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ISBN print: 978-3-0364-0544-5
ISBN eBook: 978-3-0364-1544-4
DOI book: 10.4028/b-dq1AGd
© 2026 Trans Tech Publications Ltd, All Rights Reserved

Special Materials and Processes

Edited by
Denise Bellisario

Special Materials and Processes

Special topic volume with invited peer-reviewed papers only

Edited by
Denise Bellisario

Scientific.Net

Copyright © 2026 Trans Tech Publications Ltd, Switzerland

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Volume 1187 of
Materials Science Forum
ISSN print 0255-5476
ISSN web 1662-9752

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Materials Science Forum, ISSN: 1662-9752, Vol. 1187, pp -1-0

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

Accepted:
2026-04-14

Online:
2026-04-17

Preface

The original version of this paper is available on https://www.scientific.net/MSF.1187.-1.pdf

The main topics of this special edition are non-conventional material processing technologies, both conventional materials and materials with specific properties for functional applications. The electrochemical and electro-physical machining processes, including precision pulsed electrochemical machining, laser beam cutting, additive manufacturing, layered hybrid composite tube manufacturing, etc. The edition ranges from surface engineering to forming methods of complex hybrid composite materials. The research results for numerical modelling of the mechanical behaviour of innovative non-conventional materials and hybrid structures are also presented here.

Editor

The original version of this paper is available on https://www.scientific.net/MSF.1187.-1.pdf

Bellisario Denise

Co-editors

The original version of this paper is available on https://www.scientific.net/MSF.1187.-1.pdf

Santo Loredana
Slătineanu Laurențiu
Schulze Hans-Peter

Hackert-Oschätzchen Matthias
Coteata Margareta

Boccarusso Luca

University of Rome Tor Vergata, Italy

University of Rome Tor Vergata, Italy
Gheorghe Asachi Technical University of Iași,Romania
Leukhardt Schaltanlagen Systemtechnik GmbH Magdeburg, Germany
Otto von Guericke University Magdeburg, Germany
Gheorghe Asachi Technical University of Iași Romania
University of Naples Federico II, Italy

Table of Contents

Preface

Hybrid Hemp/Carbon Fiber Reinforced Composites: Manufacturing, Mechanical Behaviour and Environmental Assessment

Dario de Fazio, Iacopo Bianchi, Luca Boccarusso, Massimo Durante, Chiara Mignanelli, Martina Panico, Fulvio Pinto, Michela Simoncini

Deep Learning-Based Quality Classification of Cold Spray Coatings with Explainable Feature Analysis

Alessia Serena Perna, Domenico Rossi, Alessia Auriemma Citarella, Dario de Fazio, Fabiola De Marco, Luigi Di Biasi, Massimo Durante, Genoveffa Tortora, Antonio Viscusi

Increasing Strength of Recycled Materials by Fiberglass Pulverization and Powder Direct Molding

Fabrizio Quadrini, Denise Bellisario, Alice Proietti, Leandro Iorio, Dounia Noqra, Giorgio Patrizii, Loredana Santo

Evaluation of Anti-Fouling Behavior of Silver Nanocomposites Made by Nano-Coating Fragmentation

Leandro Iorio, Loredana Santo, Denise Bellisario, Alice Proietti, Dounia Noqra, Giorgio Patrizii, Fabrizio Quadrini

Shape Memory Behavior of 3D-Printed Biomedical Polyurethane with Room Temperature Transition

Denise Bellisario, Leandro Iorio, Alice Proietti, Dounia Noqra, Giorgio Patrizii, Fabrizio Quadrini, Loredana Santo

Analysis of Connection Geometries for Segmented Stator Cores Machined with Laser Beam Cutting

Florian Hoffmann, Philipp Plänitz, Alexander May, Florian Winkler, Markus Barth, Sören Majcherek, Matthias Hackert-Oschätzchen

Estimation of the Lateral Gap in PECM: A Case Study on Tool Steel S390

Richard Petermann, Gunnar Meichsner, Pascal Clauß, Steven Nickel, Matthias Hackert-Oschätzchen

Beading as a Stabilising Element for Thin Wooden Panels

Ralf Förster, Andreas Loth, Jan Rösler, Jörg Hornig

Influence of Variable Radius Die Geometry on Tube Bending: A Finite Element Parametric Study

Apostolos Aslanidis, Jonas Reuter, Bernd Engel

Design and Manufacturing of Complex Geometry PEEK Component through Fused Filament Fabrication

Niccolo Burattini, Luana Bottini, Domenico Corapi, Alberto Boschetto

Development of an Initial FEM Framework to Model Particle-Substrate Contact in Fluidized Bed Finishing of L-PBF Parts

Antonio Viscusi, Andrea El Hassanin, Antonino Squillace

Biomaterial's Surface Functionalization by High Frequency Micropatterning Embossing on PEEK Samples

Marco Magro, Luigino Filice, Michele Perrelli, Domenico Mundo, Francesco Moscato, Francesco Gagliardi

Evaluating the Performance of Composite Tubes Drawn from Hybrids of Aluminum, Steel, and Carbon Fiber

Johannes Buhl, Lemopi Isidore Besong

Keywords Index

Authors Index

The original version of this eBook is available on https://www.scientific.net/book/special-materials-and-processes/978-3-0364-1544-4

Materials Science Forum, ISSN: 1662-9752, Vol. 1187, pp 1-15

doi: 10.4028/p-JN0Wa7

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

Submitted:
2026-01-19

Revised:
2026-02-23

Accepted:
2026-02-23

Online:
2026-04-17

Hybrid Hemp/Carbon Fiber Reinforced Composites: Manufacturing, Mechanical Behaviour and Environmental Assessment

The original version of this paper is available on https://www.scientific.net/MSF.1187.1.pdf

DE FAZIO Dario 1,a*, BIANCHI lacopo 2, b, BOCCARUSSO Luca 1,c, DURANTE Massimo 1, d, MIGNANELLI Chiara 2,e, PANICO Martina 1,f, PINTO Fulvio 3, g, SIMONCINI Michela 2, h

1 Department of Chemical, Materials and Production Engineering (DICMaPI), University of Naples Federico II, P.le V. Tecchio 80, 80125, Napoli, Italy

2 Department of Industrial Engineering and Mathematical Sciences (DIISM), Università Politecnica delle Marche, Via Brecce Bianche 12, 60131, Ancona, Italy

3 Material Research Centre, Department of Mechanical Engineering, University of Bath, UK

a* dario.defazio@unina.it, b i.bianchi@staff.univpm.it, cluca.boccarusso@unina.it,

d  mdurante@unina.it, e  c.mignanelli@pm.univpm.it, f  martina.panico@unina.it, gfp227@bath.ac.uk, h  m.simoncini@staff.univpm.it

Keywords: Life Cycle Assessment, Flexural Behaviour, Biocomposites.

Abstract

This study analysed the mechanical performances and the environmental sustainability of hybrid hemp/carbon fibre reinforced polymer composites produced adopting different stacking sequences. In this context, three carbon layers were replaced with hemp ones and were positioned either at the mid-plane of the laminate in a symmetric configuration ( S sample) and near to the external side of the composite material in an asymmetric configuration (A-HC sample). Additional full carbon sample (CFRP) and hemp sample (HFRP) were manufactured and used as reference materials. The mechanical behaviour of these materials was investigated through flexural, interlaminar shear and low-velocity impact (LVI) tests, and a cradle-to-grave Life Cycle Assessment (LCA) analysis was performed to quantify their environmental impacts in terms of Global Warming Potential (GWP). The experimental results revealed that hemp/carbon hybridisation in composite systems makes it possible to achieve a trade-off between mechanical performances and sustainability. Some of the investigated hybrid configurations exhibited mechanical properties comparable to conventional CFRPs thanks to strength, stiffness and enhanced energy absorption capability which depend on the stacking strategy and the presence of natural fibres that contribute to the damage mitigation. From an environmental perspective, thanks to numerous advantages in the use of hemp fibres, hybrid solutions significantly reduce the global warming potential compared to CFRPs, confirming that hemp/carbon hybridisation represents a promising strategy to balance structural performance and environmental.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1187.1.pdf

Fibre-reinforced polymer (FRP) composites are a family of materials that consists of a polymer matrix reinforced with long or short fibrous reinforcement, characterised by unique properties like high specific mechanical properties, chemical resistance and reduced thermal expansion. All these aspects, in conjunction with high levels of tailorability in terms of choice of fibre type, orientation, volume fraction or stacking strategy, have attracted over the last years a growing interest in a wide range of applications such as aerospace, sport, automotive or naval industries [1-9]. The design freedom that characterises this category of materials is not restricted to the choice of fibre's typology, the format, the adoption of a defined production technology or on the possibility of combining all these aspects during the production of the composite material. On the other hand, this freedom is extended to the possibility to introduce within the same composite system, two or more fibre's typologies to produce a fibre hybrid composite (FHC) material with unique mechanical properties [10,11]. The result of the interaction of multiple fibre's typologies is a hybrid composite material
where the intrinsic drawbacks of each reinforcement are mitigated because of a synergistic effect that leads to mechanical properties that neither the reinforcements possess [10,11].

Therefore, in light of the possibility to use different fibre's typologies within the same composite system, different strategies of fibre's hybridisations have been investigated over the years [12-15]. More recently, a growing interest of the research community has been directed toward a more environmental sustainability of fibre-reinforced polymer (FRP) composites. In particular, the increasing demand for more sustainable materials, has driven the research toward alternative solutions. In this context, a rising interest has been observed in the use of natural fibres like flax or hemp, and in their use as substitutes for traditional fibres [16,17]. These typologies of reinforcement are typically used for hybridisation with synthetic fibres since they have attracted a significant attention because of their promising potential to reduce the environmental impact of composite materials while maintaining adequate mechanical performances.

To this end, the hybridisation of carbon fibre reinforced plastic (CFRP) composites with natural fibres can mitigate some of the mechanical limitations that affect CFRPs. Thanks to the non-linear behaviour and the high damping characteristics, natural fibres promote different propagation and energy absorption mechanisms when subjected to external loads, reducing the brittle response typical of carbon reinforced composites and improving the energy dissipation efficiency [18-20].

Numerous alternatives of reinforcements can be used in hybridisation of carbon fibre composites in the panorama of natural fibres, however, flax fibres are the most widely investigated in literature where numerous studies have been focalised on impact behaviour, flexural performance, damping and micromechanical modelling to predict the mechanical properties of hybrid composites [16,17]. However, although the advantages of flax fibres, this reinforcement is affected by some drawbacks in terms of high cost, influence of the climatic conditions on the mechanical properties and vulnerability to fungal diseases which increase the storage cost and then the overall cost of the reinforcement [19].

In this scenario, hemp fibres emerge as an attractive alternative thanks to their interesting mechanical properties, low production cost and global availability. Life Cycle Assessment (LCA) studies further highlight the environmental benefit of these fibres such as carbon storage, soil requalification and low nitrate emission [21,22]. Therefore, based on these considerations, the use of hemp fibres in composite materials is increasing and attracting a significant research attention [4-6,23-27]. However, studies that address the mechanical behaviour of hybrid hemp/carbon composites remain limited since these fibres are still rarely used as reinforcement in structural applications.

This aspect has further limited the application of LCA analyses to hybrid composite materials since few research studies in literature consider at the same time both the mechanical performances of hybrid composites and the evaluation of their environmental impact through LCA investigations to assess the suitability of these materials for more sustainable industrial applications.

Therefore, the study conducted in the present research work consists of replacing three carbon plies with the same number of hemp ones in a hybrid hemp/carbon composites system and investigate on the mechanical behaviour of these composites in terms of flexural performances and failure analysis combining these results with a LCA analysis to evaluate the trade off between performances and sustainability of this category of hybrid composite materials.

Experimental Procedure

The original version of this paper is available on https://www.scientific.net/MSF.1187.1.pdf

Materials and Laminates Production. For this experimental campaign, the hybrid composite materials were manufactured using a woven hemp fabric with areal density of 160 g/m2 (supplied by Maeko Srl) and a woven carbon fabric with areal density of 200 g/m2 (supplied by Toray International Srl). SX 10 epoxy resin (supplied by Mates Srl) was used as matrix to produce hemp/carbon hybrid composite materials. Before the impregnation phase, the natural fabrics were dried in an oven at 60∘C and relative humidity of 20% for 12 hours, then a total number of 15 layers (300×300 mm2) were manually impregnated with the epoxy resin using the hand lay-up technique and were placed on a plate mould. The uncured material was sealed in an elastomeric bag under vacuum and was placed in a hydraulic press to allow for the cure. In a first step the composite material was subjected to a
pressure of 8 bar at 55∘C for 2 hours, then the cure of the laminate was completed at room temperature under a pressure of 8 bar for 24 hours. Two main hybrid stacking configurations were produced: one by placing the hemp layers in the middle of the laminate (labelled as S sample) and the other by placing the hemp layers on the top of the laminate (labelled as A-HC sample). Full carbon and full hemp laminates were also produced as references ( C and H samples respectively) are shown in Table 1.

Table 1. Main laminate's characteristics.

Stacking strategy | Label | Stacking sequence | Stacking Schematisation | Thickness [mm] | Fibre volume fraction [%]
Carbon laminate | C | [C₁₅] | Carbon laminate schematic showing 15 layers of black carbon fiber | 2.81 | 61.01
Hybrid Sandwich | S | [C₆H₃C₆] | Hybrid sandwich schematic showing carbon layers with yellow hemp core in middle | 3.01 | 55.69
Hybrid Asymmetric | A-HC | [CH₃C₁₁] | Hybrid asymmetric schematic showing hemp layers on top and carbon layers below | 3.01 | 55.69
Hemp laminate | H | [H₁₅] | Hemp laminate schematic showing 15 layers of yellow hemp fiber | 3.70 | 46.33

Mechanical characterisation. On all sample's typologies three point bending tests were performed according to the ASTM D790 standard, using an MTS Exceed E43 universal testing machine equipped with a 50 kN piezoelectric load cell. The tests were performed on a total number of 5 specimens for each sample configuration by adopting the maximum span-to-depth ratio of 60:1 admitted by the ASTM standard to reduce the shear effects. Particular attention was paid to this aspect since the hybrid configurations are characterised by a high number of hemp/carbon interfaces through the thickness, with concrete possibilities of instauration of interlaminar shear phenomena at the interface between hemp and carbon layers. Therefore, in case of CFRP and hybrid composite samples, specimens 210 mm×13.2 mm and HFRP reference 300 mm×18.2 mm were tested using a span length of 180 mm and 230 mm respectively. The flexural stress σf was then evaluated using the following Eq. 1 [28]:

σf=(32PLbd2)[1+6(DL)2−4(dL)(DL)](1)

where P is the load in N,L is the support span in mm,b and d are respectively the width and the thickness of the specimen in mm and D is the deflection of the specimen in proximity of the loading support in mm .

On the basis of what was asserted for the flexural properties of the samples, the shear characteristics were specifically evaluated through additional interlaminar shear strength (ILSS) tests performed on a total number of 5 specimens for each configuration. To this end the same testing machine was used in a short beam three point bending configuration according to the ASTM D2344 standard. All tests were carried out using a span-to-depth ratio of 5:1 to promote the shear effect in
place of the flexural one, then samples 40 mm×15 mm were tested and the shear stress τ was evaluated as follow (Eq. 2) [29]:

τ=0.75Pbd(2)

The impact properties of the laminates were evaluated using a homemade falling drop weight tower equipped with an impactor tip 16 mm in diameter and an impactor mass of 2.66 kg . The impact machine is further equipped with an anti-rebound system that limits the movement of the shuttle after the first impact. The tests were performed according to the ASTM D7136 standard, then 5 specimens for each sample typology 100 mm×150 mm clamped on their edge were tested at three energy levels (5 J,10 J and 20 J) obtained by varying the tip height while keeping constant the impactor mass.

The damage was evaluated using non-destructive tests by means of a linear pulse-echo phased array ultrasonic transducer which is characterised by 128 elements with a pitch of 0.5 mm and a central frequency of 5 MHz . An ultrasonic gel was used to connect the array probe to the specimen, ensuring proper ultrasonic wave propagation from the probe to the inspected material. The phased array was further connected to an encoder to enable a C-Scan acquisition. From these tests, two main outputs were obtained: the B-Scan, which enables damage analysis through the cross-section of the specimen by localising defects along the thickness, and the C-Scan, which provides a top-view representation of the specimen, allowing the evaluation of the damage extension and depth.

Life Cycle Assessment analysis. Aiming to evaluate the environmental impacts associated with the different stacking configurations and to identify which one is the most sustainable alternative, the standardized methodology of Life Cycle Assessment, defined by the UNI ISO 14040-14044 standards, was employed. Therefore, the four iterative phases defined in the methodology were followed: (1) Goal and Scope Definition, (2) Life Cycle Inventory, (3) Life Cycle Impact Assessment, (4) Interpretation of results.

The first phase includes the definition of the goal of the study, the Functional Unit (FU), and the system boundaries. The Functional Unit (FU) is defined as the production of a 300 mm×300 mm composite panel composed by 15 layers. An approach from cradle to grave was considered for the analysis, thus all phases from the raw materials extraction to the end-of-life phase were included within the LCA.

Four scenarios were analysed, corresponding to the four stacking sequences reported in Table 1. Scenario 1 refers to the manufacturing of a fully carbon composite panel (CFRP). Scenario 2 and Scenario 3 consider the manufacturing of hybrid composite panels, adopting the sandwich ( S ) and asymmetric (A-HC) stacking configurations, respectively.

Scenario 4 refers to the manufacturing of the fully hemp fibre reinforced polymer panel (HFRP) The analysis included the following phases:

The production and the transport of reinforcement fibres (either hemp or carbon fibres) and epoxy resin.

The drying process of hemp fibres in an oven at 60∘C for 12 hours (only for Scenario 2, 3, and 4).

The production of the mould for the manufacturing process.

The moulding process, including the consumables needed for the manual impregnation and the energy consumption related to curing and post-curing processes. The process described in the previous paragraph (i.e., moulding using a hydraulic press with a pressure equal to 8 bar and a temperature of 55∘C for 2 hours as curing process and same pressure but room temperature for 24 hours as post-curing process) was considered in the LCA analysis.

The End Of Life (EoL) of the composite panels. In particular, incineration was considered in all scenarios as it is one of the most common EoL route for composite materials.

The use phase was considered out of the system boundaries. This phase would be greatly influenced by the specific application of the panels; hence, it was not included in the analysis to ensure generality and provide reliable results.

The phases included within the system boundaries are schematically reported in Fig. 1.

[picture]

Fig. 1. Schematic representation of system boundaries.

Fig. 1. Schematic representation of system boundaries.

For the Life Cycle Inventory (LCI) phase, both directly measured and secondary data retrieved from literature and Ecoinvent database were used.

Specifically, the weight of the panels of each scenario was calculated on the basis of the defined dimensions ( 300 mm×300 mm ), the density of each material ( 1.2 g/cm3 for the epoxy resin, 1.8 g/cm3 for the carbon fibres and 1.4 g/cm3 for the hemp fibres) and the fibre volume fraction equal to 61% and 46% for carbon epoxy and hemp epoxy composite, respectively. Carbon fibres production was modelled according to scientific literature [30], while the dataset related to epoxy resin production provided by the commercial database Ecoinvent was used. As regards the hemp fibres production impacts, relevant scientific literature was considered [31]. The electric energy consumption related to the curing and post-curing processes was estimated on the basis of scientific literature [32], as well as the consumptions related to the consumable materials [33]. The electric energy mix from Italy was considered. The mould weight was estimated based on a CAD model; milling was considered for the mould production and 10% in weight of the mould metal was considered to the removed during the chipping operations. The consumption related to the drying process of hemp fibres was retrieved by literature [33]. Table 2 reports the main LCI input data employed in the LCA analysis.

Table 2. Main LCI input data.

Element | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | Unit
Materials
Carbon Fiber weight | 0.27 | 0.21 | 0.21 | 0 | kg
Hemp Fiber weight | 0 | 0.04 | 0.04 | 0.22 | kg
Epoxy resin weight | 0.11 | 0.13 | 0.13 | 0.22 | kg
Part mass | 0.38 | 0.39 | 0.39 | 0.44 | kg
Curing process
Cold press |  | 1.25 |  |  | kWh
Curing oven |  | 0.05 |  |  | kWh
Consumables
Vacuum bag |  | 0.09 |  |  | kg/m²
Release agent |  | 0.02 |  |  | kg/m²
Sealant tape |  | 0.04 |  |  | kg/m²
Mold production
Mold mass |  | 2.67 |  |  | kg
Aluminium removed by milling |  | 0.267 |  |  | kg
Mold service life |  | 750 |  |  | cycles
Fibers drying
Energy use |  | 0.75 |  |  | kWh/ kg

From Table 2 it can be seen that the weight of the materials is the same for Scenario 2 and Scenario 3. This is due to the fact that the two scenarios involve the same total number of hemp and carbon layers, differing only in the stacking sequence. Energy, consumables use and mould production are the same for all the considered scenarios. In fact, the scenarios mainly differ in terms of raw materials and any material-specific processes (e.g., drying applied only to hemp fibres).

SimaPro software was used to model the scenarios and to obtain LCA results. In particular, the environmental impacts were evaluated in terms of Global Warming Potential (GWP, IPCC methodology). GWP method quantifies the effects of the FU on global warming, and it is expressed in kg of CO2 eq.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/MSF.1187.1.pdf

Flexural and Interlaminar Shear Strength (ILSS) test results. In Fig. 2a and in Table 3 are reported respectively the typical stress-strain curves and the main flexural properties obtained from the bending tests performed on the configurations under inspection. From the curves it is possible to observe the typical flexural behaviour of a CFRP laminate (C sample), where in the initial loading phase, the laminate responds elastically to the applied load with a linear increase as the deformation progresses. During these conditions no visible signs of damages are detected, however, this behaviour continues until the material reaches a critical point that identify the transition from the elastic region to the damage propagation. From this point, internal cracks are generated on the compressive side of the laminate and propagate within the thickness of the specimen as the applied deformation increases. The internal damage is responsible for the reduction of the load-carrying capability with a sharp load drop when the failure occurs.

[picture]

Fig. 2. Typical flexural stress-strain curves (a) and Typical ILSS stress-displacement curves for each sample configuration under inspection (b).

Fig. 2. Typical flexural stress-strain curves (a) and Typical ILSS stress-displacement curves for each sample configuration under inspection (b).

Table 3. Main results of flexural and ILSS tests.

| Flexural strength [MPa] | Flexural Modulus [GPa] | Shear Strength [MPa]
| Mean value | St. dev. | Mean value | St. dev. | Mean value | St. dev.
CFRP | 736.70 | 45.00 | 56.42 | 4.64 | 41.72 | 3.16
HFRP | 64.85 | 2.34 | 6.18 | 0.25 | 16.37 | 0.45
S | 627.50 | 22.80 | 58.57 | 4.38 | 37.78 | 1.84
A-HC | 375.50 | 12.54 | 34.79 | 0.75 | 29.37 | 2.61

On the other hand, focusing the attention on the flexural behaviour of the hemp reference (H sample in Fig. 2a), it is possible to assess that this material reveals a pseudo-plastic behaviour if compared to the CFRP laminate. This aspect is typical of natural fibre composites since the internal architecture that characterises the natural reinforcement influence the mechanical behaviour of this category of composite materials [34,35]. Based on these considerations, the H sample showed the lowest value of the elastic modulus ( 6.18 GPa ) and flexural strength ( 64.85 MPa ), then respectively a reduction of almost 87.9% and 91.2% in comparison with the CFRP reference.

Looking at the hybrid configurations, it is possible to point out that the position of the natural fibre layers plays a fundamental role in the mechanical properties of the laminate. The presence of hemp layers influences the failure mode of the hybrid composite because of the difference in the flexural properties of each material and its relative position within the thickness of the laminate. The flexural response of the sandwich configuration (S sample in Fig. 2a), in which the natural fibre layers are placed in the middle plane of the laminate, highlights the effect of the hybridisation, as the presence of natural fibres alters the mechanical properties and the failure mode typical of a CFRP material. This hybrid configuration revealed a pure elastic response, with a flexural modulus comparable to that of fully carbon composite ( 58.57 GPa ), reaching a flexural strength of approximately 627.5 MPa ( −14.8% compared to the CFRP reference). This flexural behaviour can be attributed to the position of the natural fibres along the mid-plane of the laminate, which preserves the elastic properties of the hybrid laminate, as confirmed by the similar flexural modulus. However, at peak load, failure is initiated by crack propagation in the compressive region of the laminate and the hemp/carbon interface. This aspect leads to conclude that the hybridisation introduces multiple fracture and damage propagation mechanisms linked to the mechanical properties mismatch between natural and synthetic fibres.

The flexural response of the asymmetric configuration revealed (A-HC sample in Fig. 2a), as well as for the S configuration, a linear trend of the flexural curve along the initial loading phase testifying a good interaction between hemp and carbon fibres. However, unlike the other hybrid configuration, this material does not exhibit a sharp drop of the stress as indicator of the failure of the sample, on the other hand it revealed a large deformation after a threshold value of the elastic region due to the higher strain at failure that characterises hemp fibres. In this region the damage of the material starts and propagates within the material at the interface between hemp and carbon fibres revealing a global reduction of the flexural properties in comparison with the CFRP reference and S sample ( −38.3% and −40.6% respectively in case of the flexural modulus and −49% and −40.1% in case of flexural stress).

The ILSS tests revealed the shear response of all samples investigated. In Fig. 2b are reported the main results where it is possible to observe that both the hybrid configurations are characterised by shear properties similar to that of the carbon reference. However, the A-HC hybrid sample, showed a reduction of the maximum shear strength of almost 29.6% in comparison to the CFRP laminate. As it is possible to observe from the curves (Fig. 2b), the A-HC sample is characterised by a double peak before the failure of the specimen that is associated to a delamination at the interface between hemp and carbon layers. This aspect is in line with the results obtained from the flexural tests where the main failure mechanism is associated to hemp layers deformation and delamination at natural/synthetic fibres interface.

Then, based on these observations, it is possible to point out that the hybridisation of carbon fibres with hemp ones can tailor the mechanical properties of this category of materials by combining the brittle behaviour of carbon fibres with the ductile behaviour of hemp ones. It is possible to design the flexural modulus of a hybrid composite material by placing hemp fibres along the thickness of the laminate, more in detail the modulus can be close to that of pure carbon using hemp fibres as core material in a sandwich strategy or can be reduced by placing these natural fibres in an asymmetric configuration.

Low Velocity Impact (LVI) test results. In Fig. 3 are reported the representative impact curves in terms of impact force versus displacement of each configuration subjected to 10 J impact. In this figure are further reported the corresponding results of the C-Scan analysis performed on each sample typology tested at 10 J impact energy. The results of LVI at 5 J and 10 J , highlights that the carbon reference ( C sample) is characterised by an elastic behaviour since the restricted area in the forcedisplacement suggests that large part of the impact energy returned to the impactor during the rebound.

[picture]

Fig. 3. Force vs displacement curves at 10 J impact energy and C -Scan of the damaged area localisation and extension of each sample configuration.

Fig. 3. Force vs displacement curves at 10 J impact energy and C -Scan of the damaged area localisation and extension of each sample configuration.

Table 4. Main results of LVI impact and C-Scan non-destructive tests.

Energy Level [J] | Effective Impact Energy Ei [J] | Abs. Energy Ea [J] | RC [J/J] | Damaged Area [mm²]
Mean value | St. dev. | Mean value | St. dev. | Mean value | St. dev.
CFRP | 5 | 4.998 | 0.6250 | 0.0431 | 0.8749 | 0.0086 | 53.00 | 17.64
10 | 9.995 | 2.5390 | 0.3078 | 0.7460 | 0.0308 | 218.10 | 53.13
20 | 19.991 | 13.3530 | 2.3052 | 0.3320 | 0.1153 | 469.90 | 32.29
HFRP | 5 | 4.998 | 1.8980 | 0.1107 | 0.6202 | 0.0221 | 110.10 | 59.44
10 | 9.995 | 5.7350 | 0.3265 | 0.4262 | 0.0327 | 843.50 | 702.68
20 | 19.991 | 14.9210 | 0.9700 | 0.2536 | 0.0485 | 3130.70 | 675.63
S | 5 | 4.998 | 0.8010 | 0.0529 | 0.8397 | 0.0106 | 99.20 | 29.12
10 | 9.995 | 3.6960 | 0.1514 | 0.6302 | 0.0152 | 439.90 | 87.91
20 | 19.991 | 14.3950 | 0.5362 | 0.2799 | 0.0268 | 680.50 | 74.23
A-HC | 5 | 4.998 | 0.5440 | 0.1217 | 0.8912 | 0.0243 | 8.20 | 1.26
10 | 9.995 | 2.6590 | 0.2934 | 0.7340 | 0.0294 | 129.20 | 16.84
20 | 19.991 | 12.5630 | 0.4345 | 0.3716 | 0.0217 | 422.50 | 19.74

This aspect is further supported by the results in Table 4 where the absorbed energy ( Ea ) corresponds to the 12.5% and 25.4% of the impact energy ( 5 J and 10 J respectively). The same conclusions can be drawn looking at the return coefficient (RC), which value, ranging between 0 and 1 , represents the fraction of the impact energy absorbed by the laminate and it is defined as the ratio between the difference between the impact and the absorbed energies and the prescribed impact one. This coefficient in both cases is 0.87 and 0.74 ( 5 J and 10 J respectively) meaning that large part of the impact energy returned to the impactor tip. In case of impact energy of 20 J , it was observed a sharp load drop that corresponds to the initiation and propagation of internal damages in proximity of the impact zone. These observations can be further corroborated by the internal damage analysis where the impact event performed at 20 J leads to a damaged area that is more than seven time larger than that obtained from the impact event at 5 J (Table 4). This aspect confirms the brittle behaviour of the pure carbon laminate.

Compared to the C reference, the full hemp laminate (H sample) revealed a pseudo-ductile behaviour since the value of the peak force is the lowest for all the impact energies ( −36.5%,−48.1% and −57.4% respectively in case of 5 J,10 J and 20 J ) and the maximum displacement in correspondence of the maximum impact force is the highest. This behaviour can be attributed to the high damping properties and to the high energy absorbing capability of these natural fibres. These assertions are corroborated by the RC coefficient values which, among the configurations analysed, are always the lowest in all impact conditions (Table 4). The combination of these aspects leads to a larger damaged area in comparison with the CFRP reference, resulting in an increase in the damaged area that is almost double and three times respectively in case of 5 J and 10 J impact energies up to more than five times in case of 20 J impact energy.

The impact tests performed on the S hybrid configuration, revealed a damage extension that increase with the increase of the impact energy (Table 4). The impact curves at 10 J revealed that the presence of hemp layers in the midplane of the laminate do not influence the overall elasticity of the hybrid composite since the material follow a linear trend similar to that of the CFRP reference configuration (Fig. 3). What observed from the impact curves is further supported by the return coefficient (Table 4) that for all impact energies almost corresponds to the value observed in case of the pure carbon laminate.

The C-scan analysis confirmed the presence of internal delamination as consequence of the impact event, that in case of impact energies of 5 J are confined to the upper side of the laminate (the side of the laminate in contact with the indenter tip) at the interface between hemp and carbon layers with an increase of the damaged area of almost 87% compared to the C reference. In case of impact energy of 10 J , the damaged area is almost double than that of the carbon laminate because the damage propagated deeper the material and cracks grew within the laminate involving the lower interface. At 20 J the increase in the damage extension is limited to around 40%, however, severe damages involved the carbon fibres in the tensile region, leading to the total failure of the material. In all cases, the S hybrid configuration revealed an improvement in the absorbed energy linked to the presence of the hemp layers, indeed in case of impact energy of 5 J,10 J and 20 J the absorbed energy respectively increased of almost +28.2%,+45.6% and +7.8% in comparison with the CFRP reference.

When the hemp layers are positioned on the external side of the hybrid laminate in an asymmetric configuration (A-HC sample), the laminate exhibits a different impact behaviour. More in detail, the presence of hemp layers on the compressive region, then a pseudo-ductile material in correspondence of the impact location, allows for a greater deformation of the area close to the contact zone, enabling a better energy transfer between the impactor tip and the laminate. This aspect is reflected in an increased absorbed impact energy due to the deformation of hemp layers that, in conjunction with the high damping properties of these fibres, led to a reduced damage extension. These results are confirmed by the RC coefficient (Table 4) that for all the impact energies is always the highest among all the configurations and by the damaged area that is always lower than that of the CFRP reference (damaged area reduction of approximately −40.8%,−10.1% respectively in case of 10 J and 20 J impact energies).

Therefore, from the results observed in the symmetric and asymmetric configurations, it is possible to conclude that the presence of hemp layers and the relative position within the thickness of the laminate, influences the impact behaviour of hybrid composites allowing for different energy dissipation mechanisms that lead to different damage extension.

Table 5 and Fig. 4 report the LCA results of all the analysed scenarios, obtained in terms of GWP. In particular, the contributions to total impacts of each phase are shown.

Scenario 1 results as the most impactful one ( 14.22 kgCO2 eq), while Scenario 4 is the most sustainable alternative ( 3.36 kgCO2 eq). Scenario 2 and Scenario 3 are placed in an intermediate level, with the same ecological footprint ( 12.05 kgCO2 eq). The high carbon footprint of Scenario 1 is strongly related to the impacts of carbon fibres; this reinforcement is characterised by high unitary impacts (i.e., 39.9 kgCO2 eq per kg ) and their use accounts for the majority of the scenario impacts. On the other hand, Scenario 4 presents low environmental impacts due to the high sustainability of hemp fibres (i.e., 0.531 kg of CO2 eq per kg ). Even though hemp fibres require a drying phase before moulding, from a sustainability perspective, this phase is almost negligible. This shows how natural fibres represent a much more impactful solution with respect to synthetic ones. The resin contribution to the total impacts is generally low; the highest percentage contribution of the resin is obtained in Scenario 4, due to the higher quantity of epoxy resin with respect to the other scenarios. This is related to the fibres volume fraction which is lower for hemp reinforced composites with respect to those reinforced with carbon fibres. The End of Life phase presents limited contributions for all scenarios and the kg of CO2 eq associated are similar: the highest value is reached in Scenario 4 due to the weight of the panel which is higher than those of the other scenarios.

It can be observed that Scenario 2 and Scenario 3 present the same environmental impacts; this is related to the same number of carbon plies ( 12 layers) and hemp ones ( 3 layers) which involves in the same weight of each material and, subsequently, in the same kg of CO2 eq produced. Indeed, these two scenarios provide good reduction in impacts with respect to scenario 1 as part of the CFRP layers are replaced with a much more sustainable natural alternative.

The curing processes and consumable materials production give the same contribution to the total impacts of all scenarios, since the input data related to the energy consumption and the quantity of consumables are the same for all of them. In fact, all scenarios are characterised by the same resin and the same curing cycle is considered; hence, despite the differences in stacking sequences, the manufacturing processes are associated with the same carbon footprint in all scenarios. Similar considerations apply to the use of consumables, which mainly depends on part dimensions. Since the panels considered in the different scenarios do not significantly differ in terms of geometry (except for minor variations in thickness) the use of consumable materials can be assumed to be the same for all scenarios.

The production of the mould makes negligible contribution to the total footprint of all the analysed scenarios. This is due to the high number of moulding cycles that a mould can withstand before considering repairing or replacement.

The end-of-life phase was modelled considering incineration and leads to limited impacts (i.e., 0.9−1 kgCO2 eq). No energy recovery credits were considered in the analysis in order to avoid additional uncertainty related to recovery efficiency and allocation procedures. Literature shows that including energy recovery generally leads to a limited reduction in Global Warming Potential, with comparable magnitudes for synthetic and bio-based systems [36,37]. Reported impacts for incineration with energy recovery are approximately 2 kgCO2 eq /kg for synthetic composites and bio-based composites. Therefore, the results provided in this study are not expected to significantly change with the inclusion of generic recovery credits. Given the recent focus on EoL of composites, a more accurate quantification could be interesting for future studies, including measured recovery efficiency and different recycling technologies investigation.

Table 5. LCA results in terms of GWP ( kgCO2 eq).

Phase | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | Unit
Total | 14.22 | 12.05 | 12.05 | 3.36 |
Carbon Fiber | 11.73 | 9.38 | 9.38 | 0 |
Hemp Fiber | 0 | 0.03 | 0.03 | 0.13 |
Resin | 0.58 | 0.69 | 0.69 | 1.11 |
Fiber Drying | 0 | 0.017 | 0.017 | 0.08 | kg CO₂ eq
Consumables | 0.32 | 0.32 | 0.32 | 0.32 |
Mold | 0.007 | 0.007 | 0.007 | 0.007 |
Curing | 0.67 | 0.67 | 0.67 | 0.67 |
EoL | 0.91 | 0.93 | 0.93 | 1.00 |

Although Scenario 4 proved to be the most sustainable, it does not achieve the best mechanical properties.

For load-bearing structural applications, fully bio-based composites may not always represent the optimal solution. In fact, to obtain bio-based components with mechanical performance comparable to synthetic composites, increased thickness and consequently higher mass may be required; therefore, the relative environmental convenience of the different scenarios may change depending on the structural requirements associated with the functional unit. In the present study, the goal was to compare components with the same geometry, without defining structural requirements, to assess the effects of fibre substitution. In this case, bio-based and hybrid composites demonstrated great potential from a sustainability perspective. Future work should integrate mechanical performance directly into the LCA, using mechanical properties as part of the functional unit definition. This will allow for simultaneously include environmental impacts and mechanical performance in the same analysis, providing a multi decision criteria model for industrial applications.

[picture]

Fig. 4. GWP results of the analysed scenarios.

Fig. 4. GWP results of the analysed scenarios.

Conclusions

The original version of this paper is available on https://www.scientific.net/MSF.1187.1.pdf

In the present research work, the effect of the introduction of hemp fibres was investigated in hybrid composite systems by adopting different stacking strategies, and further LCA analysis were performed to evaluate the environmental impact and the sustainability of the different configuration produced, aiming to identifying the most suitable solution. In a first phase of the experimentations, it was assessed the influence of the position of natural fibre's reinforcement within the laminate's thickness on the mechanical performances of the hybrid system and subsequently, the environmental impact of each sample configuration under inspection.

It was observed that the introduction of three hemp layers in place of carbon ones significantly influence the mechanical response and the environmental impact of composite materials, with effects on the mechanical performances that are function of the stacking strategies. When hemp layers are positioned close to the neutral axis (S sample) the flexural stiffness remain comparable to that of the C reference, however, a moderate reduction in the flexural strength ( −14.8% ) was observed. On the other hand, the asymmetric configuration (A_HC sample), thanks to the ductile behaviour and the high damping capability of natural fibres, resulting in enhanced energy absorption and improved impact damage tolerance (reduction of the damaged area approximately 40% compared to C reference). Therefore, the results of the mechanical tests confirmed that the natural fibre positioning plays a key role in governing the damage mechanisms particularly with delamination phenomena at the hemp/carbon interfaces.

From the LCA analysis, it was observed that C and H samples represent respectively the most impactful and the most suitable alternative because of the high footprint of carbon fibres and the sustainability of hemp ones (GWP value respectively 39.9 and 0.531 kg∘ of CO2 eq per kg ). The hybrid configurations highlighted a clear contribution in global warming potential with a reduction of this indicator of almost 15.3% compared to the C reference.

Based on the analysis of the results, it can be asserted that the hybridisation of carbon fibres with hemp ones can be considered a promising solution since this category of composite materials can provide a significant environmental benefit compared to the conventional CFRP materials maintaining at the same time satisfying structural performances. Therefore, from the experimental investigations emerged that this combination of characteristics in terms of sustainability and mechanical properties, makes hemp/carbon hybrid composite materials very attractive and valuable from an industrial perspective. However, in light of the results obtained from the LCA analysis where it is highlighted the improved sustainability of hybrid composites compared to CFRPs, possible future research directions may include the use of different matrix typologies characterised by a higher sustainability, or the study of recycling technologies to further allow for the reduction of the environmental footprint of hybrid composites, with the aim of integrating the results into a broader LCA analysis.

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The original version of this paper is available on https://www.scientific.net/MSF.1187.1.pdf

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Materials Science Forum, ISSN: 1662-9752, Vol. 1187, pp 17-27

doi: 10.4028/p-4P6a64

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

Submitted:
2026-01-19

Revised:
2026-02-26

Accepted:
2026-02-26

Online:
2026-04-17

Deep Learning-Based Quality Classification of Cold Spray Coatings with Explainable Feature Analysis

The original version of this paper is available on https://www.scientific.net/MSF.1187.17.pdf

Perna Alessia Serena 1,a, Rossi Domenico 2, b, Auriemma Citarella Alessia 2,c, De Fazio Dario 1, d, De Marco Fabiola 2,e*, Di Biasi Luigi 2,f, Durante Massimo 1, g, Tortora Genoveffa 2, h and Viscusi Antonio 3,i

¹Department of Chemical, Materials and Production Engineering, University of Naples Federico II, Piazzale V. Tecchio 80, 80125 Napoli, Italy

2 Department of Computer Science, University of Salerno, Fisciano, SA, Italy

3 Department of Engineering and Sciences, Faculty of Technology and Innovation Sciences, Universitas Mercatorum, Piazza Mattei 10, 00186 Rome, Italy

a alessiaserena.perna@unina.it, b dorossi@unisa.it, c aauriemmacitarella@unisa.it, d  dario.defazio@unina.it, e *  fdemarco@unisa.it, f  Idibiasi@unisa.it, g  mdurante@unina.it, h tortora@unisa.it, iantonio.viscusi@unimercatorum.it

*corresponding author

Keywords: deep learning, cold spray, eXplainable artificial intelligence.

Abstract

This study investigates the applicability of deep learning models for automated quality classification of cold spray coatings, focusing on three deposition categories: good, degraded, and poor deposition. Three state-of-the-art convolutional architectures, ResNet-50, EfficientNet-B0, and ConvNeXt-Tiny, were evaluated across two training phases designed to assess the impact of dataset balancing, data augmentation, and higher input resolution. In the first phase, models were trained on an imbalanced dataset using only class weighting; EfficientNet-B0 achieved the best performance (ACC 80%, F1 77%), while ResNet-50 showed notable instability (ACC 60%, F1 56%). In the refined second phase, oversampling, advanced augmentation, 380×380 resolution, and early stopping led to substantial performance gains for all models. ConvNeXt-Tiny achieved the most robust and balanced results (ACC 93.3%, F1 90.3%), outperforming EfficientNet-B0 and ResNet-50 particularly in sensitivity and specificity for minority classes. Grad-CAM analysis provided qualitative insights into the decision-making process: poor samples elicited strong, spatially extended activations corresponding to defective regions, degraded samples produced more localized responses aligned with mid-scale irregularities, and good samples yielded diffuse, low-intensity activation patterns associated with surface uniformity. These interpretable attention maps validated the physical relevance of the learned features and confirmed the suitability of ConvNeXt-Tiny for reliable and explainable cold spray quality assessment.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1187.17.pdf

Cold spray is an advanced solid-state deposition technique that enables the formation of metallic coatings by propelling micron-sized powders at supersonic velocities onto a target substrate, without reaching the melting point of the material. This unique feature significantly limits oxidation, phase transformation, and thermal degradation, making the process particularly advantageous for the deposition of temperature-sensitive alloys and multi-material systems. Owing to these capabilities, cold spray is emerging as a key enabling technology in high-performance sectors such as aerospace, automotive, energy, and defense industries, where lightweight design, corrosion resistance, and durability are essential requirements [ 1,2 ]. The resulting coating quality, characterized in terms of thickness uniformity, porosity, hardness, adhesion strength, and microstructural integrity, is strongly influenced by a complex interplay of factors. These include powder characteristics (e.g., morphology, grain size distribution, material type), process conditions (such as gas temperature, pressure, and nozzle geometry), and substrate mechanical properties [3]. Establishing accurate relationships
between these variables and the final coating performance represents one of the most critical challenges in cold spray process optimization. Traditionally, process development and property prediction rely on costly and iterative experimental campaigns and computationally demanding simulation approaches based on the Finite Element Method (FEM). While these techniques enable detailed physical insight, their scalability is limited when exploring large design spaces or multiobjective optimization scenarios [4,5]. As industries increasingly seek flexible and rapid design-tomanufacture workflows, the need for efficient and data-driven predictive tools has become more pressing.
In this context, Deep Learning (DL) techniques are gaining prominence as powerful surrogate modeling approaches to map high-dimensional process input parameters to coating outcomes. By using experimental or synthetic training datasets, DL models can learn nonlinear correlations and predict coating quality metrics with high accuracy, drastically reducing development time and resource consumption. Recent studies demonstrate the potential of Artificial Intelligence (AI)-based techniques in supporting real-time monitoring, automated parameter tuning, and defect detection, thus paving the way toward fully digitalized and intelligent cold spray manufacturing systems [6, 7, 8]. As the technique enables solid-state bonding without melting, even subtle surface defects or nonuniform deposition patterns can significantly influence the functional performance of the resulting coatings. For this reason, the ability to reliably distinguish between good, degraded, and poor deposition states is fundamental for quality assurance, process optimization, and the prevention of premature component failure. However, manual inspection is time-consuming, subjective, and often unable to capture the fine-grained texture variations that characterize borderline quality conditions.
In this work, we address this challenge by developing an automated deep learning-based pipeline for classifying cold-spray surfaces into three quality classes: good, degraded, and poor. We evaluate three modern convolutional architectures- ResNet-50, EfficientNet-B0, and ConvNeXt-Tiny- using a two-phase training strategy designed to explore the impact of dataset imbalance, data augmentation, and image resolution. The first phase uses class weighting to partially address imbalance, while the second integrates oversampling, extensive augmentation, higher-resolution inputs, and dynamic early stopping to enhance robustness and generalization. Performance is assessed through accuracy, macroF1, macro-sensitivity, and macro-specificity to ensure an unbiased evaluation across quality classes. Model interpretability is then investigated through Grad-CAM visualizations, which highlight the image regions most relevant to the predictions and help verify the physical consistency of the learned features.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/MSF.1187.17.pdf

Dataset.

The original version of this paper is available on https://www.scientific.net/MSF.1187.17.pdf

The dataset employed in this study consists of optical images of cold spray depositions obtained under different processing conditions, resulting in a range of surface morphologies and deposition outcomes. Cold spray coatings were produced using a low pressure cold spray system (Dymet 423), with air employed as the carrier gas. The feedstock material consisted of spherical copper powders supplied by Carpenter Additive. The substrates were composed of different thermoplastic polymers, namely polyamide (PA), polycarbonate (PC), and polypropylene (PP).

The images were acquired employing a Hirox digital microscope, using the same magnification for all the samples. Each image represents the surface state of a cold-sprayed region and captures variations in texture, uniformity, and coating distribution associated with changes in particle impact behavior and bonding efficiency. All images were manually annotated by domain experts and assigned to one of three qualitative deposition classes: good, degraded, and poor. The labeling procedure was based on visual assessment of surface continuity, homogeneity, and the presence or absence of irregular deposition features, such as localized discontinuities, heterogeneous particle clustering, or extended defective regions. The original dataset comprises 78 images, with 35 samples labeled as good, 23 as degraded, and 20 as poor. This class distribution reflects the inherent imbalance commonly observed in experimental cold spray campaigns, where optimal or near-optimal
deposition conditions are more frequently achieved than severely defective ones. Image acquisition was performed under consistent optical and illumination conditions in order to minimize variability unrelated to deposition quality while preserving the intrinsic texture characteristics of each class. Validation and test sets were composed solely of original, non-augmented experimental images to ensure an unbiased evaluation of model performance under realistic inspection conditions. This dataset configuration enabled a systematic comparison between minimally processed data and a datacentric training strategy, allowing the impact of dataset composition and preprocessing choices on classification performance to be rigorously assessed.

Consequently, although the original dataset consists of 78 images, several precautions were adopted to mitigate overfitting to synthetic samples. First, validation and test sets were composed exclusively of original, non-augmented images, ensuring that performance metrics reflect generalization to real data only. Second, early stopping and macro-averaged metrics were used to monitor class-wise stability rather than raw accuracy.

Models.

The original version of this paper is available on https://www.scientific.net/MSF.1187.17.pdf

To assess the capability of deep learning models in recognizing coating quality, we tested multiple neural network architectures on a dataset of cold spray deposition images. In particular, we trained and evaluated three networks: ResNet, EfficientNet-B0 and ConvNeXt-Tiny.
The ResNet-50 is a deep convolutional neural network (CNN) architecture designed to address the vanishing gradient problem commonly encountered when training very deep models. Introduced by He et al. [9], ResNet employs a residual learning framework that allows the network to learn identity mappings through the use of shortcut (or skip) connections between non-adjacent layers. Each residual block typically consists of two or three convolutional layers followed by batch normalization and ReLU activation [10], with the input added directly to the block's output via an identity or projection shortcut. This architecture allows the model to maintain representational efficiency while avoiding the overfitting and training instability often observed in traditional deep CNNs. In image classification tasks, ResNet has demonstrated superior performance compared to other earlier architectures by achieving state-of-the-art accuracy with significantly deeper models. Its modular design also makes it a robust backbone for various computer vision applications, including feature extraction, segmentation, and defect detection [11].
The EfficientNet architecture represents a family of convolutional neural networks designed through a compound scaling strategy that uniformly scales network depth, width, and input resolution in a balanced manner. Unlike traditional approaches that arbitrarily increase one of these dimensions to improve accuracy, often at the cost of computational efficiency, EfficientNet employs a compound coefficient that optimally allocates model capacity across all three dimensions, achieving a superior trade-off between performance and efficiency [12]. EfficientNet-B0 is the baseline model of the EfficientNet family, which employs mobile inverted bottleneck convolution (MBConv) blocks combined with squeeze-and-excitation (SE) modules and the Swish activation function, enabling effective feature extraction with reduced computational cost. The architecture follows a compound scaling strategy, which uniformly balances network depth, width, and input resolution to maximize performance while minimizing parameters [13].
ConvNeXt-Tiny is a modern convolutional neural network architecture that reinterprets traditional convolutional networks through the design principles of Vision Transformers (ViTs). It retains the hierarchical structure of classic CNNs but incorporates architectural elements inspired by transformer-based models to enhance representational capacity and training stability. The Tiny variant represents the smallest configuration of the ConvNeXt family, optimized for computational efficiency while maintaining high accuracy. It consists of approximately 28 million parameters and is particularly suitable for image classification and transfer learning tasks where a balance between performance and inference cost is required [15].
Gradient-Weighted Class Activation Mapping (Grad-CAM) is a widely used interpretability technique designed to provide visual explanations for the decisions of convolutional neural networks. The method highlights the image regions that contribute most strongly to a model's prediction,
allowing researchers to inspect whether the network focuses on semantically meaningful features or on spurious artifacts [19]. By identifying the spatial regions most influential to a prediction, GradCAM provides qualitative insights into model behavior, helps detect biases or incorrect reasoning, and supports model validation in sensitive domains such as medical imaging, materials science, and quality control. Its architecture-agnostic design and compatibility with a broad range of CNN-based models make it a standard tool for explainability in deep learning.

Experimental design and Training Protocol.

The original version of this paper is available on https://www.scientific.net/MSF.1187.17.pdf

Two training phases were conducted to evaluate the performance of deep learning models in classifying cold-spray deposition quality into the three categories. The following subsections describe in detail the datasets, preprocessing strategies, training procedures, and model-specific results. In the initial training setup, the dataset exhibited a clear class imbalance, with 35 good, 23 degraded, and 20 poor samples. No resampling strategies or data augmentation techniques were applied during this first phase. Instead, class weighting was introduced during training, assigning higher weights to under-represented classes to partially mitigate imbalance effects. A fixed training schedule of 15 epochs was used for all models, without early-stopping [16].
In the second phase of experimentation, the training pipeline was substantially refined to enhance model robustness and to better address the intrinsic challenges posed by the dataset. First, the class imbalance observed in the original dataset was mitigated through oversampling of minority classes, ensuring a more uniform representation of degraded and poor samples during training. This adjustment was essential to prevent the model from developing a bias toward the majority class and to promote more equitable learning across all coating-quality categories [17]. In addition to oversampling, a comprehensive data augmentation strategy was introduced to simulate realistic variations in the visual appearance of cold-spray depositions. Techniques such as geometric transformations, intensity perturbations, and localized texture modifications were applied to artificially expand the dataset and improve generalization. These operations help mimic real-world variability in coating morphology, enabling the models to better recognize subtle texture differences associated with each quality class [18]. A further improvement involved increasing the input image resolution to 380×380 pixels. The cold-spray process generates fine-grained surface features and microtexture patterns whose discriminative details may be lost at lower resolutions. By adopting a higher spatial resolution, the models were provided with richer visual information, facilitating more accurate extraction of texture-based cues that differentiate good, degraded, and poor deposition states.

Performance Metrics.

The original version of this paper is available on https://www.scientific.net/MSF.1187.17.pdf

The performance of the classification models was assessed using several statistical indicators, including Accuracy, macro-averaged F1-score, macro-averaged Sensitivity, and macro-averaged Specificity. These metrics collectively provide a comprehensive evaluation of the ability of the models to correctly classify samples across all classes, regardless of potential class imbalance. Accuracy (ACC) quantifies the overall proportion of correctly classified samples among the total number of observations. It provides a global measure of performance but may not fully reflect classwise behavior when class distributions are unbalanced. F1-score (macro) is the harmonic mean of precision and recall, computed independently for each class and then averaged. This macro-averaging ensures that each class contributes equally to the final score, regardless of its frequency. Sensitivity (macro) (SN), also referred to as macro recall, measures the model's ability to correctly identify positive samples across all classes. It is obtained by averaging the sensitivity values computed per class. Specificity (macro) (SP) evaluates the model's capacity to correctly recognize negative samples for each class. The class-wise specificities are averaged to provide a balanced estimate across the entire dataset. The use of macro-averaged metrics is particularly appropriate for multi-class classification tasks, as it treats all classes equally, preventing dominant classes from biasing the overall performance evaluation [14].

Results

The original version of this paper is available on https://www.scientific.net/MSF.1187.17.pdf

This section presents a comprehensive analysis of the experimental results obtained from the two training phases. The first phase employed a minimally processed dataset and a static training protocol, while the second phase introduced several methodological improvements, especially oversampling, data augmentation, higher-resolution inputs, and dynamic early stopping.

Labelling of the dataset.

The original version of this paper is available on https://www.scientific.net/MSF.1187.17.pdf

The acquired images were manually labeled according to the three predefined categories. The labeling procedure was based on the traditional classification adopted within the cold spray deposition framework, which defines the deposition window as comprising three distinct regimes: the rebound region, the degradation region, and the good adhesion region. These regimes reflect the process parameter ranges that respectively prevent deposition, lead to degraded coating formation, or ensure effective deposition and adhesion of the coating to the substrate. Representative examples of coatings corresponding to the three categories are shown in Figure 1.

[picture]

Fig. 1. a) Example of a copper coating deposited on a polyamide substrate and classified as good; b) example of a copper coating deposited on a polyamide substrate and classified as degraded; c) example of a copper coating deposited on a polyamide substrate and classified as poor.

Fig. 1. a) Example of a copper coating deposited on a polyamide substrate and classified as good; b) example of a copper coating deposited on a polyamide substrate and classified as degraded; c) example of a copper coating deposited on a polyamide substrate and classified as poor.

Results of the first training phase.

The original version of this paper is available on https://www.scientific.net/MSF.1187.17.pdf

In the initial phase, models were trained with class weighting only, without any augmentation or resampling strategies. This configuration exposed the ability of each architecture to directly cope with class imbalance and limited sample variability. Table 1 summarizes the obtained results of this first phase.

Table 1. Performance Metrics (First training phase.

Model | ACC (%) | F1 macro (%) | SN macro (%) | SP macro (%)
ResNet-50 | 60 | 56 | 56.8 | 80.3
EfficientNet-B0 | 80 | 77 | 79.4 | 91.1
ConvNeXt-Tiny | 67 | 62 | 63.5 | 85

EfficientNet-B0 clearly dominates the first phase, indicating stronger generalization under limited augmentation. ConvNeXt-Tiny shows moderate performance, while ResNet-50 struggles the most, reflecting a higher sensitivity to intra-class variability.
The behavior of the three architectures during the first training phase reveals important differences in how each model interprets the visual properties of cold-spray depositions.
EfficientNet-B0 demonstrates the most reliable performance, particularly in its perfect discrimination of the degraded class. This suggests that the visual signatures associated with degraded coatings, typically characterized by clear non-uniformities or texture irregularities, are highly separable and well captured by the network's feature hierarchy. However, the model exhibits some confusion between good and poor, misclassifying a subset of good samples as poor. This behavior reflects the intrinsic visual overlap that may arise in borderline samples, where subtle irregularities in an otherwise good deposition resemble the more pronounced defects of the poor category. In contrast, ConvNeXt-Tiny shows a different error distribution. The model frequently misclassifies good ⟶ poor and poor ⟶ degraded, indicating a pessimistic bias in its decision-making process. In other words, when uncertainty is present, ConvNeXt tends to err by assigning samples to a lower-quality class. This pattern suggests that the model may be particularly sensitive to small deviations in surface texture or illumination that it interprets as defects. Despite these misclassifications, ConvNeXt-Tiny exhibits consistently strong performance on the degraded class, implying that degraded coatings contain the most distinctive and easily recognizable visual patterns among the three quality categories.
ResNet-50, on the other hand, displays the most heterogeneous and inconsistent error pattern. The model misclassifies good samples as both degraded and poor, and occasionally produces degraded → good errors, suggesting that it struggles to establish stable class boundaries without additional data regularization. Furthermore, poor samples are most often confused with degraded, highlighting difficulties in capturing the fine-grained distinctions that separate the two lower-quality classes. This error structure indicates that ResNet-50's feature extraction is less suited to the subtle and texturerich characteristics of cold-spray surfaces, particularly when trained without augmentation. The model appears unable to reliably disentangle the nuanced patterns that differentiate visually similar deposition states, resulting in lower overall performance and class-wise stability.

Results of the second training phase.

The original version of this paper is available on https://www.scientific.net/MSF.1187.17.pdf

The second training phase integrates oversampling, image augmentation, higher-resolution inputs ( 380×380 ) and dynamic early stopping. These improvements substantially enhance model performance and stability across all architectures, as we can see in Table 2.

Table 2. Performance Metrics (Second training phase).

Model | ACC (%) | F1 macro (%) | SN macro (%) | SP macro (%)
ResNet-50 | 93.3 | 86.7 | 83.3 | 95.8
EfficientNet-B0 | 86.7 | 83.3 | 89.7 | 94.9
ConvNeXt-Tiny | 93.3 | 90.3 | 94.4 | 97.4

The improvement between training phases is substantial, particularly for ConvNeXt-Tiny and ResNet-50, showing that the architectural capacity was previously underutilized due to training constraints.
EfficientNet-B0 demonstrates a generally strong and reliable classification behavior. The model achieves near-perfect recognition of the good class. This indicates that the features extracted by the network are well aligned with the morphological characteristics typically associated with high-quality coatings, such as uniform deposition and consistent surface texture. The model also performs perfectly on the poor class, successfully identifying all corresponding samples without any false positives. This suggests that the network is highly sensitive to the pronounced irregularities and defects that define poor surface quality. However, EfficientNet-B0 exhibits a single misclassification in the degraded category, assigning one degraded sample to the poor class. This particular error suggests that the model may be especially responsive to marked texture irregularities within borderline degraded cases, interpreting them as more severe than they actually are. Despite the overall performance improvement relative to the first training phase, the model remains slightly less stable on minority classes, where the number of available samples is low. This sensitivity results in minor fluctuations in how intermediate deposition defects are interpreted.
ConvNeXt-Tiny exhibits the most consistent and robust behavior across all classes. The model achieves perfect classification for both the good and poor categories, recognizing all samples correctly and without ambiguity. This is particularly relevant for industrial deployment, as accurately detecting good samples while never misclassifying poor samples as acceptable is essential for maintaining reliable quality control standards. The only error made by ConvNeXt-Tiny occurs within the degraded class, where one sample is incorrectly labeled as poor. Importantly, this misclassification reflects a conservative bias: rather than overestimating the quality of a potentially flawed deposition, the model errs on the side of caution by assigning it to a lower-quality class. In quality-critical manufacturing contexts, this behavior is generally preferable, as it minimizes the risk of approving defective components. ConvNeXt-Tiny also achieves the highest macro-F1, macrosensitivity, and macro-specificity among all tested models, confirming that it maintains balanced performance across classes. Its ability to generalize effectively despite class imbalance and subtle inter-class differences underscores its suitability for texture-rich classification tasks like cold-spray surface assessment.
ResNet-50 also performs well overall, achieving perfect classification of both the good and degraded classes, which indicates that the network can reliably distinguish high-quality coatings and moderate deposition defects. However, performance declines for the poor class, where ResNet-50 misclassifies one sample as degraded. This poor → degraded misclassification is more critical than the error observed in ConvNeXt-Tiny. While ConvNeXt's error tends to underestimate the quality of a marginal sample, ResNet-50's error has the opposite effect: it reduces the perceived severity of an actual defect, potentially allowing faulty coated components to pass inspection. In safety-critical or performance-critical settings, this type of error is far less acceptable because it compromises the reliability of the quality assurance process. Despite its strong performance on the majority classes, this limitation indicates that ResNet-50 is less reliable for identifying the most defective samples, making it less suitable for deployment in industrial inspection environments where strict defect detection is mandatory.
The results demonstrate that data quality and training strategies have a stronger influence than architecture alone. When trained with minimal preprocessing (Phase 1), models show high variance in performance and substantial confusion, particularly for the poor class. However, after implementing a more sophisticated training pipeline (Phase 2), all architectures improve, with ConvNeXt-Tiny emerging as the most reliable for industrial cold-spray quality assessment. The model's stability, superior class-wise performance, and conservative error dynamics make it highly suitable for deployment in automated inspection systems where misclassifying a poor-quality deposition as acceptable must be strictly avoided.

eXplainable results with Grad-CAM.

The original version of this paper is available on https://www.scientific.net/MSF.1187.17.pdf

To investigate whether ConvNeXt-Tiny makes decisions based on semantically meaningful features of the cold-spray depositions, we applied Grad-CAM to a representative subset of validation images from each class. Grad-CAM provides class-discriminative saliency maps by computing the gradients of the target class score with respect to the activations of a late convolutional block, averaging these gradients to obtain importance weights, and then forming a weighted sum of the feature maps followed by a ReLU. For ConvNeXt-Tiny we extracted activation maps from the final convolutional stage immediately prior to any classification head (i.e., the deepest stage that retains spatial layout), upsampled the resulting heatmaps to the original image resolution, and overlaid them on the input images. The Grad-CAM maps shown in Figure 2 highlight how the ConvNeXt-Tiny model focuses on different regions of the coating surface when predicting each class. Across all three examples, the model's attention is concentrated on texture-rich areas of the deposition.

[picture]

Fig. 2. Grad-CAM on a) degraded b) poor c) good images.

Fig. 2. Grad-CAM on a) degraded b) poor c) good images.

In Figure 2a (degraded sample), the heatmap exhibits strong, localized activations, primarily in the central band, corresponding to irregular patches and mid-scale texture disruptions typical of degraded coatings. The model concentrates on transitions in roughness and subtle clustering of particles, which serve as reliable indicators of partial deposition defects. This focused activation pattern aligns well with the model's ability to correctly isolate the degraded class and also explains the occasional conservative misclassification of borderline degraded regions as poor, as the highlighted zones often coincide with visually severe irregularities. In Figure 2b (poor sample), the activations are more widespread and intense, extending across the upper and lower regions of the image. This reflects the extensive heterogeneity, abrupt texture discontinuities, and defective local structures that define poorquality deposits. The model captures these broad defective areas, leading to a coherent and intuitive
activation map. The large, contiguous red-yellow regions in the heatmap indicate high confidence in identifying severe surface defects, consistent with the model's perfect classification of the poor class in the second phase of the experiment. In Figure 2c (good sample), the heatmap reveals a markedly different behavior: activations are diffuse and of lower intensity, often aligned with subtle texture transitions rather than clear defect zones. This suggests that, for good samples, the model relies on the absence of strong irregularities rather than on specific localized cues. The uniformity of the underlying texture corresponds to low-saliency regions in the Grad-CAM output, reflecting the model's correct identification of high-quality deposition. The sparse, low-intensity activations highlight the network's focus on subtle surface features while avoiding false attention to noise or benign variations, an essential property for robust industrial inspection. Overall, Figure 2 demonstrates that Grad-CAM provides consistent and interpretable insights into the model's internal reasoning. The network attends to defect-related features for low-quality samples while exhibiting diffuse and low-saliency focus for good-quality coatings. This behavior is both physically plausible and aligned with expert interpretation, reinforcing the trustworthiness of the ConvNeXt-Tiny model for automated cold-spray quality assessment.

Conclusion

The original version of this paper is available on https://www.scientific.net/MSF.1187.17.pdf

The results of this study highlight the combined importance of model architecture and data-centric strategies in achieving reliable classification of cold spray deposition quality. While the first training phase emphasized the limitations of relying solely on class weighting to address imbalance, the second phase clearly demonstrated that oversampling, domain-appropriate augmentation, and highresolution inputs are essential for extracting discriminative texture features characteristic of cold spray surfaces. These findings align with recent trends in automated inspection, where data quality and representativeness increasingly drive model performance beyond architectural complexity. Among the evaluated models, ConvNeXt-Tiny consistently exhibited the most stable behavior across all classes, achieving both high accuracy and balanced class-wise metrics. Its conservative error tendency, misclassifying ambiguous degraded samples as poor rather than overestimating quality, is especially valuable in industrial contexts where false negatives (i.e., poor samples classified as acceptable) pose a greater risk than false positives. In contrast, ResNet-50, although benefiting significantly from the refined training pipeline, remained less reliable for identifying the poorestquality samples. EfficientNet-B0 maintained strong performance but showed slightly higher sensitivity to borderline cases, particularly within the degraded class, confirming that architectural efficiency alone is not sufficient to ensure optimal discrimination of subtle material defect patterns. Grad-CAM analysis provided critical evidence supporting the interpretability and trustworthiness of ConvNeXt-Tiny's predictions. The model consistently focused on physically meaningful regions: broad defective zones in poor samples, localized irregularities in degraded samples, and diffuse nonsalient regions in good samples. This alignment between model attention and expert-understood morphological features reinforces confidence in the system's internal representations and supports its integration into automated quality assurance pipelines. Importantly, the absence of spurious activations indicates that the model does not rely on artefacts such as illumination gradients or background noise, a frequent concern in vision-based industrial inspection. This preliminary study demonstrates that deep convolutional models-when trained with appropriate data balancing and augmentation strategies-can achieve not only high classification accuracy but also explainable and physically grounded decision-making. ConvNeXt-Tiny, in particular, emerges as a strong candidate for deployment in real-world cold spray inspection systems due to its robustness, conservative error profile, and interpretability. Future work will focus on expanding the dataset, incorporating multimodal inputs (e.g., height maps or thermal data), and exploring transformer-based or hybrid architectures to further enhance generalization across varying deposition conditions and material systems. Since this is a preliminary study, another relevant direction for future work concerns the systematic evaluation of the robustness of Grad-CAM explanations under varying acquisition conditions. Although the current results indicate that ConvNeXt-Tiny focuses on physically meaningful defect-related regions, further experiments will be conducted to explicitly assess its
ability to distinguish real surface defects from acquisition-related artefacts. In particular, future studies will include images acquired under different lighting setups, magnifications, and controlled perturbations, as well as synthetic noise injection protocols, to verify the stability and consistency of the attention maps and to further validate the physical reliability of the model's interpretability.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/MSF.1187.17.pdf

This study was carried out within the "OPTIMA: depOsition of cold sPray in the realm of green addiTIve manufacturing through construction of MAchine learning models" and received funding from the Research Projects of Significant National Interest (PRIN) 2022 PNRR, project n. D53D23017410001.

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The original version of this paper is available on https://www.scientific.net/MSF.1187.17.pdf

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Materials Science Forum, ISSN: 1662-9752, Vol. 1187, pp 29-36

doi: 10.4028/p-4uxRbh

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

Submitted:
2025-12-11

Revised:
2026-02-10

Accepted:
2026-02-20

Online:
2026-04-17

Increasing Strength of Recycled Materials by Fiberglass Pulverization and Powder Direct Molding

The original version of this paper is available on https://www.scientific.net/MSF.1187.29.pdf

Fabrizio QUADRINI 1,a*, Denise BELLISARIO 1, b, Alice PROIETTI 1,c, Leandro IORIO 2,d , Dounia NOQRA 1,e , Giorgio PATRIZII 1,f  and Loredana SANTO 1,g

1 University of Rome Tor Vergata, via del Politecnico 1, Rome, Italy

2 Mercatorum University, p.zza Mattei 10, Rome, Italy

a  fabrizio.quadrini@uniroma2.it, b  denise.bellisario@uniroma2.it, c  alice.proietti@uniroma2.it, d leandro.iorio@unimercatorum.it, e dounia.noqra@uniroma2.it, f giorgio.patrizii@uniroma2.it, gloredana.santo@uniroma2.it 

Keywords: fiberglass recycling, direct molding, recycled composites.

Abstract

Direct molding is a technology where thermoset powders are agglomerated by compression molding without adding any additional substance or linking agent. It has been applied to powders from composite recycling, such as fiberglass. Agglomeration depends on residual reactivity of powders, the intrinsic re-activation of the particle surfaces because of the broken chemical links, and an incipient degradation mechanism during molding. In the case of continuous fiber laminates, the mechanical properties of the virgin item cannot be recovered as the recycled composite is made by particles. Nevertheless, high values may be reached, potentially interesting for such applications, depending on some precautions during the molding phase. Powders from grinding of fiberglass have been recovered from industry. They have been compression molded, and samples have been extracted from different parts of the molded plate to evaluate the distribution of the mechanical properties by bending tests. Results show that a bending strength up to 27 MPa can be achieved, without using any virgin material or additional substance, and a bending modulus over 3.5 GPa . However, pressure distribution during molding is not uniform and mechanical properties strongly vary from the periphery of the plate to the inner zones.

Introduction

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Fiber Reinforced Plastics (FRPs) are acquiring an even more increasing demand in different fields of application. They represent 3% of the total plastic market volume and account for 16−20% of the total plastic market value. Construction ( 25% ), transportation ( 24% ), electronics ( 17% ) and energy ( 13% ) are the largest segments where both continuous and discontinuous fibers are used. Currently, the mentioned FRP market is highly dominated by glass fibers representing about 90% of the global volume mainly because of their notable strength-to-price ratio [1]. This increasing demand is the driving force for new research studies in developing other FRP products [2] which can also be applied in advanced sectors such as aeronautics, where fiber reinforced composites (FRCs) with thermoplastic matrix are used in Aircraft radome [3]. Simultaneously, sustainability issues led to the development of green composites with natural fiber reinforcements, also in the field of maritime engineering [4]. In this last field polymeric matrix composites (PMCs) with glass fibers reinforcements first found applications in marine structures such as recreational boats, working vessels and boat components [5].

The widespread use of glass fibers is also due to their lightness, high strength, thermal stability, electrical insulation properties and low costs. Glass fibers in combination with polymers, develop composite materials in the form of fiberglass with excellent thermal and electrical insulation properties; moreover, they are chemical inert under many conditions, dimensionally stable and nonmagnetic. Fiberglass usually consists of a thermoplastic matrix, commonly, polyamide, poly (phenylene sulfide), polypropylene and polybutylene terephthalate) [6]. Polyurethane based composites are also acquiring great resonance because of their mechanical performance and durability
[7]. Polyester and vinylester based composites are their thermoset matrix counterparts; moreover, epoxy-based glass FRCs are also used and they find application among others, in high-voltage insulator core rods [8]. Appropriate combinations of resin matrix and glass fiber configuration (i.e., fabric or short fibers) influence mechanical performances [9]; the integration of carbon-based fillers such as carbon nanotubes or reduced graphene oxides into glass fiber reinforced plastics (GFRPs) influences mechanical performances as well; in fact, they are weakened if adhesion among fillers, glass fibers and matrix is not optimal [10]. For the mentioned applications, water absorption is also critical for mechanical performance, and in this view, epoxy matrixes are more susceptible to moisture absorption compared to vinyl-ester ones mainly due to differences in resin susceptibility to hydrolysis and matrix plasticization [11].

The wide diffusion of GFRPs opens the urgent need to introduce recycling strategies, transferable at an industrial level to avoid the commonly adopted habit of dismission to landfills. In fact, to date there are ever increasing landfill restrictions. In recycling it is important to develop re-processing solutions of recovered composite constituents to obtain second life products with limited reduction in mechanical and functional properties. The current adopted strategies are based on mechanical, thermal and chemical processes but advanced recycling methods such as electrochemical recycling, micro-wave assisted recycling and sono-chemical methods have also been emerging [12].

Mechanical recycling methods based on pulverization, grinding and shredding are the most spread ones because of their ease of process. They are commonly applied with the end of life (EoL) of turbine blades, and the recycled products can be integrated in concrete and mortar to increase tensile and compression strength [13]; in this view the optimization of sieving process is crucial to reclaim useable fiberglass for second life products [14]. Mechanical performances of compression molded composites from chopped GFRPs made of a PA 6 matrix revealed that the size of chopped fibers causes complex damage mechanisms and variability in mechanical performance [15]. Unfortunately, mechanical recycling methods, differently from chemical-based ones, weaken the recovered fiberglass [16].

Recycling strategies based on thermal processes that are combustion, pyrolysis and fluidized bed [17] are also promising but different issues still must be addressed. Combustion involves energy recovery, in fact the material to be recovered is co-processed with another one. It develops via the cement kiln route where the polymer matrix is burned as fuel for the process whereas the glass fibers provide mineral feedstock to be used as part of the cement clinker. This way of co-processing reduces the carbon footprint up to 16% and provides valuable materials to the process. Pyrolysis allows conversion of organic material in an inert atmosphere at temperatures varying from 450∘C to 800∘C depending on the nature of the composite matrix. This process induces volatilization of the resin matrix whereas fibers and fillers are isolated and recovered. Unfortunately, the recovered fibers have lower mechanical properties than virgin ones, so they can be used as fillers or replacement in the production of new composite materials. Moreover, it does not make use of chemicals. Pyrolysis can be also used in combination with oxidization process to obtain glass fibers with carbon free surface [18]. In the fluidized bed process, solid particles are transformed into a fluid state through their suspension in a hot stream of air at temperatures ranging between 450∘C and 550∘C and streamed at speed between 0.4 and 1.0 m/s. Despite pyrolysis, in fluidized bed process the rich and high-flow oxygen atmosphere allows obtaining clean fibers with very little char surface contamination. As for pyrolysis, the recovered fibers show significatively reduction in mechanical performance, up to 75%.

New perspectives are offered by chemical methods as solvolysis which uses fluid in supercritical conditions to recover polymeric matrix. The use of H2SO4 allows reaction with the epoxy constituents to obtain in situ oxonium enabling for clean glass fibers with properties like virgin ones. Epoxy matrix can also be partially recovered [19]. A recent technique using ultrasonic power and sonotrode allows separation and reconsolidation of GFRP laminates for aerospace uses. It was demonstrated that reduction in mechanical performance was about 25% [20]. Alternative solutions consisting in tailoring epoxy resin with cleavable curing agents to allow successive separation by acid digestion from glass fibers maintaining a good surface quality compared with virgin ones are offered by the market of turbine blades [21].

Recycling GFRP constituents and fabricating second life products with good mechanical performances are severe matters of interest. In the current study a "direct molding process" based on compression molding, has been used to produce GFRP plates from pulverized fiberglass. This approach uses the residual reactivity of shredded GFRP powders to allow polymerization. In addition, broken bonds on particles' external surface act as polymerization sites in further manufacturing steps. Moreover, during process any linking agents or virgin materials have been used. This manufacturing procedure has been previously used to prove the feasibility of obtaining second life products made from 100% recycled fiberglass [22] and its transfer to industries as well. Direct molding can be adopted to recycle several kinds of waste fiberglass and obtain recycled products with optimal mechanical performances [23,24]. The further goal is to evaluate the distribution of the mechanical properties on large plates by direct molding of pulverized fiberglass waste. This study investigates this aspect in the case of waste fiberglass powder from industry, where grinding is used to provide the final shape to the manufactured items.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/MSF.1187.29.pdf

Fiberglass powder. Waste fiberglass in the form of powder was supplied by a factory of technical laminates. The collected powder was the result of a grinding procedure to resize the products which were laminates used as insulating refrigerated trucks. The polymeric matrix is a polyester resin.

Manufacturing. A recycled plate was produced by hot compression molding starting from the collected powder without any addition of binder, linking agent and virgin materials. This process is known as direct molding and takes advantage of the powder residual reactivity which develops high energy because of the high area to volume ratio. Moreover, the broken bonds due to the grinding process act as polymerization sites.

The plate was produced starting from a nominal mass content of the collected powder of 100 g . The amount of powder was poured into a 150×150 mm2 cavity of a steel mold without using a release film. The whole assembly was placed between the plates of a hot parallel press. A pressure of 2 bars (0.2MPa) and a temperature of 250∘C were applied for a time of 15 min during compression molding. The whole manufacturing procedure is shown in Fig. 1. The high temperature is responsible for increasing resin mobility once the transition temperature is overcome, whereas pressure is responsible for providing the contact among the particles during agglomeration. The molding parameters were chosen to limit degradation of the resin. At the end of molding, the assembly was left cooling down to room temperature before extraction.

[picture]

Fig. 1. Compression molding of a recycled fiberglass plate.

Fig. 1. Compression molding of a recycled fiberglass plate.

A sum of seven specimens with nominal sizes of 80×10 mm2 were extracted from the molded plate through a metallographic cutting machine. Specifically, specimens were extracted, one parallel to the other, from one edge to the opposite one, to evaluate the trend of physical and mechanical properties along this direction. In Fig. 2, the concept of experimentation is shown from extraction to mechanical testing.

[picture]

Fig. 2. Sample extraction and testing.

Fig. 2. Sample extraction and testing.

Testing. Specimen density was evaluated by weighing the samples and dividing by their bulk volume, approximated to a parallelepiped. Table 1 reports physical data for all the extracted samples. An average density of 1.26 g/cm3 is inferred for the full plate. The mechanical behavior was evaluated through bending tests, carried out up to failure. Specifically, the 4-point bending test was performed according to the ASTM D6272-02 standard because of the brittle behavior of the samples. A support span of 60 mm and a load span of 20 mm,1/3 of the support span, were selected. All tests were performed with a speed rate of 1 mm/min through an Insight 5 Universal testing machine by MTS.

Table 1. Size and density of the extracted samples.

Sample ID | Thickness, mm | Density, g/cm³
Specimen 1 | 3.09±0.09 | 1.24
Specimen 2 | 3.03±0.04 | 1.35
Specimen 3 | 2.98±0.03 | 1.29
Specimen 4 | 2.94±0.06 | 1.24
Specimen 5 | 3.01±0.18 | 1.19
Specimen 6 | 2.79±0.07 | 1.27
Specimen 7 | 2.76±0.02 | 1.27

Results and Discussion

The original version of this paper is available on https://www.scientific.net/MSF.1187.29.pdf

Results from mechanical testing are reported in Table 2. Samples exhibited a brittle behavior under testing, as shown in Fig. 2. The bending curve is linear for all the samples, with very good correlation factors, always higher than 0.98 . This behavior is typical for agglomerated samples, mainly in the case of particle agglomeration. The average maximum strain of the plate is 0.64%, whereas the mean values for bending strength and modulus are 17.1 MPa and 2391 MPa , respectively. These values seem to be acceptable for such technical applications, where low strength is required, and it must be noticed that they have been achieved without using any additive or virgin substance.

Table 2. Mechanical properties from testing.

Sample ID | Maximum strain | Bending strength, MPa | Bending modulus, MPa
Specimen 1 | 0.50% | 17.5 | 3386
Specimen 2 | 0.68% | 25.1 | 3551
Specimen 3 | 0.88% | 26.8 | 2923
Specimen 4 | 0.66% | 11.9 | 1644
Specimen 5 | 0.44% | 8.3 | 1596
Specimen 6 | 0.35% | 3.0 | 1076
Specimen 7 | 0.99% | 27.0 | 2559

[picture]

Fig. 3. Property distribution along the sample length.

Fig. 3. Property distribution along the sample length.

Nevertheless, the highest value for the bending strength is 27 MPa , about 60% more than the average. Similarly, the bending modulus reached 3551MPa,50% more than the average. It is under discussion if these properties could be obtained in the full plate, instead of such points.

In Fig. 4, physical and mechanical properties of Table 1 and Table 2 are reported as a function of the sample position, which is related to the distance from one edge of the plate to the opposite other. It is visible that a trend is present for all of them, with the maximum of the properties toward the edges or close to them. The edges are the parts of the plate subjected to the highest amount of heat,
because of the presence of the lateral mold wall, whereas the internal part of the plate is heated only by the upper and lower face of the mold cavity. Nevertheless, because of the presence of the gap between the female and the male part of the mold, it is possible that the pressure is not perfectly transmitted. For this reason, if the maximum of agglomeration is not reached at the edge, it occurs a little bit far from it. Another aspect to consider is that it is difficult to apply a uniform pressure to the plate during compression molding as the powder does not flow under pressure. For this issue, it is fundamental to level perfectly the powder before closing the mold. This action becomes increasingly difficult by increasing the plate size. As expected, if one side of the plate reaches a good agglomeration, the other side of the plate receives less pressure, but a local maximum can occur at the edge because of the lateral wall. This mechanism seems to be very well represented from results of Fig. 4, as generally the left part of the plate is higher in performances than the right part, apart from anomalies at the edges.

By improving agglomeration, every property increases, comprising density, strength, stiffness and ductility. In fact, all these properties depend on the number of joined particles during the compression molding step. This fact is partially shown in Fig. 4 where the maximum strain and the bending modulus are reported for each sample as a function of the bending strength, after normalization by the maximum values among all the samples. Stiffness and ductility generally increase when the strength increases, even if data are scattered because of the brittle nature of the tested sample. This behavior is common for agglomerated samples.

[picture]

Fig. 4. Correlation between normalized mechanical properties.

Fig. 4. Correlation between normalized mechanical properties.

Summary

The original version of this paper is available on https://www.scientific.net/MSF.1187.29.pdf

The current study has obtained very high performances for recycled fiberglass in absence of any virgin substance or linking agent. In the best case, in the current experimentation, the bending strength has reached 27 MPa and the modulus has overcome 3.5 GPa . Nevertheless, it has been observed that there is a big variation in these properties in the molded plate. It is reasonable that such process limitations could have affected plate homogeneity. A possible cause is that the fiberglass powder cannot spread the applied pressure internally, and differences in powder bed thickness have led to more and less agglomerated zones. This aspect is critical to increase the size of the molded part and must be solved by adding some manufacturing steps to improve pressure distribution during molding.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/MSF.1187.29.pdf

The authors are grateful to Eng. Fabrizio Betti for the support given in the experimentation.

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The original version of this paper is available on https://www.scientific.net/MSF.1187.29.pdf

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The original version of this eBook is available on https://www.scientific.net/book/special-materials-and-processes/978-3-0364-1544-4

Materials Science Forum, ISSN: 1662-9752, Vol. 1187, pp 37-44

doi: 10.4028/p-371Pct

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

Submitted:
2026-01-16

Revised:
2026-02-15

Accepted:
2026-02-20

Online:
2026-04-17

Evaluation of Anti-Fouling Behavior of Silver Nanocomposites Made by Nano-Coating Fragmentation

The original version of this paper is available on https://www.scientific.net/MSF.1187.37.pdf

Leandro IORIO 1,a*, Loredana SANTO 2, b, Denise BELLISARIO 2,c, Alice PROIETTI 2, d, Dounia NOQRA 2,e, Giorgio PATRIZI 2,f and Fabrizio QUADRINI 2,g

1 Mercatorum University, p.zza Mattei 10, Rome, Italy

2 University of Rome Tor Vergata, via del Politecnico 1, Rome, Italy

aleandro.iorio@unimercatorum.it, b  loredana.santo@uniroma2.it, c  denise.bellisario@uniroma2.it, d alice.proietti@uniroma2.it, e dounia.noqra@uniroma2.it, f giorgio.patrizii@uniroma2.it, g  fabrizio.quadrini@uniroma2.it

Keywords: nano-coating fragmentation, silver nano-particles, smart buoy.

Abstract

Nano-coating fragmentation (NCF) is patented technology which allows producing thermoplastic matrix nanocomposites without the step of nano-particle preparation. Thermoplastic pellets are PVD (physical vapor deposition) coated by the metal of the desired nano-reinforce. In the following processing step, by extrusion or injection molding, nano-coatings are fragmented into nanoplatelets because of the action of the screw. In this study, nano-silver ( Ag ) filled nanocomposites with polypropylene (PP) matrix have manufactured by this innovative technique and tested in open environment for their anti-fouling behavior. PP pellets have been PVD coated into a large chamber with the aid of a rotating drum. Coated pellets were physically mixed with virgin in the percentage of 0,5,10,20, and 100%. Consequently, the expected Ag percentage ranged from 0.036% wt to 0.103% wt. Square nanocomposite samples ( 80×80 mm2 and 3 mm thick) were injection molded in a fully electric press. One sample for each nano-Ag content was selected to be exposed in open environment. A smart buoy, especially designed for water cleaning and monitoring, has been used for experimentation. Results show that Ag-NPs provide a significant contribution to reduce the growth of vegetation on the molded plastic surfaces. However, at very low contents, the negative effect of the Ag NPs on the surface morphology of the molded samples nullifies this contribution.

Introduction

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The current critical issues on the presence of bacterial activity and different micro and bio-organisms on the surfaces of everyday life objects, which also damage marine structures require the use of proper strategies and materials to solve these criticalities. The presence of bacteria and harmful organisms is very dangerous for human health but also for the right operation of several devices. These issues are deeply felt in the fields of biomedical devices, tissue engineering, marine structures, packaging and electronic devices. In this view different materials with anti-bacterial and anti-fouling properties are properly designed and fabricated. Specifically, anti-bacterial behavior is referred to as the property of impeding the growth and proliferation of bacteria over surfaces through direct actions aiming at inhibiting bacterial growth or breaking the bacterial membrane. On the other hand, anti-fouling behavior is referred to as the property of avoiding the accumulation of organic and biological organisms (algae, bacteria, marine fouling such as molluscs) onto surfaces. The anti-fouling behavior is obtained by developing hierarchical surfaces, specifically super hydrophobic, which impede the settlement of organisms. On the other hand, the anti-bacterial behavior is obtained by releasing chemicals which react with the bacteria constituents [1,2]. For these aims, current trends use nanomaterials (NMs) because they have optimal properties such as high surface area and area to volume ratio and consequently high reactivity [3]. Both inorganic and organic NMs are currently used. Among inorganic materials there are either metal oxides such as copper oxide ( CuO ), zinc oxide (ZnO), titanium dioxide ( TiO2 ), or metal-based NMs like gold ( Au ), silver ( Ag ), iron ( Fe ) and copper (Cu). Among organic based NMs graphene oxide (GO), carbon nanotubes (CNTs), nanodiamonds,
cellulose nanocrystals and graphitic carbon nitride are commonly used. There are also 2D materials made of transition metals (M) and carbon and/or nitrogen (X) (MXene), metal organic frameworks and polymer nanocomposites (PNCs), that are polymers filled with nano-additives. All these materials are used in anti-fouling applications such as marine structures, water treatment and medical devices [4]. Polymeric materials with anti-bacterial and anti-fouling properties are also commonly used and modified by the addition of inorganic particles or active molecules. These additives allow modifications of the commonly developed polymeric membrane obtaining nanocomposite membranes (NCMs) with enhanced thermal stability, magnetic behavior and strength [5]. They are mainly fabricated with Polyethylene Glycol (PEG). Poly(oligoethylene glycol) Methacrylate (POEGMA), Poly(2-oxazoline) (POx), Polyglycerol (PG), polyvinylidene fluoride (PVDF), polyvinylpyrrolidone (PVP) and zwitterionic polymers [6]. Apart from virgin materials, also recycled materials can be used for this aim. In fact, barrier properties of recycled polyethylene terephthalate (PET) sheets have been enhanced by aluminum (Al) nano-coating obtained through physical vapor deposition (PVD) sputtering [7]. Moreover, the commonly adopted technologies for polymers' processing can be redirected for PNCs with enhanced anti-bacterial and anti-fouling behaviours. These processes include melt processes, solvent casting, electrospinning, solution blow spinning, solid state methods (high energy ball milling), sputtering, plasma treatment and nanopatterned surfaces [8].

In polymeric materials a commonly implemented strategy is using nanoparticles (NPs) as additives to enhance the anti-fouling and anti-bacterial behaviours. The presence of metal additives like Ag , develop Ag+ions that react with the bacteria surface inhibiting their action. Another common solution is reducing Ag in nanoparticles (AgNPs) to be added in polylactic acid composite fibers containing cerium oxide to enhance antibacterial performances [9] or used in combination with TiO2 to be stabilized and using (3-Aminopropyl) triethoxysilane (APTES) crosslinking for holding with PVDF membrane [10]. TiO2 can also be used in ternary nanoparticle membrane with Ag,TiO2 and multiwalled carbon nanotubes (MWCNTs) AgTiO2MWCNT, embedded in PVDF/PVP membrane so enhancing the anti-fouling performances [11]. Membrane's anti-fouling properties can be also enhanced through the incorporation of other NPs such as Ag, Fe, silica, Al, Ti, Zn, Cu and their oxides [12]. Bifunctionalized zeolites (FAU) with hydroxylic groups (OH) and silver ions (FAU-Ag-OH) were incorporated through interfacial polymerization into a polyamide (PA) layer of a thin film NCM increasing its water permeance for wastewater treatment management as well as anti-biofouling ability with a flux recovery ratio up to 85% [13]. An easy and fast deposition of AgNPs can be achieved by electrospray even if the lack of knowledge on the behaviour of engineer NMs requires deep investigation on process parameters. Good results can be obtained by coupling electrospray with polymeric bonding to activate reaction mechanisms able to enhance the functionalization of permeable membrane [14]. AgNPs can be also treated by amine, thiol, carboxyl, and the thiolpolyvinylpyrrolidone functional groups to enhance its immobilization inside the polymeric matrix of NCMs for membrane bioreactor [15] and improving anti-microbial effect. In situ reducing of AgNPs taking advantages by phenolic hydroxyl groups of substances such as urushiol-based benzoxazines are generally adopted ways to obtain composite coatings [16].

Moreover, the in-situ reducing of silver nitrate (AgNO3) on the surface of GO with sodium borohydride three (NaBH3) has been used to prepare graphene oxyde with AgNPs(GOA) and was dropped coating in polypropylene matrix for sensor housing applications [17]. The same in situ reducing can be obtained on polyurethane (PU) substrates by a polydopamine coating directly applied to PU surfaces [18]. Wetness impregnation of silica, titania and mechanically mixed silica-titania powders was used to prepare NMs based on Cu and Ag nanoparticles and they have been further bonded with a commercial topcoat [19]. Wet impregnation of CNTs with AgNPs and further vacuum filtration on a PVDF substrate also allow enhancing anti-fouling performances [20]. Spin coating of a hybrid polymer nanoparticles mixes based on poly vinyl alcohol (PVA)-glutaraldehyde (GA) and Ag−TiO2 onto a commercial gel coated fiber reinforced polyester substrate revealed the higher antifouling properties when compared to AgNPs and TiO2NPs singularly [21]. Very recent trends aimed at developing even more advanced materials able to release Ag ions to interact with bacteria as for
the new composite with a core shell structure of halloysite by loading AgNPs and subsequently coating with chitosan. Chitosan serves as a ph-sensitive gatekeeper to release silver ions [22].

All the mentioned techniques are mainly diffused at a lab scale and realize AgNPs in a separate step; unfortunately, this choice makes the transfer to industries difficult. An innovative approach aiming at developing AgNPs within polymers during the manufacturing processes has been developed, patented and termed as nano-coating fragmentation (NCF) [23.24]. This process produces nanometric metal-based layers onto polymeric precursors (pellets) via a PVD sputtering process, which are then properly mixed with virgin PP pellets and processed by extrusion or injection moulding. In this way, PNCs with enhanced anti-bacterial properties can be obtained [25, 26]. This approach can be used also for producing a new class of composite starting from biopolymers [27]. Moreover, a non-destructive technique based on pulsed phase thermography has been also used to evaluate the dispersion of the Ag content in the bulk material [28].

In the current study the NCF process has been used to produce different samples of polypropylenebased nanocomposites with different content of AgNPs. The developed PNCs were placed on a new concept of smart buoy equipped also with oleophilic filters, obtained by cold compaction of pyrolytic carbons by recycling of tyres [29]. The buoy was settled in open environment with the expected presence of several harmful substances and organisms to evaluate the anti-fouling properties.

Materials and Methods

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Materials. The PNC plates used for exposure in a harsh environment are obtained from commercially available materials. The matrix is an injection-molding-grade polypropylene (PP) (Moplen HC500N, Lyondellbasell Europe). It is supplied in the form of pellets and commonly used in industrial applications such as nonwoven fabrics, biomedical devices, packaging and so on. Moreover, pellets have a nominal density of 0.9 g/cm3 and a characteristic dimension (diameter) ranging from 2.5 mm to 4.8 mm . The reinforcement was a 99.99% pure silver (G. Gambetti Kenologia Srl, Italy) which was used as a rectangular target with nominal dimensions of 300×125 mm2 for PVD sputtering.
Nanocoating Fragmentation and PNCs Manufacturing. The manufacturing of the PNCs is obtained through the patented solution shown in Figure 1. The process is divided into two main steps which are shown in Figure 2. The first step consists in obtaining nanometric silver coating on PP pellets via a magnetron sputtering PVD system (MITEC s.r.l., Italy) with 400 W as DC power input, 20 min as deposition time, and 29 rpm of rotation speed of the pellets during the sputtering phase. These parameters already used in a previous study [26] allowed to deposit an Ag percentage of 0.046±0.001% over the pellets' surface.

In the second step, or molding step, the obtained coated pellets are first mixed with virgin pellets of the same polymer in properly chosen proportions and then processed by extrusion or injection moulding. During the process, shear forces fragment the pellets and AgNPs are directly dispersed inside the polymer to obtain PNCs with enhanced anti-bacterial properties.

[picture]

Fig.1. Patent for nano-coating fragmentation [30].

Fig.1. Patent for nano-coating fragmentation [30].

[picture]

Fig. 2. Procedure for manufacturing anti-fouling PNC plates.

Fig. 2. Procedure for manufacturing anti-fouling PNC plates.

This second step was performed through an electric press Fanuc Roboshot S-2000i50B of 50 t (Fanuc Corporation, Japan). Square plates with nominal sizes of 80×80 mm2 and nominal thickness of 3 mm were produced either with only PP pellets, termed as Neat and either with coated pellets mixed with virgin ones and termed as PNCs. Specifically, the coated pellets were mixed with virgin ones in 4 different amounts, that is in percentage of 5%wt,10%wt,20%wt and 100%wt with the virgin ones. These percentages correspond to Ag contents varying from 0.036%wt to 0.103%wt. The adopted Ag can be considered in the form of nano particles as the sputtering step allowed to obtain a nanometric coating of about 25.3±0.5 nm.

Microscopic images of the moulded samples before and after testing have been acquired through a 3D digital microscope Hirox HR-2016E (Hirox, Japan).
Design of Smart Buoy. The PNC plates were housed around the perimeter surface of a smart buoy and mechanically joint through screws. The buoy is properly designed and produced to house oleophilic filters obtained by pyrolytic carbon from tires' recycling. In the current study such filters were replaced by PNC square plates. The smart buoy's configuration guarantees a fixed waterline to wet the nanocomposite plates for 1/3 of their height. Moreover, the buoy has a nominal diameter and height of 610 mm and 390 mm respectively and a nominal mass of 7 kg .

[picture]

Fig. 3. Positioning of the PNC samples on the smart buoy, by replacing some pyrolytic carbon filters, and exposure in open environment for the experimentation.

Fig. 3. Positioning of the PNC samples on the smart buoy, by replacing some pyrolytic carbon filters, and exposure in open environment for the experimentation.

Exposure in Harsh Environment. The anti-fouling behaviour of the manufactured PNC plates was investigated after 8 months of exposure in a drain of an industrial site, which could be potentially affected by different kinds of micro (i.e. bacteria) and macro (i.e. algae) living organisms, and suitable for understanding the whole properties of the produced nanocomposites. The anti-fouling system layout and the selected harsh environment are shown in Figure 3. After exposure, the buoy was retrieved and the PNC samples removed for analysis.

Results and Discussion

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The appearance of the exposed samples is reported in Figure 4a). They have been partially cleaned by soft water flow because of the presence of many residuals, not stuck on the sample surfaces. In fact, during 8 months in the small water channel of Figure 3, the buoys have experienced many intense natural events such as floodings and shallows. Moreover, it is reasonable that samples shown in Figure 4a) have been subjected to more severe conditions for longer times than others which have been previously tested in laboratory, as the buoy tended to take a stable position into the water flow. Direct sun exposure or shadow presence could have affected differently the sample surfaces, but a direct quantitative effect evaluation was difficult to perform. Infect, during the 8 months of exposure each sample was subjected to different and non-quantifiable sun exposure or shadow presence. Nevertheless, the very long exposure time has been able to show such interesting occurrences. A part of 1/3 of the samples has been fully immerged into the water, and this is clearly visible in all the samples, as the largest amount of vegetation (green and brown color) is present in this part. Nevertheless, samples with 20% and 100% coated pellets, during manufacturing, show very small traces in comparison with the others. Moreover, these samples are almost fully clean on the 2/3 rest of the surface, differently from the samples with 0%,5% and 10%. The positive effect of the Ag NPs seems to be verified from this first experimentation. The most affected sample is that with 10% of coated pellets, as the neat sample is better. Microscopic images of the specimen with 10% and 20% of coated pellets, which exhibited a bad and good anti-fouling behaviour respectively, are shown in Figure 4b). Apart from possible fluctuations of the exposure conditions, it is possible to identify a mechanism. In fact, NPs partially affects the roughness of molded surfaces.

[picture]

Fig. 4. PNC samples after their exposure for 8 months in open environment a) and microscopic images of the sample with a bad (10%) and with a good (20%) anti-fouling behaviour b).

Fig. 4. PNC samples after their exposure for 8 months in open environment a) and microscopic images of the sample with a bad (10%) and with a good (20%) anti-fouling behaviour b).

[picture]

Fig. 5. Microscopic images of all samples before long term exposure.

Fig. 5. Microscopic images of all samples before long term exposure.

The worst performances of the samples with 5% and 10% of coated pellets depend on the surface morphology. The average roughness Ra and the average height of the profile elements Rz of all sample range between narrow intervals, being 0.4−0.45μ m for Ra and 2.9−3.4μ m for Rz respectively. On average, samples with 5% and 10% coated pellets exhibited higher values. Moreover, the mean spacing of profile elements Rsm exhibited higher values for the samples with 5% and 10% of coated pellets as well and, among these, the highest values were measured for the specimen with 10% of coated pellets [26]. Higher Rsm values are correlated to the development of larger stagnation zones that promote the accumulation of marine vegetation. Samples with 20% and 100% coated pellets, did not exhibit a significant increase in roughness and had lower values or Rsm. Instead, roughness had small values in the case of virgin polypropylene and this characteristic, together with the hydrophobicity of this polymer, provides a certain degree of anti-fouling to molded PP products, at least at low times of exposure. Rough surfaces improve the adhesion of living organisms, partially reduce this intrinsic anti-fouling behavior. In Figure 5 a comparison of microscopic images of all samples before their exposure is shown. Surface textures are similar for all samples due to the injection moulding process. By adding Ag NPs, there are 2 opposite effects, one is on the plastic surface morphology which affects the intrinsic PP anti-fouling, and the other is the anti-fouling behavior of silver. In the end at very low content, a positive effect of the Ag NPs is covered by the worsening of the surface morphology, whereas, at higher contents, this positive effect becomes predominant. The injection moulding process develops surface morphology with variation of Ra, Rz and of Rsm which reduces the positive effect of the Ag NPs. Surface morphology can be considered a significative factor influencing the anti-fouling behaviour if a correlation between topological parameters and biological activity can be found independently by the chemical composition of the developed PNCs.

Summary

The original version of this paper is available on https://www.scientific.net/MSF.1187.37.pdf

In the current study, the anti-fouling behavior of molded polypropylene nanocomposites, made by the patented nano-coating fragmentation technique, has been tested in open environment for a long time. Results show that nanocomposites may provide a positive anti-fouling effect, if a given threshold in the Ag NPs content is reached. In fact, at very low contents, the altered surface morphology of the molded plastic can cover the positive contribution of the nanoparticles. However, silver content remains very low in absolute terms, reaching 0.1%wt in the highest case.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/MSF.1187.37.pdf

The authors are grateful to Eng. Fabrizio Betti for the support given in the experimentation. This study was carried out within the FIS-A 2023 project "ACWAM - Advanced Composite nanomaterials for WAter Management" and received funding from the MUR, Italian Ministry of University and Research, ref. FISA-2023-00229, CUP E83C25002180001.

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The original version of this paper is available on https://www.scientific.net/MSF.1187.37.pdf

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The original version of this eBook is available on https://www.scientific.net/book/special-materials-and-processes/978-3-0364-1544-4

Materials Science Forum, ISSN: 1662-9752, Vol. 1187, pp 45-54

doi: 10.4028/p-GJcp8x

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

Submitted:
2025-12-19

Revised:
2026-02-20

Accepted:
2026-02-20

Online:
2026-04-17

Shape Memory Behavior of 3D-Printed Biomedical Polyurethane with Room Temperature Transition

The original version of this paper is available on https://www.scientific.net/MSF.1187.45.pdf

Denise BELLISARIO 1,a*, Leandro IORIO 2, b, Alice PROIETTI 1,c, Dounia NOQRA 1, d, Giorgio PATRIZII 1,e, Fabrizio QUADRINI 1,f and Loredana SANTO 1,g

1 Department of Industrial Engineering, University of Rome Tor Vergata, via del Politecnico 1, Rome, Italy

2 Department of Engineering and Science' Mercatorum University, p.zza Mattei 10, Rome, Italy

a  denise.bellisario@uniroma2.it, b  leandro.iorio@unimercatorum.it, calice.proietti@uniroma2.it, d  dounia.noqra@uniroma2.it, e  giorgio.patrizii@uniroma2.it, fabrizio.quadrini@uniroma2.it, gloredana.santo@uniroma2.it

Keywords: shape memory polymers, biomedical polyurethane, 3D printing.

Abstract

Shape memory polymers (SMPs) can recover from a programmed temporary shape to their original configuration when exposed to an external stimulus, most commonly heat, making them attractive materials for soft actuation in functional and biomedical devices. Among them, thermoplastic polyurethanes (TPUs) display reliable shape-memory behavior under various conditions. The emergence of biomedical-grade TPUs and their compatibility with additive manufacturing provides new opportunities for fabricating customized components with tunable actuation capabilities. In this study, biomedical TPU filaments were processed via fused deposition modeling (FDM) to produce block-shaped specimens of controlled size and weight. The samples were mechanically deformed into a C-shaped geometry at room temperature, fixed in fridge at the temperature of −20∘C, and subsequently tested under constrained recovery at room conditions, using a universal testing machine. The recovery load has been measured for a time of 30 min . The results show that TPU-based SMPs can develop substantial recovery forces during shape restoration. The shape memory behavior has been modeled by using a logistic function, which has been able to identify a characteristic time, the same for all the samples despite their printing conditions and architecture. These findings highlight the potential of FDM-processed biomedical TPUs for compact soft-actuation systems requiring high force output.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1187.45.pdf

In recent decades, advanced polymeric materials have attracted substantial interest in both academic research and industrial practice owing to their broad functional versatility, low density, and straightforward processability. Their properties can be precisely engineered through molecular design or by incorporating targeted additives and fillers, enabling the development of materials tailored for specific functional demands. Among these, shape-memory polymers (SMPs) stand out for their capacity to undergo large, recoverable deformations and to return to their programmed permanent shape upon exposure to an external stimulus. This unique behavior has facilitated their integration into a wide range of applications, including aerospace systems, biomedical devices, actuators, sensors, and smart textiles.

SMPs can be triggered by diverse stimuli such as heat, light, electric or magnetic fields, or humidity. After being deformed and fixed into a temporary configuration, they recover their initial shape once the appropriate stimulus is applied [1-4]. Their increasing relevance stems not only from their intrinsic functional performance but also from their compatibility with lightweight and sustainable design strategies, positioning them as strong candidates for next-generation smart materials and adaptive devices.

Among SMP families, thermoplastic polyurethanes (TPUs) have emerged as particularly versatile due to their segmented molecular architecture, which consists of alternating soft and hard domains.

This structure imparts high elasticity, tunable mechanical strength, thermal stability, and excellent resistance to abrasion and chemicals [5-9]. As a result, TPUs are widely used in biomedical engineering for implants and prosthetic components [10-12], in smart textiles for wearable electronics and responsive fabrics [13,14], and in adaptive or reconfigurable systems, including deployable structures and actuation mechanisms [15-17].

A rapidly expanding application area for SMPs-especially TPU-based systems-is soft robotics, a field focused on developing flexible, compliant, and adaptive robotic platforms inspired by biological organisms. In contrast to conventional robots composed of rigid, articulated elements, soft robots employ deformable materials such as elastomers and SMPs, enabling them to bend, stretch, and safely interact with humans and delicate environments [18,19]. The combination of processability, mechanical robustness, and stimulus-responsive behavior makes TPUs highly suitable for soft robotic components, including artificial muscles, gripping devices, and wearable interfaces, where efficient and reliable shape-memory actuation is essential. Parallel to these developments, additive manufacturing-particularly fused deposition modeling (FDM)-has become an attractive route for processing TPUs into complex geometries suitable for customized functional devices. Recent advances in additive manufacturing have further expanded the applicability of TPU-based SMPs. In particular, fused deposition modeling (FDM) has proven highly suitable for processing TPUs into complex geometries while maintaining their elastomeric and shape-memory characteristics [13-16]. Several studies have demonstrated that printing parameters-such as extrusion temperature, raster orientation, layer height, and cooling rate-strongly influence the microstructure, mechanical response, and thermal-triggered recovery behavior of FDM-printed TPU components [13,14]. Early work by Koehler et al. demonstrated that TPUs can be successfully processed by FDM while preserving their ability to undergo large recoverable deformations and reliable shape-memory cycling [20]. Since then, subsequent studies have shown that key printing parameters such as extrusion temperature, raster orientation, cooling rate, and infill architecture critically influence the microstructure and thermomechanical properties of printed TPU components. For example, Ritzen et al. confirmed that the FDM process alters the polymer's internal morphology, affecting stiffness, ductility, and long-term durability [21], while Rigotti et al. demonstrated that TPU blends printed via FDM can be functionalized to incorporate thermal energy storage or controlled release capabilities [22].

Several recent studies have focused specifically on the shape-memory performance of 3D-printed TPU-based systems. Rahmatabadi et al. investigated TPU-containing blends fabricated by FDM and showed that printing conditions strongly affect shape fixity, recovery stress, and recovery ratio, highlighting the importance of process optimization when designing SMP actuators [23]. Similarly, Peng et al. reported that TPU-based polymer blends produced via 4D printing can exhibit high extensibility, efficient shape recovery, and even self-healing behavior, underscoring the potential of printed TPUs for multifunctional soft actuators [24]. Investigations by Jung and Jung further demonstrated that FDM-printed TPU structures with engineered geometries can achieve programmable shape-change pathways, enabling advanced compliance and deformation control for soft robotic components [25].

Beyond experimental characterization, predictive modeling of shape-memory polymer (SMP) behavior plays a fundamental role in understanding and designing shape-memory-based actuation systems. Over the past two decades, a wide range of modeling approaches has been proposed to describe the thermomechanical response of SMPs, including micromechanical formulations, phase-transition-based theories, and phenomenological viscoelastic frameworks [26-28]. Many constitutive models relate shape recovery to temperature-dependent phase evolution between soft and hard domains or to time-dependent molecular relaxation mechanisms governing stress release and strain recovery during activation [29,30].

In practical applications as in soft actuators or biomedical devices, the time-dependent evolution of shape recovery is often critical as well as the total recovered strain or force. As a result, simplified phenomenological models have been increasingly adopted to capture the kinetics of shape-memory activation under thermal stimuli, especially when full constitutive descriptions are not required for
system-level design [31]. In this context, sigmoidal and logistic-type functions have proven effective in representing recovery evolution over time, as they naturally describe the gradual onset, rapid transition, and saturation stages typically observed in SMP recovery processes [32]. In particular, logistic functions provide a compact mathematical description of recovery behavior using a limited number of physically interpretable parameters, such as characteristic recovery time, maximum recovered strain or force, and recovery rate. Such functions have been successfully applied to model strain recovery, displacement evolution, and thermally driven transformation kinetics in polymeric and SMP-based systems, demonstrating good agreement with experimental observations while maintaining computational simplicity [32,33]. Fig. 1 illustrates the general form of a logistic (sigmoid) function [34], highlighting its suitability for modeling the time-dependent recovery response of SMPs.

[picture]

Time, s

Figure 1. Time, s

Fig. 1. Logistic function, also known as the sigmoid function, over time

Despite these advances in experimental characterization and modeling, the thermomechanical response of FDM-printed TPU-based SMPs under constrained recovery conditions remains insufficiently explored. While many studies have focused on free recovery behavior, significantly fewer have investigated the recovery forces generated when shape restoration is mechanically constrained. Recovery force is a critical parameter for the design of soft actuators, deployable structures, and biomedical devices that must perform mechanical work or sustain external loads during activation. Furthermore, existing investigations predominantly focus on general-purpose TPUs, whereas biomedical-grade TPUs-despite their relevance for medical and assistive technologies-remain largely unexplored in terms of constrained shape-memory actuation.

The present study addresses these gaps by investigating the recovery forces of FDM-printed biomedical-grade TPU specimens under constrained recovery conditions. Block-shaped samples with controlled geometry were fabricated via fused deposition modeling, mechanically programmed into a temporary C -shaped configuration, fixed at subzero temperature, and subsequently activated at room temperature under mechanical constraint. The evolution of recovery load over time was experimentally measured and modeled using a logistic function to identify characteristic recovery parameters.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/MSF.1187.45.pdf

Modelling. The transition of such properties of organic materials as a function of time is often represented by a sigmoidal curve, as the logistic function of Fig. 1, which is the analytical solution of the Verhulst logistic differential equation [34], commonly written:

y=L1+e−k(x−x0)=L1+et−t0tc(1)

where the term L is the value of the long-time plateau, to (or x 0 ) is the curve inflection point and represents the time at which the rate of the property change reaches its maximum, and k( or −1/tc) represents the growth-rate parameter. In this case, tc is a characteristic time which influences the slope of the linear trend. This simple law has all the necessary parameters for the characterization of material behavior.
Materials. The material used in this study is a commercially available thermoplastic polyurethane (TPU), chosen to ensure reproducibility, standardization, and quality control, facilitating reliable and widely comparable results. The material was used as received, without any purification or modification. Commercial TPU pellets (DiAPLEX MM3510) were purchased from SMP Technologies Inc. (Tokyo, Japan). According to the manufacturer, the material properties are the following: glass transition temperature (Tg) of 35∘C, tensile strength of 51 MPa , bending strength and modulus of 85 MPa and 2450 MPa , respectively, and a density of 1.25 g/cm3. This TPU is designed for biomedical applications and was selected for its potential in soft actuators intended for contact with the human body, as its Tg near body temperature enhances its use for such applications.

Table 1. FDM parameters used in TPU printing

| A | B | C | D
Nozzle temperature [°C] | 240 | 240 | 240 | 240
Nozzle diameter [mm] | 0.4 | 0.4 | 0.2 | 0.8
Printer bed temperature [°C] | 50 | 50 | 50 | 50
Printing speed [mm/s] | 40 | 40 | 40 | 40
Infill | 100% | 100% | 100% | 100%
Pattern | linear | linear | linear | linear
Raster angle | 45° | 45° | 45° | 45°
Layer thickness [mm] | 0.2 | 0.3 | 0.15 | 0.3
Retraction length [mm] | 0.8 | 0.8 | 0.8 | 0.8

Table 2. Properties of the printed samples

Sample | Thickness, mm | Masst, g | Apparent density, g/cm³ | Bending load, N
A | 4.00 | 5.79 | 0.80 | 3.2
B | 5.18 | 8.64 | 0.93 | 11.9
C | 3.83 | 6.82 | 0.99 | 9.8
D | 4.31 | 8.69 | 1.12 | 11.5

3D Printing Filament Production. The TPU filament was produced using a 3DEVO Composer 450 filament extruder. The machine features a 2 L hopper for pellet loading, four independently controlled heating zones (maximum temperature 450∘C ), and a single-screw extruder with a speed range of 2− 15 rpm . A spooling system adjusts the winding speed based on real-time filament diameter measurements, and two cooling fans are installed at the nozzle exit to ensure dimensional stability. Multiple trial runs were conducted to optimize the extrusion parameters, ranging the heating zone temperatures from 160∘ to 200∘C, the extruder speed from 3.5 to 4.0 rpm , and the fan speed from 60% to 100%. The processing window for filament extrusion was determined using the operating parameters recommended by the machine manufacturer for commercial TPU. The final optimized conditions were as follows: heating zone temperatures of 170∘C,190∘C,195∘C, and 193∘C from the hopper to the nozzle, respectively; an extruder speed of 3.7 rpm ; and a fan speed of 80%. The resulting filament had a diameter of 1.75±0.1 mm. After extrusion, the polymer was automatically collected and wound onto a spool for subsequent 3D printing.

Sample Fabrication. Rectangular samples were designed for thermo-mechanical shape-memory testing, with final dimensions of 30×60 mm2. The samples were printed using a commercial Fused Deposition Modeling (FDM) printer (Prusa i3 MK3S+, Prusa Research, Czech Republic). Key parameters were adjusted during a first optimization study included nozzle temperature ( 210−240∘C ) and printing speed ( 20−40 mm/s ) [35]. In this study, four different combinations of nozzle diameter and layer thicknesses were selected as printing parameters as reported in Table 1. Some physical properties of the four different printed samples are reported in Table 2.
Recovery Force and Time Measurement. The printed TPU samples were deformed and clamped in a fixed configuration. This configuration was a curved shape with a radius of 10 mm , as reported in Fig. 2, where a deformed sample is shown together with its temperature map at the beginning of the test. The load to achieve the final sample deformation at room temperature is reported in Table 1 as bending load. In the constrained recovery test, after deformation, the samples were stored in a fridge at −22∘C to maintain the temporary shape. Recovery was initiated by transferring the samples to ambient temperature, allowing the thermally triggered shape-memory effect to occur naturally. An infrared (IR) camera (Testo 883, by Testo S.p.A., Settimo Milanese (MI), Italy) was used to monitor the sample surface temperature throughout recovery, while a universal testing machine (Insight 5 , MTS System Corp.), recorded the corresponding recovery force and the time required for the sample to return toward its original configuration.

[picture]

Fig. 2. Constrained recovery test of a 3D-printed sample and thermal image at the beginning and the end of the test

Fig. 2. Constrained recovery test of a 3D-printed sample and thermal image at the beginning and the end of the test

Specifically, the memorized sample was positioned between two compression plates, and the force exerted on the load cell (nominal maximum load 100 N , MTS Systems Corp.) during recovery phase was recorded. Small PET foam blocks were inserted to prevent direct contact with the metallic platens, while temperature evolution during recovery was monitored with a thermal camera (as shown in Fig. 2). The recovery loads and times were acquired during tests. This setup enabled precise measurement of recovery force evolution and timing under passive thermal activation conditions.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/MSF.1187.45.pdf

Results of the constrained recovery tests are reported in Fig. 3 for the 4 different printed samples. In all these cases, the load increase depends on the sample heating in room conditions, after taking the sample from the fridge. A partial recovery is expected as a result of the time required to position the sample between the platens and initiate the test, which is longer for thinner or denser specimens. Nevertheless, infrared thermography indicates that the initial conditions were highly consistent across all samples, with a minimum surface temperature below 0∘C. By considering the maximum load of the curves of Fig. 3, it reaches 3.8 N for sample A,13.3 N for sample B,8.7 N for sample C and 10.5 N for sample D. Apparently, there is no direct correlation between these load values and the sample
thickness or density, even if they approach the bending load of Table 2. The reason is that the sample thickness and void fraction influence both the sample stiffness.

[picture]

Fig. 3. Load curves from constrained recovery test of the 3D-printed samples

Fig. 3. Load curves from constrained recovery test of the 3D-printed samples

As a first approximation, the simple bent-beam model assumes that the bending stiffness of the beam is proportional to the term Vf3, where Vf is the volume fraction of the porous material with a given bulk elastic modulus (in this case, the biomedical polyurethane), and T is the sample thickness. This parameter can be calculated from the data reported in Table 2 and the bulk resin density. As shown in Fig. 4a, both the maximum load measured during the constrained recovery test (Fig. 3) and the bending load at room temperature (Table 2) exhibit a strong correlation with this parameter, indicating their dependence on sample stiffness. However, the correlation is not linear, as might be expected, and a plateau is observed at high volume fractions. This behavior suggests that nonlinear effects and possible material plasticization occur during the memory stage, enabling the material to reach the final C-shaped configuration shown in Fig. 2.

The constrained recovery curves show a quasi-sigmoidal behavior, as a small peak is reached before the plateau. This maximum is very small in curve D , and absent in curve C but evident in curves A and B . It depends on the fact that the maximum recovery load is not obtained at room temperature but an intermediate temperature between the beginning and the end of the heating phase. The occurrence and the intensity of this maximum load is strongly influenced by the heating rate of the samples, disappearing at very low rates. As the heating phase was not controlled during the test, its occurrence is not well replicated in all the samples.

The model of equation (1) has been applied after curve normalization. The values of each curve have been divided by the maximum of the same curve, which is very close to the final plateau, apart from curve A . By this normalization, the term L of equation (1) is equal to 1 for all the curves. Moreover, the curves of samples A, B and D have been shifted along the time axis to be superimposed on the curve of sample C . This shift is equal to 270 s for sample A,80 s for sample B , and 250 s for sample C. Fig. 4b shows that curves are well superimposed after this shift operation, and are optimally fitted by equation (1) with a t0 of 440 s , and a tc of 75 s .

[picture]

Fig. 4. Load data of the different 3D-printed samples (a) and optimal fitting of the experimental load curves by the proposed sigmoidal function (b)

Fig. 4. Load data of the different 3D-printed samples (a) and optimal fitting of the experimental load curves by the proposed sigmoidal function (b)

Differences in the inflection point to are related to the different transition onset, which is not directly modeled in equation (1) and is influenced by the heating conditions. A trend in to is not observed because of the different room conditions during the transition and the bulk thermal diffusivity of the samples. Nevertheless, all the curves show the same characteristic time tc, which is related to the intrinsic time for the event occurrence. A time of 75 s means that the process can be observed at naked eye but slowly. As all the 3D-printed samples show the same t0, despite the different conditions during transition, the term tc becomes representative of the nature of the printed polymer and is independent of the printing conditions.

Experimentally, not all the curves show a plateau at long times, and generally a maximum is expected after the linear trend. In fact, the maximum originates because the exerted load at low temperature is higher than the load at room temperature, being the polymer stiffer. With temperature increase, the polymer mobility improves, therefore its softness and its speed of actuation. Therefore, this aspect is related to the heating conditions during transition, as well as the onset time, but it does not affect the shape of the transition region too. The term tc can be used to characterize the polymer shape memory, apart from its printing conditions.

Summary

The original version of this paper is available on https://www.scientific.net/MSF.1187.45.pdf

Biomedical devices can be fabricated by 3D printing using biocompatible polyurethanes, with shape recovery or actuation functionalities incorporated when required. This study demonstrates that the shape-memory behavior of 3D-printed porous samples follows a common sigmoidal law, regardless of the printing conditions, indicating an intrinsic material response. Constrained recovery tests revealed that the actuation load is governed by the effective bending stiffness of the samples, which can be tailored through the printing strategy by controlling thickness and volume fraction. Although a strong correlation with stiffness was observed, nonlinear effects and material plasticization lead to a saturation of the recovery load at high volume fractions. After normalization and time alignment, all recovery curves collapsed onto a single master curve characterized by a common intrinsic time constant. Actuation loads exceeding 12 N were achieved experimentally, highlighting the potential for designing mechanically programmable, shape-memory biomedical devices.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/MSF.1187.45.pdf

This paper and the research behind it would not have been possible without the exceptional support of our laboratory technician, Fabrizio Betti. This study was carried out within the "4D Printing of smart soft robotics (4D P.Ro.)" and received funding from the Research Projects of Significant National Interest (PRIN) 2022 PNRR, project n. 2022SFF349, with CUP Master D53D23004020008.

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Materials Science Forum, ISSN: 1662-9752, Vol. 1187, pp 55-62

doi: 10.4028/p-3Tuj2F

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

Submitted:
2025-12-23

Revised:
2026-02-24

Accepted:
2026-02-24

Online:
2026-04-17

Analysis of Connection Geometries for Segmented Stator Cores Machined with Laser Beam Cutting

The original version of this paper is available on https://www.scientific.net/MSF.1187.55.pdf

Florian Hoffmann 1,a*, Philipp Plänitz 1, b, Alexander May 1,c, Florian Winkler 2, d, Markus Barth 2,e, Sören Majcherek 3,f and Matthias Hackert-Oschätzchen 1, g

1 Chair of Manufacturing Technology with Focus Machining, Faculty of Mechanical Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany

2 TEPROSA GmbH, Paul-Ecke-Straße 6, 39114 Magdeburg, Germany

3 Brandenburg University of Applied Sciences, Magdeburger Straße 50, 14770 Brandenburg an der Havel, Germany

aflorian.hoffmann@ovgu.de, b  philipp.plaenitz@ovgu.de, c  alexander2.may@st.ovgu.de, dflorian.winkler@teprosa.de, e  markus.barth@teprosa.de, f  soeren.majcherek@th-brandenburg.de, g  matthias.hackert-oschaetzchen@ovgu.de (*corresponding author)

Keywords: non-conventional manufacturing, electric motors, stator segmentation

Abstract

Current developments in the transition to renewable energy and the electrification of mobility are leading to higher demands with regard to resource efficiency in the production of electric motors. One current trend is the segmentation of the stator pack, which enables higher material utilization and novel assembly processes. In this paper, different connection geometries for individually stacked stator segments are examined. Experiments were conducted by laser beam cutting and stacking of samples with different segmentations and connection geometries. Afterwards the geometric dimension and tolerances are compared with an unsegmented stator. Furthermore, the impact of the additional connections on the mechanical behavior under load are investigated using Finite Element Analysis. The required force to join segments across different connection geometries ranges from 462 N to 1875 N , while the cylindricity of segmented stator cores spans from 33μ m to 59μ m, compared to 40μ m for the unsegmented sample. Simulation results show that the elastic strain on the connections is largely influenced by the size of air gaps between segments, as well as the geometry and number of segments.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1187.55.pdf

Stator cores are manufactured by stacking and bonding thin sheets of electrical steel. For highvolume productions, punching is a cost-effective process. For prototyping or stators with large dimensions, laser beam cutting is also commonly used [ 1,2 ]. The surface of the electrical steel sheets has a protective coating that provides corrosion resistance and electrical insulation. For joining the steel sheets into stator packs there are multiple established methods [1,3]. Laser welding of the sheets on the outer circumference of the stator yoke or the stamping of an interlocking contour on the surface are simple and cost-effective methods, but they cause electrical contact between the steel sheets, which leads to higher eddy current losses. An alternative joining method is the usage of an adhesive coating to create a bond over the entire surface and maintain the electrical insulation [4].

Segmentation of the stator core offers new opportunities for manufacturing. In addition to a higher material utilization of the electrical steel, the complexity of the winding process can be reduced, while at the same time achieving higher slot fill factors [5]. The targeted use of grain-oriented electrical steel can increase the efficiency of the electrical motor further [6]. However, the additional joining process of the segments and the effects of segmentation regarding the mechanical and electromagnetical properties of the stator core must be considered.

Koshoo et al. conducted an extensive review of current research on the subject of stator and rotor segmentation [6]. Besides a discussion about the advantages and drawbacks of segmentation, different segmentation and manufacturing methods are presented. The influence of cut edges produced with different manufacturing processes on the magnetical properties of the electrical iron is
discussed in multiple publications. Experiments show decreased permeability in the areas near the cut edges [2], and FEM models, which take those cut edge effects into account, are presented [7, 8]. The introduction of additional cut edges through stator segmentation lowers the effective permeability, which causes less magnetic flux compared to an unsegmented stator.

Another phenomenon with implications on efficiency is increased magnetostriction caused by mechanical stress [9]. For example, compressional stresses introduced with shrink fitting the stator into a housing can have a significant impact on the B(H) curve and iron losses of the stator. Local stresses can be increased further with segmentation. In [10], the stress dependent iron losses are examined on a segmented lamination core. Variation of the surface roughness of the segment interfaces show that increased roughness leads to a lower slope of the B(H) curve. The results also show increasing iron losses with higher compressive stress.

This paper examines the manufacturing of segmented stator cores machined with laser beam cutting. Experiments were conducted with different connection geometries and number of segments. After the joining process, the form and shape tolerances are compared to a non-segmented sample. Furthermore, simulation models were developed for the joining process of the segments and for the mechanical stresses to which the stator is subjected during operation. While the primary opportunities are higher material utilization of electrical steel and higher slot fill factors through novel winding techniques, the aim of this study is to evaluate the new challenges arising from stator segmentation.

Experimental Setup

The original version of this paper is available on https://www.scientific.net/MSF.1187.55.pdf

High precision laser beam cutting with a fiber laser (Kimla PowerCut) was employed in the manufacturing of the stator cores. The Electrical steel used is M235-35A [11] with a nominal thickness of 0.35 mm and a Backlack coating for adhesive bonding of the steel sheets. Fig. 1a) shows the benchmark geometry used in the experiments. This geometry represents a simplified stator design with 12 slots that includes additional grooves on the outer circumference for enhancing the alignment of sheets during the stacking process. 150 sheets are stacked and bonded in an oven with a temperature of 205∘C for 2 hours in a housing that provides continuous pressure on the lamination stack.

The segmentation type chosen for the experiment is the splitting of the back iron along the slot. Fig. 1b-d) display the investigated connection geometries. These geometries were derived from literature [12] and adapted to the stator dimensions. The interlocking geometries are designed with an air gap of 20μ m, except for the front surfaces of the dovetail (Fig. 1c) and the T-Form (Fig. 1d), which feature an air gap of 100μ m. Each segment is individually stacked and bonded, with manufacturing tolerances of each geometry being measured prior to the joining process. A total number of 10 stator cores was produced, including an unsegmented reference and variations with the three connection geometries. Each connection geometry is examined with two, three and four segments per stator ring.

[picture]

Fig. 1. Benchmark geometry of a) the unsegmented stator and chosen connection geometries: b) Circle, c) Dovetail and d) T-Form

Fig. 1. Benchmark geometry of a) the unsegmented stator and chosen connection geometries: b) Circle, c) Dovetail and d) T-Form

The joining process of the segments is realized with a manually actuated press. The setup is illustrated in Fig. 2. At first the segments are joined a few millimeters with a soft-faced hammer. Then the press is used to fully join the segments. A copper plate between piston and segment is applied to protect the surface, and the joining pressure is monitored during the process.

[picture]

Fig. 2. Joining process of a quarter-segmented stator with T-Form connection geometry

Fig. 2. Joining process of a quarter-segmented stator with T-Form connection geometry

Following the joining process, the dimensional accuracy of the samples was evaluated using a coordinate measuring machine. By probing the teeth heads, both the roundness and cylindricity of the inner diameter were measured. This measurement is crucial as the accuracy of the inner diameter significantly affects the uniformity of the air gap between the stator and rotor. Such uniformity is vital as it greatly influences the motors operational characteristics. In addition, the flatness of the upper surfaces and the lower surfaces was measured to estimate misalignment of the laminations.

Simulation Model

The original version of this paper is available on https://www.scientific.net/MSF.1187.55.pdf

The simulation models were developed using COMSOL Multiphysics. A 3D model was employed to replicate the joining process of the segments, while a 2D model was used to simulate the mechanical stresses during motor operation. The geometries shown in Fig. 1 were transferred to the models. Sharp edges on the stator and at the connection geometries were replaced with fillets in order to prevent the occurrence of singularities during simulation. A fully parametric design allows switching between different connection geometries and number of segments. The simulation models were further extended by two additional connection geometries, illustrated in Fig. 3. On the left, the L-shaped connection geometry is depicted, combined with a single-tooth segmentation that is also implemented in the 2D simulations. On the right, Fig. 3b) shows the 3D model with a rectangular connection geometry and quarter segmentation. To reduce computational effort, the neighboring segments adjacent to the one being pressed down are cut off in the 3D model.

3D Model. The purpose of the three-dimensional model is to analyze the stresses within the joints during the joining process. Experimental observations indicate a linear increase in the force required to displace the segment. This suggests that friction provides the main source of resistance. As a simplification, the laminated stator core is represented as a single solid body, while the surface roughness of the connection interfaces is characterized by a friction coefficient μ. This coefficient is derived from the measured forces during the experiments and can be used to approximate the joining forces required for the connection geometries that were not tested in the experiments.

For the simulation, the quarter segmentation illustrated in Fig. 3b) is selected. The central segment is displaced upward by 5 mm relative to the neighboring segments. The joining process up to this point is not simulated in order to keep the computational effort within reasonable limits. The maximum forces occur at the end of the joining process, and the preceding friction forces can be linearly extrapolated. For the mesh configuration, uniform triangle-elements with an element size of
0.3 mm were applied to the connection surfaces, while the rest of the volume was meshed with tetrahedron-elements.

2D Model. The two-dimensional simulation model is used to compare the stresses and deformations of segmented and unsegmented stator cores. An adaptive Triangle-Mesh was used for the model, with element-sizes ranging from 0.015 mm at the segment connections to 2 mm in uncritical areas. For the stator, an application as a traction machine with concentrated windings and characterized by high torque was assumed. A load case was defined in which a tangential reaction force corresponding to a torque of 500 Nm is applied to two opposing tooth pairs belonging to a single electrical phase of the motor. This represents an extreme scenario, as in reality the torque is generated by more than one electrical phase at any given time. This load was combined with a fixed constraint on the outer circumference of the stator.

[picture]

Fig. 3. Simulation models with additional connection geometries. a) L-Form with single tooth segmentation in 2D model, b) Rectangle-Form with quarter-segmentation in 3D

Fig. 3. Simulation models with additional connection geometries. a) L-Form with single tooth segmentation in 2D model, b) Rectangle-Form with quarter-segmentation in 3D

In a second simulation, the stresses induced by a press-fit assembly of the stator into a housing were examined. A radial force was applied to the outer circumference by defining a pre-tensioned spring, while tangential displacement at the outer boundary remains constrained. Aluminum was specified as the housing material, and the spring constant k is derived from the material data, considering an interference of 50μ m. The values used for the material properties are listed in Table 1.

Table 1. Material properties for the simulation models

Material property
Symbol

Electrical Steel

Housing Material

Young's Modulus | mathematical formula | superscript number | superscript number
Poisson's ratio | mathematical formula | 0.3 | 0.29
Density | mathematical formula | mathematical formula | superscript number

Results

The original version of this paper is available on https://www.scientific.net/MSF.1187.55.pdf

Measuring the dimensions of the connection geometries before the stacking process showed an average deviation of 9.1μ m and 9.2μ m for the dovetail and T-shaped connections, respectively. The deviation of the circular geometry was higher, at 17.8μ m. Individual packing of the segments caused additional deviations in segment height due to variations in clamping pressure, as well as temperature and time in the oven. A maximum height difference of 0.27 mm between two corresponding segments was measured.

During the joining process, the hydraulic pressure applied by the press was recorded. The joining force for different connection geometries was derived from the measured pressure and the averages of the samples with the same geometry were calculated. The values listed in Table 2 were measured when joining the final segment to complete the stator ring, where two connections were engaged
simultaneously (see Fig. 2). The lowest force of 462 N was required for the dovetail geometry, followed by the circular geometry with 1184 N and the T-shaped connection with 1875 N . Joining processes with a single connection engaged showed average joining forces approximately three times lower. The form deviations of the connection geometries and slight displacements of the steel sheets during stacking are presumed to be the primary causes of friction, even though the nominal dimensions of the connections provide a clearance of 20μ m.

The joining forces calculated with the 3D model are listed in the right column of Table 2. A representative coefficient of friction μ of 0.138 and an interference fit of +1μ m were determined in order to reproduce the joining forces of the T-shaped geometry measured in the experiments. The deviation between experiment and simulation is 2 N and 71 N for the T-Form and dovetail geometry respectively. For the circular geometry a significantly higher force of 1184 N was observed during the experiments compared to 244 N calculated with the simulation model. This difference could be attributed to the larger form deviation of the circular connection geometry observed in the produced samples, which were not considered in the simulation. Another aspect not represented by the friction based model are offsets between the sheet layers that can lead to shearing and tilting during the joining process.

Table 2. Average Force for joining of segments

Connection

geometry

Joining Force -

measured [N]

Joining Force -

calculated [N]

Circle | 1184 | 244
Dovetail | 462 | 533
T-Form | 1875 | 1877
Rectangle | - | 318
L-Form | - | 1048

After assembly, the dimensional accuracy of the inner diameter was assessed. Fig. 4a) shows the roundness, measured at 12 points at mid-height. With one exception, the diameter of the fitted circle increases with segmentation. The roundness, defined as the total deviation from the fitted circle (depicted with the error bars), amounts to 26μ m for the unsegmented stator and ranges between 13μ m and 50μ m for the segmented variants. The cylindricity of the inner diameter, determined from measurements in 5 different planes, is illustrated in Fig. 4b). The deviation from the ideal cylindrical shape increases from 40μ m for the unsegmented stator to between 52μ m and 59μ m for the samples with two segments, but decreases with a higher number of segments. Among all connection geometries, the T-shaped configuration exhibits the lowest overall values.

[picture]

Fig. 4. Form deviation of the stator's inner diameter. a) Roundness at half height, b) Cylindricity

Fig. 4. Form deviation of the stator's inner diameter. a) Roundness at half height, b) Cylindricity

For the results of the 2D simulation, the T-shaped geometry is chosen to present the two load cases in detail before comparing the connection geometries. Fig. 5a) shows the joined experimental sample, while Fig. 5b) and 5c) illustrate the 2D simulation model under two different load cases. In Fig. 5b), a tangential force is applied to the stator teeth near the segment connection. The red arrows indicate the force direction, and the surface coloration corresponds to the von Mises stress in the material. The
roots of the stator teeth experience the maximum stress in this load case, while the displacement of the connection surfaces is less than 1μ m for all geometries. The second load case, simulating a shrink fit, is depicted in Fig. 5c). The stress color scale is adjusted for the overall higher stresses compared to the first load case. Critical areas regarding stress, besides the connection points, include the inner ring of the stator and the round grooves along the outer circumference. The maximum contact pressure is 314 MPa for the T-shaped geometry, which is the highest value of the tested geometries. The dovetail connection showed the lowest maximum contact pressure of 160 MPa .

The simulation model assumes linear elastic and isotropic material properties. Because the difference between the yield point of the electrical steel (see Table 1) and the calculated stresses is sufficiently high, the exclusion of plastic deformation in the model can be considered to be legitimate. The isotropic material properties on the other hand ignore the lamination stack in the perpendicular plane and the influence of the rolling direction on the electrical steel. It was presumed that both have little influence on the considered load cases.

[picture]

Fig. 5. Segmented stator core with T-Form connection. a) experimental sample, b) simulation model with tangential load on teeth, c) simulation model with radial load on outer circumference
To compare the different connection geometries, the elastic strain energy density Wel, integrated over the arc length of the connection, is illustrated in Fig. 6. These values were calculated with the 2D simulation model and pertain to the second load case. In addition to the variations in segmentation and connection geometries, the air gap between tongue and groove was varied at 1μ m,10μ m, and 20μ m. With the exception of the L-shaped connection, the strain energy density decreases with a higher segment count and wider gaps between segments.

Fig. 5. Segmented stator core with T-Form connection. a) experimental sample, b) simulation model with tangential load on teeth, c) simulation model with radial load on outer circumference To compare the different connection geometries, the elastic strain energy density W e l , integrated over the arc length of the connection, is illustrated in Fig. 6. These values were calculated with the 2D simulation model and pertain to the second load case. In addition to the variations in segmentation and connection geometries, the air gap between tongue and groove was varied at 1 μ m , 10 μ m , and 20 μ m . With the exception of the L-shaped connection, the strain energy density decreases with a higher segment count and wider gaps between segments.

[picture]

Fig. 6. Elastic strain energy density in the connection for different connection geometries, segmentations and air gap heights

Fig. 6. Elastic strain energy density in the connection for different connection geometries, segmentations and air gap heights

For the L -shaped geometry, increasing the air gap from 1μ m to 10μ m leads to a slight increase in strain energy density for both the half and quarter segmentation. The rectangular geometry exhibits the lowest overall strain energy density, with values between 33% to 58% lower compared to the other geometries.

Summary

The original version of this paper is available on https://www.scientific.net/MSF.1187.55.pdf

In this study, the influence of various connection geometries on the mechanical and structural properties of segmented stator cores in electric traction machines was examined. Experimental data revealed an initial increase in form deviations regarding roundness and cylindricity upon segmentation. However, with a higher number of segments, these deviations decreased, eventually equaling or falling below those of the unsegmented stator sample.

By employing both two-dimensional and three-dimensional simulation models using COMSOL Multiphysics, the joining forces and stress distributions in different connection geometries were analyzed. Assuming friction as the main component in the force required to join segments, a simulation model was developed to replicate the experimentally recorded joining forces and estimate the forces for new connection geometries. Due to greater form deviations in the circular connection geometry, the average joining force for experimental samples was approximately five times higher than the simulated results.

The 2D simulations provided insights into stress distribution under various loads. Stress induced by rotor torque was considered noncritical, while simulations of shrink-fitting the stator into a housing indicated average stress levels up to a hundred times higher in a single-tooth segment. Increasing the number of segments and larger air gaps between them can reduce strain in the connection points by up to 93%, but at the expense of dimensional accuracy. Among the different connection geometries, the circular form exhibited the highest elastic strain energy density, while the rectangular shape displayed the lowest.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/MSF.1187.55.pdf

This project is funded by the State of Saxony-Anhalt with funds from the European Union under the European Regional Development Fund under the project number ZS/2024/06/187997.

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The original version of this paper is available on https://www.scientific.net/MSF.1187.55.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/special-materials-and-processes/978-3-0364-1544-4

Materials Science Forum, ISSN: 1662-9752, Vol. 1187, pp 63-73

doi: 10.4028/p-L9BoI3

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

Submitted:
2026-01-13

Revised:
2026-02-17

Accepted:
2026-02-19

Online:
2026-04-17

Estimation of the Lateral Gap in PECM: A Case Study on Tool Steel S390

The original version of this paper is available on https://www.scientific.net/MSF.1187.63.pdf

Richard Petermann 1,a*, Gunnar Meichsner 1, b, Pascal Clauß 1,c, Steven Nickel 1, d and Matthias Hackert-Oschätzchen 1,e

¹Chair of Manufacturing Technology with Focus Machining, Faculty of Mechanical Engineering, Otto von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany

a  richard.petermann@ovgu.de, b  gunnar.meichsner@ovgu.de, c  pascal.clauß@ovgu.de, d  steven.nickel@ovgu.de, e  matthias.hackert-oschaetzchen@ovgu.de (*corresponding author)

Keywords: PECM, Pulsed Electrochemical Machining, S390, lateral gap, working gap.

Abstract

Pulsed Electrochemical Machining (PECM) is an established process that is characterized by the machinability of metallic workpieces regardless of their mechanical properties. Applications of PECM, such as the manufacturing of punches made from hardened tool steel, often utilize the lateral working gap for the final shaping of the workpiece. A major challenge in designing an economically viable removal process is the prediction of the lateral gap for certain targeted feed rates. This case study presents a design strategy for the design of PECM applications utilizing the lateral gap. Based on a characterization of the material removal characteristics of the hardened tool steel S390, preliminary experiments were conducted to characterize the relation between the feed rate, the current density in the frontal gap as well as the voltage and the lateral gap. Further, multiple parameter sets were derived for the machining with a targeted lateral gap. The validity of these parameter sets was verified experimentally. Based on these results, a cathode for the manufacturing of a demonstration punch was designed and manufactured. These demonstration punches were machined and the resulting dimensions evaluated. Lastly, fine adjustments regarding the process parameters were applied to achieve the targeted geometric accuracy.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1187.63.pdf

Electrochemical machining (ECM) is a manufacturing process based on anodic metal dissolution. The material removal occurs contactless and regardless of the mechanical properties of the workpiece [1]. This allows the machining of thin structures and conventionally hard to machine metals such as stainless steels [2], hardened steels [3,4], titanium [5] or nickel-chromium superalloys [6].

Pulsed electrochemical machining (PECM) is an advanced variant of the general ECM process. This variant is characterized by the application of a pulsed direct current in conjunction with an oscillating cathode. A schematic of the process is displayed in Fig. 1a). The oscillation of the cathode is superimposed with the feed of the cathode. An electrolyte is flushed in the gap between the cathode (tool) and the anode (workpiece). Phase I is located around bottom dead center and characterized by the removal effective direct current pulse. Here, the workpiece material is dissolved into the electrolyte. The location and distribution of the material removal is governed by the distribution of current density. PECM increases the localization of the material removal compared to ECM, due to the reduced frontal gap as a result of the oscillation. Phase II is characterized by the widening of the gap due to the oscillation. This increase in gap aids the flushing of the electrolyte and ensures the removal of gases and removal products from the machining gap. The cycle is repeated until the desired machining depth is reached [1].

A major challenge associated with the application of PECM concerns the design of the removal device and the development of suitable process input parameters. Industrial applications are commonly designed based on personal experience. These designs are subsequently refined through empirical approaches consisting of multiple iterations of designs and process parameters. The utilization of Multiphysics simulation presents a complementary approach [8,9]. Here, the occurring physical and chemical phenomena are implemented to predict the resulting distribution of process
parameters such as the current density distribution and electrolyte flushing. The desired result is the determination of the shape of the final workpiece. Despite the obtainable insights, the accompanying cost associated with this approach limits the utilization in small and medium-sized companies.

[picture]

Fig. 1. a) Principle of PECM with sine oscillation and a single direct current pulse according to [3] b) Schematic of frontal gap af and lateral gap as with transition between frontal gap and lateral gap hatched with red bars according to [7]

Fig. 1. a) Principle of PECM with sine oscillation and a single direct current pulse according to [3] b) Schematic of frontal gap af and lateral gap as with transition between frontal gap and lateral gap hatched with red bars according to [7]

In many applications, including the machining of punches, the final shape of the workpiece is determined by the shape of the cathode and the resulting lateral gap as. This lateral gap is formed when external or internal cathodes are applied. Here, the frontal gap extends around the cathode. The increased distance from the conductive surface results in a decrease in local current density and therefore in a decrease in removal rate va. As a result, a point of minimal material removal is reached, resulting in the creation of a surface parallel to the feed rate [1,7]. Fig. 1b) illustrates this transition between frontal gap and lateral gap and shows the frontal gap af and lateral gap as.

It is known that the resulting lateral gap is dependent on parameters such as the design of the removal device, the workpiece specific material removal characteristics, and process parameters. [1,7-10] Previous investigations showed that the relation between the resulting lateral gap as and the voltage Uq as well as the current density in the frontal gap Jf can be correlated with the functional relation shown by Eq. 1. [7]

as(Jf(vf),Uq)=p1+p2·Jf(vf)+p3·Uq+p4·Jf(vf)·Uq(1)

The parameters p1 to p4 represent coefficients, which are determined via regression based on an experimental characterization of the lateral gap. These parameters are specific to the cathode, the workpiece material and selected process input parameters. The current density in the frontal gap Jf is derived based on the feed rate vf of the cathode. The functional relation is derived during experimental material removal characterizations. The relation is commonly described by linear functions in the format presented in Eq. 2 [2,4,11]. The parameters Vf,m and Vf,0 are the determined regression coefficients.

vf(Jf)=Vf,m·Jf+vf,0(2)

A previous study [4] investigated the removal characteristics of the hardened powder metallurgical tool steel S390. This case study applies the presented findings and derives input parameters for the characterization of the lateral gap based on these previous findings. Fig 2 displays the obtained relation between the feed rate vf and the current density J. Three distinct areas are seen, which were correlated with linear functions in the format of Eq. 2. The parameters of these functions are summarized in Table 1. [4]

[picture]

Fig. 2. Feed rate vf as a function of the current density J for PECM of S390 with a frequency of 50 Hz and a pulse width of 4 ms according to [4]

Fig. 2. Feed rate v f as a function of the current density J for PECM of S390 with a frequency of 50 Hz and a pulse width of 4 ms according to [4]

Table 1. Parameters of the correlation functions vf(J) for the material removal of S 390 with a frequency of 50 Hz and a pulse width of 4 ms according to [4]

Area

J

1 | superscript number | 0.0007 | -0.0066
2 | mathematical formula | 0.0162 | -0.5116
3 | superscript number | 0.0033 | -0.0093

The hardened tool steel S390 was investigated as it represents the class of powder metallurgical steels. The samples were hardened to a hardness of 890±HV30 [4]. Major alloying components are tungsten ( 10.4% ), cobalt ( 8.0% ), vanadium ( 4.8% ) and carbon ( 1.64% ) [4]. It is to note that around 10% of the material consists of Fe3 W3C and VC carbides [12]. Common applications of S390 are cutting tools such as skiving wheels as well as punches [4].

This study showcases a systematic approach for the design of removal devices. In contrast to the common empirical approach, a characterization of the lateral gap was initially performed. Based on this characterization, a removal device was designed. The device is then applied and the parameters were adjusted, utilizing the insights obtained from the lateral gap characterization.

Methodology

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This case study presents a novel approach for the development of removal devices for PECM. For this purpose, a device for the manufacturing of punching dies consisting of S390 was designed and trialed. In order to design the final dimensions of the cathode, an initial characterization of the lateral gap was conducted. Based on a general characterization of the material removal (see [4]), experiments were designed and conducted in order to derive an analytical model of the lateral gap (see Eq. 1) [7]. Based on this analytical model, several combinations of feed rate vf and voltage Uq were derived to achieve a targeted lateral gap. The results were then applied to machine a cloverleaf geometry with a targeted lateral gap. Lastly fine adjustments to the parameter set were performed to achieve the desired geometric accuracy. The individual steps are summarized by Table 2.

Table 2. Summary of the conducted steps

Step | Description | Results
1 | Material removal characterization (see [4]) | • Correlation vf (Jf) • Parameter combinations of vf and Uq which result in frontal gaps in the range from 10 μm to 100 μm.
2 | Characterization of the lateral gap
2.1 | Initial Experiments (series 1) →experiments applying known parameter combinations from step 1 | • General overview regarding the resulting lateral gap and frontal gap
2.2 | Additional experiments (series 2) → Investigation of additional supporting points (vf, Uq) | • Sufficient experimental data set consisting of input parameters (vf, Jf(vf), Uq) and the resulting lateral gaps as.
2.3 | Derivation of the mathematical model → Correlation of the parameters p₁ to p₄ to the experimental data | • Analytical description of the lateral gap (see Eq. 1)
2.4 | Investigation of parameter combinations resulting in a constant lateral gap (series 3) | • Experimentally validated parameter sets for a nearly constant lateral gap
3 | Design of the removal device → Assumption of a constant lateral gap based on the results of 2.4 | • Designed and manufactured removal device
4 | Commissioning of the removal device
4.1 | Initial experiments → Experiments applying the removal device of 3 with previous parameter sets from 2.4 | • Resulting geometric accuracy
4.2 | Fine adjustment → Adjustment of the voltage based on a targeted change in lateral gap | • Final parameter set • Validated reproducibility

Experimental Setup. All experiments were conducted applying a commercial machine tool PEM 800 S by PEMTec. Fig. 3 presents the applied removal device. The characterization of the lateral gap was conducted by applying a cylindrical cathode made from stainless steel AISI 304. A cylindrical sample geometry with a diameter of 12 mm was chosen for the anode material. The lateral gap was derived based on the diameter of the cathode and the resulting diameter of the anode, as shown by Eq. 3 [7].

as=Dcathode −Danode 2(3)

The dimensions of the cylindrical cathode are summarized in Table 3 The upper surfaces of the cathode are insulated. The electrolyte is flushed from top to bottom as indicated in Fig. 3. A flushing chamber is incorporated, allowing the outlet of the electrolyte to be pressurized. The pressure at the outlet pout was created by applying a valve with an opening of 4%. The dimensions of the cathode were measured applying a coordinate measuring machine (CMM) PMM 886 by Leitz. The samples were measured applying a micrometer IP65-0-25 by Mitutoyo. An underestimation of the lateral gap by an average of 1.4μ m compared to the values obtained applying a CMM is expected based on results discussed by a previous investigation [10]. The error is deemed acceptable when weighed against the associated reductions in cost and processing time, particularly in view of the method's industrial applicability.

[picture]

Fig. 3.a) Sectional representation of the removal device with illustration of the flushing direction [7] b) Removal device in the machine tool

Fig. 3. a) Sectional representation of the removal device with illustration of the flushing direction [7] b) Removal device in the machine tool

Table 3. Characteristic parameters of the cylindrical cathode [7]

Parameter | Symbol | Value
Material |  | AISI 304 (X5CrNi18-10 or 1.4301)
Diameter | Dcathode | 10.0090 [mm]
Roundness | Rcathode | 0.0086 [mm]
Height | hcathode | 0.923 [mm]

Design of Experiments. Several parameters were kept constant throughout all experiments. The frequency fosc  of 50 Hz and the pulse width tp of 4 ms were selected based on the previous material removal characterization [4]. An anode diameter of 12 mm was selected. The electrolyte was flushed from the lateral gap (top) into the frontal gap (bottom), as visualized by Fig. 3. An electrolyte inlet pressure pin  of 600 kPa was selected. A constant valve opening of 4% was applied at the outlet, resulting in a pressure at the outlet pout in the range between 190 kPa and 400 kPa depending on the resulting gap. A machining depth s of 6 mm was selected. This presents a reduction of 2 mm compared to previous investigations [7,10] in order to reduce the machining time. A NaNO3 electrolyte with around 8% mass fraction was applied. The constant parameters are summarized in Table 4.

The objective of this case study is the design of a removal device and process parameter set for the manufacturing of punches. Analyzing the removal characteristics of the S390 (see Fig. 2), it is decided to investigate current densities in the frontal gap above 40 A/cm2. This removal area is characterized by feed rates above 0.100 mm/min, making this area economically viable. Based on previous investigations [7,10], it is assumed that the lateral gap can be described by a singular parameter set p1 to p4 (see Eq. 1) for current densities above 40 A/cm2, as this area is characterized by a current efficiency above 80% (see [4]). Further, the formation of an area of minimal lateral gap is expected in the range between 30 A/cm2 and 40 A/cm2, due to the characteristic change in current efficiency.

Table 4. Constant machining parameters

Parameter | Symbol | Value
Electrolyte NaNO₃ (aq) |  | 8 [% mass fraction]
Electric conductivity | σ | 67 [mS/cm]
Electrolyte temperature | Tel | 20 [°C]
Machining depth | s | 6 [mm]
Electrolyte inlet pressure | pin | 600 [kPa]
Outlet valve opening |  | 4 [%]
Oscillation frequency | fosc | 50 [Hz]
Oscillation amplitude | hosc | 200 [μm]
Pulse width | tp | 4 [ms]

The initial 7 experiments were designed with current densities in the range between 40 A/cm2 and 150 A/cm2. The voltages were selected based on prior experiments (see [4]). Additional 3 experiments with current densities between 25 A/cm2 and 35 A/cm2 were conducted, to verify the existence of a minimal lateral gap.

The second series of experiments was comprised of 8 additional experiments with 50 A/cm2, 100 A/cm2 and 150 A/cm2 applying a wider range of voltage. The ranges of the variable parameters are summarized in Table 5.

Table 5. Variable parameters of the first and second experimental series

Series | Parameter | Symbol | Range
1 | Voltage | Uq | 5.6 [V] – 18.2 [V]
Feed rate | vf | 0.011 [mm/min] – 0.486 [mm/min]
Current density in the frontal gap | Jf | 25 [A/mm²] – 150 [A/mm²]
2 | Voltage | Uq | 7.7 [V] – 21.0 [V]
Feed rate | vf | 0.156 [mm/min], 0.222 [mm/min], 0.486 [mm/min]
Current density in the frontal gap | Jf | 50 [A/mm²], 100 [A/cm²], 150 [A/mm²]

Lateral Gap Characterization

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The resulting lateral gaps of the initial series of experiments are presented in Fig. 4 (left) as a function of the voltage. The graph is divided by applying a current density threshold of Jf=40 A/cm2. This threshold coincides with the change in the removal characteristics as previously discussed.

A nearly linear increase of the lateral gap with increasing voltage is seen for current densities above 40 A/cm2. The gap increases from around 105μ m at 7.7 V to around 260μ m at 18.2 V . Three experiments were conducted with current densities below 40 A/cm2. The resulting lateral gap ranges from around 140μ m at 5.6 V to 95μ m at around 6.3 V clearly breaking with the trend seen above 40 A/cm2. An area of minimal lateral gap seems to form around current densities of 40 A/cm2. This observation is consistent with [7], where an area of minimal lateral gap was found in the area of the characteristic change in current efficiency in the frontal gap.

[picture]

Fig. 4. Resulting lateral gap as as a function of the voltage Uq for varying current densities Jf (left) and resulting lateral gap as as a function of the voltage Uq for selected current densities Jf (right)

Fig. 4. Resulting lateral gap a s as a function of the voltage U q for varying current densities J f (left) and resulting lateral gap a s as a function of the voltage U q for selected current densities J f (right)

The diagram on the right side of Fig. 4 displays the lateral gap of the second experimental series as a function of the voltage. A linear increase of the lateral gap with increasing voltage is seen for all current densities. The resulting lateral gaps for current densities above 40 A/cm2 are correlated with a function in the format of Eq. 1. The resulting parameters are summarized in Table 6. A degree of determination R2 of 99.8% was reached. Additionally, the root mean squared deviation (RMSE) was
evaluated. The RMSE describes quadratic mean deviation of the correlation function as(Jf,Uq) from the experimental results. A RMSE of 3.4μ m was derived.

Table 6. Parameters of the correlation function (1) as(Jf,Uq) for Jf>40 A/cm2

p₁[μm] | p₂[μm/A/cm²] | p₃[μm/V] | p₄[μm/A/cm² · V]
-51.4410 | 0.1954 | 20.6655 | -0.0358

Considering the observed lateral gaps, a target lateral gap of 220μ m was chosen for the design of a removal device to explore a broad range of parameter combinations. The process input parameter combinations of feed rate vf and voltage Uq were derived based on a selected range of current densities. For the purposes of this investigation, current densities between 70 A/cm2 and 150 A/cm2 were investigated in intervals of 10 A/cm2. The resulting feed rates of the subsequent experiments were calculated applying Eq. 2 in conjunction with the parameters presented in Table 1. Eq. 4 presents Eq. 1 solved for the voltage, allowing the derivation of voltages for the subsequent experiments based on the selected current densities Jf and targeted lateral gap as.

Uq(as, Jf)=as−p2· Jf−p1p4· Jf+p3(4)

The resulting parameter ranges of the third experimental series are summarized in Table 7.

Table 7. Parameter ranges of the third experimental series

Series | Parameter | Symbol | Range
3 | Targeted lateral gap | as-target | 220 μm
| Voltage | Uq | 14.2 [V] – 15.8 [V]
| Feed rate | vf | 0.222 [mm/min] – 0.486 [mm/min]
| Current density in the frontal gap | Jf | 70 [A/cm²] – 150 [A/cm²]

Fig. 5 displays the resulting lateral gaps of the third experimental series as a function of the voltage. The targeted lateral gap of 220μ m is indicated by a dotted line. Lateral gaps in the range from 217μ m to 227μ m are observed. Further, the experiments in the range from 14.2 V to 15.4 V result in deviations of +/−3μ m from the targeted lateral gap.

[picture]

Fig. 5. Resulting lateral gaps as of the third experimental series as function of the voltage Uq with indication of the targeted lateral gap of 220μ m

Fig. 5. Resulting lateral gaps a s of the third experimental series as function of the voltage U q with indication of the targeted lateral gap of 220 μ m

Design of the Removal Device and Application

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Building upon the process input parameters and targeted lateral gap determined in the preceding section, this chapter presents the design of a removal device. The removal device is tailored for the
machining of punches with a cloverleaf-shaped contour. The general concept of the removal device is based on the previous setup (see Fig. 3).

Fig. 6a) displays the dimensions of the punch (anode). Additionally, the corresponding shape of the cathode is visualized around the contour of the punch. The characteristic radius of the cloverleaf is designed to 1.800 mm±0.005 mm. Based on the targeted lateral gap of 220μ m or 0.220 mm the corresponding radius of the cathode is planned as 2.020 mm .

[picture]

Fig. 6. a) Geometry of the punch and corresponding cathode and b) sectional representation of the designed removal device

Fig. 6. a) Geometry of the punch and corresponding cathode and b) sectional representation of the designed removal device

Fig. 6b) shows a sectional representation of the designed removal device. The resulting punch is additionally displayed. The cathode (yellow) is machined to a height of 0.981 mm . Similar to the removal device applied for the initial experiment, the upper surface of the cathode is insulated (displayed green). The shape of both the cathode and the machined samples was measured by applying an optical coordinate measuring machine Bruker Alicona μCMM. An average radius of 2.018 mm was measured. Fig. 7a) displays the machined cathode.

[picture]

Fig. 7. a) Applied cathode with cloverleaf structure, b) Machined punch c) Optical measurement of a machined punch

Fig. 7. a) Applied cathode with cloverleaf structure, b) Machined punch c) Optical measurement of a machined punch

Initial Experiments. Based on the previous characterization of the lateral gap, 3 sets of parameters were derived and applied for the machining of the demonstration punches. In total, 6 punches (2 experiments per parameter set) were machined. An image of a machined punch is displayed in Fig. 7b).

The applied parameters are summarized in Table 8. The resulting average radius of the leaves as well as the resulting frontal gap of the experiments are additionally displayed. The resulting
geometries were evaluated by investigating the achieved radii of the "leaves". The measurements revealed radii in the range of 1.74 mm to 1.75 mm , compared to the specified radius of 1.800 mm .

Table 8. Parameter sets for the manufacturing of the punches

Parameter set | 1 | 2 | 3
Feed rate [mm/min] | 0.222 | 0.321 | 0.420
Voltage [V] | 14.2 | 14.7 | 15.4
Frequency [Hz] | 50 | 50 | 50
Pulse width [ms] | 4 | 4 | 4
Machining depth [mm] | 6 | 6 | 6
Average Radius of the leaves [mm] | 1.751 | 1.749 | 1.741
Resulting frontal gap [μm] | 44 | 30 | 27

Fine adjustment. The radii of the initial experiments are deviating by around -0.05 mm from the specified radius. In order to increase the radii to 1.8 mm±0.005 mm, a reduction in lateral gap of -0.05 mm is needed. As described by [7], a reduction in lateral gap can be achieved by a) an increase in feed rate/current density in the frontal gap or b) a decrease in voltage. These adjustments both are commonly accompanied by a reduction in frontal gap [ 1,11 ]. Therefore, parameter set 1 was selected for further investigation, as the previous experiments resulted in the largest frontal gaps with 44μ m compared to set 2 with 30μ m and set 3 with 27μ m, respectively.

The effect of a change in voltage on the lateral gap for a certain current density in the frontal gap can be approximated by the factor pu(Jf) (see Eq. 5). The relation is derived from Eq. 1 as demonstrated by [7].

pU(Jf)=p3+p4·Jf(5)

Applying a current density Jf of 70 A/cm2 results in a factor pu of 18.16μ m/V. The estimated change in voltage ΔUq needed for a change in lateral gap Δas is derived by applying Eq. 6.

ΔUq=ΔaspU(6)

The initial adjustment is planned based on a targeted decrease of lateral gap of Δas=−50μ m. An initial adjustment of ΔUq=−2.7 V was derived by applying Eq. 6 . As a result, parameter set 1.1 was defined based on parameter set 1, applying a voltage Uq=11.5 V. An average radius of 1.808 mm was obtained.

A second adjustment of ΔUq=+0.5 V was applied in order to slightly decrease the radius of the cloverleaves. Parameter set 1.2 is defined by applying a voltage of Uq=12.0 V resulting in a measured average radius of 1.802 mm . The specified range of 1.800 mm±0.005 mm was therefore reached. Four additional experiments were conducted applying parameter set 1.2. The measurements of all 5 samples revealed average radii in the range between 1.800 mm and 1.804 mm satisfying the specified tolerance of 1.800 mm±0.005 mm. The adjustments and resulting radii are displayed in Fig. 8. The error bars indicate the minimal and maximal radius of each sample.

[picture]

Fig. 8. Visualization of the resulting radii of the cloverleaves as a result of the fine adjustment of the voltage with indication of the average, minimal, and maximal obtained radius of each sample

Fig. 8. Visualization of the resulting radii of the cloverleaves as a result of the fine adjustment of the voltage with indication of the average, minimal, and maximal obtained radius of each sample

Discussion and Outlook

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The initial characterization of the lateral gap validated the applicability of the previously developed model of the lateral gap as(Jf,Uq). Here the area of current efficiency above 80% correlated with an achieved RMSE of 3.4μ m. The model was then applied to derive multiple parameter sets targeting a certain, fixed lateral gap. A number of input parameter combinations (feed rate and voltage) were experimentally investigated, resulting in deviations between −3μ m and +7μ m from the targeted lateral gap.

A cathode for the machining of punches with a cloverleaf shape was designed and manufactured. The transfer of parameter sets for the initial experiments to the new cathode resulted in measurable deviations in the radii of the leaves. The radii were around -0.05 mm to -0.06 mm smaller than specified, indicating an increased material removal and lateral gap compared to the estimation.

An initial adjustment of the voltage resulted in a significant reduction in the observed deviations. The estimation of the needed adjustment showed a promising approach for the fine tuning of process parameters for industrial applications. Following a second adjustment, the target radius was successfully achieved, satisfying the specified tolerance of ±0.005 mm.

The deviations between the initial characterization of the lateral gap and the machining of the punches indicate that the shape of the cathode has a major influence on the forming gap and therefore on the resulting geometry of the workpiece. The effect of the shape of the cathode, especially the shape of the inner conductive surface, is currently not yet sufficiently investigated. As previously stated, the applied model for the characterization of the lateral gap is specific to the applied cathode.

The existence of multiple process input parameter combinations resulting in a constant lateral gap presents an important result for the design of PECM applications. This should theoretically enable a fine tuning of removal processes targeting aspects such as the productivity (via the feed rate) or process stability (focusing on the frontal gap and the flushing of the gap) while maintaining a fixed lateral gap. The existence of a minimal lateral gap further validates the observations made by [7].

Acknowledgements

The original version of this paper is available on https://www.scientific.net/MSF.1187.63.pdf

The machine tool PEM 800 S is funded by the German Research Foundation (DFG) with project number 467011871.

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Materials Science Forum, ISSN: 1662-9752, Vol. 1187, pp 75-86

doi: 10.4028/p-8gyRTS

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

Submitted:
2026-01-19

Revised:
2026-02-28

Accepted:
2026-02-28

Online:
2026-04-17

Beading as a Stabilising Element for Thin Wooden Panels

The original version of this paper is available on https://www.scientific.net/MSF.1187.75.pdf

Ralf Förster 1,a*, Andreas Loth 1,b, Jan Rösler 1,c and Jörg Hornig 1,d

1 BHT, Luxemburger Str. 10, 13353 Berlin

a* rfoerster@bht-berlin.de, b aloth@bht-berlin.de, c jroesler@bht-berlin.de, d jhornig@bht-berlin.de

*corresponding author

Keywords: wood, beading, thin wooden panels, forming, prototyping.

Abstract

Beading has been used in metal construction for decades to reinforce and stabilize thin sheets of metal. In aircraft, washing machine and car manufacturing, this allows for cost-effective, lightweight and material-saving designs to be realized. These indentations are embossed into thin metal sheets to increase their rigidity and stability, thereby preventing fluttering or deformation. The bending stiffness is significantly increased by reshaping the material. The increased stability allows thinner sheets to be used, which reduces the overall weight of the structures and components. Beading is often used on larger surfaces to prevent fluttering or vibrations and to ensure greater dimensional stability. The combination of two old production processes, beading and steam bending for wood is examined in this paper. The use of beads to reinforce thin wooden panels saves material, resources and weight, thereby making production more sustainable. The investigations carried out examined the possibilities of introducing beads into thin panels made from different types of wood. The temperature, water content, water vapour content, soaking time and pressing pressure were varied. In a first step, a test specimen was produced that serves as a mould for the surround. This shape was pressed into the thin wooden panels when varying the processing parameters shown above. In a next step, the indentation depths achieved were measured. The deflection of the thin wooden panels was then measured under different loads and compared with the calculated results.

Introduction

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Wood is one of the most important renewable raw materials and has been used in Europe for thousands of years. Wood is one of the most important renewable raw materials and has been used in Europe for thousands of years. Wood is a renewable raw material that almost always comes from sustainably managed forests. Wood stores CO2, which is removed from the atmosphere during tree growth. This remains bound in the wood as long as it is used as a building material, which contributes to reducing the carbon footprint. The importance of using wood as a construction material is steadily increasing in many areas of industry, not least in residential and modular construction. In order to expand the possible applications of wood-based materials and to continue to save materials in order to use natural resources responsibly, the corrugation of thin wood panels was investigated.
Beading is a channel-shaped indentation that is often used to reinforce thin-walled components, usually made of metal, against deformation or vibration. It is used in numerous areas such as mechanical engineering, aircraft construction and the automotive industry. Beading is also used to reinforce the outer walls of household appliances such as washing machines and dishwashers. One historically significant application of beads is in aircraft construction. All-metal aircraft such as the JU-52 were clad with beaded aluminium sheets to increase stability and reduce weight.
Beading improves the rigidity and strength of materials without the need for additional material. A well-known example of the use of beads in automotive engineering is the Citroën Transporter HY. Even today, beads are used in automotive engineering both as a design element and as a functional stiffening element. Beads can be used to create new design features while also providing stabilizing or functional advantages.
In order to be able to use these advantages for renewable raw materials such as wood, initial experiments were carried out on steam bending beads in wood.

2. Background and Related Work

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Bending of wood.

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Bending wood is one of the oldest woodworking techniques and has developed over thousands of years into a commonly used method for shaping solid wood. Two basic principles are used in this process: heat and water/water vapour. Either steam or direct heating of the wood over an open fire is used. Both methods are based on the same physical principles: heat makes the lignin and hemicellulose in the wood malleable, while moisture increases the elasticity of the fibres so that the wood can be permanently reshaped [8, 9].
Even in prehistoric cultures, wood was bent for use in tools, weapons and simple containers. Archaeological finds from this period indicate that steam was used to make wood more pliable so that it could be shaped [8, 9]. In Egypt and Greece, heated and watered wood was bent to form ship planks and furniture parts. In historical Greek shipbuilding, bent frames - probably bent over a fire - were used to make lighter and more robust hulls. Between the 10th and 17th centuries, wood bending over a fire reached its peak in European shipbuilding [8, 9]. Very old Viking ships with bent wooden planks have been found in Denmark. The age of these Viking ships has been estimated at 1000 years [1]. These early applications already demonstrate a basic understanding that wood becomes more malleable under heat and moisture. In the construction of cogs, galleons and later frigates, solid oak beams were heated over open fires until they could be bent under pressure. Fire was readily available and allowed for rapid heating, but the method required a great deal of experience to avoid charring or weakening the wood. In some regions, particularly Scandinavia, wood was prepared for bending by boiling it in water from an early stage. This results in better material quality and less tension in the wood [8].
In the course of industrialisation around 1800, steam bending became more precise and predictable thanks to the development and application of steam pipes and steam boxes. They enabled wood to be bent at constant temperatures and controlled humidity. This made it possible to plan bending times better and ensure consistent quality. This revolutionised many areas of industry - especially carriage and boat building [10, 11].
Furniture has been made from bent wooden parts for more than 200 years. The most famous piece of work in wood bending technology was developed by MICHAEL THONET. The bentwood chairs by MICHAEL THONET, also known as Viennese bentwood chairs or Viennese café chairs, which were made from bent beech wood, have become particularly well known. In the 1850s, he developed a bending process in which beech wood was plasticized with hot steam and then clamped into closed metal molds. Chair No. 14, designed in 1859, became a classic that was inexpensive to produce, extremely stable, and known worldwide. THONET's innovation made bent wood an element of modern design and later became the basis of modern series furniture production [10, 11, 12, 13, 14]. Figure 4 shows the THONET's chair number 14. However, THONET was not only involved in the manufacture of furniture, but also in the manufacture of wheels in his early days. He later combined his two developments and created the wheel shown in Figure 1.

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Fig. 1. Thonet wheel with curved spokes from the estate of Viktor Thonet [2].

Fig. 1. Thonet wheel with curved spokes from the estate of Viktor Thonet [2].

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Fig. 2. Bending tool for yoke bows.

Fig. 2. Bending tool for yoke bows.

Wood steam bending was used in Southern Europe by many non-specialized craftsmen and even farmers to produce complex components quickly and cost-effectively. Figure 2 shows a simple wooden form for bending yoke bows for cattle. This form is approximately 120 years old.

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Fig. 3. Single yoke bow with steamed bent yoke.

Fig. 3. Single yoke bow with steamed bent yoke.

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Fig. 4. Thonet Chair Nr 14 [25].

Fig. 4. Thonet Chair Nr 14 [25].

Steam bending of solid wooden parts is a well-established process in a wide variety of applications. Fig. 3 shows a single yoke for draft cattle with U-shaped steam bent wooden yoke bows that were produced in Slovenia approx. 120 years ago.
In addition to the traditional methods of wood bending described above, new technologies were developed and used in the 20th century. These include laminated bending, in which layers of thin wood veneers are glued together and bent under pressure. Another method is form gluing, in which thin wood veneers are also placed in a mold and glued. This further development allows very stable curved wood components to be produced. Controlled steam boxes are also used, which enable precise temperature and humidity regulation [8, 15]. In addition to the possibility of using high-frequency fields to heat the wood, the possibility of using microwaves is also being discussed [11]. Despite numerous alternatives, steam bending still has a significant range of applications in boat building, instrument making, and the manufacture of high-quality furniture.

Beading.

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Beading refers to elongated, channel- or ridge-like indentations or elevations in thin-walled components made of metal or plastic. They serve to increase local rigidity, reduce vibration, optimize material flow, and enhance the visual design of components. They are mainly used in car body construction, aviation, container construction, ventilation technology, and the food industry (cans). They are shaped using manual tools, beading hammers, beading sticks, beading machines, presses, or even during the injection molding of plastics. Beading was initially a purely manual technique. Before industrialization, reinforcements were hammered into sheet metal using a beading stick - a time-consuming, imprecise process that depended on the skill of the craftsman. The first applications were in sheet metal working, as sheet metal roofs, gutters, and containers required structural reinforcement at an early stage.

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Fig. 5. Can with beads [20].

Fig. 5. Can with beads [20].

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Fig. 6. Airplane Ju 52 with beads [23].

Fig. 6. Airplane Ju 52 with beads [23].

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Fig. 7. Washing machine with beads.

Fig. 7. Washing machine with beads.

The beads produced in this way were created by hand, which often allowed for only a low degree of uniformity. The use of beads was therefore functional, but not standardized. A key innovation in metal forming technology was the development of mechanical beading machines between 1861 and 1873 by the mechanical engineer Carl Erdmann Kircheis (1830-1894). His designs replaced the previously extremely laborious manual hammering technique with a roller system that produced uniform beads with significantly higher precision [17]. The early beading machines consisted of two rollers - a positive and a negative mold. The sheet metal was pulled through the rollers, which were gradually adjusted to increase the bead depth. This made beads a standardized, repeatable, and industrially usable structural form. With the growing importance of thin-walled sheet metal in mechanical engineering and vehicle construction, the function of the bead also evolved. Beads increase local bending stiffness, reduce shear sensitivity, and decrease the susceptibility of components to resonance and booming. Mainly in the automotive industry, equipment manufacturing, and the household goods industry, beads were also used for aesthetic reasons. A typical sample for beads on household goods shows Fig. 7, a washing machine with beads on the Side panel. These beads serve exclusively to stabilise the side panels of the washing machine. Fig. 5 shows a can with some beads for stabilising the thin edges around the tin can. The Fig. 6 shows a typical example of the use of beads in aircraft construction. This figure shows an aircraft, the Ju52, manufactured by Junkers Flugzeugwerk AG, Dessau. Beads were used very early on in this aluminum aircraft, which was developed in 1932, in order to save weight. Due to current requirements in lightweight construction (e.g., automotive engineering, mechanical engineering, aerospace), beads are also being used and investigated as an element for saving resources in the field of metal and plastic forming. Dissertations and research papers examine influencing factors such as shape, position, radii, material anisotropy, and component curvature in order to use beads specifically for component reinforcement [17]. Recent research approaches use topography optimization, AI-based bead patterns, and finite element analysis to automatically generate optimal bead layouts [21]. Such investigations enable predictable bead patterns that optimize mechanical performance, manufacturability, and material efficiency. Beads are used in many industries. In automotive engineering for body stiffening, crash performance, and drone reduction. In the aerospace industry for weight-efficient thin-walled constructions. In the construction industry, they are used to stiffen facade and roof panels. In the field of heating, air conditioning, and ventilation technology, they are used to reinforce and seal air ducts [19].
In the field of can manufacturing and container construction, beads increase the pressure stability of cylindrical cans and enable a significant increase in stability. The use of beads is a central component of lightweight construction. Today, beading is a highly optimized manufacturing process whose application goes beyond empirical rules and is determined by numerical simulation, algorithmic optimization, and digital manufacturing methods.

Principles of the thermoforming process of wood.

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Wood is a natural composite material composed mainly of cellulose, hemicellulose, and lignin. The composition of different kinds of wood and bamboo is shown in table 1. The most significant component in wood is cellulose. The bending process of wood is usually divided into three steps: plasticizing (softening) - bending - stabilizing (drying) [11].

Table 1. Composition of wood [4,5,6].

Component [%] |  | Oak | Beech | Pine | Mahogany
Cellulose |  | 47 | 41 | 41 | 45
Lignin |  | 29 | 27 | 29 | 24
Hemicellulose | Xylan | 22 | 13 | 9 | 28
Mannan |  | 18 | 18 |
Pectin |  |  | 1 | 3 |

The glass transition temperature determines the softening temperature of the wood components lignin and hemicellulose. In the dry state, the softening temperatures of hemicellulose and lignin are 167217∘C and 134−235∘C, respectively, while in the moist state they decrease to approximately 54-142 ∘C and 77−128∘C, respectively. This transition to the highly elastic state promotes the plasticity of the wood [7,8]. Processes such as boiling, steaming, and electromagnetic waves are used to soften wood materials. While the mechanism of action is the same in these processes, the method of heat transfer is different. During boiling and steaming, the wood surface is gradually heated, softening the wood core and then bending it into the desired shape. Electromagnetic waves simultaneously heat the core and the outer surface of the wood, ensuring rapid and even heating [7].

Production of beading in thin wooden panels.

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As described in the above sections, both wood bending and the beading of sheet metal, metals and plastics are manufacturing processes that have been known and established for a very long time. Thin wood-based materials, e.g. laminated veneer lumber, plywood or thin solid wood panels, have a favourable weight-to-load-bearing capacity ratio, but exhibit limited bending and torsional stiffness in flat areas. This leads to increased susceptibility to deformation, vibration and local instability, especially in large-area applications (interior design, furniture construction, acoustic panels, lightweight timber construction). The addition of corrugations offers a structural advantage here. As described above, this method is well established in metalworking, but has not yet been applied to thin wooden panels. The physical principles of form stiffening through the addition of corrugations can also be applied to wood-based materials. The mechanical advantages of this approach for thin wood panels are as follows: Stiffening of thin-walled structures, which have high potential for lightweight construction applications but exhibit low bending stiffness when the panel surfaces are slightly curved or completely flat. Beading improves stiffness through targeted geometric anisotropies. This principle is state of the art in sheet metal processing, but can easily be transferred to other materials as it is material-independent. Beading reduces free buckling lengths, increases buckling resistance and locally increases torsional and bending stiffness without the use of additional material [21]. The channel- or bead-like shape increases the cross-section of the wood panel. This results in significantly higher bending strength because the material is further away from the neutral fibre. Research into lightweight metal construction has shown that beads can significantly increase rigidity without adding weight. This principle can be applied 1:1 to thin wood-based materials. This paper examines the differences and challenges that can be expected when applying these principles to wood-based materials. Since beads increase the rigidity of a component without adding extra material, wooden panels can be made thinner. This principle is considered an essential design feature in lightweight construction. It enables increased load-bearing capacity with constant or lower weight. This behavior is used, among other things, in caravan/tiny house conversions, in the furniture industry, and in interior design with acoustic panels and ceiling coverings. Wooden panels with corrugations exhibit higher damping of structural vibrations, which is particularly relevant in acoustics, interior design or furniture back panels. Corrugations serve as structural dampers that reduce unwanted resonances.

3. Experimental Setup

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Four different wood materials were used for the tests: oak, beech, mahogany and spruce. The materials were 150 mm long and 44 mm wide. The thickness of the individual materials is shown in Table 2.

Table 2. Thickness of tested materials.

Thickness [mm] | Oak | Beech | Pine | Mahogany
| 1.4 | 1.3 | 1.5 | 1.45

Numerous preliminary test series were carried out in which the four different types of wood were softened at different water temperatures and for different lengths of time. The following were found to be suitable for initial tests for all types of wood. All materials were heated in a water-water vapor
mixture at a set temperature of 160∘C for one hour. The maximum pressure in the pressure vessel is approximately 1.8 bar. At this temperature, water has a boiling point of approximately 117∘C. The test components were then placed in various wire-eroded moulds and compressed to such an extent that the moulds were completely filled.

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Fig. 8. Wire-eroded bending mould.

Fig. 8. Wire-eroded bending mould.

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Fig. 9. Used bending mould (WEDM).

Fig. 9. Used bending mould (WEDM).

In order to determine the minimum possible bending radii, four different bending moulds were designed and manufactured using Wire Electro Discharge Machining (WEDM). A CUT P 350 Pro wire EDM machine from GF Machining Solutions GmbH, Schorndorf, was used to produce the various bending shapes. Two of different types of bending moulds are shown in the pictures 8 and 9 . The bending mould shown in the figure 9 was used for the investigations presented in this article. The geometry of this bending mould shown in Figures 10 an 11. Small bending radii of less than 5 mm cannot be produced with the technologies currently under investigation.

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Fig. 10. Minimum bending radii used and shape for all wooden test pieces.

Fig. 10. Minimum bending radii used and shape for all wooden test pieces.

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Fig. 11. Sketch of used bending mould.

Fig. 11. Sketch of used bending mould.

The test components were then pressed and dried in the bending mould. Fig. 12 and 13 show the device for pressing and drying the thin wooden panels. As the steel mould used made it very difficult for the wooden test components to dry, they had to dry in the mould for at least 12 hours before the mould could be opened again. If the mould is opened too early, the components bent back very quickly. Fig. 20 shows the bending back of the test pieces after premature opening of the bending mold.

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Fig. 12. Pressing of test Pieces.

Fig. 12. Pressing of test Pieces.

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Fig. 13. Pressing and drying of multi-beads in bending moulds.

Fig. 13. Pressing and drying of multi-beads in bending moulds.

4. Measuring of Deflection

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After the components had dried, the deflection was determined under various loads. For comparison purposes, the deflection of the thin, non-deformed wooden panels was also determined. The illustration shows the test setup for measuring deformation. The samples were tested in a three-point bending test. The samples were placed freely on the supports and loaded in the middle with a lineshaped force. The support spacing was 135 mm (Fig. 14, right). All samples were loaded with three different forces of 3.27 N,5.45 N and 10.75 N and the deformation was measured. Subsequently, a simulation was performed in SolidWorks using these values.

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Fig. 14. Test setup for measuring deformation and distance between support, (distance 135 mm ).

Fig. 14. Test setup for measuring deformation and distance between support, (distance 135 mm ).

The results of the bending tests for the bevelled and non-bevelled wood samples are shown in Fig. 15. It is clear to see that the bevelled samples are stiffer.

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Fig. 15. Results of the bending tests.

Fig. 15. Results of the bending tests.

5. Simulation of Deflection

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An initial FEM simulation was performed using SolidWorks simulation tools. A linear elastic model with the smallest possible mesh was used. Type of networking used was a mixed curve-based network. Network quality was high with. The percentage of elements with an aspect ratio <3 was 99.7%. Figure 16 shows the used mesh for simulation of deflection tests. Since wood is a natural material with a very wide range of material properties, average material properties were always used. The supports are made of steel and are fixed in the simulation. The wood samples are in contact with the support, one side of the wood sample can slide, the force is applied centrally and the center of the sample can slide vertically. Figure 17 shows the results of simulation of flat pine sample.

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Fig. 16. Used mesh for simulation of deflection tests.

Fig. 16. Used mesh for simulation of deflection tests.

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Fig. 17. Results of simulation of flat pine sample.

Fig. 17. Results of simulation of flat pine sample.

Table 3 compares the simulation results of flat pine with the test results of the same material. There is relatively good comparability between the two values. The error increases with increasing load, which can be explained by the errors in the natural material wood. Further investigations on this topic are still necessary.

Table 3. Comparison of simulation results with the test results of flat pine.

Test number | Material | Used E-Modulus [N/mm²] | Deflection measured [mm] | Deflection simulated [mm] | Weight [g] | Force [N]
1 | Pine flat | 11000 | 1.3 | 1.27 | 333.6 | 3.27
2 | Pine flat | 11000 | 1.75 | 2.12 | 556 | 5.45
2 | Pine flat | 11000 | 4.64 | 4.19 | 1096 | 10.75

Fig. 18 shows the results of simulation of deflection of a flat oak sample with a load of 10.75 N .

Fig. 18. Results of simulation of flat oak sample.

The comparison of the simulation results for flat oak wood with the test results, also for oak wood. Unfortunately, the values from the simulation and the tests are not comparable. On the one hand, wood is a natural material with considerable variations in its material properties; on the other hand, wood is not an isotropic material, i.e. its properties, in particular its modulus of elasticity, are highly directional. It is easy to understand that the anisotropic properties of wood have a strong influence on the test results, especially in the case of curved components. In subsequent investigations and simulations, the simulation conditions must be further adapted to the anisotropic properties of the various types of wood and the wide range of properties of wood as a natural product. Figure 19 shows a result of simulation of deflection of beaded oak test sample with a load of 10.75 N .

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Fig. 19. Results of simulation of beaded pine sample.

Fig. 19. Results of simulation of beaded pine sample.

The results of the simulation and the measurement of beaded pine test parts are not identical. Wood is a natural material whose material properties vary greatly. Furthermore, wood is not an isotropic material, which means that its properties are highly directional. This property has a strong influence on the simulation results. This plays a particularly important role in the corrugated or curved components examined here. The anisotropic properties of wood-based materials were evidently not sufficiently taken into account during the simulations carried out here.

6. Summary and Outlook

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In the initial studies presented here, the two long established manufacturing processes of wood bending using steam and heat and corrugating materials to increase stability were combined to stabilise thin wooden panels. The studies show the great potential of beading thin wood panels to increase their stability. Experiments with different types of wood have shown that thin wood panels can be stabilised by beading with steam. However, the investigations also make it clear that there is still a considerable need for research in this area of production technology. On the one hand, the process parameters (e.g. pressure, temperature, time) for beading the different types of wood need to be determined more precisely, and on the other hand, further materials for the bending moulds need to be investigated. These must be pressure-resistant and breathable materials in order to significantly reduce the drying time. If the components are removed from the mould too early, they spring back. This process is shown in Fig. 20.

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Fig. 20. Spring-loaded component after premature removal from the mould.

Fig. 20. Spring-loaded component after premature removal from the mould.

In addition, other, larger pressure vessels must be developed and used in order to soften the woodbased materials with different parameters and heat larger panel sizes so that they can be measured with standard measuring devices. Furthermore, the anisotropic material properties of the woods must be investigated more thoroughly. In further investigations and simulations, the simulation conditions must be further adapted to the anisotropic properties of the different types of wood and the highly variable properties of wood as a natural product. The behavior of the different types of wood must also be investigated and the transverse and longitudinal alignment of the wood fibres simulated. Furthermore, models must be found that describe the springback process of the various types of wood and enable the corrugation shapes to be designed accordingly. Other types of wood, wood-based materials and, if necessary, sandwich materials made of wood are to be investigated.

Acknowledgement

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The authors gratefully acknowledge the extensive experimental contributions of of Michael SteinbrücK at Berliner Hochschule für Technik and Jan Klamet the highly talented carpenter from Berlin.

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The original version of this paper is available on https://www.scientific.net/MSF.1187.75.pdf

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Materials Science Forum, ISSN: 1662-9752, Vol. 1187, pp 87-98

doi: 10.4028/p-3z29kF

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

Submitted:
2026-01-19

Revised:
2026-02-22

Accepted:
2026-02-23

Online:
2026-04-17

Influence of Variable Radius Die Geometry on Tube Bending: A Finite Element Parametric Study

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Apostolos Aslanidis 1,a*, Jonas Reuter 1, b and Bernd Engel 1,c

1 University of Siegen, Forming Technology Siegen (UTS), Breite Strasse 11, 57076 Siegen, Germany

a  apostolos.aslanidis@uni-siegen.de, b  jonas.reuter@uni-siegen.de, c  bernd.engel@uni-siegen.de

Keywords: Profile Bending, Tube Bending, Profile Forming, Compression Bending, Variable Radius Die, Variable Bending Radius, Radial Gradient

Abstract

While bending processes for producing tube bends with a constant radius have been extensively investigated in recent years, only a limited number of studies have addressed form-bound bending processes for generating variable radii. In particular, a systematic investigation of the influence of bend die geometry with a variable radius profile on tool reaction forces, geometrical and non-geometrical bent part properties is still lacking. In this study, compression bending using bend dies with a continuously varying radius is investigated by means of finite element (FE) simulations. The geometry of the bend dies is parameterized using an Archimedean spiral curve, allowing the bending radius to be described as a function of the bending angle. The introduced radial gradient, defined as the derivative of the radius with respect to the bending angle, dR/dα, serves as the central design parameter of the bend die and is systematically varied from constant radius with 0 mm/∘ to 1 mm/∘. The influence of the radial gradient dR/dα of the bend die geometry on tool reaction forces as well as on the geometrical and non-geometrical properties of the bent part is investigated by means of a numerical parametric study for a selected bending task. The results show that for small to moderate values of dR/dα, all investigated metrics exhibit a pronounced linear dependence on the radial gradient. This behavior is further confirmed by the evaluation of the maximum values of the process and geometric parameters as a function of dR/dα, yielding high coefficients of determination (R2). For larger values of dR/dα, however, the sensitivity of both process-related and geometric characteristics decreases.

Introduction

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Profile bending processes are classified into form-bound or die-dependent and kinematic or freeform methods [1,2], or, equivalently, into processes using geometry-specific and generic tools [3,4]. Furthermore, a distinction is made between continuous and incremental processes.

In free-form bending processes (e.g., TSS bending [5], three-roll push bending [6]), forming is achieved by controlled movement of the tools. As a result, the final part geometry is largely independent of the tool geometry. Adaptations to changing process conditions and part geometries can be implemented via the process kinematics. In contrast, form-bound processes (e.g., rotary draw bending or compression bending [7]) are limited by the predefined geometry of the forming tools. However, this form constraint enables extensive support of the workpiece, allowing the production of wrinkle-free parts with small radii and tight tolerances.

Due to their flexibility in process control, free-form tube and profile bending methods are particularly suitable for producing spline geometries. The target geometries are typically described using the Frenet-Serret formulas based on the moving Frenet-Serret frame, as shown among others by Groth et al. [6]. However, this continuous curve representation is often discretized into sections of constant radii to simplify execution of the bending process, see Groth et al. [6] and Wang et al. [8]. Following this, Engel et al. [9] present a method to discretize the spline geometries into segments of constant radii and directly derive the required machine parameters for three-roll push bending, also accounting for machine stiffness. Nevertheless, achieving the desired target geometry remains challenging, as transitions between radii induce complex mechanical and geometrical effects. For this reason, Groth et al. [6] developed a product planning system that anticipates such effects during the
design phase of the bent part. Furthermore, the reverse transformation of a bent part into a spline geometry, as part of quality control, presents a significant challenge. This issue is addressed by several studies, including Groth et al. [10] and Scandola et al. [11].

The application of form-bound bending processes to produce variable radii has received little attention in the scientific literature to date. One of the few examples is the work by Elyasi et al. [12], who conducted a phenomenological investigation of rotary draw bending using a bend die with a variable radius. In their study, the variable radius is defined as an involute curve. To further address this gap, the present study investigates the compression bending process with a variable radius through numerical simulation. In contrast to Elyasi et al. [12], the bend die geometry is defined by an Archimedean spiral curve. The radial gradient, defined as the derivative dR/dα, is systematically increased, and the resulting effects on tool reaction forces, geometrical and non-geometrical bent part properties are described both qualitatively and quantitatively.

The paper first outlines the compression bending process and the underlying simulation model. For a more detailed process description, reference is made to Tronvoll et al. [7]. The subsequent section presents the study design. This is followed by a detailed presentation of the results, which are then summarized, discussed, and used as a basis for further research perspectives.

Finite Element Model Setup

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FE simulations are conducted using the explicit solver Ls-Dyna (version mpp d R 12.0.0) from Livermore Software Technology, LLC. The simulation model employed in this study is based on the FE model set up by Reuter [13, 14], which was experimentally validated for constant bending radii. In the present work, the model is extended to account for variable bending radii, enabling its application to virtual investigations. The model setup is illustrated in Fig. 1.

[picture]

Fig. 1. FE simulation model setup based on [13, 14].

Fig. 1. FE simulation model setup based on [13, 14].

The tool surfaces are modeled as rigid bodies using shell elements with an average edge length of le=1.0 mm. In total, the discretization of the tools comprises 126.868 shell elements. A rigid material model (*MAT_020) is assigned. The tube is discretized using selectively reduced, fully integrated hexahedral solid elements (ELFORM =−2 in *SECTION_SOLID). The nominal element edge lengths are set to le=0.5 mm in the circumferential and le=1.5 mm in the axial direction, with five elements through the wall thickness. This results in a total of 237.750 solid elements for modeling
the tube. Additionally, shell elements with a thickness of t=0.001 mm and the same material properties as the tube are applied to the surface of the discretized tube. This approach allows for the evaluation of strains in the outer fibers.

The elastic-plastic material behavior of the tube is described using Young's modulus E, the density ρ, and the von Mises yield criterion with isotropic hardening (*MAT_024). The flow curve data are obtained from uniaxial tensile tests conducted on circular steel tubes made of E235+N, as described in detail in [13]. The experimental data are approximated using Eq. (1) following the approach proposed by El-Magd et al. [15]

kf(φ)=C1+C2·φ+C3·(1−e−φ/C4)n(1)

where kf denotes the flow stress and φ denotes the von Mises strain. The approximation parameters C1 to C4 and n are determined by curve fitting using a least-squares optimization procedure.

The rotation of the bending roll about the bending center is realized in accordance with the actual process setup by introducing an additional rotational axis into the model. This axis consists of a cylindrical structure discretized with shell elements, whose center coincides with the bending center, and which is oriented perpendicular to the bending plane. The rotational axis possesses a single rotational degree of freedom about its longitudinal axis. A time-related angular velocity profile (Fig. 2) is prescribed using the *BOUNDARY_PRESCRIBED_MOTION card and applied to the rotational axis. Via a kinematic coupling implemented with *CONSTRAINED_EXTRA_NODES, this motion is transferred to the bending roll. In addition to its rotational motion, the bending roll undergoes a radial translation toward the bending center in order to follow the variable bending radius while maintaining a constant lever arm. As a result, the bending roll rotates about the bending center along a circular path with a continuously decreasing radius. The radial displacement is implemented using *CONSTRAINED_JOINT_TRANSLATIONAL, while the time-related control of the translational velocity profile (Fig. 2) is defined using the *CONSTRAINED_JOINT_TRANSLATIONAL_MOTOR card.

[picture]

Fig. 2. Trapezoidal velocity profiles applied to the bending roll. The rise and fall times are each 10 ms . For the angular velocity, the y -scale factor in Ls -Dyna is defined as SFO= bending angle α=1.5708 and for the radial velocity, SFO=dR/dα· bending angle α=dR/dα·90∘.

Fig. 2. Trapezoidal velocity profiles applied to the bending roll. The rise and fall times are each 10 ms . For the angular velocity, the y -scale factor in Ls -Dyna is defined as S F O = bending angle α = 1.5708 and for the radial velocity, S F O = d R / d α · bending angle α = d R / d α · 90 ∘ .

The contact interaction between the tools and the tube is described by a penalty-based numerical formulation using the *CONTACT_FORMING_ONE_WAY_SURFACE_TO_SURFACE card. In the compression bending process, no significant relative motion occurs between the tools and the tube. The tube unwraps from the bend die, while the bending roll moves along the tube surface due to its bearing-supported rotation. The pressure die and the tube clamping system remain stationary throughout the process. Accordingly, the implementation of advanced friction models is omitted, and
a Coulomb friction model is applied. The static and dynamic friction coefficients are set to μstat =μdyn =0.1. This value is consistent with commonly reported data in tube bending literature [16, 17] and reflects the typically polished contact surfaces of bending tools, which provide only limited resistance to the initiation and continuation of sliding. For the bending roll-tube contact pair, both friction coefficients are set to zero instead of the roll's turning kinematic [13, 14]. Moreover, the aim of the simulation study is to isolate the effect of variable bend die radii on the tube deformation behavior. To this effect, the influence of roll on the tube is minimized. Consequently, friction between the bending roll and the tube is neglected to ensure that it does not affect the tube behavior associated with the variable bend die radii. The shell element thickness of the tools is neglected in the contact computation, as the elements are positioned directly on the actual tool surfaces.

Methodology

The original version of this paper is available on https://www.scientific.net/MSF.1187.87.pdf

To investigate form-bound bending processes for producing tubes with variable bending radii, it is essential to understand how the geometry of a bend die with a variable contour influences the process variables and the resulting bent part quality, while accounting for the tube properties. Therefore, the present study investigates the compression bending process using bend dies with a variable radius by means of numerical simulations (Fig. 3), based on the FE model described in the preceding section.

[picture]

Fig. 3. Overview of the simulation study conducted to investigate the influence of the radial gradient dR/dα in compression bending using bend dies with a variable bending radius. (a) Process description of the compression bending process; (b) Schematic representation of the bend dies with a variable radius, where dR/dα=0 mm/∘ denotes the constant radius.

Fig. 3. Overview of the simulation study conducted to investigate the influence of the radial gradient d R / d α in compression bending using bend dies with a variable bending radius. (a) Process description of the compression bending process; (b) Schematic representation of the bend dies with a variable radius, where d R / d α = 0 m m / ∘ denotes the constant radius.

The bend die geometry, which defines the target tube geometry during the process, is described by a continuously varying radius R(α) (Eq. (2)) following an Archimedean spiral curve [18, 19]. Starting from an initial bending radius R0, the radius decreases continuously with increasing bending angle α at a constant radial gradient, defined as the derivative of the radius with respect to the bending angle, dR/dα. This results in a linear radius-angle relationship

R(α)=R0−dR dα·α;dR dα≥0.(2)

The geometry of the bend die is systematically varied according to the radial gradient dR/dα, enabling the generation of different radius profiles (Fig. 3 b). The effects of these variations on the tool reaction forces, geometrical and non-geometrical bent part properties in compression bending
are investigated. The reference case with a radial gradient dR/dα=0 mm/∘ corresponds to a constant bending radius over the bending angle.

The inner radius of the bend die corresponds to the nominal inner radius of the bending task. Only circular tubes are considered. For all simulations conducted in this study, the geometric and material properties of the workpiece remain constant. The tube material is steel E235+N with an outer diameter of D=30 mm and a constant wall thickness factor of W=D/t=15. The initial bend factor is defined as B0=R0/D=4. The lever arm a=50 mm and the bending angle α=90∘ are kept constant for all parameter variations.

To evaluate the influence of the radial gradient dR/da of the bend die geometry, both process variables and the resulting geometric characteristics of the bent tubes are analyzed. The process variables considered include the tool reaction force (bending force) required during the process and the longitudinal strains in the outer and inner fibers along the extrados and intrados, respectively. The characterization of the geometric bent part features is performed based on VDI 3430 [2] and VDI 3431 [20]. The geometric characteristics include the curvature κ of the cross-sectional gravity centreline as defined in [21], the wall thickness t at the outer and inner bend, and the cross-sectional deformation of the tube, characterized by the out-of-roundness u (Fig. 4).

[picture]

Fig. 4. Determination of the out-of-roundness u of the tube cross-section at an arbitrary position in accordance with VDI 3430 [2] and 3431 [20].

Fig. 4. Determination of the out-of-roundness u of the tube cross-section at an arbitrary position in accordance with VDI 3430 [2] and 3431 [20].

The out-of-roundness u is determined according to Eq. (3)

u=HQ−HRH(3)

The evaluation of the geometric characteristics of the tube bends is performed automatically using a Python script analysis tool within the software Zeiss Inspect 2025, which is based on the geometry analysis tool for bent parts developed by Reuter [13]. The operational principle of this tool is described in detail in [13]. For the present study, the tool has been extended to analyze tube bends with a radius that varies along the bending angle. The main modifications concern the determination of the transition planes between the straight profile sections and the bend. These transition planes are identified based on a user-defined curvature threshold set close to zero, which defines the portion of the bend to be analyzed. The subsequent determination of characteristic parameters is carried out following the procedures described in [13].

All geometric characteristics are evaluated on non-springback tube bends. This procedure ensures an isolated assessment of the influence of varying bend die radii, such that the observed geometric variations can be attributed to differences in bend die radius, without interference from elastic recovery effects. For this purpose, the STL meshes of the bent tubes are exported during postprocessing immediately after completion of the bending operation, once a bending angle of 90∘ has been reached, while the bending roll remains engaged.

Results

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Fig. 5 illustrates the influence of the varying radial gradient dR/dα of the bend die geometry on the evolution of the bending force FB. The bending force corresponds to the reaction force acting on the bending roll at the bending axis.

[picture]

Fig. 5. Bending force FB during compression bending using bend dies with a variable radius for different radial gradients dR/dα, plotted as a function of the rotation angle of the bending axis.

Fig. 5. Bending force F B during compression bending using bend dies with a variable radius for different radial gradients d R / d α , plotted as a function of the rotation angle of the bending axis.

The force-angle curves show an almost identical initial increase in bending force for all investigated cases in the bend entry zone. Subsequently, the bending force increases with increasing bending angle at rates that depend on the value of dR/dα, until a maximum force is reached at the end of the bending operation. The reference case with a constant bending radius ( dR/dα=0 mm/∘ ) exhibits the lowest maximum bending force. In general, the required bending force increases with increasing dR/dα. This effect is observed over nearly the entire bending angle range and results in an overall steeper force increase for larger values of dR/dα.

A comparison of the force evolutions reveals that the influence of dR/dα is more pronounced at medium (Fig. 5, region II) to large (Fig. 5, region III) bending angles than at small (Fig. 5, region I) bending angles, while the ranking of the force levels as a function of dR/dα is largely preserved. For dR/dα=1 mm/∘, a slightly deviating trend is observed at small to medium bending angles. Between dR/dα values of 0 mm/∘ and 0.6 mm/∘, a continuous increase in the maximum bending force can be observed. In contrast, no significant differences in the maximum force are apparent between 0.8 mm/∘ und 1.0 mm/∘. This behavior indicates a stagnation of the influence of a further increase in the radial gradient on the resulting bending force.

The longitudinal strains φL in the outer and inner fibers along the defined evaluation paths at the extrados and intrados, plotted over the normalized arc length, are shown in Fig. 6 for different values of dR/dα.

[picture]

Fig. 6. Longitudinal strains φL in the outer and inner fibers along the extrados and intrados for different radial gradients dR/dα, plotted over the normalized arc length.

Fig. 6. Longitudinal strains φ L in the outer and inner fibers along the extrados and intrados for different radial gradients dR/dα, plotted over the normalized arc length.

For the reference bend ( dR/dα=0 mm/∘ ), the maximum longitudinal strains at both the outer and inner bend are the lowest and remain largely constant in the central bend region (Fig. 6, region B) before decreasing again in the bend exit zone (Fig. 6, region C). With increasing radial gradient dR/dα, the magnitudes of the longitudinal strains increase. At the same time, the longitudinal strains in the central bend region exhibit an approximately linear increase, with the slope becoming steeper and higher strain maxima being reached as dR/dα increases. For values above 0.8 mm/∘, no significant further changes in either the strain levels or their spatial distribution along the arc length are observed, indicating a stagnation of the effect.

The phenomena observed in the process variables are also reflected in the resulting geometric characteristics. Fig. 7 presents the curvature distributions κ of the cross-sectional gravity centreline (a), the wall thickness distributions t at the inner and outer bend (b), and the out-of-roundness u of the cross-section (c), each plotted over the normalized arc length for different values of dR/dα.

For the reference bend, the curvature κ attains an almost constant value in the central bend region (Fig. 7 a, region 1). With increasing radial gradient, the curvature distributions in the central bend region increase approximately linearly, with the slope becoming steeper and higher curvature values being reached as dR/dα increases. At dR/dα=0.6 mm/∘, the maximum curvature reaches nearly 1.5 times the curvature level of the reference bend, whereas a further increase from 0.8 mm/∘ to 1.0 mm/∘ results in only a minor additional increase.

The characteristics observed in the longitudinal strain distributions (Fig. 6) are also reflected in the wall thickness distributions at the outer and inner bend (Fig. 7 b). In the reference case, only minor changes in wall thickness occur at both the outer and inner bend. With increasing dR/dα, more pronounced wall thickness variations are observed. At the outer bend, increased thinning occurs, while at the inner bend, enhanced thickening is observed. Along the arc length, the wall thickness at the outer bend decreases below, and at the inner bend increases above, the corresponding values of the reference bend (Fig. 7 b , region 1). For dR/dα>0.8 mm/∘, only minor additional changes in the wall thickness distribution are observed. Both the maximum wall thickness variations and their spatial distribution along the arc length remain largely unchanged.

The out-of-roundness u of the tube cross-section is plotted over the normalized arc length in Fig. 7 c. With increasing dR/dα, the overall level of out-of-roundness exceeds that of the reference bend. For the reference bend, the out-of-roundness remains nearly constant in the central bend region (Fig. 7 c , region 1), indicating a largely homogeneous cross-sectional deformation. For dR/dα>0 mm/∘, the out-of-roundness increases, with both the slope and the magnitude of u and thus the ovalization of the tube cross-section increasing with the radial gradient. At higher values of dR/dα,
ranging from 0.8 mm/∘ to 1 mm/∘, the out-of-roundness distribution remains nearly unchanged, indicating a saturation of the effect.

[picture]

Fig. 7. Curvature κ of the cross-sectional gravity centreline (a), wall thickness t (b), and out-ofroundness u(c) for different radial gradients dR/dα, plotted over the normalized arc length.

Fig. 7. Curvature κ of the cross-sectional gravity centreline (a), wall thickness t (b), and out-ofroundness u ( c ) for different radial gradients d R / d α , plotted over the normalized arc length.

The effects of the parameter dR/dα on the tool reaction forces, geometrical and non-geometrical bent part properties observed above are confirmed by the results shown in Fig. 8. For each quantity, the absolute maximum values are extracted and plotted as a function of the radial gradient (Fig. 8 a-e). In general, all presented quantities exhibit a pronounced linear relationship with high coefficients of determination obtained from the regression analyses ( R2=0.99 ) for radial gradients between 0 mm/∘ and 0.6 mm/∘. With further increases in the radial gradient particularly from 0.8 mm/∘ to 1.0 mm/∘ a decreasing sensitivity of all investigated metrics to additional increases in dR/dα is observed.

[picture]

Fig. 8. Maximum values of the respective process variables and geometric characteristics for different radial gradients dR/dα.

Fig. 8. Maximum values of the respective process variables and geometric characteristics for different radial gradients dR/dα.

Discussion

The original version of this paper is available on https://www.scientific.net/MSF.1187.87.pdf

For radial gradients dR/dα ranging from 0 mm/∘ to 0.6 mm/∘, linear relationships are observed between the radial gradient and the maximum values of the evaluated quantities. However, for higher radial gradient dR/dα=0.8 mm/∘ and 1.0 mm/∘, deviations from this linear trend become evident.

When the absolute maximum longitudinal strains φL at the outer bend are plotted as a function of the maximum out-of-roundness u of the tube cross-section, a strong correlation between these quantities is observed, with high coefficients of determination of the linear regression of R2=0.999 for the outer bend and R2=0.991 for the inner arc (Fig. 9).

[picture]

Fig. 9. Absolute maximum longitudinal strains φL in the outer and inner fibers along the extrados and intrados, plotted as a function of the maximum out-of-roundness u for different radial gradients dR/dα.

Fig. 9. Absolute maximum longitudinal strains φ L in the outer and inner fibers along the extrados and intrados, plotted as a function of the maximum out-of-roundness u for different radial gradients d R / d α .

This indicates that beyond a certain deformation state, additional forming is preferentially accommodated by cross-sectional distortion (tube ovalization) rather than by a proportional increase in longitudinal strain. Due to the combination of wall thickness changes and the cross-sectional ovalization, a shift of the strain-neutral axis is present. Consequently, the distance between the neutral axis and the outer fiber at extrados decreases, leading to decreased longitudinal strains according to elemental bending theory. Additionally, this effect causes reduced bending stiffness, which explains the decreasing increase of bending force.

Conclusions and Outlook

The original version of this paper is available on https://www.scientific.net/MSF.1187.87.pdf

In this study, the influence of bend die geometry with a continuously varying bending radius on the tool reaction forces, geometrical and non-geometrical bent part properties in compression bending was investigated for a selected bending task using FE simulations. An FE model of the compression bending process was set up, which explicitly captures the rotationally and translationally coupled kinematics of the bending roll during the process to follow the variable bending radius while keeping the lever arm constant. The bend die geometry was parameterized according to an Archimedean spiral curve, in which the radial gradient dR/dα was introduced as a design parameter and systematically varied from constant radius with 0 mm/∘ to 1 mm/∘.

The results demonstrate that the feasibility of compression bending to produce tube bends with a variable radius is fundamentally given and depends on the specific bending task and the properties of the workpiece. Furthermore, the results show that the radial gradient of the bend die geometry influences the tool reaction forces and, consequently, the geometrical and non-geometrical bent part properties, with the magnitude of this influence depending on the level of the parameter.

For the bending task investigated in this study, the following conclusions can be drawn. The reference case with a constant bending radius ( dR/da=0 mm/∘ ) exhibits the lowest force levels as well as the most homogeneous strain and geometric distributions within the investigated parameter range. For small to moderate values of dR/dα(0 mm/∘≤dR/dα≤0.6 mm/∘), a pronounced linear relationship exists between the investigated metrics and the radial gradient. This behavior is particularly evident in the representation of the maximum values as a function of dR/dα and is confirmed by the high coefficients of determination ( R2≥0.99 ). In this parameter range, the investigated quantities respond sensitively to changes in the radius profile. An increase in dR/dα generally leads to higher required bending forces, increased longitudinal strains, and more pronounced geometric characteristics in terms of curvature distribution, wall thickness variation, and out-of-roundness. At high values of the radial gradient ( dR/dα≥0.8 mm/∘ ), an attenuation of the effects on all considered metrics is observed. A state appears to be reached in which further geometric modifications of the bend die no longer exert a significant influence on the investigated quantities.

A possible explanation for this behavior lies in cross-section-related deformation mechanisms, such as increasing ovalization and a shift of the strain-neutral axis, which contribute to the weakening of the effects of further increases in dR/dα. These phenomena will be analyzed in more detail in future investigations.

The FE model and numerical investigations will be experimentally validated by physical compression bending tests using bend dies with a variable bending radius for selected bending tasks. In addition, the transferability of the observed phenomena to further bending tasks and different workpiece properties will be examined.

Funding Acknowledgement

The original version of this paper is available on https://www.scientific.net/MSF.1187.87.pdf

This research is part of a project funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - project number 509709804.

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The original version of this eBook is available on https://www.scientific.net/book/special-materials-and-processes/978-3-0364-1544-4

Materials Science Forum, ISSN: 1662-9752, Vol. 1187, pp 99-112

doi: 10.4028/p-a49Y50

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

Submitted:
2025-12-22

Revised:
2026-02-23

Accepted:
2026-02-23

Online:
2026-04-17

Design and Manufacturing of Complex Geometry PEEK Component through Fused Filament Fabrication

The original version of this paper is available on https://www.scientific.net/MSF.1187.99.pdf

Niccolo Burattini 1,a*, Luana Bottini 1, b, Domenico Corapi 1,c, Alberto Boschetto 1, d

1 Sapienza University of Rome, via Eudossiana 18, 00184, Rome, Italy

a*niccolo.burattini@uniroma1.it,b luana.bottini@uniroma1.it, c domenico.corapi@uniroma1.it, c alberto.boschetto@uniroma1.it

Keywords: fused filament fabrication, PEEK, digital image analysis

Abstract

Complex geometry components fabricated through Fused Filament Fabrication in polyether ether ketone exhibit very interesting performances, but they are very difficult to predict. Non-standard mechanical tests allow for estimating a global structural response and do not provide local information about the failure evolution. This work investigates the integration of a Digital Image Processing method with non-standard mechanical test to improve the knowledge of the component performance through the tracking and the analysis of local failures. This way, a targeted redesign of the component can be provided: in this work the component was modified at manufacturing stage by changing the Fused Filament Fabrication infill, and at optimization stage by employing a stress line approach for locally densifying the interior.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1187.99.pdf

The growing industrial demand for flexibility and geometric complexity has driven the proliferation of Additive Manufacturing (AM) technologies. Among these technologies, Fused Filament Fabrication (FFF) has emerged due to its relative simplicity, low cost, and material versatility. FFF offers a broader range of printable materials, from easy-to-print polymers to high-performance engineering thermoplastics with superior mechanical properties, including polyetherimide, and Polyaryl Ether Ketone (PAEK).
PAEK is a family of high-performance thermoplastic polymers, consisting of an aromatic backbone molecular chain, interconnected by ketone and ether functional groups. One famous member of this group is Polyether Ether Ketone (PEEK). It can be employed to fabricate functional components aiming to substitute metallic alloy. Typically, PEEK components are processed via traditional injection molding and extrusion, nonetheless recent advancements enabled the manufacturing through FFF [1]. This high-value polymer is widely used in biomedical field for its biocompatibility [2], finding application in areas such as craniomaxillofacial reconstruction [3], joint replacement [4], and arthroscopy [5]. PEEK is considered a low-flammable material due to its high thermal stability, resistance to autoignition, and self-extinguishing behavior [6]. In addition, it does not exhibit outgassing phenomena and it is resistant to relatively high temperature, making it suitable for the aerospace industry [7]. In the automotive sector, the weight reduction allowed by high performance polymers and composite materials is crucial for decreasing fuel consumption. Furthermore, fabrication through AM provides extreme flexibility while reducing tooling and prototyping costs [8]. Although AM offers these advantages, fabrication times can be relatively long; for this reason, tools for predicting static and fatigue behavior are being investigated [9], together with the influence of process parameters [10]. Due to its intrinsic anisotropy and lack of homogeneity, the simulation of AMed parts remains challenging. Finite Element Analysis (FEA) materials databases include isotropic materials, which struggle to obtain reliable results when applied to layer-by-layer manufactured, anisotropic components. FEA simulation is a simplified model which assumes full solid body, whereas FFFed parts are composed of bonded filaments. As reported in [11], an isotropic model can predict with a certain reliability the mechanical behaviors in the elastic region; however, when the plastic deformation occurs, orthotropic material properties become more appropriate.
Although simulations struggle to achieve reliable results, their accuracy can be enhanced through calibrated models [12]. Both for calibrating numerical models and to compensate for the reliability
of FEA, experimental mechanical testing is unavoidable. Standard mechanical tests are not always representative of the behavior of complex geometries which are one of the main reasons for choosing AM technologies. Mechanical tests on custom-geometry specimens provide high quality information on AMed components. Optimizing the mechanical properties of parts fabricated using FFF usually requires a considerable number of test samples; long testing campaigns undermine the advantages of AM such as flexibility. Moreover, PEEK and other high-performance thermoplastics are relatively expensive. For these reasons, it is essential to extract maximum amount of information from each individual non-standard test.
Digital Image Processing (DIP) techniques have been exploited by researchers in the past half of century for many application fields [13]. The universal access of cameras has grown, making video data exponentially increase. The Video Object Tracking and Image Keypoint Detection are now widely used for precise localization in the fields of autonomous driving, augmented reality. Smart Factory, Industry 5.0 and Smart Cities pumped up new algorithms developments striving to efficiently determine features in manufacturing processes and structures [14]. The remote monitoring of structure features enables displacement measurement at multiple locations at a time, contrary to the contact-based sensors that allow detecting the displacement only at the places of their physical location [15]. Advancements in processing capability have led to the growing popularity of convolutional neural networks allowing for classification and detection measurements of geometrical features and damages. However, they require the availability of large datasets and a number of experimental tests accordingly. Therefore, they are not suitable whereas the aim is to return information on a single test characterized by unique conditions and behavior. DIP feature detection, is based on image keypoints and numerous registration algorithms for determining keypoints were developed but it is still difficult to build a universal framework. These algorithms are called detectors and include: Feature from Accelerated Segment Test, Binary Robust Invariant Scalable Keypoints, Speed Up Robust Feature (SURF), and many other [16]. Video Object Tracking, also known as Visual Object Tracking, faces many challenges, such as target occlusion, target deformation and so on. [17]. Observation-Centric Simple Online and Real-time Tracking employs a Kalman Filter to tracks found by Hungarian algorithm. This allows a prediction of the next state, called state of propagation, and the possible association is evaluated by Kalman estimators.
In this work the application of DIP to the rapid testing is presented. The aim is to measure displacements in a FFF part under deformation to provide feedback about the effects of the combination between geometry design and manufacturing.
The lightening of mechanical parts can be achieved through Topological Optimization (TO). This methodology utilizes a density-based formulation, such as the Solid Isotropic Material with Penalization (SIMP). Through the iterative execution of sensitivity analyses on a target objective, typically the reduction of global compliance to enhance structural rigidity under a fixed volume budget, the algorithm eliminates superfluous material and concentrates it along critical stress trajectories [18]. Another approach could consist in designing for stiffness by considering the force flow lines that originate in the principal stress directions [19]. AM technologies give full play to the complex geometries integrated with the force-flow based design to maximize structural efficiency [20]. This method, known as Michell Trusses Design method, found application in mechanical elements design indicating a significant reduction in lightweight structural modelling [21]. In [22] the weight and the vibration response of a gear are optimized by offsetting the principal stress lines. The method known as "Stress Line Additive Manufacturing" (SLAM) reconsiders the FFF technique by adding material along the principal stresses [23].
This paper explores the combination of non-standard mechanical test and DIP and its capability to improve the information through identification of local failure mechanisms. Non-standard mechanical test provides only the global structural response, whilst the integration with DIP enables the detection of local critical conditions, allowing direct modifications on specific features at the redesign phase. This work investigates how improved mechanical performance can be achieved through different manufacturing strategies, comparing a rectilinear infill, acting homogenously across the bulk material, or with a SLAM design approach which enables local modifications in regions identified as critical by the integrated analysis method.

Materials & Methods

The original version of this paper is available on https://www.scientific.net/MSF.1187.99.pdf

Design. The investigated component is an electronic device support mounted on the motorbike of the Sapienza motor student. Its original shape and topological optimized one are shown in Fig. 1a and 1b respectively. The TO design was provided by using Inspire v. 2025 Personal Edition and considering the system of loads and constraints characterizing the specific application.

[picture]

Fig. 1 Original component (a), component optimized by TO (b)

Fig. 1. Original component (a), component optimized by TO (b)

This traditional approach exhibits lack of effectiveness since the known anisotropy of FFFed parts is not considered in the TO material definition and simulation as well. In fact, the FFF part is composed of a skin made by contours and the inner part made by infill strategies. The SLAM approach was undertaken by developing an original code in McNeel's Grasshopper, a visual programming environment for the 3D modeler Rhinoceros [24]. The aim was to apply parametric geometric modelling and mechanical simulation techniques to determine a locally variant filling hatching using the principal stress flow lines criterion. The logic underlying this architecture is represented by the workflow summarized in Fig. 2.

[picture]

Fig. 2 Workflow of the architecture for the development of a geometry based on SLAM approach.

Fig. 2. Workflow of the architecture for the development of a geometry based on SLAM approach.

A number of attributes are given in input to the solver: material definition, such as modulus of elasticity, Poisson ratio, density, yield strength; the design space, i.e., the portion of the threedimensional domain that defines the shape of the mechanical component that will actually be processed by the topological optimization algorithm; the domain on which the loads are applied; the definition of the constrained domain in terms of suppressed displacements and rotations. A specified number of points are generated in random positions and used to trace the force lines; the traces are transformed into manufacturable tubes of the desired diameter. An external skin, with a parameterized thickness, that acts as a closed container of the force lines is created. Finally, both the internal tube
structures and the outer skin are assembled in a single complex geometry. The Fig. 3 shows the whole Grasshopper structure of the described workflow.

[picture]

Fig. 3 Grasshopper architecture for developing the part geometry according with SLAM approach

Fig. 3. Grasshopper architecture for developing the part geometry according with SLAM approach

Fabrication. The specimens were fabricated using a CreatBot PEEK-300 printer (Henan Creatbot Technology Limited, No. 150 Lamei Road, Zhongyuan District, Zhengzhou City, Henan Province, China), employing 1.75 mm CreatBot PEEK filament as feedstock material. According to the manufacturer, the mechanical properties of the filament are tensile: strength 100 MPa , tensile modulus 3720 MPa , flexural strength 128 MPa , and flexural modulus 2700 MPa . The glass transition temperature of the stock material, measured by Differential Scanning Calorimetry, is 143∘C and increases up to 158∘C after fabrication. The nozzle, chamber, and platform temperatures were set to 440∘C,100∘C, and 150∘C respectively. Under these conditions, the resulting crystallinity is within the range of 27.9−29.9% for edge regions and 29.6−31.1% for the internal regions of the component. Other relevant process parameters include the layer thickness ( 0.15 mm ), nozzle diameter ( 0.4 mm ), and extrusion speed ( 20 mm/s ). For slicing operations and process parameter selection, the CreatBot Creatware was used. Three infill strategies were investigated. The first consists of a rectilinear infill with variable bulk densities, namely 5%,10%, and 25%. A second component type was developed according to the SLAM design. The number of contour lines is two and kept unchanged. In Table 1 the list of specimen codes is reported together with the fabrication strategies:

Table 1 Specimen codes, infill densities, and infill strategies

Specimen code | Infill density [%] | Infill strategy
27 – PEEK – F05 | 5 | Rectilinear
20 – PEEK – F10 | 10 | Rectilinear
22 – PEEK – F10 | 10 | Rectilinear
25 – PEEK – F25 | 25 | Rectilinear
26 – PEEK – F25 | 25 | Rectilinear
23 – PEEK – ASY | - | SLAM
28 – PEEK – ASY | - | SLAM
24 – PEEK – SYM | - | SLAM
29 – PEEK – SYM | - | SLAM

Non-standard mechanical test and DIP. A non-standard mechanical test was developed to measure the mechanical resistance of the manufactured components. A S Beam Load Cell model LCM103B-250 (OMEGA Engineering 1 Omega Drive, Northbank, Irlam, Manchester, M44 5BD, United Kingdom) and a Soway SDVB20 LVDT sensor (Soway Tech Limited, No. 28 Xinfeng Road Potoubei Ailian Longgang Shenzhen China 518000) were adopted for measuring the exerted force and the displacement respectively. A stepper motor and a gear box were used to provide the lateral force for deforming the component. A 950 lm white led spotlight was positioned beside a semitransparent acrylic wall. Layout elements are shown in Fig. 4. A GoPro Hero12 was used to acquire the component during the test. The resolution was set at 3840×2160 pixel 2 and the video was captured at 50 frame/s and coded in High Efficient Video Coding (HEVC) according to ISO/IEC 23008-2 MPEG-H Part 2 [25]. The signals of the load cell and the displacement sensor were acquired by a National Instruments NI6221 (National Instruments Corporation 11500 North Mopac Expressway, Austin, TX 78759 USA), programmed in Labview 18.3.

[picture]

Fig. 4 Layout of the non-standard test device

Fig. 4. Layout of the non-standard test device

Force and displacement and video data were processed in Mathematica Wolfram 14.3. Video and array data were synchronized by capturing in the shown field of view the component and the load cell display.
The HEVC videos were completely managed in Mathematica environment by embedding v6.1.1 "Von Neumann" FFMPEG video codec support. The videos were processed accordingly to the workflow shown in Fig. 5.

[picture]

Fig. 5 DIP workflow

Fig. 5. DIP workflow

The original images were improved by means of edge enhancement. An increased noise is observed in nearly uniform regions leading to a reduced local contrast at boundaries. Therefore, a sharpening [13] was applied through a Laplacian operator which highlights local contrasted areas and suppresses smooth gradients. The image keypoints were found by using SURF algorithm and setting the strength constrain to the minimum confidence of 0.005 which typically led to about 30 elements. These points were employed in the visual tracking algorithms to find their trajectories during the deformation. For the purpose an Observation-Centric Simple Online and Realtime Tracking was adopted. It is based on classic Kalman assumptions of linear motion and Gaussian noise that can be adequate for the slow dynamic of these measurements.
A filtering was adopted to avoid noises coming from image errors and algorithm issues. The original image was convolved as in Eq. 1:

F(x,y)=∑i∑jf(x+u,y+v),G(u,v)(1)

where f(x,y) is the original image pixel at coordinates x and y,F(x,y) is the filtered image pixel at the same coordinates, u and v are local spatial variables, and G(x,y) is the Gaussian blur with standard deviation σ as reported in Eq. 2:

G(x,y)=12πσe−x2+y22σ2(2)

Each trajectory was processed to find the displacement vector fields. The vector phase θi(k) calculated on the i-th point coordinates xi(k),yi(k) of the k-th trajectory was calculated as in Eq. 3:

θi(k)=Atan2(yi(k)−yi−1(k),xi(k)−xi−1(k))(3)

where Atan2(x,y) is the inverse trigonometric function that returns the arctangent of two arguments allowing to determine unambiguously the correct quadrant among all four ones. Trajectories were smoothed by applying a Gaussian filter characterized by 12.5 px standard deviation to avoid errors coming from local reflection and noises.
In this work an indicator is proposed to highlight undesired conditions of the slope trend of each trajectory associated with critical deformation. Under the hypothesis that smooth deformations are characterized by a linear relationship of slope trend, the regression equation of the k-th trajectory is reported in Eq. 4:

y^(k)(x)=∑j(xi(k)−x¯i(k))(yi(k)−y¯i(k))∑j(xi(k)−x¯i(k))2x+y¯(k)−∑j(xi(k)−x¯i(k))(yi(k)−y¯i(k))∑j(xi(k)−x¯i(k))2x¯(k)(4)

where y^(k)(x) is the fitted value at x¯(k) and y¯(k) are the mean value of coordinate xi(k) and yi(k) respectively.
The estimated error standard deviation is considered as an unbiased indicator of the slope variability from its linear regression. In Eq. 5, the veer index is defined as the root square of the mean square error (MSE):

ξ(k)=MSE(k)=∑(yi(k)−y^i(k))2n(k)(5)

where n(k) is the number of points of the k-th trajectory.
This index is expected to be low as a regular deformation trend occurs on the specified k-th trajectory. Conversely, a break-down behavior is characterized by a big value.

Results

The original version of this paper is available on https://www.scientific.net/MSF.1187.99.pdf

The load-displacement curve of the 22-PEEK-F10 shown in Fig. 6a is a representative structural response of the specimen with respect to the imposed displacement. The first interval can be described as a quasi-elastic stage. However, this region does not represent a purely elastic structural response, as local damages already occurred. This damage includes local plastic deformation and failure mechanisms such as delamination, filament debonding, and the initiation of internal microcracks. These phenomena are further supported by the frequent audible fracture events. The second stage begins after 4.83 mm , when significant local structural failures occur. In this stage, the overall response is characterized by a load plateau, where the structure reaches new equilibrium states followed by successive failure events. In particular, the first peak load is reached at 882.7 N for an imposed displacement of 4.8 mm ; the maximum peak is reached in the second stage at 904.4 N corresponding to 9.0 mm displacement; the length of the load plateau is equal to 3.5 mm . The ratio between the final load of the first stage and the corresponding displacement is the stiffness of the
structure, in this case 182.6 N/mm. By analyzing the achieved video, some events denote some critical zones and are subjected to local failures. The points indicated in Fig. 6a are related to those events. In Fig. 6b and in Fig. 6c the selected zones are captured just one second before the event and at the failure respectively. Several damage types can be observed: cracks occur on the surface; a feature markedly rotates with respect to the surrounding elements; a local bump formation; final break on the top coupling holes.

[picture]

Fig. 6 Load-displacement curve of 22-PEEK-F10 (a), array of local failures one second before the event (b), array of local failures after the event (c)

Fig. 6. Load-displacement curve of 22-PEEK-F10 (a), array of local failures one second before the event (b), array of local failures after the event (c)

These quantitative observations highlight the need for automatic DIP technique to quantitatively analyzed each local deformation/failure overall the video frames of the test. The Fig. 7 shows the results of the application of the DIP steps developed in this work. The original image (Fig. 7a) was enhanced by using Laplacian operator. The contrast at edges is well evident in Fig. 7b and in the false colors applied to the upper right quadrants of both images. The image key points are shown in Fig. 7c. The selected SURF algorithm is based on descriptors numerically robust against translation, rotation, and scale changes. The outcomes highlight how specific image features such as shape, contrast and orientation are considered: the indicated points are represented by their confidence (i.e., the radius of the circle), their orientation (the segment inside the circle), and the sign (if the key point is lighter than its surroundings is colored in red else in yellow). Such data allowed to determine robust points to be used for the object tracking and obtain the tracks reported in Fig. 7d. Each track was smoothed by means of a Gaussian filter and the veer indexes were calculated. Fig. 7e points out how the upper curves are near linear hence the variation of the vector angles is low accordingly. As opposed, the behaviors of the tracks in the bottom are nonlinear, highlighting a critical trend in the deformation thus exhibiting a higher veer index. The final representation of the calculated indexes is
shown in Fig. 7f. It can be noticed that two of the previously manually detected zones described in Fig. 6c are markedly evidenced by this methodology.

[picture]

Fig. 7 Digital image analysis of non-standard test: original (a) and enhanced image (b), keypoints (c), feature tracks (d), veer indexes applied to smoothed tracks (e), final graphical representation (f).

Fig. 7. Digital image analysis of non-standard test: original (a) and enhanced image (b), keypoints (c), feature tracks (d), veer indexes applied to smoothed tracks (e), final graphical representation (f).

The time evolution of the veer index is now analyzed. For the purpose the trajectories 2, 6 and 11 indicated in Fig. 7f are processed. The features 2 and 11 are selected because they both present a long path while the former is smoother than the latter thus leading to lower veer index. The trajectory 6 is shorter because it is close to the constraints and exhibits a final veer index comparable to the trajectory 11 one. In Fig. 8 the veer index as a function of the time for the three trajectories is shown. The evolution of the veer index 2 does not present high slope increment and the correspondent feature is not affected by observable local failures. Conversely, the features 6 and 11 show critical local events as underlined by the before and after images in Fig. 8. The veer index of the trajectory 6 presents a sharp slope increase corresponding to the local failure of the correspondent tracked feature at time 26.2 s, as already reported in Fig. 6. Similarly, trajectory 11 shows two high slope increments related to different observed events at 31 s and 39 s . The local failure events of the tracked features are correctly detected by the veer index slope changes.

[picture]

Fig. 8 Veer index time evolutions

Fig. 8. Veer index time evolutions

In order to mitigate these localized failures, a modification has been provided at manufacturing stage by focusing on a global parameter, namely the rectilinear infill. Fig. 9a shows the results of different infill densities: 5%,10% and 25%. The 20-PEEK-F10 specimen confirms the general behavior observed for the 22-PEEK-F10 sample, exhibiting a peak load of 820.9 N at a displacement of 4.5 mm , corresponding to an estimated structural stiffness of 182.4 N/mm. The plateau length increases by 167% compared to the previous case. The end of the second region can be placed at the first severe failure occurring at 10.3 mm . After this event, the structure undergoes a reorganization, and the load increases. The 27-PEEK-F05 specimen differs from the other specimens because of its lower infill density (5%). The reduction in infill density leads to a decrease in the maximum load, as well as to a reduction in structural stiffness. The plateau length is comparable to that of the 22-PEEK-F10 specimen. The 25-PEEK-F25 and 26-PEEK-F25 exhibit an expected higher strength than the others. The non-linear quasi-static interval is negligible so the load after this point abruptly plunges showing a brittle behavior. A final observation regarding the 25% infill specimens is the absence of a load plateau region associated with local failures and subsequent structural reorganization. Structures exhibiting this behavior can therefore be classified as brittle. The DIP methodology, which results are reported in Fig. 9b, highlights the critical regions of the specimen: no reasonable criticality is present in the component top and in the right part, whilst the left part is subjected to a sudden failure evidenced by the large red circles proportional to the veer indexes.

[picture]

Fig. 9 Load-displacement curves of specimens fabricated with rectilinear infill (a), Graphical representation of the evaluated veer index onto the 26-PEEK-F25 specimen (b)

Fig. 9. Load-displacement curves of specimens fabricated with rectilinear infill (a), Graphical representation of the evaluated veer index onto the 26-PEEK-F25 specimen (b)

The SLAM approach adopted in this work allowed to locally redistribute the infill. The methodological steps are reported in Fig. 10. The generation of the points and the related flow lines (Fig. 10a) are tridimensionally thickened in pipes (Fig. 10b); the external skin, created with a desired thickness (Fig. 10c), is assembled together with the solid pipes in the final geometry of Fig. 10d.

[picture]

Fig. 10 SLAM geometry generation: points and flow lines (a); solid pipes (b); outer skin (c); final assembly (d)

Fig. 10. SLAM geometry generation: points and flow lines (a); solid pipes (b); outer skin (c); final assembly (d)

The curves of specimens 23-PEEK-ASY and 28-PEEK-ASY are shown in Fig. 11a. Both linear and non-linear quasi-elastic regions can be observed; the 23-PEEK-ASY has a load plateau larger than the 28-PEEK-ASY specimen. SLAM specimens and the 25% infilled ones have similar mass. The 25-PEEK-F25 and the 26-PEEK-F25 show higher peak load and slightly higher structural stiffness than the SLAM specimens. The DIP analysis on the 23-PEEK-ASY indicates a critical zone on the left foot of the specimen (Fig. 11b): one big circle surrounded by two smaller ones. The rest of the structure is characterized by negligible variation of the track vector phase. Hence, a modification of the SLAM design has been investigated. The 24-PEEK-SYM and 29-PEEK-SYM specimens have been designed by adding a dense distribution of structures in the evidenced critical zone leading to a symmetric counterpart. The force displacement curves are shown in Fig. 11a: the specimens exhibited a more brittle behavior with respect to the previous ones. From the DIP results a more homogeneous distribution of vector phase variation can be observed with respect to the symmetric SLAM (Fig. 10c). This result points out that this solution allows for better stress distribution and consequently the critical states are reduced. In fact, from the curves it is well evident that many initial damages occur in the asymmetric specimens before the load leak is reached whilst in the symmetric ones the quasielastic behavior is shown till the break.

[picture]

Fig. 11 Load-displacement curves of specimens designed through SLAM (a), graphical representation of the evaluated veer index onto the 23-PEEK-ASY specimen (b), graphical representation of the evaluated veer index onto the 24-PEEK-SYM specimen (c)

Fig. 11. Load-displacement curves of specimens designed through SLAM (a), graphical representation of the evaluated veer index onto the 23-PEEK-ASY specimen (b), graphical representation of the evaluated veer index onto the 24-PEEK-SYM specimen (c)

It can be concluded that this methodology provides a better understanding of the design effect on component performance. It indicates where the criticalities take place and allows for addressing the design modification. It exhibits limitations such as the quantification is demanded to an index and far from the identification of the local stress; the measure works when the failure affects the component surface. Benefits can be listed as following: the method allows for saving a lot of resources as compared to the efforts and time an industry or AM service must pay for product or prototype development; information on the component geometry local failure; flexible approach for determining the behavior on a specified load range.

Conclusions

The original version of this paper is available on https://www.scientific.net/MSF.1187.99.pdf

This paper investigates a non-standard testing methodology that combines mechanical testing with DIP. Through feature tracking, local mechanical information is extracted from PEEK components fabricated by FFF. The proposed methodology enables the evaluation of local failure mechanisms that cannot be individuated by global load-displacement data alone. Based on the results, the conclusions can be highlighted as in the following:

As expected, the infill density in components fabricated with rectilinear infill strategy affects mechanical response, including maximum load, structural stiffness, and the final failure behavior. Low infill densities lead to reduced stiffness, and lower peak load, and promote a ductile-like response characterized by load plateaus with multiple localized failures before the structure collapse. Conversely, high infill densities result in brittle behavior, and the absence of a load plateau.

Without DIP, non-standard mechanical tests provide partial information. Only the structural global response is given, failing on recognizing the onset, location, and evolution of local damage mechanisms. DIP analysis introduces additional information through the proposed phase variation index of the displacement vector field. This indicator allows the identification of regions characterized by nonlinear deformation and failure events. Through this method, the diagnostic capability of the non-standard mechanical test can be enhanced without additional sensors.

The application of the measurement methodology to SLAM designed components highlights its effectiveness in material distributions. For SLAM specimens, the DIP analysis reveals differences in local failures between asymmetric and symmetric designs, enabling the identification of critical zones. A better understanding on the local failures type and spatial distribution is particularly valuable for complex AM geometries, where standard tests on simple geometry are hardly transferable.
In conclusion, the feedback resulting from the proposed methodology enables specific design interventions. Critical regions, if correlated with specific geometric or filling features, can be redesigned concentrating the design efforts. The results show how the integration of non-standard mechanical testing with DIP can provide a tool to address AM design toward improved and more reliable mechanical performance.
Further developments of the proposed approach will regard the refinement of the descriptor and the extension to more outcome parameters. The goal will be to provide more detailed information about the type of criticality that occurs. Another important intervention will be the deepening of the indexes evolution and their analysis in term of process control. Future research direction will be the application of the method to the monitoring of components during operation. With continuous technological advancements novel monitoring techniques are expected to emerge and imaging shows promising tools for providing richer data to improve the knowledge of AMed components before and after their introduction in the mechanical systems.

Funding

The original version of this paper is available on https://www.scientific.net/MSF.1187.99.pdf

Financed by the European Union-NextGenerationEU (National Sustainable Mobility Center CN00000023, Italian Ministry of University and Research Decree n. 1033-17/06/2022, Spoke 11Innovative Materials & Lightweighting). The opinions expressed are those of the authors only and should not be considered as representative of the European Union or the European Commis-sion's
official position. Neither the European Union nor the European Commission can be held responsible for them.

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The original version of this paper is available on https://www.scientific.net/MSF.1187.99.pdf

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Materials Science Forum, ISSN: 1662-9752, Vol. 1187, pp 113-119

doi: 10.4028/p-5Dfi7O

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

Submitted:
2025-12-18

Revised:
2026-02-10

Accepted:
2026-02-17

Online:
2026-04-17

Development of an Initial FEM Framework to Model Particle-Substrate Contact in Fluidized Bed Finishing of L-PBF Parts

The original version of this paper is available on https://www.scientific.net/MSF.1187.113.pdf

Viscusi Antonio 1,a*, El Hassanin Andrea 2,b and Squillace Antonino 2,c

1Department of Engineering and Sciences, Faculty of Technology and Innovation Sciences, Universitas Mercatorum, Piazza Mattei 10, 00186 Rome, Italy

2 Department of Chemical, Materials and Production Engineering, University of Naples Federico II, P.le V. Tecchio 80, 80125 Napoli, Italy

a* antonio.viscusi@unimercatorum.it, b andrea.elhassanin@unina.it, c squillac@unina.it

*corresponding author

Keywords: numerical study, fluidized bed, surface finishing, laser-powder bed fusion.

Abstract

This work introduces an initial finite element (FE) framework for modelling particlesubstrate interaction during fluidized bed surface finishing of Laser Powder Bed Fusion (L-PBF) components. Due to the complexity of as-built surface morphology and the difficulty of experimentally observing high-speed particle impacts, the mechanisms governing material removal remain poorly understood. The proposed 3D explicit FE model simulates the impact of stainless-steel particles on representative AlSi 10 Mg asperities using Johnson-Cook plasticity model and damage formulations. Results show that erosion occurs mainly through localized brittle-like detachment rather than extensive plastic deformation. Sequential impacts and oblique trajectories significantly increase internal energy absorption, enhancing asperity fragmentation and the surface smoothing level. The framework provides a foundation for future optimization of Fluidized Bed Finishing (FBF) parameters for improved finishing of additively manufactured metal parts.

Introduction

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Additive Manufacturing (AM), commonly known as 3D printing, has evolved from a rapidprototyping tool into a viable production technology capable of creating fully functional metal components [1]. Among metal AM processes, Powder-Bed Fusion - particularly L-PBF - has gained significant industrial and research interest due to its ability to produce complex geometries directly from digital models [2]. Despite its advantages in design freedom, time reduction, and cost efficiency, L-PBF still presents challenges such as porosity, cracking, spatter formation, and poor surface quality [3].

Surface roughness in L-PBF parts is typically quite higher than that of machined surfaces due to balling effects, partially fused particles and staircase-effect, making post-processing essential [3]. Conventional finishing techniques often struggle with the intricate shapes enabled by AM, motivating alternative approaches [4]. FBF has emerged as a promising solution, offering uniform abrasive action on complex components. Previous studies indicate that abrasive particle density, shear-flow conditions, and removal of partially sintered surface particles strongly influence finishing performance [5]. However, the physical mechanisms governing particle-surface interaction remain difficult to observe experimentally due to the small spatial and temporal scales involved. Numerical modelling can therefore provide valuable insights.

Several researchers have employed the finite element methods for the simulation of the process of solid particle erosion that occurs in abrasive machining [6-10]; however, to the authors best of knowledge, no finite element studies currently address FBF of L-PBF metal parts.

The aim of this study is to establish the first FE modelling framework dedicated to describing particle-substrate contact during FBF of L-PBF specimens produced. As no previous numerical investigations exist on this topic, this initial FE approach is conceived as a foundational step toward understanding the micromechanical interaction mechanisms that govern abrasive material - substrate interaction. The framework is intended to provide a scientific basis for future model refinement and
to support the optimization of FBF process parameters for complex additively manufactured metal parts.

Numerical Procedure

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In this section, a 3D finite element model is introduced to simulate the particle-substrate interaction occurring during fluidized-bed surface finishing of additively manufactured metal components, using the commercial FEA software Abaqus. The following subsections detail the adopted geometry and material modelling assumptions.

Geometrical properties.

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The experimental campaign was conducted on 20×20×2 mm3 square specimens produced vertically on the build plate using an EOS EOSINT M280 L-PBF system and AlSi10Mg feedstock powder supplied by EOS GmbH. The case study related to the FE modeling setup described as follows refers to the best FBF condition observed experimentally elsewhere [5], in which a relative speed between the fluidized particles and the substrate was set at 2 m/s through a rotation-assisted finishing process configuration. The FBF was performed using steel particles with nominal mean diameter dp=500μ m.

For modelling purposes, the finishing mechanism was idealized as the sliding of a single spherical particle over the substrate, striking an individual surface asperity at a given velocity, which is equal to the experimental one. Still for the sake of simplicity, a spherical shape was assumed instead of the irregular or the cut wire ones selected for the experimental work on which this study is based [5], selecting then the nominal value of the mean diameter reported above. The geometry of asperity was defined based on statistical measurements obtained from SEM (HITACHI TM3000) analyses of the as-built surfaces [5] and represented as a conical feature with height dp/20 and base diameter dp/4. A schematic of the adopted in-plane geometric configuration for the FE model is presented in Fig. 1.

[picture]

Fig. 1. 2D-Schematization of geometry employed for FE development.

Fig. 1. 2D-Schematization of geometry employed for FE development.

Both the particle and the substrate were represented using a Lagrangian reference frame. The numerical simulations employed an explicit dynamic formulation with geometric nonlinearity enabled.

The particle and substrate domains were discretized using 8 -node linear brick elements with reduced integration and hourglass control (C3D8R). A nominal mesh size of dp/10 was applied to
both the particle and the substrate. To balance solution accuracy with computational efficiency, the geometries of both bodies were partitioned into multiple regions to allow local mesh refinement in the particle-substrate interaction zone. Within this refined region, the particle was meshed with an element size of dp/50 and the substrate with dp/200.

The lower surface of the substrate was fully constrained in all degrees of freedom, while symmetry boundary conditions were applied on the x−y plane faces of both the particle and the substrate. Contact interactions were modeled using the General Contact algorithm. A friction coefficient of 0.3 [11] was prescribed at all potential contact interfaces.

An initial particle velocity of 2 m/s was assigned. The temperature of both the substrate and the particle (assumed to be at room temperature ( 298 K )) was assumed as starting conditions. The complete three-dimensional FE model is depicted in Fig. 2.

[picture]

Fig. 2. 3D FE model of an AlSi10Mg substrate with a cone-shaped surface asperity impacted by a spherical steel particle.

Fig. 2. 3D FE model of an AlSi10Mg substrate with a cone-shaped surface asperity impacted by a spherical steel particle.

Materials modelling.

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A bilinear elastoplastic material model with isotropic hardening was assigned to the steel particle. For the AlSi10Mg substrate, the Johnson-Cook (J-C) plasticity model was employed.

The J-C parameters for AlSi 10 Mg material were determined by combining experimental and literature data. First, specimens of the printed material were characterized by tensile tests applying the load along the building direction, according to ASTM E8 Standard Test Method; then, starting from literature [12], the J-C parameters were properly calibrated on the experimental curve, under low strain rate conditions ( ε˙≈10−3s−1 ).

The J-C failure model was employed to predict the onset and the evolution of failure with the material constants taken from literature [13].

In summary, the material properties employed for FE modelling development for both the particle and the substrate are reported in Table 1.

Table 1. Material parameters employed for FE modelling.

Material parameter | AlSi10Mg | 416 SS
Density, [kg/m³] | 2700 | 7600
Young's modulus, [GPa] | 70.0 | 200
Poisson ratio | 0.33 | 0.20
Yielding stress [MPa] |  | 275
Ultimate tensile stress [MPa] |  | 515
Thermal conductivity, [W/m °C] | 204 |
Heat capacity, [J/kg·°C] | 904 |
Inelastic heat fraction | 0.9 |
Melting temperature, [K] | 860 |
J-C parameter, A, [MPa] | 250 |
J-C parameter, B, [MPa] | 1730 |
J-C parameter, n | 0.68 |
J-C parameter, C | 0.0166 |
J-C parameter, m | 1.571 |
J-C damage constant, d₁ | 0.04704 |
J-C damage constant, d₂ | 1.155 |
J-C damage constant, d₃ | -0.841 |
J-C damage constant, d₄ | -0.842 |
J-C damage constant, d₅ | 0 |
Reference strain rate, ε̇₀, [s⁻¹] | 0.001 |
Reference temperature, TR, [K] | 298 |

Results and Discussion

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Fig. 3 illustrates the erosion pattern generated by the impact of a single particle. The simulation reveals that material removal occurs predominantly through localized element deletion, rather than through significant plastic deformation of the substrate.

[picture]

Fig. 3. Material erosion following single-particle impact. JCCRT is Johnson-Cook Cumulative Damage Criterion.

Fig. 3. Material erosion following single-particle impact. JCCRT is Johnson-Cook Cumulative Damage Criterion.

This behavior suggests that the smoothing effect observed is primarily driven by brittle-like detachment of partially sintered asperities characteristic of L-PBF surfaces [3]. The absence of extended plastic zones further confirms that the impact energy is concentrated within a narrow region, promoting surface leveling through micro-fragmentation rather than plastic surface re-modeling.

To investigate the cumulative effect of repeated collisions, the internal energy absorbed by the substrate was evaluated for one and two consecutive particle impacts. As shown in Fig. 4, the second impact contributes substantially to the overall energy input, producing an increase of approximately 60% with respect to the single-impact condition. This enhanced energy absorption correlates with greater material removal, indicating that sequential particle interactions serve to progressively weaken the surface asperities.

[picture]

Fig. 4. Internal energy evolution of the substrate under single and double particle impacts.

Fig. 4. Internal energy evolution of the substrate under single and double particle impacts.

The influence of particle incidence angle on material erosion was examined through a singleparticle impact at 25∘ relative to the horizontal surface, as reported in Fig. 5.

[picture]

Fig. 5. Internal energy response of the substrate during a single particle impact and at a 25∘ incident angle.

Fig. 5. Internal energy response of the substrate during a single particle impact and at a 25 ∘ incident angle.

Introducing an oblique trajectory significantly increases internal energy of substrate, reaching roughly 1.7×10−4 mJ, corresponding to an enhancement of about 170% compared with the "normal" impact case. This result indicates that angled impacts are more effective in generating surface erosion due to their ability to combine normal and tangential loading, which improves crack initiation and propagation along surface irregularities.

Finally, the combined effect of multiple impacts and varying impact angles was analyzed, and the results are summarized in Fig. 6. When the substrate is subjected first to a "normal" impact and subsequently to an impact at 25∘, the internal energy reaches its maximum value of approximately 2.3×10−4 mJ. This configuration produces the most pronounced erosive effect among all tested conditions, demonstrating a synergistic interaction between the initial weakening of the surface by "normal" impact and the intensified removal produced by the angled impact. These findings suggest that, in practical FBF operations, rotating or oscillating the specimen to expose the surface to a range of angles could significantly enhance finishing efficiency.

[picture]

Fig. 6. Internal energy comparison for two sequential impacts: both at 0∘ and at combined angles ( 0∘ followed by 25∘ ).

Fig. 6. Internal energy comparison for two sequential impacts: both at 0 ∘ and at combined angles ( 0 ∘ followed by 25 ∘ ).

Conclusion

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Based on the results obtained, the following conclusions can be drawn:

Material removal is dominated by localized brittle-like detachment of surface asperities, not by significative plastic deformation.

A second particle impact increases internal energy by ~60%, promoting progressive weakening and erosion.

Oblique impacts (25∘) enhance erosion efficiency by raising internal energy by ~170% compared with "normal" impacts.

Combining "normal" and oblique impacts yields the highest erosion, demonstrating a synergistic removal mechanism.
These findings suggest that varying the incident angle-e.g., through specimen rotation in FBF could significantly improve finishing performance. For subsequent models, the authors intend to develop a more realistic representation of surface morphology, including multiple asperity geometries and nonspherical abrasive particles.

References

The original version of this paper is available on https://www.scientific.net/MSF.1187.113.pdf

Armstrong, M., Mehrabi, H., and Naveed, N., An overview of modern metal additive manufacturing technology, J. Manuf. Process. 84 (2022) 1001-1029.

Chowdhury, S., Yadaiah, N., Prakash, C., Ramakrishna, S., Dixit, S., Gupta, L. R., et al., 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 20 (2022) 2109-2172.

DebRoy, T., Wei, H. L., Zuback, J. S., Mukherjee, T., Elmer, J. W., Milewski, J. O., et al., Additive manufacturing of metallic components - Process, structure and properties, Prog. Mater. Sci. 92 (2018) 112-224.

Lee, J. Y., Nagalingam, A. P., and Yeo, S. H., A review on the state-of-the-art of surface finishing processes and related ISO/ASTM standards for metal additive manufactured components, Virtual Phys. Prototyp. 16 (2021) 68-96.

Troiano, M., El Hassanin, A., Solimene, R., Silvestri, A. T., Scala, F., Squillace, A., et al., Fluidized bed finishing of additively manufactured objects: The influence of operating parameters, Powder Technol. 432 (2024).

Pradhan, K.K.; Chakraverty, S., Generalized power law exponent based shear deformation theory for free vibration of functionally graded beams. Applied Mathematics and Computation 268 (2015) 1240-1258.

Feng, Y., Jianming, W. & Feihong, L. Numerical simulation of single particle acceleration process by SPH coupled FEM for abrasive waterjet cutting. Int J Adv Manuf Technol 59 (2012) 193-200.

Jayswal, S., Jain, V. & Dixit, P. Modeling and simulation of magnetic abrasive finishing process. Int J Adv Manuf Technol 26 (2005) 477-490.

Kumar, G.; Yadav, V. Temperature distribution in the workpiece due to plane magnetic abrasive finishing using FEM. Int. J. Adv. Manuf. Technol. 41 (2009) 1051-1058.

Chaieb, I., Ben Moussa, N., Ben Fredj, N. et al. An innovative contactless finite element simulation of the shot peening process. Int J Adv Manuf Technol 113 (2021) 2121-2136.

Yildirim B, Fukanuma H, Ando T, Gouldstone A, Müftü S, A numerical investigation into cold spray bonding processes. J Tribol 137 (2015):011102.

Akturk, M., Boy, M., Gupta, M.K., Waqar, S., Krolczyk, M.G., Korkmaz, M.E., Numerical and experimental investigations of built orientation dependent Johnson-Cook model for selective laser melting manufactured AlSi10Mg, J. Mater. Res. Technol. 15 (2021) 6244-6259.

Cai, X., Pan, C., Wang, J., Zhang, W., Fan, Z., Gao, Y., Xu, P., Sun, H., Li, J., Yang, W., Mechanical behavior, damage mode and mechanism of AlSi10Mg porous structure manufactured by selective laser melting, J. Alloys Compd. 897 (2022) 162933.

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/special-materials-and-processes/978-3-0364-1544-4

Materials Science Forum, ISSN: 1662-9752, Vol. 1187, pp 121-128

doi: 10.4028/p-BGY3tC

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

Submitted:
2026-01-16

Revised:
2026-02-23

Accepted:
2026-02-23

Online:
2026-04-17

Biomaterial's Surface Functionalization by High Frequency Micropatterning Embossing on PEEK Samples

The original version of this paper is available on https://www.scientific.net/MSF.1187.121.pdf

Marco Magro 1,a*, Luigino Filice 1, b, Michele Perrelli 1,c, Domenico Mundo 1, d, Francesco Moscato 2,e, Francesco Gagliardi 1,f

1Department of Mechanical, Energetic and Management Engineering, cube 45 C , Ponte P.Bucci, University of Calabria, Rende (CS) 87036 Italy

2 Center for Medical Physics and Biomedical Engineering, AKH Vienna, Leitsstelle 4L, Währinger Gürtel 18-20, Medical University of Vienna, 1090 Vienna, Austria

a*marco.magro1@gmail.com, bluigino.filice@unical.it, cmichele.perrelli@unical.it

d  domenico.mundo@unical.it, efrancesco.moscato@meduniwien.ac.at, f  francesco.gagliardi@unical.it,

Keywords: PEEK, biocompatibility, surface treatment, embossing, contact angle test, surface free energy.

Abstract

PEEK is a thermoplastic polymer widely employed in the orthopedic field for the fabrication of prosthetic devices, owing to a Young's modulus comparable to that of human cortical bone. Surface functionalization through biomaterial micropatterning represents an effective strategy to enhance osteointegration. To this end, an innovative vibration-assisted surface embossing process was applied to PEEK samples. The surface patterning was performed using a square punch with a side length of 0.5 mm , fabricated via CNC milling. The process is enabled by a linear actuator capable of generating controlled vibrations to induce localized heating of the polymer surface. After that, the application of a post-load is required for the embossing stage. This system allows frequency tuning in the range of 1−4kHz. Finally, the patterned surfaces were sonicated through an ultrasound cleaner and characterized through contact angle measurements and white-light interferometry, confirming the feasibility of the process and demonstrating an increase in both the polar component of the surface free energy and the hydrophilicity compared with merely polished specimens. Enhancing the polar component of surface free energy is an effective and low-cost strategy to improve biomaterial biocompatibility, confirming the relevance of the proposed surface modifications. Slightly hydrophilic surfaces promote preferential osteoblast adhesion and stable cytoskeletal organization, demonstrating the complementary roles of surface topography in shaping cellular responses.

Introduction

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Over the past decades, polymeric biomaterials have gained increasing relevance in bioengineering, tissue engineering, and implantable medical devices due to their tunable chemical composition, adaptable mechanical properties, and processing versatility. Their structural flexibility enables modulation of elasticity, rigidity, degradation rate, porosity, and surface chemistry, making them suitable for a broad range of biomedical applications [1].

Both natural and synthetic polymers are widely employed for scaffolds, membranes, coatings, and implantable devices because of their adaptability and compatibility with surface modification strategies. However, the implantation of any biomaterial inevitably triggers an inflammatory and immunological response, and the long-term clinical performance strongly depends on the nature of the biomaterial-tissue interface [2]. Suboptimal surface-tissue interactions may lead to fibrous encapsulation, functional isolation of the implant, and eventual clinical failure, sometimes requiring revision surgery to prevent acute inflammatory reactions or tissue necrosis [3].

Since the first biological events occur at the biomaterial surface, surface properties play a decisive role in determining host response. Immediately after implantation, rapid adsorption of water molecules and proteins leads to the formation of a conditioning layer that mediates subsequent cellular interactions and immune recognition [4]. Therefore, even subtle changes in surface chemistry or
morphology can significantly affect protein adsorption, cell adhesion, proliferation, and differentiation. Biocompatibility thus extends beyond the absence of cytotoxicity and includes the ability to positively interact with cells and biological fluids while ensuring long-term mechanical and functional stability [5].

Surface integrity, chemical composition, hydrophilicity, and topography are critical parameters influencing cellular behavior. Surface characteristics regulate protein adsorption and integrinmediated signaling pathways, thereby affecting cytoskeletal organization and intercellular communication [6]. For this reason, surface engineering has become a central strategy in the development of next-generation biomedical materials.

Among the approaches proposed to tailor biomaterial surfaces, chemical functionalization, bioactive coatings, and micro-/nano-topographical modification are widely investigated [7]. Surface micropatterning has attracted considerable interest because it enables control over cell morphology, alignment, and adhesion through purely physical cues. Micro- and nano-patterns can influence extracellular organization, cytoskeletal tension, and lineage-specific responses without altering the bulk properties of the material [8].

Within this context, polyetheretherketone (PEEK) represents a highly attractive polymer for orthopedic and dental applications due to its excellent mechanical performance and a Young's modulus ( 8.3 GPa ) close to that of human cortical bone ( 10−20GPa ) [9]. Despite its favorable mechanical and thermal properties, PEEK exhibits an intrinsically bioinert and hydrophobic surface, which limits protein adsorption and cell adhesion. Consequently, surface modification-either chemical or mechanical-is required to enhance immune and tissue interactions [10,11]. In the absence of surface functionalization, PEEK may demonstrate limited capacity to promote cell adhesion and ingrowth [12].

In this framework, vibration-assisted and thermomechanical surface modification techniques offer promising alternatives to conventional chemical treatments. The methodological approach considered here is based on a vibration-assisted surface micropatterning process aimed at generating controlled micrometric features while preserving the bulk structural integrity of PEEK. The process relies on localized heating induced by high-frequency vibrations applied perpendicularly to the polymer surface ( 1−4kHz ) through a microstructured mold connected to a linear actuator. The oscillating matrix promotes thermomechanical softening at the interface, enabling the imprinting of well-defined surface patterns without extensive thermal exposure of the entire component [13,14].

Other advanced techniques, such as two-photon lithography, have demonstrated the capability to fabricate highly precise microstructured PEEK surfaces with improved cell culture outcomes [15]. However, despite their high resolution, these methods are often limited by low processing rates, high equipment costs, and scalability constraints, which restrict their broader industrial adoption. These limitations highlight the need for cost-effective, scalable, and mechanically simple approaches for controlled surface pattern generation.

Surface wettability plays a complementary role in regulating protein adsorption and cell adhesion [16]. Surface topography directly affects wetting behavior, influencing how bone cells interact with microstructured textures. Slightly hydrophilic surfaces have been shown to favor adhesion and spreading of human mesenchymal stem cells (hMSCs), promoting stable focal adhesions and organized cytoskeletal structures [17].

Enhancing the polar contribution to surface free energy facilitates favorable protein conformations and modulates ligand-receptor interactions involved in osteogenic differentiation pathways [18]. Cells exposed to highly polar surfaces exhibit improved adhesion and spreading compared to those on apolar substrates, leading to enhanced osteogenic responses [19]. Consequently, increasing surface polarity represents an effective strategy for improving the biocompatibility of polymeric biomaterials.

Considering the current state of the art, the present study introduces an innovative vibrationassisted thermomechanical micropatterning process for PEEK surface optimization. Unlike conventional chemical functionalization or high-cost lithographic techniques, the proposed method employs perpendicular high-frequency oscillations ( 1−4kHz ) transmitted through a patterned matrix to induce localized interfacial softening and imprint controlled surface geometries. The primary
objective is to generate reproducible micro-patterns capable of enhancing cell adhesion and proliferation while preserving the bulk mechanical properties of PEEK. The process is designed to be cost-effective, mechanically simple, and potentially scalable, thus addressing current limitations in PEEK surface functionalization technologies and positioning vibration-assisted embossing as a promising alternative for next-generation biomedical implants.

Materials and Method

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PEEK specimens were cut from a 1 m long bar with a diameter of 15 mm , resulting in samples with a height of 5[ mm]. Subsequently, the specimens were polished using an automatic lapping machine (Struers Tegramin 25) following a customized procedure adapted from a protocol reported in the literature [20]. Specifically, the polishing sequence, detailed in Table 1, involved the use of SiC abrasive papers and Struers diamond polishing cloths. Using this approach, a surface roughness Ra of 0.2μ m was achieved, as measured by a profilometer.

Table 1. Polishing parameters.

Polishing Cloth | Lubricant | Polishing Time [min] | Applied Load [N]
SiC foil – P500 | Water | 0:15 | 20
SiC foil – P800 | Water | 0:30 | 20
MD - Largo | DiaPro Largo 9 [μm] | 1:10 | 15

The experimental setup was assembled on a supporting structure composed of a cylindrical frame and an X -axis vise, enabling micrometric positioning of the sample and precise alignment of the patterned mold at the desired location. A linear actuator (SmartShaker model K2007E01, The Modal Shop) was mounted onto the frame, and an aluminum shaft rigidly connected to the linear actuator oscillates along the load axis, transmitting vibrations to the sample through the mold fixed at its end. To improve patterning accuracy and prevent lateral misalignment, a custom-made centering bushing was designed to constrain the system to a single degree of freedom, namely translation along the load axis, thereby ensuring perpendicular contact between the mold and the PEEK sample surface. A function generator supplied a harmonic input signal to the linear actuator, allowing precise control of the excitation frequency within the 1−4kHz range. After the vibration-assisted phase, a static postload was applied along the same load axis to complete the embossing stage and ensure full replication of the mold geometry onto the PEEK sample surface, with calibrated masses positioned on threaded rods to generate the required forging load. The complete setup is shown in Fig. 1.

[picture]

Fig. 1. Experimental setup for vibration-assisted surface embossing, including the linear actuator, patterned mold, and post-loading system. The actuator applies a harmonic oscillating load driven by a function generator, and the mold, thread-connected to it, transfers the vibration to imprint the micropattern onto the PEEK surface. The loading axis is indicated by the blue dashed line. After the vibration phase, a threaded rod is used to apply calibrated weights for the final forging step.

Fig. 1. Experimental setup for vibration-assisted surface embossing, including the linear actuator, patterned mold, and post-loading system. The actuator applies a harmonic oscillating load driven by a function generator, and the mold, thread-connected to it, transfers the vibration to imprint the micropattern onto the PEEK surface. The loading axis is indicated by the blue dashed line. After the vibration phase, a threaded rod is used to apply calibrated weights for the final forging step.

To obtain the desired surface pattern with a depth of a few micrometers, it was necessary to scale the processes adopted in those studies [13,14]. This requirement arises from the fact that conventional embossing equipment was not employed; instead, relatively low applied loads on the order of several tens of newtons and excitation frequencies well below the typical 28 kHz were used. Consequently, the overall cost of the experimental setup is significantly lower than that of the systems reported in the literature, at the expense of requiring the design and fabrication of a customized mold to maximize process effectiveness. Specifically, tests were performed by varying the frequency in the range of 14 kHz and the applied mass during the post-loading phase in the range of 2−6 kg. The configurations tested are described in Table 2.

Table 2. Process parameters adopted for the two tested configurations.

Configurations | Frequency [Hz] | Post-load [kg]
a | 1000 | 2
b | 4000 | 2
c | 1000 | 6
d | 4000 | 6

Steel was selected as the material for mold fabrication, as it provides an optimal combination of machinability and mechanical strength, along with good thermal conductivity. Furthermore, steel exhibits the hardness required to effectively imprint and scratch the surface of the PEEK substrate.

The fabrication of the square mold with a side length of 0.5 mm was carried out using a conical engraving milling tool with a diameter of 0.05 mm , supplied by Meusburger (model VHZ 15136). The machining operation was performed on a CNC milling machine (Mazak Vertical Center NEXUS 420A), starting from a high-strength steel bar with a diameter of 15 mm .

At the end of the machining process, the mold was analyzed using an optical microscope (LEICA DM 4000), which confirmed that the desired geometry had been successfully achieved. Each rectangular protrusion intended to imprint its shape onto the PEEK surface exhibits a length of 0.5 mm and a width of 0.05 mm , resulting in a total contact area of 0.125 mm2. The mold can be observed in Fig. 2.

[picture]

Fig. 2. Optical microscopy image of the fabricated mold.

Fig. 2. Optical microscopy image of the fabricated mold.

The measurements of contact angle were carried out using a KRÜSS Drop Shape Analyzer and deionized water was the reference liquid. The sessile drop method was used. The surface pattern employed for contact angle testing was produced under a process configuration with an excitation frequency of 4000 Hz and an applied mass of 6 kg .

Results

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The feasibility of the process was confirmed through the analysis performed on the samples after the surface micropatterning operation. Fig. 3 displays the patterns resulting from the different excitation frequency (i.e., 1 and 4 kHz ) and the applied mass during the post-loading phase (i.e., 2 and 6 kg ). Subsequently, the surface of each patterned specimen was characterized by contact angle measurements and compared with those of polished control samples. The results indicate that patterned surfaces exhibit a lower contact angle compared with the polished control surfaces. Specifically, the optimized patterned surface shows a contact angle of 67.3∘(±2.60∘), whereas the polished control group presents a higher value of 78.76∘(±3.20∘). This demonstrates that the optimized surface is more hydrophilic. These values are reported as the grand means of contact angles, calculated from five measurements per specimen across five specimens for each surface condition (polished and patterned). The total surface free energy was found to be comparable between the patterned specimens and the polished control samples, indicating that the applied surface modification did not significantly alter the overall energetic balance of the material.

Howewer, a marked difference was observed in the polar component of the surface free energy. In particular, the patterned surfaces exhibited a polar component more than twice that of the control group, with values of 7.3(±1.19)mN/m compared to 3.03(±1.25)mN/m for the polished specimens.

[picture]

Fig. 3. Optical microscopy images of micropatterned PEEK surfaces and corresponding profilometer (Mitutoyo SJ-410) scans along the white line. Pattern definition increases with actuator frequency (ab), while imprint depth increases with post-loading mass ( c−d ). The maximum imprint depth, expressed as Ry,max, was 2.755μ m (a), 5.57μ m (b), 8.64μ m (c), and 25.39μ m (d). Letters correspond to the process configurations in Table.

Fig. 3. Optical microscopy images of micropatterned PEEK surfaces and corresponding profilometer (Mitutoyo SJ-410) scans along the white line. Pattern definition increases with actuator frequency (ab), while imprint depth increases with post-loading mass ( c − d ). The maximum imprint depth, expressed as Ry,max, was 2.755 μ m (a), 5.57 μ m (b), 8.64 μ m (c), and 25.39 μ m (d). Letters correspond to the process configurations in Table.

In Fig. 4 the graph of the polar component of SFE is shown. The process parameters used are 4000 Hz and 6 kg of post-load.

[picture]

Fig. 4. Polar component of the Surface Free Energy of structured PEEK samples, patterned with 4000 Hz and 6 kg of post-load (configuration d in Table 2), compared to the control group.

Fig. 4. Polar component of the Surface Free Energy of structured PEEK samples, patterned with 4000 Hz and 6 kg of post-load (configuration d in Table 2), compared to the control group.

Discussion

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The results obtained verified both the feasibility and the effectiveness of the proposed vibrationassisted micropatterning process for surface optimization of PEEK substrates. The process enabled the generation of highly accurate micrometric surface structuring while minimizing thermal effects on the polymer material. This represents a significant advantage over conventional thermal or laserbased surface structuring techniques, particularly for materials such as PEEK, which exhibit reduced chemical stability at elevated temperatures. Importantly, the achieved imprint depths ranged from a minimum of 2.755μ m to a maximum of 25.32μ m. This increased depth capability is considered beneficial, as it demonstrates that by properly tuning the process parameters, a wide range of surface pattern depths can be deliberately achieved.

The results of the surface wettability analyses revealed a clear reduction in the value of the contact angle due to the influence of the topography introduced in the form of micropatterns on the polished control samples. This effect indicates a certain increase in the hydrophilicity of the surface. The increase in hydrophilicity has a biological significance mainly due to the known possibility of greater cell affinity [16]. The increase in hydrophilicity implies that the proposed surface treatment might influence the surface to become more biocompatible. Despite having comparable overall surface free energy between the patterned and polished groups, there was a marked increase in the polar component of the surface free energy for micropatterned samples, reflecting values greater than twice of the control. This observation points to an elevated surface affinity for the adsorption of polar biomolecules, including proteins and ligands, playing an intrinsic mediator or facilitating role in relating to immune cells post-implantation based on interaction with implanted materials. These protein interactions have been well recognized for playing critical early-stage roles in modulating biologically-active responses [18].

It is also significant to note that the range of attained values for the contact angle, close to 60∘, is reported in the literature as the optimal range for hMSCs [21]. In fact, it is reported to be associated with improved cell adhesion, spreading, and osteogenic differentiation. This implies that the proposed method for vibration-assisted micropatterning could also be useful in the improvement of osteointegration and osteoblastic differentiation properties on PEEK surfaces. In general, the results indicate that vibration-assisted surface micropatterning could be considered a promising method with low impact for the modulation of the bioactive properties of PEEK-based devices without interfering with the material properties.

Conclusion

The original version of this paper is available on https://www.scientific.net/MSF.1187.121.pdf

This work successfully demonstrates that vibration-assisted micropatterning is an effective and low-impact technique for enhancing the surface properties of PEEK, a key orthopedic biomaterial. The process precisely imprints micro-scale patterns, significantly increasing surface hydrophilicity, and, most importantly, the polar component of surface free energy. These modifications create a more bioactive interface, optimized for the preferential adsorption of proteins and enhanced adhesion, spreading, and creates surface conditions known to promote differentiation.

By improving these critical early-stage biological interactions without altering the bulk material's properties, this scalable and cost-effective surface functionalization strategy presents a highly promising way for boosting the osteointegration and long-term clinical success of PEEK-based implants.

References

The original version of this paper is available on https://www.scientific.net/MSF.1187.121.pdf

O. Mozgova, O. Chernyayeva, A. Sroka-Bartnicka, P. Pieta, R. Nowakowski, S. Pieta, Physicochemical characterization of biodegradable polymers for biomedical applications: Insights from XPS, DRIFT, and AFM techniques, J. Polym. Environ. 33 (2025) 3477-3511.

M. Drobota, S. Ursache, M. Aflori, Surface functionalities of polymers for biomaterial applications, Polymers 14 (2022) 2307.

N. Kalashnikov, J. Barralet, J. Vorstenbosch, Implantable medical devices, biomaterials, and the foreign body response: a surgical perspective, J. Biomed. Mater. Res. A 113 (2025) e37983.

C.J. Wilson, R.E. Clegg, D.I. Leavesley, M.J. Pearcy, Mediation of biomaterial-cell interactions by adsorbed proteins: a review, Tissue Eng. 11 (2005) 1-18.

M. Rahmati, E.A. Silva, J.E. Reseland, C.A. Heyward, H.J. Haugen, Biological responses to physicochemical properties of biomaterial surfaces, Chem. Soc. Rev. 49 (2020) 5178-5224.

J. Wei, T. Igarashi, N. Okumori, T. Maetani, B. Liu, et al., Influence of surface wettability on competitive protein adsorption and initial attachment of osteoblasts, Biomed. Mater. 4 (2009) 045002.

F. Rossi, M. Van Griensven, Polymer functionalization as a powerful tool to improve scaffold performances, Tissue Eng. Part A 20 (2014) 2043-2051.

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B. Pidhatika, V.T. Widyaya, P.C. Nalam, Y.A. Swasono, R. Ardhani, Surface modifications of high-performance polymer polyetheretherketone (PEEK) to improve its biological performance in dentistry, Polymers 14 (2022) 5526.

W. Zheng, D. Wu, Y. Zhang, Y. Luo, L. Yang, X. Xu, et al., Multifunctional modifications of polyetheretherketone implants for bone repair: a comprehensive review, Biomater. Adv. 154 (2023) 213607.

Y. Chen, Z. Chen, K. Lei, J. Ding, L. Yu, Surface modification of polyetheretherketone for boosted osseointegration: a review, Biomater. Adv. (2023).

H.J. Lee, K. Park, Development of composite micro-patterns on polymer film using repetitive ultrasonic imprinting, Int. J. Precis. Eng. Manuf. Green Technol. 1 (2014) 341-345.

J. Kosloh, J. Sackmann, W.K. Schomburg, Ultrasonic fabrication of microfluidic channels from polyether ether ketone (PEEK), Microsyst. Technol. 23 (2017) 5505-5513.

M. Vostatek, E. Verin, M. Tamm, M. Rothbauer, S. Toegel, F. Moscato, Bone-mimetic osteon microtopographies on poly- -caprolactone enhance the osteogenic potential of human mesenchymal stem cells, Macromol. Biosci. 25 (2025) 2400311.

R.A. Gittens, L. Scheideler, F. Rupp, S.L. Hyzy, J. Geis-Gerstorfer, Z. Schwartz, et al., A review on the wettability of dental implant surfaces II: biological and clinical aspects, Acta Biomater. 10 (2014) 2907-2918.

A.B. Faia-Torres, S. Guimond-Lischer, M. Rottmar, M. Charnley, T. Goren, K. ManiuraWeber, et al., Differential regulation of osteogenic differentiation of stem cells on surface roughness gradients, Biomaterials 35 (2014) 9023-9032.

V.M. Villapun Puzas, L.N. Carter, C. Schröder, P.E. Colavita, D.A. Hoey, M.A. Webber, et al., Surface free energy dominates the biological interactions of postprocessed additively manufactured Ti-6Al-4V, ACS Biomater. Sci. Eng. 8 (2022) 4311-4326.

F. Awaja, E. Carletti, W. Bonani, G. Speranza, Vinculin focal adhesion of osteoblast-like cells on PEEK coated with ultra-thin polymer nanofilms, J. Appl. Polym. Sci. 132 (2015) 42181.

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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/special-materials-and-processes/978-3-0364-1544-4

Materials Science Forum, ISSN: 1662-9752, Vol. 1187, pp 129-136

doi: 10.4028/p-qcwHF8

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

Submitted:
2026-02-10

Revised:
2026-02-16

Accepted:
2026-02-17

Online:
2026-04-17

Evaluating the Performance of Composite Tubes Drawn from Hybrids of Aluminum, Steel, and Carbon Fiber

The original version of this paper is available on https://www.scientific.net/MSF.1187.129.pdf

Johannes Buhl 1,a, Lemopi Isidore Besong 1, b*

1 Institute of Metallurgy, Clausthal University of Technology, Robert-Koch-Strasse 42, 38678

Clausthal-Zellerfeld, Germany

a  Johannes.buhl@tu-clausthal.de email, b  besong.besong.lemopi.isidore@tu-clausthal.de

Keywords: tube drawing, composite, weight reduction, mechanical performance.

Abstract

Present demands for weight reduction of vehicles to decrease the carbon footprint in the transport industry have increased the need for lightweight tubes. In this paper, composite tubes are drawn from two aluminum tubes and reinforcements with the aim of maximizing mechanical performance while maintaining low weight. The reinforcements are placed between the two aluminum tubes and are made from blanks of 22 MnB 5 steel or carbon fiber laid in different quantities and patterns. The compressive stresses in tube sinking are used to hold the reinforcements in the composites without the need for resins and energy-intensive heating or curing cycles. The composites are weighed, and their performance is evaluated by mechanical test. Bending tests reveal an increase in the bending strength of the reinforced tubes by 15% for both composites reinforced by carbon fiber and 22 MnB 5 steel. However, the composites made from carbon fiber have higher stiffness and lower weight. The bending strength and residual stresses of composites manufactured with different carbon fiber layouts and quantities are evaluated to determine their performance. Increasing the carbon fiber content did not improve the stiffness and ultimate tensile strength of the composites, indicating the compressive stresses from drawing and carbon fiber content should be optimized to achieve the best mechanical performance.

Introduction

The original version of this paper is available on https://www.scientific.net/MSF.1187.129.pdf

In many industries composite tubes are required to exploit the beneficial properties of the constituents. Such is the case when high temperature resistance, high strength, and corrosion and wear resistance are required for pipes in the petrochemical industry, marine applications, power and gas, and water and heat supply. The most basic type of composite tubes are bimetallic tubes, made up of a base tube (outer) that satisfies the mechanical requirements and a cladding tube (inner) that meets other special requirements. The deformation of the base tube is usually higher than the cladding tube [1]. The process outcome in the drawing of bimetallic tubes is determined by parameters like the thicknesses of the different tubes, die angle, friction coefficients between the surfaces, and value of deformation [2]. Composites requiring more than two tubes are mainly used in thermal insulation applications [3].

Composite tubes containing carbon fibers are used in lightweight applications in the aerospace and automobile industries due to their high stiffness and high strength-to-weight ratios [4]. Carbon fibers possess high specific energy absorption and have been used as reinforcement to improve the crash resistance of tubes [4]. Fibers can be laid in different ways to create composite tubes with varying mechanical properties along the tube circumference, which allows for even further weight reduction. The optimum quantity and layout of fibers required to maximize mechanical performance of components has been the subject of a lot of research. Composite tubes are mostly made from fibers woven on metal tubes [5]. The manufacturing process chain of carbon fiber reinforced components usually includes heating and curing cycles, which are expensive and energy intensive [6].

This study investigates the tube drawing of composite tubes that consist of EN AW-6060 aluminum tubes reinforced with 22 MnB 5 steel blanks and carbon fiber. 22 MnB 5 steel was chosen as reinforcement material because its high buckling and compressive strengths are required to relieve pressure, while carbon fiber is selected because of its good tensile properties and lightweight. The

22 MnB 5 blanks and carbon fiber are arranged between two concentric tubes in the desired layouts before the drawing step, as indicated in Fig. 1. Unlike conventional hybrid composite manufacturing methods that require adhesive bonding, resins, heating, and curing cycles, the present approach uses tube drawing-induced compressive stresses to mechanically consolidate reinforcements, thus achieving a manufacturing process with a lower cost and environmental impact.

[picture]

Fig. 1. Representations of the reinforcements used in the composites. a) Four 22 MnB 5 blanks. b) One carbon fiber band and one 22 MnB 5 blank. c) Carbon fibers laid along the length and circumference of the tube. d) Control experiment.

Fig. 1. Representations of the reinforcements used in the composites. a) Four 22 MnB 5 blanks. b) One carbon fiber band and one 22 MnB 5 blank. c) Carbon fibers laid along the length and circumference of the tube. d) Control experiment.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/MSF.1187.129.pdf

Two EN AW 6060 AlMgSi 0.5 aluminum tubes with 18 mm and 20 mm external diameters were used as the matrix. The tubes were cut to 1 m length and had 1 mm wall thickness. The 18 mm tube, which serves as the inner tube, was drawn to 16 mm in one forming step. Reinforcements made from either carbon fiber or 22 MnB 5 blanks rolled to 0.2 mm thickness and 9 mm width were placed on the inner tubes. The carbon fiber designation was Tenax UTS 5131 800tex F12000 T 0 with a diameter of 7μ m and ultimate tensile strength above 4500 MPa . About 10 fibers were along the thickness of the carbon fiber bands.

The tubes were reinforced using the following reinforcement methods: 1. A carbon fiber band and one 22 MnB 5 blank. 2. Four 22 MnB 5 blanks laid along the tube length. 3. Carbon fiber laid along the length of the tube and a second layer across the length of the tube. A control experiment with no reinforcement was performed. The inner tubes with the reinforcement were placed into the larger tubes ( 20 mm ), as shown in Fig. 2. The resulting composites made from aluminum, carbon fiber, and/or steel were drawn to reduce the tube diameter from 20 mm to 18 mm in two steps, see Fig. 2c. The drawing velocity was 2 m/min. The experiments were conducted on a 500 kN drawing machine manufactured by Bültmann Maschinenbau GmbH & Co. KG.

Tensile, compression, and bending tests were performed on the formed composites on a ZwickRoell universal testing machine. Three repetitions of the tests were performed. Samples were cut from the composites and weighed.

[picture]

Fig. 2. a) Carbon fiber reinforcement placed along the length and circumference of the inner tube. b) Stacking the two tubes with carbon fiber reinforcement to form a composite. c) The composite drawing process.

Fig. 2. a) Carbon fiber reinforcement placed along the length and circumference of the inner tube. b) Stacking the two tubes with carbon fiber reinforcement to form a composite. c) The composite drawing process.

Given the dependence of the performance of composites reinforced with carbon fiber on layout and alteration in the shape of composites, the mechanical properties and residual stress of composites made from different fiber layouts were investigated. The residual stresses were measured from 0.05 mm below the surface of the tubes to 0.5 mm at increments of 0.05 mm using the hole-drilling method on a Stresstech prism hole drilling system, Stresstech Oy, Vaajakoski, Finland.

Results

The original version of this paper is available on https://www.scientific.net/MSF.1187.129.pdf

Weights. The weights of the composites were evaluated by weighing 20 mm lengths of the composites, see Table 1. As expected, the non-reinforced tubes had the minimum weight (0.2909 g/mm). The composite with carbon fiber had a higher weight of 0.3035 g/mm. The composite with carbon fiber and one band of 22 MnB 5 reinforcement weighed 0.30365 g/mm. The maximum weight was for the composite with four 22 MnB 5 blank reinforcements ( 0.33435 g/mm ). More detailed investigations were performed on the carbon fiber reinforced tubes given their potential in light weighting.

Table 1. Composite weights

Composite type

No

reinforcement

Carbon

fiber ring

mathematical formula carbon fiber band +

mathematical formula 22MnB5 blank

four 22MnB5

blanks

Normalized weight

0.29090
0.30350
0.30365
0.33435

Micrographs. Cross sections of the tubes are shown in Fig. 3. The black arrows indicate reinforcements. In between the 2 tubes, four 22 MnB 5 blanks are seen in Fig. 3a, the carbon fiber band and 22 MnB 5 blank are shown in Fig. 3b, and the carbon fiber in Fig. 3c. The size of the carbon fiber band can be further reduced by using more drawing sequences or by employing a mandrel to provide compressive forces on the inner wall. The tubes with no reinforcement are shown in Fig. 3d. The composite with a carbon fiber band and 22 MnB 5 blank in Fig. 3b appears to be less circular due to the presence of reinforcements at only two ends of the tube circumference.

[picture]

Fig. 3. a) Composite with four 22 MnB 5 blanks. b) Composite with a carbon fiber band and one 22 MnB 5 blank. c) Composite with carbon fiber laid along and across the tube length. d) Tubes with no reinforcement.

Fig. 3. a) Composite with four 22 MnB 5 blanks. b) Composite with a carbon fiber band and one 22 MnB 5 blank. c) Composite with carbon fiber laid along and across the tube length. d) Tubes with no reinforcement.

Compression tests. The forces in compression tests of the non-reinforced tubes and carbon fiber composites are presented in Fig. 4. The results reveal that the presence of the carbon fiber leads to a slight decrease in the maximum force sustained by the tubes before failure. The compressed tubes in Fig. 4 reveal larger deformed bases of the carbon-reinforced tubes than the tubes with no reinforcement.

[picture]

Fig. 4. a) Force evolution in compression tests of the composites with carbon fiber reinforcement and without reinforcement. b) Maximum force in compression tests.

Fig. 4. a) Force evolution in compression tests of the composites with carbon fiber reinforcement and without reinforcement. b) Maximum force in compression tests.

Tension tests. Tension tests on the non-reinforced tubes and carbon fiber reinforced composites are presented in Fig. 5. The composite had a higher stiffness than the non-reinforced tubes. The nonreinforced tube was stretched to 4% elongation, while failure of the carbon fiber reinforced specimen was abrupt just after the yield strength.

[picture]

Fig. 5. Force evolution in tension tests of the composites with carbon fiber reinforcement and without reinforcement.

Fig. 5. Force evolution in tension tests of the composites with carbon fiber reinforcement and without reinforcement.

Bending tests. The results of bending tests performed on the specimens are presented in Fig. 6. The least maximum bending forces were measured for the non-reinforced specimen. The composite with carbon fiber and one 22 MnB 5 blank supported a higher maximum force of about 4300 N . The composites with carbon fiber and four 22 MnB 5 blanks had higher maximum forces of about 4500 N . The highest stiffness was measured for the carbon-reinforced tube, followed by the composite with carbon fiber and one 22 MnB 5 blank, and the composite with four 22 MnB 5 blanks. There was an abrupt fall in the bending forces for the composite with four 22 MnB 5 blanks after attaining the maximum load at a displacement of about 12 mm . The drop-in force is due to the failure of some 22 MnB 5 blanks. Carbon fiber has a higher tensile strength than steel and is placed on the bottom (outside of the bend), while the 22 MnB 5 blank was placed on the inside of the bend when testing the composite reinforced with 22 MnB 5 steel and carbon fiber. After attaining the maximum bending load, the forces decrease due to wrinkle formation and fracturing of the tube. The forces at the end of the bending tests increase for the carbon fiber reinforced composites, probably due to the carbon fiber carrying the load after the matrices begin to fracture. The increase in forces at the end of the bending experiments of the composite with carbon fiber may be beneficial in crash applications, since the toughness (area under the curve) is higher.

[picture]

Fig. 6. Force evolution in bending tests.

Fig. 6. Force evolution in bending tests.

Fig. 7a presents the bent composite tubes. The widths at the bends are presented in Fig. 7b. The sudden drop in the force displacement curve of the tube reinforced with 22 MnB 5 in Fig. 6 is due to the presence of cracks, as shown in Fig. 7a, which interrupted the evolution of the width at the bend. The other specimens have no cracks. The width of the bent tube with no reinforcement was the least. The widths of the bends of the composites with carbon fiber were higher than the tube with no reinforcement and can be explained by the presence of carbon fiber. The composite with a carbon fiber and 22 MnB 5 blank had the highest width of the bend, which can be explained by the stiffness of the 22 MnB 5 blank resisting bending and causing widening of the tube.

[picture]

Fig. 7. Force evolution in bending tests.

Fig. 7. Force evolution in bending tests.

To study the fibers at fracture, the bending test of the fiber-reinforced composite was continued to fracture. Scans of the fractured composites are presented in Fig. 8. The global scan of the fractured surface shows the ends of the fractured aluminum tube and carbon fibers in Fig. 8a. Closer scans in Fig. 8c and Fig. 8d reveal that a few fibers were broken in the bending test, while most of the ≈10 fibers along the band thickness are unbroken. The first fiber layer on the inner surface sticks to the surface of the inner aluminum tube. This means that most fibers do not stretch or tear, even though the aluminum on the tension side was stretched significantly.

[picture]

Fig. 8. Scans of fractured surface of the carbon fiber reinforced composite at different magnifications.

Fig. 8. Scans of fractured surface of the carbon fiber reinforced composite at different magnifications.

Variable carbon fiber content. The carbon fiber content and layout were varied to study the bending stiffness, given that most of the fibers remain unbroken in the fiber-reinforced composite, and a smaller number of individual carbon fibers may be sufficient to achieve the same stiffness and loadcarrying capacity. The carbon fibers were laid only along the outside of the bend since a higher bending stiffness is often required in specific directions in some applications. The composites were formed using a single carbon fiber band, three carbon fiber bands spread out, and three carbon fibers stacked on each other, see the cross sections represented in the legends in Fig. 9a and Fig. 9d.

The weight of the composite made with a single band was lower than those with 3 bands, as expected. The results of the bending tests in Fig. 9 reveal that the composite with a single band had a higher bending strength ( 4285 N±70 ) than the composites made from 3 bands ( 4219 N±70 for spread and 4142 N±50 for stacked layouts). Residual stresses on the outer tube of composites with the single and three stacked carbon fiber bands are presented in Fig. 9d. The maximum residual stress was in the drawing direction for the 2 tubes on both sides of the composite with and without carbon fiber bands. The presence of carbon fiber increased the maximum residual stress, see the residual stresses in Fig. 9c and Fig. 9d. In the circumferential direction, there is no clear trend to explain the influence of carbon fiber on minor residual stress. The composite with the single band of carbon fiber had higher residual stress on the contact side than on the non-contact side of the tube, and vice versa for the composite with three stacked carbon fiber bands.

[picture]

Fig. 9. a) Forces in the bending of composites. b) Weight of composites. c) Residual stresses of the non-reinforced tube. d) Residual stresses on the side with carbon fiber and no carbon fiber.

Fig. 9. a) Forces in the bending of composites. b) Weight of composites. c) Residual stresses of the non-reinforced tube. d) Residual stresses on the side with carbon fiber and no carbon fiber.

Discussion

The original version of this paper is available on https://www.scientific.net/MSF.1187.129.pdf

Tube sinking of the composite is implemented to use the in-plane compressive stress in the process to restrain the reinforcements. Compression tests reveal no benefits accrued with the use of carbon fiber reinforced composites, instead, slightly early fracture was observed in the composites. The carbon fiber reinforced tubes had larger deformed bases than the nonreinforced tubes, which may be explained by the carbon fibers sliding past each other and reducing the friction between the two tubes and facilitating deformation. Similarly, tension tests demonstrate no benefit for the carbon fiber reinforced composite. The sudden drop in forces may be due to fracture caused by variable deformation and residual stresses induced by the presence of the carbon fiber band. In addition, the clamping condition may have influenced failure since the fracture occurred on the outer tube directly next to the clamping, as shown in Fig. 5. The bending experiments revealed that the carbon fiber reinforced composites have higher bending strength than the steel reinforced composites and the nonreinforced tube. This is due to the high ultimate tensile strength of carbon fiber ( ~4500MPa ) compared to 22MnB5(~1650MPa). However, the carbon fibers slipped past each other when 3 carbon fiber bands were used as shown in Fig. 8a, resulting in lower bending forces. Since carbon fibers are quasi-inextensible, the outer fibers slide towards the interior of the bend without stretching, resulting in lower bending forces [7]. Due to the high stiffness of carbon fiber relative to aluminum, the carbon fibers stick to the outer surface of the inner aluminum tube in bending. The detailed interactions between the fibers and aluminum tubes and between the fibers are impossible to control for the thousands of individual fibers [8]. However, properties of the components can be enhanced by reducing slippage of the carbon fibers through increasing the in-plane compressive stresses that hold the composite together. The in-plane compressive stresses can be increased by optimizing the drawing setups, the number of drawing steps, or using tube drawing with mandrels. An optimum quantity and layout of carbon fiber is needed to maximize the bending strength.

Conclusions

The original version of this paper is available on https://www.scientific.net/MSF.1187.129.pdf

Reinforcing the tubes with carbon fiber led to an increase in the maximum bending strength by 15% and a 1.2% increase in weight.

The composites made from carbon fibers and 22 MnB 5 had the same bending strength. However, the lower weight of the carbon fiber reinforced composite makes it more suitable for lightweight applications.

The carbon fiber reinforced composite had the highest stiffness, followed by the composite reinforced with carbon fiber and 22 MnB 5 steel. The composite reinforced by 22 MnB 5 steel had the least stiffness.

Compression and tensile tests on the composite reveal limited benefits of using composites over base material.

References

The original version of this paper is available on https://www.scientific.net/MSF.1187.129.pdf

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Keywords Index

#

3D Printing Shape Memory Behavior of 3D-Printed Biomedical Polyurethane with Room Temperature Transition

B

Beading Beading as a Stabilising Element for Thin Wooden Panels

Biocompatibility Biomaterial's Surface Functionalization by High Frequency Micropatterning Embossing on PEEK Samples

Biocomposites Hybrid Hemp/Carbon Fiber Reinforced Composites: Manufacturing, Mechanical Behaviour and Environmental Assessment

Biomedical Polyurethane Shape Memory Behavior of 3D-Printed Biomedical Polyurethane with Room Temperature Transition

C

Cold Spray Deep Learning-Based Quality Classification of Cold Spray Coatings with Explainable Feature Analysis

Composite Evaluating the Performance of Composite Tubes Drawn from Hybrids of Aluminum, Steel, and Carbon Fiber

Compression Bending Influence of Variable Radius Die Geometry on Tube Bending: A Finite Element Parametric Study

Contact Angle Test Biomaterial's Surface Functionalization by High Frequency Micropatterning Embossing on PEEK Samples

D

Deep Learning Deep Learning-Based Quality Classification of Cold Spray Coatings with Explainable Feature Analysis

Digital Image Analysis Design and Manufacturing of Complex Geometry PEEK Component through Fused Filament Fabrication

Direct Molding Increasing Strength of Recycled Materials by Fiberglass Pulverization and Powder Direct Molding

E

Electric Motors Analysis of Connection Geometries for Segmented Stator Cores Machined with Laser Beam Cutting

Embossing Biomaterial's Surface Functionalization by High Frequency Micropatterning Embossing on PEEK Samples

Explainable Artificial Intelligence Deep Learning-Based Quality Classification of Cold Spray Coatings with Explainable Feature Analysis

F

Fiberglass Recycling Increasing Strength of Recycled Materials by Fiberglass Pulverization and Powder Direct Molding

Flexural Behaviour Hybrid Hemp/Carbon Fiber Reinforced Composites: Manufacturing, Mechanical Behaviour and Environmental Assessment

Fluidized Bed Development of an Initial FEM Framework to Model Particle-Substrate Contact in Fluidized Bed Finishing of L-PBF Parts

Forming Beading as a Stabilising Element for Thin Wooden Panels

Fused Filament Fabrication Design and Manufacturing of Complex Geometry PEEK Component through Fused Filament Fabrication

L

Laser-powder Bed Fusion Development of an Initial FEM Framework to Model Particle-Substrate Contact in Fluidized Bed Finishing of L-PBF Parts

Lateral Gap Estimation of the Lateral Gap in PECM: A Case Study on Tool Steel S390

Life Cycle Assessment Hybrid Hemp/Carbon Fiber Reinforced Composites: Manufacturing, Mechanical Behaviour and Environmental Assessment

M

Mechanical Performance Evaluating the Performance of Composite Tubes Drawn from Hybrids of Aluminum, Steel, and Carbon Fiber

N

Nano-coating Fragmentation Evaluation of Anti-Fouling Behavior of Silver Nanocomposites Made by Nano-Coating Fragmentation

Non-conventional Manufacturing Analysis of Connection Geometries for Segmented Stator Cores Machined with Laser Beam Cutting

Numerical Study Development of an Initial FEM Framework to Model Particle-Substrate Contact in Fluidized Bed Finishing of L-PBF Parts

P

Pecm Estimation of the Lateral Gap in PECM: A Case Study on Tool Steel S390

Peek Design and Manufacturing of Complex Geometry PEEK Component through Fused Filament Fabrication; Biomaterial's Surface Functionalization by High Frequency Micropatterning Embossing on PEEK Samples

Profile Bending Influence of Variable Radius Die Geometry on Tube Bending: A Finite Element Parametric Study

Profile Forming Influence of Variable Radius Die Geometry on Tube Bending: A Finite Element Parametric Study

Prototyping Beading as a Stabilising Element for Thin Wooden Panels

Pulsed Electrochemical Machining Estimation of the Lateral Gap in PECM: A Case Study on Tool Steel S390

R

Radial Gradient Influence of Variable Radius Die Geometry on Tube Bending: A Finite Element Parametric Study

Recycled Composites Increasing Strength of Recycled Materials by Fiberglass Pulverization and Powder Direct Molding

S

S390 Estimation of the Lateral Gap in PECM: A Case Study on Tool Steel S390

Shape Memory Polymers Shape Memory Behavior of 3D-Printed Biomedical Polyurethane with Room Temperature Transition

Silver Nano-particles Evaluation of Anti-Fouling Behavior of Silver Nanocomposites Made by Nano-Coating Fragmentation

Smart Buoy Evaluation of Anti-Fouling Behavior of Silver Nanocomposites Made by Nano-Coating Fragmentation

Stator Segmentation Analysis of Connection Geometries for Segmented Stator Cores Machined with Laser Beam Cutting

Surface Finishing Development of an Initial FEM Framework to Model Particle-Substrate Contact in Fluidized Bed Finishing of L-PBF Parts

Surface Free Energy Biomaterial's Surface Functionalization by High Frequency Micropatterning Embossing on PEEK Samples

Surface Treatment Biomaterial's Surface Functionalization by High Frequency Micropatterning Embossing on PEEK Samples

T

Thin Wooden Panels Beading as a Stabilising Element for Thin Wooden Panels

Tube Bending Influence of Variable Radius Die Geometry on Tube Bending: A Finite Element Parametric Study

Tube Drawing Evaluating the Performance of Composite Tubes Drawn from Hybrids of Aluminum, Steel, and Carbon Fiber

V

Variable Bending Radius Influence of Variable Radius Die Geometry on Tube Bending: A Finite Element Parametric Study

Variable Radius Die Influence of Variable Radius Die Geometry on Tube Bending: A Finite Element Parametric Study

W

Weight Reduction Evaluating the Performance of Composite Tubes Drawn from Hybrids of Aluminum, Steel, and Carbon Fiber

Wood Beading as a Stabilising Element for Thin Wooden Panels

Working Gap Estimation of the Lateral Gap in PECM: A Case Study on Tool Steel S390

Authors Index

A

Aslanidis, A. Influence of Variable Radius Die Geometry on Tube Bending: A Finite Element Parametric Study

B

Barth, M. Analysis of Connection Geometries for Segmented Stator Cores Machined with Laser Beam Cutting

Bellisario, D. Increasing Strength of Recycled Materials by Fiberglass Pulverization and Powder Direct Molding; Evaluation of Anti-Fouling Behavior of Silver Nanocomposites Made by Nano-Coating Fragmentation; Shape Memory Behavior of 3D-Printed Biomedical Polyurethane with Room Temperature Transition

Besong, L. Evaluating the Performance of Composite Tubes Drawn from Hybrids of Aluminum, Steel, and Carbon Fiber

Bianchi, I. Hybrid Hemp/Carbon Fiber Reinforced Composites: Manufacturing, Mechanical Behaviour and Environmental Assessment

Biasi, L. Deep Learning-Based Quality Classification of Cold Spray Coatings with Explainable Feature Analysis

Boccarusso, L. Hybrid Hemp/Carbon Fiber Reinforced Composites: Manufacturing, Mechanical Behaviour and Environmental Assessment

Boschetto, A. Design and Manufacturing of Complex Geometry PEEK Component through Fused Filament Fabrication

Bottini, L. Design and Manufacturing of Complex Geometry PEEK Component through Fused Filament Fabrication

Buhl, J. Evaluating the Performance of Composite Tubes Drawn from Hybrids of Aluminum, Steel, and Carbon Fiber

Burattini, N. Design and Manufacturing of Complex Geometry PEEK Component through Fused Filament Fabrication

C

Citarella, A. Deep Learning-Based Quality Classification of Cold Spray Coatings with Explainable Feature Analysis

Clauß, P. Estimation of the Lateral Gap in PECM: A Case Study on Tool Steel S390

Corapi, D. Design and Manufacturing of Complex Geometry PEEK Component through Fused Filament Fabrication

D

Durante, M. Hybrid Hemp/Carbon Fiber Reinforced Composites: Manufacturing, Mechanical Behaviour and Environmental Assessment; Deep Learning-Based Quality Classification of Cold Spray Coatings with Explainable Feature Analysis

E

Engel, B. Influence of Variable Radius Die Geometry on Tube Bending: A Finite Element Parametric Study

F

Fazio, D. Hybrid Hemp/Carbon Fiber Reinforced Composites: Manufacturing, Mechanical Behaviour and Environmental Assessment; Deep Learning-Based Quality Classification of Cold Spray Coatings with Explainable Feature Analysis

Filice, L. Biomaterial's Surface Functionalization by High Frequency Micropatterning Embossing on PEEK Samples

Förster, R. Beading as a Stabilising Element for Thin Wooden Panels

G

Gagliardi, F. Biomaterial's Surface Functionalization by High Frequency Micropatterning Embossing on PEEK Samples

H

Hackert-Oschätzchen, M. Analysis of Connection Geometries for Segmented Stator Cores Machined with Laser Beam Cutting; Estimation of the Lateral Gap in PECM: A Case Study on Tool Steel S390

Hassanin, A. Development of an Initial FEM Framework to Model Particle-Substrate Contact in Fluidized Bed Finishing of L-PBF Parts

Hoffmann, F. Analysis of Connection Geometries for Segmented Stator Cores Machined with Laser Beam Cutting

Hornig, J. Beading as a Stabilising Element for Thin Wooden Panels

I

Iorio, L. Increasing Strength of Recycled Materials by Fiberglass Pulverization and Powder Direct Molding; Evaluation of Anti-Fouling Behavior of Silver Nanocomposites Made by Nano-Coating Fragmentation; Shape Memory Behavior of 3D-Printed Biomedical Polyurethane with Room Temperature Transition

L

Loth, A. Beading as a Stabilising Element for Thin Wooden Panels

M

Magro, M. Biomaterial's Surface Functionalization by High Frequency Micropatterning Embossing on PEEK Samples

Majcherek, S. Analysis of Connection Geometries for Segmented Stator Cores Machined with Laser Beam Cutting

Marco, F. Deep Learning-Based Quality Classification of Cold Spray Coatings with Explainable Feature Analysis

May, A. Analysis of Connection Geometries for Segmented Stator Cores Machined with Laser Beam Cutting

Meichsner, G. Estimation of the Lateral Gap in PECM: A Case Study on Tool Steel S390

Mignanelli, C. Hybrid Hemp/Carbon Fiber Reinforced Composites: Manufacturing, Mechanical Behaviour and Environmental Assessment

Moscato, F. Biomaterial's Surface Functionalization by High Frequency Micropatterning Embossing on PEEK Samples

Mundo, D. Biomaterial's Surface Functionalization by High Frequency Micropatterning Embossing on PEEK Samples

N

Nickel, S. Estimation of the Lateral Gap in PECM: A Case Study on Tool Steel S390

Noqra, D. Increasing Strength of Recycled Materials by Fiberglass Pulverization and Powder Direct Molding; Evaluation of Anti-Fouling Behavior of Silver Nanocomposites Made by Nano-Coating Fragmentation; Shape Memory Behavior of 3D-Printed Biomedical Polyurethane with Room Temperature Transition

P

Panico, M. Hybrid Hemp/Carbon Fiber Reinforced Composites: Manufacturing, Mechanical Behaviour and Environmental Assessment

Patrizii, G. Increasing Strength of Recycled Materials by Fiberglass Pulverization and Powder Direct Molding; Evaluation of Anti-Fouling Behavior of Silver Nanocomposites Made by Nano-Coating Fragmentation; Shape Memory Behavior of 3D-Printed Biomedical Polyurethane with Room Temperature Transition

Perna, A. Deep Learning-Based Quality Classification of Cold Spray Coatings with Explainable Feature Analysis

Perrelli, M. Biomaterial's Surface Functionalization by High Frequency Micropatterning Embossing on PEEK Samples

Petermann, R. Estimation of the Lateral Gap in PECM: A Case Study on Tool Steel S390

Pinto, F. Hybrid Hemp/Carbon Fiber Reinforced Composites: Manufacturing, Mechanical Behaviour and Environmental Assessment

Plänitz, P. Analysis of Connection Geometries for Segmented Stator Cores Machined with Laser Beam Cutting

Proietti, A. Increasing Strength of Recycled Materials by Fiberglass Pulverization and Powder Direct Molding; Evaluation of Anti-Fouling Behavior of Silver Nanocomposites Made by Nano-Coating Fragmentation; Shape Memory Behavior of 3D-Printed Biomedical Polyurethane with Room Temperature Transition

Q

Quadrini, F. Increasing Strength of Recycled Materials by Fiberglass Pulverization and Powder Direct Molding; Evaluation of Anti-Fouling Behavior of Silver Nanocomposites Made by Nano-Coating Fragmentation; Shape Memory Behavior of 3D-Printed Biomedical Polyurethane with Room Temperature Transition

R

Reuter, J. Influence of Variable Radius Die Geometry on Tube Bending: A Finite Element Parametric Study

Rossi, D. Deep Learning-Based Quality Classification of Cold Spray Coatings with Explainable Feature Analysis

Rösler, J. Beading as a Stabilising Element for Thin Wooden Panels

S

Santo, L. Increasing Strength of Recycled Materials by Fiberglass Pulverization and Powder Direct Molding; Evaluation of Anti-Fouling Behavior of Silver Nanocomposites Made by Nano-Coating Fragmentation; Shape Memory Behavior of 3D-Printed Biomedical Polyurethane with Room Temperature Transition

Simoncini, M. Hybrid Hemp/Carbon Fiber Reinforced Composites: Manufacturing, Mechanical Behaviour and Environmental Assessment

Squillace, A. Development of an Initial FEM Framework to Model Particle-Substrate Contact in Fluidized Bed Finishing of L-PBF Parts

T

Tortora, G. Deep Learning-Based Quality Classification of Cold Spray Coatings with Explainable Feature Analysis

V

Viscusi, A. Deep Learning-Based Quality Classification of Cold Spray Coatings with Explainable Feature Analysis; Development of an Initial FEM Framework to Model Particle-Substrate Contact in Fluidized Bed Finishing of L-PBF Parts

W

Winkler, F. Analysis of Connection Geometries for Segmented Stator Cores Machined with Laser Beam Cutting