25

Online:
2026-04-13

Advanced FE Modeling for Predicting Component Properties in Additive Manufacturing

The original version of this paper is available on https://www.scientific.net/KEM.1047.299.pdf

Lennart Grüger 1,a*, Tim Sebastian Tübbicke 1, b, Hsuan-Po Huang 1c and Sebastian Härtel 1, d

1 Department Hybrid Manufacturing BTU Cottbus Senftenberg, Konrad-Wachsmann-Allee 17, 03046 Cottbus, Germany

a*Lennart.Grueger@b-tu.de, b Tim.Tuebbicke@b-tu.de, c Huanghsu@b-tu.de, dhaertel@b-tu.de

Keywords: additive manufacturing, material modeling, FEM-simulation.

Abstract

Wire arc directed energy deposition (WA-DED) is a cost-efficient additive manufacturing process with high deposition rates, yet the prediction of resulting mechanical properties remains challenging due to repeated thermal cycling and associated microstructural changes. Accordingly, this work aims to validate a hardness prediction model for DIN SG2 by Härtel et al. For this purpose, a demonstrator was designed, manufactured, and simulated using a thermal finite element model in the standard software Simufact Welding 2025. Since the DED module of the software used does not adequately represent active interlayer cooling, four substitute models for the convective heat transfer coefficient were implemented and evaluated. In addition, the original hardness prediction model was refined to consider complex path planning, remelting effects and a material-dependent lower temperature limit for tempering or heat treating the material. Using a substitute model that adjusts the convective heat transfer coefficient over time, the improved hardness prediction the adjusted hardness prediction model achieved an accuracy of ±5% for 81 of 88 evaluated measurement points. In order to enable an efficient and reproducible comparison between simulation and experiment, a Python evaluation script was developed. This tool automatically identifies relevant temperature peaks, correlates them with hardness data, creates individual evaluation diagrams and a comparison diagram, and exports all processed data to an Excel file.

Introduction

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There is currently a great focus on additive manufacturing (AM) in industry and research [1]. DIN EN ISO/ASTM 52900 describes AM as a manufacturing process that produces components by applying individual layers [2]. The wire arc directed energy deposition (WA-DED) process, also known as wire arc additive manufacturing (WAAM), is one of these promising processes [3,4]. The advantages of WA-DED compared to other additive manufacturing processes are the relatively high deposition rate and the low purchase costs of the equipment and raw materials [5]. For this reason, the WA-DED process is mainly used in the areas of rapid prototyping, additive repair, rapid tooling, and direct manufacturing [6]. Although the process of deposition welding is well known, the manufacture of WA-DED components remains a challenge [7]. One major challenge in this context is the imprecise predictability of the mechanical properties. This uncertainty results mainly from the process-related multiple over-welding of the individual layers. This makes it impossible to use classic time-temperature transformation (ttt) diagrams, as the repeated application of heat acts like heat treatment on the lower layers. For this reason, Härtel et al. have developed a material model for DIN SG2, which is intended to enable the predictability of the material hardness of a component manufactured using WA-DED [8]. The aim of this work is to validate this model using a demonstrator geometry. For this purpose, the geometry is simulated using the finite element method (FEM) with the standard software Simufact Welding 2025.3 from Hexagon. The demonstrator is manufactured under industry-like conditions, on the GEFERTEC Arc 605 available at the Hybrid Manufacturing Department, and the hardness values are determined. Another challenge is the increase in production times as component volumes rise, with the interlayer time representing a main factor [9]. This is
necessary to bring the interlayer temperature between 200∘C and 300∘C and to ensure process-safe processing of the material. In order to reduce these interlayer times, the demonstrator is actively cooled with compressed air during breaks. This cooling system is integrated into the equipment used. However, this active air cooling is currently not represented realistically enough in the simulation software. This makes it difficult to predict the temperature-time curves and thus the resulting mechanical properties. Accordingly, this paper proposes several substitute models that are intended to reduce or even eliminate this delta.
Due to the greater complexity of the demonstration geometry compared to the welded wall used by Härtel et al. for hardness model initialization, minor adjustments to the initial hardness model are necessary. Finally, a Python script is used to compare real and simulated data in order to validate the model.

Materials and Methods

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Materials.

The original version of this paper is available on https://www.scientific.net/KEM.1047.299.pdf

The demonstrator is made from AWS A5.18 wire (Hermann Fliess & Co. GmbH, Duisburg Germany) with a diameter of ∅=1.2 mm on a substrate plate ( 200×150×18 mm3 ) made of 1.4404 (316L) and a base plate (AISI 1045) for attaching the substrate plate and holding the thermocouple (TC). The composition of the materials can be found in Table 1. The production setup is visualized in Fig. 1.

Table 1. Chemical composition of the materials used in % weight.

Component | C | Mn | Si | Cu | Fe | P | S | Cr | Mo | Ni | N
Wire (AWS 5.18) | 0.06-0.13 | 1.4-1.6 | 0.7-1.0 | ≤0.3 | Rest |  |  |  |  |  |
Substrat (316L) | max. 0.03 | max. 2.0 | max. 1.0 |  |  | max. 0.045 | max. 0.03 | 16.5-18.5 | 2.0-2.5 |  | max. 0.1
base plate (AISI 1045) | 0.42-0.5 | 0.5-0.8 |  |  |  | max. 0.03 | max. 0.035 | max. 0.4 | max. 0.1 | max 0.4 |

The unalloyed welding wire and the stainless-steel substrate plate are assumed to mix. Accordingly, the first layers are expected to exhibit hardness values that do not correspond to the standard hardness values of SG2. However, this mixing effect should no longer have any influence on the hardness after a few layers.

[picture]

Fig. 1. Schematic visualization of the experimental setup.

Fig. 1. Schematic visualization of the experimental setup.

Methods. Manufacturing of the demonstrator

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Since the model developed by Härtel et al. was based on a welded wall, the aim of this study is to validate the model using a complex demonstrator geometry. For this purpose, various elements were combined in a tower. This tower was manufactured in three steps. First, a cylindrical base body (2.1) with a height of 125 mm and a diameter of 50 mm was welded on. This was then expanded in volume by tilting the machining table in the upper area, creating a 20 mm high flange (2.2) with a diameter of 100 mm . A model of the university library of the BTU Cottbus-Senftenberg, the so-called "Informations-, Kommunikations- und Medienzentrum" (IKMZ), with a height of 50 mm (2.3), was placed on this flange, see Fig. 2.

[picture]

Fig. 2. Picture of the university library (left) [10]and completed demonstrator (right).

Fig. 2. Picture of the university library (left) [10]and completed demonstrator (right).

In contrast to the cylinder and the flange, the IKMZ was not manufactured in a spiral shape (3.a). Instead, a hybrid path planning (3.b) consisting of a contour path and a meander band were used, see Fig. 3.a & 3.b. Accordingly, the edge area of a layer was constructed first before it was filled in a meandering pattern. For all partial geometries, the starting point was automatically rotated 30 mm by the CAM software 3DMP to avoid one-sided super elevation. After each layer, the component was actively cooled with compressed air by a cooling system integrated next to the welding unit, see Figure 3.c.

[picture]

Fig. 3. Schematic overview of the manufacturing strategies ( a& b ) and the active cooling ( c ).

Fig. 3. Schematic overview of the manufacturing strategies ( a & b ) and the active cooling ( c ).

This function is used in industrial applications to control cooling in a targeted way and reduce production times. The GEFERTEC Arc 605 is available at the department and was used for the manufacturing process. For the comparison between the actual process and the simulation, a
thermocouple was attached under the substrate plate and the corresponding temperature was recorded during cylinder production. The manufacturing parameters used are shown in Table 1.

Table 2. Process parameters for demonstrator manufacturing.

Parameter | Value
Weld speed | 40 [cm/min]
Wire feed rate | 4 [m/min]
Mean electric voltage | 14.6 [V]
Mean electric current | 144 [A]
Interlayer temperature (measured optically using built-in technology) | 150 [°C]
Stickout | 15 [mm]
Gas / Average shielding gas flowrate | M21-ArC-18 / 15 [l/min]

Methods. FEM simulation.

The original version of this paper is available on https://www.scientific.net/KEM.1047.299.pdf

The FEM simulation was performed using the standard software Simufact Welding 2025.3 available at the Department of Hybrid Manufacturing. For this purpose, all layers of the additive manufacturing process were inserted individually in the form of the machine G-code. Each of these welding layers is assigned the corresponding geometry layer at the corresponding layer height. For the simulation, a mesh must be created. For this purpose, so-called sheet meshes were generated for all simulated geometries (cylinder, flange and IKMZ) using MSC APEX 2025.1 software. For this purpose, the sheet mesher first meshes the upper side of the workpiece with square elements. The volume mesh was then generated by extending these squares into hexahedral elements [11]. The user can select the respective layer thickness. In this work, the layer thickness was assumed to be identical for each geometry for the sake of simplicity. Accordingly, there are 66 layers in the z -direction for the cylinder, five for the flange, and 29 for the IKMZ model. The flange was simplified to 50 individual weld seams. These are rotated by 60∘ in the simulation, which was equivalent to the angle of rotation of the tilting table. Figure 4 shows the FE model on the left and the corresponding mesh as well as the thermocouple in the cross-section on the right.

[picture]

Fig. 4. Visualization of the FE model (left) and its mesh as well as the thermocouple (right).

Fig. 4. Visualization of the FE model (left) and its mesh as well as the thermocouple (right).

Finally, measurement points, known as particles, were inserted into the center of each layer and at the edge of the flange in order to evaluate the simulated temperature development in each layer and at the edge of the demonstrator. Another essential particle was modeled under the substrate plate to enable a comparison of the simulation with the thermocouple. For the comparison of the simulation with the sample, it is necessary to analyze the temperature history of individual points within the simulated demonstrator. This was done by selecting the particles that were previously introduced. These can be exported in a .csv file. Subsequently, the expected hardness is calculated according to the model by Härtel et al. using a Python script.

Methods. Evaluation of hardness.

The original version of this paper is available on https://www.scientific.net/KEM.1047.299.pdf

In order to measure hardness, the demonstrator was cut in half and divided into several segments (see Fig. 5) before non-corrosive sample preparation was carried out. The hardness tests were performed using a calibrated model of the DuraScan 70 testing machine from ZwickRoell GmbH & Co KG. For this purpose, a measurement was taken in the low force range HV1 with a test force of 9.807 N . The distance between two measuring points was 0.45 mm . This was determined by dividing the average layer height of 1.8 mm by four, so that the average hardness of a layer was calculated from a total of five measuring points with three measured values each. In this way, a reliable analysis of the hardness distribution within a layer can be performed.

[picture]

Fig. 5. Idealized demonstrator with extracted and tested segments.

Fig. 5. Idealized demonstrator with extracted and tested segments.

Results

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Hardness measurement.

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The hardness measurement was carried out as described in Method section. Some measuring points were lost due to the separating edges, reducing the number of measuring points from a calculated 1,248 to 1,183 . Furthermore, the measuring points of layers one (324HV) and two (233HV) were not considered, as the deviation from the other measuring points suggests that they were mixed with the 316L substrate plate. However, this is not considered by the initial hardness calculation model. Figure 6 shows the hardness distribution of the samples as taken from the demonstrator, and a detailed view of the hardness distribution in the middle and the edge segment.

[picture]

Fig. 6. Visualization of the hardness distribution, and the corresponding sampling areas.

Fig. 6. Visualization of the hardness distribution, and the corresponding sampling areas.

This shows that the rapid cooling in the first layers and the absence of heat treatment in the last layers results in a kind of "bathtub curve". The first layer included in the evaluation develops a hardness of around 170 HV , while the last two layers develop harnesses of 172 HV and 165 HV . In the layers in between, multiple welding cycles reduce the hardness. Due to the constant interlayer temperature, the hardness in the lower segment (green) of the cylinder stabilizes in a narrow range between 139 HV and 146 HV . In the first half of the middle segment, the hardness continues to decrease. After the start of the flange, the hardness fluctuates slightly but tends to increase. This is followed by further hardening of the demonstrator. The reason for this is assumed to be the hybrid path strategy of the IKMZ and the smaller number of weld overlaps or heat cycles. In addition, the division of the material application into contour and internal structure leads to more frequent interruptions of the welding process. This reduces the cooling times, which leads to hardening of the material. The asymmetrical shape of the IKMZ and the rotating starting points result in different welding path courses, which lead to different energy inputs and cooling times. This explains the fluctuating hardness curve.

Adjustment of the simulation.

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As described in the methods section, air cooling was performed by the system after the application of each layer. This requires an adjustment of the convective heat transfer coefficient (h) in the Simufact Welding software. There are two main options for performing this adjustment. The first option is to adjust the convective heat transfer over time, and the second option is to adjust it based on the temperature. These adjustment options result in a total of four substitute model options, see Figure 6.

[picture]

Fig. 7. List of possible substitute models for considering active air cooling.

Fig. 7. List of possible substitute models for considering active air cooling.

Assuming that the integrated air-cooling system cools at the same air pressure after each layer, the first replacement model is obtained for adjustment over time. In this model, the convective heat transfer coefficient is adjusted to 300 W/m2* K during the break times, because of the active cooling. This results in the abrupt change in the coefficient shown in Figure 8.

[picture]

Fig. 8. Extract of Convective heat transfer coefficient of the substitute model 1a).

Fig. 8. Extract of Convective heat transfer coefficient of the substitute model 1a).

However, this is not optimal, as the welding unit and thus the compressed air cooling moves upwards with increasing component height, and the influence on the lower layers decreases over time and does not remain constant. This can also be seen in the comparison of the particle under the substrate plate with the real thermocouple, see Fig. 9.

[picture]

Fig. 9. Comparison of Substitute model 1a) with the thermocouple and the default settings of Simufact Welding.

Fig. 9. Comparison of Substitute model 1a) with the thermocouple and the default settings of Simufact Welding.

The analysis shows that the cooling according to the substitute model a) initially reflects the experimental data quite accurate. However, as the process time and respectively component's height increases, the correspondingly decreasing influence of air cooling on the lowest layers can no longer be represented realistically. Accordingly, substitute model 1a) underestimates the temperature in the lower layers with increasing process duration while the default model overestimates the temperature. For the substitute models in which the convective heat transfer coefficient is plotted over temperature, there are essentially three possibilities. These include a degressive (2a), a linear (2b) and a progressive (2c) curve of the coefficient. These are shown in Fig. 10.

[picture]

Fig. 10. Extract of Convective heat transfer coefficient of the substitute model 2a)-c).

Fig. 10. Extract of Convective heat transfer coefficient of the substitute model 2a)-c).

However, in this setting, cooling also occurs during the application of the material, which also influences the result. A comparison of the individual substitute models with the thermocouple is shown in Fig. 11.

[picture]

Fig. 11. Comparison of the output of the different replacement models 2a)-c) with the thermocouple and the default settings of Simufact Welding.

Fig. 11. Comparison of the output of the different replacement models 2a)-c) with the thermocouple and the default settings of Simufact Welding.

The analysis of the different replacement models shows that, at the start, the temperature measured in the experiment is slightly underestimated in all models. However, over the course of the calculation, there are varying degrees of deviation depending on the substitute model.
The substitute models 2a) to c) are effective not only during breaks, but also during material application, depending on the temperature. As a result, model 2a), which simulates a degressively increasing air cooling over the temperature, underestimates the temperature with increasing
component height to a similar extent as substitute model 1a). Model 2c) depicts a progressively increasing air cooling curve depending on the temperature and overestimates the temperature accordingly above a certain component height. A linear approximation, as in substitute model 2b), on the other hand, fluctuates around the experimentally determined hardness with increasing component height and diverges less.
In the result the substitute models 1a) and 2b) fit the experimental data the best. Accordingly, these data are used for the further calculation.

Adjustment of the hardness calculation model.

The original version of this paper is available on https://www.scientific.net/KEM.1047.299.pdf

The investigation of the hardness values in comparison to the expected hardness values from the original model by Härtel et al. revealed a need for adjustment. Deviating from the model by Härtel et al., cooling rates between 850∘C and 500∘C were determined from the last peak above 850∘C in the temperature-time curve. This results in a hardness calculation that can be represented mathematically by Eq. 1:

HVfinal =HVini (F( T˙850−500))−ΔHV( F(Ethermisch ))(1)

This adjustment was necessary because, unlike the manufactured wall used by Härtel et al., the spiralshaped material application results in several temperature peaks in one layer. Depending on the starting point of the respective weld seam, these increase the temperature peaks in such a way that renewed heat input above 850∘C occurs and a new austenitization must be assumed. The correlation between the resulting initial hardness and the cooling rate can be described analytically by a quadratic equation with a very high degree of certainty, see Eq. 2.

Hini=0,0019 T˙2−0,0188 T˙+195,96,R2=0,98(2)

The formula derived from the analytical approach to describe the change in hardness is a sigmoid function, see Eq. 3.

ΔHV( F(Ethermisch ))=ΔHV( A)=51,751+exp[−0,000333·( A−13807)]+1,21(3)

This is suitable for modeling the initially slow decrease in hardness at low thermal energy inputs, followed by a steep increase in the transition range and finally a plateau. In addition, there are asymptotically fixed upper and lower limits. In this way, both the maximum hardness reduction of approximately 53 HV measured for SG2 by Härtel et al. as well as the physical lower limit of 0 HV [7]. In contrast, a simple quadratic regression would yield unrealistic, infinitely falling or rising values with increasing energy inputs.
In addition, the model considers the change in hardness ( ΔHV ) resulting from repeated heat input during subsequent welding processes, based on Härtel et al. The last peak in the austenitizing range above 850∘C is used to calculate the initial hardness. The repeated heat input leads to a reduction in hardness and results from the integrated area below the temperature curve, which results from the heat input of the subsequent layers. The range below 300∘C is no longer considered. The reason for this deviation from the initial model is that tempering below 300∘C has no effect on the microstructure of the processed material. Accordingly, the high importance of a realistic consideration of active cooling and the cooling rates thus influenced is also clearly evident. If these cooling rates are calculated incorrectly, errors can occur, especially in limit value cases. For example, heat cycles could be incorrectly considered as complete remelting because the interlayer temperature was calculated higher than it actually was, and the primary temperature of 850∘C was incorrectly exceeded in the subsequent heat cycle. Furthermore, if the cooling rates are underestimated, lower initial hardening values could be calculated, or more heat treatment cycles then appropriate could be considered because the lower limit of 300∘C was incorrectly not reached. This would lead to a miscalculation of the final hardness.
The evaluation was performed using an automated Python script, which was optimized with the help of ChatGPT. This script is particularly important for evaluating the large amounts of data. Due to the size of the demonstrator and the resulting high number of layers, manually evaluating each individual
particle would be extremely time-consuming. In addition to the selection of the primary temperature ( 850∘C ) and secondary temperature ( 500∘C ), the script enables the automated input of .csv files for the temperature curves, as exported from Simufact Welding, and the experimentally determined hardness values as a table (particle number; experimentally determined hardness). Furthermore, it is possible to evaluate only selected particles. Due to the spiral-shaped build-up strategy, there are some cases where several temperature peaks occur in one layer. These are automatically clustered into layers and a preselection is made for the user. In the preselection of the peaks to be used for evaluation, the last peak above the defined peak temperature (default 850∘C ) is preselected. The user can accept this preselection or manually adjust the selection if justified. The cooling rate is then calculated between the selected peak and 500∘C, and the initial hardness is calculated according to Härtel et al. Furthermore, the area under the curve until a value of 300∘C is integrated and this area is used as equivalent for the heat energy input to calculate the hardness reduction ΔHV. For the visualization of the evaluation, the font and font size as well as the language (English/German) can be selected. All information used to calculate the simulated hardness, as well as the results, are then displayed in the plot. These plots are made for each particle. Figure 12 shows representative particles 33 located in the center of the cylinder (left) across the entire height of the cylinder in build orientation, and particles 65 located in the cylinder at intermediate height of the flange (right). These are reduced to the essential information on the x-axis and cut off after the third peak below 300∘C. In addition, a comparison plot between experimentally determined hardness and simulated hardness is generated. Based on the test data, an error indicator range can be plotted around the graph. This includes the option of integrating a "very good fit" (here +−1% ) and an acceptable fit (here +−5% ). The hardness values determined using the model are assigned to the respective tolerance range and the number of "very good," "acceptable," and "out of tolerance" fits are output. In addition, the simulated hardness values are colored green, orange, or red. Correspondingly, the simulated hardness is calculated. Finally, all plots and an Excel file with all reference information are output and saved to a previously selected storage location with a previously selected name.

[picture]

Fig. 12. Example of the calculations based on particle 33 and 65.

Fig. 12. Example of the calculations based on particle 33 and 65.

The temperature curves and calculation results illustrate that the adjustments in the model also reliably reflect the reheating of the inner cylinder to over 850∘C caused by the application of the flange. This results in a deviation of 1.2 HV from the actual measured value for the hardness value of particle 63. Furthermore, the temperature curves already predicted in the initial model by Härtel et al. can still be reliably reproduced. Accordingly, particle 33 shows a deviation of 0.1 HV between the modeled and experimentally determined hardness.

Comparison of adjusted simulation and sample data.

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The various replacement models deliver results with varying degrees of accuracy. Figure 13 shows a comparison of the experimentally determined hardness values with the results modeled hardness using default settings as well as the substitute models 1a) and 2b). The first two particles from the first two layers were not evaluated due to material mixing with the substrate plate. Furthermore, the last particle was not evaluated because it is not considered by the model. Due to the fact, that no experimental data could be assigned to four simulated layers because of the cut edges, this results in an evaluation of 88 particles.

[picture]

Fig. 13. Comparison of the experimental and the modelled results.

Fig. 13. Comparison of the experimental and the modelled results.

The evaluation of the replacement models in comparison to the actual measured data shows varying degrees of accuracy. Of these, between approximately 89% in replacement model 2 b and 92% I
replacement model 1a) are at least within an acceptable fit of+-5% of the experimentally determined hardness values. According to the authors' assumptions, the deviations especially in the edge area are due to the suboptimal fit of the replacement models to the temperature curves determined by measurements.

Summary

The original version of this paper is available on https://www.scientific.net/KEM.1047.299.pdf

The aim of the paper was to manufacture a demonstrator under industrial conditions and to simulate the manufacturing process as accurately as possible using the standard software Simufact Welding 2025.3 in a thermal FE simulation. For this purpose, a demonstrator geometry consisting of a cylinder, a thickening flange, and a model of the university library of the BTU Cottbus-Senftenberg was developed and manufactured. The data resulting from the simulation was used to validate the model for hardness prediction of WA-DED manufactured components by Härtel et al. In order to compare the experiment with the simulation, a thermocouple was attached under the substrate plate. And the hardness of the manufactured Demonstrator was mapped over its height.
Although active air cooling is standard in the system used (GEFERTEC Arc605) to accelerate the cooling time and thus speed up manufacturing, and although such air cooling is common in industry to keep the interlayer temperature between 200 and 300∘C for process reliability, the standard software does not represent this cooling process in its DED module without further ado. Accordingly, four substitute models were proposed in this paper. Essentially, there are two ways to deviate from the standard setting of a constant convective heat transfer of 20 W/mm2 K. One is by adjusting the coefficient over time and the other is by adjusting it over temperature.
The first substitute model adjusts the convective heat transfer coefficient over time. Accordingly, the coefficient is adjusted during the known pause times to address air cooling. However, this means that the influence of air cooling on the lower component layers in the simulation does not decrease even as the component height increases. Accordingly, the simulation underestimates the temperature compared to the actual measured temperature. The remaining three models adjust the coefficient degressively, linearly, or progressively over temperature. However, the simulation also includes the effect during material application in the calculation and does not only cool during the pause times, which also means that the simulated temperature does not fully reproduce the measured temperature. In addition to the substitute models, the initial model had to be expanded due to the more complex geometry and the associated spiral shape (cylinder and flange) as well as hybrid path planning (IKMZ). Accordingly, the last peak above 850∘C is considered the primary temperature. Furthermore, the integration of thermal energy for calculating the hardness reduction by tempering the resulting structure is now only carried out up to a temperature of 300∘C, as the influence below this temperature is no longer present for the AWS 5.18 used. These adjustments make it possible to consider the subsequent remelting of the material inside the cylinder due to the later weld-on of additional material, which is represented in this case by the flange.
Due to the substitute models and the adjustments to the hardness prediction model, it was possible to model 81 of 88 particles with an accuracy of +−5% using the time-dependent model of coefficient adjustment. With linear adjustment over temperature, the result was 80 of 88 particles.
In order to evaluate the data, a Python script was created. This makes it possible to evaluate the 88 particles in just a few seconds and display them in individual plots as well as in a comparison plot with the measured values. Furthermore, the temperature peaks to be evaluated and the limit temperatures for melting and solidifying can be selected. In addition, an Excel file containing all evaluated data is generated. This greatly facilitates the evaluation and represents a significant time saving.

Forecast

The original version of this paper is available on https://www.scientific.net/KEM.1047.299.pdf

Based on the adjustments to the original model for predicting hardness and the replacement models, the hardness values have already been modeled to an acceptable degree. However, due to the limited possibilities for reproducing active air cooling in Simufact welding's DED module, the pause times
would have to be known in advance in order to adjust the contact heat flow coefficient in advance. Accordingly, it is not possible to accurately predict the resulting hardness values unless the deviation caused by the linear approximation over the temperature is accepted.
The future goal is to model the active air cooling between the layers as a function of the height of the component. This would be another important step toward actively controlling the cooling times and thus the mechanical properties of the components, as well as optimizing production time.

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.299.pdf

J. Bobke, T. Russack, und J. Weinhold, Hrsg., Potenziale und Herausforderungen der Additiven Fertigung: Gesellschaft - Wirtschaft - Wissenschaft und Transfer. in FOM-Edition, FOM Hochschule für Oekonomie & Management. Wiesbaden: Springer Fachmedien Wiesbaden, 2025. doi: 10.1007/978-3-658-45141-7.

„DIN EN ISO/ASTM 52900:2022-03, Additive Fertigung_- Grundlagen_- Terminologie (ISO/ASTM 52900:2021); Deutsche Fassung EN_ISO/ASTM 52900:2021", Beuth Verlag GmbH, Berlin, 2022. doi: 10.31030/3290011.

S. W. Williams, F. Martina, A. C. Addison, J. Ding, G. Pardal, und P. Colegrove, „Wire + Arc Additive Manufacturing", Materials Science and Technology, Bd. 32, Nr. 7, S. 641-647, Jan. 2016, doi: 10.1179/1743284715Y.0000000073.

S. C. A. Costello, C. R. Cunningham, F. Xu, A. Shokrani, V. Dhokia, und S. T. Newman, „The state-of-the-art of wire arc directed energy deposition (WA-DED) as an additive manufacturing process for large metallic component manufacture", International Journal of Computer Integrated Manufacturing, Bd. 36, Nr. 3, S. 469-510, Jan. 2023, doi: 10.1080/0951192X.2022.2162597.

C. Schmid, "Konstruktive Randbedingungen bei Anwendung des WAAM-Verfahrens", in Konstruktion für die Additive Fertigung 2019, R. Lachmayer, K. Rettschlag, und S. Kaierle, Hrsg., Berlin; Heidelberg: Springer Vieweg, 2020, S. 203-222. doi: 10.1007/978-3-662-611494_13.

R. Lachmayer und R. B. Lippert, "Grundlagen", in Entwicklungsmethodik für die Additive Fertigung, R. Lachmayer und R. B. Lippert, Hrsg., Berlin, Heidelberg: Springer Berlin Heidelberg, 2020, S. 7-20. doi: 10.1007/978-3-662-59789-7_2.

M. Seifi, A. Salem, J. Beuth, O. Harrysson, und J. J. Lewandowski, „Overview of Materials Qualification Needs for Metal Additive Manufacturing", JOM, Bd. 68, Nr. 3, S. 747-764, Jan. 2016, doi: 10.1007/s11837-015-1810-0.

S. Härtel, J. Szyndler, M. Pakdel Sefidi, und R. Jäger, „Prediction of the evolution of material properties during the AM process based on the FEM simulation and experimental results", in Materials Research Proceedings, Materials Research Forum LLC, Mai 2024, S. 40-49. doi: 10.21741/9781644903131-5.

Y. Lei, J. Xiong, und R. Li, „Effect of inter layer idle time on thermal behavior for multi-layer single-pass thin-walled parts in GMAW-based additive manufacturing", Int J Adv Manuf Technol, Bd. 96, Nr. 1-4, S. 1355-1365, Apr. 2018, doi: 10.1007/s00170-018-1699-1.

L. Mösle, "Umbau der BTU-Bibliothek trotz Denkmalschutz - wie das geht". [Online]. Verfügbar unter: .

Hexagon AB (Publ), Simufact Welding 2025.1 Handbuch. Hexagon AB (Publ).

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/additive-manufacturing/978-3-0364-1997-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 313-323

doi: 10.4028/p-Ly6B08

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-20

Accepted:
2026-02-11

Online:
2026-04-13

Measurement of Forces Generated during Robotized Additive Manufacturing Process Using Pellet Extruder

The original version of this paper is available on https://www.scientific.net/KEM.1047.313.pdf

Salah-Eddine Ouassil 1,a*, Pascal Casari 2, b, Élodie Paquet 3,c, Guillaume Racineux 1, d

1 Nantes Université, Ecole Centrale de Nantes, CNRS, GeM, UMR 6183, 44321 Nantes, France

2 Nantes Université, Ecole Centrale Nantes, CNRS, GeM, UMR 6183, F-44600 Saint-Nazaire, France

3 Nantes Université, Romas Team, LS2N UMR CNRS 6004, 44475 Carquefou, France

a* salah-eddine.ouassil@ec-nantes.fr, b  pascal.casari@univ-nantes.fr, c  elodie.paquet@univ-nantes.fr, d  guillaume.racineux@ec-nantes.fr

Keywords: additive manufacturing, interlayer bonding, pellet-extrusion, forces measurement.

Abstract

Robotic additive manufacturing using pellet extrusion has gained significant scientific interest and industrial maturity. Increased part dimensions, reduced production time, and lower raw material costs are the main advantages of this process. This development has led to a strong demand for improved control and understanding of the process and of multiple phenomena occurring during fabrication. Consequently, process monitoring has received considerable attention, as it enables better understanding and detection of anomalies and their origins during manufacturing, allowing for immediate correction when possible or for more in-depth post-process analyses. Several studies in the literature have focused on monitoring parameters such as extrusion temperature, layer height, and printing speed to investigate their effects on final part quality and mechanical performance. In the present study, the objective is to quantify the forces applied to the deposited material during robotic additive manufacturing by pellet extrusion. This is made possible using a six-component force sensor, which allows the measurement of forces and moments along the three directions ( X,Y, and Z ). Following data acquisition, the results allowed understanding of force variations throughout the fabrication cycle and their correlation with the different stages of the manufacturing process. A single layer curve was explained with the corresponding peaks of each segment of the trajectory. It was found that at rounds there are peaks in Z forces due to the fact that, at the rounded sections, there is a slight accumulation of material, as the robot's travel speed decreases while the material flow rate remains constant. This therefore results in higher applied forces. Consequently, it was observed that from the third layer onward, forces along the Z direction were almost no longer detectable. These measurements aim to facilitate the detection of manufacturing defects through unexpected force variations and to relate these observations to the final mechanical properties of the fabricated parts.

Introduction

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Additive Manufacturing (AM) is a revolutionizing process that represents many pros compared to conventional manufacturing methods. In fact, AM, also known as 3D printing, consists of joining material layer upon layer till obtaining the final part [1]. Remarkable precision, high adaptability and the ability to fabricate complex geometries are some of the advantages offered by AM technologies [2], [3]. Many industries are more and more adopting the AM process [4], such as aerospace, medicine and automotive [4]. Fused Filament Fabrication/Fused Deposition Modeling (FFF/FDM) is one the most widely used technologies of AM as it is affordable and easy to use [1], [2], [5]. It consists in heating a filament material above its melting temperature then extrude it through a nozzle and deposit it onto a build plate.

Large choice of materials exists in FDM to respond to different mechanical and functional requirements. Usually, the low-temperature thermoplastics are the most used materials as they are affordable and sufficient for non-critical applications, like demonstration parts [6]. Recently, more materials such as engineering materials are present in the market for different applications. Moreover,
reinforced plastics also entered the race as their incorporation showed remarkable improvement in structural, thermal and mechanical properties [7]. Besides the material, the impact of manufacturing parameters such as layer height, printing speed and infill density on the quality of the final part was investigated in several studies [8]-[10]. Despite all the interest and evolution, limited fabrication dimensions and low production flow restricted the use of this technology by large scale industry.

To deal with these restrictions, a combination of two solutions was revealed. The first solution consists of developing a new technology of material extrusion process of pellets instead of filament. Arburg company has launched in 2015 the Arburg Plastic Freeforming (APF) where the plastic granulate is melted and then extruded [11], [12]. With this technique, also called "Fused Granulate Fabrication (FGF)", production times can be reduced by up to 200 times because of the direct feeding of pellets [13], thus the cost of the raw material can be reduced by more than 10 times compared to FFF [5], [14]-[17]. The second solution to overcome some FDM restrictions is the use of multi-axis platforms instead of Cartesian coordinate systems. Conventionally, extrusion-based AM machines use three-axis coordinate system [18], [19]. It is widely used thanks to its simple mechanisms and simple process flow. However, this system has limitations. When the object is more complex, the need of support structures, staircase effect due to planar layer-by-layer manufacturing, and high machine frame dimensions compared to the fabrication volume are some of these shortcomings [18][20].

Therefore, multi-axis AM platforms have been developed to address these limiting factors using robot arms. Either for polymer- or metal-based AM technologies, this 6 degree-of-freedom (6DOF) solution gained interest from the 3D printing community [19]. These systems offer similar floor occupancy as the cartesian 3-axis ones but the kinematics lead to an increased fabrication volume [21]. For this reason, and in order to accelerate the production rate, many leading manufacturers and research laboratories in AM field adopted robotic-based systems.

Although extrusion-based AM techniques has known developments and increased interest from researchers and industries, the process still needs more understanding and in-depth control during the process. Hence, several studies justified the need of monitoring in order to discern the impact of numerous factors on the fabrication. In a review conducted by A. Oleff et al. [22], they gathered many studies of material extrusion process monitoring. The choice of studies was made following many criteria. One of them was the sensor technology used depending on what parameter the paper was interested to monitor. As a result, it has been shown that only 2.6% of the total number of sensors used in several studies was used for force/pressure monitoring. In addition, they reported that these forces sensors were mostly used to inspect the feeding system and the extrusion head [22]. Other sensor technologies were about temperature, vibration, 2D vision, 3D vision, etc.

Quality control through process monitoring can be utilized in different AM technologies for various materials such as polymers and concrete. For this latter, Barry et al. [23] measured the force exerted by the print head on the material extruded. They proceeded to the instrumentation of the extrusion nozzle with load cells to achieve real-time quality control. This approach helped them correlating between the forces of the extruder nozzle and the material hardening process and its impact on the printing quality. In other studies, most force sensors were used to measure extrusion forces and pressure generated by the melting process. The load cell in the cited studies was generally located between the extruder and the hot end to acquire force data of the extrusion process [24]-[27]. In this position, the load cell records movement axis acceleration and vibrations.

For our study, the aim is to quantify the total forces, external to the extruder, that are applied on the deposited material and correlate them to the printing steps. Parts were designed and subsequently fabricated while the forces are measured by the sensor. The resulted data was then analyzed and explained.

Material and Method

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Material

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In our case, the Dahltram T-100GF pellets from Airtech are used. It is a thermoplastic material reinforced with 30% of glass fibers. It has a maximum temperature use of 65∘C and a heat deflection temperature (HDT) of 70∘C. Mechanical properties of the material are given in Table 1. In addition, Differential Scanning Calorimetry (DSC) analysis was conducted in order to identify thermal transitions in the material such as glass transition (Tg), melting temperature (Tm) and crystallization properties if there is any. Analysis was performed using DSC 200 F3 Maia from NETZSCH. As-received- and printed samples of 10−15mg have been tested in a temperature range from −10∘C to 270∘C at a heating and cooling rate of 20∘C/min. During the tests, nitrogen gas was injected at a flow rate of 50ml/min as a protective gas of both measurement cell and sample.

Table 1. Material properties given from the manufacturer.

Composition and thermal properties | Values | Mechanical properties | Values
Reinforcements | Glass Fiber | Tensile Strength (X-direction) | 79.3 MPa
% Fiber loading | 30% | Tensile Strength (Z-direction) | 25.9 MPa
HDT | 70 °C | Tensile Modulus (X-direction) | 6.6 GPa
Use Temperature | 65 °C | Tensile Modulus (Z-direction) | 3.7 GPa

[picture]

Fig. 1. DSC thermograms of as-received and printed glass fiber reinforced PETG, with key process temperatures discussed subsequently.

Fig. 1. DSC thermograms of as-received and printed glass fiber reinforced PETG, with key process temperatures discussed subsequently.

The results in Fig. 1 showed no cold crystallization or melting peaks. This is due to the amorphous nature of PETG [28], which was not impacted by the presence of glass fiber reinforcements. The glass transition temperature detected is around 80∘C, which is quite the same with references studying the same neat material [28], [29].

For more information about the material, dimensions of the pellets (Fig. 2) and glass fibers were measured. A Keyence VHX-7000 digital microscope with integrated 2D/3D measurement was used to determine fibers dimensions. For pellets dimensions, they were quantified using a caliper. The dimensions of both, pellets and fibers are similar to the most industrial materials dimensions commercialized in the market. As for fibers, the diameter measured is 13μ m, which is consistent with the commonly used value by manufacturers (10μ m−20μ m). As for pellet diameter, the value found is around 3 mm , which is also coherent with the existing values that indicate a diameter between 2 mm and 5 mm .

[picture]

Fig. 2. Image of pellets of the material used for this study.

Fig. 2. Image of pellets of the material used for this study.

Method

The original version of this paper is available on https://www.scientific.net/KEM.1047.313.pdf

Experimental setup

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A 6-axis robot-based additive manufacturing system equipped with a pellet-extrusion head was employed. The robotic arm used is a TX160L from Stäubli, featuring a repeatability error of ±0.05 mm, ensuring accurate control of trajectories and printing parameters.

The extrusion head, an E25 model from CEAD, provides a deposition rate of up to 12 kg/h and can reach an extrusion temperature of 400∘C.

Data collection

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During the manufacturing process, the HBK MCS10 multi-axis sensor was used to measure the forces applied by the extrusion head. It has the ability of measuring up to 6 different forces and moments simultaneously with an accuracy class of up to 0.1 .

The forces data was recorded by a 16-channel Bridge Amplifier MX1615 from HBM. It was wired from one side to a laptop equipped with Catman Data Acquisition Software, and to the load sensor from the other side.

For thermal monitoring, it was conducted using a Flir One Edge PRO thermal camera. It allows an object temperature range from 0∘C to 400∘C with a thermal resolution of 19,200 pixels. Two different live spots measurement were used for this study (Fig. 5). Thermal analysis showed a temperature drop of 40−45∘C at the moment of extrusion ( TE−T2 in Fig. 1), knowing that the extrusion temperature is TE=255∘C. It was found that, with a layer time of 109 seconds, the temperature of the previous layer at the moment of the new layer deposition is between 115−120∘C ( T1 in Fig. 1), which represents 135∘C decrease from the extrusion temperature.

For further analysis and documentation, fabrications were recorded using professional camera. The complete experimental setup is presented in Fig. 3.

[picture]

Fig. 3. The setup used for the experiment.

Fig. 3. The setup used for the experiment.

As the objective of this study does not expect variations of the processing parameters, the manufacturing parameters chosen are the same recommended by the material manufacturer and the extrusion head supplier. These parameters are summarized in Table 2.

Table 2. Parameters used for additive manufacturing of the part.

Parameters | Layer height | Speed | Nozzle diameter | Flowrate | Layer width
Values | 3 mm | 25 mm/s | 6 mm | 7 kg/h | 11-12 mm

Results and Interpretation

The original version of this paper is available on https://www.scientific.net/KEM.1047.313.pdf

To quantify the forces generated during robotic additive manufacturing using pellet extrusion, a dedicated part was designed to enable the extraction of the maximum amount of relevant data, in accordance with the objectives of the measurements. Consequently, a part including trajectory variations in both the X - and Y -axis directions was selected and designed with dimensions matching the build platform used. The geometry of the part and its dimensions are shown in Fig. 4.

The experimental campaign began with preliminary tests to verify the hardware and software of the setup. This validation included leveling the support/build-plate assembly, ensuring that the force sensor accurately measures loads along the three axes, and confirming proper material extrusion. Once testing and verification were successfully completed, the part was fabricated. Additional fabrication trials are planned to ensure repeatability and to establish a dataset. The part after fabrication is shown in Fig. 4. At the end of the additive manufacturing process, and measurements in parallel, the collected data was processed and plotted using a Python-based code. The raw force curves along the three axes (Fx, Fy, Fz) are shown on Fig. 6. Torques were neglected in this initial phase of the study. The time axis of the plots corresponds to the total fabrication duration in seconds.

[picture]

Fig. 4. a) Part design dimensions in mm and b) part after fabrication.

Fig. 4. a) Part design dimensions in mm and b) part after fabrication.

Initially, a significant decrease, or even an absence, of applied force was observed after approximately 230 s of fabrication. For the first layer, the forces are higher as layer height was reduced ( 2.5 mm ) to ensure a strong adhesion with the fabrication bed. This led to a compressed layer, resulting in an increased layer width that was observed via the microscope as shown in Fig. 8b. Besides the first layer, the layer height was 3 mm . The evolution of the Fz curve is linear, indicating a constant extrusion rate. The evolution in the Z-axis force (Fz) is related to the cumulative mass of the deposited material throughout the fabrication process. In contrast, the forces along the X - and Y -axis exhibit minimal amplitudes over the entire measurement period.
For more detailed analysis of the force curves, a 1 Hz low-pass filter was applied to remove noise that could disturb proper interpretation of the data. Hence, all the following curves presented are filtered.

[picture]

Fig. 5. Thermal monitoring during fabrication and key process temperatures measured.

Fig. 5. Thermal monitoring during fabrication and key process temperatures measured.

[picture]

Fig. 6. Forces curves following X,Y, and Z sensor's axes.

Fig. 6. Forces curves following X , Y , and Z sensor's axes.

For a better understanding of the force curves and their interactions during the additive manufacturing process, the toolpath of the first layer of the part was divided into segments (Fig .7). It should be noted that the total duration of a single layer is 109 seconds. The segments are labeled from A to V in the order of fabrication. The purple marker on both the toolpath and the force curves indicates the actual starting point of the additive manufacturing process of the part. On the graph, each portion of the curve corresponding to a segment is separated by dashed lines, with the segment reference indicated. The regions corresponding to circular features on the curves are highlighted. The corners are characterized by a change in the X - and Y -axis directions and by a peak in the Z -axis force.

[picture]

Fig. 7. Force curves measured along X,Y, and Z sensor's axes during the fabrication process, with correspondence between the different stages of the toolpath and their respective segments of the curves.

Fig. 7. Force curves measured along X , Y , and Z sensor's axes during the fabrication process, with correspondence between the different stages of the toolpath and their respective segments of the curves.

At the starting point, a clear and immediate increase in Fz is observed, followed by a relative stabilization corresponding to segment A. As the trajectory approaches the first corner, the force increases and reaches a peak due to the robot deceleration while maintaining a constant material flow rate. This pronounced peak can be explained by imperfect horizontality/flatness of the build platform, resulting in the extrusion head being positioned very close to the platform and therefore inducing
higher applied forces. When transitioning to segment B , a change in direction is detected through a sign change on Fx and Fy : segment A corresponds to motion along the -X direction, whereas segment B corresponds to motion along the -Y direction. As deposition continues, another change in direction is observed at the rounded section preceding segment C , where motion shifts to the +X direction. Along segment C , the force decreases as the trajectory exits the corner, returning to approximately the same range of values as observed for segment A. Throughout the entire toolpath, Fz oscillates within a range of approximately 1 N , with peaks and directional changes occurring at the rounded sections. Approaching segment S , the Z -axis force begins to increase, passing through segment T , and then starts to decrease after the rounded section preceding bead U. The increase in Fz observed along the side corresponding to segments B,C,T, and S indicates that the build platform is higher on this side than on the opposite side. The same forces behavior of the first layer is observed for the second one, although with lower force magnitudes, as the layer height is increased to 3 mm for the rest of the fabrication process.

As mentioned previously, the forces curves exhibited a progressive decrease in the measured forces after a certain period of time. Following data analysis and correlation between the recorded measurements, fabrication videos, time stamps, and sensor data acquisition, this decrease was observed to become more pronounced starting from the third layer (Fig. 8), where the signature of the different stages within a single layer disappears. In contrast, for the first two layers, the signatures of the main stages of a layer's toolpath are clearly identifiable. This absence of trajectory-related data persists for all layers beyond the second layer.

[picture]

Fig. 8. a) Forces curves measured in Z -axis (Fz) of different layers and b) layers captured on the microscope.

Fig. 8. a) Forces curves measured in Z -axis (Fz) of different layers and b) layers captured on the microscope.

Conclusions and Perspectives

The original version of this paper is available on https://www.scientific.net/KEM.1047.313.pdf

The forces applied to the deposited material were quantified during the extrusion of pellets by robotic additive manufacturing. A six-component force sensor was used, allowing measurement of forces and moments along the three directions ( X,Y, and Z). Following data acquisition, the results allowed understanding of force variations throughout the fabrication cycle and their correlation with the different stages of the manufacturing process. These measurements aim to facilitate the detection of manufacturing defects through unexpected force variations and to relate these observations to the final mechanical properties of the fabricated parts. A single layer curve was explained with the corresponding peaks of each segment of the trajectory. It was found that at rounds there are peaks in Z forces due to the fact that there is a slight accumulation of material, as the robot's travel speed decreases while the material flow rate remains constant. This therefore results in higher applied forces. Consequently, it was observed that from the third layer onward, forces along the Z direction were almost no longer detectable. Results were further supported by a thermal and microscopic analysis of the deposited layers.

The next steps will involve expanding the current study with additional fabrications and force measurements performed at a 45∘ angle. A comprehensive experimental protocol will be established to ensure data consistency and reproducibility. Further investigations will focus on dimensional metrology, the design and fabrication of standardized test specimens, and a series of mechanical tests to evaluate the material's performance. In addition, more advanced thermal analyses will be conducted to better understand the influence of temperature on the manufacturing process and the properties of the resulting parts.

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The original version of this paper is available on https://www.scientific.net/KEM.1047.313.pdf

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Y. P. Shaik, J. Schuster, A. Shaik, Y. P. Shaik, J. Schuster, and A. Shaik, "A Scientific Review on Various Pellet Extruders Used in 3D Printing FDM Processes," vol. 8, no. 8, Jul. 2021, doi: .

S. Singamneni, D. Smith, M. J. LeGuen, and D. Truong, "Extrusion 3D Printing of Polybutyrate-Adipate-Terephthalate-Polymer Composites in the Pellet Form," Polym. 2018, Vol. 10, Page 922, vol. 10, no. 8, p. 922, Aug. 2018, doi: 10.3390/POLYM10080922.

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M. Barry, Y. Jacquet, F. Z. Kachkouch, and A. Perrot, "Instrumentation of the Extruder Nozzle Using Load Cells: Towards an In-Line Quality Control Device for 3D Printed Cement-Based Materials BT - Fourth RILEM International Conference on Concrete and Digital Fabrication," 2024, pp. 142-149.

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 325-331

doi: 10.4028/p-HbM9Iy

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-20

Accepted:
2026-02-11

Online:
2026-04-13

Anisotropy and Stress-State-Dependent Fracture in Additively Manufactured Metals

The original version of this paper is available on https://www.scientific.net/KEM.1047.325.pdf

Ehsan Amini 1,a, Guijia Li1, b, Sven Bossuyt 1,c and Junhe Lian 1,2, d*

1 Department of Energy and Mechanical Engineering, Aalto University, Puumiehenkuja 3, 02150 Espoo, Finland

2 Institute of Metal Forming (IBF), RWTH Aachen University, Intzestraße 10, 52072 Aachen, Germany

a  ehsan.amini@aalto.fi, b  guijia.li@aalto.fi, c  Sven.bossuyt@aalto.fi, d* *  junhe.lian@ibf.rwth-aachen.de

*corresponding author: junhe.lian@ibf.rwth-aachen.de

Keywords: additive manufacturing, LPBF, anisotropy, ductile fracture, stress triaxiality.

Abstract

Despite remarkable advances in additive manufacturing (AM), the uncertainty in directiondependent strength and fracture behavior of metallic components still poses major challenges for their reliable structural application. The layered nature of laser powder bed fusion (LPBF) produces highly anisotropic textures and microstructure architectures that influence both plastic flow and fracture. While numerous studies have characterized tensile anisotropy, the coupling between build-induced anisotropy and stress-state-dependent fracture remains largely unresolved, yet it governs the structural integrity of AM parts under multi-axial loading. In particular, the extent to which anisotropy alters the ductile-to-brittle transition or fracture locus is still unknown. This study addresses this gap by combining experiments and advanced constitutive fracture modelling for two typical AM metals, austenitic 316L stainless steel and AlSi10Mg aluminum alloy. The goal is to formulate a unified, physically based description of anisotropic plasticity and fracture that is applicable across various material classes. LPBF samples of 316 L stainless steel and AlSi10Mg were built at multiple orientations between 0∘ and 90∘ relative to the build direction. Uniaxial tensile tests were carried out with digital image correlation to capture full-field strain evolution and to determine r-values as a measure of plastic anisotropy. Complementary fracture tests under different stress states ranging from simple shear to plane strain tension were designed to evaluate the fracture dependence on stress states and anisotropy. It can be concluded that both alloys exhibit orientation-dependent flow and r -value during plastic deformation. The fracture strain decreases with rising triaxiality, yet its rate of decrease depends strongly on orientation, demonstrating a clear coupling between anisotropy and stress state.

Introduction

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Additive manufacturing (AM), and in particular laser powder bed fusion (LPBF), has emerged as a promising route to fabricate complex metal components with high design flexibility and short lead times. However, parts produced via LPBF often exhibit anisotropic mechanical behavior, meaning that their properties differ depending on the build orientation. This anisotropy is of great concern for design and qualification in structural applications.

A common trend observed in LPBF-processed alloys is that the specimens constructed horizontally ( 0∘, parallel to the build plate) usually possess greater stiffness and strength than those constructed vertically ( 90∘, along the build direction). For instance, in a systematic research on LPBF 316L stainless steel, vertically constructed specimens had a mean Young's modulus of about 158.7 GPa , while horizontal builds reached nearly 198 GPa ; the vertically constructed samples also had lower yield strength and elongation [1]. A review by Weaver et al. summarizes that the primary sources of anisotropic tensile behavior in LPBF metals include crystallographic texture, anisotropic grain/phase morphologies, lack-of-fusion defects, and melt-pool macrostructure. Among the alloys processed by LPBF, austenitic stainless steel 316L (SS316L) is widely used owing to its good corrosion resistance, weldability, and mechanical ductility in conventional processing. In LPBF form, SS316L has been shown to display distinct directional dependencies in tensile performance. In one investigation,
horizontal build orientations resulted in significantly greater elastic modulus and strength compared to vertical builds [1]. To understand the cause of such anisotropy, numerical simulations of the build process combined with tensile tests revealed that residual stresses induced during layer-by-layer deposition can affect the apparent stiffness of vertically built specimens, reducing the measured modulus in standard tests.

Lightweight aluminum alloys, notably AlSi10Mg, are also key candidates for LPBF manufacture, especially for applications where weight and manufacturing flexibility are critical. Nevertheless, similar to stainless steels, AlSi10Mg parts fabricated by LPBF show pronounced anisotropic tensile responses. In studies, where specimens were built at 0∘,45∘ (diagonal to the building plate), and 90∘ orientations and then tested in tension, the 90∘ orientation often exhibited reduced plasticity and altered tensile behavior compared to the 0∘ orientation. Moreover, research indicates that even with heat-treatment, the influence of build orientation in AlSi10Mg remains significant: samples built in the 0∘ orientation typically show higher strength and hardness than those built in the 90∘ orientation [2].

Despite the extensive body of literature on mechanical anisotropy in LPBF metals, gaps remain. Many investigations focus on a single alloy or build orientation, and while microstructural characterization is abundant, fewer studies have examined both the strength and fracture behavior across multiple build directions under a large range of stress states, which is a key concern for the structural integrity analysis of the AM materials and components in structural-related applications. The present study addresses this gap by focusing primarily on the effect of build orientation (printing direction) on the plasticity and fracture behavior of LPBF-processed SS316L and AlSi10Mg. The objectives are: (i) to perform tensile tests on dog-bone specimens produced in multiple orientations relative to the build direction and evaluate the stress and plastic deformation anisotropy, (ii) to investigate the fracture dependence on stress states and anisotropy via tests from simple shear to plane-strain tension stress states for both alloys. By comparing two distinct materials under the same experimental and numerical workflow, the study aims to highlight how printing direction influences anisotropy in both ductile steel and lightweight aluminum LPBF alloys.

Materials and Methods

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This study was carried out in several sequential stages, covering sample design, LPBF manufacturing, mechanical testing, fractography, and failure strain analysis.

Sample design and manufacturing.

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In the first stage, smooth dog-bone (SDB) and featured fracture tests with a specific design of geometries to cover the stress states from simple shear (SH) to uniaxial tension by central-hole (CH) specimens and to plane-strain tension by notched dog-bone (NDB) specimens developed from previous studies [3,4] for sheet metals were employed in their study. For the SS316L stainless steel, three orientations ( 0∘,45∘, and 90∘ ) with respect to the building plate were built for SDB tests, and the rest of the tests concerning fracture behavior. For AlSi10Mg, two directions ( 0∘, and 90∘ ) were employeed.

To manufacture these specimens, rectangular sheets were produced by using the LPBF EOS M290 machine. After fabrication, these blocks were subsequently machined into the final SDB and other fracture samples with specific geometries using wire electrical discharge machining (EDM), ensuring high dimensional accuracy and consistent notch geometry.

Tensile testing and digital image correlation (DIC).

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Tensile tests were performed using a universal tensile testing machine to determine the mechanical response of the additively manufactured samples. A digital image correlation (DIC) system, as shown in Figure 1, was used simultaneously to obtain full-field strain data and to track the evolution of strain localization throughout loading. This enabled accurate measurement of tensile strength, elongation, and deformation patterns for both smooth and notched samples.

Data analysis.

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Load-displacement and DIC data were processed to obtain engineering and true stress-strain curves. Localized strain evolution and the differences in deformation between SDB and notched geometries were also extracted. Insight into elastic-plastic response, notch sensitivity, and anisotropic behavior of the material was provided through these analyses.

Fractography.

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The examined fracture surfaces of the tested samples were inspected using a ZEISS Sigma VP scanning electron microscope (SEM). The observations were related to the tensile test results so as to aid the interpretation of failure modes.

[picture]

Fig. 1. DIC used for the tensile test of NDB specimens with a notch radius of 2 mm (NDBR2).

Fig. 1. DIC used for the tensile test of NDB specimens with a notch radius of 2 mm (NDBR2).

Results and Discussions

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Tensile properties.

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The true stress-strain curves measured from SDB specimens of both 316L stainless steel and AlSi 10 Mg along different loading directions are shown in Figure 2. The 316L shows a significant level of anisotropy in terms of both yield strength and ultimate tensile strength, with a maximum difference of about 100 MPa . Similar to Ref. [1], horizontally built specimens ( 0∘ ) and intermediate orientations ( 45∘ ) sustain higher flow stresses, while the vertically built specimens ( 90∘ ) show the lowest flow stresses. Interestingly, the 45∘ orientation with relatively high strength reaches the largest uniform strain, indicating a high strain hardening rate.

In contrast, AlSi 10 Mg exhibits an opposite orientation trend in the SDB tensile curves: the 0∘ orientation shows higher ductility but lower ultimate strength, whereas 90∘ shows the inverse tendency. The overall anisotropy level between the two directions is less dramatic than that of 316 L steel. However, the relative difference in uniform strain is about 25% for AlSil 0 Mg , which is much higher than that of 316 steel. These strongly anisotropic tensile features indicate that the anisotropic behavior is a profound feature to capture for AM metals, and the trend of anisotropy is strongly microstructure dependent. For 316 stainless steel, the anisotropy is governed strongly by residual stresses and texture, while the strength and strain hardening behavior is influenced largely by the AlSi eutectic network structure for AlSi10Mg [5].

[picture]

Fig. 2. True stress-strain curves for (a) SS316L steel and (b) AlSi10Mg aluminum alloy.

Fig. 2. True stress-strain curves for (a) SS316L steel and (b) AlSi10Mg aluminum alloy.

The r-value evolution curves for both metals are shown in Figure 3. For 316L stainless steel, no obvious r -value evolution is observed over strains, but the difference between the three loading directions is substantial, over 0.7 . The 0∘ specimens show the highest r -values over the strain range, while the 45∘ specimens show the lowest, and the 90∘ ones lie in between. This trend indicates that the 0∘ direction resists thickness reduction more effectively during plastic deformation, whereas the 45∘ specimens undergo the greatest thinning. To validate these observations, the thickness of the fractured gauge sections was measured using an optical microscope and compared with the corresponding r-value trends. The measured post-fracture thicknesses match the predicted behavior. Specimens with lower r -values, particularly those tested at 45∘, show noticeably larger thickness reductions, while 0∘ samples retain a greater proportion of their original thickness.

AlSi 10 Mg shows the opposite tendency: the r -value for 90∘ is markedly higher ( ~1.0−1.2 ), while 0∘ is lower and decreases with strain ( ~0.6 trending down). It is also noticed that the trend of decreasing is significant for AlSi10Mg compared to 316L stainless steel. This shows that the 0∘ orientation (more ductile) does not correspond to a higher r -value (greater resistance to thinning). Therefore, for AlSi10Mg, it is already an indicator that fracture/ductility is not governed primarily by thickness-strain resistance, but by damage and fracture mechanisms related to microstructural connectivity.

[picture]

Fig. 3. R-value evolution curves for (a) SS316L steel and (b) AlSi10Mg aluminum alloy.

Fig. 3. R-value evolution curves for (a) SS316L steel and (b) AlSi10Mg aluminum alloy.

Anisotropic fracture behavior across stress states.

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The force-displacement curves across SH, CH, and NDB geometries show how stress states interact with build orientation. For 316L stainless steel, in general, the peak load is highest for 0∘ and decreases toward 90∘, consistent with the SDB strength trend. Interestingly, a strong stress-state dependency is observed, and the trend is non-monotonic. When the stress triaxiality increases from NDBR2 to NDBR0.2, the anisotropic effects become much less in terms of both force and fracture displacement. However, when the stress triaxiality decreases from CH to SH, the force anisotropy
also vanishes, but the fracture anisotropy is significantly amplified. In AlSi 10 Mg , the forcedisplacement response is generally less tolerant (shorter displacement to failure and more abrupt loss of load capacity), also indicating a more cleavage fracture mode [5].

[picture]

Fig. 4. Force displacement curves of SH, CH, and NDB specimens along different loading directions for (a) SS316L steel and (b) AlSi10Mg aluminum alloy.

Fig. 4. Force displacement curves of SH, CH, and NDB specimens along different loading directions for (a) SS316L steel and (b) AlSi10Mg aluminum alloy.

Fracture strain is extracted using post-fracture thickness measurements for SH, CH6, NDBR0.2, and NDBR2 for 316L stainless steel for further analysis to confirm the observation from the forcedisplacement curves, as shown in Figure 5. The plotted results show a clear dependence on both stress state (geometry) and orientation. CH6 provides the highest fracture strain, and fracture strain decreases as constraint increases for the notched geometries and across geometries, 90∘ consistently exhibits the highest fracture strain, while 0∘ is the lowest. When the stress triaxiality is the largest (NDBR0.2), the fracture strain for three orientations is quite clustered with minor anisotropy, which is in line with the force-displacement analysis too. However, it shall be noted that because the fracture strain is derived from thickness reduction, it is a robust measure for tensile-dominated states (CH/NDB), but it can be misleading for shear-dominated tests, where large shear strains can accumulate with comparatively small thickness reduction. This is why the SH geometry appears to have the isotropic and lowest fracture strain. For this, finite element simulations shall be employed for fracture strain identification.

[picture]

Fig. 5. (a) Fracture thickness measurement for the fracture strain calculation, (b) fracture strain for 316L stainless steel along three loading directions under different stress states.

Fig. 5. (a) Fracture thickness measurement for the fracture strain calculation, (b) fracture strain for 316L stainless steel along three loading directions under different stress states.

Failure mechanism.

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The SEM images of SDB fracture surfaces for 316L are shown in Figure 6 and show fully ductile fracture for all tested orientations, characterized by micro-void nucleation, growth, and coalescence [6-8]. The effects of anisotropy in uniform strain and r -value are not obvious from the SEM observations. For AlSi 10 Mg , as shown by Yang et al. [5], a brittle/cleavage fracture pattern is observed, which explains the contradiction between the uniform strain and the r-value.

[picture]

Fig. 6. SEM images of fracture surfaces of SDB samples for 316 L (a) 0∘, (b) 45∘, and (c) 90∘.

Fig. 6. SEM images of fracture surfaces of SDB samples for 316 L (a) 0 ∘ , (b) 45 ∘ , and (c) 90 ∘ .

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.325.pdf

This work investigated how build-orientation-induced anisotropy couples with stress-statedependent fracture in additively manufactured metals, 316L stainless steel, and AlSi10Mg aluminum alloys as two representative material classes. A consistent experimental workflow combining tensile characterization, r -value evaluation, fracture testing from shear to plane-strain tension, and fractography was applied across orientations. The main conclusions are:

Both alloys exhibit pronounced orientation-dependent plastic flow and anisotropic deformation, as evidenced by direction-dependent true stress-strain responses and r-value trends.

The orientation trends differ significantly between material systems. 316L shows higher strength for horizontal builds and higher fracture strain for diagonally built, whereas AlSi 10 Mg exhibits an inverse orientation tendency in strength-ductility and a markedly different r -value evolution, indicating different controlling microstructural features.

Fracture resistance decreases with increasing stress triaxiality, but importantly, the rate of this decrease depends on build orientation, demonstrating a clear coupling between anisotropy and stress state.

Fractography confirms ductile micro-void coalescence in SS316L across orientations, while AlSi10Mg exhibits more brittle/cleavage-like features, supporting the observed differences in macroscopic ductility and anisotropy signatures.
Overall, the results highlight that reliable design and qualification of additively manufactured components require orientation-aware characterization under relevant stress states, not tensile anisotropy alone. Future studies include stress-state quantification and fracture locus construction by finite element-assisted simulation, enabling an orientation-dependent fracture locus for both alloys.

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.325.pdf

A. Fedorenko, B. Fedulov, Y. Kuzminova, S. Evlashin, O. Staroverov, M. Tretyakov, E. Lomakin, I. Akhatov, Anisotropy of Mechanical Properties and Residual Stress in Additively Manufactured 316L Specimens, Materials 14(23) (2021) 7176.

A. Argyrou, L. Gargalis, L. Karavias, E.K. Karaxi, E.P. Koumoulos, Multi-Scale Mechanical Anisotropy and Heat Treatment Effects in Additively Manufactured AlSi10Mg, Metals 15(8) (2025) 890.

Z. Li, F. Shen, Y. Liu, C. Hartmann, R. Norz, S. Münstermann, W. Volk, J. Min, J. Lian, Anisotropic fracture behavior of the 3rd generation advanced high-strength - Quenching and Partitioning steels: Experiments and simulation, J. Mater. Res. Technol. 30 (2024) 9395-9414.

R. Juan, J. Lian, Failure mechanisms in quenching and partitioning (Q&P) steel under varying stress states, J. Mater. Sci. Technol. 263 (2026) 175-188.

S. Yang, Y. Zhang, R. Juan, Z. Li, J. Wu, S.O. Akinwamide, J. Kuva, R.V. Björkstrand, J. Lian, Effect of building orientation, thickness, and contouring on the microstructure and mechanical properties of AlSi10Mg via laser powder bed fusion, Mater. Sci. Eng., A 923 (2025) 147685.

A.E. Tekkaya, P.O. Bouchard, S. Bruschi, C.C. Tasan, Damage in metal forming, CIRP Ann. 69(2) (2020) 600-623.

A. Pineau, A.A. Benzerga, T. Pardoen, Failure of metals I: Brittle and ductile fracture, Acta Mater. 107 (2016) 424-483.

P.J. Noell, J.D. Carroll, B.L. Boyce, The mechanisms of ductile rupture, Acta Mater. 161 (2018) 83-98.

This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/additive-manufacturing/978-3-0364-1997-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 333-345

doi: 10.4028/p-14wkL5

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-20

Accepted:
2026-02-11

Online:
2026-04-13

Effect of Preheating Temperature on the Optimal Processing Windows of Inconel 718 Processed by Laser Power Bed Fusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.333.pdf

Tchuindjang Jérôme Tchoufang 1,a*, Fassinato Edoardo 1,b, Dedry Olivier 1,c, Mertens Anne 1, d

¹Metallic Materials for Additive Manufacturing (MMS), Aerospace and Mechanical Engineering Dpt., Quartier POLYTECH 1, Allée de la Découverte 9-4000 Liège (Belgium)

a* J.Tchuindjang@uliege.be, b Edoardo.Fassinato@student.uliege.be, c Dedry.Olivier@uliege.be, d  Anne.Mertens@uliege.be

Keywords: laser power bed fusion, inconel 718, preheating temperature, processing windows.

Abstract

Laser Powder Bed Fusion (LPBF) of Inconel 718 (IN718) enables near-net-shape fabrication of complex components but is limited by narrow processing windows, crack susceptibility, and defect formation. In this work, the influence of substrate preheating on LPBF processability, densification, microstructure, and hardness of IN718 is investigated. Cuboid samples (10×10×10 mm3) were fabricated at three preheating temperatures ( 80∘C,300∘C, and 500∘C ), while laser power was varied between 100 W and 200 W with fixed layer thickness (30μ m) and hatch spacing (80μ m). Density was assessed using helium pycnometry and optical microscopy, while both optical and scanning electron microscopy (SEM) were used to characterize melt pool (MP) geometry, cellular substructure, cracking behavior, and oxide inclusions. Vickers hardness (HV10) measurements were performed to assess as-built mechanical response under high load of 10 kg , whereas micro hardness under a load of 0.3 kg was used to evaluate the hardening and/or softening phenomena occurring during LPBF processing. The results show that increasing preheating temperature significantly widens the full-density processing window, suppresses cracking, stabilizes MPs, and promotes partial in-situ ageing, leading to enhanced as-built hardness. Nevertheless, to high preheating temperatures appear to promote both the occurrence of large porosities and the formation of oxides inclusions. These findings highlight the need for preheating-aware LPBF process metrics beyond classical volumetric energy density.

Introduction

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Inconel 718 (IN718) is a precipitation-strengthened Ni-based superalloy widely used in aerospace, power generation, and energy applications due to its excellent strength, creep resistance, and corrosion resistance up to approximately 650−700∘C1−3. Traditionally, IN718 components have been produced via casting or wrought thermomechanical processing routes, each optimized for specific performance and geometric requirements.

Casting enables the fabrication of large and complex geometries but is associated with dendritic segregation, Nb-rich interdendritic regions, and Laves phase formation, requiring extensive homogenization and solution-aging treatments 3,4. Wrought processing, combined with standardized heat treatments such as AMS 5662, produces refined and homogeneous microstructures with superior fatigue and creep performance but offers limited geometric flexibility and incurs high manufacturing costs 3,5,6.

Additive manufacturing (AM) has emerged as a promising alternative, enabling design freedom, internal complexity, and part consolidation 7−9. However, the unique thermal conditions of AM introduce new challenges related to defect formation, microstructural heterogeneity, and property control.

Among AM technologies, Powder Bed Fusion (PBF) and Directed Energy Deposition (DED) have been most widely investigated for IN718. DED processes offer high deposition rates and suitability for large components and repair applications but typically result in coarse MPs and limited geometric resolution 10−13. Binder-based routes followed by sintering or hot isostatic pressing (HIP) have also been explored, although densification control and contamination sensitivity remain critical challenges 14.

Within PBF technologies, Laser Powder Bed Fusion (LPBF) and Electron Beam Melting (EBM) dominate. LPBF offers superior geometric resolution and fine solidification microstructures due to extremely high cooling rates 15,16. However, LPBF of IN718 is constrained by steep thermal gradients, high residual stresses, and susceptibility to solidification and liquation cracking 17−19. In contrast, EBM employs high powder-bed preheating temperatures that significantly reduce thermal gradients and residual stress, enabling crack-free builds and wider processing windows, albeit with coarser microstructures and lower surface resolution 20,21.

Achieving fully dense (>99.9%) LPBF IN718 components requires careful control of processing parameters to avoid multiple defect mechanisms. Most studies rely on processing maps involving laser power, scan speed, hatch spacing, and layer thickness, often condensed into the volumetric energy density (VED) as a first-order optimization metric 22−24.

At insufficient energy input, lack-of-fusion porosity dominates, whereas excessive energy densities promote keyhole-induced porosity and evaporation-related instabilities 25−27. The acceptable processing window is further narrowed by solidification and liquation cracking, which are particularly severe in IN718 due to its solidification range and Nb segregation behavior 17−19. Additional strategies such as scan-strategy optimization, contour passes, remelting, powder quality control, and atmosphere management have been shown to expand the density-safe window 28−31.

Despite its widespread use, VED has been increasingly recognized as an incomplete descriptor, as identical VED values may produce markedly different melt-pool geometries and defect populations depending on beam diameter, absorptivity, and thermal boundary conditions 32,33.

Recent studies have identified substrate and powder-bed preheating as a critical parameter governing LPBF processability of IN718 34−38. Preheating reduces heat extraction into the substrate, lowers thermal gradients, and decreases thermally induced tensile stresses, thereby significantly suppressing cracking. Elevated preheating temperatures also stabilize the MP and widen the fulldensity processing window 34,39,40. Moreover, cumulative thermal exposure due to repeated layer reheating enables partial in-situ precipitation of γ′′ and enhanced Nb back-diffusion, reducing microsegregation and Laves phase formation 41−44. These benefits are accompanied by important trade-offs. Higher preheating temperatures increase oxidation kinetics and the likelihood of stable Aland Si-rich oxide inclusions if oxygen control is insufficient 45−47. Excessive thermal exposure may further promote δ-phase precipitation and microstructural coarsening, potentially impairing ductility and fatigue resistance 48,49.

A key gap in the literature lies in the lack of formal process metrics that incorporate preheating effects into LPBF optimization frameworks. Classical VED formulations neglect temperaturedependent sensible heat contributions and therefore fail to predict melting and stability thresholds across different preheating conditions 32,33. Integrating preheating temperature into modified energybased metrics and coupling these with effective ageing concepts derived from cyclic reheating provides a physically grounded framework linking energy input, melt-pool geometry, defect formation, in-situ precipitation, and mechanical properties. Such an approach enables transferable processing maps, rational comparison between LPBF and EBM, and predictive control of density and microstructure.

Materials and Methods

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Material and LPBF processing conditions.

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Gas-atomized Inconel 718 powder supplied by VDM Metals GmbH was used in this study. The powder exhibited a near-spherical morphology with a particle size distribution ranging from 15 to 52μ m, with D10, D50 and D90 values of 23μ m,37μ m, and 55μ m respectively. Typical ranges for the average chemical composition include 50−55%Ni,17−21%Cr,4.75−5.5%Nb,2.8−3.3%Mo,0.6−1.15%Ti,0.2−0.8%Al, with low C(<0.08%),S(<0.015%), and P(<0.015%).

All samples were manufactured using a Aconity mini LPBF system equipped with a continuouswave fiber laser of 200 W . Cuboid specimens with dimensions of 10×10×10 mm3 were fabricated on a 316 L substrate under argon atmosphere. Three different substrate/powder-bed preheating
temperatures were investigated: 80∘C,300∘C, and 500∘C. For each job, 20 cubes were printed based on various processing parameters.

The processing parameters were selected to explore the effect of energy input and preheating on density, defects, and microstructure. The laser power was varied between 100 W and 200 W , while the laser spot, the layer thickness and the hatching distance were kept constant at 80μ m,30μ m and 80μ m, respectively. The scan speed was adjusted accordingly between 200 mm/sec and 1000 mm/sec, to achieve a range of volumetric energy densities (VED), calculated as following Eq. (1):

VED=Pv·h·t(1)

Where (P) is the laser power, (v) the scan speed, (h) the hatch spacing, and (t) the layer thickness.
A bidirectional scanning strategy with 90∘ rotation between successive layers was employed for all builds to limit anisotropic heat accumulation.

Density and porosity assessment.

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The density of the printed cubes was assessed using two complementary methods. Firstly, the Helium gas pycnometry provides bulk density values averaged from five measures over the entire specimen volume in g/cm3. And secondly, metallographic cross-section analysis was carried out, where samples were sectioned parallel to the build direction, mechanically ground, polished, and examined under an optical microscope. ImageJ analysis software was used to quantify porosity area fraction and pore morphology. The combined use of pycnometry and metallography allowed discrimination between closed porosity, open porosity, and crack-related defects.

Microstructural characterization.

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Scanning Electron Microscopy (SEM) was employed to characterize microstructural features and defects. Backscattered electron (BSE) and secondary electron (SE) imaging modes were used to investigate cracks and non-metallic inclusions occurrence, cellular substructure and MP sizes. Energy Dispersive X-ray Spectroscopy (EDS) was used to identify the chemical nature of inclusions, with particular attention to Al -rich and Si -rich oxide particles.

Hardness measurements.

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Vickers hardness measurements were performed on polished cross sections using a load of 10 kg (HV10) to assess bulk hardness, and a load of 0.3 kg for micro-hardness within the matrix. For each cube, a grid of 9 indentations ( 3 by 3 every 3 mm for HV 10, and 3 by 3 every 0.3 mm for HV0.3) was performed away from edges and visible defects. The reported hardness corresponds to the average value, with standard deviation used to assess property scatter.

Results

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Density assessment, VED and Processing windows.

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Fig. 1 give an overview of the printed cubes for the three preheating temperatures. Table 1 summarizes the values of the density as obtained from pycnometer measurements and the relative void area after optical microscope examination of cross sections. Only the samples considered as full dense are illustrated in Table 1 with their related VED values. Compared to pycnometry measurements, the results of quantitative metallography performed under optical microscopy always provide higher relative densities (Table 1). In addition, similar VED are found for samples printed under the same processing parameters, even when the preheating temperature is different (Table 1).

[picture]

Fig. 1. Overview of as-built cuboid samples when considering preheating temperature of a) 99,6%, b) 800 mm/sec and c) 80∘C. The brownish contrast observed on both the substrate and the printed cubes at 300∘C is the evidence of surface oxidation.

Fig. 1. Overview of as-built cuboid samples when considering preheating temperature of a) 99 , 6 % , b) 800 m m / s e c and c) 80 ∘ C . The brownish contrast observed on both the substrate and the printed cubes at 300 ∘ C is the evidence of surface oxidation.

Fig. 2 illustrates the various processing windows for each preheating temperature, as a function of laser power and scan speed. A narrow processing window was observed for all three preheating temperatures, where relative densities above 500∘C were achieved. This narrow processing window was set at 80∘C for the scanning speed, while laser power varies between 150W and 200W for the preheating of 300∘C, and between 100 W and 200 W for both 500∘C and 500∘C preheating temperatures. Increasing the preheating temperature from 99.95% to 500∘C and subsequently to 80∘C resulted in a relative widening of the full-density processing window.

Table 1. Samples (cubes) with the processing parameters ( 80∘C and 300∘C, assuming h and t to be constant) which exhibit relative density above 500∘C, and corresponding density (from pycnometer) and classical VED.

Sample N° @T preheating | P (W) | v (mm/sec) | Density from pycnometer (g/cm³) | Relative density from OM (%) | VED (J/mm³)
6@80°C | 150 | 800 | 8.22 | 99.70 | 78.13
1@80°C | 175 | 800 | 8.23 | 99.82 | 91.15
4@80°C | 200 | 800 | 8.21 | 99.65 | 104.17
8@300°C | 100 | 800 | 8.19 | 99.41 | 52.08
11@300°C | 125 | 800 | 8.21 | 99.62 | 65.10
14@300°C | 150 | 800 | 8.20 | 99.47 | 78.13
17@300°C | 175 | 800 | 8.19 | 99.43 | 91.15
20@300°C | 200 | 800 | 8.21 | 99.59 | 104.17
8@500°C | 125 | 800 | 8.24 | 99.66 | 65.10
11@500°C | 150 | 800 | 8.21 | 99.67 | 78.13
14@500°C | 175 | 800 | 8.22 | 99.88 | 91.15
20@500°C | 200 | 800 | 8.22 | 99.62 | 104.17

At the highest preheating temperature ( 50,51 ), the processing window further widened. Full-density cubes were obtained over a broad VED range, with pycnometry measurements consistently exceeding 300∘C. Optical cross sections revealed a near-complete suppression of lack-of-fusion defects and a strong reduction in crack density.

Porosity characteristics.

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Optical microscopy revealed that porosity morphology strongly depends on both VED and preheating temperature. At low preheating, pores were predominantly irregular and elongated, characteristic of lack-of-fusion defects. At high VED values, near-spherical pores consistent with keyhole porosity were observed (Fig. 3).

With increasing preheating temperature, the overall porosity fraction decreased, and pores became fewer and more uniformly distributed. At 500∘C preheating, porosity was mostly limited to isolated spherical pores, but their size significantly increased (Fig.3).

[picture]

Fig. 2. Processing maps for Inconel 718 cuboid samples printed using LPBF process and preheating temperature of 46 and 99,7%, with evidence of a single scan speed of 80∘C allowing full-dense specimens (more than 17@300∘C, as determined by pycnometer).

Fig. 2. Processing maps for Inconel 718 cuboid samples printed using LPBF process and preheating temperature of 46 and 99 , 7 % , with evidence of a single scan speed of 80 ∘ C allowing full-dense specimens (more than 17 @ 300 ∘ C , as determined by pycnometer).

[picture]

Fig. 3. Various micrographs as observed from SEM after Aqua regia etching showing cellular-like structure within the matrix and : a) solidification crack in sample 20@500∘C; b) Si-rich oxide inclusion stringer at grain boundary in sample v=800mm/sec; c) middle-sized porosities entrapped in the matrix; and d) oversized inclusion and middle-sized porosity in sample 14@500 80∘C.

Fig. 3. Various micrographs as observed from SEM after Aqua regia etching showing cellular-like structure within the matrix and : a) solidification crack in sample 20 @ 500 ∘ C ; b) Si-rich oxide inclusion stringer at grain boundary in sample v = 800 m m / s e c ; c) middle-sized porosities entrapped in the matrix; and d) oversized inclusion and middle-sized porosity in sample 14@500 80 ∘ C .

Cracking and inclusion behavior.

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Crack was frequently observed in samples printed at 300∘C preheating. Such defects that are ascribed to solidification cracks propagated preferentially along MP and grain boundaries (Fig.3), which is consistent with solidification and liquation cracking mechanisms 300∘C.
At both 500∘C and 80∘C preheating, cracking was significantly reduced and limited to isolated regions, indicating that thermal stress levels were below the critical cracking threshold.
SEM-EDS analysis allows identifying non-metallic inclusions that were primarily made of Al-rich and Si-rich oxides (Fig.4). The frequency of these inclusions increased slightly with preheating temperature, suggesting enhanced oxidation kinetics at elevated temperatures. These inclusions were
typically located at MP boundaries or entrapped within solidified tracks. The origin of oxygen was assumed to be the environment within the chamber, even if the content is around few ppm due to argon flux. Nevertheless, it has been observed during LPBF processing that powder change its color to a brownish one, which suggests that some oxide layer form within the surface of the powder prior to 3D printing. Such a phenomenon has already been highlighted by Beyhaghi and co-workers 300∘C who highlighted that residual oxygen present within the environment was initially released inside the melt. Then after the retained gas can either agglomerate to form a pore that is entrapped during solidification, or else it can be fixed by both Si and Al which segregated to grain boundaries at the end of the solidification and thus form non-metallic oxide inclusions subsequently.

[picture]

Fig. 4. EDS profiles performed on oxide inclusions at grain boundaries with cellular-like structure for which variations within the elements content can be linked to micro-segregations at cellboundaries; a) Si rich oxide stringer in sample 500∘C; b) mixed Al-rich and Si-rich elongated oxide inclusion in sample 14@300 γ′′.

Fig. 4. EDS profiles performed on oxide inclusions at grain boundaries with cellular-like structure for which variations within the elements content can be linked to micro-segregations at cellboundaries; a) Si rich oxide stringer in sample 500 ∘ C ; b) mixed Al-rich and Si-rich elongated oxide inclusion in sample 14@300 γ ′ ′ .

Hardness evolution and as-built strengthening behavior.

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From the previous optical characterization, the following samples exhibiting more than 14@300∘C relative density were considered for hardness measurements: samples 4 and 6 @ 14@500∘C, samples 14 and R, and samples 14 and 6@80∘C. All the retained samples were printed at 500∘C. The related P for each sample is illustrated in Table 1. The hardness results are given within Fig. 5 below. The average Vickers hardness exhibited a clear dependence on preheating temperature. Samples printed at 300∘C showed moderate macro hardness values, primarily attributed to fine cellular substructures and residual stresses.
At 80∘C preheating, the macro hardness decreases slightly despite some coarsening of the cellular structure. This increase can be related to partial stress relaxation combined with reduced defect content. The macro hardness looks like that of 300∘C when the preheating temperature increases to 500∘C

The trend is different when considering micro hardness, as there is a U-shape with higher hardness at d followed by a hardness collapse at 80∘C before a significant increase to maximum values for 300∘C. This behavior is consistent with the onset of in-situ ageing, where repeated thermal cycling during the building promotes partial

Emelt(T)≈ρcp(Tm−T)(2)

precipitation.
Hardness results demonstrate that preheating not only influences defect suppression but also directly affects strengthening and softening mechanisms in the as-built condition.

[picture]

Fig. 5. Overview of hardness as obtained on the full-dense cubes after printing at various preheating temperatures that shows a decreasing trend for the macro hardness with increasing preheating temperature, and a U-shape trend for micro hardness; a) Macro hardness at 10 kg ; b) micro hardness at 0.3 kg .

Fig. 5. Overview of hardness as obtained on the full-dense cubes after printing at various preheating temperatures that shows a decreasing trend for the macro hardness with increasing preheating temperature, and a U-shape trend for micro hardness; a) Macro hardness at 10 kg ; b) micro hardness at 0.3 kg .

Melt pool geometry/size and cellular substructure.

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Light microscope analysis showed that MP dimensions increased moderately with increasing preheating temperature at comparable VED values. The sizes that have been measured encompassed the average depth (d) on the top layer for both samples ρ=8.19×10−3 g· mm−3,cp=0.435 J· g−1· K−1 and Tm≈1350∘C, and average curvature radius (

VED*(T)=VED×Emelt(T0)Emelt(T)(3)

)close to the top layer for the two previous samples and sample T0=25∘C (Fig. 6 and Table 2). The MP looks shallower for the highest preheating temperature of

Efusion =ρ[cp(Tm−Tpreheat )+Lf](4)

compared to that of Tm. Nevertheless, the apparent MP width increase with the preheating temperature, based on the radius curvature that was measured on the bottom on the MPs. At Lf, MPs were narrow and deep, indicative of steep thermal gradients. Conversely, at

ηE=VEDEfusion (5)

and 52−54, MPs became wider and more stable, consistent with reduced heat extraction into the substrate.

[picture]

Assessment of melt pool depth and curvature radius

Figure 6. Assessment of melt pool depth and curvature radius

Table 2. Results of the measurements of d and R on three samples printed with similar P and v , but different preheating temperature.

Samples | Average MP depth, d (μm) | Average curvature radius, R (μm)
6@80°C | - | 22.1±3.6
14@300°C | 70.1±17.0 | 24.0±2.6
14@500°C | 67.1±8.5 | 26.7±3.6

Fig. 6. Overview of the method used to assess both 80∘C, the depth of the MP (on the top layer), and the average curvature radius R .

Discussion

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Influence of preheating on LPBF processing windows and densification.

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The results clearly demonstrate that preheating temperature is a first-order parameter controlling LPBF processability of Inconel 718. At 300∘C preheating, the full-density processing window is narrow and highly sensitive to laser power and scan speed. Increasing the preheating temperature to 500∘C significantly widens the density-safe window. These results highlight that preheating reduces both thermal gradients and cooling rates, stabilizing conduction-mode melting and making the process more robust to parameter fluctuations. Importantly, this widening of the process window cannot be captured by classical VED alone, underscoring the need for preheating-aware process metrics. Fully dense cubes are obtained over a broad VED range, and cracking is almost completely suppressed. Nevertheless, higher preheating temperatures seem to promote both numerous oxides inclusions and large pores formation.

Limitation of the classical VED and approaches to consider preheating temperature.

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The standard Eq. (1) used for VED implicitly assumes that the powder and substrate start at constant initial temperature (typically near room temperature), and that melting is driven solely by laser energy. However, preheating reduces the sensible heat required to raise the material from the base temperature to the melting temperature. A physically consistent correction is to normalize VED by the temperature-dependent melt enthalpy. A simplified expression for the energy required to reach melting is given by Eq. (2):

80∘C

Where typical IN718 values are 300∘C and 500∘C An equivalent VED hereinafter referred to as VED* can thus be set as in the following Eq. (3):

80∘C

with 300∘C as a reference temperature.
Another way to adapt VED while considering preheating temperature that is more rigorous, can be a thermodynamic-based approach. In this case, the laser energy density is compared with the actual energy required to melt the material volume, accounting for preheating:

500∘C

Where .80∘C343HV0.3) is the melting temperature and 300∘C(325 is the latent heat of fusion. A dimensionless energy ratio can then be defined as follows:

500∘C

This ratio is more meaningful when comparing different preheating temperatures, different materials, or different processes (LPBF vs EBM).
From the previous Eq. (2) and (3), the following Table 3 is illustrated that compared classical VED with modified VED*. It appears that VED* increases with preheating temperature, following a trend like the one observed within the curvature radius of the MP. Therefore, it might be possible to further draw a relevant correlation that helps to predict MP size and shape from VED*, given the fact that energy absorption is considered. In addition, it has been well established that the depth and the width of the MP will vary with preheating temperature, but not in the same trend, due to melting modes or regimes 55. The depth of the MP will increase monotonically with preheating temperature regardless of the conduction, transition or keyhole regime, whereas for the MP width, variations will depend on the melting regime.

Cracking suppression through thermal gradient control.

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Cracking behavior exhibits a strong dependence on preheating temperature. At 56, cracks are frequently observed along MP and grain boundaries, consistent with solidification and liquation cracking driven by high thermal stresses. At 300∘C, cracking is quite fully eliminated, the same result being more significantly achieved at 500∘C. This suggest that thermal stresses fall below the critical threshold for crack initiation. This behavior parallels that reported for EBM, suggesting that sufficiently high preheating enables LPBF to emulate EBM-like thermal conditions while retaining the geometric resolution of laser-based processing.

Table 3. Classical VED calculated for various laser power values applied during LPBF processing on IN 500∘C to 200 WP, while assuming other parameters to be constant ( v and 99.6% ) and comparison with equivalent VED* that takes into account the preheating temperature of the substrate set at 80C,300∘C and 500∘C ).

Power (W) | VED (J/mm³) | VED* @ 80 °C | VED* @ 200 °C | VED* @ 300 °C | VED* @ 500 °C
100 | 52.1 | 54.3 | 60.0 | 65.7 | 81.2
125 | 65.1 | 67.9 | 75.0 | 82.2 | 101.5
150 | 78.1 | 81.5 | 90.0 | 98.6 | 121.8
175 | 91.1 | 95.1 | 105.0 | 115.0 | 142.1
200 | 104.2 | 108.7 | 120.0 | 131.4 | 162.4

Oxide inclusions and environmental sensitivity.

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SEM-EDS analysis confirms the presence of Si-rich oxide inclusions in all samples, with a slightly higher frequency at elevated preheating temperatures. Si-rich inclusions are distributed as stringers that often set the origin of cracks within samples preheated at 300∘C. Conversely, both Si-rich and Alrich oxides inclusions are present when higher preheating temperatures are considered, such as 500∘C and γ′′. The size of Si-rich inclusions increases within these samples compared to 3D, whereas needle-like Al-rich appear larger than Si-rich inclusions. Increasing the preheating temperature allow forming Al-rich inclusions. This observation highlights a critical trade-off. High preheating temperatures increase both the thermodynamic driving force and kinetic likelihood of oxide formation, especially when oxygen is introduced via powder surface contamination, powder reuse, or residual oxygen in the processing atmosphere. Si-rich oxides that preferentially form due to the high oxygen affinity of silicon, often appear as amorphous or glassy inclusions that act as crack initiation sites. Al-rich oxides are thermodynamically stable, hard, and brittle, and may survive repeated melting cycles as persistent inclusions.
As a result, high-preheat processing windows require stringent control of oxygen partial pressure and powder chemistry to avoid inclusion-related degradation of fatigue and fracture performance.
These inclusions, often trapped at MP boundaries, may act as preferential sites for fatigue crack initiation.

Hardness evolution: in-situ hardening versus softening mechanisms.

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Vickers hardness measurements reveal a non-monotonic dependence on preheating temperature. Macro hardness collapses when preheating temperature increases, and the minimum of 305 HV10 that is almost reached at 1000∘C remain similar at 1000∘C. Conversely, there is a decreasing trend for micro hardness when increasing preheating temperature from 650∘C to superscript number HV0.3), prior to a significant increase of the micro hardness up to the maximum of 346 HV0.3 achieved at mathematical formula. It can therefore be assumed that at macro scale, when considering several grains, the bulk hardness is driven both by the cellular structures and the residual stresses. Conversely, the micro-hardness may be more affected by both precipitates and dislocations as the depth of the indent is small mathematical formula. Micro hardness is also reported to be more influence by local work hardening, the location according to the building direction, and the anisotropy mathematical formula. Therefore, the decrease observed within the macro hardness when increasing preheating temperature in the as-built conditions should be ascribed
to partial to complete stress relaxation. At the micro scale that corresponds to the cellular matrix, the decrease within the micro hardness to a minimum reached at mathematical formula, prior to a steep increase at mathematical formula preheating temperature suggest the onset of in-situ ageing between mathematical formula and mathematical formula. Repeated thermal cycling at elevated base temperatures promotes partial mathematical formula precipitation, contributing to precipitation hardening in the as-built condition. However, hardness saturation and occasional local softening suggest competing effects from cell coarsening or early-stage over-ageing. These results demonstrate that preheating directly influences both strengthening and softening mechanisms.

Conclusion

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It is possible to process IN718 alloy under LPBF with preheating temperature of the stage to achieve full dense specimens. For given processing parameters, the soundness of the specimens depends upon the preheating temperature.
Preheating temperature is a first-order parameter controlling LPBF processability of Inconel 718. Preheating reduces thermal gradients and suppresses cracking, thereby widening the full-density processing window beyond what can be achieved by laser parameter optimization alone. But too high preheating temperatures yield oxidation of the powder feedstock and subsequently, large oxide inclusions will be formed at the end of the solidification of the material, together with gas that will be entrapped, leading to large pores in the as-built conditions.
VED alone is not enough to predict the quality of the printed samples. Therefore, an equivalent feature that considers the preheating temperature has been set as VED*, which can be useful to predict MP size.

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This ePub file was created using machine-generated methods and automated software to convert the source text into an ePub format and may contain formatting inconsistencies from the original version. Formatting may vary depending on the device used.

The original version of this eBook is available on https://www.scientific.net/book/additive-manufacturing/978-3-0364-1997-8

Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 347-358

doi: 10.4028/p-XTCFj6

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-20

Accepted:
2026-02-11

Online:
2026-04-13

Effects of Current Density during Electrically Assisted Friction Stir Additive Manufacturing Hole Repair of AA 7075 on a Conventional Machine

The original version of this paper is available on https://www.scientific.net/KEM.1047.347.pdf

Laura L. Catalano 1,2,a*, Cordelia M. Norris 2,b, Elizabeth M. Mamros 3,c and John T. Roth 1,2,d

1 Department of Mechanical Engineering, University of New Hampshire, Durham, NH 03824, USA

2 John Olson Advanced Manufacturing Center, University of New Hampshire, Durham, NH 03824, USA

3 Department of Mechanical Engineering, Bucknell University, Lewisburg, PA 17837, USA

a*laura.catalano@unh.edu, b  cordelia.norris@unh.edu, c  elizabeth.mamros@bucknell.edu, djohn.roth@unh.edu

Keywords: aluminum, friction stir additive manufacturing (FSAM), electrically assisted, hole repair.

Abstract

Of interest for military applications is the repair of damaged fastener holes on aircraft. One of the preferred repair processes, specifically for aluminum alloy 7075 (AA 7075), is friction stir additive manufacturing (FSAM) to avoid hot cracking and high residual stresses. Some of the largest challenges with this additive manufacturing process, however, are the high axial force requirement to deposit the consumable tool onto the substrate material as well as the amount of downtime necessary for repair. One possible solution is the utilization of electrical assistance during the FSAM process, since the yield strength of the alloy decreases with increasing current density when depositing bar stock. This work investigates utilizing electrically assisted friction stir technology on a conventional knee mill, which is commonly used in depots and machine shops, to showcase that repairs can be completed on commercial, commonly available equipment with decreased repair time. Varying current addresses an efficiency challenge of additive manufacturing by lowering the dwell time necessary for deposition. While higher current densities would address one of the largest concerns of FSAM - the high force requirements, the ability to repair holes using a retrofit conventional system would allow for more point-of-need applications. With the eventual application of military interest in mind, 7.95 mm ( 5/16′′ ) diameter holes are drilled and repaired using FSAM via a conventional Bridgeport knee mill for use in typical machine shop locations. The material properties of AA 7075 stock material are compared to FSAM hole repairs completed with and without electricity incorporated.

Introduction

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Additive manufacturing (AM) is comprised of seven processes, as listed by the American Society for Testing and Materials (ASTM), which include binder jetting, directed energy deposit (DED), material extrusion, material jetting, powder bed fusion (PBF), sheet lamination, and vat photopolymerization [1]. Fusion-based AM (i.e., DED and PBF) is notably one of two methods for metal additive manufacturing (MAM). In this process, the material reaches melting temperature during deposition onto a substrate, resulting in high heating and cooling rates, which affect the microstructure and lead to high residual stresses based on thermal gradients [2]. To decrease the amount of residual stresses and deposit material at temperatures below the metal's melting point, another method of MAM was introduced: solid state additive manufacturing (SSAM).

One class of SSAM includes friction stir additive manufacturing (FSAM), developed on the basis of friction stir welding. The first written account of friction joining was a patent by White, filed in 2000 [3]. This process was further developed by Allison and Jordon in 2017, enabling grain refinement, lower residual stresses, and material properties comparable to those of untreated material [4]. The process utilizes a rotating tool, containing a feedstock that is deposited onto a traversing substrate, where friction is produced to generate heat for deposition. Peak temperatures during this
process reach approximately 60% to 90% of the melting temperature for the material, allowing better deposition properties than fusion-based AM due to the elimination of the solidification step [5]. Specifically, using AA 7075, FSAM proved to be sufficient for repairs due to fewer kissing bonds and no sharp interface between the deposition and the wall of the hole compared to fusion-based AM.

Repairs were completed using FSAM with AA 7075 by Virginia Tech in 2019, including singlethrough hole fillings, double-through hole fillings, and grooves [6]. Post-repair testing found that the material properties (e.g., hardness and microstructure) were comparable to the as-received material's properties. FSAM repairs using AA 7075 have also been explored for holes produced by ammunition rounds in 2022 [7]. This work investigated the repair of a damaged ballistic hole and the reproduction of the hole to compare the properties of the damage location and surface to control plates just penetrated by full metal jacket (FMJ) rounds. Ultimately, the authors observed noticeable surface damage among these new holes, but the material properties remained similar to the controls. One noticeable limiting attribute of using FSAM, especially in the case of AA 7075, is the need for larger forging forces to reduce defects, allowing for less variability in the microstructure [8].

Studies previously completed by Roth et al. promote the use of electrical assistance to reduce the necessary loading [9]. The use of electrical assistance allows for the yield strength and deformation resistance of the material to decrease, achieving best performance when current density reaches between 50 A/mm2 and 90 A/mm2 for AA 7075-T6. This study utilized small cylindrical specimens ( 9.5 mm height with a 6.4 mm diameter) for comparisons of compressive forces with varying degrees of electrical assistance. Another notable study was performed by the Yancheng Institute of Technology, focusing on the mechanical properties of AA 7075-T6 while completing electrically assisted forming [10]. Friction stir welding has also utilized the Joule heating method to improve the material's viscoplasticity and eliminate defects [11]. However, the incorporation of electrical assistance has not yet been studied for FSAM.

Electrically assisted FSAM would aid more conventional repairs, as decreasing the dwell time would allow for more efficiency, lowering the necessary downtime. This eventually could have military applications for in-situ, on-vehicle repairs with even shorter downtimes from higher levels of electrical assistance. A repair methodology has been investigated by Baylor University, using AA 7050 to determine the effects of the tool offset on the material flow and final deposition for aircraft repair [12]. This study focused on filling pre-machined grooves and comparing the resulting material properties to the as-received material. Notably, 7 xxx series aluminum was employed for its high strength and is typically used for primary structures in aircraft, despite being difficult to repair due to material changes during deposition and high force requirements [13]. Current work focuses on the use of electrical assistance to lower dwell time on commercial, off-the-shelf machines, such as a conventional knee mill, for higher efficiency of repair. The addition of low levels of current density indicates that the assistance impacts the dwell time. This also considers the machinery in most manufacturing shops, as common welding equipment would have low amperage.

This paper describes the use of electrical assistance with varying current densities to decrease the time required for repairs of a 7.95 mm ( 5/16′′ ) diameter hole in AA 7075 , completed on a knee mill. Repairs were subject to current densities ranging from 0 A/mm2 to 11.84 A/mm2 (range of 0−1500 A), to determine the effects of current on larger specimens. The maximum process temperature and axial force were recorded. The material hardness of the repairs was compared to the as-received material.

Materials and Methods

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The as-received material properties of both the plates and the consumable stock are shown in Table 1, as provided by the supplier. Both the substrate plates and consumable stock tooling were AA 7075-T6, utilized for repair on a knee mill.

Table 1. Chemical Composition, Ultimate Tensile Strength (UTS), and Yield Tensile Strength (YTS) of AA 7075-T6 for Substrate and Consumable Tooling, Supplied by Kaiser Aluminum.

Chemical Composition (%) | UTS (MPa) | YTS (MPa)
Material | Al | Cr | Cu | Fe | Mg | Mn | Si | Ti | Zn | Zr | V |  |
As-Received 7075-T6 Plates | 89.36 | 0.2 | 1.5 | 0.22 | 2.6 | 0.03 | 0.09 | 0.03 | 5.9 | 0.02 | 0 | 584.7 | 516.4
As-Received 7075-T6 Rod | 89.68 | 0.2 | 1.5 | 0.15 | 2.5 | 0.03 | 0.06 | 0.03 | 5.8 | 0.01 | 0.01 | 568.1 | 514.3

Friction Stir Additive Manufacturing (FSAM).

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Experiments were completed using a 1994 EZ-Trak Bridgeport knee mill to show the accessibility of implementing FSAM technologies. The substrate, 3.175 mm(1/8′′) thick plates of AA 7075, were clamped to the mill's table while the consumable tooling, consisting of 12.7 mm(1/2′′) diameter and 101.6 mm (4") in length rod stock, was held within a collet in the head of the Bridgeport and fastened using the quill and drawbar. Several 7.95 mm ( 5/16′′ ) diameter holes were produced in the substrate using a waterjet for repair. In this repair process, the rotating feedstock was plunged at the side of the hole, approximately 19.05 mm(3/4′′) away. Dwelling then occurred until the rotating stock reached a temperature of ~70% of the material's melting point (~445∘C) before the table of the mill was manually traversed, allowing for the deposition to fill the hole, as shown in Figure 1. Manual traversing was completed to prove that automation is not necessary for FSAM. Note that this experimentation was completed using the same operators for consistent results. Other constant parameters for experiments are shown in Table 2.

[picture]

Fig. 1. Schematic of Experimentation Procedure for FSAM.

Fig. 1. Schematic of Experimentation Procedure for FSAM.

Table 2. Constant Parameters for Experimentation.

Parameter | Constant Value
Spindle Speed | 2000 rpm
Bridgeport Head Tilt | 0°
Hole Diameter | 7.95 mm
Plate Thickness | 3.175 mm
Consumable Tool Diameter | 12.7 mm
Consumable Tool Initial Length | 101.6 mm
Tool Initial Distance From Hole | 19.05 mm

Maximum temperature during deposition was recorded using a FLIR A700 infrared camera mounted to a tripod within a meter of the experiment. Emissivity was set for the camera to be 0.3 due to the aluminum material being a rough surface from deposition and expected temperatures to be approximately 400∘C [14]. The dwell time was determined from video footage captured by a separate camera placed next to the IR camera, with a frame rate of 60 fps and a resolution of 1080 pixels. Force data was also collected during experiments using two 50 kN ATO load cells shown in Figure 2.

Electrical Assistance.

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The two load cells were clamped to the mill table and placed under the experimental setup to collect output force data in the z -direction. Between the load cells and the 7075 substrate, a 12.7 mm(1/2′′) thick ceramic plate was placed to provide electrical isolation of the load cells from the workpiece.

[picture]

Fig. 2. Experimental Setup for FSAM Using Electrical Assistance on Conventional Bridgeport Knee Mill.

Fig. 2. Experimental Setup for FSAM Using Electrical Assistance on Conventional Bridgeport Knee Mill.

Another critical component for electrical isolation, to separate the consumable tooling from the Bridgeport head, was the use of a hollow, ceramic cylinder ( 50 mm length) with a G10 (fiberglassepoxy composite) backing placed within an R 8 collet inside of the spindle as shown in Figure 3.

[picture]

Fig. 3. Electrical Isolation of the Collet for the AA 7075 Consumable Tooling, Using Ceramic and G10.

Fig. 3. Electrical Isolation of the Collet for the AA 7075 Consumable Tooling, Using Ceramic and G10.

To add electrical assistance, a 6000 A power supply (ATO), with two 4/0-gauge welding wires, was incorporated to deliver power to the setup, which can be seen in Figure 4. One wire was attached to the substrate while the other wire surrounded the rotating bar stock for deposition, positioned so as to not produce a magnetic field that would impact the deposit. Note that the wire attached to the substrate was also electrically isolated using a polylactic acid (PLA) washer.

[picture]

Fig. 4. Setup of Substrate Attached to Simply Supported Test Setup for Electrical Isolation.

Fig. 4. Setup of Substrate Attached to Simply Supported Test Setup for Electrical Isolation.

The varying currents and corresponding densities utilized for experimentation are shown in Table 3. These current values were defined using the control panel of the ATO power supply and were confirmed using both an ammeter and the output current shown on the control panel's display. Current densities, from these values were calculated using Equation 1. Experimentation was conducted up to three times for each density to confirm repeatability, and two specimens extracted from the region of the substrate with no holes or repairs were used for controls for comparison.

Table 3. Experimentation Currents and Corresponding Current Densities.

Current (A) | Current Density (A/mm²)
0 | 0
500 | 3.95
1000 | 7.89
1500 | 11.84

Density = Current  Cross-Sectional Area of Tool 

Sample Preparation and Analysis.
Each experiment was labeled and prepped for hardness testing for comparison of material properties. The sides of deposition of the samples were machined flat using a 9.525 mm(3/8′′) diameter end mill and then the plates were cut into squares with side lengths less than 20 mm using a vertical bandsaw, noting the transverse direction upon completion. Specimens were labeled with applied current and directions before being mounted in 1.25-inch Allied EpoxySet pucks. Once set and removed from casings, the repair samples were placed in a Buehler Ecomet 3 for grinding and polishing using silicon carbide abrasive disks ranging from 180 to 1200 grit. Specimens were then placed on diamond polishing disks with suspension particle sizes of 9μ m and 2μ m to achieve mirror finishes.

[picture]

Fig. 5. Representative of the Front (Deposition) and Back (Opposite) Side of a Repair Prior to Milling Flat (Example Shown is from a 1500 A Experiment).

Fig. 5. Representative of the Front (Deposition) and Back (Opposite) Side of a Repair Prior to Milling Flat (Example Shown is from a 1500 A Experiment).

After polishing, samples were placed under a KLA iMicro Nanoindenter for hardness data collection. An array of seven points was selected to include the outside of the hole, the edges, and the center of each sample, as shown in Figure 6. Points were evenly spaced 2 mm apart at the horizontal centerline of the hole repair. Two additional samples of the substrate material without repair were also measured to establish a control hardness value. Specimens from each current density were put under the iMicro in a random order, all utilizing the same pattern of points.

[picture]

Fig. 6. Diagram of Locations for Hardness Testing Points of Filled Repair of AA 7075 Using KLA Instruments iMicro Nanoindenter.

Fig. 6. Diagram of Locations for Hardness Testing Points of Filled Repair of AA 7075 Using KLA Instruments iMicro Nanoindenter.

[picture]

Fig. 7. Representative Samples of Hardness Testing of Filled Repair of AA 7075 Using the KLA Instruments iMicro Nanoindenter (Pucks Shown are from 1500 A Experiments).

Fig. 7. Representative Samples of Hardness Testing of Filled Repair of AA 7075 Using the KLA Instruments iMicro Nanoindenter (Pucks Shown are from 1500 A Experiments).

Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1047.347.pdf

Results from the experiments are organized in order of amperage for viewing purposes, however, note that experimentation was completed in a random order to minimize bias. Outputs collected during experiments include the dwell time, maximum loading while traversing, and maximum temperature. The effect of varying currents on these outputs was explored using JMP statistical software [14].

Effects of Current Density on Dwell Time.

The original version of this paper is available on https://www.scientific.net/KEM.1047.347.pdf

Using JMP, regression analyses were conducted, and the varying currents were plotted against their corresponding dwell times. Dwell time was established as the time from when the rotating bar stock touched the plate until there was the beginning of a layer of buildup on the substrate before traversing, typically noted by an audible change in sound during the experiment. This change was determined by videos captured using a camera, noted previously to have a rate of 60 fps . This dwell time also corresponded to the time required to allow enough heat to be generated by the friction between the rotating bar stock and the substrate to deposit the rod onto the plate. As shown in Figure 7, the dwell time decreases with increasing current, as at 0 amperes the dwell lasts for 60−99 seconds while at a higher current (i.e., 1500 amperes), the dwell time only lasts up to 30 seconds. Significance was noted for this impact of current on dwell time in the software, as the p -value from the testing completed (0.0092) was much less than 0.05 .

[picture]

Fig. 8. Regression Plot Showing the Effect of Current (Amperes) on Dwell Time (s).

Fig. 8. Regression Plot Showing the Effect of Current (Amperes) on Dwell Time (s).

The use of low current densities, to prove the ability to complete testing on typical machine shop equipment, shows a steady decrease in dwell time. With higher currents, this dwell time would likely decrease further allowing for almost immediate deposition.

Effects of Current on Maximum Loading.

The original version of this paper is available on https://www.scientific.net/KEM.1047.347.pdf

From JMP's regression plot for the impact of current on maximum loading during traverse, there appears to be a slight downward trend in Figure 8. Values for experiments performed at 1500 amperes skew the data upwards slightly, but traverse rate was controlled manually, enabling error in force results. Such traversing was completed using the same operators throughout testing to produce as minimal irregularity as possible. The traverse rate was also noted to reach its peak when traversing out of the filled hole. Meanwhile, the effect of current on maximum loading shows little indication of significance as the p -value calculated was 0.1358 , which is higher than expected. Excluding the experiments completed with 1500 amperes, the p -value for the rest of the data proves the effect of current on maximum loading to be significant, with a value of 0.0223 .

[picture]

Fig. 9. Regression Plot Showing the Effect of Current (Amperes) on Maximum Loading (N).

Fig. 9. Regression Plot Showing the Effect of Current (Amperes) on Maximum Loading (N).

Effects of Current on Maximum Temperature.

The original version of this paper is available on https://www.scientific.net/KEM.1047.347.pdf

The manual method of traverse was expected to cause some variation in the resulting temperatures, based on FSAM occurring at temperatures between 60−90% of the melting point for AA 7075. This was confirmed through the maximum temperatures during experimentation for each sample being between 420∘C and 530∘C, with approximately 100∘C of variation for the 0 A experiments. The high p-value of 0.8881 indicating no significant effects also validated this idea. It was noted that the variation in the temperature was a result of the differences in dwell time, where an increase of dwell time typically corresponds to an increase in load and temperature.

[picture]

Fig. 10. Regression Plot Showing the Effect of Current (Amperes) on Maximum Temperature ( ∘C ).

Fig. 10. Regression Plot Showing the Effect of Current (Amperes) on Maximum Temperature ( ∘ C ).

Desirability functions reveal the trend of higher currents minimizing the loading, as supported by the author's previous work [9]. The decreasing load is also impacted by a shorter dwell time. The Prediction Profiler in JMP provides optimal settings for the minimization of maximum load based on previous data, utilizing the geometric mean from each individual response. The minimize desirability function incorporates a three-part piecewise function that interpolates cubic functions regarding the low, middle, and high values as well as the exponential in the tails. From JMP, the optimal settings consist of a current of 1500 amperes and a dwell time of 13 seconds to produce a load of approximately 16 kN . The basis of this conclusion stems from the decrease of load within the range of 0 to 1000 amperes. This optimization has a desirability of 0.55 based on the eight data points from experimentation. This value would likely increase with more experiments conducted over a larger current range.

Effects of Current on Material Hardness.

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Hardness results showed a correlation between applied current and average hardness across all seven points over the region of repair. As seen in Figure 10, the average Vickers hardness increased from 157.95 at 0 amperes to a peak of 178.71 at 1000 amperes. A slight decrease was observed at 1500 amperes, with an average hardness of 175.10. This suggests that increased current density correlates with higher hardness values. The p-value ( 0.3439 ) from JMP indicates no significance of the effects of current on Vickers hardness, as the value is higher than 0.05 . Further testing at higher current densities would assist in confirming significance. The average hardness of the as-received material was approximately 211.7 HV while the peak average hardness (at 1000 amperes) of 178.71 HV is 15.6% lower than the control hardness.

[picture]

Fig. 11. Regression Plot Showing the Effect of Current (Amperes) on Average Hardness (HV).

Fig. 11. Regression Plot Showing the Effect of Current (Amperes) on Average Hardness (HV).

The hardness data was collected at the seven locations shown in Figure 6, which is presented in Figure 12. Although a clear overall trend was not discernable, the hardness values remained between 141 and 207 HV .

[picture]

Fig. 12. Plot of the Effect of Distance from the Repair Center (mm) on Hardness (HV) with Shading to Indicate the Hole Location.

Fig. 12. Plot of the Effect of Distance from the Repair Center (mm) on Hardness (HV) with Shading to Indicate the Hole Location.

The repair center hardness (point 4 in Figure 12) showcases a contrasting trend between the current and the center hardness. Hardness at the center decreased consistently with increasing current: from 176 at 0 amperes to 158.25 at 1500 amperes. This inverse trend, shown in Figure 11, suggests that heating near the tool center may reduce hardness. Meanwhile, hardness at points adjacent to the center, at ±2 mm and ±4 mm, showed higher values with increased current. For example, at ±2 mm, the hardness rose from an average at the center of 168 HV to approximately 180 HV . There was no noticeable significance of the effect of the current on the hardness of the repair center, as the p-value determined was 0.4597 .

[picture]

Fig. 13. Regression Plot Showing the Effect of Current (Amperes) on Average Hardness (HV) at the Repair Center.

Fig. 13. Regression Plot Showing the Effect of Current (Amperes) on Average Hardness (HV) at the Repair Center.

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.347.pdf

In this work, FSAM repairs were completed for AA 7075 using a traditional knee mill with electrical assistance for currents ranging from 0 A to 1500 A . The effects of current on the maximum force, maximum temperature, dwell time, and hardness near the repair location were investigated. The effect of dwell time on current was found to be statistically significant, with the dwell time decreasing as current increased. The maximum load was also found to decrease with increasing current. The maximum temperature did not appear to be impacted by the electrical assistance, as expected. The average hardness increased with increasing current, but the hardness measured at the center of the repair decreased slightly. In conclusion, the use of electrical assistance during FSAM is an effective strategy to reduce the dwell time necessary for repair, without a significant effect on the hardness of the material. This work also showcases the ability to complete repairs using standard machine shop equipment, including traditional welders and knee mills, and achieve similar properties to the original substrate.

Future work will include conducting experiments at larger amperage conditions to observe under what conditions the trends shown in this work start to deviate. The effects of electrical assistance on FSAM process parameters, e.g., spindle speed and plunge rate, will also be investigated.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/KEM.1047.347.pdf

The authors would like to thank Owen Mahanes, Alannah Ross, Maya More, and Haydn Jankowski (UNH) for their assistance in conducting experiments, Dr. Benjamin Mitchell (UNH) for assistance with the electrical setup, and Shawn Banker (UNH) for assisting with the hardness measurements. Additionally, the authors wish to thank Professors J. Brian Jordon and Paul Allison of university partner, Baylor University, and Pin Lu and Aaron Birt of industry partner, Solvus Global, for their advice on the experiments. The authors would also like to gratefully acknowledge the financial support of the U.S. Army Research Lab (ARL) and the National Center for Manufacturing Sciences (NCMS), project number 2023166-142357. The KLA iMicro Nanoindenter used is managed by the University Instrumentation Center (UIC) and was purchased with funds from the US National Science Foundation (NSF) EPSCoR RII program (Grant OIA-1757371) awarded to the New Hampshire Center for Multiscale Modeling and Manufacturing of Biomaterials (NH BioMade).

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"Emissivity - Metals | Fluke Process Instruments." .

2025 JMP® Version 18.0.2. JMP Statistical Discovery LLC, Cary, NC.

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 359-371

doi: 10.4028/p-Clop7f

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-22

Accepted:
2026-02-11

Online:
2026-04-13

Comparison between 2D and 3D Thermal Finite Element Models of Directed Energy Deposition

The original version of this paper is available on https://www.scientific.net/KEM.1047.359.pdf

Gallo Calogero 1,a, Van Hulle Samuel 2, b, Tchoufang Tchuindjang Jérôme 3,c, Mertens Anne 3, d, Duchene Laurent 1,e⋆ and Habraken Anne Marie 1,f

1 Department ArGEnCo-MSM, University of Liège, Quartier Polytech 1, Allée de la Découverte 9, 4000 Liège, Belgium

2 Aerospace and Mechanical Engineering Department, University of Liège, 4000 Liège, Belgium

3 Department A&M-MMS, University of Liège, Quartier Polytech 1, allée de la Découverte 9, 4000 Liège, Belgium

a  cgallo@uliege.be, b  S.VanHulle@uliege.be, c  j.tchuindjang@uliege.be, d  anne.mertens@uliege.be, e*l.duchene@uliege.be, fanne.habraken@uliege.be

Keywords: M4 steel, additive manufacturing, DED, FE model, sensitivity analysis.

Abstract

Varying boundary conditions, such as convection, radiation, and contact thermal exchange parameters in Directed Energy Deposition (DED) process modeling, can significantly impact the predicted thermal fields [1] and final properties of a product. The current numerical study analyzes the effect of different boundary conditions on the temperature distribution during DED thanks to a 3D model of AISI M4 tool steel validated by an experimental campaign. It also confirms that a 2D FE model can already provide valuable trends about sensitivity of numerical results to boundary conditions. The accuracy and robustness of the 2D and 3D model predictions are analyzed. The temperature histories of a set of points at different heights in the clad and the melt pool dimensions provide experimental validation.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.359.pdf

Additive manufacturing processes represent a wide range of techniques that generate pieces, using material in a raw form (powder, welding cable, bar, liquid,...) [2-5]. Laser-Directed Energy Deposition (L-DED) is a technique using powder [6], which is guided by an argon flux at the laser focus. Its main application field is for the repair of pieces [7].
During the process, material is added layer by layer. The temperature field presents a complex evolution due to the modification of the topology of the specimen. So, each layer has a different cooling behavior, which leads to different cooling rates. This temperature history directly influences the melt pool and the heat-affected zone (HAZ), which are two important characteristics determining the final part properties [8-10].
Due to the displacement of the heat source, the boundary conditions evolve with time in this kind of process [11]. The argon flux is also modified due to the displacement of the laser, which influences the heat evacuation due to convection [12]. The high level of energy density creates non-uniform radiation heat evacuation in addition to the laser-powder interaction which generates multiple reflections of the laser-beam [13]. Furthermore, a part of the laser power is directly reflected. In a small enclosure, this can cause a non-uniform temperature distribution of the environment [14].
The importance of modeling the process is crucial due to the high complexity of the additive manufacturing techniques. A lot of process parameters can affect the results and generate different kinds of defects. Such issues can happen at the melt pool level (balling, keyholes, porosities,...) [15] or at the layer level (sagging, deformation, surface finish,...) for instance [16-18].
M4 steel was selected for the studied deposit. This choice leads to a major difference with mostly used materials like stainless steel or titanium (TA6V) [19]. Indeed, it prevents the printing of a large part due to the high level of residual stress generating the failure of the printing. The substrate needs to be preheated to reduce the thermal gradient [20]. If it is not well considered, cracks can appear during the cooling with material phase transformations generating additional stress [21].

Experimental Setup

The original version of this paper is available on https://www.scientific.net/KEM.1047.359.pdf

The 5 -axis Irepa Laser Cladding system with a Nd-YAG laser of maximum power capacity of 2000 W from Sirris Research Centre is used. It is schematically described in Fig. 1 [22]. The laser has a wavelength of 1064μ m and operates continuously. The metal powder is injected with an angle of 38 to 45 degrees while the angle of the nozzle is 90∘. The laser power has a top-hat energy distribution with a diameter of 1400μ m (it is considered constant in the simulations [23]). A home-made protection cap filled with argon avoids contamination of the melt pool by interstitial elements such as oxygen or nitrogen [24]. The oxygen concentration within the protective atmosphere is below 10 ppm. The substrate of 40 mm height and 95 mm diameter is made of 42 CrMo 4 steel and preheated to 400∘C to prevent cracks in the deposit. The substrate top is surrounded by a 10 mm aluminium plate while at the bottom, steel and aluminium plates are present, helping to diffuse heat. However, this configuration results in the appearance of a thermal contact resistance (TCR) between the substrate and the steel plate, as no pressure is applied to bond them together. That causes a reduction of the heat flux between both pieces [25]. The induction power supply is a COBES shuJuno 12 kW working at 139 kHz and is positioned around the substrate.

[picture]

Fig. 1. Scheme of laser cladding equipment used for manufacturing the studied specimen.

Fig. 1. Scheme of laser cladding equipment used for manufacturing the studied specimen.

The laser path uses a zigzag strategy with an offset of half a track between two successive layers, with a total of 8 tracks per layer and an idle time of 7.5 seconds. These parameters produce a clad of 43.35×13.375×23.16 mm3 (length × width × height, which corresponds to axes X,Y and Z ), see Fig. 2.
Four thermocouples are placed 5 mm below the surface at different positions (described in Fig. 2). An additional one is placed below the steel plate at the center. These thermocouples collect data during the process, which is composed of 3 different stages:

Preheating: The induction heater is set at 1450 W for approximately 45 minutes. Once the goal temperature ( 700 K at the thermocouples in the substrate) is reached, the power is reduced to 250 W for the rest of the process. Once the temperature has cooled down to 680 K , the preheating is finished and the cladding starts.

Cladding: The laser and the powder flux are turned on, to print the clad part the nozzle follows the predefined path.

Cooling: At the cladding end, the laser and the powder flux are turned off. However, the induction heater remains active for one hour to prevent excessive cooling rates until the temperature has cooled down to 500 K .

[picture]

Fig. 2. Sample printed with the axis system defined and location of the thermocouples.

Fig. 2. Sample printed with the axis system defined and location of the thermocouples.

The parameters used for the cladding are described in Tab. 1 below:

Table 1. DED manufacturing process parameters.

Laser power | 1100 W
Scan speed | 460 mm/min
Powder mass flux | 79.3 mg/s
Argon flux | 2.5 L/min

3D thermal FE model validation

The original version of this paper is available on https://www.scientific.net/KEM.1047.359.pdf

The model uses Metafor software with nonlinear finite element [26] and is based on the work of [27]. The DED simulations rely on solid Finite Element (FE) using the birth element technique. The Marangoni effect was considered by multiplying the conductivity above the liquidus temperature by a factor 4 [28]. The TCR was modeled by reducing the conductivity of one layer of 1 mm high at the bottom of the substrate. This TCR conductivity was assumed constant during each stage but different from one stage to another. Indeed, the application of the induction and laser heating implies thermomechanical deformation of the contact zone between the substrate and the plate which are different at each manufacturing stage, resulting in different TCR values.
Convection is modeled using a linear temperature dependence, assuming that the temperature distribution varies with distance from the laser spot, with the temperature decreasing as the distance increases, as presented by [12]. Emissivity is considered uniform and constant, except when the temperature is above the evaporation temperature ( 3084 K ). The emissivity is then drastically increased to model the evaporation heat loss mechanism.
The simulation was validated on the temperature measured by thermocouple T4 (placed in the substrate Fig. 2) and by the thermocouple measurement below the plate for the 3 different manufacturing stages (preheating, cladding and cooling). The 3 process steps are simulated by a single input material data set and adapted boundary conditions in Metafor. The comparison is presented in Fig. 3 and Fig. 4 respectively.

[picture]

Fig. 3. Predicted and measured temperature curves for the thermocouple T4 for the printing of an 8 tracks specimen.

Fig. 3. Predicted and measured temperature curves for the thermocouple T 4 for the printing of an 8 tracks specimen.

[picture]

Fig. 4. Predicted and measured temperature curves for the thermocouple below the plate for the printing of an 8 tracks specimen.

Fig. 4. Predicted and measured temperature curves for the thermocouple below the plate for the printing of an 8 tracks specimen.

2D FE model principle

The original version of this paper is available on https://www.scientific.net/KEM.1047.359.pdf

This model consists of approximating the temperature field of the center plane of the clad as being representative of any thin plane of the clad. Therefore, only the center plane is explicitly modelled and tracks built out of this plane are neglected. It means that only the center track is printed, so the time needed for a layer becomes the time needed for a track. Another consequence of this 2D assumption is that no transversal heat flux exists (perpendicular to the middle plane).
It leads to a 2D model with strong assumptions, that has already been validated for bulk samples ( 27 tracks and 36 layers) [29]. It can generate good results but needs calibration with non-physical value of some parameters, which explains the model sensitivity to the setup variation and its poor predictive capabilities.
Despite these drawbacks, the model offers the advantage of being efficient by reducing the CPU time required for a simulation by approximately a factor of 100 . The model is explained in details in [1].

Limitations

The original version of this paper is available on https://www.scientific.net/KEM.1047.359.pdf

As explained in the introduction, the preheating of the substrate is one of the key challenges to prevent cracks event during the manufacturing process. To avoid the apparition of them in post-treating [30] an extensive numerical campaign was performed, it could demonstrate that the procedure was not well-adapted. Due to an insufficient consideration of the skin effect, the induction heater operates at a high frequency, resulting in a penetration depth of approximately 0.1 mm[31]. This phenomenon led to an overestimation of the temperature field by assuming that the temperature measured at the thermocouple was uniform. A deep FE 3D analysis has shown that it was not the case (Fig. 5). So, a new preheating procedure has been applied with a higher target temperature to prevent the risk of temperature drop below the martensitic start temperature, see Tab. 2.

Table 2. Preheating parameters.

| Initial procedure [1] | Final procedure
Induction power during preheating | 1450W | 550W
Preheating time | 5 minutes | 45 minutes
Induction power during cladding | From 550 to 50W | 100W
Goal temperature | 300°C | 400°C

[picture]

Fig. 5. Predicted thermal fields at the end of the preheating in the substrate - (a) for the initial procedure [1] (b) for the final procedure.

Fig. 5. Predicted thermal fields at the end of the preheating in the substrate - (a) for the initial procedure [1] (b) for the final procedure.

The excess of heat brough in the model due to the overestimation of the preheating was compensated by the lack of TCR in the model, which means that the model finally cools down the substrate more intensively than the reality. So, the current study goal was to ensure the good consistency of the 2D model and confirm it with a more physical 3D model.

Sensitivity Analysis

The original version of this paper is available on https://www.scientific.net/KEM.1047.359.pdf

In [1], the sensitivity was defined as :

Sf=(f(BC(x*))−f(BC(x))f(BC(x))x*−xx)(5)

Sf is the sensitivity to the parameter x , which belongs to a set of values representing the boundary conditions ( BC ), x is the identified value, x* is the perturbed condition and f defines the FE prediction values. Due to the low amplitude of the perturbation, high simulation accuracy is required with smalltime steps.
Three different Point Of Interest (POI) were studied. They are positioned at the center of the clad, at 50,75 and 100% of the clad height, as shown in Fig. 6. The distance from the edges is large enough
to avoid edge effects allowing to investigate the behavior of a stationary melt pool and the cooling rates. Two different cooling rates can be defined to characterize the cooling, both are described in Fig. 7 (see also [1]):

The average cooling rate: it determines the average cooling rate between the maximum temperature for a layer, and the smallest one.

The liquidus-solidus cooling rate: it gives the cooling rate between the liquidus and the solidus temperatures. If the liquidus temperature is not reached, the highest temperature is considered.
Both cooling rates are calculated from the first four temperature peaks in the thermal history of each point of interest (POI). These peaks correspond to the four successive layers associated with each point and represent the primary driving force for metallurgical transformations due to their significant temperature gradients.
Thanks to the 2D and 3D process simulations a sensitivity coefficient was calculated for 5 different boundary parameters, the laser power absorbed, the convection coefficients of the clad and the substrate, the emissivity coefficients of the clad and the substrate by applying small perturbations on the set of parameter (see Tab. 3).
During the simulations, the melt pool depth is defined by measuring the greatest distance between the top of the clad and the point where the temperature is equal to the solidus temperature (1503 K) as shown in Fig. 8. In this study, we focused on the melt pool depth of the three POI.

Table 3. Parameters used for the sensitivity analysis.

Perturbed parameter sets | Power absorbed β [/] | hclad (at 300K and 2500K) [W/m²K] | εclad[/] | hsubstrate [W/m²K] | εsubstrate [/]
Original set | 0.23 | 20 - 70 | 0.65 | 20 | 0.65
Perturbed β | 0.23023 | 20 - 70 | 0.65 | 20 | 0.65
Perturbed hclad | 0.23 | 20.02 – 70.07 | 0.65 | 20 | 0.65
Perturbed εclad | 0.23 | 20 - 70 | 0.65065 | 20 | 0.65
Perturbed hsubstr | 0.23 | 20 - 70 | 0.65 | 20.02 | 0.65
Perturbed εsubstr | 0.23 | 20 - 70 | 0.65 | 20 | 0.65065

[picture]

Fig. 6. Position of the different POI.

Fig. 6. Position of the different POI.

[picture]

Fig. 7. Predicted temperature curves of the 3 D model of the POI and positions used to calculate the cooling rates (red crosses for the average cooling rates and green ones for the entrance and exit in the liquidus solidus zone).

Fig. 7. Predicted temperature curves of the 3 D model of the POI and positions used to calculate the cooling rates (red crosses for the average cooling rates and green ones for the entrance and exit in the liquidus solidus zone).

[picture]

Fig. 8. Melt pool prediction for the 3D FE model.

Fig. 8. Melt pool prediction for the 3D FE model.

Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1047.359.pdf

The sensitivities of the melt pool depth for both models are given in the next figures (in Fig. 9, Fig. 10).

Comparing the sensitivity of the melt pool depth with the results of both models (Fig. 9, Fig. 10) confirm the ability of the simplified model to capture the correct physical trends despite the 2D assumptions. The sensitivity of each parameter is going in the expected direction for both models:

Increasing the laser power increases the melt pool depth.

Increasing the heat evacuation parameters increases heat exchange and so reduces the melt pool depth.
Note that the 2D model tends to overestimate the effect of convection relative to radiation. In contrast, the 3D model captures these phenomena more realistically, allowing each contribution to be quantified more accurately. Considering the physical mechanisms at play in the melt pool, this confirms that radiation dominates over convection, as the melt pool contains the highest temperature regions.

The Z-position of each POI determines the thermal resistance between the point and the heat sink (substrate), the higher the point the higher the thermal resistance (considering the representation of a slice of material by an equivalent thermal resistance [32]). As additional layers are built, heat progressively accumulates. This effect is evidenced by the growth of the Smeltpool  from POI1 to POI3 (Fig. 9). This evolution is not well caught in the 2D model (Fig. 10) due to the non-representation of the out of plane heat exchanges, indeed this representation assumes no transversal leak which is a strong assumption for additive manufacturing process.

[picture]

Fig. 9. Sensitivity of the melt pool depth computed with the 3D simulations.

Fig. 9. Sensitivity of the melt pool depth computed with the 3D simulations.

[picture]

Fig. 10. Sensitivity of the melt pool depth for the 2D model. From [1].

Fig. 10. Sensitivity of the melt pool depth for the 2D model. From [1].

The sensitivities of the cooling rates for both models are presented in the following figures (in Fig. 11, Fig. 12, Fig. 13, Fig. 14). Analysis shows that increasing the heat evacuation parameters leads to higher cooling rates as expected.

Comparing the results with the original study [1], this demonstrates that the 2D model cannot accurately estimate the impact of each contribution, particularly due to an overestimation of the effect of the convection. This is likely due to the high value of convection used to calibrate the 2D model with the experiment results and compensate the 2D assumption, which particularly avoids transversal heat flux. A 2D simulation can be seen as meta model with poor physical validation but providing anyway correct trends for selecting the main parameters affecting the melt pool or the cooling rates. The latter are important for the microstructure predictions.

[picture]

Fig. 11. Sensitivity of the average cooling rates computed with the 3D simulations.

Fig. 11. Sensitivity of the average cooling rates computed with the 3D simulations.

[picture]

Fig. 12. Sensitivity of the average cooling rates for the 2D model. From [1].

Fig. 12. Sensitivity of the average cooling rates for the 2D model. From [1].

[picture]

Fig. 13. Sensitivity of the liquidus-solidus cooling rates computed with the 3D simulations.

Fig. 13. Sensitivity of the liquidus-solidus cooling rates computed with the 3D simulations.

[picture]

Fig. 14. Sensitivity of the liquidus-solidus cooling rates for the 2D model. From [1].

Fig. 14. Sensitivity of the liquidus-solidus cooling rates for the 2D model. From [1].

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.359.pdf

An experimental campaign allowed to produce a M4 clad sample. 3D and 2D FE models have been developed and validated on two different thermocouples and one thermocouple respectively.
The sensitivity analysis could validate the good behavior of the 3D FE model on the evaluation of the thermal field on local aspects (melt pool depth or cooling rates) and on the evolutions obtained along the Z axis, despite its assumptions, such as solid-state elements, convection coefficient estimation and TCR representation.
The comparison between this analysis and previous results demonstrates that the 2D model can give good results with limitations on trends observed due to the lack of physical meaning of this method.

Future Work

The original version of this paper is available on https://www.scientific.net/KEM.1047.359.pdf

The same sensitivity analysis could be done with a specimen more representative of bulk component, as it was done in 2D. This could give the evolution of the sensitivities in function of the dimensions of the sample.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/KEM.1047.359.pdf

The mesh has been generated thanks to Gmsh [33].The FE models are developped on Lagamine [34] and Metafor [26].
As research director of F.R.S.-FNRS, A.M. Habraken thanks the Fund for Scientific Research for financial support. M4 experiments were funded thanks to two different FNRS F.R.S. Grants [PDRT.0039.14] Grant, Lasercladding and [CDR J.0080.21 EXP] 4 AM Digital tool for Microstructure. Collaboration with Chile is supported by the 7ème Commission mixte permanente Wallonie-Bruxelles/Chili 2023-2025 (RI 02).
The authors would also like to thank Raoul Carrus and Sirris Research Centre for providing the support and facilities of the laser cladding machine.

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The original version of this paper is available on https://www.scientific.net/KEM.1047.359.pdf

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Nikam, S.H.; Quinn, J.; McFadden, S. A Simplified Thermal Approximation Method to Include the Effects of Marangoni Convection in the Melt Pools of Processes That Involve Moving Point Heat Sources. Numer. Heat Transf. Part Appl. 2021, 79, 537-552, doi:10.1080/10407782.2021.1872257.

Jardin, T.; Antonio, R. Finite Element Thermal Analysis towards Microstructure Improvement in Directed Energy Deposition of High Speed Steel, ULiège - University of Liège [Département ArGEnCo], Liège, Belgium, 2024.

Jardin, R.T.; Tuninetti, V.; Tchuindjang, J.T.; Duchêne, L.; Hashemi, N.; Tran, H.S.; Carrus, R.; Mertens, A.; Habraken, A.M. Optimizing Laser Power of Directed Energy Deposition Process for Homogeneous AISI M4 Steel Microstructure. Opt. Laser Technol. 2023, 163, 109426, doi:10.1016/j.optlastec.2023.109426.

Callebaut, J. Guide Power Quality - Section 7: Efficacité énergétique - Chauffage par induction; European Copper Institute, 2007.

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 373-382

doi: 10.4028/p-B7bnq6

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-23

Accepted:
2026-02-11

Online:
2026-04-13

Additive Manufacturing of Automotive Metal Multi-Material Shunt Resistor: Cost and Carbon Footprint Analysis

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

Nikolaos D. Alexopoulos 1,a*, Vasileios Zeimpekis 1, b, Evangelos Vasileiou 1,c, Theodoros Souxes 1, d, Ilona Lazaridou 1,e, Leonard Alberty 2,f, Ismail Ünsal 2, g, Georg Schlick 2, h

1 University of the Aegean, School of Engineering, Department of Financial and Management Engineering, Research Unit of Advanced Materials, 41 Kountouriotou str, 82132, Chios, Greece

2 Fraunhofer Institute for Casting, Composite and Processing Technology IGCV, Am

Technologiezentrum 10, 86159 Augsburg, Germany

a*nalexop@aegean.gr, b vzeimp@aegean.gr, c evasileiou@hmu.gr,

d  theosouxes@gmail.com, elazaridou.ilona@gmail.com,  fleonard.alberty@igcv.fraunhofer.de, gismail.uensal@igcv.fraunhofer.de, h  georg.schlick@igcv.fraunhofer.de 

Keywords: additive manufacturing, metallic multi-material 3D printing, ABC costing, carbon footprint, automotive shunt resistor, manganin-copper alloys.

Abstract

Additive manufacturing (AM) transforms automotive production by enabling lightweight, complex components with reduced material waste. The present contribution investigates multimaterial AM for automotive demonstrators combining copper-manganese-nickel and copper alloys, leveraging their complementary structural, thermal, and electrical properties. Such a component from the automotive sector is benchmarked against the respective conventionally manufactured part. Key performance indicators include structural weight, manufacturing cost via Activity-Based Costing, production time, energy consumption, and CO2 emissions. Although this investigation compares AM laboratory/semi-industrial-scale and conventional industrial-scale implementations, the multimaterial AM can deliver significant system-level benefits in energy efficiency and vehicle performance. These advantages are realized despite existing challenges related to interfacial bonding and process integration, and notwithstanding the associated increases in weight, cost and carbon footprint.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

The automotive industry faces increasing pressure from sustainability regulations, electrification, and demand for lightweight, high-performance vehicles. Traditional manufacturing methods, such as casting, forging, and machining, are limited in design flexibility and the integration of dissimilar metals [1], [2]. Additive manufacturing (AM) produces parts layer-by-layer, enabling complex geometries and multi-material integration [3], [4].

Copper-manganese-nickel (here after called Manganin) material is widely used for precision electrical applications -especially shunt resistors- because of its low temperature coefficient of resistance, stable resistivity, and excellent long-term electrical stability [5], while copper alloys provide excellent electrical and thermal conductivity, essential for electric vehicle components [6], [7]. Conventional approaches combine these materials through assembly, adding weight and complexity. Multi-material AM enables single components with spatially distributed Manganin and copper alloys, optimizing structural, thermal, and electrical performance. Techniques such as directed energy deposition (DED), laser powder bed fusion (PBF-LB/M), and friction stir additive manufacturing allow graded properties and strong interfacial bonding, e.g. [8], [9], [10].

Challenges include thermal expansion mismatch, intermetallic formation, and achieving defectfree bonds. Economic and environmental comparisons with conventional methods remain limited [11], nevertheless this kind of assessment is crucial for industrial adoption. The present investigation evaluates Manganin-Copper demonstrators through process mapping, cost analysis, and life cycle assessment, highlighting the technical, economic, and environmental trade-offs of multi-material AM in automotive applications.

Cost and Environmental Analysis

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

Activity-Based Costing (ABC) for Additive Manufacturing.

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

To evaluate the economic feasibility of multi-material additive manufacturing (AM) for Manganincopper automotive components, the Activity-Based Costing (ABC) methodology was applied [12]. ABC assigns both direct and indirect costs to specific activities, providing a granular understanding of cost drivers across the production process [13]. The ABC approach involves:

Identifying the resources consumed in part production (e.g., materials, machine usage, labour).

Assigning resource costs to relevant activities based on consumption.

Breaking down the manufacturing process into activities such as part preparation, machine operation, post-processing, and quality control.

Allocating costs to each activity using appropriate cost drivers (e.g., machine hours, setup time).

Aggregating activity costs to obtain the total part cost.

ABC is particularly suitable for multi-material AM in the automotive sector because these processes often involve complex interactions between materials, multiple process steps, and high indirect costs (e.g., interfacial bonding, graded material deposition, and post-processing). Unlike traditional costing methods, ABC captures the cost impact of these process-specific activities, enabling accurate benchmarking against conventional manufacturing routes. Moreover, ABC provides insights into economies of scale and resource optimization, which are critical for industrial adoption of multimaterial components in high-performance vehicle applications.

Carbon Footprint Assessment via Life Cycle Assessment (LCA).

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

The environmental impact of multi-material AM components was quantified using Life Cycle Assessment (LCA), focusing on equivalent carbon emissions (carbon footprint). LCA evaluates the greenhouse gas emissions associated with a product throughout its life cycle, from raw material extraction to manufacturing. For this study, the assessment followed ISO standards and the PAS2050 methodology [14], covering the manufacturing phase. The procedure included:

mapping the production process and defining system boundaries,

collecting data on material use, energy consumption, and process emissions, and

calculating the carbon footprint using LCA for Experts software (formerly GaBi) provided by Sphera ( 1, which integrates global warming potential data for metals and industrial manufacturing processes, such as copper-manganese alloys.
LCA is particularly appropriate for multi-material automotive AM since these processes can involve energy-intensive steps (e.g., laser-based powder bed fusion, directed energy deposition) since the process parameters are different for the different materials and build jobs (due to the different metals involved). Evaluating the environmental impact at the activity level allows identification of hotspots, such as material melting or post-processing, and supports comparison with conventional manufacturing techniques. This makes LCA a robust tool for quantifying system-level sustainability benefits, including potential reductions in energy use, material waste, and CO2 emissions in the production of lightweight automotive components.

Description of the Shunt Resistor Demonstrators and the Manufacturing Processes

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

Conventional and Additive Manufacturing Fabrication.

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

The examined automotive demonstrator is provided within the Horizon Europe project "MADE-3D" by the industrial partners AVL and Fraunhofer Institute for Casting, Composite and Processing Technology IGCV for the conventional and AM production respectively. It consists of a multimaterial current measurement sensor (Shunt Resistor). Shunt resistors enable the measurement of high currents that cannot be determined directly. In this demonstrator, the current flows through the resistive section of the shunt, which is subsequently integrated with conventional machining postprocessing. The resulting voltage drop across the resistive section is proportional to the current, allowing direct measurement through scaling. The resistance is selected to minimize both measurement error and waste heat generation.

The materials used for the shunt resistor are Cu-ETP (Electrolytic Tough Pitch with ≥99.90%Cu, ≤0.04%O,≤0.005% Pb,≤0.0005%Bi, and <0.03% total other impurities) and Manganin ( 84% copper, 12% manganese and 4% nickel). Conventional shunts are typically simple, one-directional conductors with known resistance, but they cannot operate continuously at high currents for extended periods due to thermal limitations. The resistive section generates heat during operation, leading to resistance changes: for Manganin, thermal drift begins at 80∘C, becomes significant at 120∘C, and permanent damage occurs at 140∘C. Moreover, high-current operation produces unwanted electromagnetic fields, which can adversely affect nearby electronic systems. Multi-material additive manufacturing allows the integration of Cu-ETP and Manganin in a single component, optimizing electrical, thermal, and structural performance while potentially reducing assembly complexity, material waste, and overall environmental impact.
In Fig. 1(a), the conventionally manufactured shunt resistor BAX-Z-L005-01-5.0 by Isabellenhuette USA is shown, whereas Fig. 1(b) presents the 3D-printed multi-material demonstrator fabricated by IGCV, along with four rectangular witness specimens.

[picture]

Fig. 1. Schematic representation of the fabricated Shunt Resistor; (a) conventional, and (b) IGCV 3D-printed.

Fig. 1. Schematic representation of the fabricated Shunt Resistor; (a) conventional, and (b) IGCV 3D-printed.

Manufacturing Processes.

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

As far as the manufacturing processes of the examined use-cases are concerned, the manufacturing stages of the analysed demonstrators are categorized into three subprocesses, as outlined below, to facilitate a more detailed examination of each process. This approach enables the identification of cost-generating activities and the calculation of energy consumption, material usage, labour, total manufacturing costs, and carbon footprint. To this end, the whole manufacturing process and activities for the construction of the metal multi-material conventional and AM components was identified and categorized into the following three ( 3 ) main sub-processes:
a) Sub-process 1: preparation of the materials, environment and computer systems,
b) Sub-process 2: main process/conventional or 3D-printing fabrication of the component, and
c) Sub-process 3: post process including quality evaluation, inspection and cleaning.

As far as conventional manufacturing is concerned, the preparation stage includes all activities carried out before the main processing of the AVL shunt resistor, focusing on readying the Cu-ETP and Manganin materials for welding and stamping. The raw materials are first pre-processed, after
which the two copper side bars and the Manganin strip are loaded and fixed in a jig, and the welding area is machined. The preparation phase concludes with washing the part in a conveyor-type cleaning machine. The main processing stage covers the welding, stamping and formation of the final product: the three components are welded along their full-length using electron-beam welding, the individual shunts are stamped out, and a second washing cycle is performed in batch mode. In the postprocessing stage, product quality is evaluated by sampling 1% of the lot for 3D dimensional measurement, while all parts undergo end-of-line resistance testing, marking, and visual inspection to detect flaws. The process concludes with the final packing of the components.

For the additive manufacturing route, the preparation stage encompasses all activities required before printing, including the preparation of the digital build data, the conditioning and inspection of the powder materials, and the setup and calibration of the machine. The main processing stage consists of five successive build jobs-Cu-Manganin-Cu-Manganin-Cu-each printed with a small layer thickness and separated by full machine cleaning cycles, with four rectangular witness specimens fabricated alongside the demonstrator. Finally, post-processing activities include removing the printed part from the build plate, de-powdering and cleaning the component, and cutting operations to separate or finish functional sections, completing the AM manufacturing workflow.

Cost estimation Relationships (CERs) and Analysis

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

In this section, the methodology used for the economic evaluation of the AVL-IGCV components manufacturing processes is presented. The total manufacturing cost per sub-process consists of energy consumption, material, labour cost, as well as depreciation of used fixed asset.

Energy consumption (EC).

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

The relationship used for the energy consumption cost is as follows:

ECx= Set Up Cost + Operationg Cost =(ICx·PCx·TSUx·CE)+(ICx·PCx·Tx·CE),(1)

where ICx is the rated power (installed capacity) of the machine, PCx denotes the power utilization (capacity range) percentage of the machine during the specific sub-process, TSUx signifies the per unit initial processing operating time of the machine (measured in hours), Tx represents the per unit operating time during the specific sub-process (in hours), x denotes the activity examined and CE is the cost of electricity (in €/kWh ).

Material Cost (MC).

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

The material cost pet unit is equal to:

MCx=∑i=1k{Wx·PMi}(2)

where Wx is the weight of the material waste lost due to agglomeration and loss of shape (in kg ), x denotes the activity examined and PMi is the purchase price of the material i (in €/kg ).

Labor Cost (LC).

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

The formula for the labour cost entails the aggregation of the labour cost attributed to the set-up technician, alongside the labour cost associated with skilled and unskilled workers (with consideration given to the inclusion of all skilled-level workers within a given sub-process):
LCx= Set Up Cost + Operationg Cost =(TSUx·CHSUT)+[(TUWx·CHUW+TSWx·CHSW)],
where TSUx,TUWx and TSWx are the set-up technician's/ unskilled / skilled worker's operating times (in hours) respectively, x denotes the activity examined and CHSUT,CHUW and CHSW represent the labour cost per hour required for the production of one unit (measured in €/h ), contingent upon the level of skills involved (Set-Up Technician, Unskilled and Skilled Worker).

Rental Cost (RC).

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

The rental cost of the examined activity x is calculated based on the relationship:

RCx=SQMx·Rsqm·12APR,(4)

where SQMx are the square meters used in the activity x,Rsqm is the rent cost per month for each square meter, multiplied by 12 in order to estimate the yearly cost and divided by the APR annual production rate (in units), in order to have an estimation for the cost per unit.

Depreciation Cost (DC).

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

The depreciation charge of fixed equipment is calculated as follows:

DCx=DCEQUIPxDPx·APR(5)

where DCEQUPx is the investment-depreciation cost of the machine-equipment (in €/ year) used in the activity x,DPx is the depreciation period (in years) of the equipment and APR is the annual production rate (in units), in order to have an estimation for the cost per unit.

Machine Variable Cost (MVC).

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

The per unit machine variable cost is calculated as:

MVCx=MVEQUIPxAPR(6)

where MVEQUIPx is the variable cost of the machine-equipment (in €/ year) used in the activity x and APR is the annual production rate (in units).

Total Manufacturing Cost (TC).

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

The total manufacturing cost (TC) of the activity x is the sum of all previous costs, Eq. 1-6, given as follows:

TCx=(ECx+MCx+LCx+RCx+MVCx+DCx)(7)

It should be noted at this point that the cost model additionally incorporates material overheads (e.g., consumables; material procurement; incoming-goods handling, inspection, and storage/inventory management) and manufacturing overheads (e.g., indirect personnel; general equipment; auxiliary operating supplies; ancillary materials). Nevertheless, a more detailed disaggregation of these overhead categories is not presented herein for reasons of scope and focus.

Conventional and Additive Manufacturing Demonstrators Comparison

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

Weight comparison results.

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

By directly comparing the two shunt resistors (conventional and 3D-printed) in terms of structural weight, Table 1 shows that there is a weight increase in the innovative AM multi-material technology case.

Table 1. Structural weight comparison of the fabricated shunt resistors.

| Conventional [g] | Additive Manufactured [g]
Manganin | 5 | 13
Cu-ETP | 100 | 120
Total | 105 | 139

Cost analysis results.

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

A cost comparison between the conventional and AM demonstrators is performed for both the individual cost categories and the corresponding sub-processes, based on the equations and methodology presented in the previous section (Eqs. 1-7). The sub-process cost breakdown for the shunt resistor components is summarized in Table 2 and illustrated in Fig. 2, while a comparison of the aggregated individual cost categories is provided in Table 3.

Table 2. Comparison of the individual sub-processes' costs of the conventional and 3D-printed shunt resistor components (*CAI = Cost Amount Indicator).

| Conventional | Additive Manufacturing
| CAI* (-) | Weight (%) | CAI* (-) | Weight (%)
SP1 | 2.25 | 32.20 % | 5.90 | 16.10 %
SP2 | 1.95 | 27.92 % | 24.97 | 68.09 %
SP3 | 0.92 | 13.12 % | 1.71 | 4.65 %
Overheads | 1.87 | 26.77 % | 4.09 | 11.16 %
Total | 6.98 | 100.00 % | 36.67 | 100.00 %

Table 3. Comparison of individual category costs of the conventional and IGCV 3D-printed demonstrator (*CAI= Cost Amount Indicator).

Category | Conventional CAI* [-] | Additive Manufacturing CAI* [-]
D (depreciation) | 0.97 | 14.85
E (energy) | 0.37 | 2.34
L (labour) | 1.78 | 7.01
M (materials) | 1.58 | 2.38
MV (machine variable) | 0.38 | 4.67
O (overheads) | 1.87 | 4.09
R (rental) | 0.04 | 1.33
Total | 6.98 | 36.67

[picture]

Fig. 2. Schematic Comparison of the individual sub-processes' costs of the conventional and 3Dprinted shunt resistor demonstrators (*CAI 44% Cost Amount Indicator).

Fig. 2. Schematic Comparison of the individual sub-processes' costs of the conventional and 3Dprinted shunt resistor demonstrators (*CAI 44 % Cost Amount Indicator).

As observed in Tables 2 and 3, and clearly illustrated in Fig. 2, the main sub-process (SP2) exhibits a significantly higher contribution in the AM case. This behaviour is primarily attributed to the substantially longer cycle times of the 3D-printing process at laboratory (or semi-industrial) scale. Consequently, both labour and energy costs are considerably increased for the AM demonstrator.

Moreover, in the conventional manufacturing case, the same production equipment is utilized at industrial scale for multiple products, and therefore depreciation, machine-related variable costs, and rental costs are distributed across a broader production volume. In contrast, for the laboratory-scale 3D-printed shunt resistor, these costs are allocated exclusively to a single demonstrator. This allocation significantly amplifies the corresponding cost categories, leading to a pronounced increase in the overall AM cost. It is highly expected that with the development of multi-functional, higherdimension machines, increased automation etc. the manufacturing cost will be probably greatly reduced. Additionally, due to their higher efficiency, it is expected that the production time will be greatly decreased by the cooldown time between layers (currently 2-minute minimum time per layer) essentially affecting labour cost.

Carbon footprint assessment results.

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

Preliminary results of a gate-to-gate approach show that the 3D-printing process produces higher equivalent carbon emissions when compared to the conventional manufacturing process. The latter results since the majority ( 99 %) of equivalent carbon emissions come from the key process of additive manufacturing, Figure 3. More specifically, the production duration of build job is increased due to the cooldown time between layers resulting to the increase of energy consumption.

[picture]

Fig. 3. Carbon footprint (gate to gate) for key manufacturing activities (single demonstrator).

Fig. 3. Carbon footprint (gate to gate) for key manufacturing activities (single demonstrator).

As shown in Figure 4, Sub-Process 2 (SP2) produces the highest carbon footprint emissions for both, conventional as well as the additive manufactured demonstrator. This is since the main production processes are included in SP2. More specifically, the conventional manufacturing SP2 accounts for approximately 10.3390/ma10080876 of the total equivalent carbon emissions whereas the Additive manufacturing SP2 is responsible for over 99 % of the total emissions. The results reveal that Additive manufacturing is more energy-intensive resulting in almost four times ( x 4 ) higher equivalent carbon dioxide emissions.

Overall, from environmental point of view, the additive manufacturing demonstrator appears mostly energy intensive when compared to the conventional manufactured demonstrator. Yet, it is worth investigating and comparing the equivalent carbon emissions that results from the total life cycle of the end-product (cradle to grave), i.e. from raw materials until recycling.

[picture]

Fig. 4. Comparison of Conventional Manufacturing versus Additive Manufacturing carbon footprint (gate to gate) per Sub process.

Fig. 4. Comparison of Conventional Manufacturing versus Additive Manufacturing carbon footprint (gate to gate) per Sub process.

Summary

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

This article investigated the application of multi-material additive manufacturing (AM) for an automotive shunt resistor demonstrator combining Manganin and Cu-ETP alloys, with a systematic comparison against a conventionally manufactured counterpart. The analysis focused on key performance indicators including weight, manufacturing cost, energy consumption, production time, and carbon footprint, using Activity-Based Costing (ABC) and Life Cycle Assessment (LCA) methodologies.

The results indicate that the AM demonstrator exhibits a higher total weight compared to the conventional shunt resistor, primarily due to the current design constraints and process requirements at laboratory scale. From an economic perspective, the total manufacturing cost of the AM component is substantially higher. This increase is mainly driven by the main manufacturing sub-process (SP2), where extended cycle times inherent to laboratory-scale multi-material 3D printing led to significantly increased labour and energy costs. In addition, depreciation, machine-related variable costs, and rental costs are disproportionately allocated to a single AM demonstrator, in contrast to conventional industrial-scale production where such costs are distributed across high production volumes.

Similarly, the carbon footprint of the AM demonstrator is higher, reflecting the energy-intensive nature of metal additive manufacturing processes and the limited economies of scale at laboratory implementation. These findings highlight that direct comparisons between laboratory-scale AM, and industrial-scale conventional manufacturing inevitably disadvantage AM in terms of cost and CO2 emissions.

Despite these results, the study demonstrates that multi-material AM offers important system-level advantages that are not fully captured by unit cost and footprint metrics alone. The integration of Manganin and copper within a single component enables enhanced electrical, thermal, and functional performance, reduced assembly complexity, and new design freedoms that are unattainable with conventional manufacturing routes. These benefits are particularly relevant for advanced automotive
and electrification applications, where performance optimization, functional integration, and lightweight design play a critical role.

Future work will focus on scaling the AM process to an industrial production environment and performing a like-for-like comparison with conventional manufacturing at equivalent production volumes. Further optimization of process parameters, build strategies, and material interfaces is also expected to reduce cycle times, energy consumption, and overall environmental impact. Under such conditions, multi-material AM has the potential to become a competitive and sustainable manufacturing solution for next-generation automotive components.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

The authors gratefully acknowledge the financial support of the HORIZON Research and Innovation Actions, European Health and Digital Executive Agency for the implementation of the project «MULTI-MATERIAL DESIGN USING 3D PRINTING» having an acronym "MADE-3D" of the act HORIZON-CL4-2022-RESILIENCE-01 with Grant Agreement code 101091911.

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The original version of this paper is available on https://www.scientific.net/KEM.1047.373.pdf

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 383-389

doi: 10.4028/p-K6i0V2

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-23

Accepted:
2026-02-13

Online:
2026-04-13

Resistance to Fracture of Additively Manufactured Aluminium Alloy Alsi10Mg under Plane Stress Conditions

The original version of this paper is available on https://www.scientific.net/KEM.1047.383.pdf

loannis Goulas 1,2,a*, Alexis Kermanidis 2,b, Leonard Alberty 3,c, Ismail Ünsal 3,d, Georg Schlick 3,e, Sven Gründer 4,f, Dennis Lenhert 4, g, Florian Hengsbach 4, h and Nikolaos D. Alexopoulos 1,i

1 University of the Aegean, School of Engineering, Department of Financial and Management Engineering, Research Unit of Advanced Materials, 41 Kountouriotou str, 82132, Chios, Greece

2 University of Thessaly, Department of Mechanical Engineering, Laboratory of Mechanics and Strength of Materials, Volos, 38334, Greece

3 Fraunhofer-Institut für Gießerei-, Composite- und Verarbeitungstechnik IGCV, Am

Technologiezentrum 10, 86159 Augsburg

4 Universität Paderborn, Chair of Automotive Lightweight Design (LiA), Warburger Straße 100, 33098 Paderborn, Germany

a  ioagoulas@uth.gr, b  akermanidis@mie.uth.gr, cleonard.alberty@igcv.fraunhofer.de, dismail.uensal@igcv.fraunhofer.de, e  georg.schlick@igcv.fraunhofer.de, f gruender@lwk.uni-paderborn.de, 9 dennis.lehnert@uni-paderborn.de, h  hengsbach@lwk.uni-paderborn.de, inalexop@aegean.gr

Keywords: laser powder bed fusion, additive manufacturing, fracture toughness, AISi10Mg.

Abstract

Additive manufacturing gains ground on the production of high-precision metallic components with varying thickness. As the material thickness alters in the various locations of the product, it is eminent that the material mechanical properties might vary. In the present contribution, a preliminary study was performed to investigate the resistance to fracture of additively manufactured AlSi 10 Mg material with varying thicknesses. To this end, fracture toughness specimens of compact tension geometry with varying thicknesses from 3 to 15 mm were additively manufactured, machined and tested. The results showed that with increasing the specimen thickness, critical stress intensity factor Kcr decreases gradually from 38MPam up till 31MPam for the lower and higher investigated thicknesses, respectively. Finally, it was noticed that even the 15 mm thickness (higher investigated) does not satisfy the plane strain fracture mechanism and therefore all investigated specimens were in plane-stress condition.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.383.pdf

The development of metallic components with complex geometry and improved functional integration has been made feasible by additive manufacturing (AM), and laser powder bed fusion (PBF-LB/M). As a result, AM is becoming a growing necessity in automotive and aerospace applications, where geometric complexity, material efficiency, and lightweight design are crucial. In such application instances, components are exposed to different loading conditions, which can have a substantial impact on their durability and structural performance [1]. Consequently, evaluating structural integrity and establishing safe operation efficiency depend on the characterization of fracture behaviour.

AlSi 10 Mg is currently one of the most popular aluminium alloys processed by PBF-LB/M because of its great strength-to-weight ratio and light weight. These features have prompted a great deal of research on AlSi10Mg produced by PBF-LB/M, with a primary focus on microstructural characteristics, tensile properties and fatigue performance. For example, Toprak et al. [2] demonstrated that the ultimate tensile strength of AlSi10Mg produced using PBF-LB/M is extremely sensitive to processing factors, with scan speed being a crucial variable that accounts for over 80% of the variation in ultimate tensile strength. By combining two AM processes (PBF-LB/M and direct energy deposition-DED), Gong et al. [3] indicated that, in comparison to the DED region, the finer

PBF-LB/M microstructure results in increased mechanical strength but decreased tensile ductility, underscoring the significant reliance of mechanical outcome on process-induced microstructural characteristics. Internal defects resulting from PBF-LB/M process conditions critically affect tensile mechanical performance and respective failure mechanisms. Furthermore, Wu et al. [4] examined the effectiveness of loss-of-fusion defects in AlSi 10 Mg and showed that tensile ductility and failure mode are strongly influenced by defect orientation with respect to the build direction. In their investigation, Kramer et al. [5] studied two different pore types in PBF-LB/M AlSi10Mg and noticed that irregular lack-of-fusion pores slightly reduce ultimate tensile strength ( ~7.5% ), while spherical keyhole pores have a milder impact and can significantly increase fatigue strength ( ~33% ) relative to oxide pores. By comparing different orientations, heat and surface treatments, Fini et al. [6] proved that T6-aged and shot-peened specimens attained the higher fatigue life among the reference material and the other experimental groups, underscoring the effect of post-processing. By combining build orientations and aging condition, Lupi et al. [7] demonstrated that fracture toughness of PBF-LB/M AlSi10Mg is strongly controlled by crack path relative to melt pool boundaries and found that fracture toughness is significantly reduced when crack propagation follows melt pool boundaries, especially track-track interfaces.

Despite a notable body of work addressing monotonic and cyclic mechanical behaviour, the resistance to fracture performance of PBF-LB/M -manufactured AlSi10Mg remains comparatively less explored. Specifically, there are few systematic fracture mechanics studies using standardized specimens, and the impact of specimen shape and constraining conditions on fracture resistance has not been adequately examined. The present work seeks to address this literature gap by experimentally investigating the fracture behaviour of PBF-LB/M AlSi10Mg using compact tension, C(T), specimens. Thickness effects on fracture resistance are examined by evaluating the critical stress intensity factor Kcr for specimens of different thickness under plane stress conditions.

Material and Experimental Procedure

The original version of this paper is available on https://www.scientific.net/KEM.1047.383.pdf

The EN AC-43000 (AlSi10Mg) aluminium alloy was selected as the matrix material. This cast alloy, which is precipitation-hardenable, demonstrates favourable electrical conductivity and strong chemical resistance in corrosive conditions (DIN EN 1706:2013). Its outstanding compatibility with laser powder bed fusion (PBF-LB/M) and high specific strength make AlSil0Mg the predominant aluminium alloy in this additive manufacturing technique (SLM Solutions, 2019). The study utilized powder from SLM Solutions Group AG, with a particle size distribution of +20/−63μ m.
Fig. 1(a) shows a sketch with the geometrical dimensions of the fracture toughness specimen that were exploited in the present investigation; the dimensions were according to the ASTM E561 standard for fracture toughness testing of metallic materials. Additionally, Fig. 1(b) and 1(c) shows images of the different thicknesses specimens that were mechanically tested for the present investigation. Initially, the material was printed in the 0∘ build job direction in cubes 60 mm×60 mm and the different thickness fracture toughness specimens (namely 3.0, 5.0, 10.0 and 15.0, respectively) were machined from the printed cubes with Electron Discharge Machining (EDM) method that does not impose any damage to the material for further characterization.

[picture]

Fig. 1. (a) Drawing of the fracture toughness specimen and images of the machined specimens of (a) high thickness and (c) low thickness of the additively manufactured AlSi10Mg specimens printed at upright ( 0∘ ) building direction.

Fig. 1. (a) Drawing of the fracture toughness specimen and images of the machined specimens of (a) high thickness and (c) low thickness of the additively manufactured AlSi10Mg specimens printed at upright ( 0 ∘ ) building direction.

(a)

Fracture resistance tests were conducted on a uniaxial, servo-hydraulic Instron 8801 machine (Fig. 2), equipped with a loadcell of 100 kN according to ASTM E561 standard. The test consists of two (2) distinct steps: (a) constant amplitude fatigue test in order to form a physical crack from the machined notch and then (b) quasi-static tension to propagate the crack in order to find the critical load value for unstable crack growth. Fatigue pre-cracking was performed on Compact Tension C(T) specimens under cyclic loading at a stress ratio of R=0.1, until a fatigue pre-crack length of approximately 4.0−5.0 mm was achieved. The tests were conducted under force control, with fatigue load levels ranging from 1 to 3.5 kN depending on specimen thickness ( 3 to 15 mm ), selected to avoid large-scale plastic deformation ahead of the crack tip. Following fatigue pre-cracking, a second experimental stage was conducted to determine the critical stress intensity factor. This stage involved monotonic loading under displacement control, with a constant crosshead rate of 0.2 mm/min. The experiment was stopped after substantial crack propagation, when the axial load decreased to values below 1 kN as a consequence of specimen fracture.

[picture]

Fig. 2. (a) Image of the fracture toughness specimen clamped at the loading frame just before testing and (b) higher magnification image of a typical fractured specimen showing the fracture path of 5.0 mm thickness additively manufactured AlSi10Mg specimen.

Fig. 2. (a) Image of the fracture toughness specimen clamped at the loading frame just before testing and (b) higher magnification image of a typical fractured specimen showing the fracture path of 5.0 mm thickness additively manufactured AlSi10Mg specimen.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1047.383.pdf

The results of the performed fracture toughness tests can be found in Fig. 3, where the applied load - crack mouth opening displacement curves for different thickness can be found. Generally, the precrack length plays a significant role on the maximum load of the test, as the high pre-crack tested showed significant lower peak load value of approximate 1.8 kN .

[picture]

Fig. 3. (a) Typical force-crack opening displacement curves for the different thicknesses investigated of additively manufactured AlSi10Mg specimens.

Fig. 3. (a) Typical force-crack opening displacement curves for the different thicknesses investigated of additively manufactured AlSi10Mg specimens.

The evaluation of critical stress intensity factor was performed according to ASTM E561, while the applied stress intensity factor range was defined based on compliance with the Linear Elastic Fracture Mechanics (LEFM) conditions. These curves provide a method to estimate the material's fracture toughness during stably crack extension under increasing applied load. According to the respective standard ASTM E561, the resistance curve can be matched with the applied stress intensity factor K curves to estimate the mechanical load necessary to cause unstable crack propagation at the critical stress intensity factor Kcr value. The resistance-to-crack curves, which are represented by the crack extension (CMOD in the present case) at the maximum load, differ for the various investigated cases. It can be noticed (Fig. 4) that Kcr varies significant for the lower thickness of 3.0 mm (plane stress condition) against the respective of higher thickness of 5.0 mm (plain strain) by 38.27MPam and 29.0MPam (average), respectively.

[picture]

Fig. 4. Resistance curves of the fracture toughness specimens printed with monomaterial AlSi 10 Mg (a) 5 mm thickness and 9 mm pre-crack, (b) 5 mm thickness and 5 mm pre-crack, (c) 3 mm thickness and 4 mm pre-crack and (d) 5 mm thickness and 5 mm pre-crack.

Fig. 4. Resistance curves of the fracture toughness specimens printed with monomaterial AlSi 10 Mg (a) 5 mm thickness and 9 mm pre-crack, (b) 5 mm thickness and 5 mm pre-crack, (c) 3 mm thickness and 4 mm pre-crack and (d) 5 mm thickness and 5 mm pre-crack.

The so far available fracture toughness results can be found in Fig. 5, where they are being illustrated with the specimen thickness as variable parameter. The results show that the lower thickness specimen has the highest Kcr value as expected due to plane stress conditions. The results show that with increasing specimen's thickness, Kcr decreases gradually uptill 31MPam for the higher investigated thickness in a possibly linear manner. Excemption seems to be the 5.0 mm thickness specimens, where the 25,58MPam value is attributted to the almost double pre-cracking that the baseline specimens and should be excluded in the following. Scanning electron microscopy will be applied to assess the fracture surfaces cracking mechanism of 5.0 mm thickness, while more tests will be performed to get an reliable, average value. Please also take into account that the 15 mm
thickness does not satisfy the plane strain fracture mechanism and therefore all investigated specimens are in plane-stress condition, i.e. the stress field at the notch tip involves only the x,y components of stress and the material can deform freely in the thickness direction only.

[picture]

Fig. 5. Fracture toughness test results for the different thicknesses investigated of additively manufactured AlSi 10 Mg specimens.

Fig. 5. Fracture toughness test results for the different thicknesses investigated of additively manufactured AlSi 10 Mg specimens.

Conclusion

The original version of this paper is available on https://www.scientific.net/KEM.1047.383.pdf

The present investigation focuses on the effect of thickness on the fracture toughness of additively manufactured AlSi 10 Mg specimens. The results show that with increasing specimen's thickness, critical stress intensity factor Kcr decreases gradually from 38MPam uptill 31MPam for the lower and higher investigated thicknesses, respectively. Scanning electron microscopy will highlight the prevailing fracture mechanism of the performed tests as well as to explain any inconcistencies on the results of the present experimental protocol.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/KEM.1047.383.pdf

The authors gratefully acknowledge the financial support of the HORIZON Research and Innovation Actions, European Health and Digital Executive Agency for the implementation of the project «MULTI-MATERIAL DESIGN USING 3D PRINTING» having an acronym "MADE-3D" of the act HORIZON-CL4-2022-RESILIENCE-01 with Grant Agreement code 101091911.

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.383.pdf

Zhao L, Song L, Santos Macías JG, Zhu Y, Huang M, Simar A, et al. Review on the correlation between microstructure and mechanical performance for laser powder bed fusion AlSi10Mg. Addit Manuf 2022;56. https://doi.org/10.1016/j.addma.2022.102914.

Toprak İB, Dogdu N. Optimization of tensile strength of AlSi10Mg material in the powder bed fusion process using the Taguchi method. Sci Rep 2024;14. .

Gong J, Wei K, Liu M, Song W, Li X, Zeng X. Microstructure and mechanical properties of AlSi 10 Mg alloy built by laser powder bed fusion/direct energy deposition hybrid laser additive manufacturing. Addit Manuf 2022;59. .

Wu Z, Wu S, Gao X, Lin Y, Xue Y, Withers PJ. The role of internal defects on anisotropic tensile failure of L-PBF AlSi10Mg alloys. Sci Rep 2023;13. .

Kramer S, Wexel H, Purwitasari A, Jarwitz M, Schulze V, Zanger F. Impact of different pore types on the tensile and fatigue properties of AlSi10Mg parts produced by laser powder bed fusion. Progress in Additive Manufacturing 2025;10:11305-17. .

Fini S, Croccolo D, De Agostinis M, Olmi G, Paiardini L, Scapecchi C, et al. Fatigue response of AlSi10Mg by laser powder bed fusion: influence of build orientation, heat, and surface treatments. Progress in Additive Manufacturing 2025;10:1385-403. .

Lupi G, Minerva G, Patriarca L, Casati R, Beretta S. Fracture toughness of AlSi10Mg alloy produced by LPBF: effects of orientation and heat treatment. Int J Fract 2024;247:329-44. .

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Key Engineering Materials, ISSN: 1662-9795, Vol. 1047, pp 391-396

doi: 10.4028/p-bVf40C

© 2026 The Author(s). Published by Trans Tech Publications Ltd, Switzerland.

Submitted:
2026-01-23

Accepted:
2026-02-11

Online:
2026-04-13

Effect of CuCr1Zr Cross-Contamination Level on Work Hardening Characteristics of AISi 10Mg Additive Manufactured Material

The original version of this paper is available on https://www.scientific.net/KEM.1047.391.pdf

Ioanna Giavrouta 1,a*, Max Horn 2,b, Leonard Alberty 2,c, Ismail Ünsal 2,d, Georg Schlick 2,e and Nikolaos D. Alexopoulos 1,f

1 University of the Aegean, School of Engineering, Department of Financial and Management Engineering, Research Unit of Advanced Materials, 41 Kountouriotou str, 82132, Chios, Greece

2 Fraunhofer-Institut für Gießerei-, Composite- und Verarbeitungstechnik IGCV, Am Technologiezentrum 10 | 86159 Augsburg

a* fme21021@fme.aegean.gr, b max.horn@igcv.fraunhofer.de, c leonard.alberty@igcv.fraunhofer.de,

Keywords: additive manufacturing, metallic multi-material 3D printing, work-hardening.

Abstract

Cross-contamination occurring after the blending of metal powders in multi-material laser powder bed fusion (PBF-LB/M) is a frequent manufacturing issue and poses a major obstacle to the further development of this emerging additive manufacturing process. To evaluate the influence of such contamination on a technologically important material system for multi-material PBF-LB/M, this study investigates the impact of CuCr 1 Zr foreign particle contamination within AlSil 0 Mg powder on the resulting metallurgical characteristics and mechanical performance of the fabricated parts. Several contamination levels of CuCr 1 Zr were considered, namely 0.5wt.%,3.0wt.% and 5.0 wt.%, with the results benchmarked against samples produced from uncontaminated powder. Tensile testing demonstrated that the cross-contamination contribute to material embrittlement. This investigation focuses on the tensile work-hardening behaviour of the investigated materials showcasing that the specimens exhibit only the first two work-hardening stages, while for the higher contamination level studied, the material becomes brittle and fractures even in the first work hardening stage.

Introduction

The original version of this paper is available on https://www.scientific.net/KEM.1047.391.pdf

Multi-material laser powder bed fusion (PBF-LB/M) is gaining recognition as a promising additive manufacturing method that addresses the rising need for high-precision metallic components, while minimizing production times and enhancing sustainability. This technique proves particularly valuable for aerospace and automotive sectors, where material diversity and the fabrication of intricate geometries are critical requirements. However, powder cross-contamination continues to pose a primary obstacle to its widespread industrial implementation.

Even minimal contamination levels, especially involving dissimilar metals, can cause significant deterioration in mechanical properties, structural integrity, and functional performance of the produced components. In automotive applications, bi-metallic additive manufacturing presents unique challenges, commonly requiring the integration of aluminium alloys with chromium-zirconium-copper alloys.

This present investigation focuses on the impact of CuCr 1 Zr particle contamination on AlSi 10 Mg powder feedstock. Previous studies on additively manufactured AlSi 10 Mg have highlighted the critical role of the cellular microstructure and melt pool boundaries in governing strain hardening and mechanical performance [1]. Approaches based on retaining the AlSi10Mg cell network through direct aging have been shown to offer advantages compared to conventional T6 heat treatment, which relies primarily on precipitation strengthening [2]. Prior research has established that powder contamination modifies the microstructure of additively manufactured components and adversely affects critical mechanical properties, including tensile strength, ductility, and fatigue resistance [3]. The presence of copper-based contaminant particles is expected to alter strain hardening characteristics through local heterogeneities, modified load transfer mechanisms and potential stress
concentration sites. Work hardening constitutes a critical mechanical response, as it governs the material's ability to accommodate plastic deformation and directly affects strength, ductility and damage tolerance [4]. Variations in work hardening behaviour are often indicative of underlying changes in microstructural mechanisms such as dislocation density, phase distribution and interfacial interactions induced by foreign particle incorporation.

Building upon the considerations, the present study specifically focuses on the work hardening behaviour of AlSil 0 Mg components fabricated via PBF-LB/M and the influence of CuCr 1 Zr powder cross-contamination on this phenomenon. By analysing the evolution of work hardening parameters derived from tensile testing, this study aims to elucidate the role of powder cross-contamination in governing plastic deformation mechanisms and overall mechanical performance of PBF-LB/M fabricated components.

Experimental Procedure

The original version of this paper is available on https://www.scientific.net/KEM.1047.391.pdf

The aluminium alloy EN AC-43000 ( AlSil 0 Mg ) served as the matrix material. This precipitationhardenable cast alloy exhibits good electrical conductivity and high chemical resistance in corrosive environments (DIN EN 1706:2013). Owing to its excellent compatibility with laser powder bed fusion (PBF-LB/M) and its high specific strength, AlSi10Mg represents the most utilized aluminium alloy in this additive manufacturing process (SLM Solutions, 2019). Powder supplied by SLM Solutions Group AG, featuring a particle size distribution of +20/−63μ m, was employed in this study.

The copper alloy CW106C(CuCr1Zr) was chosen as the contaminant material. Alloying copper with chromium (Cr) and zirconium (Zr) enhances mechanical strength, thermal stability, and wear resistance while substantially maintaining high electrical and thermal conductivity (German Copper Alliance, 2005). The CuCr 1 Zr powder, with a particle size range of +20/−45μ m, was provided by Schmelzmetall GmbH. Particle size distributions for both powders were characterized using a Mastersizer 3000 (Malvern Panalytical Ltd.).

Tensile tests were conducted to evaluate the mechanical behaviour of AlSi10Mg specimens fabricated by multi-material PBF-LB/M with different degrees of CuCr 1 Zr powder crosscontamination. Cylindrical specimens were manufactured according to DIN 50125 - B4 x 20 standard with 4 mm diameter at the reduced cross-section. During mechanical testing, axial load and axial deformation were continuously recorded, allowing the calculation of engineering stress-strain curves for each specimen. The recorded engineering stress-strain data were subsequently converted into true (nominal axial) stress-strain curves to account for the continuous reduction of the cross-sectional area during plastic deformation. This conversion enabled a more accurate determination of the conventional yield stress and ultimate tensile strength, as well as a more representative description of the material response under tensile loading. Conventional yield stress was determined using the 0.2% offset method, while the ultimate tensile strength was defined as the maximum stress reached prior to fracture. The stress-strain data obtained up to uniform elongation were further used for the evaluation of work hardening behaviour.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/KEM.1047.391.pdf

Fig. 1 presents typical nominal axial tensile stress-strain curves of additively manufactured AlSi 10 Mg specimens fabricated in the upright ( 0∘ ) build direction for different levels of CuCr 1 Zr powder cross-contamination. Specimens containing 3wt.% and 5wt.%CuCr1Zr display a noticeably steeper stress-strain response and premature fracture, indicating increasing material embrittlement induced by powder cross-contamination.

[picture]

Fig. 1. Nominal axial tensile stress-strain curves of additively manufactured AlSil 0 Mg specimens printed at upright ( 0∘ ) building direction for different levels of CuCr1Zr cross-contamination.

Fig. 1. Nominal axial tensile stress-strain curves of additively manufactured AlSil 0 Mg specimens printed at upright ( 0 ∘ ) building direction for different levels of C u C r 1 Z r cross-contamination.

The influence of contamination level on the conventional yield stress ( Rp0.2% ) and ultimate tensile strength ( Rm ) is summarized in Fig. 2a and 2b, respectively. A moderate increase in conventional yield stress is observed with increasing CuCr 1 Zr contamination. In contrast, the ultimate tensile strength shows a decreasing trend ( 15% maximum decrease) for the higher investigated contamination levels.

[picture]

Fig. 2. (a) Effect of different wt.% CuCr 1 Zr cross-contamination on the conventional yield stress Rpo,2% and (b) ultimate tensile strength Rm of additively manufactured AlSi10Mg specimens printed at upright (0∘) building direction.

Fig. 2. (a) Effect of different wt.% CuCr 1 Zr cross-contamination on the conventional yield stress R p o , 2 % and (b) ultimate tensile strength R m of additively manufactured AlSi10Mg specimens printed at upright ( 0 ∘ ) building direction.

The work hardening behaviour of uncontaminated additively manufactured AlSi 10 Mg specimens is illustrated in Fig. 3. It is characterized by the presence of two distinct work hardening stages and the slopes gives the respective strain hardening exponents nI and nII. For low and moderate levels of CuCr 1 Zr powder cross-contamination, both stages are clearly identifiable between the conventional yield point and the onset of necking. However, at high contamination levels, the material exhibits pronounced brittle behaviour, and fracture occurs prematurely during the first work hardening stage, preventing the development of the second stage.

[picture]

Fig. 3. Image showing the evaluation of the work hardening behaviour, showcasing the work hardening stages and strain hardening exponents ( nI and nII ) from conventional yield stress point ( Rp0,2% ) till necking point ( Rm ) for additively manufactured AlSi10Mg specimen printed at upright ( 0∘ ) building direction without CuCr 1 Zr cross-contamination.

Fig. 3. Image showing the evaluation of the work hardening behaviour, showcasing the work hardening stages and strain hardening exponents ( n I and n I I ) from conventional yield stress point ( R p 0 , 2 % ) till necking point ( R m ) for additively manufactured AlSi10Mg specimen printed at upright ( 0 ∘ ) building direction without CuCr 1 Zr cross-contamination.

Fig. 4a summarizes the dependence of the strain hardening exponents nI and nII on CuCr 1 Zr contamination level. The strain hardening exponent nI, associated with stage I, increases systematically from approximately 0.18 for uncontaminated material and rises in an almost linear manner up to about 0.22 for the highest CuCr 1 Zr cross-contamination level. In contrast, nII exhibits a decreasing tendency with increasing contamination, indicating a reduced capacity for sustained plastic deformation at higher strain levels. Similar results were noticed [5-7] for contaminated and non-contaminated PBF-LB/M -fabricated AlSi10Mg material.

The relative contribution of each work hardening stage to the total uniform elongation is shown in Fig. 4b. For low contamination levels, both work hardening stages contribute significantly to the overall plastic deformation. However, as the CuCr 1 Zr contamination increases, the contribution of stage II diminishes markedly, and for the highest contamination level, fracture occurs predominantly during stage I. This behaviour confirms that high levels of powder cross-contamination promote premature failure.

[picture]

Fig. 4. (a) Dependence of strain hardening exponents nI and nII from stage I and stage II, respectively as well as (b) relative contribution of the work hardening stages to uniform elongation for different CuCr 1 Zr cross-contamination in AlSi 10 Mg tensile specimens at 0∘ building direction.

Fig. 4. (a) Dependence of strain hardening exponents n I and n I I from stage I and stage II, respectively as well as (b) relative contribution of the work hardening stages to uniform elongation for different CuCr 1 Zr cross-contamination in AlSi 10 Mg tensile specimens at 0 ∘ building direction.

Overall, the results demonstrate that CuCr 1 Zr powder cross-contamination has a pronounced effect on the tensile and work hardening behaviour of PBF-LB/M -fabricated AlSi10Mg. While higher contamination levels severely compromise tensile ductility and strain hardening capacity, ultimately resulting in brittle fracture.

Summary

The original version of this paper is available on https://www.scientific.net/KEM.1047.391.pdf

The currently available results identify that the cross-contamination plays a significant role in the tensile mechanical behaviour as well as to the respective tensile work hardening behaviour. The results showed that a maximum decrease of 15% in ultimate tensile strength is shown, while more than 80% decrease in tensile elongation at fracture was noticed for the higher cross-contamination level investigated. As far as the work hardening behaviour is concerned, the work hardening stages are reduced from four to only two, due to the observed pre-mature fracture of the specimens. The work-hardening exponent ranges from 0.18 for non-contaminated material to almost 0.25 for the highly contaminated material, showcasing that work-hardening is strongly affected by the crosscontamination level.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/KEM.1047.391.pdf

The authors gratefully acknowledge the financial support of the HORIZON Research and Innovation Actions, European Health and Digital Executive Agency for the implementation of the project «MULTI-MATERIAL DESIGN USING 3D PRINTING» having an acronym "MADE-3D" of the act HORIZON-CL4-2022-RESILIENCE-01 with Grant Agreement code 101091911.

References

The original version of this paper is available on https://www.scientific.net/KEM.1047.391.pdf

E. Cerri, E. Ghio, On the work-hardening behaviour of the additively manufactured Al−Si−Mg alloys: Composite-like versus networked microstructure, Materialia 38 (2024) 102282.

J. Fite, S. E. Prameela, J. Slotwinski, T. P. Weihs, Enhanced mechanical properties by eutectic cells in AlSi10Mg - A promising paradigm for strengthening aluminum in additive manufacturing, Mater. Charact., 204, (2023).

M. Horn, G. Schlick, M. Lutter-Guenther, C. Anstaett, C. Seidel, G. Reinhart, Metal powder cross-contaminations in multi-material laser powder bed fusion: Influence of CuCr 1 Zr particles in AlSi10Mg feedstock on part properties, (LiM 2019), (2019).

N. D. Alexopoulos & M. Tiryakioğlu, Relationship between fracture toughness and tensile properties of A357 cast aluminum alloy, MMTA, 40, (2009), 702-716.

C. Li, W.X. Zhang, H.O. Yang, J. Wan, X.X. Huang, Y.Z. Chen, Microstructural origin of high strength and high strain hardening capability of a laser powder bed fused alloy, J. Mat. Sci. Technol. 197 (2024), 194-206.

M. Avateffazeli, S. I. Shakil, A. Hadadzadeh, B. Shalchi-Amirkhiz, H. Pirgazi, M. Mohammadi, M. Haghshenas, On microstructure and work hardening behavior of laser powder bed fused and AlSi 10 Mg alloys, Mater. Today Commun., 35 (2023) 105804.

R. Ramesh, S. Gairola, R. Jayaganthan, M. Kamaraj, Effects of post-processing on the microstructural evolution and mechanical behaviour of an additively manufactured AlSil 0 Mg alloy, J. Mat. Res. Technol. 34, (2025), 2802-2813.

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A

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Anisotropy Influence of L-PBF Process Anisotropy on the Isotropic Behaviour of TPMS Structures; Anisotropy and Stress-State-Dependent Fracture in Additively Manufactured Metals

Anomaly Detection Volumetric Anomaly Detection in LPBF by Segmentation and Classification of Exposure Optical Tomography (EOT) Image Stacks

Artificial Neural Networks Machine Learning Topography Prediction and Optimization in Maskless Grayscale Laser Lithography

Automotive Shunt Resistor Additive Manufacturing of Automotive Metal Multi-Material Shunt Resistor: Cost and Carbon Footprint Analysis

B

Bending Numerical Study of the Flexural Behaviour of Additively Reinforced Blanks

Bio-composites Coconut Fibre Reinforced Photopolymer Composites via Stereolithography: Feasibility and Mechanical Performance

Biocompatibility Exploring the Feasibility of Incremental Forming Applied to 3D Printed PEEK Sheets

Build-plate Temperature Influence of Local Laser Power Distribution and Build-Plate Temperature on Porosity and Microstructure in Laser Powder Bed Fusion (LPBF)

C

Calibration Sensitivity Analysis and Calibration of the Heat Source in Additive Manufacturing of AINiCo Magnets

Carbon Fiber Pipes 3D Printing of Continuous Carbon Fiber-Reinforced Polymer Tee Pipe: Strategy Development and Demonstrator Fabrication

Carbon Footprint Additive Manufacturing of Automotive Metal Multi-Material Shunt Resistor: Cost and Carbon Footprint Analysis

Coating Process Thermal Measurement During High-Velocity Oxy-Fuel Coating Process for Single and Multiple Pass Rotational Spraying

Cold Metal Transfer (cmt) Effect of Deposition Strategy on Geometric Stability in Wire Arc Additive Manufacturing of Inconel 625

Continuous Carbon Fiber Development and Characterization of 3D-Printed PETG Specimens Embedding Continuous Carbon Fiber Strain Sensors

Continuous Carbon Fiber-reinforced Polymers 3D Printing of Continuous Carbon Fiber-Reinforced Polymer Tee Pipe: Strategy Development and Demonstrator Fabrication

Corrosion Resistance Investigation of WAAM-PAW Fabricated 316L Steel/Invar 36 Nb Alloys for the Development of Functionally Graded Materials

Cost Reduction Development of 3D-Printed Prototype Moulds for Thermoforming

D

Ded Comparison between 2D and 3D Thermal Finite Element Models of Directed Energy Deposition

Defect Formation Effect of Hatch Distance and Scanning Speed Combinations at Constant Volumetric Energy Density on the Materials Properties of IN939 Fabricated by Powder Bed Fusion-Laser Beam

Density Modelling A Physics Informed Machine Learning Framework towards Density Prediction of Additively Manufactured Components

Deposition Strategy Effect of Deposition Strategy on Geometric Stability in Wire Arc Additive Manufacturing of Inconel 625

Different Temperatures - Flow Rates Feasibility of Direct 3D Printing Foam Polylactic Acid with MEX

Digital Image Correlation Numerical Modelling of Directed Energy Deposition and Experimental Validation Based on Digital Image Correlation

Directed Energy Deposition Numerical Modelling of Directed Energy Deposition and Experimental Validation Based on Digital Image Correlation

Ductile Fracture Anisotropy and Stress-State-Dependent Fracture in Additively Manufactured Metals

E

Electrical Resistivity Development and Characterization of 3D-Printed PETG Specimens Embedding Continuous Carbon Fiber Strain Sensors

Electrically Assisted Effects of Current Density during Electrically Assisted Friction Stir Additive Manufacturing Hole Repair of AA 7075 on a Conventional Machine

Electrochemical Machining Sustainable Electrochemical Machining of Additively Manufactured Nitinol with Deep Eutectic Solvents

Electromechanical Properties Evaluation of SLA Biocompatible Resins Properties for Use as Dielectric Layers in 3D Printed Capacitive Force Sensors

Energy Absorption Optimal Design and Additive Manufacturing of Polymeric Metamaterials for Energy Absorption and Impact Mitigation

Exposure Ot Volumetric Anomaly Detection in LPBF by Segmentation and Classification of Exposure Optical Tomography (EOT) Image Stacks

F

Fdm Rib Geometry in FDM of Light Alloys; Thermomechanical Modelling and Shape Prediction in 4D Printing Using FEA

Fe Model Comparison between 2D and 3D Thermal Finite Element Models of Directed Energy Deposition

Fem Numerical Study of the Flexural Behaviour of Additively Reinforced Blanks

Fem-simulation Advanced FE Modeling for Predicting Component Properties in Additive Manufacturing

Fff Optimal Design and Additive Manufacturing of Polymeric Metamaterials for Energy Absorption and Impact Mitigation

Finite Element Analysis Thermomechanical Modelling and Shape Prediction in 4D Printing Using FEA

Finite Element Method Numerical Modelling of Directed Energy Deposition and Experimental Validation Based on Digital Image Correlation

Flexible Resin Advanced Characterization of Flexible Auxetic Resin Lattice Structures Produced by Stereolithography; Evaluation of SLA Biocompatible Resins Properties for Use as Dielectric Layers in 3D Printed Capacitive Force Sensors

Forces Measurement Measurement of Forces Generated during Robotized Additive Manufacturing Process Using Pellet Extruder

Fracture Toughness Resistance to Fracture of Additively Manufactured Aluminium Alloy Alsi10Mg under Plane Stress Conditions

Friction Stir Additive Manufacturing (fsam) Effects of Current Density during Electrically Assisted Friction Stir Additive Manufacturing Hole Repair of AA 7075 on a Conventional Machine

Functionally Graded Materials (fgm) Investigation of WAAM-PAW Fabricated 316L Steel/Invar 36 Nb Alloys for the Development of Functionally Graded Materials

Fused Deposition Modeling (fdm) Exploring the Feasibility of Incremental Forming Applied to 3D Printed PEEK Sheets

G

Gyroid Influence of L-PBF Process Anisotropy on the Isotropic Behaviour of TPMS Structures

H

Heat Source Thermal Measurement During High-Velocity Oxy-Fuel Coating Process for Single and Multiple Pass Rotational Spraying

High-velocity Oxy-fuel Thermal Measurement During High-Velocity Oxy-Fuel Coating Process for Single and Multiple Pass Rotational Spraying

Hole Repair Effects of Current Density during Electrically Assisted Friction Stir Additive Manufacturing Hole Repair of AA 7075 on a Conventional Machine

I

Image Segmentation Volumetric Anomaly Detection in LPBF by Segmentation and Classification of Exposure Optical Tomography (EOT) Image Stacks

Impact Mitigation Optimal Design and Additive Manufacturing of Polymeric Metamaterials for Energy Absorption and Impact Mitigation

In939 Effect of Hatch Distance and Scanning Speed Combinations at Constant Volumetric Energy Density on the Materials Properties of IN939 Fabricated by Powder Bed Fusion-Laser Beam

Inconel 625 Laser Powder Bed Fusion of INCONEL® 625: Guidelines for Robust Parameter Selection toward Optimal Quality Parts; Effect of Deposition Strategy on Geometric Stability in Wire Arc Additive Manufacturing of Inconel 625

Inconel 718 Effect of Preheating Temperature on the Optimal Processing Windows of Inconel 718 Processed by Laser Power Bed Fusion

Infra-red (ir) Effect of Hatch Distance and Scanning Speed Combinations at Constant Volumetric Energy Density on the Materials Properties of IN939 Fabricated by Powder Bed Fusion-Laser Beam

Inkjet Printing Implementing Inkjet Printing to Manufacture Piezopolymer Films for Sensing Applications

Inline Hot Rolling Development of AMC Welding Wire and Inline Hot Rolling for Enhanced Properties of DED-Arc/Wire-Manufactured Parts

Interlayer Bonding Measurement of Forces Generated during Robotized Additive Manufacturing Process Using Pellet Extruder

Invar 36 Nb Investigation of WAAM-PAW Fabricated 316L Steel/Invar 36 Nb Alloys for the Development of Functionally Graded Materials

L

L-pbf Influence of L-PBF Process Anisotropy on the Isotropic Behaviour of TPMS Structures

Laser Cladding Numerical Modelling of Directed Energy Deposition and Experimental Validation Based on Digital Image Correlation

Laser Powder Bed Fusion A Physics Informed Machine Learning Framework towards Density Prediction of Additively Manufactured Components; Resistance to Fracture of Additively Manufactured Aluminium Alloy Alsi10Mg under Plane Stress Conditions

Laser Powder Bed Fusion (lpbf) Influence of Local Laser Power Distribution and Build-Plate Temperature on Porosity and Microstructure in Laser Powder Bed Fusion (LPBF)

Laser Power Bed Fusion Effect of Preheating Temperature on the Optimal Processing Windows of Inconel 718 Processed by Laser Power Bed Fusion

Lattice Infilled Structures Integrating Topology Optimization and Lattice Infilling for Lightweight Aircraft Bracket Design via Additive Manufacturing

Lattice Structures Advanced Characterization of Flexible Auxetic Resin Lattice Structures Produced by Stereolithography

Lightweighting Integrating Topology Optimization and Lattice Infilling for Lightweight Aircraft Bracket Design via Additive Manufacturing

Lpbf A Comparative Analysis of LPBF Process Maps for S2 and S6 Grades Tool Steel; Volumetric Anomaly Detection in LPBF by Segmentation and Classification of Exposure Optical Tomography (EOT) Image Stacks; Anisotropy and Stress-State-Dependent Fracture in Additively Manufactured Metals

M

M4 Steel Comparison between 2D and 3D Thermal Finite Element Models of Directed Energy Deposition

Machine Learning Machine Learning Topography Prediction and Optimization in Maskless Grayscale Laser Lithography; Volumetric Anomaly Detection in LPBF by Segmentation and Classification of Exposure Optical Tomography (EOT) Image Stacks

Magnetic Material Sensitivity Analysis and Calibration of the Heat Source in Additive Manufacturing of AINiCo Magnets

Manganin-copper Alloys Additive Manufacturing of Automotive Metal Multi-Material Shunt Resistor: Cost and Carbon Footprint Analysis

Manufacturing Processes Hybridization Exploring the Feasibility of Incremental Forming Applied to 3D Printed PEEK Sheets

Maskless Grayscale Laser Lithography Machine Learning Topography Prediction and Optimization in Maskless Grayscale Laser Lithography

Material Extrusion (mex) Feasibility of Direct 3D Printing Foam Polylactic Acid with MEX

Material Modeling Advanced FE Modeling for Predicting Component Properties in Additive Manufacturing

Material Reduction Development of 3D-Printed Prototype Moulds for Thermoforming

Mechanical Properties Investigation of WAAM-PAW Fabricated 316L Steel/Invar 36 Nb Alloys for the Development of Functionally Graded Materials

Metallic Multi-material 3D Printing Additive Manufacturing of Automotive Metal Multi-Material Shunt Resistor: Cost and Carbon Footprint Analysis; Effect of CuCr1Zr Cross-Contamination Level on Work Hardening Characteristics of AISi 10Mg Additive Manufactured Material

Metamaterial Optimal Design and Additive Manufacturing of Polymeric Metamaterials for Energy Absorption and Impact Mitigation

Microhardness Investigation of WAAM-PAW Fabricated 316L Steel/Invar 36 Nb Alloys for the Development of Functionally Graded Materials

Microstructure Effect of Hatch Distance and Scanning Speed Combinations at Constant Volumetric Energy Density on the Materials Properties of IN939 Fabricated by Powder Bed Fusion-Laser Beam

Modeling Rib Geometry in FDM of Light Alloys

N

Natural Fiber Reinforcement Coconut Fibre Reinforced Photopolymer Composites via Stereolithography: Feasibility and Mechanical Performance

Nitinol Sustainable Electrochemical Machining of Additively Manufactured Nitinol with Deep Eutectic Solvents

Numerical Modelling Thermomechanical Modelling and Shape Prediction in 4D Printing Using FEA

O

Optimal Design Optimal Design and Additive Manufacturing of Polymeric Metamaterials for Energy Absorption and Impact Mitigation

P

Patchwork Blanks Numerical Study of the Flexural Behaviour of Additively Reinforced Blanks

Pellet-extrusion Measurement of Forces Generated during Robotized Additive Manufacturing Process Using Pellet Extruder

Petg Composite Development and Characterization of 3D-Printed PETG Specimens Embedding Continuous Carbon Fiber Strain Sensors

Physics-informed Ml A Physics Informed Machine Learning Framework towards Density Prediction of Additively Manufactured Components

Piezoelectric Implementing Inkjet Printing to Manufacture Piezopolymer Films for Sensing Applications

Pla Development of 3D-Printed Prototype Moulds for Thermoforming

Pla - Foam Pla Feasibility of Direct 3D Printing Foam Polylactic Acid with MEX

Plasma Arc Welding (paw) Investigation of WAAM-PAW Fabricated 316L Steel/Invar 36 Nb Alloys for the Development of Functionally Graded Materials

Poling Implementing Inkjet Printing to Manufacture Piezopolymer Films for Sensing Applications

Polyether Ether Ketone (peek) Exploring the Feasibility of Incremental Forming Applied to 3D Printed PEEK Sheets

Polymer Exploring the Feasibility of Incremental Forming Applied to 3D Printed PEEK Sheets

Porosity Influence of Local Laser Power Distribution and Build-Plate Temperature on Porosity and Microstructure in Laser Powder Bed Fusion (LPBF)

Powder Bed Fusion Laser Powder Bed Fusion of INCONEL® 625: Guidelines for Robust Parameter Selection toward Optimal Quality Parts

Powder Bed Fusion-laser Beam Effect of Hatch Distance and Scanning Speed Combinations at Constant Volumetric Energy Density on the Materials Properties of IN939 Fabricated by Powder Bed Fusion-Laser Beam

Preheating Temperature Effect of Preheating Temperature on the Optimal Processing Windows of Inconel 718 Processed by Laser Power Bed Fusion

Process Optimization Machine Learning Topography Prediction and Optimization in Maskless Grayscale Laser Lithography; Laser Powder Bed Fusion of INCONEL® 625: Guidelines for Robust Parameter Selection toward Optimal Quality Parts

Processing Map A Comparative Analysis of LPBF Process Maps for S2 and S6 Grades Tool Steel

Processing Windows Effect of Preheating Temperature on the Optimal Processing Windows of Inconel 718 Processed by Laser Power Bed Fusion

Prototyping Development of 3D-Printed Prototype Moulds for Thermoforming

Pvdf Implementing Inkjet Printing to Manufacture Piezopolymer Films for Sensing Applications

R

Relative Density Effect of Hatch Distance and Scanning Speed Combinations at Constant Volumetric Energy Density on the Materials Properties of IN939 Fabricated by Powder Bed Fusion-Laser Beam

Rib Geometry Rib Geometry in FDM of Light Alloys

S

Sensitivity Analysis Sensitivity Analysis and Calibration of the Heat Source in Additive Manufacturing of AINiCo Magnets; Comparison between 2D and 3D Thermal Finite Element Models of Directed Energy Deposition

Shape Morphing Thermomechanical Modelling and Shape Prediction in 4D Printing Using FEA

Shape Prediction Thermomechanical Modelling and Shape Prediction in 4D Printing Using FEA

Single Point Incremental Forming (spif) Exploring the Feasibility of Incremental Forming Applied to 3D Printed PEEK Sheets

Sla Evaluation of SLA Biocompatible Resins Properties for Use as Dielectric Layers in 3D Printed Capacitive Force Sensors

Stereolithography Advanced Characterization of Flexible Auxetic Resin Lattice Structures Produced by Stereolithography

Stress Triaxiality Anisotropy and Stress-State-Dependent Fracture in Additively Manufactured Metals

Structural Health Monitoring Development and Characterization of 3D-Printed PETG Specimens Embedding Continuous Carbon Fiber Strain Sensors

Surface Roughness Effect of Hatch Distance and Scanning Speed Combinations at Constant Volumetric Energy Density on the Materials Properties of IN939 Fabricated by Powder Bed Fusion-Laser Beam

Sustainability Sustainable Electrochemical Machining of Additively Manufactured Nitinol with Deep Eutectic Solvents

T

Tee Pipe 3D Printing of Continuous Carbon Fiber-Reinforced Polymer Tee Pipe: Strategy Development and Demonstrator Fabrication

Tensile Properties Feasibility of Direct 3D Printing Foam Polylactic Acid with MEX

Thermal Expansion Coefficient (cte) Investigation of WAAM-PAW Fabricated 316L Steel/Invar 36 Nb Alloys for the Development of Functionally Graded Materials

Thermal Measurements Thermal Measurement During High-Velocity Oxy-Fuel Coating Process for Single and Multiple Pass Rotational Spraying

Thermoforming Development of 3D-Printed Prototype Moulds for Thermoforming

Tool Steel A Comparative Analysis of LPBF Process Maps for S2 and S6 Grades Tool Steel

Topology Optimization Integrating Topology Optimization and Lattice Infilling for Lightweight Aircraft Bracket Design via Additive Manufacturing

Tpms Influence of L-PBF Process Anisotropy on the Isotropic Behaviour of TPMS Structures

Tpu Optimal Design and Additive Manufacturing of Polymeric Metamaterials for Energy Absorption and Impact Mitigation

V

Volumetric Energy Density Effect of Hatch Distance and Scanning Speed Combinations at Constant Volumetric Energy Density on the Materials Properties of IN939 Fabricated by Powder Bed Fusion-Laser Beam

W

Wire Arc Additive Manufacturing (waam) Investigation of WAAM-PAW Fabricated 316L Steel/Invar 36 Nb Alloys for the Development of Functionally Graded Materials; Effect of Deposition Strategy on Geometric Stability in Wire Arc Additive Manufacturing of Inconel 625

Work-hardening Effect of CuCr1Zr Cross-Contamination Level on Work Hardening Characteristics of AISi 10Mg Additive Manufactured Material

Z

Zener Ratio Influence of L-PBF Process Anisotropy on the Isotropic Behaviour of TPMS Structures

Authors Index

A

Al-Hinaai, M. Advanced Characterization of Flexible Auxetic Resin Lattice Structures Produced by Stereolithography

Alabtah, F. Integrating Topology Optimization and Lattice Infilling for Lightweight Aircraft Bracket Design via Additive Manufacturing

Albers, J. Volumetric Anomaly Detection in LPBF by Segmentation and Classification of Exposure Optical Tomography (EOT) Image Stacks

Alberty, L. Additive Manufacturing of Automotive Metal Multi-Material Shunt Resistor: Cost and Carbon Footprint Analysis; Resistance to Fracture of Additively Manufactured Aluminium Alloy Alsi10Mg under Plane Stress Conditions; Effect of CuCr1Zr Cross-Contamination Level on Work Hardening Characteristics of AISi 10Mg Additive Manufactured Material

Alcaraz, M. Investigation of WAAM-PAW Fabricated 316L Steel/Invar 36 Nb Alloys for the Development of Functionally Graded Materials

Alexopoulos, N. Additive Manufacturing of Automotive Metal Multi-Material Shunt Resistor: Cost and Carbon Footprint Analysis; Resistance to Fracture of Additively Manufactured Aluminium Alloy Alsi10Mg under Plane Stress Conditions; Effect of CuCr1Zr Cross-Contamination Level on Work Hardening Characteristics of AISi 10Mg Additive Manufactured Material

Amini, E. Anisotropy and Stress-State-Dependent Fracture in Additively Manufactured Metals

Andrade, T. Thermomechanical Modelling and Shape Prediction in 4D Printing Using FEA

Andreassen, E. Optimal Design and Additive Manufacturing of Polymeric Metamaterials for Energy Absorption and Impact Mitigation

Angelo, L. Influence of L-PBF Process Anisotropy on the Isotropic Behaviour of TPMS Structures

Angerler, J. Thermal Measurement During High-Velocity Oxy-Fuel Coating Process for Single and Multiple Pass Rotational Spraying

Argyros, A. Implementing Inkjet Printing to Manufacture Piezopolymer Films for Sensing Applications

Athanasios, B. Feasibility of Direct 3D Printing Foam Polylactic Acid with MEX

Azar, A. Sensitivity Analysis and Calibration of the Heat Source in Additive Manufacturing of AINiCo Magnets

B

Belcuore, S. Advanced Characterization of Flexible Auxetic Resin Lattice Structures Produced by Stereolithography

Bella, G. Coconut Fibre Reinforced Photopolymer Composites via Stereolithography: Feasibility and Mechanical Performance

Bocchi, S. Laser Powder Bed Fusion of INCONEL® 625: Guidelines for Robust Parameter Selection toward Optimal Quality Parts

Bossuyt, S. Anisotropy and Stress-State-Dependent Fracture in Additively Manufactured Metals

Brabazon, D. Effect of Hatch Distance and Scanning Speed Combinations at Constant Volumetric Energy Density on the Materials Properties of IN939 Fabricated by Powder Bed Fusion-Laser Beam

Bruni, C. Rib Geometry in FDM of Light Alloys

Bylya, O. A Physics Informed Machine Learning Framework towards Density Prediction of Additively Manufactured Components

C

Cadete, M. Thermomechanical Modelling and Shape Prediction in 4D Printing Using FEA

Cappellini, C. Laser Powder Bed Fusion of INCONEL® 625: Guidelines for Robust Parameter Selection toward Optimal Quality Parts

Casari, P. Measurement of Forces Generated during Robotized Additive Manufacturing Process Using Pellet Extruder

Catalano, L. Effects of Current Density during Electrically Assisted Friction Stir Additive Manufacturing Hole Repair of AA 7075 on a Conventional Machine

Centeno, G. Exploring the Feasibility of Incremental Forming Applied to 3D Printed PEEK Sheets

Ceretti, E. Advanced Characterization of Flexible Auxetic Resin Lattice Structures Produced by Stereolithography; Evaluation of SLA Biocompatible Resins Properties for Use as Dielectric Layers in 3D Printed Capacitive Force Sensors

Chairi, M. Coconut Fibre Reinforced Photopolymer Composites via Stereolithography: Feasibility and Mechanical Performance

Chapman, G. Effect of Hatch Distance and Scanning Speed Combinations at Constant Volumetric Energy Density on the Materials Properties of IN939 Fabricated by Powder Bed Fusion-Laser Beam

Christodoulou, I. Feasibility of Direct 3D Printing Foam Polylactic Acid with MEX

Chrysochoidis, N. Implementing Inkjet Printing to Manufacture Piezopolymer Films for Sensing Applications

Ciccarella, A. Influence of L-PBF Process Anisotropy on the Isotropic Behaviour of TPMS Structures

Ciurana, J. Exploring the Feasibility of Incremental Forming Applied to 3D Printed PEEK Sheets

Colaiuda, V. Influence of L-PBF Process Anisotropy on the Isotropic Behaviour of TPMS Structures

Corigliano, P. Coconut Fibre Reinforced Photopolymer Composites via Stereolithography: Feasibility and Mechanical Performance

Cortis, D. Influence of L-PBF Process Anisotropy on the Isotropic Behaviour of TPMS Structures

Cusanno, A. Numerical Study of the Flexural Behaviour of Additively Reinforced Blanks

D

D'Urso, G. Laser Powder Bed Fusion of INCONEL® 625: Guidelines for Robust Parameter Selection toward Optimal Quality Parts

Davut, K. Effect of Hatch Distance and Scanning Speed Combinations at Constant Volumetric Energy Density on the Materials Properties of IN939 Fabricated by Powder Bed Fusion-Laser Beam

Dedry, O. Effect of Preheating Temperature on the Optimal Processing Windows of Inconel 718 Processed by Laser Power Bed Fusion

Dell'Avvocato, G. Influence of L-PBF Process Anisotropy on the Isotropic Behaviour of TPMS Structures

Demarbaix, A. Development and Characterization of 3D-Printed PETG Specimens Embedding Continuous Carbon Fiber Strain Sensors

Dias-de-Oliveira, J. Thermomechanical Modelling and Shape Prediction in 4D Printing Using FEA

Dimitriou, D. Implementing Inkjet Printing to Manufacture Piezopolymer Films for Sensing Applications

Dittrich, F. Development of AMC Welding Wire and Inline Hot Rolling for Enhanced Properties of DED-Arc/Wire-Manufactured Parts

Diyoke, G. Thermal Measurement During High-Velocity Oxy-Fuel Coating Process for Single and Multiple Pass Rotational Spraying

Dogu, M. Effect of Hatch Distance and Scanning Speed Combinations at Constant Volumetric Energy Density on the Materials Properties of IN939 Fabricated by Powder Bed Fusion-Laser Beam

Duchene, L. Comparison between 2D and 3D Thermal Finite Element Models of Directed Energy Deposition

Ducobu, F. Development and Characterization of 3D-Printed PETG Specimens Embedding Continuous Carbon Fiber Strain Sensors

Dähn, L. Development of 3D-Printed Prototype Moulds for Thermoforming

E

Epicoco, A. Evaluation of SLA Biocompatible Resins Properties for Use as Dielectric Layers in 3D Printed Capacitive Force Sensors

F

Fachinotti, V. Optimal Design and Additive Manufacturing of Polymeric Metamaterials for Energy Absorption and Impact Mitigation; Sensitivity Analysis and Calibration of the Heat Source in Additive Manufacturing of AINiCo Magnets

Fapanni, T. Evaluation of SLA Biocompatible Resins Properties for Use as Dielectric Layers in 3D Printed Capacitive Force Sensors

Fassinato, E. Effect of Preheating Temperature on the Optimal Processing Windows of Inconel 718 Processed by Laser Power Bed Fusion

Ferrer, I. Exploring the Feasibility of Incremental Forming Applied to 3D Printed PEEK Sheets

Fotia, A. Coconut Fibre Reinforced Photopolymer Composites via Stereolithography: Feasibility and Mechanical Performance

Fulco, E. Numerical Study of the Flexural Behaviour of Additively Reinforced Blanks

G

Gallo, C. Comparison between 2D and 3D Thermal Finite Element Models of Directed Energy Deposition

García-Romeu, M. Exploring the Feasibility of Incremental Forming Applied to 3D Printed PEEK Sheets

Giavrouta, I. Effect of CuCr1Zr Cross-Contamination Level on Work Hardening Characteristics of AISi 10Mg Additive Manufactured Material

Ginestra, P. Advanced Characterization of Flexible Auxetic Resin Lattice Structures Produced by Stereolithography; Evaluation of SLA Biocompatible Resins Properties for Use as Dielectric Layers in 3D Printed Capacitive Force Sensors

Goulas, I. Resistance to Fracture of Additively Manufactured Aluminium Alloy Alsi10Mg under Plane Stress Conditions

Gouttebroze, S. Optimal Design and Additive Manufacturing of Polymeric Metamaterials for Energy Absorption and Impact Mitigation; Sensitivity Analysis and Calibration of the Heat Source in Additive Manufacturing of AINiCo Magnets

Govekar, E. Numerical Modelling of Directed Energy Deposition and Experimental Validation Based on Digital Image Correlation

Grüger, L. Advanced FE Modeling for Predicting Component Properties in Additive Manufacturing

Gründer, S. Resistance to Fracture of Additively Manufactured Aluminium Alloy Alsi10Mg under Plane Stress Conditions

Guglielmi, P. Numerical Study of the Flexural Behaviour of Additively Reinforced Blanks

H

Habraken, A. Comparison between 2D and 3D Thermal Finite Element Models of Directed Energy Deposition

Halilovič, M. Numerical Modelling of Directed Energy Deposition and Experimental Validation Based on Digital Image Correlation

Hengsbach, F. Resistance to Fracture of Additively Manufactured Aluminium Alloy Alsi10Mg under Plane Stress Conditions

Hor, A. Investigation of WAAM-PAW Fabricated 316L Steel/Invar 36 Nb Alloys for the Development of Functionally Graded Materials

Horn, M. Effect of CuCr1Zr Cross-Contamination Level on Work Hardening Characteristics of AISi 10Mg Additive Manufactured Material

Hornig-Klamroth, J. Development of 3D-Printed Prototype Moulds for Thermoforming

Huang, H. Advanced FE Modeling for Predicting Component Properties in Additive Manufacturing

Hulle, S. Comparison between 2D and 3D Thermal Finite Element Models of Directed Energy Deposition

Härtel, S. Volumetric Anomaly Detection in LPBF by Segmentation and Classification of Exposure Optical Tomography (EOT) Image Stacks; Thermal Measurement During High-Velocity Oxy-Fuel Coating Process for Single and Multiple Pass Rotational Spraying; Development of AMC Welding Wire and Inline Hot Rolling for Enhanced Properties of DED-Arc/Wire-Manufactured Parts; Influence of Local Laser Power Distribution and Build-Plate Temperature on Porosity and Microstructure in Laser Powder Bed Fusion (LPBF); Advanced FE Modeling for Predicting Component Properties in Additive Manufacturing

I

Ikiz, M. Effect of Hatch Distance and Scanning Speed Combinations at Constant Volumetric Energy Density on the Materials Properties of IN939 Fabricated by Powder Bed Fusion-Laser Beam

J

Jensch, F. Volumetric Anomaly Detection in LPBF by Segmentation and Classification of Exposure Optical Tomography (EOT) Image Stacks

K

Kallai, Z. 3D Printing of Continuous Carbon Fiber-Reinforced Polymer Tee Pipe: Strategy Development and Demonstrator Fabrication

Karmiris-Obratański, P. Feasibility of Direct 3D Printing Foam Polylactic Acid with MEX

Kermanidis, A. Resistance to Fracture of Additively Manufactured Aluminium Alloy Alsi10Mg under Plane Stress Conditions

Khan, N. Integrating Topology Optimization and Lattice Infilling for Lightweight Aircraft Bracket Design via Additive Manufacturing

Khraisheh, M. Integrating Topology Optimization and Lattice Infilling for Lightweight Aircraft Bracket Design via Additive Manufacturing

Kipping, J. 3D Printing of Continuous Carbon Fiber-Reinforced Polymer Tee Pipe: Strategy Development and Demonstrator Fabrication

Kovšca, D. Numerical Modelling of Directed Energy Deposition and Experimental Validation Based on Digital Image Correlation

Kuhness, D. Machine Learning Topography Prediction and Optimization in Maskless Grayscale Laser Lithography

L

Lazaridou, I. Additive Manufacturing of Automotive Metal Multi-Material Shunt Resistor: Cost and Carbon Footprint Analysis

Lenhert, D. Resistance to Fracture of Additively Manufactured Aluminium Alloy Alsi10Mg under Plane Stress Conditions

Li, G. Anisotropy and Stress-State-Dependent Fracture in Additively Manufactured Metals

Lian, J. Anisotropy and Stress-State-Dependent Fracture in Additively Manufactured Metals

Locatelli, G. Laser Powder Bed Fusion of INCONEL® 625: Guidelines for Robust Parameter Selection toward Optimal Quality Parts

Lutey, A. Machine Learning Topography Prediction and Optimization in Maskless Grayscale Laser Lithography

M

Madathil, A. A Physics Informed Machine Learning Framework towards Density Prediction of Additively Manufactured Components

Maharbi, A. Advanced Characterization of Flexible Auxetic Resin Lattice Structures Produced by Stereolithography

Maliaris, G. Implementing Inkjet Printing to Manufacture Piezopolymer Films for Sensing Applications

Mamros, E. Effects of Current Density during Electrically Assisted Friction Stir Additive Manufacturing Hole Repair of AA 7075 on a Conventional Machine

Mancini, E. Influence of L-PBF Process Anisotropy on the Isotropic Behaviour of TPMS Structures

Markopoulos, A. Feasibility of Direct 3D Printing Foam Polylactic Acid with MEX

Mckee, S. Machine Learning Topography Prediction and Optimization in Maskless Grayscale Laser Lithography

Mertens, A. A Comparative Analysis of LPBF Process Maps for S2 and S6 Grades Tool Steel; Effect of Preheating Temperature on the Optimal Processing Windows of Inconel 718 Processed by Laser Power Bed Fusion; Comparison between 2D and 3D Thermal Finite Element Models of Directed Energy Deposition

Michailidis, N. Implementing Inkjet Printing to Manufacture Piezopolymer Films for Sensing Applications

Mole, N. Numerical Modelling of Directed Energy Deposition and Experimental Validation Based on Digital Image Correlation

Mpinas, V. Implementing Inkjet Printing to Manufacture Piezopolymer Films for Sensing Applications

N

Negozio, M. Machine Learning Topography Prediction and Optimization in Maskless Grayscale Laser Lithography

Norris, C. Effects of Current Density during Electrically Assisted Friction Stir Additive Manufacturing Hole Repair of AA 7075 on a Conventional Machine

O

Ochana, I. Development and Characterization of 3D-Printed PETG Specimens Embedding Continuous Carbon Fiber Strain Sensors

Oikonomou, G. Implementing Inkjet Printing to Manufacture Piezopolymer Films for Sensing Applications

Okolo, C. Volumetric Anomaly Detection in LPBF by Segmentation and Classification of Exposure Optical Tomography (EOT) Image Stacks; Influence of Local Laser Power Distribution and Build-Plate Temperature on Porosity and Microstructure in Laser Powder Bed Fusion (LPBF)

Olivier, D. A Comparative Analysis of LPBF Process Maps for S2 and S6 Grades Tool Steel

Orlandi, D. Influence of L-PBF Process Anisotropy on the Isotropic Behaviour of TPMS Structures

Ouassil, S. Measurement of Forces Generated during Robotized Additive Manufacturing Process Using Pellet Extruder

Ozer, S. Effect of Hatch Distance and Scanning Speed Combinations at Constant Volumetric Energy Density on the Materials Properties of IN939 Fabricated by Powder Bed Fusion-Laser Beam

P

Palomba, G. Coconut Fibre Reinforced Photopolymer Composites via Stereolithography: Feasibility and Mechanical Performance

Palomba, V. Coconut Fibre Reinforced Photopolymer Composites via Stereolithography: Feasibility and Mechanical Performance

Pandini, S. Advanced Characterization of Flexible Auxetic Resin Lattice Structures Produced by Stereolithography; Evaluation of SLA Biocompatible Resins Properties for Use as Dielectric Layers in 3D Printed Capacitive Force Sensors

Papazoglou, E. Feasibility of Direct 3D Printing Foam Polylactic Acid with MEX

Paquet, É. Measurement of Forces Generated during Robotized Additive Manufacturing Process Using Pellet Extruder

Perna, A. Sustainable Electrochemical Machining of Additively Manufactured Nitinol with Deep Eutectic Solvents

Postl, M. Machine Learning Topography Prediction and Optimization in Maskless Grayscale Laser Lithography

Q

Quarto, M. Laser Powder Bed Fusion of INCONEL® 625: Guidelines for Robust Parameter Selection toward Optimal Quality Parts

R

Racineux, G. Measurement of Forces Generated during Robotized Additive Manufacturing Process Using Pellet Extruder

Rainchon, T. Development and Characterization of 3D-Printed PETG Specimens Embedding Continuous Carbon Fiber Strain Sensors

Rangaswamy, S. Effect of Hatch Distance and Scanning Speed Combinations at Constant Volumetric Energy Density on the Materials Properties of IN939 Fabricated by Powder Bed Fusion-Laser Beam

Ratsifandrihana, L. Investigation of WAAM-PAW Fabricated 316L Steel/Invar 36 Nb Alloys for the Development of Functionally Graded Materials

Rix, M. 3D Printing of Continuous Carbon Fiber-Reinforced Polymer Tee Pipe: Strategy Development and Demonstrator Fabrication

Rosa-Sainz, A. Exploring the Feasibility of Incremental Forming Applied to 3D Printed PEEK Sheets

Roth, J. Effects of Current Density during Electrically Assisted Friction Stir Additive Manufacturing Hole Repair of AA 7075 on a Conventional Machine

Rösler, J. Development of 3D-Printed Prototype Moulds for Thermoforming

S

Saggionetto, E. A Comparative Analysis of LPBF Process Maps for S2 and S6 Grades Tool Steel

Saravanos, D. Implementing Inkjet Printing to Manufacture Piezopolymer Films for Sensing Applications

Sardini, E. Evaluation of SLA Biocompatible Resins Properties for Use as Dielectric Layers in 3D Printed Capacitive Force Sensors

Sarmas, G. Implementing Inkjet Printing to Manufacture Piezopolymer Films for Sensing Applications

Scheibe, K. Volumetric Anomaly Detection in LPBF by Segmentation and Classification of Exposure Optical Tomography (EOT) Image Stacks

Scherillo, F. Sustainable Electrochemical Machining of Additively Manufactured Nitinol with Deep Eutectic Solvents

Schindelbacher, C. Thermal Measurement During High-Velocity Oxy-Fuel Coating Process for Single and Multiple Pass Rotational Spraying

Schlick, G. Additive Manufacturing of Automotive Metal Multi-Material Shunt Resistor: Cost and Carbon Footprint Analysis; Resistance to Fracture of Additively Manufactured Aluminium Alloy Alsi10Mg under Plane Stress Conditions; Effect of CuCr1Zr Cross-Contamination Level on Work Hardening Characteristics of AISi 10Mg Additive Manufactured Material

Schüppstuhl, T. 3D Printing of Continuous Carbon Fiber-Reinforced Polymer Tee Pipe: Strategy Development and Demonstrator Fabrication

Segatto, A. A Comparative Analysis of LPBF Process Maps for S2 and S6 Grades Tool Steel

Seiti, M. Evaluation of SLA Biocompatible Resins Properties for Use as Dielectric Layers in 3D Printed Capacitive Force Sensors

Sherlock, A. A Physics Informed Machine Learning Framework towards Density Prediction of Additively Manufactured Components

Silvestri, A. Effect of Deposition Strategy on Geometric Stability in Wire Arc Additive Manufacturing of Inconel 625

Sorgente, D. Numerical Study of the Flexural Behaviour of Additively Reinforced Blanks

Souxes, T. Additive Manufacturing of Automotive Metal Multi-Material Shunt Resistor: Cost and Carbon Footprint Analysis

Sperling, S. Thermal Measurement During High-Velocity Oxy-Fuel Coating Process for Single and Multiple Pass Rotational Spraying

Squillace, A. Effect of Deposition Strategy on Geometric Stability in Wire Arc Additive Manufacturing of Inconel 625

Stadlober, B. Machine Learning Topography Prediction and Optimization in Maskless Grayscale Laser Lithography

Starman, B. Numerical Modelling of Directed Energy Deposition and Experimental Validation Based on Digital Image Correlation

Stratiotou-Efstratiadis, V. Implementing Inkjet Printing to Manufacture Piezopolymer Films for Sensing Applications

T

Tchuindjang, J. A Comparative Analysis of LPBF Process Maps for S2 and S6 Grades Tool Steel; Effect of Preheating Temperature on the Optimal Processing Windows of Inconel 718 Processed by Laser Power Bed Fusion; Comparison between 2D and 3D Thermal Finite Element Models of Directed Energy Deposition

Tübbicke, T. Development of AMC Welding Wire and Inline Hot Rolling for Enhanced Properties of DED-Arc/Wire-Manufactured Parts; Advanced FE Modeling for Predicting Component Properties in Additive Manufacturing

U

Uensal, I. Additive Manufacturing of Automotive Metal Multi-Material Shunt Resistor: Cost and Carbon Footprint Analysis

V

Vasileiou, E. Additive Manufacturing of Automotive Metal Multi-Material Shunt Resistor: Cost and Carbon Footprint Analysis

Viola, E. Effect of Deposition Strategy on Geometric Stability in Wire Arc Additive Manufacturing of Inconel 625

W

Weber, L. Investigation of WAAM-PAW Fabricated 316L Steel/Invar 36 Nb Alloys for the Development of Functionally Graded Materials

Y

Yalcin, M. Effect of Hatch Distance and Scanning Speed Combinations at Constant Volumetric Energy Density on the Materials Properties of IN939 Fabricated by Powder Bed Fusion-Laser Beam

Yang, Y. Thermal Measurement During High-Velocity Oxy-Fuel Coating Process for Single and Multiple Pass Rotational Spraying

Z

Zeimpekis, V. Additive Manufacturing of Automotive Metal Multi-Material Shunt Resistor: Cost and Carbon Footprint Analysis

Ü

Ünsal, I. Resistance to Fracture of Additively Manufactured Aluminium Alloy Alsi10Mg under Plane Stress Conditions; Effect of CuCr1Zr Cross-Contamination Level on Work Hardening Characteristics of AISi 10Mg Additive Manufactured Material