Cover

ISBN print: 978-3-0364-0996-2
ISBN eBook: 978-3-0364-1996-1
DOI book: 10.4028/b-dM5At3
© 2026 Trans Tech Publications Ltd, All Rights Reserved

Composites Forming Processes

Edited by
Luise Kärger
Pierpaolo Carlone

Composites Forming Processes

Special topic volume with invited peer-reviewed papers only

Edited by Luise Kärger and Pierpaolo Carlone

Scientific.Net

Copyright © 2026 Trans Tech Publications Ltd, Switzerland

All rights reserved. No part of the contents of this publication may be reproduced, processed or transmitted in any form or by any means without the written permission of the publisher.

Trans Tech Publications Ltd
Seestrasse 24c
CH-8806 Baech
Switzerland
https://www.scientific.net

ISSN 0377-6883

Full text available online at https://www.scientific.net/book/composites-forming-processes/978-3-0364-1996-1

Distributed worldwide by
Trans Tech Publications Ltd
Seestrasse 24c
CH-8806 Baech
Switzerland
Phone: +41 (44) 9221022
e-mail: sales@scientific.net

Solid State Phenomena, ISSN: 1662-9779, Vol. 387, pp -1-0

© 2026 Trans Tech Publications Ltd, All Rights Reserved

Accepted:
2026-04-09

Online:
2026-04-14

Preface

The original version of this paper is available on https://www.scientific.net/SSP.387.-1.pdf

High-level applications of composite materials at affordable development and manufacturing costs create significant demand for scientific knowledge and computational tools for composite manufacturing. The mechanical behavior of composite parts in service is dominated by fiber orientation and density, which in turn, are determined by the forming process. Hence, predicting inservice performance requires full understanding and, preferably, prediction of the complex material behavior during manufacturing.

Experimental and numerical "benchmarks" are set up and discussed within the composites forming issue. The topics of it concern: Material characterization; Constitutive laws; Contact and friction; Forming simulations; Multiscale analyses; Textile forming, Thermoforming; Resin injection; Compression Molding; Fiber suspensions; Natural fibers; Additive manufacturing of composites; Benchmark efforts.

Editors

The original version of this paper is available on https://www.scientific.net/SSP.387.-1.pdf

Kärger, Luise
Carlone, Pierpaolo

Co-editors

The original version of this paper is available on https://www.scientific.net/SSP.387.-1.pdf

Akkerman, Remko
Grouve, Wouter
Ivanov, Dmitry
Lomov, Stepan
Sherwood, James A.
Vidal Salle, Emmanuelle

Karlsruhe Institute of Technology
University of Salerno

University of Twente
University of Twente
University of Bristol
KU Leuven
University of Massachusetts
INSA Lyon

Table of Contents

Preface

Topological Optimization of Shell Laminate Composites Parts Manufactured by the TFP Process

Peng Yu, Jessy Simon, Christophe Binetruy, Nahiene Hamila

Process Influences on Plastificate and Fiber Microstructure Formation: An LFT-D Compression Molding Parameter Study

Christoph Schelleis, Luca Meckes, Frank Henning

Stability of Tubular Preforms from Multi-Layer Winding of Composite Tapes Prior to Thermoforming

Nagui Elgaby, Guillaume Helbert, Nahiene Hamila, Rubén Campos, Audrey Sanchez, Bertrand Laine

Influence of Fiber Orientation Clustering Methods in Virtual Process Chains of Discontinuous-Fiber Composite Components

Louis Schreyer, Luca Salvatore Pusineri Yaluff, Constantin Krauß, Luise Kärger

Wrinkling Behaviour of Engineering Fabrics in Diaphragm-Assisted Deep Drawing

Ali Tabatabaeian, Conor Blair, Kaspar Ambuehl, Philip Harrison

Fibre Tension-Induced Stresses in Filament-Wound Composites

Jurui Liu, Stepan V. Lomov, Chung Hae Park, Jan Ivens

Thermo-Chemical Modeling and Experimental Validation of Pultruded Glass Fiber Reinforced Composites

Jacob Harris, Sangharsha Gharat, Luohaoran Wang, Ali Zolali, Alan Taub, Mihaela Banu

Controlled Preform Consolidation for Manufacturing Difficult Resin System Composites

Dominic R. Palubiski, Natasha Shirshova, Emile S. Greenhalgh, Milo S. P. Shaffer, Dmitry S. Ivanov

Multi-Stage Consolidation Strategy to Improve Impregnation Quality in Thermoplastic UD Tapes

Oguz Eryilmaz, Onur Yuksel, Maissaloun El-Jakl, Nursel Karakaya, Guralp Ozkoc, Baris Caglar

Cost-Effective One-Shot Stamping of Steel-Based High-Performance Lightweight Multi-Material Structures

Hao Wu, Mohamed Mohamed, Chunyi Gao, Zerong Ding, Manikandan Ganapathy, Pinaki Biswas, Rahul K. Verma, Ambrose Taylor, Nan Li

A CT-Based Methodology for Mesostructural Characterization of Unidirectional Continuous-Fiber Polymer Composites Using Line-Profile Descriptors

Roberto Mele, Paolo De Sio, Fausto Tucci, Pierpaolo Carlone

Environmental Impact Assessment of Thermoplastic Pultrusion of GF-PP Pre-Consolidated Tapes

Paolo De Sio, Ersilia Cozzolino, Fausto Tucci, Antonello Astarita

The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Connie Qian, Miro Duhovic, Louis Schreyer, Marcel Olma, Andreas Gebhard, Wouter Johannes Bernardus Grouve, Dominic Schommer, Florian Gortner, Thomas Neumeyer, Luise Kärger, Martin Hohberg, Hao Yuan, Anna Julia Imbsweiler, Leah Senn Ramirez, Sepehr Simaafrookhteh, Jan Ivens, Stepan V. Lomov, Gerben Bieleman, Francois Mahé, Christophe Binetruy, Andreas Kapshammer, Zoltan Major, Dmitry S. Ivanov, Jonathan P.H. Belnoue, Anatoly Koptelov, Jakub Jakimow, Yvonne Aitomaki, Dimitra Ramantani

Iterative Un-Forming of Highly Aligned Discontinuous Preform/Material

Yuncong Feng, Hao Yuan, Burak Ogun Yavuz, Ian Hamerton, Byung Chul Kim, Stephen R. Hallett, Jonathan P.H. Belnoue

Mechanical Response of Flax Rovings under Compaction: Experimental Investigation

Samir Zidi, Tiffany Larquemin, Audrey Hivet, Gilles Hivet, Davy Duriatti

Keywords Index

Authors Index

The original version of this eBook is available on https://www.scientific.net/book/composites-forming-processes/978-3-0364-1996-1

Solid State Phenomena, ISSN: 1662-9779, Vol. 387, pp 1-8

doi: 10.4028/p-pK41bI

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

Submitted:
2025-12-17

Revised:
2026-02-05

Accepted:
2026-02-14

Online:
2026-04-14

Topological Optimization of Shell Laminate Composites Parts Manufactured by the TFP Process

The original version of this paper is available on https://www.scientific.net/SSP.387.1.pdf

Peng YU 1,a*, Jessy SIMON 2, b, Christophe BINETRUY 2,c and Nahiene HAMILA 1,d

1 IRDL UMR CNRS 6027, Bretagne INP, Brest, F-29200, France

2 GeM, UMR CNRS 6183, Nantes Université, Ecole Centrale Nantes, Nantes, F-44000, France

a  peng.yu@enib.fr, b  jessy.simon@ec-nantes.fr, c  christophe.binetruy@ec-nantes.fr, d  nahiene.hamila@enib.fr

Keywords: Topology optimization, TFP process, curvilinear fibers

Abstract

While the use of composite materials increases the specific stiffness of structural parts, their manufacture using automated fiber placement processes such as Tailored Fiber Placement (TFP) allows for the addition of functionalization. An example of such a part is the hydrofoil, which can gain hydrodynamic performance if its shape adapts to the different loads encountered in the three modes of navigation. One method that can meet these requirements is passive functionalization. In this context, the development of digital design support tools is essential. Among them, topology optimization is a well-established method. This work focuses on the development of a strategy for optimizing the topology or the fiber density distribution of the part and the orientation of the fibers for composite materials with an objective function of path generating type allowing passive functionalization. A method for generating fiber trajectories for the TFP process is also presented. The topology optimization results of a cantilever type test case and a shell plate are shown and discussed.

Introduction

The original version of this paper is available on https://www.scientific.net/SSP.387.1.pdf

Topology optimization is one of the most advanced structural design methods, aiming to automatically find the optimal structure within the design domain based on loads, given constraints, and an objective function to be satisfied. In a topology optimization problem for a mechanical part, satisfying the objective involves determining the material arrangement within the design space. It can be defined as a binary problem consisting of finding the distribution of void or filled areas that minimizes the work done by external forces or compliance subjected to a volume constraint [1]. Some other problems with different objectives functions and constraint can also be solved. For instance, finding the lightest structure which meets a specific failure criterion [1]. Numerical methods for topological optimization have been extensively studied, notably by Bendsøe [2] who introduced a homogenization approach [3], but also the density-based method with the SIMP (Solid Isotropic Material with Penalization) scheme [1,4] and level-set-based topological optimization [5,6].

The TFP (Tailored Fiber Placement) process is a relatively new automated textile process for producing composites reinforcements with curvilinear fiber paths [7]. When combined with the forming process of continuous fiber-reinforced composite materials, the TFP process offers a high degree of design freedom by allowing the fibers to be arranged in an optimal manner. The principle of TFP is illustrated in Fig. 1. The embroidery head fixes a continuous tow of fiber to a flat backing material by creating a zigzag stitch. The TFP has demonstrated significant potential for application in the manufacture of small and medium-sized lightweight composite structures in the aerospace, automotive, and energy industries [8, 9, 10].

[picture]

Fig. 1 Schematic representation of the TFP principle [11]

Fig. 1. Schematic representation of the TFP principle [11]

For the TFP process, the most important factor is determining the fiber deposition path, which will significantly influence the mechanical performance of the composite part. In the article by Papapetrou et al. [12], three different methods were applied and compared to generate and optimize the fiber path. The first is the offset method, which creates contours parallel to the boundary of the solid domain at equal intervals until they intersect or encounter void. This method, while applicable to very complex geometries, can introduce abrupt changes along the paths. The second is called the EQS method, it uniformly distributes a constant number of points along each cross-section of the optimized solid area, and then these points are connected in the span direction. Finally, the third is the streamline method, which will be explained with more details in the Methods section.

The energy method [12] is commonly employed to enhance the smoothness and continuity of fiber paths, where fiber orientations are determined by minimizing the strain or stress energy. Gea and Luo [13] investigated this method and demonstrated that the resulting orientations generally align with the principal stress or strain directions for materials that are orthotropic with relatively weak shear coupling, as well as for certain strongly coupled materials under shear.

Methods

The original version of this paper is available on https://www.scientific.net/SSP.387.1.pdf

Material model. The orthotropic material model with variable fiber volume fraction vf[14] is used in this work. The following micromechanical models are used to determine the effective properties of the composite as function of vf :

E1=vfEf+(1−vf)Em,E2=EfEmvfEm+(1−vf)Ef,G12=GfGmvfGm+(1−vf)Gf,v12=vfvf+(1−vf)vm.

(1)(2)(3)(4)

where E1 is Young's modulus along axis 1,E2 is Young's modulus along axis 2,v12 is Poisson's ratio, G12 is in-plane shear modulus. Ef,Gf,vf represent Young's modulus, in-plane shear modulus and Poisson's ratio of the fiber material, respectively, and Em,Gm,vm represent Young's modulus, in-plane shear modulus and Poisson's ratio of the matrix material, respectively. The axis 1 is determined by fiber orientation θ and the axes 2 and 3 are taken to be orthogonal to axis 1 .

In numerical computation, the vf of each element is obtained by a continuous design variable fiber density ρf by following equation:

vfi=(1−ρfi)vf,min+ρfivf,max(5)

where vf, min  and vf, max  represent the minimum and maximum local fiber volume fraction, the intermediate values of vf can thus be determined through linear interpolation.

The 2D orthotropic stiffness tensor 𝐂 is defined as follows:

𝐂=[E11−v12v21v12E21−v12v210v21E11−v12v21E21−v12v21000G12](6)

The fiber orientation θ is considered through transformation matrix 𝐓 :

𝐓=[cos2θsin2θ2sinθcosθsin2θcos2θ−2sinθcosθ−sinθcosθsinθcosθcos2θ−sin2θ](7)

For 3D shell element, the reduced constitutive tensor and SB8 solid-shell element from the work of M. Dia [15] are applied in order to model correctly shell elements behavior. It easier to implement the cinematics of SB8 element since it's based only on displacements. Quadrangle-type elements will be used for 2D calculations and the 3D solid-shell elements will be used for a laminate subjected to out-of-plane forces.

Topology optimization: Density-based method. The density-based method is a classic topological optimization method [1]. Depending on the type of material, the method scheme may differ, for example, SIMP for isotropic materials and SOMP (Solid Orthotropic Material With penalization). In this method, a density ρ between 0 and 1 is assigned to each element to represent its state: density 0 corresponds to the void and density 1 to the solid. Intermediate densities are obtained by interpolation. Thus, a discretized binary problem is converted into a continuous problem.

The minimization of compliance topology optimization problem with SOMP scheme can be formulated as follows:

 find ρ=[ρ1,ρ2,…,ρn]T,θ=[θ1,θ2,…,θn]T minimize c(ρphys ,θ)=𝐟T𝐮(ρphys ,θ) subjected to V(ρphys )=ρphys T𝐕−Vlim ≤0ρ∈x,x=[ρ∈ℝn:0≤ρ≤1](8)

where ρ is the element density, ρphys  is the filtered density, θ is the fiber orientation, n is the number of elements, c is the compliance, 𝐟 is the force vector, 𝐮 is the displacement vector, 𝐕 is a vector containing the volumes of all elements and Vlim  is the prescribed threshold of volume of the final structure.

Besides minimization of compliance, the path generating problem is also studied in this paper. This type of problem aims to achieve a target displacement at specified locations so the objective function can be written as the sum of the error between the actual displacement and the target displacement at the target locations in least squares form.

 find ρf=[ρf1,ρf2,…,ρfn]T,θ=[θ1,θ2,…,θn]T minimize ε(ρf,θ)=12(𝐮−𝐮target )T𝐃(𝐮−𝐮target ) subjected to 𝐊(ρf)𝐮(ρf)=𝐟ρf∈x,x=[ρf∈ℝn:0≤ρf≤1](9)

where ρf is the fiber density, θ is the fiber orientation, n is the number of elements, ε is the error, 𝐮 is the displacement vector, 𝐮target  is the target displacement vector that only has prescribed values on the target degrees of freedom, the matrix 𝐃 is a diagonal matrix where all elements on the diagonal are 0 except for the degree of freedoms representing the target displacement, 𝐊 is the global stiffness matrix and 𝐟 is the force vector.

In the first formulation, the aim is to minimize the compliance with a constraint of the total volume and fiber orientation, therefore the density of element and the fiber orientation are chosen as design variables. In the second formulation, we are interested in a target deformation and we only manipulate the fiber volume fraction and the orientation, no material will be removed. In engineering application, there is also no reason to remove some elements and leave a hole when we want to fabricate a composite plate. Based on this context, unlike Eq. 8, the total volume constraint is removed from Eq. 9 and we choose fiber density and orientation as design variables to solve path generating problem.

Fiber path generation: Streamline method. Since the orientation field θ is a discrete solution to the optimization problem, manufacturable continuous fiber paths with the TFP process must be determined. For this, the streamline method is used. This method calculates a function ψ whose gradient is orthogonal to the fiber orientations θ. Thus, if ψ is calculated on a mesh, a scalar field of Ψ is obtained, allowing the generation of continuous trajectories by drawing level-sets from the values of Ψ. These level-sets represent the fiber paths. A smoothing method for the change in orientation between neighboring elements is also applied to obtain a more continuous and smoother trajectory.

The streamlines are therefore calculated using the following minimization scheme:

minψ𝐈(ψ)=12∫α1(x)·|∇ψ−k𝐕⟂|2dΩ where: α1(x)=0, if x∈Ωvoid α1(x)=1, if x∈Ωsolid (10)

where k is a coefficient that allows control of the spacing between the tow of fibers and 𝐕⟂ is the vector perpendicular to the fibers directly obtained from the fiber angle θ.

Results and Discussions

The original version of this paper is available on https://www.scientific.net/SSP.387.1.pdf

In this section, two numerical tests are conducted to evaluate the performance of the proposed method. For the first formulation, a test from the work of Papapetrou et al. [12] is reproduced to validate the topology optimization method, then the proposed fiber path generation approach is also tested. For the second formulation, a simple shell plate is tested, which necessitates the integration of shell-like element such as SB8. In this test, we want to assess how the fiber density distribution and fiber orientation will influence the final fiber paths, therefore such a flexion plate with fiber density initially uniform everywhere and aligned with X direction is chosen.

Case 1: cantilever2D. The topology optimization with the first formulation (Eq. 8) is carried out on a 2D cantilever model as shown in Fig. 2. The design domain is a 200×100 rectangle. Four-nodes quad elements of dimension 2×2 is used. The left side of domain is clamped and a concentrated force of 1×105 N is applied at the middle of the right edge of design domain.

As for the properties of material, in Eq. 8 there is no fiber volume fraction, so it is considered that the fiber density ρf in Eq. 5 is 1 everywhere in the material model. Other properties are taken from the paper of Papapetrou et al. [12]: E1 is 80GPa,E2 is 35GPa,G12 is 30.1 GPa and v12 is 0.33 . It is noticed that the properties adopted here do not correspond to a specific industrial composite material, but are introduced for numerical validation of our model.

[picture]

Fig. 2 Mesh, boundary conditions and loads of case 1: cantilever2D

Fig. 2. Mesh, boundary conditions and loads of case 1: cantilever2D

Fig. 3 shows the results of topology optimization of case 1. In the results, the elements having a density ρ smaller than 0.5 are considered as void so removed. The red arrows represent the fiber orientation, which is first aligned with the first principal direction of each element, then averaged by a filter within a certain range with their neighbor elements. The orientations are averaged and smoothed in the purpose of generating a smooth and continuous fiber trajectory, avoiding sudden change of direction, which is not suitable in TFP process. The yellow lines represent the fiber paths generated based on the fiber orientation, it can be seen that with a symmetric fiber orientations field, we obtain smooth and symmetric fiber paths, they tend to converge at the point where the load is applied.

It should be noticed that the fiber paths near the boundary are sometimes disconnected. This is for the reason of stair-like boundaries. In the future study, the mesh will be refined and some smoothen algorithm will be applied to have more continuous paths on the boundaries.

[picture]

Fig. 3 Results of case 1: topology (black), fiber orientation (yellow) and fiber paths (red)

Fig. 3. Results of case 1: topology (black), fiber orientation (yellow) and fiber paths (red)

Case 2: flexion plate. A path generating problem with the second formulation (Eq. 9) is studied on a 1-layer 3D flexion shell plate model as shown in Fig. 4. The design domain is a 200×100 rectangle with layer thickness of 0.2 . Solid-shell SB8 elements of dimension 2×2×0.2 is used. The
left end surface of domain is clamped and a uniform distributed force of 10 N is applied on the right end surface of design domain.

[picture]

Fig. 4 Mesh, boundary conditions and loads of case 2: flexion plate

Fig. 4. Mesh, boundary conditions and loads of case 2: flexion plate

Material properties are set as follows [14]: Ef is 110GPa,Em is 2.3GPa,Gf is 6.2GPa,Gm is 1.5 GPa and vf and vm are 0.31 . The maximum and minimum local fiber volume fraction are 0.6 and 0.2 , respectively. The target displacements set on the right end surface to the negative Z direction with the magnitude of 2.5 while the theoretic mechanical solution of the displacement on the right end is between 1.18 and 3.32, which corresponds to maximum and minimum fiber volume everywhere with fiber aligned with the x -axis, respectively.

[picture]

Fig. 5 Results of case 2: distribution of vf, fiber orientation (black) and fiber paths (green)

Fig. 5. Results of case 2: distribution of v f , fiber orientation (black) and fiber paths (green)

Fig. 5 shows the results of topology optimization of case 2. The black arrows represent the fiber orientation and the green lines represent the fiber paths generated based on the fiber orientation. The fiber volume fractions are in the range of 0.2 to 0.4 . Extracting the results from the first iteration, it can be known that the fiber density is still almost uniform in 0.5 (initialized value), the fibers are all aligned with the x -axis and the generated fiber paths are almost parallel lines. The Z component of displacement of the right end is about -1.7 , this means the plate is too rigid to achieve the target displacement at the start. Based on this situation, it can be seen that, in order to achieve the target uniform displacements, the fiber volume fraction of the whole plate is not homogeneous but has a gradient from left end to right end. On the left end, the fiber volume fraction is almost the minimum, it decreased a lot compared with the first iteration to make the plate more flexible, so the fiber paths are less densified. Along the X direction, the fiber volume fraction increases so the distance between adjacent fiber paths becomes smaller. On the right end of the plate, the fiber paths are much more densified that on the left end. Furthermore, it can be seen that the fiber orientations do not change much and remains aligned with X direction, while the fiber paths on the left end are not exactly
aligned with the fiber orientations since they are influenced by a lower fiber volume fraction, which seems consistent. By checking the sensibility of the objective function with respect to the orientation, it can be found that the sensibility of orientation is almost zero in this case, that's why only the fiber volume fraction changed over iterations.

[picture]

Fig. 6 Result of case 2: displacement on the right end surface along the width direction

Fig. 6. Result of case 2: displacement on the right end surface along the width direction

In Fig. 6, the red curve shows the Z component of displacement at the middle of the right end surface along the width direction and the blue line represents the target displacement. The final objective function is 6.368e−4. It can be seen that, the results are varied from -2.4925 to -2.5035 , very close to the target displacements value -2.5 . It is also interesting to observe that, even though the results are very close to the target, they are not unform but change in a quadratic form along the width direction, which is expected for a plate under bending.

Conclusion

The original version of this paper is available on https://www.scientific.net/SSP.387.1.pdf

This work focuses on the design of composite parts, especially shell plate with target displacement, through topology and material optimization. Unlike previous works, the volume constraint on density or fiber density is removed from the formulation of path generating problem, which makes the problem more relevant to engineering practice. The presented method for generating continuous fiber paths from a discrete vector field resulting from the optimization of the topology and fiber orientation will be used to manufacture parts using the TFP process and experimental testing will be conducted to validate the optimized design.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/SSP.387.1.pdf

The authors acknowledge financial support from Bretagne region and Carnots ARTS project.

References

The original version of this paper is available on https://www.scientific.net/SSP.387.1.pdf

M.P. Bendsoe, O. Sigmund, Topology optimization: theory, methods, and applications, Springer Science & Business Media, 2013.

M.P. Bendsøe and N. Kikuchi, "Generating optimal topologies in structural design using a homogenization method," Computer Methods in Applied Mechanics and Engineering, vol. 71, no. 2, pp. 197-224, Nov. 1988, doi: .

Y. Wang, X. Li, K. Long, and P. Wei, "Open-Source Codes of Topology Optimization: A Summary for Beginners to Start Their Research," Computer Modeling in Engineering & Sciences, vol. 137, no. 1, pp.1-34, Jan. 2023, doi: .

M.P. Bendsøe, "Optimal shape design as a material distribution problem," Structural Optimization, vol. 1, no. 4, pp. 193-202, Dec. 1989, doi: .

M. Y. Wang, X. Wang, and D. Guo, "A level set method for structural topology optimization," Computer Methods in Applied Mechanics and Engineering, vol. 192, no. 1-2, pp. 227-246, Jan. 2003, doi: .

G. Allaire, F. Jouve, and A.-M. Toader, "Structural optimization using sensitivity analysis and a level-set method," Journal of Computational Physics, vol. 194, no. 1, pp. 363-393, Feb. 2004, doi: .

P. Mattheij, K. Gliesche, and D. Feltin, "Tailored Fiber Placement-Mechanical Properties and Applications," Journal of Reinforced Plastics and Composites, vol. 17, no. 9, pp. 774-786, Jun. 1998, doi: .

Axel Spickenheuer, M. Schulz, Konrad Gliesche, and G. Heinrich, "Using tailored fibre placement technology for stress adapted design of composite structures," Plastics Rubber and Composites, vol. 37, no. 5, pp. 227-232, Jun. 2008, doi: .

K. Uhlig, A. Spickenheuer, L. Bittrich, and G. Heinrich, "Development of a Highly Stressed Bladed Rotor Made of a CFRP Using the Tailored Fiber Placement Technology," Mechanics of Composite Materials, vol. 49, no. 2, pp. 201-210, May 2013, doi: .

H.S. Almeida, Lars Bittrich, T. Nomura, and Axel Spickenheuer, "Cross-section optimization of topologically-optimized variable-axial anisotropic composite structures," Composite structures, vol. 225, pp. 111150-111150, Oct. 2019, doi: .

J. Simon, "Numerical simulation and experimental investigation of the forming of tailored fibre placement preforms: a mixed embedded-ALE finite element formulation," PhD thesis, École centrales de Nantes, , Jun. 2022, doi: .

V.S. Papapetrou, C. Patel, and A.Y. Tamijani, "Stiffness-based optimization framework for the topology and fiber paths of continuous fiber composites," Composites Part B: Engineering, vol. 183, p. 107681, Feb. 2020, doi: .

H. C. Gea and J. H. Luo, "On the stress-based and strain-based methods for predicting optimal orientation of orthotropic materials," Structural and Multidisciplinary Optimization, vol. 26, no. 3-4, pp. 229-234, Feb. 2004, doi: .

F. Zhang, B. Li, W. Wo, X. Hu, M. Chang, and P. Jin, "Topology Design of 3D Printing Continuous Fiber-Reinforced Structure Considering Strength and Non-Equidistant Fiber," Advanced Engineering Materials, vol. 26, no. 1, Nov. 2023, doi: .

M. Dia, "Hexahedral and prismatic solid-shell for nonlinear analysis of thin and medium-thick structures" PhD thesis, Université de Lyon, Hal.science, Jun. 2020, doi: .

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/composites-forming-processes/978-3-0364-1996-1

Solid State Phenomena, ISSN: 1662-9779, Vol. 387, pp 9-19

doi: 10.4028/p-ZoJg4h

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

Submitted:
2025-12-18

Revised:
2026-02-10

Accepted:
2026-02-17

Online:
2026-04-14

Process Influences on Plastificate and Fiber Microstructure Formation: An LFT-D Compression Molding Parameter Study

The original version of this paper is available on https://www.scientific.net/SSP.387.9.pdf

Christoph Schelleis 1,a*, Luca Meckes 1, b and Frank Henning 1,c

¹Fraunhofer Institute for Chemical Technology ICT, Joseph-von-Fraunhofer-Str. 7, 76327 Pfinztal, Germany

a* christoph.schelleis@ict.fraunhofer.de, b luca-meckes@web.de, cfrank.henning  @ict.fraunhofer.de 

a  christoph.schelleis@ict.fraunhofer.de *corresponding author

Keywords: FRTP, composites, processing, charge, plastificate, lofting, fiber content, fiber orientation.

Abstract

Direct compounding of long fiber thermoplastic (LFT-D) materials in compression molding are two complex processes in series linked by the plastificate. Continuous compounding and sequential compression create a time-dependent property progression along the extrusion direction of the plastificate. Under variation of secondary parameters, extruder die temperature, and die height of the LFT-D line, samples of plastificates, flow fronts and plates are manufactured and characterized. The plastificate density progression along the extrusion direction is primarily influenced by the temperature of the die. Lofting of the plastificate is higher at high temperatures while the density difference along the extrusion direction is lower. This density difference is known to influence fiber orientations and mechanical properties. The flow front of the material filling the mold is skewed because of the density difference. We show that the skewness is mainly influenced by the die height and is lower at high die heights. The fiber content distribution in the plate is discussed and found to be influenced by the length of the plastificate which is in turn determined by the secondary parameters. These secondary parameters of the LFT-D line can play a role in process optimization once the primary parameters are selected. This work provides clues and observations of principles for such optimizations.

Motivation and State of the Art

The original version of this paper is available on https://www.scientific.net/SSP.387.9.pdf

Earth's resources are finite and need to be spent only with great care [1]. Transportation of goods as well as personal mobility are pillars of wealth of our society, yet at the same time this sector accounts for roughly a quarter of the worlds CO2 emissions [2]. A balance must be struck to conserve both the environment and society's wealth.
In vehicle manufacturing, identical parts are often used across model series and even brands [3]. These are an impactful lever for producing resource-saving components. With the proliferation of battery-powered aircraft, manned and unmanned aircraft, to be manufactured in similar quantities, the correct process description is becoming increasingly important. Long fiber reinforced thermoplastic (LFT) materials are a widely used material class in the automotive industry [4]. Polymer matrix and fibers, usually glass, are produced in various ways balancing processability and part performance [5]. Compression molding is an effective processing route with lower fiber attrition compared to injection molding [6]. The LFT-Direct (LFT-D) process has, since its inception more than two decades ago, pushed for ever higher market shares [7].
A processing scheme for LFT-D compression molding is given in Fig. 1. In a) two twin screw extruders (TSE) are, compounding the polymer melt from granulates and additives (TSE1), and gently incorporating the reinforcing fiber from continuous rovings (TSE2). A detailed description of the processing route is presented by Schelleis et al. [8], as are parameter studies [9] and selected mechanical properties [10,11]. At the end of TSE2 the plastificate or charge is cut to size and exits the LFT-D line to be compression molded immediately. The fibers in the plastificate are arranged in a spring-like double helix pattern [12-14] which relaxes as soon as the restrictions of the die are removed. This relaxation is called lofting [15] and will lead to an increase in porosity and conversely
a decrease in density [16]. The LFT-D compression molding process alternates from continuous compounding to discontinuous compression molding. This results in the plastificate having an old and a new end and a time gradient in between. Material will be sitting on a heated chain belt until enough material to fill the mold is produced. The material has an age in this process, and both ends of the plastificate can be referred to as old and new end (Fig. 1 a)). Once this amount has been extruded, the new end is formed. Accordingly, the relaxation lasts longest at the old end and the density of the plastificate ρplast  is expected to be lowest here. Two conclusions can be drawn from this. First, the actual density does not equal the calculated density ρcalc of the composite. Second, the center of volume does not coincide with the center of mass [17]. This is described in Fig. 1 a) with the actual length of the plastificate lplast  and the nominal length of the plastificate lplast,calc  if it was pore-free. The pore-content decreases towards the new end, as was shown by Blarr [16]. The distance between the centers of mass and volume created by lofting is called D1.
The plastificate is placed in the open mold and molded under high pressure, usually 200 bar [18]. Shown in Fig. 1 b ) and c) is the side-aligned placement of the plastificate representing a distinct direction of flow (grey arrow). During the compression molding step, the distinct fiber microstructure of an LFT-D compression molding material is formed. A shell-core effect of perpendicularly aligned fiber orientations can be found in the charge area.

[picture]

Fig. 1. Depiction of compounding a), compression molding b), and sampling c) schemes. Plastificate measurement terminology is given in a).

Fig. 1. Depiction of compounding a), compression molding b), and sampling c) schemes. Plastificate measurement terminology is given in a).

Microstructure.

The original version of this paper is available on https://www.scientific.net/SSP.387.9.pdf

The fiber microstructure defines the mechanical properties of the part [19]. A differentiation between charge and flow area (square and triangle in Fig. 1 c )) is regularly considered in the state of the art. Fiber mass content wf is subject to shear-induced migration phenomena and does increase with flow path in the direction of flow (cf. grey arrow Fig. 1 c)) [20,21]. Deviations in fiber orientation as small as 10∘ can result in a significant drop in tensile properties [22]. The fiber orientation in LFTD samples is often reported to be out of 0∘ alignment [23-25]. This was linked to a skewed flow front (cf. sample: flow front in Fig. 1 c)) caused by the distribution of density ρplas in the plastificate [26].

In fact, the density has a gradient ∇ρ from old to new end, being denser at the new end. This gradient is calculated according to Eq. 1 and stands for the density decrease along the length of the plastificate while correcting for different lplast.  It can be understood as an incline.

∇ρ=Δρ/lplast. (1)

The plastificate does link both the compounding and compression molding processes. A discussion of the influence of the main LFT-D processing factors extruder screw speed, polymer throughput, and roving amount on the plastificate and further the flow front skewness sff fiber orientation and wf was presented by Schelleis [27].

Statement of Need and Approach

The original version of this paper is available on https://www.scientific.net/SSP.387.9.pdf

The process-microstructure relation must be described as accurately as possible and is essential for the precise simulation of the material [19,28,29]. In this work the die of TSE2 is the center of the investigation. The die has a fixed width but can be set to different heights and temperatures. An experimental plan is developed where and as well as the closing speed of the press are varied. Plastificates, flow fronts and square plates are produced and characterized (cf. Fig. 1 c for depictions of the samples). Quality criteria of the investigation are ρplas and ∇ρ,sff and wf.

Materials, Processing and Methods

The original version of this paper is available on https://www.scientific.net/SSP.387.9.pdf

Material.

The original version of this paper is available on https://www.scientific.net/SSP.387.9.pdf

In this study a polyamide 6 material, Stabamid S22, with a matching masterbatch was provided by DOMO Chemicals. Glass fiber direct rovings JM StarRov895 were provided by Johns Manville Germany. The PA6 granulate was dried at 85∘C for 120 minutes immediately before processing.

Processing.

The original version of this paper is available on https://www.scientific.net/SSP.387.9.pdf

The materials were compounded on an LFT-D ILC line from Dieffenbacher, Eppingen, Germany. The die is 75 mm wide and set to hdie =24 mm and hdie =38 mm. The die temperature Tdie  is set to Tdie =265∘C and Tdie =280∘C. The primary LFT-D factors, extruder screw speed n TSE, polymer throughput mpol  and roving amount nrov  are kept constant, so the throughput per hour mLFT-D  as well as wf are constant for all set points of the experiment. All relevant processing parameters are shown in Table 1. The time to produce the LFT-D amount needed to fill the mold tLFT-D  is thus the same throughout the experiment. The length of the plastificate lplast  will change when the same amount of material is extruded through a die of smaller area. Selected plastificates are quenched in water for characterization immediately after the cut at the new end is made. Some plastificate are left to cool on their own to characterize the difference.

Table 1. Processing parameters of the LFT-D line and their set points for the experiments.

LFT-D production parameters |  | Set point | Resulting properties
primary | Extruder screw speed nTSE | 67.5 1/min | mLFT-D = 44.5 kg/h wf = 33 %
Polymer amount mpol | 30 kg/h
Roving amount nrov | 16 pcs.
secondary | TSE2 temperature TTSE2 | 280 °C |
TSE2 die temperature Tdie | 265 °C or 280 °C
TSE2 die height hdie | 24 mm or 38 mm

Compression molding was done on a Dieffenbacher DYL 630 parallel guided, hydraulic press. Flow fronts and plates were molded in a 400 mm×400 mm square mold heated to 80∘C (upper mold) and 85∘C (lower mold). Metal spacers were fixed to the mold for the fabrication of flow fronts, halting the molding process at a gap height of approximately 8 mm . The closing profile is given in Table 2 . The closing speed towards the end of the compression molding process is a factor of the experiment
and changed from 5 mm/s to 10 mm/s. The closing speed is maintained for as long as the resistance of the material allows and the press switches to the force-profile. Molding is finished with 3200 kN (200 bar on the square mold). The difference between the profiles is visualized in Fig. 2 as a red curve (10 mm/s) and black curve ( 5 mm/s ).

Table 2. Compression molding profile comprising position of the upper mold relative to the lower mold hmold  and closing speed of the upper mold vpress. 

Gap height mathematical formula

in mm

Closing speed mathematical formula

in mm/s

40 | 80
30 | 40
20 | 30
15 | 5 or 10
0 | 5 or 10

[picture]

Fig. 2. Visualization of the press closing profile.

Fig. 2. Visualization of the press closing profile.

Methods.

The original version of this paper is available on https://www.scientific.net/SSP.387.9.pdf

Density characterizations of the plastificate were conducted by calculation after weighing and 3D scanning the volume [26,30]. The old and new ends are cut off at a length of 30 mm each as they are very rugged and difficult to scan. The density of these cut sections is not representative as the cut at the end of TSE2 staunches the plastificates. The rest is cut into four segments of the same length lseg  along the extrusion direction considering the time gradient. Preliminary investigations have shown that higher resolutions, for example eighths or sixteenths, do not provide any further insights [31]. The effective density of the entire plastificate ρplast  is calculated as a mean value from all four segments. The density of the old end pold has been calculated as a mean from the densities of the two older segments. The same approach was taken with ρnew  at the new end.
Flow fronts are photographed and analyzed. The flow-front skewness s ff value is a representation of the mold being filled unevenly [26]. In Fig. 6 a scheme is given visualizing how to interpret s ff. The flow front can be understood as a function that maps the flow progress over the extrusion length. This function has a minimum where the flow of the material has progressed the least. Here, a horizontal line can be drawn (solid red line in Fig. 6). The area, shown in grey in Fig. 6, between the function and the horizontal line has a center of area. Here, the sff value is a measure of the distance of the center of area to the mold middle (dashed black line in Fig. 6). To characterize the fiber migration, the plate is segmented into 80 mm squares that are burned off in an oven at 650∘C for 12 hours. The wf is calculated from the mass of the polymer lost during this process. [20]

Results and Discussion

The original version of this paper is available on https://www.scientific.net/SSP.387.9.pdf

The results are presented in order of processing, starting with the plastificate, following with the flow fronts and ending with the plates. Unless stated otherwise a minimum number of five samples were taken to ensure sound statistics.

Plastificate.

The original version of this paper is available on https://www.scientific.net/SSP.387.9.pdf

Generally, the cut surfaces can be discussed. Two examples are shown in Fig. 3 a). Noticeably, the cut surface at hdie =24 mm is coarse-pored than at hdie =38 mm. Usual locations for big pores, shown in a), are between the two spiral impressions of the screw threads and on the outside. These pores
increase the volume, lower the density and are a source of uncertainty regarding the volume measurement [16]. In Fig. 3 b) the difference in lplast  resulting from the setting of hdie  can be observed.

[picture]

Fig. 3. Cut plastificate surface perpendicular to the extrusion direction a). Length comparison and results of ρplast  measurement in relation to processing parameters Tdie  and hdie b ).

Fig. 3. Cut plastificate surface perpendicular to the extrusion direction a). Length comparison and results of ρ plast measurement in relation to processing parameters T die and h die b ).

The density measured across four segments is shown exemplary for two settings of hdie =38 mm and hdie =24 mm in Fig. 4. While plastificates vary individually (grey lines) a general trend can be seen once the measurements are averaged (red line). Once a linear fit function is drawn (dashed red line) the tendency is clear, density does decrease towards the old end. The ρplast  at both the new and old ends is shown as a mean value of the densities of the first two and last two segments respectively. The density measurements show a high degree of deviation, especially the results of Fig. 4 b). The general tendencies of the density gradient or linear fit are valid, nonetheless. While the plastificates are quenched immediately after production a minimal amount of handling is required that can lead to changes in the shape and volume accordingly. Generally, at lower densities, the occurrence of big blisters below the shell layer of the plastificate can lead to a higher volume at lower weight and accordingly lower density.

[picture]

Fig. 4. Density measurements of four plastificate segments each for five plastificates along the extrusion direction for varied hdie .

Fig. 4. Density measurements of four plastificate segments each for five plastificates along the extrusion direction for varied h die .

The following Fig. 5 shows ρplast  at the old and new end (checkered) of the plastificate for the parameter variation at the LFT-D line. The cut segments are indicated on the left. Both Tdie  and hdie  are significant parameters influencing ρplast  with hdie  being the stronger influence (6.2% difference in ρplast ). Raising Tdie  from 265∘C to 280∘C lowers ρplast  and rising hdie  from 24 mm to 38 mm increases ρplast.  Lofting can be understood as a relaxation of inner tensions in the plastificate as soon as the confines of the TSE2 die are gone. A colder surface of the plastificate will resist these forces and keep
the shape and volume closer to the dimensions of the die. The ∇ρ is higher at low Tdie  (blue columns) and slightly lower at higher hdie . The influence of Tdie  is stronger.
At wf=33%, ρcalc  of the plastificate would be 1.4 g/cm3, implying a pore content around 45% depending on choice of parameters. If ρplast  is too low, processing can become difficult as needle grippers might lose the plastificate during handling. Also, material degradation is accelerated by a larger surface area as plastificates are open pored when lofting. A noticeable browning of plates can be observed.

[picture]

Fig. 5. Results of ρplast  measurement in relation to processing parameters Tdie  and hdie.  A plastificate with overlayed segments at old and new end is shown on the left.

Fig. 5. Results of ρ plast measurement in relation to processing parameters T die and h die. A plastificate with overlayed segments at old and new end is shown on the left.

Flow front.

The original version of this paper is available on https://www.scientific.net/SSP.387.9.pdf

Influences of hdie ,Tdie  and vpress  on sff  are shown in Fig. 6. A photo of a flow front is shown on the left. The greyed area above the red line is noticeably skewed from the mold middle towards the new end of the plastificate (dashed charge area). The sff value does fluctuate, resulting in a big standard error and considerable coefficient of variation (CV) between 16.5% and 83.3%. A larger die height does consequently decrease the mean sff (arrows). A sff=0 would mean that the material is distributed evenly left and right of the mold middle. The other parameters Tdie  and νpress  are not significant, while step patterns can be seen nonetheless. The sff does greatly depend on ∇ ρ which is also influenced by the factors so an excluding interaction must be considered. Noticeably, the lowest and highest sff are found at Tdie =280∘C. The influence of vpress  is higher at Tdie =280∘C.

[picture]

Fig. 6. The influence of hdie ,Tdie  and vpress  on sfff . The relation of sff  to the old and new plastificate ends in shown on the left.

Fig. 6. The influence of h die , T die and v press on s fff . The relation of s ff to the old and new plastificate ends in shown on the left.

Plate.

The original version of this paper is available on https://www.scientific.net/SSP.387.9.pdf

Fig. 7 features wf for each of the twenty-five 80 mm by 80 mm segments of a plate. The difference is hdie  and resulting from that, lplast . The size of the charge area is noticeably larger for hdie =24 mm shown as a white frame in Fig. 7 a). The fiber-matrix-separation starts as soon as the material starts flowing. The outer areas of the flow freeze on the cold mold wall. The closing mold gap pushes the LFT-D material into the remaining mold cavity. Shear between the static, frozen, material at the mold walls and the accelerating material in the center leads to migration of fiber in the polymer suspension [20,21]. Thus, wf is higher outside the white frames marking the initial charge position. While wf, mean  across the entire plate is the same for hdie =24 mm and hdie =38 mm the CV is higher for hdie =24 mm when calculated from all segments. The plate is segmented into zones of mean fiber content in charge area wf,C, and flow area wf,F, by red-dashed lines. The change in wf across the flow length from charge to flow is greater at hdie =24 mm. Here, there is a difference of 7.1%pt. while it is only 5.5%pt. at hdie =38 mm.
The density gradient as well as the position of the old and new end does not seem to play a role here. The choice of plastificate length through various parameters at the LFT-D line will influence the fiber microstructure in flow direction as well as perpendicular to the flow direction in the charge area (Fig. 7 b)).

[picture]

Fig. 7. Segment-wise distribution of wf for low a) and high b) hdie.

Fig. 7. Segment-wise distribution of w f for low a) and high b) h d i e .

Processing Recommendations

The original version of this paper is available on https://www.scientific.net/SSP.387.9.pdf

Following Fig. 8 is an attempt to visualize the mechanisms behind ∇ρ and processing influences in regards to lofting. The x -axis marks the extrusion time. At tend  the material for one plate is extruded, the plastificate is cut and molded directly. The plastificate is shown on top of the graph with the segments extruded continuously from told  to tend . Lofting starts for every segment once it is extruded, this point in time is for example told  for the oldest segment. Note the extrusion direction from right to left (black arrow). Lofting can be seen as a function describing the decrease in density influenced by time t,Tdie  and hdie  (as well as other factors not discussed here like nTSE  [27]). The shape of this function is assumed to be the same for all segments processed at the same set of parameters only influenced by parameter change. The shape of this function is based on observations that lofting starts and subsides quickly because the tension of the fiber decreases and the outer layer cools down. It is neither a continuous nor linear process. When plastificates are left to cool down uninterrupted the density gradient disappears as all segments loft to a similar extent.
Now considering two segments at the old and new end of the plastificate, this function can be drawn starting at different ti during extrusion of a plastificate. For every segment, and every sub-segment and so on, the lofting starts at different ti. The resulting density gradient ∇ρ can be seen at the time tend  where the plastificate is quenched or molded.

The influence of Tdie  and hdie  on the shape of the function is shown in Fig. 8 b). Temperature stretches or staunches the curve in x-direction, the plastificate decreases its density quicker at higher Tdie  (red arrow in b)) This is shown in Fig. 5 where ρ is lower for higher Tdie . The final extent of lofting is influenced by hdie  mainly where the plastificate, if left to cool on air alone, will have a lower density at a low hdie ( yellow arrow in b)).

[picture]

Fig. 8. Schematic explanation of ∇ρ between two functions a). Influencing factors on function shape from the experimental b).

Fig. 8. Schematic explanation of ∇ ρ between two functions a). Influencing factors on function shape from the experimental b).

The following Fig. 9 is an attempt to serve as a quick reference when dealing with a lofting related challenge in LFT-D compression molding. It is plausible, that ∇ρ depends on both factors hdie  and Tdie  interacting in a way that leads to the results presented in Fig. 5 and Fig. 6. Parameter pairings from hdie  and Tdie  can be chosen in the first column, the color coding is coherent to Fig. 8 where the influence on the ρ function is explained. Two things need to be considered. The extent of lofting and ∇ρ are not dependent on each other. A plastificate can be lofted a lot while not having a big ∇ρ. The combination of a larger die height, high hdie , and high Tdie  will lead to low lofting but quickly. The densities of the segments are at their final value quickly. This means, that when the plastificate is cut, the density of the first and last segments are very similar and ∇ρ is small (Fig. 9 a)).

[picture]

Fig. 9. Visualized influence of parameter settings on lofting and density gradient.

Fig. 9. Visualized influence of parameter settings on lofting and density gradient.

Summary

The original version of this paper is available on https://www.scientific.net/SSP.387.9.pdf

It was established, that the density of LFT-D plastificates does decrease with extrusion time towards its old end that has been out of the extruder the longest [ 30,31 ]. The pore content does increase in turn [16]. The spread of measured density and the difference between the old and new end is influenced by processing parameters especially the opening height of the extruder die and the temperature of the die (cf. Fig. 5). This uneven density distribution in the plastificate does influence mold filling and is known to influence the fiber orientations [26]. The plastificate length is highly influenced by the extruder die height and shown to impact the fiber content distribution across the plate (cf. Fig. 7) [20].
The plastificate is the crucial link between compounding and compression molding. It is directly influenced by the core factors of the LFT-D process such as screw speed, but also by secondary factors as demonstrated in this work. The plastificate does in turn influence important microstructure characteristics such as fiber orientation and fiber content. Further studies could investigate the influence of processing parameters on internal fiber orientation in the plastificate.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/SSP.387.9.pdf

The initial research documented in this manuscript was funded by the Deutsche Forschungsgemeinschaft (DFG) (German Research Foundation), Project Number 255730231, within the International Research Training Group "Integrated engineering of continuous-discontinuous long fiber reinforced polymer structures" (GRK 2078).
The documentation and analysis of the results presented in this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-202400397400).

Support from DOMO Chemicals GmbH, as well as Johns Manville Europe GmbH in the form of trial materials is gratefully acknowledged.

References

The original version of this paper is available on https://www.scientific.net/SSP.387.9.pdf

M. Wackernagel, N.B. Schulz, D. Deumling, A.C. Linares, M. Jenkins, V. Kapos, C. Monfreda, J. Loh, N. Myers, R. Norgaard, J. Randers, Tracking the ecological overshoot of the human economy, Proc. Natl. Acad. Sci. U. S. A. 99 (2002) 9266-9271. https://doi.org/10.1073/pnas.142033699.

F. Henning, E. Moeller, Handbuch Leichtbau: Methoden, Werkstoffe, Fertigung, second Edition, Hanser, München, Wien, 2020.

A. Kampker, H.H. Heimes, Elektromobilität: Grundlagen einer Fortschrittstechnologie, third Edition, Springer Vieweg, Berlin, Heidelberg, 2024.

M. Schemme, LFT - development status and perspectives, Reinforced Plastics 52 (2008) 3239. .

F. Henning, H. Ernst, R. Brüssel, LFTs for automotive applications, Reinforced Plastics 49 (2005) 24-33.

T.A. Osswald, G. Menges, Materials science of polymers for engineers, third Edition, Hanser, Munich, Germany, 2012.

E. Witten, V. Mathes, The European Market for Fibre-Reinforced Plastics and Composites 2022: Market developments, trends, challenges and outlook, AVK Market Report (2023).

C. Schelleis, B.M. Scheuring, W.V. Liebig, A.N. Hrymak, F. Henning, Approaching Polycarbonate as an LFT-D Material: Processing and Mechanical Properties, Polymers (Basel) 15 (2023) 2041-2065. .

C. Schelleis, A. Hrymak, F. Henning, Optimizing processing parameters for glass fiber reinforced polycarbonate LFT-D composites, in: Proceedings of the SAMPE Europe Conference, Madrid, Spain, 2023.

C. Schelleis, F. Henning, A. Hrymak, Comprehensive material development of glass fiber reinforced polyamide 6 LFT-D, in: Proceedings of ITHEC, Bremen, Germany, 2024, pp. 9497.

C. Schelleis, B.M. Scheuring, A. Hrymak, F. Henning, Study on mechanical characteristics of glass fiber-reinforced polycarbonate LFT-D for codico structures, in: Proceedings of the 23rd ICCM, Belfast, UK, 2023.

K. Brast, Verarbeitung von langfaserverstärkten Thermoplasten im direkten Plastifizier-/ Pressverfahren. Dissertation (in german), Aachen, Germany, 2001.

L. Schreyer, J. Blarr, K. Höger, N. Meyer, L. Kärger, Generation of Initial Fiber Orientation States for Long Fiber Reinforced Thermoplastic Compression Molding Simulation, in: Proceedings of the 20th ECCM, Lausanne, Switzerland, 2022.

C. Perez, T. Osswald, S. Goris, Study on the fiber properties of a LFT strand, in: Proceedings of the 13th Annual SPE ACCE, Novi, MI, USA, 2013, pp. 1115-1126.

F. Truckenmüller, H.-G. Fritz, Injection molding of long fiber-reinforced thermoplastics: A comparison of extruded and pultruded materials with direct addition of roving strands, Polymer Engineering & Science 31 (1991) 1316-1329. .

J. Blarr, Development of Computational, Image Processing and Deep Learning Methods for the Microstructure Characterization of Carbon Fiber Reinforced Polyamide 6 Based on CT Images, Karlsruher Institut für Technologie (KIT), 2024.

B.M. Scheuring, N. Christ, J. Blarr, W.V. Liebig, J. Hohe, J. Montesano, K.A. Weidenmann, Experimental and homogenized orientation-dependent properties of hybrid long fiberreinforced thermoplastics, International Journal of Mechanical Sciences 280 (2024) 109470109488. .

F. Henning, Verfahrensentwicklung für lang- und endlosglasfaserverstärkte thermoplastische Sandwich-Bauteile mit geschlossenem Werkstoff-Kreislauf. Dissertation (in german), Fraunhofer-IRB-Verl., Stuttgart, Germany, 2001.

U.N. Gandhi, S. Goris, T.A. Osswald, Y.-Y. Song, Discontinuous Fiber-Reinforced Composites: Fundamentals and applications, Hanser Publishers, Munich, Germany, 2020.

C. Schelleis, L. Meckes, F. Henning, Fiber migration in compression molded LFT-D materials: characterization proposal and first results, in: Material Forming: ESAFORM 2025, Paestum, Italy, Materials Research Forum LLC, 2025, pp. 468-477.

S. Goris, T.A. Osswald, Process-induced fiber matrix separation in long fiber-reinforced thermoplastics, Composites Part A: Applied Science and Manufacturing 105 (2018) 321-333. .

T. Osswald, Understanding Polymer Processing: Processes and Governing Equations, Hanser, Munich, Germany, 2018.

B.M. Scheuring, Effect of hybridization in CoDico-FRTPs: Orientation-dependent characterization and analytical modeling in various climatic conditions. Dissertation, Karlsruhe, Germany, 2024.

F. Garesci, S. Fliegener, Young's modulus prediction of long fiber reinforced thermoplastics, Composites Science and Technology 85 (2013) 142-147. .

J. Blarr, T. Sabiston, C. Krauß, J.K. Bauer, W.V. Liebig, K. Inal, K.A. Weidenmann, Implementation and comparison of algebraic and machine learning based tensor interpolation methods applied to fiber orientation tensor fields obtained from CT images, Computational Materials Science 228 (2023) 112286. .

C. Schelleis, B.M. Scheuring, L. Schreyer, W.V. Liebig, A. Hrymak, L. Kärger, K.A. Weidenmann, F. Henning, Process-induced skewness of flow fronts and fiber orientations in LFT-D compression molding considering processing, characterization, and simulation, Journal of Thermoplastic Composite Materials 38 (2025) 2922-2944. .

C.S. Schelleis, Characterization of Process Influences on Microstructure and Mechanical Properties of Long Glass Fiber Reinforced Polyamide 6 Plates in Compounding and Compression Molding. Dissertation, Karlsruher Institut für Technologie (KIT), Karlsruhe, Germany, 2025.

F. Folgar, C.L. Tucker, Orientation Behavior of Fibers in Concentrated Suspensions, Journal of Reinforced Plastics and Composites 3 (1984) 98-119. .

S. Tröster, Materialentwicklung und -charakterisierung für thermoplastische Faserverbundwerkstoffe im Direktverfahren. Dissertation (in german), Fraunhofer-IRB-Verl., Stuttgart, Germany, 2004.

S. Löwe, Charakterisierung des Plastifikates an der Schnittstelle zwischen Compounding und Fließpressen langfaserverstärkter thermoplastischer Formmassen. Bachelor Thesis (in german), Karlsruhe, Germany, 2022.

L. Meckes, Charakterisierung des LFT-D Plastifikates: Methodenentwicklung und Ermittlung der Prozesszusammenhänge mit Fokus auf die Dichteeigenschaften. Master Thesis (in german), Fraunhofer Society, Karlsruhe, Germany, 2024.

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/composites-forming-processes/978-3-0364-1996-1

Solid State Phenomena, ISSN: 1662-9779, Vol. 387, pp 21-31

doi: 10.4028/p-91sVZi

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

Submitted:
2025-12-19

Revised:
2026-02-14

Accepted:
2026-02-14

Online:
2026-04-14

Stability of Tubular Preforms from Multi-Layer Winding of Composite Tapes Prior to Thermoforming

The original version of this paper is available on https://www.scientific.net/SSP.387.21.pdf

Nagui ELGABY 1,2,a*, Guillaume HELBERT 1,b, Nahiene HAMILA 1,c Ruben CAMPOS 2, d, Audrey SANCHEZ 2,e, Bertrand LAINE 2,f

1 CNRS, IRDL UMR 6027, Bretagne INP, Plouzané, 29280, France

2 OLLOW, 7 Avenue de Guitayne, 33610 Canéjan, France

a  nagui.elgaby@enib.fr, b  guillaume.helbert@enib.fr, c  nahiene.hamila@enib.fr

d rcampos@ollow-tech.com, e asanchez@ollow-tech.com, f blaine@ollow-tech.com

Keywords: Tape winding, Stability, Tubular composites

Abstract

Some composite preforms lose dimensional stability after mandrel removal during tube winding, leading to pronounced longitudinal expansion and radial contraction for specific layup configurations. This work presents an experimental method for tracking the reorganization of individual wound tapes during mandrel release and uses these measurements to validate a finite element (FE) model designed to capture the underlying deformation mechanisms. The experiments reveal a nonlinear dependence of ribbon elongation on the initial winding angle. Complementary FE parametric studies investigate the effects of tape geometry and mechanical properties, demonstrating that increased mechanical anisotropy amplifies the elongation response. The study also introduces a layer-equivalent stiffness formulation and concludes with FE simulations of multi-tape preforms. Together, these results support the development of improved winding strategies aimed at reducing defects, lowering production costs, and enhancing the structural performance of tubular composite components.

Introduction

The original version of this paper is available on https://www.scientific.net/SSP.387.21.pdf

Tubular composite structures are increasingly utilized across sectors such as oil and gas, civil engineering, automotive, and aerospace, owing to their high specific strength and stiffness, which make them well suited for lightweight load bearing applications [1]. Their corrosion and chemical resistance further enable use in demanding environments, including fluid-transport pipelines and storage vessels. Industrial adoption has grown rapidly in the past decade, driven by the need for durable, high-performance, and lightweight components.

A variety of manufacturing techniques are employed to produce tubular composites, ranging from manual fabrication to highly automated processes [2-5]. Established methods such as filament winding, Automated Fiber Placement (AFP), and Pull winding provide different benefits in terms of fiber control, production speed, and geometric versatility. Despite this progress, key challenges persist, including maintaining consistent part quality (e.g., minimizing voids and fiber misalignment), reducing overall manufacturing costs, and mitigating the environmental impact of composite fabrication and disposal.

To address these manufacturing challenges, new process concepts are being developed to enhance efficiency, quality, and sustainability. Among them is OLLOW's tape-winding approach [6], which draws inspiration from filament winding and automated fiber placement. In this method, unidirectional thermoplastic tapes are helically deposited onto a solid mandrel at prescribed angles withoutimmediatematrix consolidation. The process is illustrated in Figure 1.Local weld points at the preform ends provide sufficient stability for handling. After winding, the preform is removed from the mandrel and transferred into a rigid metallic mold, where high pressure using an internal silicone membrane, and temperature are applied to produce the final straight or curved tube (Figure 2).

This decoupled winding/consolidation workflow offers notable advantages over traditional techniques. Automated tape winding improves repeatability and dimensional accuracy, while separating consolidation from deposition shortens production cycles and supports high-throughput
manufacturing. The reliance on thermoplastic matrices further decreases cycle times [7] and enables recyclability, reducing the overall environmental impact. As a result, this process shows strong promise for scalable, cost-efficient, and sustainable production of tubular composite structures across diverse industrial sectors.

[picture]

Fig. 1. The manufacturing process involves two main stages: dry winding onto the mandrel and thermoforming inside a rigid die, using heat and pressure.

Fig. 1. The manufacturing process involves two main stages: dry winding onto the mandrel and thermoforming inside a rigid die, using heat and pressure.

[picture]

Fig. 2. A bicycle handlebar constructed from several consolidated tubes, each featuring a different cross-sectional shape

Fig. 2. A bicycle handlebar constructed from several consolidated tubes, each featuring a different cross-sectional shape

Although the technology offers clear advantages, it is not without challenges. A key issue examined in this study is the loss of dimensional stability that occurs when the preform is removed from the mandrel. Certain layup configurations, particularly specific combinations of winding angles, exhibit pronounced instability upon release: the preforms undergo significant axial expansion and radial contraction, triggering a rapid geometric collapse. This sudden deformation forces the individual tapes to reorganize, disrupting the intended fiber orientations and compromising the mechanical properties of the final part. In severe cases, as illustrated in Figure 3, the preforms become unusable and must be discarded. While industrial mitigation strategies exist, they typically require adding extra tape layers to stabilize the layup, which increases cycle time, defect risk, and final component weight. A deeper understanding of the instability mechanisms of helically wound tapes is therefore essential for reducing scrap and improving overall manufacturing efficiency.

[picture]

Fig.3. Upon removal from the mandrel, some preforms undergo significant axial expansion and a reduction in their inner diameter

Fig.3. Upon removal from the mandrel, some preforms undergo significant axial expansion and a reduction in their inner diameter

Helical stability of slender structures has been investigated in the literature. It has been demonstrated that certain helical configurations of elastic rods remain stable only when their length is sufficiently short, typically for helices comprising less than one full revolution [8-10]. Borum et al. [11] analyzed the stability of helical equilibria of inextensible, isotropic Kirchhoff rods clamped at both ends, employing the conjugate point method to determine the maximum stable length of a helical equilibrium. The stability of strips/rods with asymmetric cross sections has also been addressed in previous studies. Goriely et al. [12] showed that only configurations without intrinsic twist, with the principal bending direction aligned with either the normal ( fr2― in figure 2) or binormal vector ( fr3― in
figure 2) of the local Frenet frame, can form helical equilibria from an initially straight state. In this context, wound ribbons can be interpreted as binormal strips. According to Goriely et al. [13], infinitely long binormal strips bent into a helical configuration are inherently unstable, whereas finitelength strips can attain stability if their cross-section is sufficiently flat, that is, if the thickness to width ratio is small. Additional studies have explored instability in helically deformed structures arising from mechanical anisotropy, where geometric or material parameters govern spontaneous shape transitions [14-15]. The stiffness of helices under tension has also been quantified: Starostin et al. [16] derived an expression for the equivalent spring stiffness as a function of helix angle and crosssectional properties, later validated experimentally by Prévost et al. [17]. These findings collectively indicate that helix stiffness decreases with unwinding length, helix angle, and torsional-to-bending stiffness ratio.

Despite these advances, the stability of helically wound ribbons composed of anisotropic materials remains largely unexplored. The mechanical response of such ribbons upon release of winding constraints is, to date, insufficiently studied.

This study examines how wound tapes reorganize during preform release, focusing first on the displacement of individual tapes under varying properties. An experimental setup is developed to replicate mandrel removal, and its results are used to validate a numerical model. The work also introduces the concept of layer equivalent stiffness to clarify how multi-layer behavior emerges and concludes with simulations of full multi-layer preforms to assess their global stability during release.

Stability of the Individual Composite Tape

The original version of this paper is available on https://www.scientific.net/SSP.387.21.pdf

Before qualifying the instability problem on the scale of the preform, the phenomenon is studied at the scale of an individual carbon fiber reinforced polymer (CFRP) tape. Numerical as well as experimental procedures are designed in this work to reproduce this behavior. An original experimental setup was built and used to simulate the winding and releasing of the tapes. Its results were then compared to numerical ones using the Finite element (FE) software ABAQUS. The stress state and deformation mechanisms were also evaluated using the numerical model, which was later used to conduct a parametric study of the tape geometric and mechanical properties.

[picture]

Fig. 4. An individual tape wound onto a mandrel, along with the corresponding geometric properties

Fig. 4. An individual tape wound onto a mandrel, along with the corresponding geometric properties

The considered geometric properties of the tapes are the following: tape winding angle α, winding radius R , tape width w , tape thickness t , tape unwound length L , number of revolutions Nrev , and tape wound height H . The parameters α,R, and H will evolve once the mandrel is released. For this reason, quantities referring to the as-wound configuration (prior to mandrel removal) are denoted with a subscript 0 . The initial winding angle α0 (cf. Figure 4) is defined as the angle between the tape's tangent vector (expressed in the conventional Frenet frame of the helix shown in the figure) and the mandrel axis. Consequently, α0=0∘ corresponds to a tape laid flat and parallel to the mandrel axis, whereas α0=90∘ corresponds to a tape wound into a closed ring. These parameters are related through the helix geometry given in Eq. 1, which hold for any regular helix.

{L=2πNrevR01sin(α0)H0=2πNrevR0tanα0(1)

Experimental procedure. The experimental setup presented in Figure 5 was specifically designed to simulate the releasing phase of the preforms from the mandrel. It consists of two plastic wire supports through which nylon wires are threaded. The wires are fixed to one support (the left support in Figure 5) and tensioned at the opposite end using a wire-tensioning mechanism. This mechanism includes a disc to which the wires are bolted; the disc is actuated by a pulley system to apply tension. Under tension, the wires form a quasi-mandrel onto which the tapes are wound. The design of both the wire supports and the tensioning system enables the creation of a fully collapsible mandrel.

Each support contains a set of evenly spaced holes along the perimeter of a circle corresponding to the desired winding radius. When the wires are tensioned, they assume this circular geometry, whereas releasing the tension causes the mandrel to collapse entirely. The winding radius used in all experiments is 20 mm .

The tape is secured in the wound configuration at two fixation regions. At segment A, the tape is rigidly clamped using a bolted fixture, constraining all degrees of freedom (DoF). At segment B, the tape is mounted on a linear slide and fixed with bolts such that only axial translation along the mandrel direction (the z-axis) is permitted. After winding, the wire tensioning system is released, allowing the mandrel to collapse and enabling the tape to translate along the linear slide. Depending on the winding angle and tape geometry, this results in either an increase or a decrease in the tape height.
The test is performed 3 times and the elongation of the ribbon ΔH is then averaged. Finally, the relative elongation ΔH/H0 is then reported for each winding angle.

[picture]

Fig.5. Experimental setup for releasing individual wound tapes

Fig.5. Experimental setup for releasing individual wound tapes

Carbon fiber reinforced thermoplastic tapes with a polyamide-6 matrix (CF-PA6) are investigated in this study. They are referenced to as CF6 tapes in this work. The transversely isotropic elastic properties and cross-sectional geometrical characteristics of the tapes are summarized in Table 1, where Vf denotes the fiber volume fraction. The mechanical properties of the constituent fibers and matrix were obtained from literature sources [18,19] and from manufacturers' datasheets. These properties were subsequently used to derive the effective transversely isotropic properties of the unidirectional tapes using classical rule of mixtures formulations, which provide an adequate representation for unidirectional composite laminates [18].
Experimental tests were conducted for initial winding angles α0 ranging from 20∘ to 70∘ with a single revolution ( Nrev=1 ). Smaller winding angles could not be investigated for larger values of Nrev due to geometric limitations of the experimental setup. Moreover, for increasing α0, a higher number of revolutions significantly amplifies ribbon instability, rendering the post-release response highly
sensitive to experimental conditions such as release rate, ambient vibrations, and small variations in the initial angle. As previously reported in [11,12], helically wound structures exhibit pronounced instability as their length increases. Consequently, instability becomes a limiting factor for experiments involving large Nrev . To mitigate these effects, the mandrel was released quasi-statically during the experiments to promote the formation of the elongated ribbon configuration. Rapid release of the wire-tensioning system was found to introduce vibrations that strongly affect the response in highly unstable configurations. Gradual release of the applied tension effectively suppresses these unwanted vibratory effects.

Tape specimens were cut to the exact required length using the geometric relations given in Eq. 1. Prior to winding, the samples were kept straight under applied weights to ensure an initially straight configuration. The tapes were then mounted onto the experimental setup by bolting their extremities at fixing segments A and B . The initial winding angles were measured using a protractor in these fixing regions. Before mandrel release, the wound height H0 and mandrel radius R0 were also measured to verify the accuracy of the prescribed initial winding angle.

Numerical model. A finite element model is developed using the commercial software ABAQUS to simulate the mechanical response of individual composite tapes. The numerical results are subsequently compared with the corresponding experimental observations.
A dedicated pre-processing stage is implemented using a Python script to generate the spatial trajectories of the tape nodes and to construct the finite element mesh. Based on this information, an ABAQUS input file is automatically generated, containing all geometric, material, and boundarycondition data required for the simulations.
The analysis is performed using two quasi-static steps with geometric nonlinearity enabled to account for large displacements and rotations. Both steps are solved using an implicit time-integration scheme. In the first step, the initially straight, stress-free tape is clamped at one extremity, corresponding to segment A in the experimental setup, while prescribed displacement boundary conditions, computed by the Python pre-processing code, are applied to impose the wound configuration. This winding step is illustrated in Figure 6.

[picture]

Fig. 6. The tapes are helically wound in the first step using displacement control of each node

Fig. 6. The tapes are helically wound in the first step using displacement control of each node

In the second step, the prescribed displacement boundary conditions are removed to simulate mandrel release. The node set at the left extremity of the tape (segment A) is constrained in all DoFs. The right extremity of the tape (segment B) is then constrained in all DoFs except for translation along the mandrel axis (the z -direction), reproducing the experimental boundary conditions during release. The boundary conditions on the tape after completion of the winding step are chosen in accordance to the experimental ones. The tapes are meshed using quadrilateral shell elements. The mesh is constructed such that all element edges have equal length, ensuring uniform discretization. The number of elements across the tape width, denoted Nelements/width, is selected as the primary meshing parameter.  A mesh convergence study is therefore performed to determine an appropriate discretization level. Convergence with sufficient accuracy is achieved for Nelements/width =14, which is retained for the
remainder of the study as a compromise between numerical accuracy and computational cost. Linear elastic law with lamina anisotropic material section is created for transversely isotropic materials. The material properties used are taken from table 1 for model validation and for further parametric analysis.

Table 1. Tape geometric and mechanical properties

Material

t

w

CF-PA6 | 0.15 | 12.7 | 48 | 110 | 4.19 | 0.29 | 1.54 | 1.53

[picture]

Fig. 7. Experimental (left) and numerical results validation (right) of the CF6 tape

Fig. 7. Experimental (left) and numerical results validation (right) of the CF6 tape

Results Figure 7 shows the results of releasing of the tape CF6. As shown in Figure 7 (left), the reported experimental results predominantly exhibit a positive relative elongation following mandrel release. Small negative elongation is observed only in a limited number of cases, primarily for small initial winding angle α0=20∘, where measured elongation reaches −0.04%. A pronounced increase in the elongation response is observed with increasing α0 indicating a strong dependence of the postrelease deformation on the initial winding angle. In addition, increasing the number of revolutions Nrev  leads to a larger elongation response, manifested by higher curves. The response also becomes progressively smoother and less affected by experimental noise as Nrev increases.
Because both extremities of the ribbon are rotationally clamped, the achievable elongation is constrained by boundary effects. However, increasing the number of revolutions introduces a larger central region of the helix that is less influenced by these constraints and can deform more freely. Consequently, the influence of the boundary conditions is progressively reduced as Nrev increases. To validate the numerical model, finite element (FE) results are compared with experimental measurements for the case Nrev=1, which remains numerically stable across all investigated winding angles.
The numerical and experimental results are plotted together in Figure 7 (right). A good agreement is observed between the FE predictions and experimental measurements of relative elongation, both qualitatively and quantitatively, supporting the validity of the proposed numerical model.
Upon release from the mandrel, the ribbon evolves toward a configuration that minimizes its internal strain energy. Under the imposed boundary conditions, this relaxation occurs through changes in the helix height H , radius R , and winding angle α, which are the only geometric parameters free to evolve.

Effect of geometric and mechanical properties of the tape. The study has concluded the following after a parametric study of the tape mechanical and geometric properties:

The initial winding angle α0 has a strong influence on the mechanical response of the ribbons. For the composite tape considered in this study, the response becomes increasingly sharp as α0 increases. The relative elongation-winding angle curves exhibit a pronounced nonlinear behavior. In particular, for larger winding angles, the composite tapes undergo significantly higher elongation.

The cross-sectional geometry of the tape significantly influences its mechanical response. An increase in tape width is found to reduce the relative elongation, particularly at larger winding angles. In contrast, variations in tape thickness within the investigated range do not produce a noticeable effect on the elongation response.

The winding radius has a positive influence on the elongation response of the composite tapes, particularly at larger winding angles. Analysis of the post-release angular profiles for tapes with different initial winding radii indicates the presence of a preferred equilibrium angle in the high angle regime. The equilibrium angle depends on the mechanical and geometric properties of the tape, and is typically in the range of 20∘ to 30∘. Reaching this equilibrium configuration requires larger axial elongation than that of smaller radii, which explains the increased elongation observed for larger winding radii.

The influence of the mechanical properties in the longitudinal and transverse directions was investigated through a parametric variation of the material constants. In the longitudinal direction, an increase in the Young's modulus E1 enhances the bending stiffness of the tape, thereby amplifying the elongation response. Conversely, increasing the transverse modulus E2 has a mitigating effect on the response.

The in-plane shear modulus G12 is found to significantly limit the elongation for larger initial winding angles α0. Since G12 directly contributes to the torsional stiffness of the tape, increasing its value enhances the resistance to reductions in the helix angle during release, a deformation mode associated with higher torsional strains. Consequently, larger values of G12 suppress the elongation response in the high angle regime.

Layer Equivalent Stiffness

The original version of this paper is available on https://www.scientific.net/SSP.387.21.pdf

According to the results observed experimentally and obtained numerically, the wound strips behave like a loaded spring, either in tension or compression. When the mandrel is released, these springs return to their length at equilibrium. It would be interesting to evaluate the equivalent stiffness of these springs keq, which would allow us to predict the behavior of a preform when several tapes coexist. At first approximation, it can be assumed that the multi-tape preform consists of several springs in parallel, with different stiffnesses and equilibrium lengths. Starostin et al. [16] derived an analytical expression for the equivalent axial stiffness of a helical spring, expressed as a function of the helix angle as well as the cross-sectional geometric and mechanical properties. Their formulation accounts for the coupling between torsional and bending deformations inherent to helical geometries, thereby providing a comprehensive description of the global mechanical response under axial loading. Subsequently, Prévost et al. [17] experimentally validated this theoretical model, confirming its predictive capability across a range of geometrical configurations. The combined theoretical and experimental results demonstrated that the effective stiffness of a helical coil systematically decreases with increasing unwound length, increasing helix angle (winding angle), and with reductions in both torsional and bending stiffnesses of the constituent material. In the context of the present work, this framework is adapted to describe the stiffness of helically wound ribbons, wherein similar geometric and mechanical dependencies govern the effective stiffness response. The corresponding expression, tailored to the assumptions and boundary conditions of the current study, is presented in Eq. 2.

kribbon =[2π NrevR03sin2(α0)(1csin2(α0)+1 Bcos2(α0))]−1(2)

where B and C represent the bending and torsional stiffness, respectively. The equivalent spring stiffness kribbon relates the force that the ribbon exerts in its extremities in its wound state, to the displacement in the axial direction when the winding boundary conditions are released. This expression reveals that the equivalent stiffness of the strips decreases as α0 increases. Consequently, using smaller angles in the preform results in a 'stiffer' tape that stretches relatively less. In order to compare this expression to our case, a study was conducted using the FE model. After winding, a reference point in kinematically tied to the fixing segment B . The axial reaction force of the Reference

Point (RP) at the end of the ribbon RF3 is measured in the as wound state. It represents the force that the ribbon exerts in the axial direction when wound.

[picture]

Fig.8. Method for calculating the equivalent wound tape stiffness

Fig.8. Method for calculating the equivalent wound tape stiffness

The displacement U3 of the same RP is measured after release of the winding boundary conditions. The equivalent stiffness kFE is calculated using the following formula:

kFE=RF3U3(3)

[picture]

Fig. 9. Evolution of the equivalent stiffness of wound strips: comparison of the analytical and numerical models.

Fig. 9. Evolution of the equivalent stiffness of wound strips: comparison of the analytical and numerical models.

Figure 9 shows a comparison of the analytical stiffnesses calculated by [16] and [17] and that calculated by the finite element model. We can see the same non-linear decreasing trend in stiffness with the winding angle. This clearly shows that the larger angles that elongate the most have the lowest stiffness. On a complete preform, smaller angles that elongate less and have greater stiffness would need to be combined to prevent or limit this elongation.

Stability of Preforms

The original version of this paper is available on https://www.scientific.net/SSP.387.21.pdf

After studying the stability of ribbons on a simple scale, more complex cases can be examined. Preforms consisting of several ribbons can be simulated using the same finite element model described above, in order to analyze the influence of combining different winding angles. In this study, a preform consisting of two layers of five ribbons is simulated. The initial height of the preforms H0 is set at 300 mm . The objective is to observe the interaction between layers with different winding angles. The ribbons used to manufacture the preforms have the same properties as those given in Table 1. After the winding step, surface-to-surface contact is activated between the two layers of the preform, and the layers are welded together at their ends using rigid connectors. The purpose of this welding is to connect all the ribbons in such a way as to prevent excessive isolated displacement of an individual ribbon, which could lead to numerical instability and divergence of the
analysis. The winding orientation is alternated for each layer (right-hand helices in the first layer and left-hand helices in the second layer), and the ribbons are positioned to minimize gaps in the preform. The smallest angles are always placed in the inner layer.

[picture]

Fig. 10. Overview of the simulated preforms: The layups are laid in pairs of as-wound (left) and released (right). From left to right the preforms are.

Fig. 10. Overview of the simulated preforms: The layups are laid in pairs of as-wound (left) and released (right). From left to right the preforms are.

Preform A : [5 ×20∘,5×−40∘ ] ΔH=−0.2 mm, Preform B : [ 5×30∘,5×−40∘ ] ΔH=5.7 mm,
Preform C : [5×40∘,5×−40∘]ΔH=9.5 mm, Preform D : [5×40∘,5×−50∘]ΔH=34.0 mm
The reference preform studied has winding angles of 40∘ in the inner layer and −40∘ in the outer layer. Other preform configurations are compared to this one. The following layups are simulated: [5×20∘,5×−40∘],[5×30∘,5×−40∘],[5×40∘,5×−40∘] and [5×40∘,5×50∘]. The results are presented in Figure 10, where each wound preform is illustrated alongside its deformed configuration after removal of the imposed displacements. As expected, the preform with the most dimensional stability is the first one, consisting of tapes at 20∘(ΔH=−0.2 mm). As shown previously, small winding angles result in the lowest elongation values. As shown in Figure 10, axial expansion occurs gradually for increasingly higher angles. The phenomenon of preform collapse is evident for the last preform on the right, which has the highest pitch angle ( ΔH=34 mm ). Compared to the reference preform, the combination of 20∘ strips limits expansion by up to 99%. By combining different angles, the structural behavior of the preform can be compared to that of a parallel spring system. By examining the equivalent spring stiffness of each wound ribbon, the final expansion of the preform can be estimated. This proves that combining lower angles with higher ones would limit the instability greatly.

Conclusion

The original version of this paper is available on https://www.scientific.net/SSP.387.21.pdf

In this work, the post-release behavior of helically wound ribbons was examined through a combined experimental and finite element (FE) approach. The FE model was validated against the experimental measurements and subsequently employed to identify the principal factors governing the axial response of the structure. The results demonstrate that both the mechanical properties and the crosssectional geometry of the ribbon play a critical role in determining the axial displacement that develops following mandrel removal. An equivalent spring stiffness associated with a wound ribbon was derived, providing a first order approximation of the instability of a multi-layer preform, modeled as a system of parallel springs with distinct stiffnesses and rest lengths. The validated FE model was further used to simulate the behavior at the scale of complete preforms, revealing the interactions between layers wound at different angles. These simulations confirm that the inclusion of layers with smaller winding angles contributes to an overall increase in the stability of the layup.

Future research should address multi-layer preforms, as combining tapes of different materials or cross-sectional shapes offers a potential strategy for controlling structural instability. The influence of inter-layer friction should also be considered, as frictional interactions may restrict relative sliding between layers and thus affect the collapse behavior. Additionally, tailored welding strategies, such as welding unstable layers to more stable ones, could further enhance the dimensional stability of the final preform.

References

The original version of this paper is available on https://www.scientific.net/SSP.387.21.pdf

S.M. Kennedy, R.J. Robert, R.M.R. Prince, G. Hikku and M. Kaliraj, "A comprehensive overview of the fabrication and testing methods of frp composite pipes," MethodsX, vol. 13, p.102990, 2024.

J. Singh, K. Singh, J. S. Saini, and Mohammed S.J. Hashmi, "Processing of Polymers and Their Composites: A Review," Elsevier eBooks, pp. 577-603, Jan. 2018.

M. Azeem, H. H. Ya, M. A. Alam, M. Kumar, P. Stabla, M. Smolnicki, L. Gemi, R. Khan, T. Ahmed, Q. Ma et al., "Application of filament winding technology in composite pressure vessels and challenges: a review," Journal of Energy Storage, vol. 49, p. 103468, 2022.

A. Brasington, C. Sacco, J. Halbritter, R. Wehbe, and R. Harik, "Automated fiber placement: a review of history, current technologies, and future paths forward," Composites Part C: Open Access, vol. 6, p. 100182, 2021.

M. Volk, O. Yuksel, I. Baran, J. H. Hattel, J. Spangenberg, and M. Sandberg, "Cost-efficient, automated, and sustainable composite profile manufacture: A review of the state of the art, innovations, and future of pultrusion technologies," Composites Part B: Engineering, vol. 246, p. 110135, 2022.

B. Laine and A. Azran, "Device for the manufacturing of an unconsolidated textile elongate member." Patent WO 2023/089 051 A1, WO 2023/089051 A1. May, 2023.

M. Kropka, M. Muehlbacher, T. Neumeyer, and V. Altstaedt, "From ud-tape to final part-a comprehensive approach towards thermoplastic composites," Procedia CIRP, vol. 66, pp. 96100, 2017.

N. Chouaieb, A. Goriely, and J. H. Maddocks, "Helices," Proceedings of the National Academy of Sciences, vol. 103, no. 25, pp. 9398-9403, 2006.

A. Majumdar and A. Raisch, "Stability of twisted rods, helices and buckling solutions in three dimensions," Nonlinearity, vol. 27, no. 12, p. 2841, 2014.

A. Goriely and M. Tabor, "The nonlinear dynamics of filaments," Nonlinear Dynamics, vol. 21,pp. 101-133, 2000.

A. Borum and T. Bretl, "When is a helix stable?" Physical review letters, vol. 125, no. 8, p. 088001, 2020.

A. Goriely, M. Nizette, and M. Tabor, "On the dynamics of elastic strips," Journal of Nonlinear Science, vol. 11, pp. 3-45, 2001.

A. Goriely and P. Shipman, "Dynamics of helical strips," Physical Review E, vol. 61, no. 4, p. 4508, 2000.

Q. Guo, A. K. Mehta, M. A. Grover, W. Chen, D. G. Lynn, and Z. Chen, "Shape selection and multi-stability in helical ribbons," Applied Physics Letters, vol. 104, no. 21, 2014.

Z. Chen, "Shape transition and multi-stability of helical ribbons: a finite element method study,"Archive of Applied Mechanics, vol. 85, pp. 331-338, 2015.

E. Starostin and G. Van Der Heijden, "Tension-induced multistability in inextensible helical ribbons," Physical review letters, vol. 101, no. 8, p. 084301, 2008.

L. Prévost, A. Lindner, and O. Du Roure, "Pitch controls the flexibility of helical ribbons," Extreme Mechanics Letters, vol. 58, p. 101923, 2023.

L. N. McCartney, "Predicting properties of undamaged and damaged carbon fibre reinforced composites," in The Structural Integrity of Carbon Fiber Composites: Fifty Years of Progress and Achievement of the Science, Development, and Applications. Springer, 2016, pp. 425-467.

E. Kurkin, M. Spirina, O. U. Espinosa Barcenas, and E. Kurkina, "Calibration of the pa6 shortfiber reinforced material model for to carbon mass fraction mechanical characteristic prediction," Polymers, vol. 14, no. 9, p. 1781, 2022.

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/composites-forming-processes/978-3-0364-1996-1

Solid State Phenomena, ISSN: 1662-9779, Vol. 387, pp 33-41

doi: 10.4028/p-fNrp4N

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

Submitted:
2025-12-19

Revised:
2026-02-16

Accepted:
2026-02-16

Online:
2026-04-14

Influence of Fiber Orientation Clustering Methods in Virtual Process Chains of Discontinuous-Fiber Composite Components

The original version of this paper is available on https://www.scientific.net/SSP.387.33.pdf

Louis Schreyer a*, Luca Salvatore Pusineri Yaluff b, Constantin Krauß c, and Luise Kärger d

Karlsruhe Institute of Technology (KIT), Institute for Vehicle System Technology (FAST) Lightweight Engineering, Rintheimer Querallee 2, 76131 Karlsruhe, Germany

a  louis.schreyer@kit.edu, b  luca.yaluff@student.kit.edu, c  constantin.krauss@partner.kit.edu, dluise.kaerger@kit.edu

Keywords: Fiber Orientation, Clustering, Molding Simulation, Structural Simulation

Abstract

In virtual process chains for discontinuous fiber-reinforced polymers, clustering of fiber ori-entation tensors reduces the number of macroscopic material cards required for downstream structural and warpage simulations. However, it remains unclear whether including the additional information provided by the fourth-order fiber orientation tensor improves clustering quality. This study investi-gates the influence of second-order vs. fourth-order informed clustering on clustering outcomes and the resulting orientation-averaged mechanical properties. Using parameterizations based on harmonic decomposition, rotation-invariant clustering is performed in both the secondorder and fourth-order parameter spaces. Results from injection molding simulation data indicate that the level of tensorial information has limited effect when the fourth-order tensor is computed via a closure approximation, as the deviatoric parameters are nonlinearly dependent on the secondorder parameters. In contrast, the choice of clustering algorithm - KMeans vs. Birch - has a more pronounced influence on cluster shapes and allocations. Furthermore, we demonstrate that clustering affects orientation-averaged stiff-ness properties, with deviations most pronounced near cluster boundaries and rarely occurring tensor shapes.

1. Introduction

The original version of this paper is available on https://www.scientific.net/SSP.387.33.pdf

In virtual process chains for discontinuous fiber-reinforced polymers (DiCoFRP), the fiber orientation is obtained from mold filling simulations [1-3]. This orientation information is transferred to down-stream warpage or structural analyses using appropriate mapping techniques [4]. The local macro-scopic properties at each material point are then determined through meanfield homogenization and orientation averaging based on the corresponding (local) fiber orientation, resulting in a unique ma-terial card per material point. However, the fiber orientation field often contains regions with similar tensorial shapes. Grouping these can drastically reduce the number of required material cards - with-out compromising model fidelity - thus simplifying pre- and postprocessing. An efficient description for fiber orientation is given by the fiber orientation tensor (FOT) A⟨k⟩ of k th order following the works of [5] and [6]:

A⟨k⟩=∫𝒮2Ψ(p)p⊗k dS(1)

where Ψ(p) is the fiber orientation distribution function (FODF), p∈𝒮2 describes a fiber's orientation and 𝒮2 is the unit sphere. Thus, the FOT represents the statistical moments of the FODF. While molding simulations generally employ the second-order FOT A for computational efficiency, orientation average of mechanical properties require fourth-order FOT 𝔸. The latter are obtained from second-order tensors using a closure approximation.

A common approach for grouping FOT is KMeans clustering of the eigenvalues of the secondorder FOT [7]. In the context of composites, the KMeans algorithm has also been applied to plastic strain data [8]. An alternative non-adaptive approach is provided by triangularization of the eigenspace [9]. However, these works do not address whether including information from the fourth-order FOT
in the clustering procedure improves the grouping quality. Similar to the influence of the closure and mapping sequence in CAE chains [10], the level of tensorial information considered may affect clustering behavior and, consequently, the accuracy of the predicted (orientation-averaged) mechanical properties. This motivates the central question of this study: Does fourth-order informed clustering, which incorporates the additional information provided by the fourth-order FOT, yield improved results compared to second-order informed clustering? To this end, we study and quantify the differences between these clustering approaches based on the resulting fourth-order FOT. Furthermore, we demonstrate the implications of clustering on the orientation-averaged linear elastic stiffnesses.

This contribution is structured as follows. First, we introduce parameterizations of the secondand fourth-order FOT based on harmonic decomposition, which enable rotation-invariant clustering. Then, we describe the clustering procedure and compare the two approaches - second-order informed and fourth-order informed clustering - using orientation fields from injection molding simulations. Finally, we discuss the implications of clustering on orientation-averaged mechanical properties.

2. Parameterization of Fiber Orientation Tensors

The original version of this paper is available on https://www.scientific.net/SSP.387.33.pdf

A parameterization of the rotation-invariant shape of the second-order FOT A is given by its eigenvalues λi,i=1..3, with λ1≥λ2≥λ3 and corresponding eigenvectors vi :

A(λ1,λ2)=λ1v1⊗v1+λ2v2⊗v2+(1−λ1−λ2)v3⊗v3,(2)

where we use the normalization condition ∑i3λi=1. Thus, the shape of A is fully described by its two largest eigenvalues λ1 and λ2. The admissible set of eigenvalues is known as the orientation triangle, with the extrema: unidirectional (UD; λ1=1 ), planar isotropic ( λ1=λ2=1/2 ), and isotropic ( λi=1/3 ). The transformation between the orientation coordinate system (OCS) {vi} spanned by the eigenvectors vi of A and a fixed global coordinate system {ei} is given by the orthogonal basis transformation Q=vi⊗ei.

An alternative representation of the rotation-invariant shape is given by the non-orthogonal decomposition following [11] as

A(α1,α3)=Aiso+α1Htv1+α3Htv3,(3)

with Aiso =I/3, the second-order identity tensor I, the transversely isotropic deviatoric structure tensors

Htv1=(100−1/20 sym −1/2)vi⊗vj and Htv3=(−1/200−1/20 sym 1)vi⊗vj,(4)

and

0≤α1≤1 and α12−13≤α3≤0.(5)

The isotropic state corresponds to α1=α3=0. For positive α1, a more aligned fiber orientation state in v1-direction is achieved, while α3 describes the deviation towards the planar isotropic orientation in v3-direction. Visualizations of these parameterizations can be found in [11].

Based on harmonic decomposition [12, 13], [11] present a parameterization for arbitrary fourthorder FOT. This decomposition contains a constant isotropic part 𝔸iso =1/5sym(I⊗I), a deviatoric part linear in A and a deviatoric fourth-order structure tensor ℍ :

𝔸=𝔸iso +(6/7)sym(A′⊗I)+ℍ,(6)

with the operator sym ( ⋅ ) denoting full index symmetrization. The deviatoric structure tensor ℍ has at most nine degrees of freedom di,i=1..9, within the OCS, corresponding to triclinic material
symmetry. Since common fourth-order closure approximations, e.g., [14], preserve the orthotropic symmetry of A, we restrict to ℍortho =ℍ(d1,d2,d3,0,0,0,0,0,0), which within a Mandel basis Bξv with ξ=1..6 spanned by the OCS is defined as [11]:

ℍ(d1,d2,d3)·(Bξv⊗Bζv)=().(7)

Thus, the orientation-invariant shape of an orthotropic fourth-order FOT within the OCS is described by the five parameters: α1,α3,d1,d2, and d3. In an engineering context, the first two parameters directly follow from the second-order FOT known from molding simulations, while the last three are estimated by an (orthotropic) closure approximation:

(d1,d2,d3)≈fClosure (α1,α3),f:ℝ2→ℝ3. (8)

The nonlinear mapping of the (orthotropic) IBOF closure [14] is shown in Figure 1.

[picture]

Fig. 1: Nonlinear mapping of the deviatoric parameters d1,d2,d3 as a function of α1 and α3 for the IBOF closure [14].

Fig. 1. Nonlinear mapping of the deviatoric parameters d 1 , d 2 , d 3 as a function of α 1 and α 3 for the IBOF closure [14].

3. Clustering Procedure

The original version of this paper is available on https://www.scientific.net/SSP.387.33.pdf

As discussed above, grouping FOT with similar tensorial shapes reduces the number of material cards required in downstream simulations. The two approaches for performing this data reduction are depicted in Figure 2. In both cases, the FOT field obtained from molding simulations AI(Src) , transformed to the OCS, is the starting point for obtaining the clustered fourth-order FOT \AA^J(Cluster) . In fourth-order informed clustering (top row), the fourth-order fiber orientation tensor 𝔸I(Src) is first approximated via a closure, which then serves as input for clustering. In contrast, in second-order informed clustering (bottom row), AI(Src) is clustered directly, and the closure is subsequently applied to the clustered secondorder FOT A^J(Cluster ) to obtain 𝔸^J(Cluster ). Depending on the order of the FOT, either ( α1,α3,d1,d2,d3 )
(top row) or (α1,α3) (bottom row) are clustered. Alternatively, the eigenvalue-based parameterization (λ1,λ2) can be used instead of (α1,α3); however, as the conversion between the eigenspace- and harmonic-parameterizations is (linear) oblique, mixing notations between second- and fourth-order clustering does not permit a fair comparison.

[picture]

Fig. 2: Possible clustering routes in virtual process chains. Fourth-order informed clustering is depicted in the top row and the bottom row shows second-order informed clustering.

Fig. 2. Possible clustering routes in virtual process chains. Fourth-order informed clustering is depicted in the top row and the bottom row shows second-order informed clustering.

In the following section, we present clustering results for the two approaches depicted in Figure 2 utilizing second-order FOT data from an injection molding simulation dataset [15], which comprises 629 part geometries ranging from plate-like shapes to complex three-dimensional structures, such as a mounting bracket. The fourth-order FOT is approximated using the IBOF closure [14]. In addition to the influence of the level of tensorial information, we also discuss the influence of the clustering algorithm. In this work, the KMeans [16, 17] and Birch [18] clustering algorithms available within the scikit-learn Python package [19] are considered. The KMeans algorithm minimizes the withincluster sum-of-squares, i.e., the sum of squared Euclidean distances between each data point and its corresponding cluster centroid. The Birch algorithm builds a hierarchical tree structure by incrementally inserting data points into subclusters, which are then aggregated to form the final clusters. This approach is memory-efficient and scales well to large orientation fields.

4. Results and Discussion

The original version of this paper is available on https://www.scientific.net/SSP.387.33.pdf

Influence of level of tensorial information. We investigate the influence of the level of tensorial information (second-order vs. fourth-order informed clustering) based on clustering results for several parts with n=10 clusters, as depicted in Figure 3. The results are visualized in the eigenspace, as this representation is more widely used. The colors indicate cluster affiliation, and the " X " markers denote the corresponding cluster centroids. For consistent coloring across different clustering methods, colors are assigned based on the centroid's λ1 value. The probability density functions (PDF) of each cluster in λ1 and λ2 are shown on the top and right axes, respectively. The selected parts exhibit distinctive geometrical features, resulting in different distributions of (rotation-invariant) fiber orientation shapes. For the parts in the upper two rows, the second- and fourth-order clustering results (first two columns) yield similar cluster centroids and allocations. Only the results for the bottom row part show notable differences, which can be attributed to two reasons. First, the deviatoric parameters d1,d2,d3 span a smaller range than α1 and α3 (cf. Figure 1), causing the latter to dominate the within-cluster variance. This bias could be mitigated through parameter normalization; however, normalization is not considered in this study. Second, the deviatoric parameters d1,d2,d3 depend nonlinearly on α1 and α3, and are thus not independent. As a result, the dimensionality does not increase when including the deviatoric parameters; instead, the two-dimensional space spanned by α1 and α3 becomes curved (cf. Equation 8). A true increase in dimensionality would require omitting the closure approximation, thereby allowing general orthotropic states - for instance, by reconstructing the fourth-order FOT directly from the FODF. The extreme case is represented by triclinic orientation states.

[picture]

Fig. 3: Results of KMeans and Birch clustering for α1,α3 (second-order informed; first column) and α1,α3,d1,d2,d3 (fourth-order informed; second and third column) with n=10 clusters for three injection-molded part geometries (top, center, bottom) from the molding dataset [15]. The coloring represents cluster allocation and is sorted by the cluster centroid's λ1 value. The corresponding probability density functions in λ1 and λ2 are shown on the top and right axes.

Fig. 3. Results of KMeans and Birch clustering for α 1 , α 3 (second-order informed; first column) and α 1 , α 3 , d 1 , d 2 , d 3 (fourth-order informed; second and third column) with n = 10 clusters for three injection-molded part geometries (top, center, bottom) from the molding dataset [15]. The coloring represents cluster allocation and is sorted by the cluster centroid's λ 1 value. The corresponding probability density functions in λ 1 and λ 2 are shown on the top and right axes.

Influence of clustering algorithm. Compared to the differences between the levels of tensorial information (due to the implications of the closure), the choice of clustering algorithm has a more pronounced effect on the outcome (cf. second vs. third column in Figure 3). This stems from fundamental algorithmic differences. KMeans iteratively minimizes the within-cluster variance, whereas Birch incrementally builds a hierarchical tree structure based on a threshold parameter. As a result, the Birch algorithm may produce clusters with more irregular shapes and higher variance, as evident in the PDF of the first two rows of Figure 3.
Influence of number of clusters. An important free parameter is the number of clusters n. In Figure 3, a small number of clusters is chosen deliberately to emphasize the differences between methods. To quantify the deviation between individual data points and their associated cluster centroids, we introduce the following relative measure:

‖Δ𝔸‖=‖𝔸−𝔸Cluster ‖‖𝔸‖.(9)

Clearly, as n→ns,‖Δ𝔸‖→0, where ns denotes the total number of data points of the simulation. The required number of clusters depends on the component geometry and the fiber-flow interaction. Accordingly, fewer clusters are needed to adequately represent the (rotation-invariant) FOT field in a geometrically simple part (e.g., a plate) compared to a complex one. Figure 4 shows clustering results using fourth-order informed clustering with KMeans for the part in the second row of Figure 3, with n∈{50,100,200} clusters. Elements exceeding thresholds of 5%,10%, and 15% are marked in red. As expected, the number of flagged elements decreases with increasing threshold and with a larger number of clusters. Notably, the flagged elements are predominantly located at complex geometrical features. This can be explained by two factors. First, the mesh is quasi-uniform, and second, each data point is weighted equally during clustering. Consequently, regions with complex geometry have limited influence on the within-cluster variance. However, these regions are often prone to stress concentrations, e.g., due to notch effects. This issue could potentially be addressed by assigning higher weights to data points in such regions.

[picture]

Fig. 4: Influence of number of clusters for fourth-order informed clustering using the KMeans algorithm. Red-marked regions exceed the relative deviation threshold ‖Δ𝔸‖ of 5%,10%, or 15%. The simulation model comprises 111,931 tetrahedral elements.

Fig. 4. Influence of number of clusters for fourth-order informed clustering using the KMeans algorithm. Red-marked regions exceed the relative deviation threshold ‖ Δ 𝔸 ‖ of 5 % , 10 % , or 15 % . The simulation model comprises 111,931 tetrahedral elements.

Implications on mechanical properties. Finally, we examine the influence of the clustering on the orientation-averaged mechanical properties. Figure shows the orientation-dependent Young's modulus E(s)=((s⊗s)·ℂ−1[s⊗s])−1, where s∈𝒮2 is a unit direction, at sample locations Pi for a generic GF-PP DiCoFRP with an aspect ratio of 50 and a fiber mass content of 30%. The composite's effective transversely isotropic mechanical properties are obtained via mean-field homogenization following [20], and the orientation average is computed according to [6]. The sample points are selected to highlight differences arising from the clustering approach (second-order vs. fourth-order informed) and the choice of clustering algorithm. Note that increasing the fiber aspect ratio amplifies the anisotropy of the orientation-averaged Young's modulus.

Pronounced variations in the Young's modulus occur at data points located near cluster boundaries. This effect is particularly evident for data points with highly aligned fiber orientations ( λ1→1; gray-colored cluster in Figure ). Depending on the level of tensorial information or the clustering algorithm, a data point's assigned centroid may correspond to a more isotropic or more aligned state. For instance, at sample point P3, the Young's modulus exhibits stronger anisotropy for second-order informed clustering (first column) compared to fourth-order informed clustering (second column).

[picture]

Fig. 5: Influence of tensorial information level (first two columns) and clustering algorithm (last two columns) on the orientation-averaged fourth-order stiffness tensor ⟨ℂ(𝔸)⟩𝒮2 at sample positions Pi of the part depicted in the bottom row of Figure 3. The first row shows the clustering results in the eigenspace and the corresponding part colored by cluster labels. Subsequent rows display the stiffness bodies EPi(s) [21] of the cluster centroids in the OCS {vi}. Colors represent cluster allocation and are sorted by the centroid's λ1 value.

Fig. 5. Influence of tensorial information level (first two columns) and clustering algorithm (last two columns) on the orientation-averaged fourth-order stiffness tensor ⟨ ℂ ( 𝔸 ) ⟩ 𝒮 2 at sample positions P i of the part depicted in the bottom row of Figure 3. The first row shows the clustering results in the eigenspace and the corresponding part colored by cluster labels. Subsequent rows display the stiffness bodies E P i ( s ) [21] of the cluster centroids in the OCS { v i } . Colors represent cluster allocation and are sorted by the centroid's λ 1 value.

The clustering of FOT thus affects the predicted stress distribution in structural simulations and the formation of residual stresses in warpage simulations. These results further indicate that global scalar metrics, such as mean squared error or Euclidean distance, are insufficient for evaluating clustering quality, as they disregard directional and spatial information.

5. Conclusion

The original version of this paper is available on https://www.scientific.net/SSP.387.33.pdf

This study investigates the influence of the level of tensorial information - second-order vs. fourthorder informed - on fiber orientation tensor clustering in virtual process chains for discontinuous fiberreinforced polymers. Using parameterizations based on harmonic decomposition, we enable rotationinvariant clustering in both the second-order ( α1,α3 ) and fourth-order ( α1,α3,d1,d2,d3 ) parameter spaces.

The results indicate that the level of tensorial information has a limited effect on clustering outcomes when the fourth-order fiber orientation tensor is derived from the second-order tensor via an (orthotropic) closure approximation. This is attributed to the nonlinear dependence of the deviatoric parameters d1,d2,d3 on the second-order fiber orientation tensor parameters α1 and α3 imposed by the closure approximation, which effectively curves the parameter space rather than increasing its dimensionality. In addition, the deviatoric parameters span a small range relative to α1 and α3. In contrast, the choice of clustering algorithm - KMeans vs. Birch - has a more pronounced influence on cluster shapes and allocations.

The question of which clustering approach yields superior results cannot be answered definitively. However, from a theoretical standpoint, fourth-order informed clustering should be preferred, as it incorporates additional information from the closure approximation. The alternative - clustering in the second-order space and subsequently applying the closure - effectively discards this information and relies on estimating the deviatoric parameters from the cluster centroids.

Furthermore, we demonstrated that clustering affects orientation-averaged mechanical properties, with pronounced deviations occurring near cluster boundaries and low-occurrence tensor shapes. Global metrics such as mean squared error or Euclidean distance are insufficient for evaluating clustering quality, as they do not account for directionality or spatial position. Future work could address this limitation by incorporating directional error measures or by introducing spatially weighted clustering schemes that prioritize regions prone to stress concentrations.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/SSP.387.33.pdf

The research documented in this manuscript has been funded initially by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) as part of the EcoDynamicSMC research project, and subsequently by the German Research Foundation (DFG) as part of the project MeproSi (project no.: 464119659). The work is also part of the Heisenberg project "Digitalization of fiber-reinforced polymer processes for resource-efficient manufacturing of lightweight components" (455807141), funded by the DFG. The support by the DFG and BMWK is gratefully acknowledged.

References

The original version of this paper is available on https://www.scientific.net/SSP.387.33.pdf

F. Wittemann, R. Maertens, L. Kärger, and F. Henning, "Injection molding simulation of short fiber reinforced thermosets with anisotropic and non-Newtonian flow behavior," Composites Part A: Applied Science and Manufacturing, vol. 124, p. 105476, Sept. 2019.

J. Görthofer, N. Meyer, T. D. Pallicity, L. Schöttl, A. Trauth, M. Schemmann, M. Hohberg, P. Pinter, P. Elsner, F. Henning, A. N. Hrymak, T. Seelig, K. Weidenmann, L. Kärger, and T. Böhlke, "Virtual process chain of sheet molding compound: Development, validation and perspectives," Composites Part B: Engineering, vol. 169, pp. 133-147, July 2019.

P. Kennedy and R. Zheng, Flow analysis of injection molds. Carl Hanser Verlag GmbH & Co. KG, Apr. 2013.

C. Krauß and L. Kärger, "Tensor interpolation in virtual manufacturing chains for fiber reinforced composites," International Journal of Mechanical Sciences, vol. 226, p. 107378, July 2022.

K.-I. Kanatani, "Distribution of directional data and fabric tensors," International Journal of Engineering Science, vol. 22, pp. 149-164, Jan. 1984.

S. G. Advani and C. L. Tucker, "The use of tensors to describe and predict fiber orientation in short fiber composites," Journal of Rheology, vol. 31, pp. 751-784, Nov. 1987.

M. Hohberg, Experimental investigation and process simulation of the compression molding process of sheet molding compound (SMC) with local reinforcements. PhD thesis, Karlsruhe Institute of Technology (KIT), Germany, 2022.

V. Romanenko, Materialcharakterisierung und durchgängige 3D-Prozesssimulation für kohlenstofffaserverstärktes Sheet Molding Compound. PhD thesis, RPTU University KaiserslauternLandau, Kaiserslautern, 2021.

J. Köbler, M. Schneider, F. Ospald, H. Andrä, and R. Müller, "Fiber orientation interpolation for the multiscale analysis of short fiber reinforced composite parts," Computational Mechanics, vol. 61, no. 6, pp. 729-750, 2018.

C. Krauß, J. K. Bauer, J. Mitsch, T. Böhlke, and L. Kärger, "On the averaging and closure of fiber orientation tensors in virtual process chains," Journal of Elasticity, vol. 156, pp. 279-306, Aug. 2024.

J. K. Bauer and T. Böhlke, "Variety of fiber orientation tensors," Mathematics and Mechanics of Solids, vol. 27, no. 7, pp. 1185-1211, 2022.

S. Forte and M. Vianello, "Symmetry classes for elasticity tensors," Journal of Elasticity, vol. 43, pp. 81-108, May 1996.

J. Rychlewski, "A qualitative approach to Hooke's tensors. Part I," Archives of Mechanics, vol. 52, pp. 737-759, Jan. 2000.

D. H. Chung and T. H. Kwon, "Invariant-based optimal fitting closure approximation for the numerical prediction of flow-induced fiber orientation," Journal of Rheology, vol. 46, no. 1, pp. 169-194, 2002.

N. Meyer and J. Greif, "Injection molding simulations," May 2024.

J. MacQueen, "Multivariate observations," in Proceedings of the 5th Berkeley symposium on mathematical statistics and probability, vol. 1, pp. 281-297, 1967.

S. Lloyd, "Least squares quantization in PCM," IEEE transactions on information theory, vol. 28, no. 2, pp. 129-137, 1982.

T. Zhang, R. Ramakrishnan, and M. Livny, "BIRCH: An efficient data clustering method for very large databases," SIGMOD Rec., vol. 25, pp. 103-114, June 1996.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, "Scikit-learn: Machine learning in Python," Journal of Machine Learning Research, vol. 12, no. 85, pp. 2825-2830, 2011.

G. P. Tandon and G. J. Weng, "The effect of aspect ratio of inclusions on the elastic properties of unidirectionally aligned composites," Polymer Composites, vol. 5, pp. 327-333, Oct. 1984.

T. Böhlke and C. Brüggemann, "Graphical representation of the generalized Hooke's law," Technische Mechanik, vol. 21, no. 2, p. 145, 2001.

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/composites-forming-processes/978-3-0364-1996-1

Solid State Phenomena, ISSN: 1662-9779, Vol. 387, pp 43-51

doi: 10.4028/p-mkOd5g

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

Submitted:
2025-12-27

Revised:
2026-02-10

Accepted:
2026-02-20

Online:
2026-04-14

Wrinkling Behaviour of Engineering Fabrics in Diaphragm-Assisted Deep Drawing

The original version of this paper is available on https://www.scientific.net/SSP.387.43.pdf

Ali Tabatabaeian 1,a*, Conor Blair 1, b, Kaspar Ambuehl 1,c, Philip Harrison 1, d

1 James Watt School of Engineering, University of Glasgow, Glasgow, UK

a  ali.tabatabaeian@glasgow.ac.uk, b 2662267 B @student.gla.ac.uk, c 2520880 A @student.gla.ac.uk, d  philip.harrison@glasgow.ac.uk

(*corresponding author)

Keywords: Deep draw, engineering fabrics, wrinkling, lubrication, diaphragm forming.

Abstract

Wrinkling during diaphragm forming of engineering fabrics compromises structural integrity and surface quality. This study investigates two strategies-diaphragm pre-tensioning and fabric inter-ply lubrication-to mitigate wrinkle formation. A pre-tensioning (PT) blank holder was designed and evaluated through deep-draw experiments supported by finite element analysis (FEA). Results show that pre-tensioning ( 50% strain) significantly reduces wrinkle severity compared to a conventional blank holder. The PT blank-holder creates initial equi-biaxial strain consistent with analytical and FEA predictions. Resin inter-ply lubrication also decreased wrinkling and reduced forming force by approximately 70%, primarily by lowering inter-ply friction. However, pretensioning proved more effective overall. These findings demonstrate the potential of diaphragm pretensioning for improving forming quality and provide a foundation for advanced multi-step thermoforming processes.

Introduction

The original version of this paper is available on https://www.scientific.net/SSP.387.43.pdf

Forming multi-axial composite preforms and consolidated blanks into complex geometries remains a significant challenge for automotive and aerospace applications, primarily due to wrinkling, which compromises structural integrity and surface quality of the final parts. Conventional matched-die forming and blank-holder systems can manage certain lay-ups, such as unidirectional or simple cross-ply configurations, but often fail to suppress wrinkles in complex lay-ups and geometries [1,2]. Various approaches have been investigated to improve the forming process and reduce forming-induced defects, particularly wrinkling [3-9]. Previous studies have shown that reducing the forming rate [3,10,11], increasing the forming temperature [11,12], decreasing the preform size and increasing diaphragm stiffness [3] can mitigate wrinkle formation. Codolini et al. [8] conducted a combined experimental and numerical study to investigate the influence of key process parameters on wrinkling during double-diaphragm forming. Their results demonstrated that variability in diaphragm pre-tensioning has the greatest influence on wrinkle amplitude, followed by variations in the friction coefficient between fabric plies. This finding highlights the critical role of these two parameters in wrinkle initiation and growth.

Previous studies have shown the feasibility of a diaphragm assisted multi-step thermo-forming process using lubricated blanks [9]. Diaphragm-assisted press forming, although more complex and costly, offers unique advantages for multi-step thermo-forming processes involving lubricated blanks, including lubricant containment, void removal, and protection of the multi-step tooling from matrix ingress [9]. The present study explores the potential of pre-tensioned diaphragms to further mitigate wrinkle formation during double diaphragm press forming operations. To investigate this, a pre-tensioning device was designed and manufactured and its performance was evaluated using a deep-draw test configuration through both numerical and experimental investigations. Experiments were conducted on dry woven glass fabrics at room temperature, with and without lubrication. The study examines the influence of diaphragm pre-tensioning and inter-ply lubrication - two key process parameters that can improve forming behaviour by reducing wrinkling [8]. By establishing the benefits and challenges of diaphragm pre-tensioning, this work provides a foundation for its
application in more advanced configurations, such as diaphragm-assisted multi-step thermo-forming processes for lubricated multi-axial preforms with complex geometries.

Experimental and FEA Methods

The original version of this paper is available on https://www.scientific.net/SSP.387.43.pdf

Materials, Design, and Manufacturing. Diaphragm materials consisted of general-purpose translucent silicone rubber sheets ( 40∘ Shore hardness, 1 mm thick) supplied by Silex [13]. The fabric material used was woven glass fabric with an average thickness of 0.26±0.02 mm and diameter of 240 mm [14].

Two blank-holder systems were developed, including a conventional no-tension (NT) holder and an innovative pre-tension (PT) holder (see Fig. 1). The design of the PT blank holder was based on (a) kinematic calculations of the engineering strain created by the deformation during full diaphragm draw-in into the blank-holder cavity under vacuum pressure (assuming symmetric draw-in of the upper and lower diaphragms and also zero-friction) and (b) a finite element analysis (FEA) performed using Abaqus software. Both blank holders were manufactured from aluminium. O-rings along with bolts and nuts were used for sealing and fastening. For a better understanding of the working principle of the PT blank holder, see Fig. 5.

[picture]

Fig. 1. CAD representation of the blank holders manufactured in this study: a) no-tension (NT), b) pre-tension (PT) (diaphragms indicated in red).

Fig. 1. CAD representation of the blank holders manufactured in this study: a) no-tension (NT), b) pre-tension (PT) (diaphragms indicated in red).

Changing the dimensions of two design parameters of the PT blank holder - cavity depth ( 2A ) and radius (a+b) - outlined in the following analytical formulation, enabled the achievement of desirable strain (pre-stretch) values (see Fig. 2 and Eq. 1). A pre-stretch of 50% was intended for the design and manufacturing of the PT blank holder.

ε=ubu=b(2T+t+A)−aD(a+b)(1)

where, b is radius of interest, T is flange overhang, t is flange thickness, A is cavity half depth, a is "diaphragm radius −b ", and D is a hole depth that diaphragms are deep drawn into after vacuum draw-in has been completed.

[picture]

Fig. 2. Schematic of the PT blank holder and its design parameters (with values shown in Fig. 2(b) to achieve 50% theoretical pre-stretch).

Fig. 2. Schematic of the PT blank holder and its design parameters (with values shown in Fig. 2(b) to achieve 50% theoretical pre-stretch).

Following [14], for the deep-draw tests, a cylindrical punch and a matching female die were designed and manufactured from aluminium (see Fig. 3). The punch diameter was 50 mm , while the inner and outer diameters of the female die were 60 mm and 120 mm , respectively, providing a 5 mm clearance on each side between the punch and the inner wall of the die that is identical to a previously developed setup, which allows for direct comparison with that study [14].

Deep Draw Tests. A StepLab machine was set to a displacement rate of 25 mm/min, with a travel limit of 80 mm , starting from the moment the punch contacted the upper diaphragm. The vacuum was activated prior to the test and maintained throughout the experiment. A custom-designed 3D printed tool was used to ensure all components of the setup were centrally aligned. A silicone lubricant spray (3-IN-ONE brand) was applied to all external surfaces, including the interfaces between the diaphragms and the tools, to reduce friction. All tests were performed with three fabric plies, with lay-up arrangement of [(0/90)/±45/(0/90)], placed between the two diaphragms. Videos from the top view were recorded throughout the entire test duration, and photographs were taken at the end of each test. Circles with known radii were marked on unstretched diaphragms to experimentally measure the subsequent strains. To examine the effect of diaphragm pre-tensioning, tests were conducted using both NT and PT blank holders. To assess the influence of inter-ply lubrication, tests were performed with and without resin lubrication in the NT blank holder. Here, resin lubrication refers to applying 20 mL of a mixture of EL2 epoxy laminating resin and AT30 epoxy hardener (supplied by Easy Composites and prepared with mixing ratio of 100 (resin) - to - 30 (hardener)) between fabric plies. Note that the resin was applied only between the fabric layers, not between the fabric and diaphragm surfaces (referred to here as partial lubrication).

[picture]

Fig. 3. Deep draw test setup with NT blank holder: a) experimental, b) CAD.

Fig. 3. Deep draw test setup with NT blank holder: a) experimental, b) CAD.

Finite Element Analysis. The blank holder and forming tools were modelled as discrete rigid shell revolutions, while the diaphragms were defined as deformable planar shells (S4R) with thickness assigned via a homogeneous shell section. The hyperelastic material properties of the diaphragms and the friction properties were characterised previously and reported in [13]; therefore, all these data were taken from this reference. Each rigid body was linked to a unique reference point using a rigid-tie constraint. Gravity and a uniformly distributed pressure load of 100,000 Pa were applied using linearly increasing amplitudes. Time steps of 0.25 s and 1 s were used for the application of gravity and pressure respectively, using an explicit analysis. Note that FEA gives logarithmic or true strains (LE11 and LE22), from which the numerical engineering strain (pre-stretch) can be calculated ( εeng =eεtrue −1 ).

[picture]

Fig. 4. Schematic of the FEA model (diaphragms indicated in red).

Fig. 4. Schematic of the FEA model (diaphragms indicated in red).

Results and Discussion

The original version of this paper is available on https://www.scientific.net/SSP.387.43.pdf

Evaluating the Performance of Pre-Tension Blank Holder. The dimensions of the PT blank holder used for FEA modelling were selected based on a theoretical pre-stretch target of 50%. The FEA results confirmed the expected diaphragm deformation under vacuum-the diaphragms gradually approached each other and remained at half the cavity depth under full vacuum-achieving a prestretch of 59% near the centre of the upper diaphragm (Fig. 5). Similarly, vacuum draw-in experiments demonstrated stable diaphragm behaviour, with the diaphragm rest-position close to the mid-depth of the cavity wall (see the vacuum fitting in Fig. 6(a)), and strains near the central area measuring 55%. The slightly higher strains observed in both FEA and experimental measurements compared to the analytical solution can be attributed to the assumption of a frictionless scenario in the analytical model, whereas in FEA and experiments, friction between the diaphragms and between the tools and diaphragms was not zero, creating slightly non-homogeneous deformation with localised regions of higher/lower deformation. The experimental results confirm that the PT blank-holder induces an equi-biaxial strain state, as visually evident in Fig. 6 and quantitatively verified through measurements obtained during the draw-in tests. The deformation can be considered stable because the diaphragms met at approximately half the cavity depth and remained flat throughout the process, indicating the absence of instability. The equi-biaxial nature of the deformation is further confirmed by diameter change measurements of the marked circles before and after vacuum draw-in, which show equal strains along the two in-plane principal directions. In addition, visual inspection shows that both the fabric ply between the diaphragms and the marked circles on the diaphragms retained a perfectly circular shape after deformation, providing further evidence of a uniform equi-biaxial strain state.

[picture]

Fig. 5. FEA results showing vacuum draw-in process.

Fig. 5. FEA results showing vacuum draw-in process.

[picture]

Fig. 6. Experimental vacuum draw-in results showing stable and equi-biaxial diaphragm deformation: a) without fabric layers between diaphragms, b) with fabric layers between diaphragms.

Fig. 6. Experimental vacuum draw-in results showing stable and equi-biaxial diaphragm deformation: a) without fabric layers between diaphragms, b) with fabric layers between diaphragms.

Influence of Diaphragm Pre-Tensioning. The influence of diaphragm pre-tensioning (by 50%) can be evaluated by analysing visual inspection images (Fig. 7) and force-displacement graphs (Fig. 8). As shown in Fig. 7(a) and Fig. 7(b), pre-tensioning significantly reduced wrinkling, both in number and severity. In the test with the PT blank holder (Fig. 7(b)), only a few minor wrinkles appeared along the inner edge of the die, whereas numerous pronounced wrinkles were observed at the same displacement in the NT blank holder test (Fig. 7(a)). Regarding the force-displacement curves, both tests exhibited non-linear behaviour. Up to a displacement of 30 mm , the curves were nearly identical; beyond this point, they diverged. The maximum force recorded at the end of the test was approximately 2450 N for the NT blank holder and 1850 N for the PT blank holder.

[picture]

Fig. 7. Wrinkling behaviour at 80 mm displacement: a) NT blank holder, b) PT blank holder.

Fig. 7. Wrinkling behaviour at 80 mm displacement: a) NT blank holder, b) PT blank holder.

[picture]

Fig. 8. Comparison of load-displacement graphs for tests with NT and PT blank holders.

Fig. 8. Comparison of load-displacement graphs for tests with NT and PT blank holders.

Influence of Lubrication. Similarly, the effect of resin inter-ply lubrication using the NT blankholder can be assessed by analysing visual inspection images (Fig. 9) and force-displacement graphs (Fig. 10). Lubrication also significantly reduced wrinkling: in the test with lubricated fabric layers (Fig. 9(b)), only a few minor wrinkles appeared shortly before the end of the test, whereas in the test without lubrication (Fig. 9(a)), numerous larger wrinkles developed much earlier. The forcedisplacement curves for the two tests differed markedly. Without lubrication, the curve was nonlinear, and the maximum force reached approximately 2450 N . In contrast, with lubrication, the curve was nearly linear, indicating that resin applied between fabric plies substantially reduced inter-ply friction, resulting in a maximum force of about 700 N . Another factor contributing to the higher force in the non-lubricated test was the presence of large wrinkles from the early stages, which were gradually drawn into the die cavity, filling the gap between the punch and die and increasing friction. In the lubricated test, the punch moved freely without contacting the die. This is evident in Fig. 11, which shows the lubricated sample after curing for 24 hours at room temperature; some wrinkles remain visible on both the inner and outer edges of the hole.

Although both pre-tensioning and lubrication reduced wrinkling, pre-tensioning had a more pronounced effect, producing fewer and smaller wrinkles than lubrication. Future work will investigate the combined effect of these approaches and further examine pre-tensioning through detailed FEA modelling.

[picture]

Fig. 9. Wrinkling behaviour at 80 mm displacement: a) no lubrication, b) with lubrication.

Fig. 9. Wrinkling behaviour at 80 mm displacement: a) no lubrication, b) with lubrication.

[picture]

Fig. 10. Comparison of load-displacement graphs for tests with and without lubrication.

Fig. 10. Comparison of load-displacement graphs for tests with and without lubrication.

[picture]

Fig. 11. Deep-drawn sample on NT blank holder (with lubrication) after curing for 24 hours at room temperature.

Fig. 11. Deep-drawn sample on NT blank holder (with lubrication) after curing for 24 hours at room temperature.

Summary

The original version of this paper is available on https://www.scientific.net/SSP.387.43.pdf

This study examined the effectiveness of diaphragm pre-tensioning and inter-ply lubrication in mitigating wrinkle formation during diaphragm-assisted deep drawing of woven glass fabrics. A bespoke pre-tensioning blank holder was designed and validated through deep-draw experiments supported by finite element analysis, confirming both feasibility and performance benefits. The PT blank holder delivered favourable results, achieving uniform biaxial strain consistent with analytical predictions and numerical simulations. Although both lubrication and pre-tensioning improved formability, the latter demonstrated a more pronounced impact on wrinkle suppression. Future work will investigate the synergistic effect of these strategies and employ advanced finite element modelling to optimise process parameters and accurately predict wrinkle behaviour of the fabrics.

References

The original version of this paper is available on https://www.scientific.net/SSP.387.43.pdf

LT. Harper, S. Chen, Forming of non-crimp fabrics. Advanced Structural Textile Composites Forming: Characterization, Modeling, and Simulation, Woodhead Publishing; (2025), p. 10943. https://doi.org/10.1016/B978-0-443-21578-0.00004-4.

P. Harrison, Characterizing the forming mechanics of woven engineering fabrics. Advanced Structural Textile Composites Forming: Characterization, Modeling, and Simulation, Woodhead Publishing;(2025), p.57-85..

CM. O'Brádaigh, RB. Pipes, PJ. Mallon, Issues in diaphragm forming of continuous fiber reinforced thermoplastic composites. Polym Compos; (1991), p. 246-56. .

GD. Lawrence, S. Chen, NA. Warrior, LT. Harper, The influence of inter-ply friction during double-diaphragm forming of biaxial NCFs. Compos Part A Appl Sci Manuf; (2023). .

S. Chen, OPL. McGregor, LT. Harper, A. Endruweit, NA. Warrior, Optimisation of local inplane constraining forces in double diaphragm forming. Compos Struct; (2018), P. 570-81. .

F. Yu, S. Chen, GD. Lawrence, NA. Warrior, LT. Harper, A global-to-local sub modelling approach to investigate the effect of lubrication during double diaphragm forming of multi-ply biaxial non-crimp fabric preforms. Compos B Eng; (2023). .

G. Miris, M. Ravandi, A. Cardew-Hall, B. Eisenbart, A. Di Pietro, Efficient numerical analysis of in-plane compression-induced defects in thick multi-ply woven textile preforms during double diaphragm forming. Compos Part A Appl Sci Manuf; (2024). .

A. Codolini, S. Chen, GD. Lawrence, LT. Harper, MPF. Sutcliffe, Characterisation of processinduced variability in wrinkle defects during double diaphragm forming of non-crimp fabric. Compos B Eng; (2024). .

P. Harrison, I. Campbell, E. Guliyev, B. McLelland, R. Gomes, N. Curado-Correia, et al., Induction melt thermoforming of advanced multi-axial thermoplastic composite laminates. J Manuf Process; (2020), p. 673-83. .

SG. Pantelakis, EA. Baxevani, Optimization of the diaphragm forming process with regard to product quality and cost. Compos Part A Appl Sci Manuf; (2002), p. 459-70.

XX. Bian, YZ. Gu, J. Sun, M. Li, WP. Liu, ZG. Zhang, Effects of processing parameters on the forming quality of C -shaped thermosetting composite laminates in hot diaphragm forming process. Applied Composite Materials; (2013), p.927-45. .

J. Sun, Y. Gu, M. Li, X. Ma, Z. Zhang, Effect of forming temperature on the quality of hot diaphragm formed C-shaped thermosetting composite laminates. Journal of Reinforced Plastics and Composites; (2012), p. 1074-87. .

G. Street. Thermomechanical Forming Simulation for Fibre Reinforced Thermoplastic Laminates. PhD Thesis at University of Nottingham; (2025).

P. Harrison, LF. Gonzalez Camacho, Deep draw induced wrinkling of engineering fabrics. Int J Solids Struct; (2021), p. 220-36. .

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/composites-forming-processes/978-3-0364-1996-1

Solid State Phenomena, ISSN: 1662-9779, Vol. 387, pp 53-60

doi: 10.4028/p-gBukM0

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

Submitted:
2026-01-04

Revised:
2026-02-15

Accepted:
2026-02-17

Online:
2026-04-14

Fibre Tension-Induced Stresses in Filament-Wound Composites

The original version of this paper is available on https://www.scientific.net/SSP.387.53.pdf

Jurui Liu 1,3,a*, Stepan V. Lomov 2,b, Chung Hae Park 3,c and Jan Ivens 1,d

1 KU Leuven, Department of Materials Engineering Campus De Nayer, J. De Nayerlaan 5, 2860 Sint-Katelijne-Waver, Belgium

2KU Leuven, Department of Materials Engineering, Kasteelpark Arenberg 44 box 2450, 3001, Leuven, Belgium

3 IMT Nord Europe, Institut Mines-Télécom, University of Lille, Centre for Materials and Processes, 59500, Lille, France

Email: ajurui.liu@kuleuven.be, b stepan.lomov@kuleuven.be, c  chung-hae.park@imt-nord-europe.fr, djan.ivens@kuleuven.be

Keywords: Filament winding, fibre tension, residual stress

Abstract

Fibre tension is an important process parameter during filament winding. It strongly affects the void content and fibre volume fraction, which in turn determines the mechanical performance of the part. Fibre tension also contributes to the residual stresses that develop in the filament-wound material.

This study focuses on how the fibre tension shapes the stress distribution in filament-wound composites. To this end, a numerical predictive model was developed. Experimental validation was conducted using a force sensor, and good agreement was observed between the model predictions and the experimental measurements. These findings provide deeper insight into the role of fibre tension in filament winding and offer practical guidance for optimising the process to enhance the performance of composite pressure vessels.

Introduction

The original version of this paper is available on https://www.scientific.net/SSP.387.53.pdf

Filament winding is an advanced composite manufacturing technique that winds continuous fibre tows or prepreg on the mandrel and processes it as high-performance composites. It has been widely used in various industries, including the manufacturing of pressure vessels, aerospace components, as well as sports and recreational equipment.

Several parameters can be independently controlled during the filament winding process, for instance winding speed, fibre tension, and internal pressure in the mandrel. Among these, fibre tension has been identified as a critical factor influencing the quality and performance of filamentwound products [1]. The filament-wound parts manufactured with high fibre tension can have enhanced interlayer compaction, reduced void content, increased fibre volume fraction and consequently improved strength and stiffness of the composite [1-3].

The development of fibre-tension-induced stress during filament winding has been the subject of a number of studies. Early research focused on analytical methods. These analytical models were built upon the plane-stress hypothesis and relied on the superposition principle to represent the incremental build-up of the wound structure [4]. Subsequent developments introduced twodimensional models based on thick-wall cylinder theory, enabling the estimation of tension-induced stress distributions with accuracy comparable to finite element predictions [5-7]. These models were later extended to a three-dimensional case to better represent the spatially varying stress state in wound composites [8,9].

Apart from analytical modelling, finite element analysis has become a widely adopted tool for predicting fibre-tension-induced stresses. Accurate prediction of tension-induced stress during the winding process requires combining both layer activation and the application of tension within the numerical framework. For this purpose, the birth-death element technique is commonly employed, where newly deposited layers are activated while inactive layers are excluded from the computation [10-12]. Among the available strategies for applying fibre tension, the temperature-parameter method
is particularly prevalent. This approach introduces a virtual coefficient of thermal expansion to the composite layers and applies a negative temperature change to generate a contraction equivalent to the target tension force [7,11,13]. Alternative implementations treat the tension as a predefined stress field combined with the birth-death method, offering more direct load control and improved accuracy in maintaining element thickness during activation [12]. The aforementioned methodologies have advanced the design of tension control and improved the understanding of the influence of fibre tension in filament-wound materials. However, several limitations and challenges remain in the existing studies. Transversely isotropic material properties are commonly adopted for modelling wound materials, including prepregs and fibre tows. The thermal and shrinkage behaviour of these materials is often neglected. The input properties of the wound material, especially those of the fibre tow, have not been fully investigated. Some studies [5,14] obtained the transverse modulus of the fibre tow by fitting numerical results with experimental data. Additionally, limited experimental validation has been reported, and the accuracy of the models has not been thoroughly assessed.

The present model is based on the birth-death element method and the predefined stress method. Track elements were introduced into the numerical model to control layer thickness and improve numerical convergence. Previous work has overlooked the compaction behaviour of fibre tows in determining internal stress development. To address this limitation, a power-law compaction model was incorporated to capture the evolution of the transverse modulus of the fibre tow. Thermal and shrinkage effects are not considered in the current model. We demonstrate the accuracy of the model through comparison with a direct measurement of the radial stress resulting from fibre tension. This study provides a deeper understanding of the fibre tension influence on the filament wound material and a guidance for the optimisation of the filament winding technology.

Experiment

The original version of this paper is available on https://www.scientific.net/SSP.387.53.pdf

Material and setup

The original version of this paper is available on https://www.scientific.net/SSP.387.53.pdf

To monitor the stress evolution induced by the fibre tension during the winding process, a thin film force sensor was employed in this study.
The wound material in this study was Toray T700SC-F0E 12K carbon fibre tows, with 0.13 mm thickness and 6.5 mm width. Radial forces acting on the mandrel surface during winding were measured using a Tekscan FlexiForce A201 force sensor with 0.203 mm thickness [15].The experimental setup used to record the radial force is shown in Fig. 1.

[picture]

Fig. 1 (a). Overview of the experimental set-up (b). Close-up view showing the force sensor and fibre tows

Fig. 1. (a). Overview of the experimental set-up (b). Close-up view showing the force sensor and fibre tows

The mandrel was made of Teflon with a diameter of 150 mm and a thickness of 10 mm . The force sensor was attached to the outer surface of the mandrel at the winding location. A dedicated fixture was designed to accommodate the sensor electronics and wiring, ensuring stable positioning and reliable signal acquisition during the winding process.

Prior to each test, the force sensor was calibrated using known weights. The sensor output voltage varies with the applied normal load on the sensing area, and a calibration curve was established to convert the measured voltage into force values. A constant fibre tension of 10 N was applied during winding, and four repeated tests were conducted to ensure repeatability.
The winding speed was set to 1.6 rpm to minimize dynamic effects. Dry fibre tows were used without resin. Hoop winding was performed over the sensor's sensing area. During the winding process, the fibre tows were continuously wound at the same circumferential position, resulting in a progressive increase in the number of layers. The radial force exerted on the sensor was continuously recorded throughout the winding process.

Model Development

The original version of this paper is available on https://www.scientific.net/SSP.387.53.pdf

Geometry

The original version of this paper is available on https://www.scientific.net/SSP.387.53.pdf

The dimensions of mandrel and fibre tow are listed in Table 1. A quarter 2D finite element model representing the winding system was established, as shown in Fig. 2. To accurately capture the stress development in the layers, three elements are meshed through the layer thickness.

Table 1 Dimensions of mandrel and fibre tow

Parameter | Value
Mandrel outer diameter | 150 mm
Mandrel inner diameter | 130 mm
Fibre tow thickness | 0.13 mm
Fibre tow width | 6.5 mm

[picture]

Fig. 2 Finite element model of filament winding system

Fig. 2. Finite element model of filament winding system

Boundary Conditions

The original version of this paper is available on https://www.scientific.net/SSP.387.53.pdf

In the numerical model, the fibre tows were modelled as hoop layers following the experimental hoop-winding configuration. Tie constraints were applied between adjacent fibre tows as well as between the first layer of fibre tows and the mandrel surface, ensuring no slippage occurs during the simulation. This boundary condition is consistent with the no-slippage behaviour typically observed in geodesic winding [16]. Symmetric constraints are imposed on the symmetric surfaces of the model. The inner surface of the mandrel is fixed in accordance with the real situation, where two ends of the mandrel are connected to a central shaft.
A model change was implemented to simulate the material deposition process. Each filament-wound layer was applied in an individual analysis step. In the first analysis step, only the mandrel was
activated. After the initial step, each layer was activated sequentially to mimic the winding process. The equilibrium was continuously updated when a new layer was activated on the mandrel.
During the winding process, the mandrel underwent radial deformation under fibre tension, and the activated layers followed this deformation, whereas the deactivated layers remained fixed. When a new layer was activated, its top surface remained at the undeformed position, while its bottom surface was constrained to the radially displaced surface of the previously activated layer. This mismatch led to artificial stretching of the newly activated layer, resulting in non-physical tensile stresses and unrealistically large thickness strains in the newly activated elements.
To address this issue, a track element method was adopted. All layer elements were duplicated at the same location and shared nodes with the original elements, while their activation sequences were reversed. Initially, all track elements were active, as each wound layer was activated, the corresponding track elements were deactivated and replaced. This procedure enabled the newly activated elements to inherit the deformation history of the previously deposited layers, thereby avoiding artificial stress generation. The overall process is illustrated in Fig. 3.

[picture]

Fig. 3 Illustration for birth-death element method and track elements approach. (a) Traditional birth-death element method. (b) Birth-death element method with track elements

Fig. 3. Illustration for birth-death element method and track elements approach. (a) Traditional birth-death element method. (b) Birth-death element method with track elements

The method was implemented by modifying the input file using the elcopy keyword. To minimise their influence, the track elements were assigned isotropic material properties with a low stiffness of 100 MPa , which was chosen based on the parameter sensitivity analysis.
The fibre tension was applied via a predefined stress field. The fibre tension induced stress in the fibre direction can be obtained through the following equation:

σ=Tt·w(1)

where σ is the fibre tension stress in the layers, T is the applied tension force, t and w are the thickness and width of the fibre tow, respectively.

Material Properties

The original version of this paper is available on https://www.scientific.net/SSP.387.53.pdf

The mandrel was made of Teflon, whose properties are listed in Table 2.

Table 2 Material properties of mandrel

Parameter | Symbol | value
Young's modulus | E | 500 MPa
Poisson's ratio | v | 0.45

The material properties of Toray T700 fibre were taken from the datasheet [17]. The in-plane shear modulus was taken from the literature [18]. Hahn [5] and Kempner [19] obtained the transverse modulus by fitting the numerical results to experimental data. They reported that the transverse modulus is fibre-tension-dependent, and the magnitude is much smaller than the longitudinal modulus. To accurately determine the transverse modulus, the compaction behaviour of fibre tow was considered in this model. In filament winding, compaction arises from radial pressure acting on the bottom surface due to force equilibrium, as well as from pressure applied at the top surface when a new layer is wound. The power law for pressure-fibre volume fraction dependency, commonly used in literature (e.g. [20,21]) captures the evolution of fibre volume fraction under different levels of compaction, except at the initial stage where no compaction is applied. In the absence of compaction, this formulation leads to a small non-physical transverse stress. Therefore, a tangent modulus formulation was adopted in the simulation to update the stress: the transverse modulus during the compaction process can be expressed as:

ET=dσ33 dε33=ab( Vfoexp(ε33))b(2)

where ET is transverse modulus, σ33 is the transverse stress, and σ33=−P(P being the compaction pressure), ε33 is transverse strain, Vf0 is initial fibre volume fraction.

The transverse modulus of fibre tow was evaluated during the simulation by incorporating equation (2) into ABAQUS via UMAT user subroutine. The input parameters a and b were obtained from the literature [20], where they were identified from compaction experiments on dry carbon fibre tows. The input data for the fibre tows are summarized in Table 3.

Table 3 Input material data for T700 fibre tow

Parameter | Symbol | value | Unit
Fibre longitudinal modulus | Ef | 230 000 | MPa
Poisson's ratio | v₁₂ | 0 |
In-plane shear modulus | G₁₂ | 4980 | MPa
Initial Fibre volume fraction | Vf0 | 0.40 |
Power law parameter | a | 4975 | MPa
Power law parameter | b | 13.42 |

Results and Discussion

The original version of this paper is available on https://www.scientific.net/SSP.387.53.pdf

The experimental and simulation results of radial pressure on the outer surface of the mandrel are presented in Fig. 4.

[picture]

Fig. 4 Numerical and experimental results

Fig. 4. Numerical and experimental results

The results show that the model can accurately capture both the initial stress development and final saturation behaviour. The corresponding numerical hoop and radial stress distribution through the thickness of the composite layers are presented in Fig. 5. The hoop stress remains tensile throughout the thickness. The stress decreases from the innermost layer to nearly zero between the 6th  and 11th  layers, and then increases to approximately 10 MPa toward the outer layer. The radial stress decreases from the innermost layer and remains at approximately 0.02 MPa from the 2nd  layer to the 18th  layer, before dropping to zero at the outer surface. These results indicate that the compaction capability of fibre tow is limited, and that the increasing number of winding layers cannot further increase the radial stress in the previously wound layers.
In addition to stress prediction, the model also provides the fibre volume fraction distribution along the thickness direction, as well as the thickness of each wound layer after completion of the winding process. The predicted fibre volume fraction distribution is shown in Fig. 6. The results show that the fibre volume fraction distribution follows the radial stress pattern: higher compaction forces in the inner layers lead to higher fibre volume fractions, while lower compaction levels are observed towards the outer layers. The predicted thickness of the wound layers and the fibre volume fraction distribution will be validated in future experimental work.

[picture]

Fig. 5 Fibre tension induced stress distribution across thickness (a) Hoop stress and (b) Radial stress. The radial coordinate is discretised by the layer index after winding completion, where 0 corresponds to the inner surface of the wound material.

Fig. 5. Fibre tension induced stress distribution across thickness (a) Hoop stress and (b) Radial stress. The radial coordinate is discretised by the layer index after winding completion, where 0 corresponds to the inner surface of the wound material.

[picture]

Fig. 6 Predicted fibre volume fraction distribution across thickness after winding completion

Fig. 6. Predicted fibre volume fraction distribution across thickness after winding completion

Conclusion

The original version of this paper is available on https://www.scientific.net/SSP.387.53.pdf

In this study, a numerical model was presented to predict the fibre tension induced stress. Birth-death element and predefined stress were used to simulate the winding process and application of fibre tension. Track elements were implemented to accurately capture the thickness of wound layers and stress development. The consolidation behaviour of the fibre tow during the winding process was considered into the model by a power law. A validation experiment using a force sensor on the mandrel surface was performed to record the radial stress development on the outer surface of the mandrel during the winding process. The good agreement between the predicted and experimental results demonstrates the reliability of the model. The validated model thus provides a useful tool for optimizing process parameters and improving product quality in filament winding.

Acknowledgements

The original version of this paper is available on https://www.scientific.net/SSP.387.53.pdf

This work has been performed within the framework of the ECOHYDRO project (Grant No. 101138008), which is financially supported by the Clean Hydrogen JU and the European Commission through the Horizon Europe program.

References

The original version of this paper is available on https://www.scientific.net/SSP.387.53.pdf

Cohen D. Influence of filament winding parameters on composite vessel quality and strength. Composites Part A: Applied Science and Manufacturing 1997; 28: 1035-47. https://doi.org/10.1016/S1359-835X(97)00073-0.

Mertiny P, Ellyin F. Influence of the filament winding tension on physical and mechanical properties of reinforced composites. Composites Part A: Applied Science and Manufacturing 2002; 33: 1615-22. .

Błachut A, Kaleta J, Detyna J, Kmiecik B, Ziętek G, Panek M, et al. Multiscale analysis of composite pressure vessel structures wound with different fiber tensile force. Composite Structures 2024; 337:118065. .

Lee S-Y, Springer GS. Filament Winding Cylinders: I. Process Model. Journal of Composite Materials 1990;24:1270-98. .

Hahn HT, Kempner EA, Lee SS. The stress development during filament winding of thick cylinders. Composites Manufacturing 1993;4:147-56. .

Kang C, Shi Y, Deng B, Yu T, Sun P. Determination of Residual Stress and Design of Process Parameters for Composite Cylinder in Filament Winding. Advances in Materials Science and Engineering 2018;2018:1821342. .

Zu L, Xu H, Zhang B, Li D, Wang H, Zi B. Filament-wound composite sleeves of permanent magnet motor rotors with ultra-high fiber tension. Composite Structures 2018;204:525-35. .

Liu C, Shi Y. Analytical model for the winding process-induced residual stresses of the multilayered filament wound cylindrical composite parts. Mater Res Express 2019;6:105354. .

Liu C, Shi Y. Design optimization for filament wound cylindrical composite internal pressure vessels considering process-induced residual stresses. Composite Structures 2020;235:111755. .

Zu L, Xu H, Zhang B, Li D, Zi B. Design of filament-wound composite structures with archshaped cross sections considering fiber tension simulation. Composite Structures 2018;194:11925. .

Zu L, Xu H, Zhang Q, Jia X, Jin S, Li D. Investigation on mechanical behavior of composite electromagnetic gun barrel based on the high tension winding. Composite Structures 2020;248:112521. .

Xie X, Xie X, Li H, Shen L, Zhu J, Yu H, et al. Tension relaxation during the NOL ring winding process based on a novel tracking element method. Journal of Reinforced Plastics and Composites 2024:07316844241278554. .

Xu H, Wang Y, Song H, Li K, Hu J. Analysis of fiber stresses during high-tension winding molding of thermoplastic composites. Journal of Thermoplastic Composite Materials 2025:08927057251318714. .

Kempner EA, Hahn HT. The Effect of Radial Stress Relaxation on Fiber Stresses in Thick Filament-Wound Cylinders n.d.

Tekscan Inc. FlexiForce A201 Sensor Datasheet. (Rev I). Available at: (accessed 11 December 2025).

Wang R, Jiao W, Liu W, Yang F, He X. Slippage coefficient measurement for non-geodesic filament-winding process. Composites Part A: Applied Science and Manufacturing 2011;42:3039. .

Toray Composite Materials America, Inc., T700G Technical Data Sheet, Technical datasheet, 2021. Available at:

Ng S-P, Tse P-C, Lau K-J. Numerical and experimental determination of in-plane elastic properties of 2/2 twill weave fabric composites. Composites Part B: Engineering 1998;29:73544. .

Kempner EA, Hahn HT. Effect of radial stress relaxation on fibre stress in filament winding of thick composites. Composites Manufacturing 1995;6:67-77. .

Lu S, Evans A, Turner T. Analysis of roller compaction pressure distribution in automated dry fibre placement. Composite Structures 2023; 316: 117048. . 2023.117048.

Robitaille F, Gauvin R. Compaction of textile reinforcements for composites manufacturing. I: Review of experimental results. Polymer Composites 1998; 19: 198-216. .

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/composites-forming-processes/978-3-0364-1996-1

Solid State Phenomena, ISSN: 1662-9779, Vol. 387, pp 61-73

doi: 10.4028/p-Wf7rKC

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

Submitted:
2026-01-14

Revised:
2026-02-16

Accepted:
2026-02-17

Online:
2026-04-14

Thermo-Chemical Modeling and Experimental Validation of Pultruded Glass Fiber Reinforced Composites

The original version of this paper is available on https://www.scientific.net/SSP.387.61.pdf

Jacob Harris 1,a*, Sangharsha Gharat 1, b, Luohaoran Wang 1,c, Ali Zolali 2, d, Alan Taub 1,e and Mihaela Banu 1,f

1 University of Michigan, Ann Arbor, USA

2 BASF, Wyandotte, USA

a jchari@umich.edu, b  sgharat@umich.edu, clhrwang  @umich.edu, d  ali.zolali@basf.com, e alantaub@umich.edu, f mbanu@umich.edu

Keywords: pultrusion, thermo-chemical modeling, cure kinetics, glass fiber composites.

Abstract

This work develops and validates thermo-chemical models for pultrusion of glass fiberreinforced polyurethane composites on an industrial PulFlex production line. A reduced onedimensional model combining a calibrated Kamal-Sourour (KS) cure law with an Arrhenius type chemo-rheological viscosity formulation is cross-validated against a three-dimensional ANSYS Composite Cure Simulation using identical material inputs along a three-zone, 0.9144 m heated die. Embedded thermocouples provide in-die temperature histories at 50.8 cm· min−1 for calibration, while additional differential scanning calorimetry (DSC) measurements supply degree of cure (DoC) profiles for independent validation. At the industrial operating speed of 50.8 cm· min−1, the mathematical and ANSYS models both reproduce the measured temperature peak location and exit temperature within a few degrees Celsius and predict a die-exit DoC of approximately 0.95 , confirming near-complete curing. Using these calibrated fields as inputs to an analytical pullingresistance formulation, both models predict comparable pulling force magnitudes and plateau behavior, demonstrating that the simplified 1D framework can capture not only thermo-chemical evolution but also process resistance trends over a range of pulling speeds. The validated 1D model therefore enables efficient exploration of speed-temperature-force tradeoffs for process window design, while the 3D ANSYS model provides a higher-fidelity reference for local gradients and future thermo-chemo-mechanical extensions.

1. Introduction

The original version of this paper is available on https://www.scientific.net/SSP.387.61.pdf

The manufacturing of continuous fiber-reinforced polymer composites through pultrusion is widely valued for its ability to streamline production through a highly automated and continuous process [1,2]. Because the material is shaped as it is pulled through the die, long, straight components can be produced in a single pass without additional joining operations, reducing manufacturing time, cost, and material waste [1,2−5]. These advantages have driven the adoption of pultruded composites across automotive, aerospace, construction, and renewable energy applications [4,5].

Despite its industrial maturity, pultrusion is inherently complex due to strong coupling among heat transfer, resin flow, chemical cure reactions, pressure evolution, and stress development, all of which influence process stability, final part quality, and dimensional accuracy [1,2,4−6]. The process involves multiple critical stages including fiber alignment, resin impregnation, heating, curing, and controlled pulling speed, with thermal management playing a central role in gelation and cure completion [1-7].

Physics-based computational modeling has therefore become an essential complement to empirical process development in pultrusion [6,8,9]. Coupling finite-element heat transfer with cure kinetics enables prediction of temperature evolution and DoC progression along the die, providing early insight into process behavior prior to physical trials [6-10]. For example, Liu et al. demonstrated that increasing pulling speed delays the exothermic peak and reduces in-die cure due to shortened residence time [10]. Similar modeling frameworks have been validated under a range of processing
conditions and extended to impregnation modeling in closed injection pultrusion, where flow through porous fiber beds becomes dominant [11].

To ensure reliability under industrial conditions, such models must be validated against representative process measurements. In this study, we develop and experimentally validate thermochemical pultrusion models for glass fiber-reinforced polyurethane composites processed on an industrial pultrusion line at PulFlex, Pittsburgh, PA. Embedded thermocouples in the die's provide transient temperature histories for model calibration and validation. In addition, a reduced onedimensional mathematical model is cross-compared against a detailed three-dimensional ANSYS Composite Cure Simulation using identical cure kinetics and chemo-rheological inputs. This direct comparison isolates the influence of modeling dimensionality and heat-transfer assumptions, providing a quantitative basis for assessing the applicability of the reduced model for parametric studies while retaining ANSYS as a higher-fidelity reference for local effects.

2. Methodology

The original version of this paper is available on https://www.scientific.net/SSP.387.61.pdf

2.1. Process configuration and operating conditions

Pultrusion experiments were conducted on the PulFlex Technologies industrial line, consisting of a closed resin-impregnation/injection stage, a heated forming/cure die, and dual-belt pullers (Fig. 1(ac)). Continuous fiber rovings were guided from creels through alignment cards into the closed injection box. A two-component polyurethane (PU) resin system was supplied by a metering/mixing unit that combined the polyol component (Elastocoat 74850R) and isocyanate component (Elastocoat 74850 T ) at a target stoichiometric ratio of 1.17:1 (isocyanate: polyol).

[picture]

Fig. 1. Schematic of the PulFlex line pultrusion process: (a) resin impregnation via MVP injection box, (b) three-zone heated forming die, (c) in-line puller.

Fig. 1. Schematic of the PulFlex line pultrusion process: (a) resin impregnation via MVP injection box, (b) three-zone heated forming die, (c) in-line puller.

Immediately downstream of the injection region, the saturated fiber bundle entered a three-zone steel forming die (Fig. 1(b)) that produced a constant rectangular profile of 127 mm×3.2 mm (Fig. 2). The die contained three independently controlled electric heating zones set to 133∘C,177∘C, and 154∘C (Zones 1-3), with a combined heated length of approximately 0.914 m . The pulling speed was varied between 48.3 and 63.5 cm· min−1. An operating condition of 50.8 cm· min−1 provided stable production with consistent part quality and was selected as the reference condition for model calibration and ANSYS-mathematical model comparison. Post-cure measurements confirmed the part cross-section was consistent with the die design (Fig. 2).

[picture]

Fig. 2. Pultruded part at die exit (left: optical image) and measured cross-sectional dimensions (right: cross section of the die: 127 mm width ×3.2 mm thickness).

Fig. 2. Pultruded part at die exit (left: optical image) and measured cross-sectional dimensions (right: cross section of the die: 127 mm width × 3.2 m m thickness).

2.2 Experimental trials, data acquisition and instrumentation

To capture the in-die thermal history, a K-type thermocouple was embedded directly within the reinforcement bundle by tying it to a central tow upstream of the injection box. The sensor provided a continuous temperature record as a function of time and position along the die (Fig. 3(a)). The temperature-position measurement at the pulling speed of 50.8 cm· min−1 (Fig. 3(b)) was used as the primary dataset for thermo-chemical model calibration. Die-wall thermocouples in each heating zone monitored die surface temperature and verified that programmed setpoints were maintained during each trial.

To validate the curing behavior extracted from simulation, a Differential Scanning Calorimetry (DSC) methodology by TA Discovery DSC 2500 employed a specific thermal profile designed to characterize the resin's curing kinetics under conditions that simulate the rapid heating of the real pultrusion process by PulFlex, Pittsburgh, PA. The material was subjected to a multi-step temperature program characterized by aggressive ramp rates of 200.00∘C/min starting from 50∘C, reaching setpoints of 133.00∘C,177.00∘C, and 156.00∘C, interspersed with short 0.3 -minute isothermal holds. This rapid thermal cycling mimicked the environment within the die where the resin transitions from a liquid to a rubbery status (gelation), eventually leading to the final cure. The final stages of the test involved cooling down to 25.00∘C followed by a standard ramp to 200.00∘C, likely to assess the residual cure of the processed PU.

[picture]

Fig. 3. Thermocouple-based temperature acquisition: (a) sensor placement in composite preform, (b) temperature profile along die length at 50.8 cm· min−1.

Fig. 3. Thermocouple-based temperature acquisition: (a) sensor placement in composite preform, (b) temperature profile along die length at 50.8 c m · m i n − 1 .

In this work, the thermo-chemical model focuses on three coupled physical phenomena which are axial heat transfer along the die, resin cure kinetics, and the resulting evolution of viscosity. These physical processes were chosen as they directly control the temperature history, gelation position, degree of cure, and pulling resistance in thermoset pultrusion [3]. Other mechanisms such as compaction in a tapered entrance and detailed resin flow in the injection region was not modeled explicitly, since the PulFlex configuration employs a constant-section die without a compaction taper and the trials showed stable impregnation in the glass-fiber baseline trials. Prior studies indicate that viscous drag and friction dominate the pull force while compaction and local flow variations make a comparatively minor contribution [12].

3. Finite Element Modeling of Thermal and Curing Phenomena in Pultrusion Manufacturing

The original version of this paper is available on https://www.scientific.net/SSP.387.61.pdf

3.1 Geometric configuration, mesh generation and boundary conditions

The composite laminate was modeled in ANSYS Composite PrepPost (ACP) to capture throughthickness thermal gradients and anisotropy. The laminate consisted of 0.2 mm plies (total thickness 3.2 mm ) with a width of 127 mm , matching the experimental profile.

The three-dimensional finite element simulation was implemented to capture the full thermalchemical coupling during the pultrusion process. The composite laminate was meshed using 8 -node
brick elements (SOLID278 in ANSYS) with body sizing and a smallest feature size of 0.2 mm . Additionally, side biasing was applied at corners to capture high gradient regions, resulting in approximately 2.1 million elements for the full composite length. The die structure was meshed with a smallest feature size of 3 mm using a 4-biased boundary-constrained mesh scheme. For the internal surface of the die, face meshing was applied with a minimum element size of 0.3 mm to ensure accurate resolution of the die-composite interface thermal interactions. These meshing strategies enabled precise representation of heat transfer at critical interfaces.

To complete the thermal model formulation, boundary conditions were applied to represent the physical heat transfer mechanisms. Contact between the die and composite surfaces was defined using surface-to-surface contact pairs, with the die internal surface as the target and the composite outer surface as the contact. A coefficient of convective heat transfer of 5000.0 W/(m2·∘C) was applied at this interface, with a friction coefficient of 0.25 to capture resistance during pulling. Natural convection with a heat transfer coefficient of 10 W/m2· K at 25∘C ambient was applied to external die surfaces. These thermal boundary conditions, coupled with the material properties and cure kinetics, establish the complete computational framework for process simulation.

[picture]

Fig. 4. Longitudinal die-composite system geometry and actual dimensions of the PulFlex pultruder, and the cross section (Section A-A) of the last segment of the die which define the final pultruded geometry.

Fig. 4. Longitudinal die-composite system geometry and actual dimensions of the PulFlex pultruder, and the cross section (Section A-A) of the last segment of the die which define the final pultruded geometry.

The dimensions presented in Fig. 4 were used for creating the pultrusion finite element simulation displayed in the following paragraph.

3.2 Computational framework

The study of thermo-chemical evolution and polymerization kinetics in pultrusion required developing a computational model of both the die and fiber-reinforced polymer matrix. This approach enabled quantification of heat transfer mechanisms and thermo-mechanical effects under industrial conditions. Simulations used ANSYS 2025 R1 with the ANSYS Composite Cure Simulation (ACCS) module for polymerization, thermal fields, and multiphysics coupling in thermoset composites. The transient energy conservation equation for the curing composite incorporated diffusive and advective transport with volumetric heat from exothermic reactions, as presented in Eq. 1:

ρcp((∂T∂t)+u(∂T∂xc))=−((∂∂xo)Qo)+Hi.(1)

where ρ is the density of material, cp is the specific heat of the composite material, T is the temperature, Qo is the heat flux, Hi characterizes the rate of internal heat generation per unit volume due to curing reactions, and the convective term u(∂T∂xc) accounts for thermal advection resulting from material transport through the die at the prescribed pulling velocity.

For the metallic die structure, thermal transport was governed by classical Fourier conduction with material advection in the primary pulling direction, as presented in Eq. 2:

ρcp(u∂T∂x)=Kx∂2T∂x2+Ky∂2T∂y2+Kz∂2T∂z2+q.(2)

where ρ is the density of matrix, cp is the specific heat, Kx,Ky,Kz represent the directional thermal conductivities along the longitudinal ( x ), and transverse ( y,z ) axes, while q denotes internally generated heat. The pulling velocity u in the longitudinal direction introduces a Eulerian advection term that reflects the continuous material flow through the stationary die assembly.

Equivalent pressure stress p is a scalar measure of the hydrostatic component of the stress tensor, as presented in Eq. 3:

p=−13×(σ11+σ22+σ33)(3)

where σ11 is normal stress in the pultrusion direction, σ22 is normal stress in the transverse direction, and σ33 is normal stress in the thickness direction.

The total pulling force required during the pultrusion process was determined as the sum of three force components [12], as presented in Eq. 4:

Fd, total =Fcom +Fvis+Ffric (4)

where Fcom is the compaction force, Fvis is the viscous drag force, and Ffric is the friction force. The compaction force, given by equation (5), was zero in this study as no tapered die inlet was employed ( θ=0∘ ):

Fcom =∫x=0x=Lt2w(p˙+σ)tanθdx(5)

The viscous force was calculated using equation (6) by integrating the shear stress over the pregelation region from the die entrance to position xin :

Fvis=∫x=0x=xin2wτdx(6)

where w is the composite width (0.127 m) and τ is the shear stress calculated using the viscosity at different pulling speeds and thickness. The friction force acting in the post-gelation region was determined as by equation (7):

Ffric =∫x=xin x=xL2wfσdx(7)

where f is the coefficient of friction ( 0.3 ) [13], and xL is the die exit position ( 0.9144 m ). Material shrinkage effects were not considered in this analysis.

3.3 Cure evolution and flow behavior modeling

Curing of the PU was characterized through DSC measurements leading to the following set of values (Table 1), where Ea is the activation energy for curing, Htot  is the total heat released, A1 and A2 are the pre-exponential factors and m is the autocatalytic reaction order and n is the reaction order for remaining reactants.

Table 1. Curing Kinetics of PU Derived from DSC Measurements.

Parameters | Values | Units
Activation energy (Ea) | 46.12 | [kJ/mol]
Total heat released (Htot) | 127.17 | [kJ/kg]
A₁ | 1.0 × 10⁻² | [1/s]
A₂ | 3.52 × 10⁴ | [1/s]
m | 0.11 | -
n | 1.64 | -

In the same time, the cure evolution was modeled using the KS autocatalytic kinetic law, as presented in Eq. 8:

dαdt=(E1+E2αm)(1−α)n(8)

Here, α is DoC(0 to 1), and E1,E2,m, and n define the reaction rate and autocatalytic behavior. Resin viscosity was modeled as a coupled function of temperature T and the degree of crystallinity (DoC), denoted α, as presented in Eq. 9:

η(T,α)=η∞exp(URT+Kηα)(9)

In Eq. (9), η∞,U, and Kη are empirical constants and R is the universal gas constant. The viscosity and cure inputs were coupled through the thermal-cure solution governed by Eqs. (1-2, 8-9).

3.4. Integration of Viscosity for Pulling Force Prediction

To predict the pulling force requirements as a function of pulling speed, a coupled thermo-chemomechanical framework was implemented. A displacement-controlled boundary condition was applied at the composite inlet, where the displacement magnitude at each time step corresponded to the specified pulling velocity (displacement = velocity ×Δt, with Δt=1 second). This approach simulated the continuous material flow through the die under quasi-steady-state conditions.

The viscosity field, obtained from the chemo-rheological model as a function of local temperature and degree of cure, was computed at every integration point throughout the simulation. This viscosity distribution served as the constitutive input for calculating shear stresses in the pre-gelation region. Simultaneously, the simulation calculated normal stress components (σ11,σ22,σ33) at each position, which were extracted to determine the pressure distribution acting on the composite-die interface. The gelation position was identified, separating the viscous-dominated pre-gelation zone from the friction-dominated post-gelation zone.

The total pulling force was determined by integrating the viscosity-dependent shear stress over the pre-gelation region and adding the friction force contribution from the post-gelation region, calculated using the extracted normal stresses and a constant friction coefficient ( μ=0.3 ). Material shrinkage effects were not considered in this analysis. The constant friction coefficient results in constant friction force post-gelation, causing the total pulling resistance to plateau after gelation.

3.5. Computational results and process parameter analysis

The transient simulation employed a time step of 1 second with the three heating zones set to 133∘C (Zone 1), 177∘C (Zone 2), and 154∘C (Zone 3). As the composite progresses through the die, the polymer undergoes distinct phase transformations: in Zone 1 (starting at x=88.9 mm ), the material remains in a low-viscosity liquid state as temperature rises; upon entering Zone 2 (starting at x= 368.3 mm ), the elevated temperature triggers rapid polymerization and the material transitions through gelation into a rubber-like state; by the end of Zone 2 (ending at x=571.5 mm ) and through Zone 3 (ending at x=806.45 mm ), the material approaches vitrification, transforming into a rigid, fully consolidated solid. The simulated temperature distribution at a pulling speed of 50.8 cm· min−1 is shown in Fig. 5, with a peak temperature of approximately 174∘C in the mid-die region after complete simulation.

[picture]

Fig. 5. Temperature contours along the die and composite in ANSYS 2025 R1.

Fig. 5. Temperature contours along the die and composite in ANSYS 2025 R1.

[picture]

Fig. 6. Cross-sectional temperature contour distributions at three axial locations: (a) length x=0 mm , (b) length x=380 mm, (c) length x=914.4 mm.

Fig. 6. Cross-sectional temperature contour distributions at three axial locations: (a) length x = 0 mm , (b) length x = 380 m m , (c) length x = 914.4 m m .

Three cross-sections are also shown in Fig. 6, at: (a) x=0 mm at the die entrance, (b) x=380 mm after the start of zone 2 heating at 177∘C, and (c) x=914.4 mm at the die exit. To quantify process sensitivity, the validated ACCS model was evaluated across a range of pulling speeds, and Fig. 7 summarizes the coupled evolution of (a) temperature, (b) DoC, (c) pulling force, and (d) die-exit conversion versus speed along the 0.9144 m die length.

The temperature profiles along the die for different pull speeds are shown in Fig. 7(a). In all cases, the temperature increases from the inlet, reaches a peak of about 170−180∘C in the mid-die region (around 0.45−0.6 m ) due to die heating and reaction exotherm, and then decreases toward the die exit. Fig. 7(b) presents the degree of cure (DoC) evolution along the die. Although the cure develops progressively along the heated zone, the key difference is observed at the die exit, where the DoC decreases with increasing pull speed. Lower pull speeds achieve faster cure development and nearcomplete conversion earlier in the die, whereas higher speeds shift the cure onset downstream and result in lower conversion at the exit, highlighting the strong influence of residence time on cure kinetics.

Figure 7(c) shows the evolution of pulling force along the die length for different pulling speeds ranging from 10 to 90 cm· min−1. At lower pulling speeds ( 10−20 cm· min−1 ), the force rise occurs
earlier in the die and reaches plateau values of approximately 6500−7000 N, while higher speeds ( 48.3−90 cm· min−1 ) shift the force increase downstream and result in higher plateau forces ( 7000− 8500 N ). This trend reflects the delayed gelation at faster speeds due to reduced residence time, which increases the pre-gelation viscous contribution before the transition to friction-dominated pulling. The sharp increase in all curves confirms the transition of the gelation, while the constant postgelation force validates the assumption of constant friction coefficient in the cured composite region.

In Fig. 7(d) the DoC at die exit as a function of pull speed decreases monotonically from about 99.52% at 10 cm· min−1 to 88.38% at 90 cm· min−1. This trend demonstrates the tradeoff between productivity and cure completeness, indicating that pull speeds above ~50 cm· min−1 may lead to under-cured profiles for the present die and resin system unless compensated by modified thermal conditions or post-curing.

[picture]

Fig. 7. Coupled (a) temperature, (b) DoC, (c) pulling force, and (d) cure at die exit for pulling speeds ranging from 10−90 cm· min−1.

Fig. 7. Coupled (a) temperature, (b) DoC, (c) pulling force, and (d) cure at die exit for pulling speeds ranging from 10 − 90 c m · m i n − 1 .

4. Mathematical Modeling of Thermal and Curing Phenomena in Pultrusion Manufacturing

The original version of this paper is available on https://www.scientific.net/SSP.387.61.pdf

4.1. Modeling framework and parameter identification

A reduced-order thermo-chemical model was developed to reproduce the PulFlex glass fiber pultrusion trials and enable rapid, speed-dependent predictions of temperature, DoC, and viscosity along the heated die. Glass fibers (ECT469PH CPIC Abahsain Fiberglass) were used for calibration due to their stable thermocouple measurements. The heated die length was set to 0.9144 m and divided into three heater zones. Zone setpoints were represented as piecewise-constant heater bands superimposed on a smooth baseline centerline temperature profile T(x) (Fig. 8). The baseline T(x) was calibrated using the 50.8 cm· min−1 thermocouple trace and exhibits a single peak near x≈0.47 m followed by cooling toward the die exit, consistent with prior pultrusion modeling studies [14].

Predictions at other pulling speeds v were obtained by applying speed-dependent scaling to the peak temperature, exit temperature, and rise/decay exponents of T(x), while preserving the experimentally observed peak location and accounting for reduced residence time at higher speeds. The local DoC α(x) was computed by integrating the KS autocatalytic kinetic model defined in Eq. (8), using the local thermal history provided by T(x). Kinetic parameters were identified from DSC measurements following established procedures [15,16] and are summarized in Table 1.

The governing cure equation was integrated into a Lagrangian frame with Δt=Δx/v, using a two-stage explicit time-integration scheme with midpoint temperature evaluation. Resin viscosity was computed using the Arrhenius-type chemo-rheological relationship given in Eq. (9), consistent with the formulation used in the ANSYS Composite Cure Simulation module. In Eq. (9), η∞,U, and K denote the reference viscosity, apparent activation energy for flow, and cure-dependent stiffening parameter, respectively. These viscosity parameters were calibrated such that the predicted centerline viscosity at 50.8 cm· min−1 matched the ANSYS results and were subsequently held fixed across all pulling speeds. Following common practice for reactive thermosets, gelation was defined as the axial location where η≥104 Pa· s [16,17], with residence time given by t(x)=x/v. The composite was treated as a one-dimensional unidirectional laminate with a fiber volume fraction of Vf=0.60.

4.2 Results and discussion of the mathematical model

Fig. 8 presents the predicted centerline temperature distributions along the 0.9144 m heated die for pulling speeds of 5.0,20.0,48.3,50.8, and 90.0 cm· min−1. In all cases, temperature increases rapidly from the die entrance to approximately 0.45−0.50 m, followed by a gradual decrease toward the exit. At lower pulling speeds (5−20 cm· min−1), the longer residence time allows the composite to approach the heater-zone setpoint temperatures farther upstream in the die. As pulling speed increases, reduced residence time lowers both peak and exit temperatures. These trends are consistent with classical pultrusion heat transfer behavior, in which increasing speed shifts thermal features downstream and limits internal temperature rise [14].

[picture]

Fig. 8. Mathematical modeling of composite temperature profile along heating die.

Fig. 8. Mathematical modeling of composite temperature profile along heating die.

The corresponding DoC evolution is shown in Fig. 9(a). For all pulling speeds, the cure profiles exhibit the characteristic Kamal-Sourour S-shaped behavior. Increasing pulling speed shifts the main cure rise downstream and reduces die exit DoC due to shortened residence time. At the reference speed of 50.8 cm· min−1, the model predicts a die-exit DoC of approximately 0.95 , consistent with experimental observations of near-complete curing under this condition.

The predicted pulling force evolution along the 0.9144 m die is presented in Fig. 9(b). For each pulling speed, the total force increases from near zero at the inlet to a plateau in the post-gelation
region as the contribution from viscous drag is replaced by friction at the die-composite interface. At lower speeds ( 10−20 cm· min−1 ), the force rise occurs earlier in the die and plateaus at moderate levels, reflecting earlier gelation and shorter viscous-dominated length. At higher speeds ( 48.3−90cm·min−1 ), the force increase is shifted downstream, and the plateau magnitude becomes larger, indicating a longer pre-gelation shear zone and greater integrated resistance before friction dominates. The sharp change in slope around the gel point in all curves confirms the transition between viscousdominated and friction dominated pulling resistance assumed in the analytical formulation.

The dependence of die-exit DoC on pulling speed is summarized in Fig. 9(c). The model predicts a monotonic decrease in exit DoC with increasing speed. Using a practical in-die DoC target of 95%, the current heater schedule supports pulling speeds up to approximately 50−55 cm· min−1 without significant cure deficit. Speeds well below 20 cm· min−1 provide only marginal gains in exit DoC while reducing throughput, whereas higher speeds push the primary gelation transition too close to, or beyond, the die exit. The predicted temperature, cure, and viscosity fields at 50.8 cm· min−1 are subsequently used as baseline inputs for comparison with the full ANSYS thermo-chemical simulation in Section 5.

[picture]

Fig. 9. Mathematical modeling of (a) DoC, (b) Pulling force, and (c) DoC at die exit.

Fig. 9. Mathematical modeling of (a) DoC, (b) Pulling force, and (c) DoC at die exit.

5. Comparison of ANSYS Simulation and Mathematical Modeling in Pultrusion Manufacturing

The original version of this paper is available on https://www.scientific.net/SSP.387.61.pdf

To evaluate the reliability of the reduced mathematical model and its suitability for process design, its predictions were compared directly with both experimental measurements and the full thermochemical ANSYS simulation at a pulling speed of 50.8 cm· min−1. All three modeling approaches employed identical experimentally calibrated cure kinetics, while the ANSYS and mathematical models shared the same chemo-rheological viscosity formulation used for pulling-force prediction.

Figure 10(a) compares the predicted and measured centerline temperature evolution along the heated die. The PulFlex thermocouple trace exhibits rapid heating from the inlet, a single peak in the mid-die region, and cooling toward the exit. Both the ANSYS and mathematical models reproduce this overall shape, capturing the peak location and exit temperature within a small offset that likely reflects local heat-transfer non-uniformities and sensor placement uncertainty. The DSC based temperature representation, constructed from the programmed oven history used in the cure kinetics experiments, shows a slightly sharper thermal ramp but follows the same peak range, reinforcing consistency between the laboratory characterization protocol and the industrial die conditions.

Figure 10(b) presents a comparison of DoC along the die between the mathematical model, the ANSYS Composite Cure Simulation, and the DSC-derived degree of cure at 50.8 cm· min−1. The mathematical and ANSYS predictions exhibit closely aligned S-shaped cure profiles, with similar onset, inflection position, and die-exit DoC values ( ~0.95 ), consistent with near-complete curing inferred from post-process characterization. The DSC based DoC curve, when mapped to the die length using the measured temperature history, falls between the two model predictions and confirms that both frameworks reproduce the effective cure progression under the industrial heating schedule. Small offsets near the peak reaction zone are attributed to differences in heating rate between the pultruder and DSC protocol as well as through-thickness averaging in the 3D simulation

Figure 10(c) compares the pulling-force evolution along the die for the ANSYS based and mathematical models at 50.8 cm· min−1. Despite their different dimensionality and numerical implementation, both models predict a similar structure: a gradual force increase in the upstream, low-viscosity region, a sharper rise around the gelation point, and an approximately constant plateau in the post-gelation segment where friction dominates. The predicted plateau levels differ by a modest margin that is within the combined uncertainty associated with friction coefficient, pressure distribution, and die-composite contact assumptions. Overall, the agreement in temperature, DoC, and pulling force indicates that the reduced 1D model is sufficient to reproduce key thermo-chemical and process-resistance features at the baseline operating condition, making it a practical tool for parametric studies while retaining the 3D ANSYS model as a benchmark for localized effects such as through-thickness gradients and die corner regions.

[picture]

Fig. 10. Experimental and modeling prediction comparison at a 50.8 cm· min−1 pulling speed for composite (a) Temperature, (b) DoC, and (c) Pulling force.

Fig. 10. Experimental and modeling prediction comparison at a 50.8 c m · m i n − 1 pulling speed for composite (a) Temperature, (b) DoC, and (c) Pulling force.

The current thermo-chemo framework predicts temperature, DoC, and pulling force along the PulFlex line but does not yet capture the mechanical response of the profile. Future work will extend the model to a coupled thermo-chemo-mechanical formulation that tracks modulus, stress, and strain evolution through the heated die, enabling prediction of residual stresses and cracking in complex profiles [18]. This extension will allow direct links between heater setpoints, pulling speed, and resulting mechanical performance.

In parallel, in-situ ultrasonic monitoring will be integrated to detect internal defects such as porosity and delamination during processing. Prior studies demonstrate the effectiveness of ultrasonic through-thickness, guided waves, and phased array techniques for defect detection in composite manufacturing [19,20]. These measurements will be combined with simulation outputs to predict defect localization and process optimization. Data assisted correction strategies can also be implemented to improve accuracy under industrial noise [21,22]. Finally, the framework will be extended to additional fiber and resin systems by updating kinetic, rheological, and modulus parameters to explore processing windows for various composite architectures [18].

Conclusion

The original version of this paper is available on https://www.scientific.net/SSP.387.61.pdf

This work developed and experimentally validated a thermo-chemical modeling framework for polyurethane-based pultrusion on an industrial PulFlex line using a glass fiber baseline. A reduced one-dimensional mathematical model combining a calibrated KS cure law with an Arrhenius-type chemo-rheological viscosity formulation was implemented to predict temperature, degree of cure, and pulling resistance along a three-zone, 0.9144 m heated die. In parallel, a three-dimensional ANSYS Composite Cure Simulation of the die-composite system was constructed using identical kinetic and rheological inputs, enabling direct comparison between reduced and finite element approaches.

Both models capture the dominant thermo-chemical behavior of the process and reproduce measured in-die temperature profiles at 50.8 cm· min−1 within a few percent. The predicted DoC fields from ANSYS and the mathematical model agree closely with each other and with DSC-based reconstructions, yielding a die-exit DoC of approximately 0.95 and confirming near-complete curing
at the industrially preferred pulling speed. When these fields are used as inputs to an analytical pulling-force formulation, the ANSYS and reduced models predict similar force rise and plateau behavior along the die, demonstrating that the simplified 1D framework can reproduce not only thermal and cure evolution but also process resistance trends over a range of pulling speeds.

Parametric analysis with the mathematical model quantifies the tradeoff between productivity and cure completeness, showing that increasing pulling speed reduces die-exit DoC and shifts gelation and peak force downstream, while lower speeds provide diminishing gains in cure. Within the current heater configuration, pulling speeds up to approximately 50−55 cm· min−1 achieve a practical in-die DoC target of 95% without excessive pulling resistance. Overall, the validated 1D model offers a computationally efficient tool for process window exploration and pulling force prediction, while the 3D ANSYS framework provides a complementary higher fidelity benchmark that can be extended toward coupled thermo-chemo-mechanical simulations of modulus, stress, and defect evolution in future work.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/SSP.387.61.pdf

The research is funded within the Department of Energy DE-EE0011184 Project entitled "Pultrusion of structural components made of lignin-based carbon fiber". We would also like to acknowledge BASF Inc. North America for providing polyurethane resin. Special thanks to Dr. Ali Zolali and Dr. Elias Shakour. PulFlex Techonologies, LLC provided pultrusion lines.

References

The original version of this paper is available on https://www.scientific.net/SSP.387.61.pdf

AM Fairuz, SM Sapuan, ES Zainudin, and CNA Jaafar. Polymer composite manufacturing using a pultrusion process: A review. American Journal of Applied Sciences, 11(10):1798, 2014.

Nik Poppe, Michael Wilhelm, and Luise Karger. Pultrusion process simulation-modelling of the injection and impregnation chamber. In Proceedings of the 2023 International Conference on Composite Materials, 2023.

Pierpaolo Carlone, Ismet Baran, Jesper Henri Hattel, and Gaetano Salvatore Palazzo. Computational approaches for modeling the multiphysics in pultrusion process. Advances in Mechanical Engineering, 5:301875, 2013.

Segun Isaac Talabi, Jim Tobin, Benjamin Strom, Ian Brownstein, Vlastimil Kunc, and Ahmed Arabi Hassen. Recent and future developments in pultrusion technology with consideration for curved geometries: A review. Composites Part B: Engineering, 283:111678, 2024.

Costa Dias, R.C. Pultrusion of thermoset-based profiles - state of the art regarding materials, process set-ups, process modeling, and process simulation. Doctoral Thesis, Montanuniversität Leoben, Leoben, Austria, February 2020.

Evgeny Barkanov, Pavel Akishin, Nora L Miazza, and Santiago Galvez. Ansys-based algorithms for a simulation of pultrusion processes. Mechanics of Advanced Materials and Structures, 24(5):377-384, 2017.

Adam Douglas Freed. Modeling the pultrusion process to obtain low void fraction composites. West Virginia University, 2002.

P Akishin, E Barkanov, and A Bondarchuk. Finite element modelling and analysis of conventional pultrusion processes. In IOP Conference Series: Materials Science and Engineering, volume 96, page 012012. IOP Publishing, 2015.

G Ganesan and G Kumaran. An experimental study on the behaviour of gfrp pultruded i beam reinforced with cfrp laminates. International Journal of Advanced Technology and Engineering Exploration, 5(45):232-242, 2018.

Xiao Lin Liu, IG Crouch, and YC Lam. Simulation of heat transfer and cure in pultrusion with a general-purpose finite element package. Composites Science and Technology, 60(6):857864, 2000.

Xueliang Ding, Quanguo He, Qun Yang, Suwei Wang, and Ke Chen. Numerical simulation of impregnation process of reactive injection pultrusion for glass fiber/pa6 composites. Polymers, 14(4):666, 2022.

Arindam Mukherji and James Njuguna. An assessment on effect of process parameters on pull force during pultrusion. The International Journal of Advanced Manufacturing Technology, 121(5):3419-3438, 2022.

Gibson Leonard Batch. Crosslinking free radical kinetics and the pultrusion processing of composites. University of Minnesota, 1989.

Alexander A Safonov, Pierpaolo Carlone, and Iskander Akhatov. Mathematical simulation of pultrusion processes: A review. Composite Structures, 184:153-177, 2018.

Claudia Garschke, Patricia P Parlevliet, Christian Weimer, and Bronwyn Louise Fox. Cure kinetics and viscosity modelling of a high-performance epoxy resin film. Polymer Testing, 32(1):150-157, 2013.

Ismet Baran, Remko Akkerman, and Jesper H Hattel. Material characterization of a polyester resin system for the pultrusion process. Composites Part B: Engineering, 64:194-201, 2014.

RP Theriault, TA Osswald, and JM Castro. A numerical model of the viscosity of an epoxy prepreg resin system. Polymer composites, 20(5):628-633, 1999.

San Michael Sandberg, Onur Yuksel, Raphael Benjamin Comminal, Mads Rostgaard Sonne, Masoud Jabbari, Martin Larsen, Filip Bo Salling, Ismet Baran, Jon Spangenberg, and Jesper H Hattel. Numerical modeling of the mechanics of pultrusion. In Mechanics of materials in modern manufacturing methods and processing techniques, pages 173-195. Elsevier, 2020.

Ranjeetkumar Gupta, Daniel Mitchell, Jamie Blanche, Sam Harper, Wenshuo Tang, Ketan Pancholi, Lee Baines, David G Bucknall, and David Flynn. A review of sensing technologies for non-destructive evaluation of structural composite materials. Journal of Composites Science, 5(12):319, 2021.

Helena Rocha, Christopher Semprimoschnig, and João P Nunes. Sensors for process and structural health monitoring of aerospace composites: A review. Engineering Structures, 237:112231, 2021.

Omogbai Oleghe. A predictive noise correction methodology for manufacturing process datasets. Journal of Big Data, 7(1):89, 2020.

Wilma Polini and Andrea Corrado. Digital twin of composite assembly manufacturing process. International Journal of Production Research, 58(17):5238-5252, 2020.

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/composites-forming-processes/978-3-0364-1996-1

Solid State Phenomena, ISSN: 1662-9779, Vol. 387, pp 75-84

doi: 10.4028/p-eB7jZs

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

Submitted:
2026-01-17

Revised:
2026-02-04

Accepted:
2026-02-14

Online:
2026-04-14

Controlled Preform Consolidation for Manufacturing Difficult Resin System Composites

The original version of this paper is available on https://www.scientific.net/SSP.387.75.pdf

Dominic R. PALUBISKI 1,a*, Natasha SHIRSHOVA 2, b, Emile S. GREENHALGH 3,c, Milo S. P. SHAFFER 4, d and Dmitry S. IVANOV 1,e*

1 Bristol Composites Institute, School of Civil, Aerospace, and Design Engineering, Faculty of Science and Engineering, University of Bristol, Queen's Building, University Walk, Bristol, BS8 1TR, U.K.

2 Department of Engineering, Lower Mountjoy, South Road, Durham University, Durham DH1 3LE, U.K.

3 Faculty of Engineering, Department of Aeronautics, Imperial College London, 58 Princess Gate, London, SW7 1AY, U.K.

4 Faculty of Natural Sciences, Department of Chemistry, Imperial College London, 80 Wood Lane, London, W12, 7TA U.K.

a  dominic.palubiski@bristol.ac.uk, b  natasha.shirshova@durham.ac.uk, c e.greenhalgh@imperial.ac.uk, d m.shaffer@imperial.ac.uk, e dmitry.ivanov@bristol.ac.uk (*corresponding authors)

Keywords: In-situ preform deformation, multifunctional resin systems, liquid composite molding.

Abstract

Novel multifunctional resins and composites present multiple manufacturing challenges that need to be overcome to allow for wider adoption. These challenges include high viscosity, short cure windows, low permeability preforms, and non-standard cure kinetics. Standard liquid composite molding methods (such as resin infusion under flexible tooling [RIFT], resin transfer molding [RTM], or compaction-RTM) are poorly equipped to manufacture these new materials. A new system is presented that combines the abilities of RIFT/RTM/c-RTM while introducing controlled deformation of the preform during infusion to remove flow through the preform. This manufacturing method allows the preform to lie uncompacted, while still under vacuum, during infusion which allows resin to flow unrestricted between plies. Then once infusion has occurred, compaction proceeds to produce the final composite geometry. This method has been successfully implemented to manufacture structural power devices with a biphasic resin system and metal coated carbon aerogel preform, as well as a vitrimer composite with a high-viscosity/short-cure resin infused through high weight carbon fiber preform. The novel and flexible manufacturing parameters of this new system present a low-cost route towards optimizing the manufacture of challenging and novel resin systems, allowing for a faster understanding and implementation of these materials.

Introduction

The original version of this paper is available on https://www.scientific.net/SSP.387.75.pdf

Resins for modern composite matrices have historically been optimized for mechanical performance and processing requirements. While achieving exceptional properties in these areas, the expanding application of composites necessitates the development of more complex, multifunctional resins to enable greater efficiency in composite structures. These resins can deliver a range of essential functions, such as repairability or energy storage, but they also introduce a new set of processing and manufacturability challenges. They are often more viscous, exhibit narrower processing windows and more demanding handling conditions, and can adversely affect the formability and compressibility of preforms. As a result, substantial modification or reinvention of conventional manufacturing methods is required. In this paper, we examine the requirements for, and potential solutions to, new manufacturing approaches that can be flexibly adapted to a variety of resins, and we present two representative case studies.

Composites with Covalent Adaptive Networks (CANS, aka vitrimers) matrices [ 1−3 ] are one such example. Vitrimers are crosslinked polymers, similar to thermosets, that possess side chain chemistry
that disassociates above a set temperature. This allows the material to flow post curing and behave similar to a thermoplastic. Both abilities produce a performative and creep resistant resin that can be repaired [4-6] and reshaped [7,8]. While the processability of this system requires lower heat and pressure than other high-performance thermoplastic systems, vitrimers can be far more viscous and have shorter cure times than comparable thermoset resins. Especially if the reversable side chain chemistry is present in the initial components (rather than synthesized during manufacture). Manufacturing with such systems requires careful consideration to ensure rapid infusion, short flow paths, full preform infusion, achieving good consolidation of infused preforms and high fiber volume fraction, and good surface finish without accumulation of excess resins on ply surface.

Another manufacturing challenge presented in current multifunctional resin research is the development of structural power composites, where the ability to transfer structural load alongside electrical load promises significant weight savings in a wide range of applications. With a specific focus upon structural supercapacitors [9-11], there have been a wide range of novel material developments to improve their electrochemical and mechanical performance in tandem. To allow for the transfer of load and ion transfer concurrently, a biphasic multifunctional resin system has been developed [12]. During cure, this resin undergoes a phase separation to produce a solid, load carrying phase and a liquid, ion carrying phase. This transformation occurs with a complex rheological profile and shortened cure times compared to the base line resin systems it is based on.

The biphasic resin is infused into structural electrodes, which are the carbon woven preforms containing carbon aerogel (CAG) for larger surface area and energy storage capacity. These preforms need to be thin so spread-tow fabric is the most attractive option. CAG occupies the space available between fibers and present much lower permeability. The inclusion of a current collecting film of metal across the entire upper and lower surface of the device further impedes the prospects of throughthickness flow.

The restrictions outlined above produce manufacturing challenges that typical liquid composite molding (LCM) manufacturing processes simply cannot overcome. Resin Infusion under Flexible Tooling (RIFT) [13] is widely used in research settings due to its low cost and flexibility. RIFT relies solely upon a vacuum at the inlet both to produce both the pressure gradient for resin to flow and to apply consolidation pressure. This is typically sufficient for low viscosity commercial resin systems with long cure times but is practically not applicable to a high viscosity vitrimer such as the one explored here. Additionally, the resin distribution mesh creates a rough layer of resin which has considerable thickness compared to the thickness of thin electrodes.

Resin Transfer Molding (RTM) [14], on the other hand, permits to apply positive injection pressure as the preform is constrained by double-sided solid molds. While positive resin pressure gives higher chances for viscous resin to reach the vent, the flow front propagates only in the plane of the preform, which increases the flow length compared to RIFT, while preform is highly compacted, which lowers its permeability. As a result, benefits of positive pressure may be compromised. Inflexibility of RTM also presents difficulties for adjusting it to preforms of various density and compressibility.

Compression-RTM (cRTM) [15] could be an attractive alternative to RIFT and RTM in the context of functional resins. Resin is injected into a mold in a similar manner to RTM but with an open cavity above the preform or with the preform compacted to lower fiber volume fraction that intended. After the injection of the required amount of resin, the upper mold is closed to force the resin through the plies (or drained from the mold), producing the final component geometry. cRTM is capable to take advantage of positive injection pressure, high permeability during infusion, and high level of consolidation achieved in post-infusion compaction. The main limitations of cRTM in the context of structural power applications are the constraints on through-thickness flow imposed by the current collector plies and rather rigid nature of CAG modified carbon fibers preform preventing preform spring back and lowering permeability of the preform during infusion. Infusion trials with a vitrimer resin highlighted limitations in through thickness flow, where almost no vitrimer could be forced through a very limited number of plies of the preform. Complexity and cost of cRTM makes it also rather inconvenient to tune process to the properties of multi-functional resins.

To tackle these challenges a new manufacturing approach is required that can remove the limitation of through thickness flow for novel complex resin and fiber systems. Two trial systems are presented to highlight the abilities of such a novel manufacturing system, the structural power device and vitrimer composites noted above. The proposed manufacturing solution is based around a novel manufacturing system utilizing vacuum induced preform relaxation (VIPR, referred to as vacuum relaxation) [16] to induce the separation of individual preform plies during infusion, as well as independent control of infusion and consolidation pressure. Below we discuss the processing abilities and challenges of this manufacturing route in achieving a cost-effective solution.

Manufacturing System and Materials

The original version of this paper is available on https://www.scientific.net/SSP.387.75.pdf

Manufacturing system. The novel manufacturing system was designed in house to implement a range of manufacturing capabilities flexibly joining features of RIFT, RTM, cRTM, and VIPR such as positive injection pressure, vacuum at the vent, independent control of consolidation and injection pressures, vacuum relaxation, flexible tooling, and in-tool heating. This has been implemented using a two-chamber design in a rigid tool (Figure 1a). The chambers are separated by a flexible membrane with the bottom half hosting preform, inlets for resin injection under pressure and vacuum vents (Figure 1b). The upper chamber has ports for both applying vacuum or consolidation pressure. The tool is placed in a press to sustain the consolidation and injection pressure, and the flexible membrane serves as sealing as well as pressure transmitting medium.

[picture]

Fig. 1. Infusion system used in this work. Part a presents an overview of the system including all primary components, while part b presents the tool face and how it is set up for infusion. The preform is within the lower chamber of the two-chamber pressurized tool, alongside thermocouples, distribution mesh around (but not on) the preform, and a semi-permeable membrane. Both the upper and lower chambers have a pressure inlet and a vacuum outlet. The lower chamber inlet is connected to a heated pressure pot, via a heated hose, for resin injection. The two-chamber tool is held together using a press to contain infusion and consolidation pressures.

Fig. 1. Infusion system used in this work. Part a presents an overview of the system including all primary components, while part b presents the tool face and how it is set up for infusion. The preform is within the lower chamber of the two-chamber pressurized tool, alongside thermocouples, distribution mesh around (but not on) the preform, and a semi-permeable membrane. Both the upper and lower chambers have a pressure inlet and a vacuum outlet. The lower chamber inlet is connected to a heated pressure pot, via a heated hose, for resin injection. The two-chamber tool is held together using a press to contain infusion and consolidation pressures.

A typical infusion schematic can be seen in Figure 2. When vacuum is applied in both the chambers, the preform will not experience compaction as there is no resulting force upon the silicone membrane. This helps to infuse resin at higher preform permeability and propagate the resin between the plies (Figure 2a). Resin is then infused in-plane driven by the pressure differential between the vent and positive injection pressure. The flow of resin, especially viscous resins, can cause the preform to shift during infusions. While this issue occurred, the resultant force was minimal and small tabs of temperature stable tape (on two opposing corners of the preform) were suitable to prevent movement. The use of a semi-permeable membrane (permeable for gases but not liquid) over the vacuum outlet ensures that resin cannot leave the lower chamber and the full volume is quickly filled, forcing resin into any available spaces such as those between plies (Figure 2b). With the lower chamber filled and the resin delivered between each individual ply, consolidation pressure is then applied to the empty upper chamber. When the upper chamber pressure is greater than the lower chamber, excess resin is pushed back through the injection port and reverse deformation of the preform commences. Extra volume of resin is also accommodated in distribution mesh placed around the preform (Figure 2c). Once all excess resin has been removed the preform forms its final geometry after infusion has occurred. The resin-tailored cure cycle then commences by activating the heating elements built within the mold (Figure 2d).

By balancing and sequencing the application of injection and consolidation pressure, various manufacturing routes can be implemented making continuous transition from RIFT to cRTM depending on the processing parameters.

[picture]

Fig. 2. Composite manufacturing steps in the designed infusion system. a): Both the upper and lower chambers are evacuated to vacuum, with the inlets sealed off. The resulting force upon the membrane is zero, allowing the preform to relax and deform. b): Resin is injected into the lower chamber under pressure. The semi-permeable membrane allows any residual air to escape but stops any resin leaving the lower chamber. As vacuum is still existent in the upper chamber the membrane deforms upwards. c): Once infusion has been completed the vacuum lines, for both top and bottom chambers, are sealed off and the upper chamber is pressurized. Once the upper chamber pressure exceeds that of the lower chamber the membrane is forced down and compaction occurs, forcing excess resin out of the chamber back through the injection port. d): Once all excess resin has left the lower chamber the preform is considered infused and cure progresses.

Fig. 2. Composite manufacturing steps in the designed infusion system. a): Both the upper and lower chambers are evacuated to vacuum, with the inlets sealed off. The resulting force upon the membrane is zero, allowing the preform to relax and deform. b): Resin is injected into the lower chamber under pressure. The semi-permeable membrane allows any residual air to escape but stops any resin leaving the lower chamber. As vacuum is still existent in the upper chamber the membrane deforms upwards. c): Once infusion has been completed the vacuum lines, for both top and bottom chambers, are sealed off and the upper chamber is pressurized. Once the upper chamber pressure exceeds that of the lower chamber the membrane is forced down and compaction occurs, forcing excess resin out of the chamber back through the injection port. d): Once all excess resin has left the lower chamber the preform is considered infused and cure progresses.

Structural Supercapacitor Device (SPD). The general design of the structural supercapacitor device is following the methodology developed by Nguen et al [10]. The layup consists of two plies of CAG'd spread tow woven carbon fibers (TeXtreme 1134, 12k plain weave 53 gsm fabric, supplied by TeXtreme [17], modified by CAG addition) with a single sheet of a separator between the two plies ( 1 k plain weave Kevlar, 50 gsm was used in this case). Both pieces of CAG modified carbon
fibers have a film of aluminum current collector (3M AL-36FR Tape, purchased from Mouser Electronics [18]) glued, with conductive adhesive, to the outside faces of the laminate. The total laminate thickness is 0.2 mm , with a maximum size of 210×148 mm (a5 paper size). Trial infusions were of this size but samples for electrochemical testing were 40×40 mm, following outlined protocols of Greenhalgh et al [19]. No flow medium was placed above or below the preform, but to aid with excess resin removal during compaction the rest of the tool face was covered in distribution mesh (Figure 1b).

The structural electrolyte (SE) infusion resin, modified from the original design by Wengdong et al [12], consisted of Huntsman Tactix 742 epoxy (RD4, supplied by Huntsman Resin [20]) and isophorodiamine hardener (iPDA, purchased from Merck [21]) mixed at a 1:4 wt. ratio respectively. The epoxy was heated to 65∘C and 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (EMIM TFSI, >99.5%, purchased from Iolitec [22]), aka ionic liquid (IL) was added at 40 volume% to the reaction mixture and stirred by hand. Once cooled, the hardener was added, mixed by hand, and then formed solution was used to infuse at room temperature with 6 bar of injection pressure while the preform was held under vacuum relaxation. After the chamber was filled with SE the injection pressure was lowered to 2 bar and the upper chamber was slowly pressurized to 6 bar. The infusion was considered complete once excess resin stopped flowing out of the chamber, and the cure cycle followed. The resin cure cycle was as follows: 16 hours at 25∘C,1 hour at 60∘C,2 hours at 80∘C, 2 hours at 120∘C. At room temperature the resulting resin has a viscosity of ~20 Pa.s. Phase separation starts to occur around 60 minutes after mixing, with a resin pot life of about 100 minutes.

Vitrimer Composites (VC). Four plies of NCF unidirectional 50k, 600 gsm carbon fiber fabric (with tricot synthetic stitching, layup [0,90]s) was used as the infusion preform. This was chosen both for the challenge of the large tow size, as well as the heavy material weight acting against preform relaxation. Infusion occurred with a flow/deformation guide within the infusion chamber to aid both the deformation of the preform and to reduce the internal volume of the mold. Mallinda VHM epoxy and hardener [23] was used as the infusion resin. Both parts of the resin were heated to 85∘C before being mixed in the manufacturers recommended ratio of 1:1.5 of epoxy to hardener respectively. The pressurized injection pot and infusion line were heated to 80∘C (equipment maximum) and the tool heated to 85∘C. The mixed resin has a viscosity of ~6 Pa.s, with a pot life of 150 minutes. The resin was injected at 6 bar of pressure into the vacuum relaxed lower chamber and held until the volume was filled. The injection pressure was then lowered to 2 bar and 6.5 bar of consolidation force was slowly applied. The infusion was considered complete when excess resin was no longer flowing back out of the infusion chamber. Resin cure then progressed with 1 hour at 85∘C.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/SSP.387.75.pdf

Optimization of the infusion system was required before high quality infusions were achieved. Parameters such as infusion time, infusion pressure, tool heating, distribution mesh use and lay-up, infusion chamber volume, consolidation chamber volume, chamber volume ratios, and consolidation pressure were all explored during initial trials. Chamber volume, and the specific ratio between the upper and lower chamber, was discovered to be key to ensuring optimum infusion. If the upper chamber was too large for example, then the membrane would lift significantly during vacuum relaxation. The resulting excessive volume in the infusion chamber either (i) appears difficult to drain in post-infusion consolidation particularly when resin quickly gels, which leaves substantial resin rich domains on the composites surface (Figure 3a), or (ii) if the chamber is not fully flooded, accumulates in the distribution mesh surrounding preform but losing pressure gradient at the flow front to spread over the entire area leaving the preform dry (Figure 3b). In addition, the large volume would require an excessive amount of resin to fill the volume, making infusions both expensive and wasteful. Meanwhile, if the chamber volume is too small, vacuum relaxation does not permit the accumulation of sufficient amount of resin. The cavity thickness was varied in sequential trials and different optimum thicknesses were found for the two infusions explored here. All infusions required a lower internal height equal to, or slightly greater than, their final product thickness. However, the upper chamber height was dependent on the height of the dry fiber preform when unrestrained. The VC
(with a 4 mm final product thickness) required an upper chamber height of roughly 3 mm , while the SPD infusions (with 0.2 mm final product thickness) required an upper chamber height of less than 1 mm . These values are to be taken alongside the lower chamber height. For example, the VC composite infusion had a lower chamber height of 4 mm , while an upper chamber height of 3 mm , combined to 7 mm . This 7 mm is the height of the preform when unrestricted during layup, therefore in its most uncompacted state and providing least resin flow restriction. Ensuring the combined chamber heights allow for full fiber relaxation, without producing too large a volume (making it difficult to remove excess resin after infusion, is key to producing high quality infusions.

[picture]

Fig. 3. a): Failed infusion of SPD device using a monofunctional version of the SE resin, where excess upper chamber volume caused a trapped vein of resin during consolidation. Purple color was added to the resin in early infusions to aid in resin identification and flow understanding, along with minial use of flow distribution mesh. b): Failed infusion of preform with vitrimer resin. The internal volume was too small to allow for adequate vacuum relaxation. Of note is the white dry fiber stitching still visible in the center of the laminate that has been removed around the edges of the preform. When comparing to Figure 1b, the thermocouples are not visible, the vacuum outlet port is hidden under the semi-permeable membrane, and the silicone gasket has been removed.

Fig. 3. a): Failed infusion of SPD device using a monofunctional version of the SE resin, where excess upper chamber volume caused a trapped vein of resin during consolidation. Purple color was added to the resin in early infusions to aid in resin identification and flow understanding, along with minial use of flow distribution mesh. b): Failed infusion of preform with vitrimer resin. The internal volume was too small to allow for adequate vacuum relaxation. Of note is the white dry fiber stitching still visible in the center of the laminate that has been removed around the edges of the preform. When comparing to Figure 1b, the thermocouples are not visible, the vacuum outlet port is hidden under the semi-permeable membrane, and the silicone gasket has been removed.

Early infusion trials, such as one shown in Figure 3b, also tended to only partially infuse the preform. This was due to the internal height within the infusion and vacuum relaxation chambers being too small. Interestingly, where infusion has occurred the white stitching yarns of the preform have been removed (thought to be dissolved). An optical image of the center of a later, successfully infused VC laminate is shown in Figure 4. It still has the stitching present (darker fibers), while the surface of the laminate has no stitching present like in Figure 3b. While the fibers are thought to be dissolved in the resin, there appears to be a limiting factor with the designed infusion method that is limiting the amount of stitching that can be dissolved in the center of laminates. To fully understand this process, further understanding of the flow mechanisms and characteristics within the preform during vacuum relaxation is necessary. The SPD infusions may also have similar factors occurring during infusion but they are not noticed due to the lack of an indicator such as stitching. If these mechanisms can be better understood, then the removal of the stitching may aid in compaction and improving future infusions.

Additional work was also undertaken on the infusion aids within the chamber. Initial runs aimed to use no flow medium at all. However, it was quickly discovered that resin ended up being trapped between the tool and the membrane during compaction, leading to large excess resin regions across both the tool and part. Thie necessitated the inclusion of distribution mesh across the bare tool face, but not on the preform itself, to allow for excess resin to flow out the chamber during compaction. Many infusion trials were undertaken to optimize these parameters to improve infusions, with
emphasis placed on minimizing resin use (influenced by the high cost of the IL used in this work) alongside high part quality with minimal voids.

[picture]

Fig. 4. Optical microscope image of the infused vitrimer samples. The white fibers are the carbon fibers of the laminate, while the darker fibers are the NCF fabric stitching.

Fig. 4. Optical microscope image of the infused vitrimer samples. The white fibers are the carbon fibers of the laminate, while the darker fibers are the NCF fabric stitching.

Utilization of the preform deformation during the SPD manufacture produced a uniform laminate (Figure 5), with almost no voids. The RD4 SE microstructure in SPD was even and uniform across the entire device. Figure 6 shows an SEM image of the infused separator, with the gaps between the Kelvar fully infused with RD4 SE resin.

[picture]

Fig. 5. The infused structural supercapacitor 40×40 mm test device (a.) and the same device dissected for SEM imaging (b.). The current collector is the metallic top layer seen in image a with electrical connection tabs running off the left side of the device for both positive and negative connections, with the black CAG'd carbon fiber and yellow Kevlar electrical separator (outlined with blue dashes in a) can be seen in both images. The distribution mesh is outlined in purple dots in image a. In image b , the infused RD4 SE is the white seen on the peeled carbon fiber.

Fig. 5. The infused structural supercapacitor 40 × 40 m m test device (a.) and the same device dissected for SEM imaging (b.). The current collector is the metallic top layer seen in image a with electrical connection tabs running off the left side of the device for both positive and negative connections, with the black CAG'd carbon fiber and yellow Kevlar electrical separator (outlined with blue dashes in a) can be seen in both images. The distribution mesh is outlined in purple dots in image a. In image b , the infused RD4 SE is the white seen on the peeled carbon fiber.

Infusion of the vitrimer material underwent similar optimization before successful infusions were possible, where upon full infusion through all plies and into the center of the 50k tows (Figure 4) was possible. Vacuum relaxation ensured that the preform had no inter-ply flow restrictions (along the xand y-axis, between plies). This greatly aided the infusion by reducing the resin flow through thickness to only half of a ply, compared to the full z-thickness in typical RIFT/c-RTM infusion. While the compaction of the laminate does appear limited due to the presence of resin-rich
interlaminar regions, this is thought to be due to the inclusion of the flow/deformation guide and the interlaminar stitching not fully being removed during infusion. The flow/deformation guide greatly reduced the volume within the lower infusion chamber by occupying all space that was not the inlet port, outlet port, and the preform between them. However, this silicone sheet possibly supported the chamber separating membrane during compaction and reduced the pressure applied directly to the preform. Overall the infusion was still far more successful than any other VC manufacturing trialed and presents a viable route for vitrimer composite manufacturing with short cure, high viscosity resins.

[picture]

Fig. 6. SEM image of the electrical separator infused with structural electrolyte (SE), separated after infusion.

Fig. 6. SEM image of the electrical separator infused with structural electrolyte (SE), separated after infusion.

Conclusion

The original version of this paper is available on https://www.scientific.net/SSP.387.75.pdf

The successful manufacture of challenging multifunctional resin systems has been achieved utilizing designed preform deformation within a novel manufacturing solution, showcased through the production of a structural supercapacitor device and a vitrimer composite. The novel solutions has allowed for the infusion around low-permeability preforms, reduced required resin flow paths to only half a ply, and enabled a cost-effective solution to trialing novel material systems. The incorporation of elements from a range of typical infusion techniques (RIFT, RTM, cRTM) and the introduction of VIPR has produced a working hybrid that enables manufacture that none of the typical methods could achieve themselves. While the flexibility and low cost of the designed system is beneficial, the higher use of resin can be limiting. The entire chamber volume requires being filled with resin before compaction. The explored examples here are relatively thin, but even the 4 mm thick vitrimer composite required a volume reducing guide to ensure the full chamber volume was filled, and compacted, before gel point was reached. Overall though, the designed system appears to allow for rapid optimization of an infusion process, enabling key infusion parameters to be explored with greater flexibility than traditional pressure infusion systems (RTM and cRTM), while not sacrificing much ease of use when compared to typical RIFT infusion. Going forward, further experimental validation on new complex resin systems will enable far quicker optimization of manufacturing conditions with the added advantage of vacuum relaxation to allow for the controlled deformation of preforms to reduce flow restrictions throughout the preform.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/SSP.387.75.pdf

The work undertaken in this paper was supported by the EPSRC grant EP/W03526X/1 "Realising Structural Power: Addressing the Manufacturing Challenges".

References

The original version of this paper is available on https://www.scientific.net/SSP.387.75.pdf

Leibler L, Tournilhac F, Capelot M, Montarnal D. Silica-Like Malleable Materials from Permanent Organic Networks. Science (1979) 2011;334:965-8. https://doi.org/10.1126/science.1211649.

Sharma H, Rana S, Singh P, Hayashi M, Binder WH, Rossegger E, et al. Self-healable fiberreinforced vitrimer composites: overview and future prospects. RSC Adv 2022;12:32569-82. .

Schenk V, Labastie K, Destarac M, Olivier P, Guerre M. Vitrimer composites: current status and future challenges. Mater Adv 2022;3:8012-29. .

Palubiski DR, Longana ML, Dulieu-Barton JM, Hamerton I, Ivanov DS. Multi-matrix continuously-reinforced composites: A novel route to sustainable repair of composite structures. Mater Des 2023;235:112446. .

Kandemir A, Longana ML, Hamerton I, Eichhorn SJ. Developing aligned discontinuous flax fibre composites: Sustainable matrix selection and repair performance of vitrimers. Compos B Eng 2022;243:110139. .

Messmer LL, Kandemir A, Yavuz BO, Longana ML, Hamerton I. Mechanical Behaviour of As-Manufactured and Repaired Aligned Discontinuous Basalt Fibre-Reinforced Vitrimer Composites. Polymers (Basel) 2024;16:1089. .

Rath JE, Nettig D, Palubiski DR, Schüppstuhl T, Ivanov DS. Single point incremental forming of multi-matrix continuously-reinforced composites: A feasibility study. Material Forming ESAFORM 2025, Paestrum: 2025, p. 517-25. .

Palubiski DR, Longana ML, Hamerton I, Dulieu-Barton JM, Ivanov DS. Composite forming post-manufacture: reducing complexity and de-risking manufacture. In: Anna Carla Araujo, Arthur Cantarel, France Chabert, Adrian Korycki, Philippe Olivier, Fabrice Schmidt, editors. Material Forming - ESAFORM 2024, Toulouse: 2024, p. 440-8. .

Shirshova N, Qian H, Shaffer MSP, Steinke JHG, Greenhalgh ES, Curtis PT, et al. Structural composite supercapacitors. Compos Part A Appl Sci Manuf 2013;46:96-107. .

Nguyen S, Anthony DB, Katafiasz T, Qi G, Razavi S, Senokos E, et al. Manufacture and characterisation of a structural supercapacitor demonstrator. Compos Sci Technol 2023:110339. .

Greenhalgh ES, Nguyen S, Valkova M, Shirshova N, Shaffer MSP, Kucernak ARJ. A critical review of structural supercapacitors and outlook on future research challenges. Compos Sci Technol 2023;235:109968. .

Wendong Q, Dent J, Arrighi V, Cavalcanti L, Shaffer MSP, Shirshova N. Biphasic epoxyionic liquid structural electrolytes: minimising feature size through cure cycle and multifunctional block-copolymer addition. Multifunctional Materials 2021;4:035003. .

Williams C, Summerscales J, Grove S. Resin Infusion under Flexible Tooling (RIFT): a review. Compos Part A Appl Sci Manuf 1996;27:517-24. .

Potter K. Resin Transfer Moulding. 1st ed. London: Springer Netherlands; 1997.

Bickerton S, Kelly PA. Compression resin transfer moulding (CRTM) in polymer matrix composites. Manufacturing Techniques for Polymer Matrix Composites (PMCs), Elsevier; 2012, p. 348-80. .

Alms JB, Advani SG, Glancey JL. Liquid Composite Molding control methodologies using Vacuum Induced Preform Relaxation. Compos Part A Appl Sci Manuf 2011;42:57-65. .

Textreme n.d. (accessed December 18, 2025).

Mouser Electronics. 3M AL-36FR n.d.

Greenhalgh ES, Nguyen S, Asp LE, Bici A, Bismarck A, Fam D, et al. Characterization and Reporting Protocols for Structural Power Composites: A Perspective. Adv Energy Mater 2025;15. .

Huntsman. Tactix 742 n.d. (accessed November 24, 2025).

Merck. Isophorodiamine n.d. 814123?srsltid=AfmBOopeBUqRLvRMjTqcdcm7PdqLRITjGL2jZoef_2_JBG7j7DNx4Byi (accessed November 24, 2025).

Iolitec. 1-Ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide n.d. (accessed November 24, 2025).

Mallinda n.d. (accessed November 24, 2025).

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/composites-forming-processes/978-3-0364-1996-1

Solid State Phenomena, ISSN: 1662-9779, Vol. 387, pp 85-93

doi: 10.4028/p-ol0WyU

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

Submitted:
2026-01-19

Revised:
2026-02-16

Accepted:
2026-02-18

Online:
2026-04-14

Multi-Stage Consolidation Strategy to Improve Impregnation Quality in Thermoplastic UD Tapes

The original version of this paper is available on https://www.scientific.net/SSP.387.85.pdf

Oguz Eryilmaz 1,a, Onur Yuksel 1, b, Maissaloun El-Jakl 1,c, Nursel Karakaya 2, d, Guralp Ozkoc 2,3,e and Baris Caglar 1,f

1 Aerospace Structures and Materials Department, Faculty of Aerospace Engineering, Delft University of Technology, Delft, the Netherlands

2 Xplore Instruments B.V., 6135 KT Sittard, the Netherlands

3 Department of Chemistry, Istinye University, Istanbul 34396, Türkiye

a  o.eryilmaz@tudelft.nl, b  o.yuksel@tudelft.nl, c  M.El-Jakl@tudelft.nl,

d  nursel.karakaya@xplore-together.com, e  guralp.ozkoc@xplore-together.com, f  b.caglar@tudelft.nl

Keywords: thermoplastic composites, UD tape, process optimization, multi-stage consolidation.

Abstract

Incomplete impregnation is a remaining challenge in the production of thermoplastic unidirectional (UD) tapes, particularly for the melt impregnation. This study investigates an approach analogous to multi-stage die impregnation by re-calendaring partially impregnated UD thermoplastic tapes, where different closing forces under controlled processing conditions are used to enhance impregnation quality. In this work, polypropylene-carbon fiber (PP-CF) tapes with a width of 20 mm and a thickness of 400μ m were produced by using an Xplore UD Tape Line with a closed pultrusion die. As a model sample, these tapes were deliberately produced under conditions expected to result in partial impregnation by setting the die pressure and pultrusion rate accordingly. To improve impregnation, the tapes were subsequently subjected to calendaring with different nip force passes in a different line, thereby mimicking a staged or multi-die consolidation approach. Results show that calendaring induces pronounced geometric reconfiguration accompanied by improved impregnation quality. Tape thickness was reduced by up to approximately 65%, while tape width increased by up to approximately 70%, indicating effective lateral spreading under compressive and shear stresses. Optical microscopy of polished cross-sections revealed a reduction of dry fibre regions and improved resin continuity within inter-filament gaps at intermediate calendaring nip force. Density-based fiber volume fraction of the tape measurements showed only a slight increase in the range of 2−3%, suggesting that consolidation was governed primarily by fibre rearrangement and spreading rather than significant resin squeeze-out. The findings provide practical insights into how the calendaring unit of a thermoplastic tape manufacturing line can be adapted for multi-stage consolidation, offering improved impregnation quality in thicker thermoplastic tapes that are more prone to impregnation defects. This approach may also serve as a bridge solution when the pressure build-up is limited.

Introduction

The original version of this paper is available on https://www.scientific.net/SSP.387.85.pdf

Thermoplastic unidirectional (UD) tapes are increasingly adopted in automated composite manufacturing because of their recyclability, rapid processing capability, and suitability for out-ofautoclave consolidation [1-3]. Their performance, however, is governed by the degree of impregnation achieved during production. In thermoplastic extrusion-based impregnation processes (TPR), the molten polymer must penetrate tightly packed fibre bundles or spread tows, yet the high melt viscosity of thermoplastics and the limited pressure build-up inside the die often lead to partially impregnated regions and persistent micro-voids [4, 5]. Achieving consistent impregnation is particularly challenging for thermoplastic tapes produced via TPR, where resin viscosity, insufficient pressure generation in the closed die, and incomplete tow spreading restrict resin mobility and capillary flow within the fibre bundle [6, 7].
The Delft TapeLab is a unique open research facility for unidirectional thermoplastic tapes and their applications, where it facilitates research-scale (UD Tape line, Xplore Instruments, the Netherlands) and semi-industrial unidirectional tape manufacturing line (Van Wees, the Netherlands). The Xplore

UD Tape line consists of a benchtop microcompounder (MC15HT) attached to a benchtop UD Tape line consisting of a de-winding unit, spreaders, pultrusion die, and take-up winders. The semiindustrial unidirectional tape manufacturing line includes a creel cabinet for continuous fibre feeding, a heated main roll where polymer and fibres are combined, calender rolls for pressing and shaping the composite into a tape, and cooled rolls for stabilising and winding the product. The line also offers three interchangeable impregnation routes: thermoplastic extrusion impregnation (TPR), where the extruded melt is deposited onto fibres on a heated roller; kiss-roll impregnation (DPR), where fibres pick up polymer dispersion from a bath before water evaporation and melting on the main roller; and nip-roll impregnation (NPR), where fibres are introduced horizontally between rolls that both transfer polymer dispersion, followed by drying and melting.
Improving impregnation within the constraints of existing equipment remains a critical objective for both research-scale and industrial tape manufacturing. A more flexible strategy is to exploit consolidation units already present in most thermoplastic tapelines. Within this system, the calendaring unit plays a crucial role. It applies localized compressive and shear stresses to the molten or semi-molten tape, promoting resin mobility and facilitating penetration into micro-scale inter-fibre gaps [8, 9]. When applied in different stages, calendaring can emulate a multi-die impregnation strategy by providing repeated opportunities for void elimination and resin redistribution without requiring the die to produce an impregnated tape. A limitation in using calendaring as a consolidation tool is the narrow processing window. Excessive pressure can distort fibre architecture, cause nonuniform tow spreading, or squeeze out resin, unintentionally altering the fibre volume fraction. Insufficient temperature, on the other hand, reduces resin mobility and makes additional passes ineffective. Detailed insight into how parameters such as roll temperature, nip force, and pass count affect microstructural evolution is, therefore, essential for determining when calendaring improves impregnation. Despite this potential, the effect of calendaring on thermoplastic tape impregnation quality has not been systematically explored, particularly for narrow tapes with severe pressure gradients. At present no clear framework exists describing how calendaring influences filament packing, resin distribution, void content, and the evolution of tape geometry.
In this study, we investigated whether the calendaring unit can be used as a controlled multi-stage consolidation method to enhance impregnation quality in partially impregnated thermoplastic UD tapes. For this purpose, a modular Xplore UD Tape Line was employed to investigate the role of downstream consolidation on the impregnation quality of thermoplastic unidirectional (UD) tapes. The system enables the production of UD tapes with tailored width, thickness, and fibre volume fraction (Vf), allowing flexible adaptation to material systems and application-driven requirements. By optimizing the parameters, a fully impregnated carbon fiber-based UD tape can be obtained by using this lab-scale line. A representative tape, produced using a die with 12.7 mm width and an exit thickness of 170μ m, employing 24 K carbon fibre tows (DowAksa) and polypropylene (SABIC PP 519A), is shown in Figure 1. This representative micrograph of the tape illustrates the achievable tape uniformity and impregnation state with a calculated volume fraction of 49,12%.

[picture]

Fig. 1. Micrograph for a representative CF/PP tape produced with Xplore UD Tape Line (Die temperature of 225∘C, and line speed of 0.3 m/min.).

Fig. 1. Micrograph for a representative C F / P P tape produced with Xplore UD Tape Line (Die temperature of 225 ∘ C , and line speed of 0.3 m / m i n .).

In this study, polypropylene-carbon fibre (PP-CF) tapes with a width of 20 mm and an initial thickness of 400μ m were manufactured using an Xplore UD Tape Line with a die under conditions deliberately selected to yield partial impregnation. These tapes were subsequently processed on a separate tape manufacturing line where multiple calendering passes were applied under well-defined temperatures and different closing forces of the belts. Tape quality after each calendaring stage was characterized through optical microscopy of polished cross-sections and quantitative image analysis to assess voids. Thickness, width, and areal weight were monitored to quantify geometric evolution, while density measurements provided estimates of fibre volume fraction and void content. Together, these results enabled the identification of processing conditions where impregnation is improved without inducing undesirable changes in dimensional stability or fibre architecture.

2. Materials

The original version of this paper is available on https://www.scientific.net/SSP.387.85.pdf

The materials used in this study consisted of T800SC-10E-24K carbon fibres supplied by TORAY and Polypropylene SABIC PP 519A, a homopolymer grade with a melt flow index of 35 g/10 min(230∘C/2.16 kg). The polymer and fibres were processed in continuous form without any preimpregnated intermediates. The PP pellets were dried at 80∘C for 4 h prior to use to minimise moisture during melt impregnation.

3. Experimental Campaign

The original version of this paper is available on https://www.scientific.net/SSP.387.85.pdf

3.1. Narrow Tape Production with Xplore UD Tape Line

The initial stage of tape manufacturing was carried out using an Xplore 15 cc micro-compounder (MC15HT, Xplore Instruments, the Netherlands) in combination with the Xplore UD Tape Line (Figure 2), which is equipped with a spreading unit, melt impregnation die, pulling and winding unit. T800SC-10E-24K carbon-fiber rovings were guided through the melt impregnation die, where molten polypropylene (PP) was introduced into the die at a processing temperature of 225∘C. With the help of the continuous feeders on MC15HT, the feed rate of PP was kept constant. A die exit geometry with 20 mm width and a thickness of 400μ m was utilized. Two manufacturing configurations were investigated at this stage, differing in the number of rovings fed into the die, as 2×24 K and 4×24 K rovings. The theoretical volume fraction is the ideal situation for filling the die without any gaps. The resulting fibre volume fractions (Vf) of the as-produced tapes were measured as 28.52% for Rov2XP and 37.52% for Rov4-XP (Table 1). One should note that the UD tapes were deliberately produced partially impregnated by adjusting the line speed and die pressure accordingly. Both configurations exhibited pronounced dry regions and non-impregnated fibre regions, indicating insufficient pressure build-up within the closed die during melt impregnation. These partially impregnated (as-produced) tapes were therefore used as baseline material for the subsequent consolidation step.

Table 1. Process parameters used to manufacture tapes at the Xplore setup.

Number of Roving | Sample Code | Pulling Speed (m/min) | Die Temp (°C) | Theoretical Vf (%) | Calculated Vf (%)
2 | Rov2-XP | 0.5 | 225 | 14.30 | 28.52
4 | Rov4-XP | 0.5 | 225 | 28.61 | 37.52

[picture]

Fig. 2. Xplore's UD tape line with an interchangeable number of roving creel setup and closeup image of the spreading and die units.

Fig. 2. Xplore's UD tape line with an interchangeable number of roving creel setup and closeup image of the spreading and die units.

3.2. Calendaring Stage with Semi-industrial Tapeline

The second stage of processing was performed on a UD thermoplastic tape manufacturing line, focusing on the calendaring unit (Figure 3).

[picture]

Fig. 3. Semi-Industrial UD thermoplastic tape manufacturing line and calendaring stage of partially impregnated tapes.

Fig. 3. Semi-Industrial UD thermoplastic tape manufacturing line and calendaring stage of partially impregnated tapes.

In this stage, the produced partially impregnated baseline tapes were processed through a line equipped with a calendar and double-belt system to enhance consolidation and impregnation. The calendaring unit functioned as a controlled consolidation zone, allowing independent adjustment of nip force and roll temperature. Each calendaring condition applied a defined nip load while maintaining the roll temperature at 220∘C, matching the die temperature used during the initial Xplore processing in order to preserve comparable melt viscosity and flow behavior. Following each calendaring pass, the tape exited the belt and cooled under ambient conditions. No additional tension was applied beyond the standard feeding tension of the line, ensuring that any observed changes in tape geometry were solely attributable to the calendaring process. For each baseline configuration (Rov2-XP and Rov4-XP), three progressively increasing nip forces were investigated: 500 N,1000 N , and 1500 N (Table 2).

Table 2. Process parameters used to calendar tapes at the semi-industrial tapeline.

Sample code

Nip (Closing) Force

Pulling Speed

Rov2-500, Rov4-500 | 500 | 1.0
Rov2-1000, Rov4-1000 | 1000 | 1.0
Rov2-1500, Rov4-1500 | 1500 | 1.0

The pulling speed was maintained at 1 m/min, which is lower than typical industrial production rates but was deliberately selected to allow detailed investigation of impregnation and fibre spreading behavior during secondary consolidation. The complete schematic of the multi-stage consolidation approach, spanning from the Xplore system to the semi-industrial tape manufacturing line, is presented in Figure 4.

[picture]

Fig. 4. Schematic of the multi-stage consolidation approach.

Fig. 4. Schematic of the multi-stage consolidation approach.

4. Characterization Steps

The original version of this paper is available on https://www.scientific.net/SSP.387.85.pdf

Various characterization techniques were employed to evaluate the influence of the calendering processes on the spreading and quality of CF/PP tapes. The applied methods included geometrical measurements, volume fraction analysis by weighing, and optical microscopy.
Geometrical characterization was conducted to quantify changes in tape dimensions induced by spreading at the calendaring stage. The width and thickness of the CF/PP tapes were measured after spreading and compared with the as-produced reference tapes from Xplore tapeline ( 0% spreading). For each tape condition, at least 10 measurements of maximum width and maximum thickness were taken at intervals of 20 cm along a total tape length of 2 m to capture longitudinal variability. Tape width was measured using a Mitutoyo Series 500 vernier caliper with a resolution of 0.01 mm , while thickness measurements were performed using a Mitutoyo Series 293 micrometer with a resolution of 0.001 mm . Based on these measurements, the cross-sectional area (CSA) was calculated to assess whether the spreading process altered the tape geometry. The fiber volume fraction of the tapes was determined by weighing a 1 -meter specimen and applying the known densities of the carbon fiber ( 1.8 g/cm3 ) and polypropylene resin ( 0.9 g/cm3 ), thereby accounting for any resin loss resulting from squeeze-out during the calendering process.
For microstructural analysis, samples were extracted from both Xplore-produced and calendered tapes, embedded in epoxy, and subsequently ground and polished in an automated polishing machine (Struers Tegramin-30) to obtain high-quality cross-sections. A Keyence VK-X1000 confocal laser scanning microscope was used to investigate the effect of spreading on fibre distribution, resin, and the presence of dry regions or voids. Three samples were analyzed for each condition, covering eight sample groups: non-calendered Xplore tapes produced with 2 and 4 rovings, and tapes calendered under 500 N,1000 N, and 1500 N . Image processing techniques were applied to the acquired micrographs to see the changes in tape composition, including fibre/resin-rich areas and void regions.

5. Results and Discussions

The original version of this paper is available on https://www.scientific.net/SSP.387.85.pdf

5.1. Geometrical Measurements

Geometrical measurements are shown in Figure 5, which gives a clear response of CF/PP tapes after the calendaring process.

[picture]

Fig. 5. Changes with calendar nip force vs. (a) Tape width, (b) Tape thickness, (c) Cross-sectional area.

Fig. 5. Changes with calendar nip force vs. (a) Tape width, (b) Tape thickness, (c) Cross-sectional area.

Calendaring induced a geometrical reconfiguration of the tapes, characterized primarily by thickness reduction and lateral spreading, while only marginally affecting the fibre volume fraction. For the Rov2 configuration, increasing calendar nip force from the as-produced state to 1500 N led to a reduction in average thickness from ~501μ m to ~168μ m, corresponding to a thickness reduction of approximately 66%. In parallel, the tape width increased substantially from 15.1 mm to 28.1 mm , representing a ~73% width increase (Figure 6). The calculated cross-sectional area (CSA) decreased from 7.58 mm2 to ~4.38 mm2 with increasing calendaring force. As the CSA was derived from nominal width and locally measured thickness, the absolute values are influenced by surface roughness and thickness non-uniformity. Therefore, the observed reduction reflects profile homogenisation and compaction during calendaring. A similar but less pronounced trend was observed for the Rov4 configuration, where the higher initial fibre content constrained deformation. The tape thickness decreased from 423μ m to 237μ m at 1500 N , corresponding to a thickness reduction of approximately 44%, while the width increased from 17.6 mm to 24.1 mm(~37% increase). CSA decreased from 7.45 mm2 to ~5.72 mm2, showing a more moderate consolidation response compared to Rov2.

[picture]

Fig. 6. The width difference of (a) number of 2 (b) number of 4 roving produced tapes.

Fig. 6. The width difference of (a) number of 2 (b) number of 4 roving produced tapes.

Despite significant thickness reduction and lateral widening of the tapes during calendaring, the fibre volume fraction increased only slightly (from 28.5% to ~31.9% for two-rovings tapes, and from 37.5% to 39.2% for four-rovings tapes, Table 3 ). This suggests that the main effect of calendaring is fibre rearrangement and tow spreading, rather than substantial resin squeeze-out, which would have led to a larger increase in Vf.

Table 3. Measured fiber volume fraction and resin loss at different calendaring forces.

Calendaring Force

(N)

Rov2 Vf

(%)

Rov2 Resin Loss

(%)

Rov4 Vf

(%)

Rov4 Resin Loss

(%)

0 (XP) | 28.52 | - | 37.52 | -
500 | 30.13 | 7.47 | 38.12 | 2.51
1000 | 31.55 | 13.43 | 38.48 | 3.98
1500 | 31.87 | 14.70 | 39.15 | 6.67

Clancy et al. investigated in-line spreading of CF/PEEK pre-preg tapes using a dedicated spreading device integrated into an LATP head, where tape width was increased by applying pressure and heat close to the melt temperature of PEEK [10]. Their results showed that increasing applied pressure led to a systematic increase in tape width and a corresponding decrease in thickness, while the crosssectional area and fibre volume fraction remained approximately constant across spreading levels up to ~62% width increase. This behaviour was attributed to transverse squeeze flow, where the viscous thermoplastic matrix enables lateral redistribution of fibres under compression without significant resin loss [10,11].

5.2. Confocal Microscopy

Figure 7 presents representative polished cross-sections of partially impregnated as-is tapes (Rov2XP and Rov4-XP) and the same tapes after secondary calendering at increasing nip forces of 500, 1000 , and 1500 N . The as-is UD tapes exhibit clear evidence of incomplete impregnation, characterized by localized resin-rich regions, dry fiber bundles, and non-uniform filament packing. These features are particularly pronounced for the Rov2-XP configuration, where limited pressure build-up during melt impregnation restricts resin penetration into inter-filament gaps. The partially impregnated Rov2 and Rov4 tapes exhibit non-planar geometry, irregular thickness profiles, and extended dry fibre regions. In these areas, the contrast between dry fibre bundles and the embedding resin is insufficient for reliable threshold-based segmentation, leading to significant uncertainty in automated void quantification. For this reason, void evolution is discussed qualitatively in the present work, supported by mass-per-length measurements and fibre volume fraction data. Following secondary calendering, a clear nip force-dependent evolution of tape geometry and microstructure is observed. At 500 N , both Rov2 and Rov4 tapes show a substantial reduction in thickness accompanied by an increase in tape width, indicating the onset of lateral spreading under compressive and shear
stresses imposed by the calender rolls. Microstructurally, this stage promotes improved filament rearrangement and partial filling of inter-bundle voids, although residual dry regions remain visible, especially in the Rov2 configuration. At 1000 N , calendering produces the most homogeneous spreading response for both Rov2 and Rov4. The tape width increases further while the thickness is reduced in a controlled manner, leading to a more uniform filament distribution across the tape crosssection. Micrographs reveal enhanced resin continuity along the fiber bed, reduced clustering of dry filaments, and a smoother impregnation front. This nip force level appears to provide sufficient resin mobility to promote filament-level rearrangement without inducing excessive resin displacement. The improved spreading at this stage is consistent with the measured geometric trends, where width increase and thickness reduction occur concurrently while the overall cross-sectional area remains relatively stable. Increasing the nip force to 1500 N results in diminishing returns and the onset of adverse effects, particularly for the Rov2 tapes. Although further width increase and thickness reduction are observed, microstructural inspection reveals localized fiber splitting, increased surface roughness, and the re-emergence of dry fiber regions, especially for Rov4 sample. These features are indicative of resin squeeze-out and non-uniform pressure transmission through the fiber bed, leading to localized resin depletion rather than further impregnation improvement. In the Rov4 configuration, the higher initial fiber content provides greater resistance to excessive spreading, yet signs of resin redistribution and localized heterogeneity are still evident at the highest calandering nip force.

[picture]

Fig. 7. Micrographs for the CF/PP tape produced in the current study.

Fig. 7. Micrographs for the CF/PP tape produced in the current study.

6. Conclusion

The original version of this paper is available on https://www.scientific.net/SSP.387.85.pdf

This study demonstrates that secondary calendaring can be used as a multi-stage consolidation strategy to enhance the microstructural homogeneity of partially impregnated PP-CF UD tapes. Increasing calendaring pressure induced pronounced thickness reduction and lateral spreading, with thickness reductions of up to ~66% (Rov2) and ~44% (Rov4), accompanied by width increases of ~73% and ~37%, respectively.
Qualitative examination of the cross-sections indicates a visible reduction in void regions after calendaring. However, due to the geometric irregularity and surface roughness of Rov2_XP and Rov4_XP tapes, quantitative void content analysis from micrographs was not considered sufficiently reliable. The observed increase in fibre volume fraction and measured resin loss further support the occurrence of densification and matrix redistribution during calendaring.
Microstructural observations showed that intermediate pressure ( 1000 N ) provided the most homogeneous fibre distribution and resin continuity, while higher nip force ( 1500 N ) led to fibre splitting and local resin loss. Despite significant geometric changes, fibre volume fraction increased only marginally ( 3−4% ), indicating that consolidation was dominated by fibre rearrangement rather than resin squeeze-out.
The tapes studied are intentionally thick and narrow, representing a challenging impregnation case where pressure build-up is limited. The results are therefore mostly applicable to such geometries. For thinner and wider tapes, transverse flow during calendaring is expected to be more constrained;
however, the findings may still remain relevant in demonstrating how multi-stage pressure application enhances microscale impregnation, even when full transverse flow is limited.
Overall, an optimum processing window was identified where impregnation quality is enhanced without compromising tape integrity. Future work will investigate microstructural in-depth analysis, laminate-level mechanical performance, and crystallization morphology to assess the structural implications of multi-stage consolidation.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/SSP.387.85.pdf

This project is made possible in part by a contribution from the National Growth Fund program NXTGEN HIGHTECH. The authors would also like to thank Xplore Instruments B.V. for their cooperation.

References

The original version of this paper is available on https://www.scientific.net/SSP.387.85.pdf

Schaefer, Y.T., L. Raps, and A.R. Chadwick, Ultrasonic and micrograph-based quantification of material characteristics for in-situ AFP composite structures. Journal of Thermoplastic Composite Materials, 2024. 38(3): p. 933-949.

Fricke, D., L. Raps, and I. Schiel, Prediction of warping in thermoplastic AFP-manufactured laminates through simulation and experimentation. Advanced Manufacturing: Polymer & Composites Science, 2022. 8(1): p. 1-10.

Yassin, K. and M. Hojjati, Processing of thermoplastic matrix composites through automated fiber placement and tape laying methods: A review. Journal of Thermoplastic Composite Materials, 2017. 31(12): p. 1676-1725.

Hopmann, C., et al., Investigation of the influence of melt-impregnation parameters on the morphology of thermoplastic UD-tapes and a method for quantifying the same. Journal of Thermoplastic Composite Materials, 2019. 34(9): p. 1299-1312.

Moradi, A., et al., Technology for lateral spreading of fibre bundles for applications in manufacturing thermoplastic composites. Composites Part A: Applied Science and Manufacturing, 2023. 167: p. 107422.

Garofalo, J. and D. Walczyk, In situ impregnation of continuous thermoplastic composite prepreg for additive manufacturing and automated fiber placement. Composites Part A: Applied Science and Manufacturing, 2021. 147: p. 106446.

Talabi, S.I., et al., Fiber orientation and porosity in large-format extrusion process: The role of processing parameters. Composites Part A: Applied Science and Manufacturing, 2025. 194: p. 108891.

Wang, J., F. Song, and M. Yu, Unidirectional continuous fiber-reinforced polypropylene single-polymer composites prepared by extrusion-calendering process. Journal of Thermoplastic Composite Materials, 2019. 35(3): p. 303-319.

Yuksel, O., et al., Saturated transverse permeability of unidirectional rovings for pultrusion: The effect of microstructural evolution through compaction. Polymer Composites, 2024. 45(7): p. 5935-5952.

Clancy, G., et al., In-line variable spreading of carbon fibre/thermoplastic pre-preg tapes for application in automatic tape placement. Materials & Design, 2020. 194: p. 108967.

Kobler, E., et al., Modeling the anisotropic squeeze flow during hot press consolidation of thermoplastic unidirectional fiber-reinforced tapes. Journal of Thermoplastic Composite Materials, 2023. 37(8): p. 2775-2801.

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/composites-forming-processes/978-3-0364-1996-1

Solid State Phenomena, ISSN: 1662-9779, Vol. 387, pp 95-101

doi: 10.4028/p-B5pfph

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

Submitted:
2026-01-19

Revised:
2026-02-16

Accepted:
2026-02-20

Online:
2026-04-14

Cost-Effective One-Shot Stamping of Steel-Based High-Performance Lightweight Multi-Material Structures

The original version of this paper is available on https://www.scientific.net/SSP.387.95.pdf

Hao Wu 1,a, Mohamed Mohamed 23, b, Chunyi Gao 1,c, Zerong Ding 1, d, Manikandan Ganapathy 4,e, Pinaki Biswas 5,f, Rahul K Verma 5, g, Ambrose Taylor 2,h and Nan Li 1,i *

1 Dyson School of Design Engineering, Imperial College London, London, SW7 2DB, UK

2 Department of Mechanical Engineering, Imperial College London, London, SW7 2AZ, UK

3 Department of Mechanical Engineering, Faculty of Engineering, Helwan University, Helwan, Egypt

4 R&D, Centre for Innovation in Mobility, Tata Steel Itd, IITMRP Campus, 600113, India

5 R&D, Tata Steel Itd, Jamshedpur, 831001, India

an  h.wu@imperial.ac.uk, b  m.mohamed08@imperial.ac.uk, c  chunyi.gao21@imperial.ac.uk, d  z.ding19@imperial.ac.uk, e  manikandan.g@tatasteel.com, f  pinaki.biswas@tatasteel.com, g  rahul.verma@tatasteel.com, h  a.c.taylor@imperial.ac.uk, in.li09@imperial.ac.uk

Keywords: multi-material structures, sheet forming, advanced high-strength steel, DP780, glass fibre, PA6

Abstract

To address the challenges met in the manufacturing of state-of-the-art multi-material structures, this work employs a novel one-shot stamping process with single-stamp forming of advanced high-strength steels (AHSS)-based multi-material structures consisting of dual-phase steel (DP780) and low-cost glass fibre-reinforced polyamide 6 (GF/PA6). The effects of DP780 surface treatment and forming temperature on interfacial bonding with GF/PA6 were first assessed using double cantilever beam (DCB) tests, alongside tensile tests of DP780 to assess post-stamping performance. Sandblasting on DP780 significantly improved bonding strength compared to nontreated surface, while the interfacial fracture energy ( GC ) increased with forming temperature up to 350∘C before decreasing at higher temperatures, which is attributed to PA6 squeeze-out and DP780 surface oxidation. Although the tensile strength for DP780 decreased with increasing temperature, the yield strength peaked at 350∘C, identifying sandblasting and a forming temperature of 350∘C as the optimal processing conditions for DP780. Based on the optimal conditions determined, highquality U-shaped demonstrator components were successfully produced with good surface finish, minimal polymer squeeze-out, and no observable defects, via further optimisation of the forming conditions.

Introduction

The original version of this paper is available on https://www.scientific.net/SSP.387.95.pdf

Ongoing efforts have been made to improve fuel efficiency in the automotive industry to meet the urgent and growing demand for reducing carbon footprint and enhancing vehicle energy efficiency through the substitution of lightweight materials. Polymer matrix composites, notably continuous fibre-reinforced polymers (FRPs) [1], together with commonly used alloys such as high-strength steels and aluminium alloys [2,3], have therefore attracted increasing attention.

Multi-material structures, composed of alternating layers of metal and FRP sheets, were initially developed for aviation to enable the forming of lightweight structures due to high strength-to-weight ratio, corrosion resistance and improved fatigue resistance [4]. In recent years, driven by increasingly stringent CO2 e regulations and lightweighting demands, such structures have also attracted growing interest in automotive industry, particularly for high-performance and safety-critical components that require high structural strength at reduced weight. Several demonstrator and pre-production applications have shown that multi-material concepts can effectively balance structural performance, weight reduction, and cost in automotive body-in-white structures. Such structures have demonstrated
great potential in achieving comparable structural performance while offering up to a 25% weight reduction compared to full-metal automotive counterparts [5].

However, the wider adoption of multi-material structures is hindered by manufacturing limitations: conventional heated-tool one-shot thermoforming is energy-intensive, has a slow cycle time (>20 min[6]. Separate forming followed by bonding and fastening can overcome this limitation, but it requires multiple tools, adhesives or fasteners, introducing complexity and additional processing time [7,8].

To address the challenges mentioned above, this work employs a novel one-shot stamping process with single-stamp forming of AHSS-based multi-material structures consisting of dual-phase steel (DP780) and low-cost glass-fibre-reinforced polyamide 6 (GF/PA6). The process achieves a rapid cycle time with stamping and in-die quenching around 30 s using a cold tool and requires no adhesives or curing. The tailored thermal route of the process bridges the temperature gap between the steel and polymer interface, achieving robust interfacial bonding while avoiding polymer degradation.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/SSP.387.95.pdf

Materials. In the work presented, an advanced high-strength steel (AHSS), dual-phase DP780, was selected as the metallic material, and low-cost glass-fibre polyamide 6 (GF/PA6) thermoplastics laminate was chosen as the FRP material of the multi-material structure.

The component. Figure 1 shows the design and the dimension of the U-shaped demonstrator component to validate the ability of this novel one-shot stamping in forming beam-like multi-material structure. The component is designed with a drawing depth of 33.9 mm , a width of 40 mm , and a draft angle of 5∘. The DP780 layer was placed at the bottom with a thickness of 1.2 mm , while a 2 mm thick GF/PA6 laminate was positioned on top.

[picture]

Fig. 1. (a) Design and (b) the major dimensions (in mm ) of the U-shaped demonstrator component.

Fig. 1. (a) Design and (b) the major dimensions (in mm ) of the U-shaped demonstrator component.

Stamping equipment and process. Figure 2a shows the design of the stamping rig and the forming tool set mounted onto the rig for one-shot stamping of the demonstrator component. The key elements of the forming tool set include a die, a punch, and a blank holder. The dimensions of the die correspond to the upper surface of the U-shaped component, whereas those of the punch correspond to the lower surface. Figure 2b shows the layout of the heating and forming equipment of one-shot stamping and the forming tool set being assembled onto a high-rate 250 kN hydraulic press. Two furnaces were placed on each side of the machine for heating DP780 and GF/PA6, respectively.

The illustration of the one-shot stamping process is shown in Figure 2c, including the following key steps: first, the two blanks are heated to the targeted forming temperatures (Steps 1 and 2); the heated blanks are then successively transferred to the cold tool, where the GF/PA6 is stacked onto DP780 (Steps 3 and 4); next, the two layers are stamped simultaneously in a single press operation, allowing their shapes to be conformed at the same time, and the newly formed multi-material component is held within the cold tool to rapidly quench to stabilise its geometry and enable effective interfacial adhesion, completing the one-shot process (Step 5).

[picture]

Fig. 2. (a) Design of the forming rig and tool sets for forming U-shaped and flat components; (b) laboratory equipment layout, with the corresponding one-shot stamping steps indicated; and (c) schematic illustration of the one-shot stamping process.

Fig. 2. (a) Design of the forming rig and tool sets for forming U-shaped and flat components; (b) laboratory equipment layout, with the corresponding one-shot stamping steps indicated; and (c) schematic illustration of the one-shot stamping process.

Pre- and post-stamping tests. Prior to the stamping experiments of the U-shaped component, 150 ×40 mm flat multi-material component were first produced to determine the effects of DP780 surface treatment and forming temperature on the interfacial bonding strength and to identify the optimal forming temperature window. These tests included testing the bonding strength of as-received and sandblasted DP780 surfaces (shown in Figure 3a) and evaluating different DP780 stamping temperatures ranging from 250∘C to 500∘C with respect to bonding strength. The stamping temperature of the GF/PA6 was fixed close to the melting temperature of PA6, i.e. 230∘C. This helps to suppress excessive through-thickness polymer flow at the interface, which could otherwise lead to interfacial polymer depletion and weakened bonding, while still minimising void formation and delamination.

The bonding performance was characterised by double cantilever beam (DCB) tests, with the fracture energy GC used to quantify the effects of pre-forming sandblasting and forming temperature on interfacial bonding strength. The values of GC were obtained using the experimental compliance method in accordance with the relevant test protocol [9]. The DCB specimens were water-jet cut from the centre of the flat multi-material components, with a size of 120×20 mm. A total pre-crack length of 50 mm was created by inserting ethylene tetrafluoroethylene (ETFE) films with a thickness of 12 μm at the interface. An Instron 3369 uniaxial tester was used for the DCB tests to record the crosshead displacement and load during the tests. During the DCB tests, a camera was used to capture and measure crack growth on the specimens, with distances labelled along the load line prior to testing.

[picture]

Fig. 3. (a) Surface appearance of DP780 in the as-received condition and after sandblasting prior to stamping, and (b) surface appearance after heat treatment at different temperatures (sectioned for tensile testing).

Fig. 3. (a) Surface appearance of DP780 in the as-received condition and after sandblasting prior to stamping, and (b) surface appearance after heat treatment at different temperatures (sectioned for tensile testing).

Tensile tests were conducted to investigate the effect of heating temperatures on the post-stamping mechanical properties of DP780. The surface appearance of DP780 specimens treated at different temperatures (for post-stamping tensile testing) is presented in Figure 3b. The surface colours varied due to different levels of oxidation. The tests were performed using an Instron 5584 tester, and the specimens were prepared based on ASTM E8 (Figure 3b). The specimens were water-jet cut from 1.2 mm thick DP780 sheets. Digital image correlation (DIC) was used for real-time measurement of the gauge length and width of the specimen to obtain engineering stress and strain. The crosshead speed was consistently set at 12.5 mm/s throughout the tests.

The micrographs of the post-stamping component were captured using a Leica Microsystems optical microscope. The transverse cross-section of the as-formed component was sectioned and ground using silicon carbide papers from coarse to progressively finer grades for polishing. A minimum material removal of 3 mm from the component edge was ensured to prevent the GF/PA6 microstructure from being affected by polymer squeeze-out near the edge.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/SSP.387.95.pdf

Figure 4a shows the effect of sandblasting of DP780 on interfacial bonding strength under identical optimal forming condition. It can be seen that sandblasted surfaces significantly increase the debonding load to over 25 N compared with the untreated as-received specimens, which exhibit debonding loads of only around 10 N . This improvement in debonding load is likely attributed to the increase of surface roughness and removal of surface contaminants and oxide layers, which increases the effective bonding area and promotes mechanical interlocking with the molten PA6.

Figure 4b shows the effect of DP780 forming temperature on the fracture energy, GC. It is observed that GC increased as the DP780 forming temperature rose from 250∘C to 350∘C, where a maximum was reached. Further increases in the forming temperature resulted in a decrease in GC, with the value returning to the lowest level at 500∘C. Two factors are likely responsible for this trend. First, higher DP780 temperatures during forming promote a greater extent of polymer squeeze-out, thereby depleting interfacial PA6. Second, surface oxidation of DP780 becomes more severe at higher temperatures, which further reduces interfacial bonding quality.

[picture]

Fig.4. (a) Effects of surface treatment of DP780 on debonding load and (b) effects of DP780 forming temperatures on the fracture energy GC.

Fig.4. (a) Effects of surface treatment of DP780 on debonding load and (b) effects of DP780 forming temperatures on the fracture energy G C .

Figure 5 shows the relationship between forming temperature and the yield strength of DP780 in the range from 250∘C to 450∘C. The yield strength increases with forming temperature, reaching a maximum of approximately 700 MPa at 350∘C, before decreasing at higher temperatures and falling below the as-received level at 500∘C. This trend is in good agreement with the literature, where the strength degradation of DP780 is mainly attributed to martensite tempering, including carbide precipitation and coarsening, and the associated softening of the martensitic phase in the temperature range of 300−500∘C [10].

[picture]

Fig. 5. Effect of forming temperature on the yield stress of DP780.

Fig. 5. Effect of forming temperature on the yield stress of DP780.

Combining the results of the DCB and tensile tests, sandblasting of DP780 prior to forming and a forming temperature of 350∘C were preliminarily identified as the optimal processing conditions. The sandblasted surface exhibited a significantly higher debonding load than the as-received condition. In addition, components formed at 350∘C showed the highest interfacial fracture energy (GC), together with the highest level of post-stamping yield strength and a relatively high tensile strength.

Furthermore, a series of forming experiments was conducted to identify a suitable processing window for the one-shot stamping process. These included improving the consistency of material handling combined with temperature measurements to ensure accurate control of the forming temperatures, fine-tuning the forming temperatures of GF/PA6 and DP780 around 230∘C and 350∘C, respectively, and employing two forming speeds of 100 mm/s and 150 mm/s. Figure 6a shows the micrograph of the as-received GF/PA6, with distinct layering of 0∘ and 90∘ glass fibres and PA6 filling the regions between the fibre layers. Figure 6b displays the U-shaped multi-material component produced at a forming speed of 100 mm/s. It can be seen that both delamination within the GF/PA6 and debonding at the GF/PA6-DP780 interface occurred (as indicated by the red arrows in the micrograph), primarily due to non-optimal shear flow within the GF/PA6 laminate at the lower forming rate, which led to excessive squeeze-out of PA6 and, consequently, ineffective adhesion. In contrast, as shown in Figure 6c, the component produced at a forming speed of 150 mm/s exhibited good bonding across the entire GF/PA-DP780 interface together with a defect-free GF/PA6 laminate, with the material distribution remaining nearly undisturbed by the one-shot stamping process compared with the as-received state.

[picture]

Fig.6. (a) Micrograph of as-received GF/PA6 laminate; and top and front views of the laboratory Ushaped demonstrator components with optical micrographs captured on the on the sidewall of the components produced by (b) 100 mm/s and (c) 150 mm/s forming speeds.

Fig.6. (a) Micrograph of as-received GF/PA6 laminate; and top and front views of the laboratory Ushaped demonstrator components with optical micrographs captured on the on the sidewall of the components produced by (b) 100 m m / s and (c) 150 m m / s forming speeds.

Conclusion

The original version of this paper is available on https://www.scientific.net/SSP.387.95.pdf

This study investigated the cost-effective one-shot stamping process for manufacturing steel-based multi-material structures consisting of DP780 steel and GF/PA6 laminate. Flat multi-material components were first produced to systematically evaluate the effects of DP780 surface treatment and forming temperature on interfacial bonding quality, using DCB tests. In parallel, tensile tests were conducted to assess the influence of forming temperature on the post-stamping mechanical properties of DP780. Based on the optimised processing window identified from these tests, laboratory U-shaped demonstrator components were manufactured to validate the feasibility of the proposed one-shot stamping process for producing multi-material structures.

The results show that both surface preparation and forming temperature are crucial in achieving robust interfacial bonding while preserving mechanical performance. Sandblasting of DP780 prior to forming significantly increased the debonding load to over 25 N compared with the as-received condition of around 10 N , owing to the removal of surface contaminants and weak oxide layers, as well as increased effective adhesion area and enhanced interlocking with PA6. The interfacial fracture energy, GC, peaked at a DP780 forming temperature of 350∘C. Tensile testing revealed that the yield strength peaked at approximately 350∘C, indicating a good balance between interfacial bonding and strength at this temperature. Based these findings, sandblasting and a DP780 forming temperature of 350∘C were preliminarily identified as the optimal processing conditions. Following further optimisation of the forming conditions, high-quality U-shaped multi-material components were produced with sound FRP surface finish, minimal polymer squeeze-out, and no observable delamination or porosity, demonstrating the potential of the one-shot stamping process for achieving high-quality, high-performance automotive lightweight manufacturing.

This work demonstrates the capability of the one-shot stamping process to produce multi-material structures with high forming quality and mechanical performance. In the present study, the metal and FRP materials were heated separately using two furnaces to facilitate manual handling during laboratory-scale trials. However, in recent trials, which the two materials were heated in a single furnace with different timing, indicate the potential to employ only one furnace, provided that accurate material transfer and timing control can be achieved. Such an approach is particularly feasible in industrial production environments. Compared with carbon fibre-reinforced polymer composites, the GF/PA6 used in this study offers a significant reduction in material cost while still providing a high level of strength and toughness. Moreover, the results suggest that the proposed oneshot stamping process is applicable to both glass fibre- and carbon fibre-reinforced thermoplastic composites, and can be further extended to alternative material configurations, such as combinations with high-strength aluminium alloys, thereby enabling cost-effective and lightweight solutions for automotive and broader applications.

References

The original version of this paper is available on https://www.scientific.net/SSP.387.95.pdf

F.L. Matthews, R.D. Rawlings, Composite Materials, Woodhead Publishing, 1999.

M. Tisza, I. Czinege, Comparative study of the application of steels and aluminium in lightweight production of automotive parts, International Journal of Lightweight Materials and Manufacture 1 (2018) 229-238. .

E.A. Starke, J.T. Staley, Application of modern aluminum alloys to aircraft, Progress in Aerospace Sciences 32 (1996) 131-172. .

A.D. Vlot, Glare: History of the Development of a New Aircraft Material, Springer Dordrecht, 2001.

M. Harhash, T. Fischer, M. Grubenmann, W. Hua, J. Heingärtner, M. Kuhtz, M. Gude, P. Hora, G. Ziegmann, H. Palkowski, Top-hat crashboxes of thermoplastic fibre-metal-laminates processed in one-step thermoforming: Experimental and numerical study, Composites Part B: Engineering 226 (2021). .

Y. Guo, C. Zhai, F. Li, X. Zhu, F. Xu, X. Wu, Formability, defects and strengthening effect of steel/CFRP structures fabricated by using the differential temperature forming process, Composite Structures 216 (2019) 32-38. .

J. Dau, C. Lauter, U. Damerow, W. Homberg, T. Tröster, Multi-material systems for tailored automotive structural components, in: 18th International Conferences on Composite Materials, International Committee on Composite Materials, Jeju Island, 2011.

G. Gardiner, Is the BMW 7 series the future of autocomposites?, CompositesWorld (2016).

B. Blackman, A. Kinloch, Protocol for the Determination of the Mode I Adhesive Fracture Energy, GIC, of Structural Adhesives using the Double Cantilever Beam (DCB) and Tapered Double Cantilever Beam (TDCB) Specimens, European Structural Integrity Society 00-08 (2000).

G.C. Krishna, M.J. Quamar, N.K. Babu, G.V.S. Kumar, B. Bandi, M.K. Talari, Investigations of Microstructure and Mechanical Properties of Post-Weld Heat-Treated DP780 Steel TIG Welds, Fusion Science and Technology 80 (2024) 215-229. .

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/composites-forming-processes/978-3-0364-1996-1

Solid State Phenomena, ISSN: 1662-9779, Vol. 387, pp 103-117

doi: 10.4028/p-sDKXB6

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

Submitted:
2026-01-19

Revised:
2026-02-16

Accepted:
2026-02-20

Online:
2026-04-14

A CT-Based Methodology for Mesostructural Characterization of Unidirectional Continuous-Fiber Polymer Composites Using Line-Profile Descriptors

The original version of this paper is available on https://www.scientific.net/SSP.387.103.pdf

MELE Roberto 1,a*, DE SIO Paolo 1,b, TUCCI Fausto 1,c and CARLONE Pierpaolo 1, d

¹Department of Industrial Engineering, University of Salerno, 132 Via Giovanni Paolo II, 84084

Fisciano (SA), Italy

a  rmele@.unisa.it, b  pdesio@unisa.it, cftucci@unisa.it, d  pcarlone@unisa.it

Keywords: X-ray micro-computed tomography, unidirectional continuous- fiber composites, mesostructure characterization, line-profile features.

Abstract

X-ray micro-computed tomography enables three-dimensional inspection of fiberreinforced polymer composites. Quantitative mesostructural characterization remains challenging when voxel size does not permit reliable phase segmentation. This study presents a CT-based methodology for mesostructural characterization of unidirectional continuous-fiber polymer composites using line-profile descriptors. The approach extracts fixed one-dimensional intensity profiles within a defined internal volume of interest and computes a compact set of statistical and spatial descriptors. These include distributional moments, entropy, gradient-based measures, autocorrelation-derived correlation length, spectral band-energy ratios, and percentile-based runlength metrics. A technical quality control procedure verifies numerical consistency of the extracted feature tables. A one-at-a-time sensitivity analysis quantifies the influence of descriptor hyperparameters and identifies parameter groups that alter signal partitioning, particularly spectral cut-offs and run-length thresholds. Applied to pultruded composites, the descriptors resolve transverse heterogeneity across the section and systematic through-thickness trends in attenuation level, dispersion, spatial scale, and persistence of low-attenuation domains. The methodology provides a traceable low-dimensional representation of attenuation structure that can inform finiteelement modeling through spatially parameterized material fields. Mechanical validation and descriptor-property calibration remain subjects for future work.

Introduction

The original version of this paper is available on https://www.scientific.net/SSP.387.103.pdf

Unidirectional continuous-fiber polymer matrix composites are widely used in lightweight structural applications due to their high specific stiffness and strength and their pronounced anisotropy. Their mechanical response depends not only on nominal fiber volume fraction and layup, but also on process-induced microstructure. Relevant features include voids, resin-rich domains, fiber misalignment, fiber waviness, inter-bundle heterogeneity, and spatial variability at the roving or tape scale [1,2,3]. These non-idealities degrade matrix-dominated properties such as interlaminar shear strength, transverse compression resistance, and fracture toughness, and they promote localized damage initiation [4, 5]. Studies on voids show that performance depends not only on void volume fraction but also on void morphology and spatial distribution [6, 7]. This evidence motivates quantitative three-dimensional characterization rather than scalar porosity metrics alone. In addition, the realized architecture of unidirectional reinforcements often deviates from the nominal fiber direction. Misalignment and waviness arise during consolidation, impregnation, placement, and pulling operations. Under compressive loading, these defects promote micro-buckling and stabilitydriven failure. Reviews describe fiber waviness as a process-dependent, multiscale defect for which robust metrology and quantitative property correlation remain challenging, especially when imaging resolution does not allow fiber-by-fiber discrimination [8, 9, 10]. X-ray micro-computed tomography is widely used as a non-destructive technique for three-dimensional inspection of composite microstructures. It enables visualization and quantification of internal defects, analysis of damage
and fracture mechanisms, and generation of image-based geometries for numerical modelling. Reviews on quantitative tomography highlight both the advantages of true three-dimensional morphology and spatial statistics and the limitations imposed by artefacts, resolution constraints, and quantitative uncertainty [11, 12]. Applications to fiber-reinforced laminates demonstrate the ability of micro-CT to resolve delamination and microcracking, but also reveal practical trade-offs between field of view, magnification, and detectability of fine damage features [13]. The transition from qualitative visualization to reproducible quantitative metrology requires standardized acquisition protocols and explicit quality control procedures. Industrial standards such as ASTM E1441 define general practices for computed tomography. Metrology-oriented guidelines, including VDI/VDE 2630 for CT-based dimensional measurement, specify performance verification and comparability principles [14, 15, 16]. In practice, the micro-CT analysis pipeline involves multiple parameter choices across acquisition, reconstruction, pre-processing, and feature extraction. When voxel size is on the order of several micrometers to tens of micrometers, individual fibers, matrix, and voids cannot be reliably resolved. Partial-volume effects, noise, and restricted contrast make multi-class segmentation unstable and often ill-posed. Under such conditions, segmentation-free or segmentation-light strategies have been proposed. These include structure-tensor-based approaches for estimating local anisotropy and orientation without explicit fiber identification, with explicit discussion of resolution and image-quality constraints [17, 18, 19, 20]. Image-based modelling studies further emphasize that pipeline reliability depends either on high-fidelity segmentation or on the use of robust, low-dimensional descriptors when segmentation is ambiguous, especially in multiscale architectures [21]. A recurrent limitation in composite micro-CT studies is the trade-off between spatial resolution and field of view. Voxel sizes small enough to resolve fine-scale constituents require reduced imaging volumes. Conversely, capturing representative mesostructural variability requires larger fields of view and therefore coarser voxel sizes or longer acquisition times, with direct consequences for detectability and quantitative uncertainty [22,23]. This trade-off defines a widely used operational regime with voxel sizes on the order of 5−20μ m. In this range, micro-CT resolves mesostructural features such as rovings, resin-rich zones, coarse porosity, and roving-scale waviness, but does not allow reliable multi-phase segmentation at the fiber scale. When segmentation is feasible, void- and defect-oriented analyses are well established [6, 7]. When segmentation is unstable, however, methodological practices are less standardized. In such cases, analysis must rely on statistical and spatial descriptors extracted directly from attenuation signals, together with explicit parameter reporting, sensitivity assessment, and numerical quality control. This need is reinforced by current industrial trends. Automated layup processes may introduce defects such as wrinkles, bridging, and gap or overlap regions, which require objective structural verification [24, 25]. In liquid composite molding and related processes, permeability and impregnation quality influence porosity and heterogeneity. Experimental and numerical studies show that reinforcement architecture and modelling assumptions affect inferred permeability and predicted impregnation behavior [26,27]. In parallel, increasing attention to sustainability and thermoplastic processing routes strengthens the demand for reproducible structural metrics that enable meaningful comparison across materials and processes [28]. Against this background, the present work addresses quantitative micro-CT characterization in the limited separability regime. It proposes a low-dimensional set of statistical and spatial descriptors computed from controlled one-dimensional intensity profiles. The descriptor set includes distributional moments and quantiles, entropy, gradient-based measures, autocorrelationderived correlation length, spectral energy ratios, and run-length statistics. The extraction procedure is accompanied by explicit numerical quality control and parameter sensitivity analysis to ensure reproducibility. The objective is to provide a traceable attenuation-based representation of mesostructural variability that supports intra- and inter-sample comparison and for subsequent mechanical interpretation and modelling

Materials and Data

The original version of this paper is available on https://www.scientific.net/SSP.387.103.pdf

Unidirectional glass fiber-reinforced polypropylene pultruded bars are investigated. The material is manufactured at the University of Salerno using a laboratory-scale pultrusion line designed and
operated in-house. The reinforcement consists of forty continuous unidirectional glass fiber tapes preimpregnated with polypropylene. The pultrusion process produces rectangular bars with constant cross-section and nominally aligned fiber reinforcement. Five reduced-size specimens are machined from the pultruded bars for microstructural analysis. Each specimen is scanned by X-ray microcomputed tomography to characterize the internal architecture at the mesostructural scale. Micro-CT acquisitions are performed using a Rigaku CT Lab HX 130 system. Scans are conducted at an accelerating voltage of 110 kV and a tube current of 72μ A. A 0.1 mm copper filter attenuates lowenergy photons and reduces beam-hardening effects. Volumes are reconstructed with an isotropic voxel size of 10.5μ m using vendor-provided reconstruction software. The reconstructed grayscale volumes are exported in .vox format, inspected using Dragonfly, and converted to 16-bit TIFF stacks for slice-wise processing and feature extraction. Acquisition and reconstruction parameters are kept constant across all specimens to ensure reproducibility and enable direct inter-scan comparison.

Methodology

The original version of this paper is available on https://www.scientific.net/SSP.387.103.pdf

Fig. 1 summarizes the proposed processing pipeline. Starting from the reconstructed micro-CT volume, a volume of interest (VOI) and a fixed line-sampling scheme are defined. One-dimensional intensity profiles are extracted slice-wise within the VOI. Structural descriptors are computed from both raw and normalized intensities. The workflow produces traceable outputs for structural characterization, quality control, and sensitivity analysis. The reconstructed datasets are converted to 16-bit grayscale image stacks and processed as volumetric arrays. Slices are ordered according to file names to preserve the physical stacking direction and to prevent index permutation. Each slice is imported as a single-channel intensity image. When multi-channel data are present, the grayscale channel is retained to ensure a scalar intensity field. Let Ik(x,y) denote the intensity field of the k-th slice. The three-dimensional volume is assembled as I(x,y,z=k)=Ik(x,y),k=1,…,Nz, yielding a scalar field with dimensions (Ny,Nx,Nz). All computations use the voxel spacing provided by the micro-CT reconstruction. No interpolation or resampling is applied. The volume is stored using the native integer bit-depth to avoid unintended intensity rescaling during import. Pre-processing is intentionally minimal. Spatial filtering and denoising are not applied, as they may alter local gradients, autocorrelation structure, and high-frequency spectral content. Intensity normalization is applied only after VOI definition and is explicitly parameterized. This separation decouples acquisition-dependent intensity scaling from descriptor computation and ensures reproducible comparisons across slices and specimens.

[picture]

Fig. 1. Overview of the proposed micro-CT line-profile pipeline.

Fig. 1. Overview of the proposed micro-CT line-profile pipeline.

Processing settings and signal extraction. A three-dimensional volume of interest is defined to restrict the analysis to an internal region of the specimen. Background air and boundary zones affected by partial volume effects are excluded. The VOI is parameterized by axis aligned bounds xmin,xmax, ymin,ymax, and zmin,zmax in the reconstructed volume coordinates. A central slice z=kz is selected
as reference to define the in-plane VOI extent [xmin,xmax]×[ymin,ymax]. The through-thickness extent is defined by selecting a contiguous set of slices centered on kz. The bounds along z are set as

zmin=max(1,kz−Δz),zmax=min(Nz,kz+Δz)(1)

where Δz is the prescribed half-range expressed in number of slices and Nz is the total number of slices in the stack. This procedure yields a reproducible VOI with dimensions ( NxVOI ,NyVOI ,NzVOI  ). To characterize spatial heterogeneity within the VOI while retaining a low dimensional representation, one-dimensional intensity profiles are sampled along the x-direction at fixed y coordinates. Three horizontal lines y=yi, with i=1,2,3, are defined inside the VOI and uniformly spaced across the interval [ymin ,ymax ] to probe transverse variability. For each slice z∈[zmin ,zmax ], the corresponding intensity profile is extracted as

pi(x;z)=I(x,yi,z),x∈[xmin,xmax](2)

yielding a set of profiles {pi(·;z)} of length NxVOI  for each slice. This sampling strategy provides both slice wise and line wise descriptions of the attenuation signal. It enables the assessment of transverse variability and through thickness stability without requiring full three-dimensional phase segmentation. Restricting the analysis to an internal VOI reduces edge related artefacts, while fixedy sampling ensures controlled and repeatable probing of the microstructural signal. The use of multiple equidistant line profiles enables detection of spatial non uniformity with limited additional complexity. To enable quantitative comparisons across slices and, when applicable, across specimens, grayscale intensities are transformed using a relative calibration based on two reference levels. The calibration uses a low attenuation background reference and a high intensity reference representative of the solid phase. Let I(𝐫) denote the reconstructed grayscale intensity at voxel location 𝐫. The background reference gair  is estimated from a region selected outside the specimen and summarized using a robust statistic to limit the influence of outliers. The high intensity reference gref  is defined as a high percentile Pq of the intensity distribution within the VOI, with q=99.5 unless otherwise stated. This specimen specific reference provides a stable upper anchor while remaining insensitive to isolated extreme values. The use of a global VOI-based reference avoids slice wise normalization and preserves through-thickness trends. The normalized intensity field is computed through an affine transformation,

Inorm(𝐫)=I(𝐫)−gairgref−gair(3)

followed, when required, by clipping to a bounded range to improve numerical stability and limit the influence of rare extreme values,

Inorm (𝐫)←min(max(Inorm (𝐫),0),1)(4)

The same transformation is applied to all extracted line profiles, yielding paired datasets of raw profiles pi(x;z) and normalized profiles pinorm (x;z). Structural descriptors are computed on both representations. Raw features preserve the original reconstruction scale and remain sensitive to acquisition-dependent intensity shifts. Normalized features reduce scan to scan scaling effects and support quantitative comparison across slices and specimens. All calibration parameters gair ,gref , and q are recorded to ensure full traceability and reproducibility.

Feature definition. For each line profile pi(x;z) sampled along the x-direction at fixed (yi,z), first-order descriptors are computed to summarize the marginal distribution of grayscale intensities. These descriptors are independent of the spatial ordering of samples along the profile. Let {xn}n=1N denote the N discrete intensity samples of a profile, either raw or normalized. The sample mean μ and sample standard deviation σ are defined as

μ=1N∑n=1Nxn and σ=1N−1∑n=1N(xn−μ)2. (5,6)

Relative dispersion is quantified using the coefficient of variation,

CV=σμ,(7)

which is reported only when μ≠0. Distributional asymmetry and tail behavior are characterized using central standardized moments. Skewness γ1 and excess kurtosis γ2 are computed as

γ1=1N∑n=1N(xn−μ)3σ3, and γ2=1N∑n=1N(xn−μ)4σ4−3,(8,9)

with γ2=0 for a Gaussian distribution by construction. Robust location and scale are further described using empirical percentiles. The 10th, 50th (median), and 90th percentiles are denoted as P10,P50, and P90, respectively. The interquartile range is defined as

IQR=P75−P25(10)

First-order descriptors provide a compact representation of the attenuation statistics sampled along each line. They capture shifts in average intensity level, dispersion, and the presence of extreme values. Because they ignore spatial ordering, these descriptors are insensitive to the spatial arrangement of phases and therefore complement the order-dependent descriptors introduced in the following sections. To quantify distributional complexity beyond moment-based statistics, the Shannon entropy of the intensity histogram is computed for each line profile. For a given profile {xn}n=1N, a discrete probability mass function is estimated by binning the samples into B fixed width bins over a prescribed intensity range. Let cb denote the count in bin b and pb=cb/∑b=1Bcb the corresponding empirical probability. The Shannon entropy is defined as

H=−∑b=1Bpblog2(pb), with pb>0.(11)

The binning parameters are held constant within each analysis to ensure comparability across slices and lines. When normalized intensities are used, the intensity range is naturally bounded and the same binning strategy is applied. First-order statistics and histogram entropy do not account for the spatial ordering of samples along the profile. To capture spatial variability along the sampled direction, descriptors derived from the first discrete difference of the intensity signal are computed. For a profile xn, the discrete gradient is defined as

Δxn=xn+1−xn,n=1,…,N−1.(12)

From this gradient signal, two scalar descriptors are computed: the root-mean-square gradient magnitude and the mean absolute gradient,

GRMS=1N−1∑n=1N−1(Δxn)2, and GABS=1N−1∑n=1N−1|Δxn|.(13,14)

These descriptors increase with the amplitude and frequency of local intensity transitions along the profile and therefore provide compact proxies for edge density and fine-scale texture in the sampled direction.
To quantify the characteristic spatial scale of intensity fluctuations along each profile, an autocorrelation-based correlation length is computed. For a given line profile {xn}n=1N, the mean value is first removed to suppress the DC component, xn′=xn−μ The normalized autocorrelation function is then evaluated for non-negative integer lags l as

R(ℓ)=∑n=1N−ℓxn′xn+ℓ′∑n=1N(xn′)2,ℓ=0,1,…,ℓmax(15)

with R(0)=1 by construction. The correlation length Lcorr, e is defined as the smallest lag l≥1 at which the autocorrelation decays below a prescribed threshold T,

Lcorr ,e=min{ℓ≥1∣R(ℓ)<T}(16)

with T=e−1 unless otherwise stated. To ensure consistent behavior across profiles of different lengths and to limit computational cost, the maximum evaluated lag is capped at lmax. If the threshold
is not crossed within the interval [1,lmax], the correlation length is set equal to lmax. Profiles with negligible variance, defined by σ≈0, are excluded from this computation. The correlation length Lcorr,e  estimates the typical distance, expressed in pixels, over which intensities remain correlated along the sampled direction. Larger values correspond to slowly varying profiles dominated by extended domains, whereas smaller values indicate rapid decorrelation consistent with finer-scale texture or frequent intensity transitions. To characterize the distribution of spatial frequencies in each line profile, the one-dimensional power spectrum of the mean-centered signal is analyzed. For a profile xn, the mean-centered signal is defined as xn′=xn−μ. Let X(k) denote the discrete Fourier transform of xn′, with k=0,…,N−1. The one-sided power spectrum P(fk) is computed from 1 X(k)|2 using standard one-sided scaling. The normalized spatial frequency axis is defined as

fk=kN,fk∈[0,0.5](17)

where f=0.5 corresponds to the Nyquist frequency expressed in cycles per pixel. The zerofrequency component is excluded to focus on spatial variations. The total spectral energy is defined as

Etot=∑fk>0P(fk).(18)

To obtain a compact multi-scale description, the spectrum is partitioned into three non-overlapping frequency bands. Low-, mid-, and high-frequency bands are defined by two cut-off frequencies flow  and fmid , with 0<flow <fmid ≤0.5. The corresponding band energies are computed as

Elow =∑0<fk≤flow P(fk),Emid =∑flow <fk≤fmid P(fk) and Ehigh =∑fmid <fk≤0.5P(fk).

(19,20)(21)

The normalized band-energy ratios are then defined as

ηlow=ElowEtot and ηmid=EmidEtot and ηhigh=EhighEtot(22,23,24)

which satisfy ηlow +ηmid +ηhigh ≈1 when Etot >0. Low-frequency energy reflects slowly varying intensity profiles dominated by large-scale domains, whereas high-frequency energy reflects rapid intensity fluctuations and fine-scale texture along the sampled direction. The cut-off frequencies flow  and fmid  control how spectral energy is redistributed among bands and therefore directly influence the resulting descriptors. These cut-offs are treated as explicit hyperparameters and their impact is quantified in the sensitivity analysis. To quantify the spatial extent and frequency of contiguous high- and low-intensity domains along each line profile, run-length descriptors based on percentile thresholds are computed. For a given profile {xn}n=1N, two robust thresholds are defined from the empirical intensity distribution. The lower threshold is set to the 10th percentile τlo=P10, and the upper threshold is set to the 90 th percentile τhi=P90. These percentiles target the distribution tails while remaining robust to isolated outliers. Two binary indicator sequences are then constructed as

bnhi=𝕀(xn>τhi) and bnlo=𝕀(xn<τlo),(25,26)

where 𝕀(·) denotes the indicator function. A run is defined as a maximal contiguous sequence of ones in the corresponding binary sequence. For each binary sequence, the number of runs Nrun , the maximum run length Lmax , and the mean run length Lmean  are computed. If no runs are present, these descriptors are set to zero. To enable comparisons across profiles of different lengths, all run-length descriptors are also reported in normalized form,

Nrun*=NrunN and Lmax*=LmaxN and Lmean *=LmeanN.(27,28,29)

High-tail run descriptors capture the occurrence and spatial persistence of contiguous high-intensity domains along the sampled direction. Low-tail run descriptors capture the occurrence and persistence
of low-intensity domains. Because run formation depends explicitly on the chosen percentile thresholds, these descriptors are intrinsically sensitive to threshold selection. This dependence is addressed explicitly in the sensitivity analysis. All descriptors are computed at the profile level, that is, for each sampled line i and each slice z. This produces a long-format dataset in which each record corresponds to a unique ( i,z ) pair and contains the full set of feature values derived from the corresponding profile. This representation preserves slice-wise variability and provides the input for quality control and sensitivity analysis. To enable compact comparisons between transverse locations, each feature is further aggregated along the slice direction. For each line i, summary statistics over z are computed, including mean, standard deviation, median, minimum, and maximum. The resulting aggregated by line representation provides low-dimensional descriptors that summarize throughthickness variability while retaining sensitivity to persistent structural differences between lines. When required, linear trends with respect to z are estimated by least-squares fitting to quantify systematic drifts along the volume.

Quality control and sensitivity analysis. A technical quality control procedure is applied to ensure numerical validity, internal consistency, and comparability of the extracted descriptors. Control is performed at both the profile level and the aggregated by line level, and separately for raw and normalized features when both representations are available. Each feature record must correspond to a unique sampling location identified by the pair ( i,z ), where i denotes the line index and z the slice index within the VOI. The number of records is verified against the expected cardinality Nlines ×NzVOI . Duplicate or missing keys are not permitted. When raw and normalized tables are both present, key sets must match exactly to allow one to one alignment. For each feature, the fraction of non-finite values is quantified at the profile and aggregated levels. Concentrated missingness patterns are flagged, as they may indicate nearly constant profiles or ill-posed computations such as divisions by μ≈0. Numerical validity is enforced by checking that all statistics satisfy their analytical prerequisites, for example σ>0 for standardized moments. Each descriptor is checked against constraints implied by its definition. Entropy and gradient-based descriptors must be non-negative. Run counts and run lengths must be non-negative. Normalized spectral energy ratios must lie within the interval [i,z]. For the spectral band descriptors, approximate closure is verified,

ηlow+ηmid+ηhigh≈1(31)

whenever Etot >0. For the autocorrelation-based correlation length, it is verified that Lcorr,e  lies within the evaluated lag range[ 1,lmax ] when defined. Any violation is traced back to the corresponding ( i,z ) key. When both raw and normalized feature tables are available, records are aligned using identical keys. The absence of mismatches is verified, and normalization is checked to ensure that it does not introduce degenerate behavior beyond the expected effects of clipping. This step ensures that intensity rescaling preserves the structural information encoded in the profiles. Several descriptors depend on explicit hyperparameters, including the autocorrelation threshold T and maximum lag lmax , the spectral band cut-offs flow  and fmid , the histogram bin count, and the percentile thresholds used in run-length definitions. To assess robustness with respect to these choices, a one at a time sensitivity analysis is performed. One hyperparameter group is varied while all other processing steps, including VOI definition and line sampling, are kept fixed. For each descriptor family, a baseline parameter set Θ0 is defined. Perturbed configurations {Θj} are generated by modifying the corresponding hyperparameters within plausible ranges. Features are recomputed on the same set of sampled profiles for each configuration and compared to the baseline at both the profile level and the aggregated by line level. Robustness is quantified using three complementary metrics. First, the Spearman rank correlation between baseline and perturbed values is computed to assess preservation of relative ordering. Second, the magnitude of deviation is evaluated through the median and upper quantile of the absolute relative difference,

δ=|f(Θj)−f(Θ0)||f(Θ0)|+ε(32)

where ε>0 ensures numerical stability. Third, degeneracy is monitored by tracking the variance and number of unique values under each configuration. This step identifies descriptors that collapse toward nearly constant outputs under specific parameter choices. Robustness results are summarized through a scorecard that ranks parameter configurations within each descriptor family. For each feature, a composite score combines rank preservation, deviation magnitude, and penalties for degeneracy. Scores are then aggregated across features to obtain a per-configuration robustness ranking at both representation levels. The scorecard supports transparent selection of parameter values that yield stable and reproducible descriptors. The sensitivity patterns observed in the scorecard reflect the structural role of each hyperparameter in the descriptor definition. Spectral band cut-offs directly determine how the total spectral energy is partitioned across low, mid, and high frequency ranges. Because the band-energy ratios are normalized by the same total energy, shifting the cut-off frequencies redistributes energy between bands even when the underlying spectrum remains unchanged. As a result, moderate variations in flow  and fmid  can produce non-negligible changes in the reported band ratios and in their relative ordering across profiles. Run-length descriptors exhibit stronger sensitivity because the percentile thresholds alter the binary segmentation of the intensity signal itself. Changing P10 or P90 modifies which samples are classified as tail events, thereby redefining the set of contiguous runs. Since run counts and run lengths are computed on this thresholded signal, small shifts in percentile values can induce discrete changes in run formation, leading to larger variations than those observed for moment-based or correlation-based descriptors. In contrast, autocorrelation threshold and histogram bin count affect only secondary aspects of their respective computations and do not modify the underlying signal partitioning. Their influence is therefore comparatively limited within the tested parameter ranges.

Results and Discussion

The original version of this paper is available on https://www.scientific.net/SSP.387.103.pdf

Case study. The proposed framework is applied to a representative pultruded GF/PP micro-CT volume to demonstrate end to end execution under fixed processing settings. An internal volume of interest is defined using axis-aligned bounds to exclude background and boundary regions. Within the VOI, three horizontal line profiles are sampled along the x-direction at uniformly spaced y coordinates. The same line locations are used for all slices to ensure consistent transverse comparison and through-thickness tracking. Fig. 2 shows a representative reconstructed slice with the selected VOI and the three fixed line profiles. The VOI contains Nz=1931 slices. Each line profile spans Nx=2190 pixels, corresponding to a spatial length of 22.995 mm at a voxel size of 10.5μ m. The sampling scheme produces 5793 profiles per intensity domain. For each slice z, descriptors are computed from full-length profiles using both raw and normalized intensities. Unless otherwise stated, results are reported for normalized intensities to reduce scan-scale effects while preserving relative through-thickness variation. Descriptors are summarized at the line level by aggregating across z using median and interquartile range. Run-length descriptors use the 10th and 90th percentiles to define lower and upper tail thresholds.

[picture]

Fig. 2. Representative reconstructed slice showing the selected internal volume of interest and three uniformly spaced line profiles sampled along the x-direction.

Fig. 2. Representative reconstructed slice showing the selected internal volume of interest and three uniformly spaced line profiles sampled along the x -direction.

Quality control and sensitivity analysis results. The report confirms full integrity of the extracted profile dataset. Raw and normalized outputs contain 5793 profiles each and show perfect key consistency with no missing entries in either domain. The pipeline produces complete and numerically stable features for all descriptors and no domain-constraint violations. Raw-norm consistency follows the expected behavior, scale-invariant descriptors remain unchanged while scaledependent descriptors rescale under normalization but preserve ordering. Entropy shows a lower correlation ( r=0.869 ) because normalization changes the histogram support and bin occupancy, which the pipeline treats as expected. Sensitivity outcomes reveal distinct influence regimes across parameter groups. These regimes are quantified through rank correlation, relative deviation, and composite score magnitude at both aggregated and profile levels. Autocorrelation parameters and entropy bin counts show limited influence within the tested ranges. At both representation levels, Spearman rank correlation remains above 0.99 and median relative deviations remain below 0.04 . Composite scores remain confined to a narrow interval between 0.10 and 0.18 . These parameters modify decay threshold or histogram resolution without altering the structural partitioning of the signal. Spectral band cut-offs exhibit substantially larger deviations. For perturbed FFT configurations, relative median deviations reach values close to unity and composite scores exceed 1.5 in the most extreme cases. Rank correlation decreases to approximately 0.89 at the profile level. This behavior arises from redistribution of spectral energy across frequency bands when cut-off frequencies are modified. Because band-energy ratios are defined by energy partitioning, shifting the boundaries alters descriptor magnitudes even if the underlying spectrum remains unchanged. Runlength thresholds represent the strongest sensitivity driver. Changing percentile thresholds modifies the binary indicator sequences used to construct contiguous runs. This redefinition of the thresholded signal produces discrete changes in run topology rather than gradual magnitude shifts. At the aggregated level, composite scores exceed 1.1 and rank correlation drops below 0.5 for the most extreme threshold variation. These values are markedly higher than those observed for other descriptor families. The scorecard therefore separates parameter groups that induce smooth metric perturbations from those that alter the structural segmentation of the signal. Based on these results, the baseline configuration used for structural interpretation adopts an autocorrelation threshold T=e−1 with lmax=100, a 256 -bin histogram discretization, spectral cut-offs at normalized frequencies 0.10 and 0.30 cycles per pixel, and run-length thresholds defined by the 10th and 90th percentile.

Structural characterization. To assess transverse uniformity within the VOI, three fixed transverse positions are sampled using line profiles oriented along the x-direction and repeated for each slice z. Fig. 3 shows a representative line profile extracted from a single slice together with the percentile thresholds used for run-length analysis. Each profile is a one-dimensional sequence of normalized voxel intensities. In micro-CT, voxel intensity reflects local X-ray attenuation. In this composite system, attenuation variations primarily reflect differences in effective density and phase composition. Higher intensity values are consistent with fiber-rich regions intersected by the sampling line. Lower intensity values are consistent with resin-rich or lower-density regions. No phase segmentation is performed. Intensity statistics are therefore treated as indirect descriptors of the spatial distribution of attenuation contrast along the sampled direction. Fig. 3 also illustrates how lowtail and high-tail runs are defined relative to percentile thresholds. Run counts and run lengths quantify the spatial persistence of extreme attenuation domains along the line.

[picture]

Fig. 3. Representative normalized intensity profile extracted along the x-direction within the VOI. Dashed lines indicate the 10th and 90th percentile thresholds used for run-length analysis.
Highlighted segments correspond to low-tail runs.

Fig. 3. Representative normalized intensity profile extracted along the x -direction within the VOI. Dashed lines indicate the 10th and 90th percentile thresholds used for run-length analysis. Highlighted segments correspond to low-tail runs.

Slice-wise descriptors are computed for each line and summarized across the VOI using median and interquartile range. Transverse comparison reveals systematic differences between the three sampled positions. The mean attenuation μ, within-profile dispersion, and correlation length Lcorr,  exhibit distinct central tendencies across lines, indicating non-uniform attenuation structure across the section. These differences are consistent with mesostructural heterogeneity at the tape or roving scale. Normalized high-frequency energy ratios and run-length descriptors further differentiate transverse locations. Lines exhibiting higher ηhigh  show increased fine-scale intensity fluctuations. Lines with larger normalized low-tail run lengths exhibit more persistent low-attenuation domains. Throughthickness trends are evaluated by analyzing descriptor evolution along z. For selected lines, μ and Lcorr,  show gradual monotonic drift, indicating slow variation of attenuation structure along the stack. The largest increase in correlation length is observed for line 3 over the analyzed range. These observations indicate that fixed 1D profile descriptors capture both transverse heterogeneity and slow through-thickness modulation of attenuation structure. The descriptors provide quantitative measures of attenuation variability and spatial scale without requiring explicit phase segmentation. They allow controlled comparison of transverse positions and systematic tracking along the stack. However, intensity-based descriptors do not directly yield phase volume fractions or fiber orientation. They quantify statistical and spatial properties of the attenuation field under fixed acquisition conditions. Line sampling reduces dimensionality and does not capture full two- or three-dimensional morphology. Results may be influenced by reconstruction artefacts and partial-volume effects. Validation against independent structural or mechanical measurements remains necessary. Fig. 4 reports boxplots of slice-wise descriptor values computed from normalized line profiles. The plotted quantities are mean intensity μ, interquartile range (IQR), autocorrelation-based correlation length Lcorr, , high-frequency spectral energy ratio ηhigh , and normalized maximum low-tail run length Lmax , lo *. Each boxplot summarizes the distribution of a descriptor across the full slice range for a fixed transverse position. Differences between lines reflect transverse heterogeneity. Spread within each box reflects through-thickness variability at that location. The mean intensity μ differs systematically across transverse positions. The central tendency of μ shows limited overlap between lines. Line 1 exhibits the highest median value, line 2 the lowest, and line 3 intermediate values. These differences indicate that the average attenuation level sampled along x varies across the section.

Lower median μ at line 2 corresponds to a higher contribution of lower-attenuation regions along that transverse location. Higher median μ at line 1 corresponds to a greater contribution of higherattenuation regions. The interquartile range quantifies the spread of intensity values along each profile. Line 3 exhibits higher median IQR values compared to lines 1 and 2, indicating stronger intensity contrast along x at that transverse position. Line 2 shows a broader slice-wise distribution of IQR values, indicating larger through-thickness variability in local heterogeneity at that location. This suggests that the attenuation structure sampled at line 2 varies more strongly along z than at the other transverse positions. The autocorrelation-based correlation length Lcorr,e  increases progressively from line 1 to line 3 with limited overlap between distributions. Because Lcorr,e  estimates the spatial scale over which intensities remain correlated, larger values correspond to more slowly varying profiles along x. The observed increase from line 1 to line 3 therefore indicates a transverse shift toward larger-scale attenuation domains at line 3 . The normalized high-frequency energy ratio ηhigh  quantifies the fraction of spectral energy located in the high-frequency band. Larger values indicate stronger rapid intensity fluctuations along the sampled direction. Line 2 exhibits the highest median ηhigh . Lines 1 and 2 display heavier upper tails compared to line 3 , indicating the presence of slices with locally enhanced fine-scale fluctuations. The normalized maximum low-tail run length Lmax , lo * measures the longest contiguous segment of low-intensity values along a profile, expressed relative to profile length. This descriptor quantifies the spatial persistence of lowattenuation domains. Line 2 shows the highest median Lmax , lo *, indicating more persistent lowattenuation segments at that transverse position. Line 3 exhibits fewer low-tail events overall but occasional longer excursions, consistent with a broader upper spread in the corresponding boxplot. Across all descriptors, the attenuation field within the VOI is not transversely uniform. The three sampled positions exhibit distinct combinations of average attenuation level, within-profile dispersion, correlation length, high-frequency spectral content and persistence of low-attenuation domains. These differences are consistent with mesostructural heterogeneity across the section at the sampling scale considered. Through-thickness trends are evaluated by fitting slice-wise descriptor values as a function of z for each transverse line. Slopes are estimated using block-bootstrap confidence intervals to account for serial dependence along the stack. Line 2 exhibits a consistent drift pattern. The mean attenuation μ increases with z, while IQR and Lcorr, e decrease. In parallel, ηhigh  and Lmax ,lo * show positive slopes. This combination indicates a progressive shift toward shorter correlation lengths and increased persistence of low-attenuation segments along the stack. Line 3 exhibits a different trend. The mean attenuation decreases with z, while the correlation length increases. This behaviour indicates a shift toward more slowly varying attenuation profiles along the sampled direction. These results demonstrate that slice-wise descriptors detect systematic attenuation changes along the stack, in addition to transverse heterogeneity across the section.

[picture]

Fig. 4. Boxplots of slice-wise descriptor values computed from normalized profiles at three transverse positions. Boxes represent median and interquartile range. Whiskers extend to 1.5× IQR. Points denote outliers.

Fig. 4. Boxplots of slice-wise descriptor values computed from normalized profiles at three transverse positions. Boxes represent median and interquartile range. Whiskers extend to 1.5 × IQR. Points denote outliers.

Conclusions

The original version of this paper is available on https://www.scientific.net/SSP.387.103.pdf

This study establishes a reproducible descriptor-based representation of attenuation structure in unidirectional pultruded composites directly from micro-CT intensity profiles, without relying on explicit phase segmentation. The extracted line-profile descriptors resolve measurable transverse heterogeneity across the section. Mean attenuation separates transverse locations with limited
overlap. Within-profile dispersion and spectral content reveal line-dependent differences in fine-scale variability. The autocorrelation-based correlation length identifies systematic transitions in characteristic spatial scale across the section. Run-length descriptors quantify the persistence of lowattenuation domains and distinguish transverse regions through differences in contiguous segment length. Slice-wise analysis further reveals coherent through-thickness trends. Selected descriptors exhibit monotonic drifts along the stack, demonstrating that low-dimensional intensity-based metrics detect organized structural modulation rather than purely stochastic slice-to-slice fluctuation. Quality control confirms numerical completeness and internal consistency of the descriptor tables. Sensitivity analysis quantitatively separates weakly influential parameter groups from structurally influential ones. Autocorrelation and entropy settings show limited impact within the tested ranges, whereas spectral band limits and percentile thresholds materially affect descriptor magnitude and ordering. This establishes a transparent parameter policy for reproducible reporting. The principal output of the work is therefore a compact, traceable set of mesostructural indicators that quantify attenuation level, dispersion, spatial scale, fine-scale fluctuation intensity, and persistence of low-attenuation domains at fixed transverse positions. Future work will focus on integrating these descriptors into Abaqusbased finite-element workflows. In that context, slice-wise or aggregated descriptor fields can be mapped onto spatially varying material parameters using field-dependent properties or user-defined material subroutines. This will enable variability-aware simulations in which CT-derived structural indicators inform effective property distributions, without requiring full three-dimensional phase reconstruction. Mechanical validation and calibration of descriptor-property relationships will be addressed in subsequent studies.

Acknowledgments

The original version of this paper is available on https://www.scientific.net/SSP.387.103.pdf

This work was supported by the Ministero dell'Istruzione, dell'Università e della Ricerca, PRIN2022: Recycling of plastic wastes integrating extrusion and additive manufacturing techniques (RELIVE), code 2022FBB37P, CUP: D53D23003520006.

References

The original version of this paper is available on https://www.scientific.net/SSP.387.103.pdf

D. Wilhelmsson, R. Gutkin, F. Edgren, and L. E. Asp, "An experimental study of fibre waviness and its effects on compressive properties of unidirectional NCF composites," Compos. Part A Appl. Sci. Manuf., vol. 107, pp. 665-674, Apr. 2018, doi: 10.1016/j.compositesa.2018.02.013.

A. S. Mahmood, J. Summerscales, and M. N. James, "Resin-Rich Volumes (RRV) and the Performance of Fibre-Reinforced Composites: A Review," Journal of Composites Science, vol. 6, no. 2, p. 53, Feb. 2022, doi: 10.3390/jcs6020053.

M. Mehdikhani, L. Gorbatikh, I. Verpoest, and S. V. Lomov, "Voids in fiber-reinforced polymer composites: A review on their formation, characteristics, and effects on mechanical performance," May 01, 2019, SAGE Publications Ltd. doi: 10.1177/0021998318772152.

Y. Fu and X. Yao, "A review on manufacturing defects and their detection of fiber reinforced resin matrix composites," Composites Part C: Open Access, vol. 8, p. 100276, Jul. 2022, doi: 10.1016/j.jcomc.2022.100276.

I. Baran, I. Straumit, O. Shishkina, and S. V. Lomov, "X-ray computed tomography characterization of manufacturing induced defects in a glass/polyester pultruded profile," Compos. Struct., vol. 195, pp. 74-82, Jul. 2018, doi: 10.1016/j.compstruct.2018.04.030.

M. Mehdikhani, I. Straumit, L. Gorbatikh, and S. V. Lomov, "Detailed characterization of voids in multidirectional carbon fiber/epoxy composite laminates using X-ray micro-computed tomography," Compos. Part A Appl. Sci. Manuf., vol. 125, Oct. 2019, doi: 10.1016/j.compositesa.2019.105532.

M. Elkolali, L. P. Nogueira, P. O. Rønning, and A. Alcocer, "Void Content Determination of Carbon Fiber Reinforced Polymers: A Comparison between Destructive and Non-Destructive Methods," Polymers (Basel)., vol. 14, no. 6, Mar. 2022, doi: 10.3390/polym14061212.

M. P. Alves, C. A. Cimini Junior, and S. K. Ha, "Fiber waviness and its effect on the mechanical performance of fiber reinforced polymer composites: An enhanced review," Oct. 01, 2021, Elsevier Ltd. doi: 10.1016/j.compositesa.2021.106526.

M. R. Wisnom, "The Effect of Fibre Waviness on the Relationship between Compressive and Flexural Strengths of Unidirectional Composites," J. Compos. Mater., vol. 28, no. 1, pp. 6676, Jan. 1994, doi: 10.1177/002199839402800105.

P. Kulkarni, K. D. Mali, and S. Singh, "An overview of the formation of fibre waviness and its effect on the mechanical performance of fibre reinforced polymer composites," Oct. 01, 2020, Elsevier Ltd. doi: 10.1016/j.compositesa.2020.106013.

P. J. Withers et al., "X-ray Computed Tomography."

Y. Gao, W. Hu, S. Xin, and L. Sun, "A review of applications of CT imaging on fiber reinforced composites," J. Compos. Mater., vol. 56, no. 1, pp. 133-164, Jan. 2022, doi: 10.1177/00219983211050705.

P. J. Schilling, B. P. R. Karedla, A. K. Tatiparthi, M. A. Verges, and P. D. Herrington, "X-ray computed microtomography of internal damage in fiber reinforced polymer matrix composites," Compos. Sci. Technol., vol. 65, no. 14, pp. 2071-2078, Nov. 2005, doi: 10.1016/j.compscitech.2005.05.014.

E. A. Zwanenburg, M. A. Williams, and J. M. Warnett, "Performance testing of dimensional X-ray computed tomography systems," Precis. Eng., vol. 77, pp. 179-193, Sep. 2022, doi: 10.1016/j.precisioneng.2022.05.005.

"Guide for Computed Tomography (CT) Imaging," Jul. 01, 2019, ASTM International, West Conshohocken, PA. doi: 10.1520/E1441-19.

"DIN EN ISO 10360-7:2011-09, Geometrische Produktspezifikation (GPS)- Teil_7: KMG mit Bildverarbeitungssystemen (ISO_10360-7:2011); Deutsche Fassung EN_ISO_103607:2011," Sep. 2011, DIN Media GmbH, Berlin. doi: 10.31030/1726376.

R. Karamov, L. M. Martulli, M. Kerschbaum, I. Sergeichev, Y. Swolfs, and S. V. Lomov, "Micro-CT based structure tensor analysis of fibre orientation in random fibre composites versus high-fidelity fibre identification methods," Compos. Struct., vol. 235, Mar. 2020, doi: 10.1016/j.compstruct.2019.111818.

N. Jeppesen, L. P. Mikkelsen, A. B. Dahl, A. N. Christensen, and V. A. Dahl, "Quantifying effects of manufacturing methods on fiber orientation in unidirectional composites using structure tensor analysis," Compos. Part A Appl. Sci. Manuf., vol. 149, Oct. 2021, doi: 10.1016/j.compositesa.2021.106541.

R. Blanc, C. Germain, J. P. Da Costa, P. Baylou, and M. Cataldi, "Fiber orientation measurements in composite materials," Compos. Part A Appl. Sci. Manuf., vol. 37, no. 2, pp. 197-206, Feb. 2006, doi: 10.1016/j.compositesa.2005.04.021.

M. Krause, J. M. Hausherr, B. Burgeth, C. Herrmann, and W. Krenkel, "Determination of the fibre orientation in composites using the structure tensor and local X-ray transform," J. Mater. Sci., vol. 45, no. 4, pp. 888-896, Feb. 2010, doi: 10.1007/s10853-009-4016-4.

D. He et al., "Multiscale image-based modelling of composite materials," Dec. 01, 2025, SAGE Publications Inc. doi: 10.1177/09506608251363653.

P. Galvez-Hernandez, R. Smith, K. Gaska, M. Mavrogordato, I. Sinclair, and J. Kratz, "The effect of X-ray computed tomography scan parameters on porosity assessment of carbon fibre reinfored plastics laminates," J. Compos. Mater., vol. 57, no. 29, pp. 4535-4548, Dec. 2023, doi: 10.1177/00219983231209383.

J. M. Sietins, W. H. Green, and J. S. Jones, "Materials Evaluation Using X-ray Computed Tomography," in Comprehensive Structural Integrity, Elsevier, 2023, pp. 159-199. doi: 10.1016/B978-0-12-822944-6.00038-4.

A. Gambardella, V. Esperto, F. Tucci, and P. Carlone, "Defects Reduction in the Robotic Layup Process," in Key Engineering Materials, Trans Tech Publications Ltd, 2022, pp. 14371444. doi: 10.4028/p-7v9349.

V. Esperto, A. Gambardella, G. Pasquino, F. Tucci, M. Durante, and P. Carlone, "Modeling and simulation of the robotic layup of fibrous preforms for liquid composite molding," in ESAFORM 2021-24th International Conference on Material Forming, PoPuPS (University of LiFge Library), 2021. doi: 10.25518/esaform21.475.

J. S. U. Schell, M. Renggli, G. H. van Lenthe, R. Müller, and P. Ermanni, "Micro-computed tomography determination of glass fibre reinforced polymer meso-structure," Compos. Sci. Technol., vol. 66, no. 13, pp. 2016-2022, Oct. 2006, doi: 10.1016/j.compscitech.2006.01.003.

V. Esperto, L. Boccarusso, M. Durante, L. Carrino, and P. Carlone, "Permeability analysis of natural and artificial fiber textiles for liquid composite molding process," in Procedia Manufacturing, Elsevier B.V., 2020, pp. 435-439. doi: 10.1016/j.promfg.2020.04.328.

P. De Sio, M. Gaito, V. Esperto, E. Cozzolino, A. Astarita, and F. Tucci, "Life Cycle Assessment of a Composite Prototype Battery Enclosure for Electric Vehicles," Sustainability (Switzerland), vol. 17, no. 4, Feb. 2025, doi: 10.3390/su17041579.

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/composites-forming-processes/978-3-0364-1996-1

Solid State Phenomena, ISSN: 1662-9779, Vol. 387, pp 119-130

doi: 10.4028/p-ZUEM4y

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

Submitted:
2026-01-19

Revised:
2026-02-16

Accepted:
2026-02-17

Online:
2026-04-14

Environmental Impact Assessment of Thermoplastic Pultrusion of GF-PP Pre-Consolidated Tapes

The original version of this paper is available on https://www.scientific.net/SSP.387.119.pdf

Paolo De Sio 1,2,a*, Ersilia Cozzolino 2, b, Fausto Tucci 1,c, Antonello Astarita 2, d

1 Department of Industrial Engineering, University of Salerno, 132 Via Giovanni Paolo II, 84084

Fisciano (SA), Italy

2 Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, Naples, ITALY

a  paolo.desio@unina.it, b  ersilia.cozzolino@unina.it, c  ftucci@unisa.it, dantonello.astarita@unina.it,

Keywords: Sustainability, Life Cycle Assessment, Pultrusion,

Abstract

This work focuses on the pultrusion of pre-consolidated tapes made of virgin polypropylene reinforced with glass fiber. A specially designed laboratory-scale pultrusion line was used, consisting of a heating/forming mold, a cooling mold, and the pulling system. A life cycle assessment was conducted to evaluate the environmental impact of producing a pultruded composite material with a constant cross-section of 100 mm2 and a length of one meter. The cradle-to-gate approach was chosen to model the pultrusion process, which involves the three stages mentioned above. The analysis was performed using the CML 2016 method in the LCA for Experts (Sphera) software. The data used in this work to model the cradle-to-gate scenario are mainly derived from experimental measurements taken during the pultrusion process using sensors and from the literature. The specific Energy Consumption (SEC) was calculated for both operational conditions ( 1.41MJ/m at v120 to 0.905MJ/m at v180). Despite the defects found, the samples taken from the pultruded profile showed significant interlaminar shear strength ( 120 mm/min of 83.7±9.6MPa compared to 63.5±17.3MPa at 180 mm/min ).

Introduction

The original version of this paper is available on https://www.scientific.net/SSP.387.119.pdf

Thermoplastic matrix composites offer many significant advantages, both mechanically and physically, compared to the more common fiber-reinforced thermoset composites [1], [2]. For this reason, in recent years they have attracted the interest of industrial sectors requiring high-performance materials, such as the nautical, aerospace, and automotive sectors. Another attractive feature is their ability to be post-formed and welded. However, their high viscosity during processing has prevented their widespread use as a composite matrix. This characteristic makes impregnation with the reinforcement difficult, increasing the risk of defects and, consequently, production costs [3]. For this reason, research is increasingly pushing for solutions that utilize pre-consolidated materials, thus reducing the viscosity of thermoplastics[4], [5]. Thermoplastic pultrusion is the most advantageous composite manufacturing process for producing continuous profiles of theoretically infinite length [4], [6], [7]. A non-reactive pultrusion line for thermoplastic composites consists of pre-impregnated tapes, a guiding system, a preheating chamber, a heated forming die, a cooling die, a puller system and a cutting saw. In recent years, this process has gained popularity in various industries, including marine, automotive, aerospace and construction, due to its ability to produce lightweight, strong and recyclable components [6], [8], [9], [10]. Indeed, with the increasing demand for more sustainable manufacturing practices, thermoplastic pultrusion represents an opportunity to promote recyclability and thus reduce environmental impact compared to traditional thermoset pultrusion. However, to date, the number of studies and publications in the field of thermoset pultrusion is an order of magnitude higher than that of thermoplastics, despite the advantages offered by the latter. To evaluate the sustainability of a product or process, it is possible to resort to Life Cycle Assessment. It is a structured and internationally standardized method that allows us to quantify the potential environmental and human health impacts associated with a good or service, based on their resource consumption and emissions [11]. In this context, the primary objective of this work is to conduct a
cradle-to-gate preliminary LCA analysis of the thermoplastic pultrusion of pre-consolidated GF/PP tapes using experimentally measured energy data. Furthermore, the study further investigates how varying pultrusion speed affects specific energy consumption (SEC), the associated environmental impacts in different midpoint categories, and interlaminar shear strength (ILSS) as an indicator of consolidation quality. The pultrusion speed was chosen as the primary process variable because it directly controls the residence time in the heated mold. Therefore, evaluating its effect allows for a preliminary assessment of process intensification strategies from both an environmental and mechanical perspective. This approach allows us to quantify the extent to which increasing production speed effectively reduces environmental burdens per functional unit and whether such reductions occur without compromising interlaminar performance. By integrating environmental and mechanical parameters into the same experimental framework, this study provides a first processlevel quantification of the relationship between production rate, energy intensity, and interlaminar integrity in thermoplastic pultrusion.

Materials and Methods

The original version of this paper is available on https://www.scientific.net/SSP.387.119.pdf

A commercial tape produced by CompTape BV (Delft, Netherlands) is composed of a continuous filament E-glass roving (Owens Corning SE4849, tex. number 2400) and a polypropylene matrix (Moplen RP348U). The pre-consolidated tapes have a width of 6.35 mm , and a thickness of about 0.6 mm . Thirty-four tapes were used to produce a composite profile with a rectangular cross-section 25 mm wide and 4 mm thick. The number of tapes used is sufficient to oversaturate the heating die by 30%, increasing the fiber volume fraction of the pultruded product. The thermoplastic matrix must be heated above its melting temperature (Tm), consolidated, and then cooled to obtain a solid composite profile. Therefore, the laboratory-scale pultrusion line used consists of a preheating die to gradually increase the temperature, a heating/forming die where the polymer softens and melts, resulting in compaction, a cooling die responsible for solidification, and finally, the traction system.

[picture]

Fig. 1. Thermoplastic pultrusion dies

Fig. 1. Thermoplastic pultrusion dies

The heating/forming die is 180 mm long and divided into a 160 mm long converging section and a 20 mm long constant section. The cooling system is located in the next 100 mm long section of the mold. Fig. 1 shows the geometry of the mold cavities. Thermal energy is provided by three pairs of electric heating plates monitored by J-type thermocouples and regulated by PID controllers to maintain the temperature at the Tset point ±5∘C. The cooling system consists of a water-cooling
system regulated by an industrial chiller with a temperature setpoint of 13∘C. To ensure process continuity, a track conveyor at the end of the line acts as a towing system, allowing the material to move along the entire line. Table 1 shows the process parameters. DSC analysis performed on the PP made it possible to determine the temperature window and process speeds. In particular, the temperatures of the die zones were set to ensure that the melting range of the PP was completely exceeded in the hot section, ensuring adequate viscosity reduction with a consequent improvement in impregnation, while in the cooling section, controlled passage through the crystallization region was ensured in order to stabilize the microstructure before extraction. The pulling speed was the only variable modified to modulate the residence time in the die, while the other process parameters remained unchanged.

Table 1. Process parameters

Pre-Heater

Temperature

Heating Die

Set point chiller

Velocity

mathematical formula | mathematical formula | superscript number | superscript number | 120
mathematical formula | mathematical formula | superscript number | superscript number | 180

To verify that the chosen process parameters, under steady-state conditions, allowed the temperatures at the core of the pultruded material to be reached sufficiently to allow the polymer to melt and solidify, the mobile thermocouple method was used [12].

Life Cycle Assessment. Life Cycle Assessment (LCA) is the internationally recognized method used to assess the environmental performance of a product or of a service. ISO 14040:2006 [13]. This study aims to assess the environmental impacts of a composite material made with glass fiber ( GF ) and polypropylene (PP) through a cradle-to-gate LCA, shown in Fig. 2.

[picture]

Fig. 2. System boundary

Fig. 2. System boundary

Functional unit (FU) is defined as 1 meter of pultruded glass-polypropylene composite (GF/PP) profile, having a rectangular solid cross-section of 25×4 mm, made under steady-state process conditions. The mass corresponding to FU is around 0.126 kg , measured experimentally. Primary data were acquired over a total length. However, the last meter made was selected for each experimental trial to avoid the transient phase. All matter and energy fluxes are normalized with respect to the functional unit. The analyses have been carried out by using LCA for Experts - Sphera (GaBi). The LCA model used is the CML 2016 impact assessment method (August 2016 version). This method uses midpoint indicators to model the effects of substances on the environment at an early stage (also known as the problem-oriented approach), which minimizes uncertainties. The environmental impact category chosen are: Global Warming Potential (GWP 100) [ kgCO2 eq], Abiotic Depletion Potential - Fossil Fuels (ADPf) [MJ], Abiotic Depletion Potential - Elements (ADPe) [kg Sb eq], Acidification Potential (AP) [kg SO2 eq], Eutrophication Potential (EP) [kg PO4 3−
eq], Ozone Depletion Potential (ODP) [kg CFC-11-eq], Photochemical Ozone Creation Potential (POCP) [kg C2H4-eq]. Thus, raw material production (fiber, PP) is included in the system boundaries, while tapes production in this first stage of analysis was omitted as it was considered negligible compared to the raw material production stage and process. The authors considered focusing on the process by neglecting all transportation, infrastructure (machinery, building), maintenance, personnel, and measuring instruments at this first stage, according to the cut-off principle.

Life cycle inventory (LCI). This section is devoted to the second phase of the LCA study, the Life Cycle Inventory (LCI), in which all input and output flows present in the system boundary are reported. Table 2 reports all elementary flows used to model the cradle-to-gate scenarios, which are mainly derived from experimental measurements made during the pultrusion process using current and voltage sensors and from the LCA modeling software database. Current and voltage sensors were used during the process to monitor the power absorbed by each element in the line and its power consumption. As shown in Fig. 3, junction boxes were used for monitoring to allow measurement of both current and voltage and a DAQ connected to a pc for real-time data acquisition. A sampling frequency of 20 kHz was chosen to perform the power analysis. For energy calculation, reference was made only to the active power required to carry out experimental tests, considering the different systems present (mono-phase and three-phase). Specific energy consumption (SEC) was adopted to evaluate the energy efficiency of the pultrusion process. It is energy consumption to make the F.U. unit by pultrusion and in our study it is expressed in Eq.1. Where E (MJ) is electrical energy consumption during the process to make the F.U.

SEC=EF.U.(1)

[picture]

Fig. 3. Set up of electricity measurement

Fig. 3. Set up of electricity measurement

Table 2 reports the data used in this work to model the cradle-to-gate scenario, which are mainly derived from experimental measurements taken during the pultrusion process using current and voltage sensors and from the database of software.

Table 2. Life Cycle Inventory

Inventory | Measure Units |  | Source
Raw Materials
E - Glass fibers (GF) | kg | 0.082 | 0.082 | Gabi Database
Polypropylene (PP) | kg | 0.044 | 0.044 | Gabi Database
Thermoplastic Pultrusion
INPUT |  | T200-V120 | T200-V180 |
Tapes GF/PP | kg | 0.126 | 0.126 | Primary Data
Electricity | MJ |  |  |
Suction hood |  | 0.434 | 0.282 | Primary Data
Heating Die |  | 0.603 | 0.361 | Primary Data
Cooling Die |  | 0.303 | 0.202 | Primary Data
Caterpillar |  | 0.068 | 0.060 | Primary Data
Total [MJ] |  | 1.41 | 0.905 |
OUTPUT |  |  |  |
Pultruded composite | kg | 0.126 | 0.126 |

For the modeling of upstream (background) flows such as glass fibers and polypropylene, it was not possible to use primary inventories directly referring to the suppliers used in the experimental trials. Therefore, secondary datasets from the GaBi database were used, selected to maximize consistency in terms of production technology and geographic representativeness. Specifically, fiber was represented through a dataset with reference to Germany, while PP was modeled through an average EU-28 dataset. It should be emphasized that these datasets do not necessarily correspond to the exact specifications of the materials actually used and therefore introduce systematic uncertainty into the estimation of absolute impacts associated with raw materials. However, since the comparison between scenarios was set up with equal bill of materials and with differences mainly on speed as a process parameter, the use of datasets is considered appropriate for the comparative analysis, with the understanding that the sensitivity of the results to the choice of feedstock dataset is discussed in the uncertainty/limits section. As mentioned above, for the electricity consumed by the pultrusion line (foreground), the inventory was constructed from experimental current and voltage measurements, and the associated impact was calculated using an Italian electricity grid mix, consistent with the location of the production site and the specific emissions of the national electricity system.

Interlaminar shear strength test. A mechanical interlaminar shear strength test was performed on the pultruded profile in accordance with the ISO D2334. The mechanical test was performed using the Galdabini universal testing machine. At least 5 samples were tested to obtain the ILSS according to Eq. 2.

ILSS=0.75*Pm b* h(2)

Results and Discussion

The original version of this paper is available on https://www.scientific.net/SSP.387.119.pdf

At a visual inspection, both produced profiles appeared well compacted and with constant cross section, even if the external surface presented marked irregularities, as visible in Fig. 4.

[picture]

Fig. 4. Pultruded composite (P5 - T200 v120; P6 - T200 v180)

Fig. 4. Pultruded composite (P5 - T200 v120; P6 - T200 v180)

Life Cycle Impact Assessment. This section is dedicated to the third phase of the LCA study, the life cycle impact assessment (LCIA), in which the life cycle inventory information on elementary flows is translated into the chosen environmental impact categories. The analysis is performed by using the CML 2016 method in the LCA for Experts (Sphera) software.

Table 3. Life Cycle Impact Assessment

Impact | T200-v120 | T200-v180
Photochemical Ozone Creation Potential (POCP) [kg C2H4-eq] | 9.62 | 9.08
Global Warming Potential (GWP 100) [kg CO₂ eq] | 0.37 | 0.33
Eutrophication Potential (EP) [kg PO₄³⁻ eq] | 10.2 | 9.19
Acidification Potential (AP) [kg SO₂ eq] | 1.33 | 1.25
Abiotic Depletion Potential – Elements (ADPe) [kg Sb eq] | 7.52 | 7.51
Abiotic Depletion Potential – Fossil Fuels (ADPf) [MJ] | 6.95 | 6.37
Ozone Depletion Potential (ODP) [kg CFC-11-eq] | 5.28 | 4.17

As can be seen from the results shown in aggregate in Table 3 for all impact categories analyzed, the scenario processed at the highest pull speed (v 180 mm/min ) has lower total impacts, with the only marginal exception of ADPe, which remains essentially unchanged as it is dominated by upstream material contributions rather than process-related energy consumption. Overall, the results clearly indicate that increasing the pull rate leads to a systematic improvement in environmental performance per functional unit. The observed reductions in environmental impacts are mainly due to the decrease in specific energy consumption per meter of pultruded profile. The total energy requirement (SEC) decreases from 1.41MJ/m at v120 to 0.905MJ/m at v180, representing a reduction of approximately 35.8 percent. All energy subsystems benefit from increased traction speed. Heating energy decreases from 0.603 to 0.361MJ/m ( −40.1% ), intake from 0.434 to 0.282MJ/m ( −35.0% ), cooling from 0.303 to 0.202MJ/m(−33.3%), and traction from 0.068 to 0.060MJ/m(−11.8%). Despite the reduction in absolute values, the relative distribution among subsystems remains approximately constant, with heating accounting for about 40−43% of total energy demand, extraction 31−32%, cooling 21−22%, and traction 5−7%. From a process perspective, this behavior reflects the partial amortization of quasistationary thermal and auxiliary loads at a higher production rate, resulting in reduced energy intensity per unit length. To perform a comprehensive analysis, the authors reported the impacts in disaggregated form in Figures 5-11 to evaluate hotspots by impact category.

[picture]

Fig.5. Non-aggregate impacts of POCP

Fig.5. Non-aggregate impacts of POCP

[picture]

Fig. 6. Non-aggregate impacts of GWP

Fig. 6. Non-aggregate impacts of GWP

[picture]

Fig.7. Non-aggregate impacts of EP

Fig.7. Non-aggregate impacts of EP

[picture]

Fig. 8. Non-aggregate impacts of ADPf

Fig. 8. Non-aggregate impacts of ADPf

[picture]

Fig. 9. Non-aggregate impacts of ADPe

Fig. 9. Non-aggregate impacts of ADPe

[picture]

Fig. 10. Non-aggregate impacts of ODP

Fig. 10. Non-aggregate impacts of ODP

[picture]

Fig. 11. Non-aggregate impacts of AP

Fig. 11. Non-aggregate impacts of AP

ADPe is almost entirely dominated by E-glass fibers, which account for more than 99 percent of the total impact in both scenarios. As a result, process optimization by varying the tensile speed has a negligible effect on this category, and any significant reduction would require material-level interventions, such as sourcing alternative fibers, substituting the material, or improving the glass fiber manufacturing process. POCP and AP are also primarily driven by material production, with Eglass and polypropylene together contributing more than 80% of the total impact. Thus, the reduction achieved at v180 is moderate and mainly attributable to decreased energy demand from heating and auxiliary systems. Regarding GWP, the contribution from process energy is more significant (approximately 30−40% ), resulting in a more pronounced reduction as drive speed increases. Similarly, the same trend is observed for EP, where the energy contribution decreases significantly at higher speeds, shifting the dominant contribution toward the material phase. The results show that increasing traction speed improves environmental performance in almost all impact categories by reducing the specific energy demand. However, the contribution analysis indicates that not all environmental burdens can be effectively mitigated solely through process intensification. Categories dominated by upstream material production, such as ADPe and partially ADPf, require materialoriented strategies, while energy-dominated categories mainly benefit from operational optimization and thermal management.
The temperature profiles at the core of the pultruded material, acquired using the mobile thermocouple technique, are shown in Fig. 12. The preheating system allows the strips passing through it to gradually increase in temperature from ambient temperature ( 25−30∘C ). However, the setpoint temperature of 90∘C of pre-heater was not able to provide the heat necessary to bring the temperature of the strips close to the melting point. At the entrance to the heating die (330, vertical line), an increase in the dT/dx slope is observed, consistent with entering a zone of greater heat exchange. The increase becomes marked until it reaches a thermal maximum near the entrance to the constant section (480, dashed line) at the compaction condition.

[picture]

Fig. 12. Temperature at core of pultruded during the process

Fig. 12. Temperature at core of pultruded during the process

For the 120 mm/min condition, a temperature of approximately 182∘C is reached, while at 180 mm/min the peak is approximately 174∘C. The difference between the maximums is consistent with the effect of thermal permanence: at lower speeds, the material remains longer in the heated zone and accumulates more heat, approaching the conditions necessary for the softening/melting of PP. In both conditions, the temperatures reached are higher than the melting temperature of the polypropylene in question, thus ensuring a reduction in the viscosity of the PP flow.
The samples subjected to interlaminar shear stress showed large plastic deformations. The maximum load observed in the test at speeds of 120 mm/min and 180 mm/min was approximately 3806 N and 3934 N, respectively. According to ASTM D2344, the maximum interlaminar shear strength of the pultruded composite has a higher value at 120 mm/min of 83.7±9.6MPa compared to 63.5±17.3 MPa at 180 mm/min, indicating better consolidation and interlaminar adhesion, while the greater variance suggests the presence of more defects. This trend is consistent with the thermal histories measured at the core of the pultruded composite. Indeed, the maximum temperature decreases, as does the residence time above the melting temperature, reflecting a shorter thermal residence time in the heated section. The reduced time above the melt and the shorter compaction time affect polymer flow and pressure-induced redistribution, resulting in lower interlaminar adhesion. Thus, while increased speed improves energy efficiency per unit length, it also introduces a trade-off between consolidation and intensification, which is critical for structural applications.

Conclusions

The original version of this paper is available on https://www.scientific.net/SSP.387.119.pdf

In conclusion, this study examined the thermoplastic pultrusion of glass fiber-reinforced PP tapes using a laboratory-scale line. A cradle-to-gate LCA analysis was performed to assess the environmental impact of the process. The results showed that the raw material extraction phase is predominant for specific impact categories, such as ADPe and ADPf, while factors such as GWP and ODP are significantly influenced by the process speed. This is a preliminary LCA study to analyze the impacts per sub-process, laying the foundation for future work aimed at optimizing process parameters. The pultruded products obtained were continuous and compact; however, the authors intend to study their microstructure in future work using computed microtomography to identify any connections between process parameters and defects. Despite the surface defects found, the samples taken from the pultruded profile showed significant interlaminar shear strength with higher strength at lower speeds. Although the study is based on primary data, it has several limitations in terms of the number of experimental tests performed and process variables considered. It is important to note that this is a preliminary LCA on the pultrusion process of these materials. Consequently, future developments will involve applying this study to a wide range of process parameters, such as extensive Design of Experiments (DoE) that varies preheating and heating temperatures. This approach aims to establish guidelines for decision support in industry, identifying the optimal tradeoff to simultaneously optimize mechanical properties and minimize environmental impact.

References

The original version of this paper is available on https://www.scientific.net/SSP.387.119.pdf

F. Tucci, V. Esperto, G. Pasquino, and P. Carlone, "Injection Pultrusion of Glass-Reinforced Epoxy: Cure Kinetics, Rheology, and Force Analysis," Polymers, vol. 16, no. 12, p. 1642, Jun. 2024, doi: 10.3390 /polym16121642.

S. Dong, P. Zhou, Z. Ning, X. Wu, C. Li, and G. Xian, "Durability of carbon- and glass-fiber reinforced thermoplastic polymer composites: A literature review," J. Build. Eng., vol. 98, p. 111055, Dec. 2024, doi: 10.1016/j.jobe.2024.111055.

V. Esperto, C. Gallo, S. Lomov, J. Soete, and P. Carlone, "System Integration for Advanced Manufacturing of Composites by Microwave Preheated Resin Infusion: An Experimental Study," J. Mater. Eng. Perform., vol. 34, no. 10, pp. 8759-8767, May 2025, doi: 10.1007/s11665-024-10397-7.

F. Tucci, F. Rubino, V. Esperto, G. Pasquino, and P. Carlone, "Investigation on the pultrusion of thermoplastic preimpregnated polypropylene-glass tapes," presented at the Material Forming, May 2023, pp. 393-398. doi: 10.21741/9781644902479-43.

K. Minchenkov, A. Vedernikov, A. Safonov, and I. Akhatov, "Thermoplastic Pultrusion: A Review," Polymers, vol. 13, no. 2, p. 180, Jan. 2021, doi: 10.3390/polym13020180.

"Pultruded materials and structures: A review - Alexander Vedernikov, Alexander Safonov, Fausto Tucci, Pierpaolo Carlone, Iskander Akhatov, 2020." Accessed: Jan. 19, 2026. [Online]. Available:

F. Tucci, F. Rubino, V. Esperto, and P. Carlone, "Integrated modeling of injection pultrusion," presented at the PROCEEDINGS OF THE 22ND INTERNATIONAL ESAFORM CONFERENCE ON MATERIAL FORMING: ESAFORM 2019, Vitoria-Gasteiz, Spain, 2019, p. 060006. doi: 10.1063/1.5112601.

V. Esperto, A. Gambardella, G. Pasquino, F. Tucci, M. Durante, and P. Carlone, "Modeling and Simulation of the Robotic Layup of Fibrous Preforms for Liquid Composite Molding," ESAFORM 2021, Apr. 2021, doi: 10.25518/esaform21.475.

"Marine Application of Fiber Reinforced Composites: A Review." Accessed: Jan. 19, 2026. [Online]. Available:

A. H. Miller, N. Dodds, J. M. Hale, and A. G. Gibson, "High speed pultrusion of thermoplastic matrix composites," Compos. Part Appl. Sci. Manuf., vol. 29, no. 7, pp. 773-782, Jul. 1998, doi: 10.1016/S1359-835X(98)00006-2.

P. De Sio, M. Gaito, V. Esperto, E. Cozzolino, A. Astarita, and F. Tucci, "Life Cycle Assessment of a Composite Prototype Battery Enclosure for Electric Vehicles," Sustainability, vol. 17, no. 4, p. 1579, Feb. 2025, doi: 10.3390/su17041579.

F. Tucci, V. Esperto, F. Rubino, and P. Carlone, "Experimental Measurement of the Resistant Load in Injection Pultrusion Processes".

M. Finkbeiner, A. Inaba, R. Tan, K. Christiansen, and H.-J. Klüppel, "The New International Standards for Life Cycle Assessment: ISO 14040 and ISO 14044," Int. J. Life Cycle Assess., vol. 11, no. 2, pp. 80-85, Mar. 2006, doi: 10.1065/lca2006.02.002.

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/composites-forming-processes/978-3-0364-1996-1

Solid State Phenomena, ISSN: 1662-9779, Vol. 387, pp 131-141

doi: 10.4028/p-j6M0hN

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

Submitted:
2026-01-19

Revised:
2026-02-16

Accepted:
2026-02-17

Online:
2026-04-14

The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

The original version of this paper is available on https://www.scientific.net/SSP.387.131.pdf

Connie Qian 1,a*, Miro Duhovic 2, b*, Louis Schreyer 3,c, Marcel Olma 3, d, Andreas Gebhard 2,e, Wouter Grouve 4,f, Dominic Schommer 2, g, Florian Gortner 2, h, Thomas Neumeyer 2,i, Luise Kärger 3,j, Martin Hohberg 5,k, Hao Yuan 6,1, Anna Julia Imbsweiler 7, m, Leah Senn Ramirez 7,n, Sepehr Simaafrookhteh 8,0, Jan Ivens 8,p, Stepan V. Lomov 8,q, Gerben Bieleman 4,9,r, Francois Mahé 10, s, Christophe Binetruy 11,t, Andreas Kapshammer 12,u, Zoltan Major 12,v, Dmitry S. Ivanov 13,w, Jonathan P.-H. Belnoue 13,x, Anatoly Koptelov 13,y, Jakub Jakimow 13,z, Yvonne Aitomaki 14,aa, Dimitra Ramantani 14,bb

1 School of MAC, University of Sheffield, Sheffield S1 4DT, United Kingdom

2 Leibniz-Institut für Verbundwerkstoffe, 67663 Kaiserslautern, Germany

3 Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany

4 Production Technology, University of Twente, 7522 NB Enschede, the Netherlands

5 SIMUTENCE GmbH, 76131 Karlsruhe, Germany

66 WMG, University of Warwick, Coventry CV4 7AL, United Kingdom

7 Technical University of Munich, 85748 Garching, Germany

8 Department of Materials Engineering, KU Leuven, 443001 Leuven, Belgium

9 ThermoPlastic composites Research Center (TPRC), 7521 PN Enschede, the Netherlands

10 Arts et Métiers Institute of Technology, LAMPA, HESAM Université, F-49035 Angers, France

11 Nantes Université, Ecolé Centrale de Nantes, 44000 Nantes, France

12 Institute of Polymer Product Engineering, Johannes Kepler University Linz, Linz, 4040, Austria

13 Bristol Composites Institute, University of Bristol, Bristol BS8 1TR, United Kingdom

14 RISE Research Institutes of Sweden, Materials and Production, Sweden

a c.qian@sheffield.ac.uk, b Miro.Duhovic@ivw.uni-kl.de, c louis.schreyer@kit.edu, d  marcel.olma@kit.edu, e  Andreas.Gebhard@ivw.uni-kl.de, f  w.j.b.grouve@utwente.nl,

e Dominic.Schommer@ivw.uni-kl.de, h Florian.Gortner@ivw.uni-kl.de,

i  Thomas.Neumeyer@ivw.uni-kl.de, jluise.kaerger@kit.edu, k  martin.hohberg@simutence.de,

'hao.yuan@warwick.ac.uk, , ,anna.julia.imbsweiler@tum.de, "leah.senn@tum.de,

r  g.bieleman@utwente.nl, sfrancois.mahe@ensam.eu, t  christophe.binetruy@ec-nantes.fr,

uandreas.kapshammer@jku.at, v  zoltan.major@jku.at, w  dmitry.ivanov@bristol.ac.uk, 

xjonathan.belnoue@bristol.ac.uk, yanatoly.koptelov@bristol.ac.uk, zjakub.jakimow@bristol.ac.uk,

aa  yvonne.aitomaki@ri.se, bb  dimitra.ramantani@ri.se (*corresponding author)

Keywords: C-SMC, squeeze flow testing, compression moulding, benchmark exercise

Abstract

Squeeze-flow testing is a commonly used experimental method for characterising the flow behaviour of high-performance C-SMCs under typical compression moulding conditions. A European benchmark exercise involving 14 research institutions is currently being conducted to identify the sources of variability in squeeze flow testing results and to support the development of a standardised testing methodology. Experimental testing of five C-SMC materials has been completed using a well-defined testing procedure. This paper focuses on the data processing stage of the benchmark exercise, in which experimental data collected from all participants are processed and

analysed to extract information on raw material variability, force-gap height relationships, and flowfront profiles. Quantitative assessment of these results is used to identify the critical sources of variability, which subsequently informs a more detailed statistical analysis.

Introduction

The original version of this paper is available on https://www.scientific.net/SSP.387.131.pdf

High-performance carbon fibre sheet moulding compounds (C-SMCs) are a type of discontinuous fibre reinforced composite material characterised by long carbon fibre reinforcement (typically 25 mm roving length) and high fibre content (greater than 40wt% ). C-SMCs can be compression moulded under high pressure (typically greater than 100 bar) to form parts with complex geometries and excellent mechanical performance. Understanding the flow behaviour of C-SMCs is key for accurately predicting manufacturing outcomes. Several research institutions have adopted squeeze flow testing for characterising the flow behaviour of SMCs [1-5], but due to the lack of a standardised testing procedure, and the unique and complex flow behaviour of high-performance SMCs, the reliability and repeatability of this testing method remain low.

The high variability observed in SMC squeeze-flow testing can be attributed to several factors. First, the raw material (material prior to processing) itself is inherently stochastic. The compounding process introduces local variations in fibre volume fraction, fibre tow size (due to filamentation), and fibre orientation, particularly during the fibre deposition stage. In addition, for thermosetting SMCs, the matrix system consists of a pre-mixed resin and hardener that has been B-staged, requiring strict control of temperature and humidity during transport, storage, and handling to minimise premature curing. These requirements are especially demanding for modern SMCs containing fast-curing resin systems, as even subtle changes in storage temperature or humidity can lead to significant variations in the degree of cure of the matrix. These, in turn, affect the matrix viscosity at processing temperatures and consequently influences the flow behaviour of the SMC.

The variation in testing equipment used also contributes to the observed variability in several ways. Squeeze flow testing is typically performed on a load frame or press with either a static or hydraulic drive, and differences in drive type can lead to variations in the accuracy of speed and position control. The choices of sensors and measurement approaches for force and displacement are other important consideration, with factors such as measurement range, linearity, and sensor location all influencing the measured results. The heating method is another significant source of variability, because the type and layout of the heating system can strongly affect the temperature distribution within the test specimen. For SMC specimens with fast-curing matrix systems, non-uniform heating can result in inhomogeneous curing and lead to variations in viscosity throughout the specimen. Finally, loss of platen parallelism is a well-known source of error in squeeze flow testing, particularly for heterogeneous materials such as SMC, as it can lead to asymmetrical pressure distributions during the test.

The actual testing procedure is another area in which large differences among research institutes are commonly observed in the literature. Unlike factors such as fibre architecture, the environmental history of the raw material, and the testing equipment, variations in the testing procedure is often strongly influenced by researchers' interpretations of representative processing conditions. The affected parameters include for example specimen size, number of layers, testing speed, specimen loading time and the tooling surface conditions.

A benchmark exercise on squeeze flow testing of C-SMCs is currently being conducted by a consortium of European research institutions with in-house squeeze flow testing facilities. The primary objective of this exercise is to identify the sources of variability in experimentally measured data and to investigate ways to improve the reliability and repeatability of the testing. A previous paper, presented at ESAFORM 2025 [6] summarised the experimental methodologies adopted in the benchmark, with a focus on the materials, testing facilities, and testing procedures. The present paper focuses on the data-processing methodology required to enable comparison of results across all participants. Based on initial observations of the processed data, a strategy for detailed quantitative analysis is proposed to establish statistical and physical correlations among variables in the experimental methodology and the measured results.

Experimental Methodology

The original version of this paper is available on https://www.scientific.net/SSP.387.131.pdf

The benchmark exercise involves 14 participating institutions across Europe, operating a total of 10 squeeze flow testing facilities. Participating institutions include École Centrale de Nantes (ECN) and HESAM Université (HESAM), Leibniz-Institut für Verbundwerkstoffe (IVW), Johannes Kepler University Linz (JKU), Karlsruhe Institute of Technology (KIT) and Simutence GmbH (SIM), KU Leuven (KUL), the University of Twente (UTW) and the ThermoPlastic Composites Research Center (TPRC), RISE Research Institutes of Sweden AB (RISE), the Technical University of Munich (TUM), the University of Bristol (UOB), Warwick Manufacturing Group (WMG), and the University of Sheffield (UOS).

Five vinyl ester (VE) based C-SMC materials (Table 1) are investigated in this benchmark exercise to examine the effects of fibre content and tow size on the flow behaviour of C-SMCs. All materials studied contain the same resin system, featuring a specialised formulation that allows the material to remain processable for up to 12 weeks when stored at room temperature (up to 23∘C ). The long shelf life at room temperature is expected to reduce material variability arising from large, unpredictable environmental fluctuations, since controlling temperature variations at room temperature is considerably easier than maintaining frozen conditions. All materials were kept in sealed packaging with the original protective films during transport and storage to mitigate humidity fluctuations and evaporation loss of low molecular weight resin components. In addition, temperature and humidity sensors were kept with the material at all times to record any variations in storage conditions among the different participants.

Table 1. Summary of the materials tested in the first European C-SMC squeeze flow benchmark exercise [6].

Product code | Fibre tow size | Fibre length [mm] | Fibre weight fraction [%] | Target areal weight [g/m²]
24 CF50-50K | 50K | 25.4 | 50 | 1800
24 CF50-12K | 12K | 25.4 | 50 | 1800
24 CF50-3K | 3K | 25.4 | 50 | 1800
24 CF40-12K | 12K | 25.4 | 40 | 1800
24 CF60-12K | 12K | 25.4 | 60 | 1800

A well-defined experimental procedure was implemented to mitigate the contribution of procedural variation to results variability. All test specimens were square-shaped and consisted of six plies laid up in a consistent orientation (in relation to the roll direction). Two different specimen sizes were investigated, with edge lengths of 50 mm and 100 mm . Depending on the size of testing equipment available, participants tested either both specimen sizes or only the 50 mm specimens. Specific guidance, including time limits for specimen cutting and lay-up, was provided to all participants to minimise the duration for which raw material was exposed to ambient conditions.

All testing was performed using an open-cavity, constant-mass configuration, as shown in Figure 1 , with a constant mould surface temperature of 140∘C. To minimise variation in boundary conditions across participants, all testing surfaces were lined with polyimide films coated with each participant's choice of release agent. A specimen loading time of 10 s was imposed to reduce variability in preheating time, which could otherwise lead to significant differences in the initial degree of cure between tests. All tests started at an initial cavity height of 11 mm and ended at final cavity heights of 7 mm,5 mm, or 3 mm . Each specimen was then cured for a minimum of 210 s before being removed from the testing surface. Six repeats were performed for each testing and specimen configuration, with the test order fully randomised to eliminate systematic drift associated with material aging and time-dependent variations in operator or machine behaviour. The detailed experimental methodology can be found in the previous paper [6].

[picture]

Fig. 1. Schematic illustration of the C-SMC squeeze flow test [6].

Fig. 1. Schematic illustration of the C-SMC squeeze flow test [6].

Data Processing Methodology

The original version of this paper is available on https://www.scientific.net/SSP.387.131.pdf

Following the completion of the testing campaign, four sets of raw data were collected from each participant, including testing information, unprocessed force-displacement or force-gap height data for each specimen, scanned plan view images of the specimens after testing and temperature-humidity history data for the materials from raw material production to final pressing. Information of specimens prior to testing, including thickness, density and areal weight, were used to investigate material variability. The force-displacement or force-gap height data were further processed and plotted using an in-house Python-based program developed by KIT to enable visual comparison across all participants, as well as a subsequent statistical analysis. The specimen images were processed using an in-house Python-based program developed by IVW to extract the flow front profiles for further analysis. Both data processing programs were centrally developed and executed to avoid errors and discrepancies that could arise if data processing were performed independently by individual participants.

The following sections describe the data processing methodology, starting with the weight and thickness of the raw specimens, followed by the force-gap height curves, and finally the flow-front profiles that were extracted from the specimen images.

Variability in raw material. The material variability was assessed from the thickness, density and areal weight data for raw specimens collected from all participants. Prior to each test, participants were required to measure the weight and thickness of the specimen. The density and areal weight were subsequently calculated based on the nominal specimen size.

Force-gap height relationship. The unprocessed force-displacement or force-gap height data were collected from each participant as machine-readable files. Participants who recorded both displacement and gap height data were asked to identify which measurement they considered to be more reliable and should therefore be selected for downstream data processing and analysis. A separate comparison study was performed to examine differences between the two datasets; however, details of this study are not discussed in the present paper.

The unprocessed data were imported and processed as follows. First, the data-reading function mapped the material type, specimen size ( 50 mm or 100 mm ), and final gap height ( 7 mm,5 mm, or 3 mm) according to each participant's testing plan. Then, the data columns containing force, displacement, and gap height values were processed. For data manipulation, the force, displacement, and gap height data were first converted to a consistent unit system and sign convention. The gap height or force data were not further processed unless a systematic offset was identified in the recorded measurements. During code development, the data processing functions for individual participants were sanity-checked through blind comparison between sample force-gap height curves
manually plotted by each participant following a written version of the algorithm and the corresponding curves generated by the program.

Further data processing was performed to enable calculation of the mean and standard deviation across the six repeats for each testing and specimen configuration, as well as subsequent quantitative comparison among participants. Linear interpolation was applied to align all force-gap height data at common gap height values. Interpolation was performed using a uniform interval of 0.01 mm between the initial gap height of 11 mm and the final gap heights of 7 mm,5 mm, and 3 mm . To ensure consistency across datasets, all interpolated data were cropped to the common upper and lower gap height limits defined by the maximum and minimum values observed across all repeats of the same testing and specimen configuration. The mean and standard deviation were then calculated at each interpolated gap height point to obtain the average response and variability of the datasets.

Flow front profiles. Flow front profiles were determined from 24-bit colour images downscaled to 8-bit greyscale images of all individual specimens. All images were obtained by the participants using flatbed scanners of their choice, with a fixed spatial resolution of 600 dpi . A ruler was placed adjacent to each specimen in every image to enable spatial calibration where a reference length of 200 mm corresponded to 1182 pixels, resulting in a conversion factor of 0.1691 mm/px.

A global binary inverse threshold was applied to each image using a threshold value of 254 to generate a binary mask. This threshold value was selected based on the assumption of a near-white background while allowing for minor scanner noise. Following thresholding, the specimen appeared as the foreground region in the binary mask. A rectangular region-of-interest (ROI) mask was then applied to restrict specimen detection to a known area, excluding scanner borders and edge artefacts near the image boundaries.

The flow front profile was determined using a contour-based edge detection approach. External contours were extracted from the binary mask and represented as ordered lists of pixel coordinates in a Cartesian x−y coordinate system with a resolution of one pixel. Among all detected contours, the longest contour (i.e., the contour containing the largest number of points) was selected as the flow front profile of the specimen.

A specimen centre was subsequently determined by computing the mean of all pixel coordinates along the extracted flow front profile. All flow front coordinates were then re-centred relative to this centre and converted from pixel units to physical dimensions in mm using the established conversion factor of 0.1691 mm/px.

To obtain a representative average flow front for specimens of the same type, individual flow front profiles were combined using a correspondence-based averaging approach. The shortest profile (i.e., the profile containing the smallest number of points) was selected as a reference. For each point on this reference profile, the closest points on all other specimen profiles were identified and then averaged to produce a representative mean flow front profile for the specimen type.

Results and Discussions

The original version of this paper is available on https://www.scientific.net/SSP.387.131.pdf

Variability in raw material. Figure 2 presents an overview of the experimentally measured thickness, density, and areal weight of the raw specimens. The data presented in Figure 2 were averaged across all participants and all specimens, including both 50 mm and 100 mm specimens. It can be observed that CF4012K exhibits the highest coefficient of variation (COV) in both thickness and areal weight, with values of 7.22% and 7.51%, respectively. For density, CF4012K shows a comparatively high COV of 6.03%, second only to CF503K, which exhibits a marginally higher density COV of 6.05%. Overall, the observed levels of variability (approximately 5−8% ) are consistent with the ranges commonly reported for raw SMC materials in the literature [7]. In addition, the mean areal weights of all five materials lie within 5.88% of the target areal weight of 1800 g/m2, which is generally regarded as very good consistency for this class of material.

[picture]

Fig. 2. Thickness, density and areal weight results for raw specimens. Data were averaged across all participants and specimens (including both 50 mm and 100 mm specimens)

Fig. 2. Thickness, density and areal weight results for raw specimens. Data were averaged across all participants and specimens (including both 50 mm and 100 mm specimens)

It should be noted that the variations observed in the three quantities shown in Figure 2 can arise from different sources of material variability. Areal weight is primarily affected by the local fibre content and is therefore most strongly influenced by the fibre deposition stage of the SMC manufacturing process. During this stage, parameters such as tow cutting speed and carrier foil transport velocity must be carefully controlled to achieve the target areal weight. Maintaining such control becomes increasingly challenging for larger tow sizes or wider ranges of fibre content, particularly when using equipment optimised for a nominal fibre mass fraction of approximately 50% and a tow size of 12 K . Thickness and density, on the other hand, are also affected by downstream impregnation and lay-up processes. Several authors have reported that raw SMC layups commonly exhibit increased apparent thickness due to trapped air and lofting effects between plies of nonuniform thickness. Apparent volume increases of up to approximately 20% have been reported for such materials [2,7], which directly result in lower calculated densities prior to consolidation.

Force-gap height relationship. Figure 3 presents example force-gap height graphs of four out of the five materials tested in the 100×100 mm2 specimen size configuration for the participants capable of testing this specimen size. The results show a high level of variation in the measured forces among participants despite the fact that a well-prescribed testing procedure was implemented. All curves show an increase in measured force with reducing gap height, with most curves exhibiting an initial steep slope, once contact has been established that levels off in the medium compression range, and then increases again near the end.

It should be noted that the force-gap height curves obtained by IVW exhibit a higher level of noise compared with those from the other participants, with negative forces observed at low levels of compression (gap heights greater than approximately 8 mm ). This behaviour is likely attributable to the larger hydraulic press used by IVW, which may introduce greater inertia effects and structural vibration than the smaller-scale testing machines employed by the other participants. In addition, IVW used a load cell with a measurement range of 8000 kN , far exceeding the force levels recorded in the benchmark (below 100 kN ). By comparison, the other participants selected load cells rated up to 250 kN . As a result, IVW's measurements were more susceptible to reduced accuracy at the low end of the measured force range.

[picture]

Fig. 3. Example force-gap height curves for four of the five materials in the 100×100 mm2 specimen size configuration. For the purpose of clarification, only four data markers and corresponding error bars are displayed for each curve.

Fig. 3. Example force-gap height curves for four of the five materials in the 100 × 100 m m 2 specimen size configuration. For the purpose of clarification, only four data markers and corresponding error bars are displayed for each curve.

Figure 3a-c illustrate the effect of increasing fibre weight fraction from 40% to 60%. At low levels of compression (gap height of approximately 8 mm ), the measured forces show no strong correlation with fibre content, with values typically ranging from 3 to 10 kN at a gap height of 8 mm across all participants. This behaviour is attributed to deformation mechanisms dominated by the removal of trapped air and lofting between plies, with negligible deformation of the fibre network. As a result, the compressive forces at this stage are largely independent of fibre content. At intermediate levels of compression (gap height of approximately 6 mm ), no noticeable increase in force is observed as the fibre weight fraction increases from 40% to 50%. However, a substantial increase in force of approximately 50% is observed when the fibre weight fraction increases from 50% (approximately 5−15kN ) to 60% (approximately 10−25kN ) at a gap height of 6 mm . At high levels of compression (gap height of approximately 4 mm ), the influence of fibre content becomes more pronounced. An increase in force of approximately 30% is observed as the fibre weight fraction increases from 40% (approximately 9−17kN ) to 50% (approximately 12−22kN ), followed by a further increase of approximately 60% as the fibre weight fraction increases to 60% (approximately 18−36kN ). In
addition, the standard deviation of the measured forces decreases with increasing fibre content, as indicated by the shorter error bars. In general, an increase in fibre content is expected to result in higher forces beyond the initial compression regime, after trapped air has been released or compressed. The higher force is attributed to increased fibre-fibre interactions and tow compaction, which lead to elevated shear and compressive stresses within the material. At higher levels of compression, this effect is expected to intensify, as internal stresses exhibit a nonlinear relationship with compression due to the progressive stiffening of the fibre network.

Figure 3b and 3d illustrate the effect of decreasing the tow size from 12 K to 3 K , for which a pronounced increase in force is observed across all levels of compression. At low levels of compression (gap height of approximately 8 mm ), the measured force range increases by more than a factor of 3 as the tow size decreases from 12 K (approximately 3−7kN ) to 3 K (approximately 10− 30 kN ). At intermediate levels of compression (gap height of approximately 6 mm ), the force increases by approximately a factor of 4 , from around 7−15kN for 12 K to 30−50kN for 3 K . At high levels of compression (gap height of approximately 4 mm ), the force increases by just over a factor of 2, from approximately 12−22kN for 12 K to 40−70kN for 3 K . No noticeable difference in the level of standard deviation is observed between the two tow sizes. In theory, for a given fibre content, reducing tow size increases the number of tows within the same volume of material, leading to higher levels of fibre-fibre interaction and consequently, an accelerated increase in force with increasing compression. This effect is expected to dominate primarily beyond the initial compression stage, once trapped air has been fully removed. While the data presented in Figure 3 do not fully substantiate this assumption, the current analysis is limited to a qualitative assessment based on overall force ranges across all participants. Further in-depth analyses will be conducted with a focus on the detailed trends of individual force-gap height curves for each participant.

As discussed in the Introduction, the observed variability in the results can be attributed to a range of factors. For example, some participants (IVW, JKU, WMG) employed presses or testing frames with hydraulic drives, while others (UOB, KUL, KIT, UTW) used screw-driven electromechanical testing machines. These differences can lead to variations in testing control accuracy, as electromechanical drives generally provide more precise displacement control within the speed range adopted in this benchmark, while hydraulic drives may exhibit greater speed fluctuations under these conditions. The specific implications of IVW's large press size and load cell capacity on the measured force signals have also been discussed earlier in this section. In addition, the type of heating system varied among participants. Some facilities employed self-heated platens with cartridge or fluid-based heating, whereas others used convection heating within an environmental chamber. These differences can lead to substantial variations in the steady-state temperature distribution at the tooling surfaces, while the use of an environmental chamber may introduce additional temperature fluctuations during specimen loading and unloading. The most critical sources of variability have therefore been categorised into testing procedure, testing equipment, and material related factors, as summarised in Table 2.

Table 2. Parameters considered in the statistical analysis.

Category | Parameters | Typical values/range
Testing procedure | Specimen loading time | 10-30 s
Release agent | Loctite-frekote-700NC Marbocote 227 CE Chemlease PMR 90
Testing equipment | Machine type | Electromechanical testing frame Hydraulic testing frame Hydraulic press
Heating method | Environmental chamber Heated platens
Load cell range | 50-8000 kN
Displacement measurement | Video extensometer/Optical Crosshead displacement LVDT
Material Specimen | Environmental conditions | Temperature 15-19 °C Humidity 33%-76%
Material location | (Specific labelling system for roll and batch tracking)
Testing dates | 22 October 2024 – 28 November 2024
Specimen size | 50 x 50 mm² 100 x 100 mm²

Flow front profiles. Figure 4 shows the processed flow front profile data for the same four example materials shown in Figure 3. It should be noted that with the current method, the centre of each flow front profile was calculated as the mathematical averaging of all the x−y coordinates on the profile, which is not necessarily aligned with the true specimen centre, as the flow might not be symmetrical due to factors such as mould tilting and material heterogeneity. This approach was adopted due to the lack of reference marking of the specimen centre, which is a shortfall that should be addressed in future squeeze flow testing.

It can be seen in Figure 4 that at the final gap height of 3 mm , CF 503 K exhibits the shortest flow distances, where the shape of the flow front profile also shows the smallest deviation from the original square specimen shape. CF4012K on the other hand shows the longest flow distances as well as largest deviation from the original square specimen shape. The observed difference in flow distances between corresponds well to the force data, where CF503K and CF4012K exhibited the highest and lowest forces respectively to reach the 3 mm final gap height due to a combined effect of fibre content and tow size as discussed in the force-gap height section.

For the CF503K and CF4012K materials, there is also a noticeable discrepancy in the level of variation among participants. This noticeable discrepancy suggests that the variations in testing procedure and testing equipment among participants is likely to amplify the effects of material variations. Other than the clear outlier (TUM) for CF503K, CF503K and CF6012K show the lowest level of variations both within and among participants. Analogous to the force curves, the CF4012K and CF5012K exhibit higher levels of variations both within and among the participants than CF 6012 K and CF503K. It can therefore be considered that C-SMC materials with higher fibre content and smaller tow size tend to be less sensitive to the variability in testing procedure and testing equipment. It is interesting to note that such materials tend to provide better mechanical properties such as stiffness and strength, although at a higher material cost.

[picture]

Fig. 4. Example flow front profiles extracted from four of the five materials with 100×100 mm2 specimen size configuration.

Fig. 4. Example flow front profiles extracted from four of the five materials with 100 × 100 m m 2 specimen size configuration.

Conclusion and Future Work

The original version of this paper is available on https://www.scientific.net/SSP.387.131.pdf

Initial data processing has been conducted to provide an initial quantitative overview of the experimental results, including variability in the raw material, force-gap height relationships, and flow front profiles. The levels of variability observed in the raw material, in terms of specimen thickness, density, and areal weight, are consistent with those reported in the literature. Preliminary assessment of the force-gap height curves indicates that material parameters such as fibre content and tow size have a strong influence on the compressive forces once the material has passed the initial compression stage, during which trapped air is fully removed. Specifically, the measured forces increase with increasing fibre content and decreasing tow size, which is attributed to enhanced fibrefibre interactions. Analysis of the flow front profiles suggests that materials requiring higher compressive forces-such as those with higher fibre contents and smaller tow sizes-tend to exhibit shorter flow distances under the same compression displacement and show reduced deviation from the original square specimen geometry. Variations observed in both the force-gap height relationships and flow front profiles can be attributed to a combination of factors related to the testing procedure, testing equipment, and material or specimen parameters. In general, materials with lower fibre
contents and larger tow sizes appear less sensitive to variability arising from differences in testing procedure and equipment.

The next phase of the study will focus on a more comprehensive and quantitative evaluation of the collected dataset. Statistical analyses are currently being performed to quantify variability in the processed force-gap height relationships and flow front profiles, and to identify correlations between the factors summarised in Table 2 and the observed variability. This analysis will assess repeatability and reproducibility within and across participants, evaluate the consistency of mean responses, and distinguish the relative contributions of the different sources of variability.

The outcomes of this benchmark exercise will support the development of a robust testing procedure and associated guidance for squeeze flow testing of high-performance C-SMCs, and will contribute towards the standardisation of this testing method. In addition, these efforts aim to improve the reliability of flow-modelling input data for C-SMC composite part manufacturing simulations.

Acknowledgement

The original version of this paper is available on https://www.scientific.net/SSP.387.131.pdf

The authors thank Polynt Composites Germany GmbH for their generous donation of all the materials used in this benchmark exercise.

References

The original version of this paper is available on https://www.scientific.net/SSP.387.131.pdf

Romanenko, V., et al., Advanced process simulation of compression molded carbon fiber sheet molding compound (C-SMC) parts in automotive series applications. Composites Part A: Applied Science and Manufacturing, 2022. 157: p. 106924.

Yuan, H., et al., Experimental process characterisation for high-volume compression moulding of hybrid-architecture composites. Composites Part A: Applied Science and Manufacturing, 2024. 181: p. 108137.

Mahé, F., et al., A revisited compression flow model for concentrated suspensions with fiberfriction interactions. Journal of Non-Newtonian Fluid Mechanics, 2023. 319: p. 105090.

Schreyer, L., et al., Characterization and modeling of the anisotropic flow behavior of long carbon fiber reinforced thermoplastic compression molding. Composites Part A: Applied Science and Manufacturing, 2025. 198: p. 109053.

Kapshammer, A., et al., An Advanced Compression Molding Simulation and Validation of a Thick-Walled Carbon Fiber Sheet Molding Compound Brake Caliper. Journal of Manufacturing and Materials Processing, 2025. 9(4): p. 137.

Qian, C., et al., The first European benchmark exercise on squeeze flow testing of highperformance carbon fibre sheet moulding compounds. 2025. 676-685.

Schommer, D.M., Charakterisierung und Modellierung des Materialverhaltens von kohlenstofffaserverstärkten Sheet Molding Compounds während des Fließpressens. 2025, Leibniz-Institut für Verbundwerkstoffe GmbH: Kaiserslautern.

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/composites-forming-processes/978-3-0364-1996-1

Solid State Phenomena, ISSN: 1662-9779, Vol. 387, pp 143-151

doi: 10.4028/p-9qL9HL

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

Submitted:
2026-01-24

Revised:
2026-02-14

Accepted:
2026-02-17

Online:
2026-04-14

Iterative Un-Forming of Highly Aligned Discontinuous Preform/Material

The original version of this paper is available on https://www.scientific.net/SSP.387.143.pdf

Yuncong Feng 1,a*, Hao Yuan 1,b, Burak Ogun Yavuz 1,c, Ian Hamerton 1,d, Byung Chul Kim 1,e, Stephen R Hallett 1,f and Jonathan P.-H. Belnoue 1, g

1 Bristol Composite Institute, University of Bristol, UK

a  ol24008@bristol.ac.uk, b  hao.yuan@bristol.ac.uk, c  ogun.yavuz@bristol.ac.uk, dian.hamerton@bristol.ac.uk, eB.C.Eric.Kim@bristol.ac.uk, f  Stephen.Hallett@bristol.ac.uk,  9jonathan.belnoue@bristol.ac.uk, (*corresponding author)

Keywords: Forming process, Finite Element Modelling, Automated Fibre Placement.

Abstract

Inherent limitations restrict the application of automated fibre placement (AFP) for manufacturing small components with complex geometries. As an alternative, flat, tailored preforms composed of fibre tows are first created and then formed into desired 3D geometries. However, the fibre orientation deviations between the as-manufactured and as-designed parts are inevitably introduced during forming. To address this issue, this study presents an iterative numerical forming/un-forming framework for the manufacture of highly aligned discontinuous preforms. At first, the as-designed preforms are "un-formed," i.e., a reverse forming simulation, to achieve the corresponding flat preforms using the finite element modelling (FEM) method. To mitigate the deviations during forming, a pre-compensation strategy is then introduced by adjusting the initial fibre orientations derived from the un-forming analysis based on the calculated deviations through iterative re-forming simulations. A hypo-viscoelastic constitutive model implemented through a userdefined material subroutine captures the rate-dependent and orthotropic behaviour of the preforms during un-forming and re-forming. The FEM simulation results demonstrate significant reductions in fibre orientation deviation of formed 3D preforms through the iterative forming/un-forming framework, validating its applicability to a discontinuous fibre material on complex geometries.

1. Introduction

The original version of this paper is available on https://www.scientific.net/SSP.387.143.pdf

Automated Fibre Placement (AFP) is an efficient technique for large-scale composite manufacturing with minimal human error. However, its application to small components remains limited due to the relatively large size of AFP heads and machines, as well as challenges in depositing fibres on small radii of curvature.

To address these limitations, our research group has proposed an alternative method that enables fast manufacturing of low-defect highly-optimised doubly curved parts. The approach starts with the creation of flat, tailored preforms composed of fibre tows, followed by forming onto the desired 3D geometry. Deviations between the as-manufactured and as-designed parts occur in the forming process due to shear deformation of tows. To mitigate these deviations, a virtual un-forming process is introduced, in which the as-designed 3D part is computationally un-formed into flat tailored preforms [5,6]. The flat preform fibre paths extracted from the analyses are used to manufacture physical preform through the continuous tow shearing method [7].

Previous studies have modelled preforms with continuous fibres in the un-forming process [7]. However, with the growing emphasis on sustainability, discontinuous fibres are increasingly favoured because they can come from recycled material. Given the distinct mechanical behaviours of preforms reinforced with continuous and discontinuous fibres, the applicability of the un-forming approach requires verification for discontinuous-fibre systems. Moreover, in existing studies, the 2D fibre paths were obtained directly from the un-forming simulation, which is difficult to realise for complex geometries with sharp curvature variations, such as automotive parts.

In this study, highly aligned discontinuous fibre prepregs with a thermoset resin matrix are investigated [1,2]. The preforms consisting of thermoset resin matrix, reinforced by highly aligned discontinuous fibres, are formed utilising the double diaphragm forming (DDF) technique. A user subroutine based on the HypoDrape Abaqus VUMAT, developed at BCI and introduced in Section

5, is employed to describe the behaviour of preforms during the forming and un-forming processes [3]. To enable general applicability to parts with complex geometries and viscoelastic behaviours not previously reported in the literature, an iterative forming-unforming design strategy is introduced to improve the accuracy of the existing unforming model, thereby supporting more robust composite manufacturing design.

2. Modelling and Automation Strategy

The original version of this paper is available on https://www.scientific.net/SSP.387.143.pdf

Figure 1 illustrates the workflow of forming/un-forming is depicted in. First, a DDF process is simulated on a "surrogate" preform to obtain the deformation history of the nodes of both diaphragms. This displacement history is then reversely applied to the diaphragms, with the surrogate preform replaced by preforms with realistic material properties, in the un-forming simulation. Subsequently, the resulting flat preforms are re-formed to verify if fibre paths in the formed part agree with those in the as-designed part. If the deviation in the fibre path exceeds a certain tolerance, corrected fibre orientations, determined through a pre-compensation algorithm, are then applied to the flat preforms before repeating the re-forming process. These procedures are repeated until the deviation falls within the specific tolerance. Finally, the corrected fibre paths from the un-forming simulation are obtained as the input for the future AFP manufacturing.

[picture]

Fig. 1. The flowchart of forming and un-forming processes

Fig. 1. The flowchart of forming and un-forming processes

In the forming/unforming workflow, key information, such as nodal displacements and fibre orientations, is exported from the Abaqus database, processed, and then re-imported into the Abaqus/Explicit solver for further analyses. The fibre orientation, in particular, requires these procedures to be repeated iteratively. Performing this process manually, step by step, is timeconsuming; therefore, Python scripts were developed to automate and streamline the entire workflow, enabling one click execution. For the initial forming simulation, a Python script was used to export the nodal displacement histories of the diaphragms, along with the node and element information of the surrogate preform and formed diaphragms at the end of the simulation. The histories were then processed in reverse time and exported as include files containing the settings of boundary conditions and their corresponding amplitudes. These files were then incorporated into an input (.inp) file also automatically generated by the script - for the un-forming simulation, which included the diaphragm and preform models and all relevant analysis settings. The script allowed the number of preforms to be specified, and the assembly module was adjusted accordingly to accommodate the defined configuration. Finally, the input file was submitted to Abaqus Explicit by the script. Afterwards, another Python script was created to export and correct fibre orientations from the unforming analysis, and to generate an input file containing the corrected orientations for the re-forming simulations. At current, the correction requires manual intervention, but this will be fully automated in the future work.

3. Mould and Specimen Design

The original version of this paper is available on https://www.scientific.net/SSP.387.143.pdf

A revolving-swept and double-curved 3D surface was designed to demonstrate the advantages of the proposed forming/un-forming framework and to validate the overall workflow. A margin region with the length of 5 mm from the surface was introduced to mitigate the edge effects on preform deformation in the forming process. The 2D project of the overall geometry is a square with the length of 114.3 mm , as demonstrated in Figure 2(a). It should be noted that only the top surface of the virtual mould, i.e. the region that interacts with the preforms and diaphragms, was modelled, to simplify the creation of geometric model. The CAD geometry was built in CATIA V5 and then imported into Abaqus, where it was discretised into membrane elements with a rigid body assignment.

[picture]

Fig.2. (a) Basic dimensions of the mould surface (units: mm ). (b) Schematic of the DDF setup.

Fig.2. (a) Basic dimensions of the mould surface (units: mm ). (b) Schematic of the DDF setup.

Typically, the 3D surface is imported into Abaqus again to serve as the specimen, after which a structural mesh is applied to align element profiles with fibre paths, therefore preventing potential numerical errors. However, as structural mesh follows the contour gradient of the geometry, the gradually converging gradient along the diagonal direction towards the corner of the specimen in this case leads to highly irregular element distributions. To tackle this issue, the structural mesh of the target 3D surface was instead obtained from a forming simulation on a "surrogate" preform, the details of which are provided in Section 4.

4. Initial Forming Simulation

The original version of this paper is available on https://www.scientific.net/SSP.387.143.pdf

The initial forming model consists of two diaphragms, a "surrogate" preform in between the diaphragms and the mould surface on the bottom, as shown in Figure 2(b). The edges of the diaphragms were constrained to move only in the vertical direction, while the mould surface was completely fixed. Atmospheric pressure was applied to the top diaphragm to form the doublediaphragm system. It should be noted that the purpose of the initial forming simulation was to extract the displacement histories of diaphragms for use in the subsequent un-forming simulation. Because the preform bending stiffness is negligible relative to diaphragm interaction forces [7]. its configuration does not meaningfully influence diaphragm deformation. The surrogate preform is used solely to provide mesh continuity; an isotropic model is therefore sufficient. Specifically, Young's modulus and Poisson's ratio were set to 160 MPa and 0.1 , respectively, to increase the shear resistance and thereby reduce localised shear deformation. Bulk viscosity was added into the system to suppress element distortion. Friction is a crucial factor that affects the forming pattern of diaphragms and the preform. The penalty coefficients for frictions between diaphragm-diaphragm and diaphragm-preform were set to 0.6 and 0.53 , respectively, based on the results of characterisation experiments reported in [8]. For the material model of diaphragms, Ogden's hyperelastic material model was adopted with the same parameters used in [3], which are listed in Table 1. The diaphragms and the preform were structurally meshed using S4R shell elements. The element thicknesses were 0.2 mm and 0.15 mm for the diaphragms and the preform, respectively, with uniform element sizes of 4.5 mm for the diaphragms and 2 mm for the preform. The element
size was selected with reference to [7]. The primary objective of this work is to develop and demonstrate the iterative forming/un-forming framework rather than to resolve local stress and strain fields. Moreover, as the displacement fields of both the preforms and diaphragms are smooth, further mesh refinement would not alter the conclusion of this work while significantly increasing computational cost. Consequently, mesh convergence analysis was not conducted in this study.

Table 1. Material parameters for Odgen's hyperelastic model for the diaphragms from [3].

μ₁ (MPa) | μ₂ (MPa) | α₁ | α₂
0.63 | 0.0012 | 1.3 | 5.0

After forming, the preform with a uniform structural mesh was obtained, as shown in Figure 3(a) and (b), and was used as the discretised geometry model of the as-designed part for the un-forming simulation. In addition, the nodal displacement histories of both diaphragms were extracted and timereversed to serve as prescribed boundary conditions for the diaphragms in the un-forming analysis.

5. Un-Forming and Re-Forming Simulations

The original version of this paper is available on https://www.scientific.net/SSP.387.143.pdf

In this study, two preform sheets, denoted as Sheet0 and Sheet1, were un-formed as as-designed preforms. Both sheets used the meshed geometry of the preform obtained in the initial forming analysis, but their fibre directions were configured differently. In Sheet0, the fibre paths were aligned with element edges along the x axis, whereas in Sheet1, the fibre paths were oriented along the y axis, as illustrated in Figure 3 (a) and (b). Due to the viscoelastic nature of the thermoset resin, the mechanical response of the preform is strongly dependent on the deformation rate. Accordingly, an analytical viscoelastic constitutive model developed in our previous work [4] was employed to describe the behaviour of the preform in the un-forming and re-forming simulations. In addition, to capture the orthotropic characteristics of the preform accurately, its constitutive behaviour was implemented using a hypo-viscoelastic material model via a VUMAT subroutine in Abaqus/Explicit. To eliminate the effect of rigid-body rotation of fibres, which can be significant during un-forming and re-forming, on the stress update, the deformed fibre direction was first calculated using the current deformation gradient to construct a fibre-aligned work frame. The strain increment under the GreenNaghdi (GN) work frame, i.e. the default Abaqus VUMAT work frame, was converted to the fibre work frame, where the stress increments were computed according to the constitutive relations and then converted back to the GN work frame for the next increment.

[picture]

Fig. 3. (a) The as-designed 3D Sheet0 and (b) the as-designed 3D Sheet1. (c) The un-formed 2D Sheet0 and (d) the un-formed 2D Sheet1.

Fig. 3. (a) The as-designed 3D Sheet0 and (b) the as-designed 3D Sheet1. (c) The un-formed 2D Sheet0 and (d) the un-formed 2D Sheet1.

Due to the decoupling between the in-plane and out-of-plane behaviour of the preform, shell and membrane elements were utilised to simulate the collective material response of the preform sheets. In particular, shell elements (S4R in Abaqus/Explicit) accounted for out-of-plane bending, while in-plane deformation was evenly shared by both shell and membrane elements (M3D4R). Both element types were superimposed by sharing common nodes and assigned the same thickness, as described in Section 4. The material properties of the shell and membrane elements, obtained from characterisation experiments, are documented in Table 2. At the end of the un-forming simulation, the 3D sheets were reversely formed back into flat sheets, as demonstrated in Figure 3(c) and (d).

Table 2. Material input parameters for membrane and shell elements

Element type | E₁ (MPa) | E₂ (MPa) | G₁₂ (MPa)
Membrane | Analytical model [4] | 0.05 | Analytical model [4]
Shell | Analytical model [4] | 0.05 | 1 × 10⁻⁸

To verify the un-forming results, the preforms were virtually re-formed and then compared with the as-designed preforms in terms of fibre orientation. To this end, the diaphragms and the flat preforms with updated fibre orientations from the un-forming analysis were input to the re-forming simulation as the initial state. Boundary conditions in the re-forming simulation remained the same with those in the initial forming simulation, while the surrogate preform was replaced by the unformed 2D preforms with the viscoelastic material model applied. Afterwards, the fibre orientations of the re-formed 3D preforms were extracted for the calibration and correction of the deviation in fibre orientations.

6. Calibration and Correction of Fibre Orientation Deviations

The original version of this paper is available on https://www.scientific.net/SSP.387.143.pdf

The angular deviation between the fibre orientations of the as-designed and re-formed preforms was calculated and used as the basis for the subsequent correction. Its magnitude was obtained using the following equation:

|Δf|=acos(fa·fr|fa|·|fr|)(1)

where fa and fr represent the fibre orientation vectors of the as-designed and re-formed preforms, respectively. acos is the inverse cosine function, and Δ denotes the angular deviation. In addition, the sign of the deviation is crucial for determining the direction of the correction. To this end, the 3D fibre vectors were projected onto a 2D plane, where fa was transformed into a unit reference vector, i.e., (1,0) for Sheet0 and (0,1) for Sheet1. The deviation was defined as positive when the component of fr along the secondary axis was positive. In this case, the y and x axes served as the secondary axes for Sheet0 and Sheet1, respectively. Python scripts were developed to visualise the angular deviation on the preforms to facilitate further verification. The deviation distributions after the reforming process are demonstrated in Figure 4.

[picture]

Fig. 4. The angular deviation distributions of (a) Sheet0 and (b) Sheet1 after the 1st  re-forming simulation.

Fig. 4. The angular deviation distributions of (a) Sheet0 and (b) Sheet1 after the 1 st re-forming simulation.

For the correction, the angular deviations obtained from the re-formed 3D preforms were inversely applied to the corresponding un-formed flat preforms, assuming that fibre reorientation during forming is systematic. To avoid over-correction, only a fraction of the deviation was pre-compensated in the initial fibre orientation of the flat preforms. The correction procedure is summarized as follows:

The initial angular deviation was calculated as the difference between the as-designed fibre orientation and the fibre orientation from the first re-formed preform simulation results.

A corrective rotation equal to α×(−Δf) was applied to the initial fibre orientation of the flat preform, where α denotes the compensation factor.

The corrected flat preform was re-formed, and the resulting fibre orientation deviation was evaluated.

The value of α was updated according to a parametric optimisation method as described in the following five paragraphs.

Steps 2-4 were repeated until the minimum deviation was achieved.

Deviations on Sheet0 and Sheet1 were both positive, indicating that the re-formed orientations had different angular positions relative to the as-designed orientations in the two preforms, as illustrated in Figure 4. Therefore, opposite rotational pre-compensation was applied to the two preform sheets. In this study, anticlockwise compensation was defined as positive, and clockwise adjustment was defined as negative. To quantify the correction performance, the root mean squared error (RMSE) and a severity index were introduced, where the severity index represents the number of elements with the angular deviation exceeding half of the maximum deviation. As shown in Figure 4, the maximum deviation is 13.1∘, and the corresponding severity threshold is therefore set to 6.55∘. RMSE provides a global measure of the average magnitude of the angular deviation, while the halfmaximum threshold prevents the severity index from becoming overly polarized by either isolated extreme values or a large population of minor deviations. A similar half-maximum-based thresholding strategy has been adopted in [9].

To identify the optimised α for each sheet, a commonly used approach is the bisection method [10], where α is halved repeatedly to ensure convergence toward the minimal deviation. However, as the number of compensation factors increases with the number of preform layers, the problem becomes multi-variable, rendering the bisection method less efficient. Therefore, a parametric optimisation strategy was adopted [11].

Initially, α=1 and -1 were selected for Sheet0 and Sheet1, respectively; however, both RMSE and the severity index of the corrected un-forming results exceeded those obtained without correction (RMSE = 4.41 and 4.12 for Sheet0 and Sheet1, respectively; severity index =279 and 218 for Sheet0 and Sheet1, respectively), indicating overcorrection when the full angular deviation was applied to the un-forming analysis. Therefore, only a portion of the angular deviation was compensated. First, the α value for Sheet 1 was fixed at 0.1 , while the α value for Sheet 0 was varied from 0.1 to the anticipated upper limit of 1.0 to investigate the influence of α value for Sheet 0 on angular deviation. It should be noted that the specific choice of α for Sheet1 at this stage does not
affect the outcome. Due to the small bending of the preforms, the configuration of one preform has a negligible influence on the angular distribution of the other. This assumption is validated by the results shown in Figure 5 (a) and 5(b).

For Sheet0, a sharp increase in both RMSE and the severity index was observed when α exceeded 0.25 , indicating that continuously increasing α beyond 0.4 is unnecessary. Consequently, the effective range of α for Sheet0 was limited to 0.1−0.4. As illustrated in Figure 5 (a) and 5(b), the RMSE for Sheet0 reaches a low-level plateau between α=0.1 and 0.2 , whereas the severity index remains stable up to α=0.25. Based on these observations, α values of 0.1,0.2, and 0.25 were selected for Sheet0. For each of these cases, α for Sheet1 was varied from -0.1 to the anticipated lower limit of -1.0 .

The results for α=0.1 for Sheet 0 and α values ranging from -0.1 to -0.4 for Sheet 1 are presented in Figure 5 (c) and 5(d). Although RMSE continued to decrease with decreasing α for Sheet1, an increasing trend in the severity index was observed beyond α=−0.25. This result was deemed unacceptable, as it would be undesirable to permit a noticeable number of elements with large angular deviations, even if the majority exhibit only minor deviations. Therefore, the lower bound of α for Sheet 1 was set to -0.4 .

Similar trends were observed when Sheet 0 was fixed at α=0.2 and 0.25 . However, the combination of α=0.1 for Sheet0 and α=−0.25 for Sheet1 resulted in the smallest overall angular deviations. Accordingly, these values were selected as the optimal compensation factors, and the corresponding angular deviation distributions are presented in Figure 6.

Admittedly, the parametric approach cannot reduce all deviations to their absolute minimum. More localised optimisation strategies will therefore be explored in future work.

Compared to the 1st  re-forming results, overall RMSE for Sheet0 and Sheet1 after final precompensation decreased by 6.5% and 15.8%, respectively. It should be noted that a large proportion of the preforms exhibited very small angular deviations (<1.7∘). When focusing on the critical regions with relatively large deviations, the improvement in RMSE becomes considerably more pronounced. In addition, the number of severe elements was reduced by 43.2% and 221.9% for Sheet0 and Sheet1, respectively. These results show that the effectiveness of the proposed forming/un-forming framework combined with the pre-compensation strategy.

[picture]

Fig. 5. (a) RMSE and (b) severity index for a set of re-forming simulation results with varying α for Sheet0 and the constant α of 0.1 for Sheet1. (c) RMSE and (d) severity index for a set of re-forming simulation results with the constant α of 0.1 for Sheet 0 and varying α for Sheet 1 .

Fig. 5. (a) RMSE and (b) severity index for a set of re-forming simulation results with varying α for Sheet0 and the constant α of 0.1 for Sheet1. (c) RMSE and (d) severity index for a set of re-forming simulation results with the constant α of 0.1 for Sheet 0 and varying α for Sheet 1 .

[picture]

Fig. 6. Angular deviation distributions of (a) Sheet0 and (b) Sheet1 after final pre-compensation.

Fig. 6. Angular deviation distributions of (a) Sheet0 and (b) Sheet1 after final pre-compensation.

The parametric optimisation algorithm will be implemented as a Python script to determine the optimised automatically the compensation factor for each preform sheet. The resulting corrected fibre orientations will then be automatically converted into fibre paths for AFP manufacturing.

7. Conclusion

The original version of this paper is available on https://www.scientific.net/SSP.387.143.pdf

An iterative forming/un-forming framework was developed to improve the accuracy of unforming simulations for discontinuous preforms, representing a clear improvement over direct un-forming analyses, particularly for complex geometries. A hypo-viscoelastic model was employed to accurately capture the preform behaviour during forming and un-forming. To mitigate the fibre orientation deviation introduced during forming, a pre-compensation strategy was introduced by optimising the compensation factor applied to the initial fibre orientations of the un-formed preforms through a parametric study. Comparative results before and after pre-compensation demonstrate a significant reduction in the angular deviation, confirming the effectiveness of the proposed iterative forming/un-forming framework.

References

The original version of this paper is available on https://www.scientific.net/SSP.387.143.pdf

D. Heider, J. Tierney, M.A. Henchir, V. Gargitter, S. Yarlagadda, J.W. Gillespie Jr, J. Sun, J.M. Sietins, D. Knorr, Microstructural evaluation of aligned, short fiber Tuff material, SAMPE 2019-Charlotte, NC, May 2019 (2019).

S. Yarlagadda, J. Deitzel, D. Heider, J. Tierney, J.W. Gillespie Jr, Tailorable universal feedstock for forming (TUFF): overview and performance, SAMPE 2019-Charlotte, NC, May 2019 (2019).

A.J. Thompson, J.P. Belnoue, S.R. Hallett, Modelling defect formation in textiles during the double diaphragm forming process, Composites Part B: Engineering 202 (2020) 108357.

B.O. Yavuz, I. Hamerton, M.L. Longana, J.P.H. Belnoue, Modelling the tensile behaviour of aligned discontinuous carbon fibre thermoplastic matrix composites under processing conditions, Composites Science and Technology 269 (2025) 111252.

E. Loukaides, J. Allwood, Automatic design of sheet metal forming processes by "un-forming", International Journal of Mechanical Sciences 113 (2016) 61-70.

J. Joustra, K. Brans, I. Fernandez Villegas, J. Sinke, J. Teuwen, Reverse forming thermoplastic composites: Design and process development, Composites Part C: Open Access 16 (2025) 100550.

X. Sun, J.P.H. Belnoue, W.-T. Wang, B.C. Kim, S.R. Hallett, "Un-forming" fibre-steered preforms: Towards fast and reliable production of complex composites parts, Composites Science and Technology 216 (2021).

S. Chen, O. McGregor, A. Endruweit, M. Elsmore, D. De Focatiis, L. Harper, N. Warrior, Double diaphragm forming simulation for complex composite structures, Composites Part A: Applied Science and Manufacturing 95 (2017) 346-358.

R. Karim, P. Bhagirath, P. Claus, R.J. Housden, Z. Chen, Z. Karimaghaloo, H.-M. Sohn, L.L. Rodríguez, S. Vera, X. Albà, Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images, Medical image analysis 30 (2016) 95-107.

R.M. Freund, The steepest descent algorithm for unconstrained optimization and a bisection line-search method, Journal of Massachusetts Institute of Technology. United States of america 131 (2004).

R. Binali, A.D. Patange, M. Kuntoğlu, T. Mikolajczyk, E. Salur, Energy saving by parametric optimization and advanced lubri-cooling techniques in the machining of composites and superalloys: A systematic review, Energies 15(21) (2022) 8313.

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/composites-forming-processes/978-3-0364-1996-1

Solid State Phenomena, ISSN: 1662-9779, Vol. 387, pp 153-162

doi: 10.4028/p-W4LGjL

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

Submitted:
2025-12-19

Revised:
2026-02-17

Accepted:
2026-02-18

Online:
2026-04-14

Mechanical Response of Flax Rovings under Compaction: Experimental Investigation

The original version of this paper is available on https://www.scientific.net/SSP.387.153.pdf

Samir ZIDI 1,a*, Tiffany LARQUEMIN 1,b, Audrey HIVET 1,c, Gilles HIVET 1,d and Davy DURIATTI 2,e

1 Laboratoire de mécanique Gabriel LaMé, University of Orléans, Chartres, France

2 Depestele Group, Bourguébus, France

a  samir.zidi@univ-orleans.fr, b tiffany.larquemin@univ-orleans.fr, c  audrey.hivet@univ-orleans.fr, d  gilles.hivet@univ-orleans.fr, e  dduriatti@depestele.com

Keywords: Flax Rovings; Compaction behavior; Mechanical response; Experimental investigation; Mesoscopic scale.

Abstract

This study investigates the compaction behavior of woven flax rovings, a crucial aspect of the preforming stage in composite manufacturing. It focuses specifically on the challenges posed by their inherent heterogeneity and structural variability. Although the durability of flax-based composites is widely recognized, accurate numerical modeling of their processing, particularly for liquid composite molding (LCM), remains limited by the lack of detailed experimental data on roving mechanical behavior, including data capturing the inherent variability of the material. This research used a combined experimental approach, comprising computational microscopy for microscale deformation analysis and macroscopic compression tests, to characterize the mechanical response of flax rovings under compaction. Results highlight the need to develop sophisticated simulation frameworks that account for statistical variations in material properties but also the specificities of the flax roving response, which differs considerably from that of synthetic fibers rovings. The experimental dataset generated provides a valuable basis for identifying material parameters and validating advanced simulation frameworks aimed at improving performance predictions of manufactured components.

Introduction

The original version of this paper is available on https://www.scientific.net/SSP.387.153.pdf

The design of modern structures is increasingly constrained by the need to develop lightweight, high-performance, sustainable solutions in an environmental context that demands reducing the ecological impact of materials and processes [1,2]. This transition is pushing the field of engineering toward bio-based composites, which replace traditional synthetic fibers (such as glass and carbon) with renewable resources, thereby reducing dependence on fossil fuels [3]. Some of these composites are biodegradable, which contributes to a circular economy [4,5].

Bio-based composites are being adopted in many sectors, including automotive (interior components and reinforcements), aerospace, and construction (insulation and structures), as well as packaging [6]. A wide range of natural fibers (e.g., flax, hemp, and jute) is available, each with specific intrinsic properties [7].

Among the many natural fibers that have been studied for use in composites, flax is a particularly strategic material. Its importance stems from its technical and logistical advantages [8]. First, its mechanical properties, particularly its strength-to-density ratio, rival those of conventional glass fibers. This property enables the production of lightweight structures for high-tech applications without compromising structural performance. Second, flax is cultivated throughout Europe, primarily in France, which offers a considerable logistical advantage [9]. Short transport distances for raw materials reduce the carbon footprint of the finished product and boost the local and regional economies. In addition to these mechanical and logistical benefits, flax offers valuable functional advantages. It has intrinsic thermal and acoustic insulating properties in high demand for industrial applications [10, 11, 12].

It is important to note that the processing flax fibers is complex and greatly affects the quality of the final material. The performance of the resulting composites depends on controlling the steps after harvest. Key stages of the process, such as retting, scutching, and combing during sliver preparation, are particularly important. Poor management of these initial treatments can introduce variability that significantly impacts the composites' final mechanical performance [13,14].

Despite their potential, existing models fail to predict the compaction behavior of flax slivers [15,16]. This study aims to address this gap with a two-pronged approach. First, we characterized the compaction behavior experimentally using a multi-scale approach that integrates microscopic observations and macroscopic measurements. Second, we analyzed the deformation mechanisms that occur at the fiber level. Our approach is original in that we combine innovative experimental methodologies to quantify the variability of this behavior and establish direct links among the properties of the fibers, the observed mechanisms, and the macroscopic response of the wicks. This information is crucial for developing accurate numerical models.

2. Materials and Methods

The original version of this paper is available on https://www.scientific.net/SSP.387.153.pdf

2.1 Materials. This study used woven flax rovings supplied by the Depestele Group.These rovings were used as a reinforcement material. The roving count was 520±20 tex, which indicates the fineness of the fibers they contain. The average thickness of the rovings was 0.15±0.05 mm, and the average width was 5.1±1.4 mm. The linear density, representing the mass per unit length, was 0.52 ±0.02 g/m. To ensure that the experimental results accurately reflect the material's commercial behavior, the rovings were used as is, without pretreatment or surface modification. Using the as-is rovings enabled us to measure this specific flax's actual compaction behavior consistently without introducing external factors that could alter the fibers' natural mechanical response.
2.2 Compaction Test Setup. We conducted compaction tests in a Keyence VHX-5000 digital microscope equipped with a loading stage, to enable detailed observation of fiber deformation during compression. A custom-made compression stage was mounted directly onto the microscope platform to apply a controlled compressive force. The microscope had an XY resolution of 3.0−6.0μ m and an estimated Z-axis accuracy of less than 10μ m (Fig. 1). These specifications were essential for capturing subtle variations in the sample cross-section. The load capacity of the stage in the microscope was 1 kg , and the maximum observation length was 12 mm . The microscope was equipped with a high-resolution camera capable of acquiring images at magnifications ranging from 50x to 200x using transmitted light illumination. The stage guides a glass plate, allowing the load to be applied and the roving to be observed. Guidance is provided by two axes and ball bushings, ensuring parallelism, rigidity and low friction. Loading is carried out by adding masses, ensuring a discontinuous but precise control. Preliminary tests enabled verification that the glass plate does not distort the image.

[picture]

Fig. 1. Compaction device under microscope

Fig. 1. Compaction device under microscope

The goal was to observe the cross-sectional variations (thickness and width) of the strand under load. The flax roving samples were carefully positioned on the compression stage to enable observation of the fiber structure as shown in Fig. 2. Acquiring and processing the images for ten measurements took approximately two hours. All tests were conducted under controlled environmental conditions of an ambient temperature of 23∘C and a relative humidity of 50%.

[picture]

Fig. 2. Compaction device under microscope

Fig. 2. Compaction device under microscope

2.3 Compression Test Setup. Macroscopic compression tests were performed using a carefully instrumented Zwick tensile testing machine to record the force-displacement response of the flax strands under compressive load (Fig. 3). Before each compression test, particular attention was paid to determining the initial dimensions of the sample. The thickness and width of each strand were measured under initial compression test conditions using the previously described Keyence VHX5000 digital microscope. These preliminary measurements enabled the precise calculation of the average apparent pressure exerted on the sample and the variation in thickness undergone by the tow during the test. To ensure uniform application of the compressive load, tests were carried out using rectangular PMMA plates. The quality of the parallelism was also verified using a Tekscan pressure sensor inserted between the two plates (Fig 3-B). This sensor validates the uniform distribution of pressure applied to the specimen during compression. Given the small thickness and small displacements, it is critical to maintain strict parallelism and mitigate the effects of clearances and machine rigidity, which was achieved with this protocol. A key aspect of this setup was the use of a
high-resolution camera that achieved a resolution of 0.007 mm/ pixel. This high resolution enabled detailed visual tracking of sample deformation throughout the test by measuring the distance between two markers positioned on the two plates (Fig 3). The tests were performed at a quasistatic speed of 0.125 mm/min. This slow, controlled speed mitigated dynamic effects and ensured accurate measurement of the material's behavior. The main objective of this experimental protocol was to observe and understand the critical transition between two phenomena: the internal reorganization of fibers within the tow and the deformation of individual fibers resulting from locking and interactions within the tow network.

[picture]

Fig. 3. Microscope (A) Compression test setup (B) Visualization of the pressure distribution observed from the Tekscan sensor during calibration of the platen orientation.

Fig. 3. Microscope (A) Compression test setup (B) Visualization of the pressure distribution observed from the Tekscan sensor during calibration of the platen orientation.

3. Results and Discussion

The original version of this paper is available on https://www.scientific.net/SSP.387.153.pdf

3.1 Compaction/Compression Test.

One of the most striking results of the compaction tests was the nearly perfect preservation of the samples' transverse geometry as the loads increased. Contrary to the usual expectation for fibrous materials under stress, there was no lateral expansion. The Coil Rovings' (CR) specimen profiles remained strictly superimposable throughout the test, with no detectable widening, as illustrated in Figs. 4 and 5. We quantified this dimensional stability by considering measurement uncertainties, especially those due to fiber protrusion and the presence of free fibers. The maximum relative error was estimated at 2%. The absence of broadening, even at low pressures (up to 0.1 MPa ), demonstrates that the fiber reorganization mechanisms typically observed in synthetic fiber rovings do not exist, as shown in Fig. 4.

[picture]

Fig. 4. Superposition and evolution during testing of a profile

Fig. 4. Superposition and evolution during testing of a profile

Another key observation is that surface irregularities remained visible in the cross-sectional profiles throughout compaction. One might assume that with increasing pressure, the internal structure of the reinforcement would reorganize to smooth out the surface undulations, producing a more uniform cross-section. However, the results show that, although the amplitude of the irregularities decreases slightly, the overall topology of the profile remains unchanged, regardless of the type of specimen tested (Fig. 5). Initially, it seemed possible that rigid body motion limited deformation at the beginning of the test. However, under increasing pressure, this assumption loses validity, particularly around 0.1 MPa , where actual compaction would be expected. The stagnation of thickness variation in the final increments confirms this hypothesis, suggesting that maximum compaction is reached without uniforming of the external surface.

Therefore, it can be concluded that the flax rovings exhibit atypical "compaction behavior," as their microstructure reorganization differs significantly from the local densification that is classically observed for synthetic rovings due to their high fiber mobility. The atypical compaction behavior originates from the internal cohesion resulting from the roving manufacturing process which limits the relative mobility of the fibers. Furthermore, the composition of flax yarn differs significantly from that of technical rovings. Technical flax fibers consist of an assembly of short flax filaments and interfacial lamellae after the hackling step. Therefore, flax rovings cannot be considered to consist of individual filaments that are free to move relative to each other. This structure tends to form fiber clusters that move together, reducing microstructural reorganization. In conclusion, flax rovings exhibit a specific compaction behavior resulting from the particular constitution of technical flax fibers and the specificities of their manufacturing process, which reduces local mobility and microstructural reorganization. Despite the increased pressure from the flat plate, surface irregularities persist.

[picture]

Fig. 5. Evolution of the thickness of the profiles of a sample

Fig. 5. Evolution of the thickness of the profiles of a sample

The compaction behavior of each sample was characterized by analyzing the experimental data with MATLAB's lsqnonlin algorithm, which is based on Toll's equation [17]. This approach enabled us to determine the primary mechanical parameters: initial compressive stiffness ( Kc ), compaction rate, and fiber surface fraction. We used two approaches for these parameters: the "apparent" approach, based on idealized geometry, and the real approach, based on measured profiles. The results show that curves derived from apparent data are smoother and more consistent due to geometric smoothing. However, parameters extracted from real data more accurately reflect the actual behavior of the material, albeit being more sensitive to local irregularities. This discrepancy underscores the importance of considering real geometry in simulations to predict the mechanical response of reinforcements accurately.

Curve fitting revealed two significant phenomena. First, there was an absence of transverse swelling during compaction. Second, surface irregularities persisted. These phenomena suggest that deformation is dominated by overall settlement rather than by local fiber reorganization (Fig. 6). These observations reflect atypical behavior strongly influenced by the size and internal structure of the roving. This finding justifies the need for in-depth studies, particularly in situ tomography, to visualize these mechanisms.

[picture]

Fig. 6. Interpretation of data using Toll's equation

Fig. 6. Interpretation of data using Toll's equation

3.2. Compression Test Results.

Macroscopic compression tests analyzing the behavior of flax rovings extracted from coil rovings for testing provided crucial information on how they respond to applied pressure. Using marker displacement during compression followed by image analysis, we determined the apparent fiber volume fraction as a function of applied pressure, as shown in Fig. 7.

It is important to recognize the limitations of the measurement technique, especially when working with very high-volume fractions. At these stages, thickness variation becomes minimal, yet the relative measurement uncertainty of the image analysis technique increases. Thanks to the optical strategy implemented, the estimated uncertainty in the marker position at the pixel level, caused by limitations in image resolution, can lead to a totally acceptable variation (up to 2% ) in the calculated fiber volume fraction even at high volume fractions.

As is often the case with fiber reinforcements, our analysis revealed significant variability in compression behavior among the tested samples (CR1, CR2, CR3, CR4, and CR5).

The initial compression phase, exhibited a very small stiffness, thus finite strains under low loads. This step is primarily due to fiber reorientation and void filling within the roving structure under pressure. This initial phase is crucial because it determines the material's initial stiffness and consolidation behavior, two parameters that influence the final composite's performance.

Additionally, the variability observed in compressive response highlights the inherent complexity of natural fiber structures compared to more uniform synthetic fibers. Differences in compressive response are likely strongly influenced by heterogeneity in the microstructure and packing density. These trends have important implications for modeling the overall behavior of composite materials, suggesting the need for models capable of accounting for the stochastic nature of natural fiber arrangements.

[picture]

Fig. 7. Compressive behavior obtained for different samples from image analysis and zoomed in the view of the behavior of the samples over the 0−0.2MPa range.

Fig. 7. Compressive behavior obtained for different samples from image analysis and zoomed in the view of the behavior of the samples over the 0 − 0.2 M P a range.

To evaluate the applicability of existing compaction theories, we applied Toll's equation to our experimental results. While the model generally reproduces the shape of the compression curves, it deviates considerably from the values measured during the initial low-load phase, as illustrated in Fig. 8. This poor fit suggests that the physical assumptions underlying the Toll and Manson models [18], that compaction results exclusively from fiber sliding in voids and direct contact, are insufficient for this initial stage. In reality, the initial compressive strength is governed by many subtle factors, including fiber instability, organization within bundles, inter-fiber friction, and surface coating adhesion. The divergence observed in the low-stress domain confirms that while the Toll and Manson
equation provides a usable basis of reference for the overall response, it fundamentally lacks the ability to describe the dynamic structural changes that flax cables undergo during initial densification.

[picture]

Fig. 8. Fitting compression behavior by the Toll model.

Fig. 8. Fitting compression behavior by the Toll model.

4. Conclusion

The original version of this paper is available on https://www.scientific.net/SSP.387.153.pdf

Compaction/Compression tests have been performed on flax rovings. The use of a high-resolution numerical microscope and in-situ compaction measurement enabled the tracking of the evolution of the external microstructure under low pressure. Results revealed atypical mechanical behavior. The samples maintained their cross-sectional geometry exceptionally well under increasing load. Contrary to conventional fiber materials, there was no significant lateral expansion, and surface irregularities persisted throughout compaction, even at high pressure. This dimensional stability, with a maximum relative variation of 2%, suggests that the classic reorganization and local densification mechanisms observed in synthetic fiber rovings do not occur. This therefore leads to considering the difference between the apparent pressure and the real contact pressure in the analysis and modeling of the compression behavior of flax reinforcements.

This behavior can be explained by the flax rovings' specific internal cohesion. The manufacturing process involves specific flax yarn composition consisting of short filaments and interfacial lamellae that form fiber clusters rather than individual filaments. This process significantly limits the relative mobility of the fibers. The intrinsic rigidity of the fiber network limits microstructural reorganization and fiber fluidity under stress.

Compression tests were also performed to obtain the compression behavior at high pressure, complementing the previous compaction tests. The two types of test proved to be consistent. The lack of internal reorganization and the persistence of surface heterogeneities suggest that conventional compaction models based on the assumption of high fiber mobility are inadequate for accurately describing the behaviors of these materials. Thus, macroscopic compression tests revealed significant variability between samples and highlighted the limitations of the traditional Toll model in capturing the nuanced deformation mechanisms of flax rovings.

While Toll's model may be used as a first approximation, it is necessary to look for other models to account for the specificities of flax rovings.

These observations have important implications for modeling and predicting the behavior of flaxbased composites. Although the observed variability in response is uncorrelated with fiber orientation within the reinforcement, it highlights the importance of considering the stochastic nature of natural fiber arrangement for precise modeling.

Acknowledgment

The original version of this paper is available on https://www.scientific.net/SSP.387.153.pdf

The authors gratefully acknowledge the DEPESTELE Group and ANRT for funding T. Larquemin's thesis.

References

The original version of this paper is available on https://www.scientific.net/SSP.387.153.pdf

Korniejenko, K., Oliwa, K., Gądek, S., Dynowski, P., Źróbek, A., & Lin, W.-T. (2025). A review of additive manufacturing techniques in artificial reef construction: Materials, processes, and ecological impact. Applied Sciences (Basel, Switzerland), 15(8), 4216. https://doi.org/10.3390/app15084216

Jha, R. K., Kim, S.-W., & Kim, S. (2024). Naturally bred Korean silkworm cocoon showing strong green fluorescence and its textile-based environmental sensing applications. Journal of Natural Fibers, 21(1).

Rova, L., Gallet--Pandellé, A., Wang, Z., Kurita, H., & Narita, F. (2024). Japanese washi-paperbased green composites: Fabrication, mechanical characterization, and evaluation of biodegradability, " Compos. Compos. Part A Appl. Sci. Manuf, 184(108261).

Carvalho, D., Ferreira, N., França, B., Marques, R., Silva, M., Silva, S., ... Oliveira, C. (2024). Advancing sustainability in the automotive industry: Bioprepregs and fully bio-based composites. Composites Part C: Open Access, 14(100459), 100459. doi:10.1016/j.jcomc.2024.100459.

Moutinho, L. G., Soares, E., & Oliveira, M. (2025). Bio-based thermoplastic composites reinforced with natural fillers for sustainable packaging: A comprehensive review. Polymer Composites, 46(11), 9703-9727. doi:10.1002/pc. 29589

Zidi, S., Miraoui, I., & Moussaoui, Y. (2024). Optimized integration of Opuntia ficus-indica fibers for improved performance in plaster-based composites. Multiscale and Multidisciplinary Modeling Experiments and Design. doi:10.1007/s41939-024-00518-z

Gupta, S., & Mahmood, A. H. (2022). A multi-method investigation into rheological properties, hydration, and early-age strength of cement composites with admixtures recovered from inorganic and bio-based waste streams. Construction and Building Materials, 347(128529), 128529. doi:10.1016/j.conbuildmat.2022.128529

Nwankwo, C., Mahachi, J., Olukanni, D., & Musonda, I. (2025). Alkali treatment of flax fibres: effects on tensile strength, thermal performance, and moisture absorption. Composite Interfaces, 32(9), 1329-1346. doi:10.1080/09276440.2025.2467700

Ouagne, P., Soulat, D., Tephany, C., Moothoo, J., Allaoui, S., Hivet, G., & Duriatti, D. (2012). Complex shape forming of flax based woven fabrics - analysis of the yarn tensile strain during the process. Key Engineering Materials, 504-506, 231-236. doi:10.4028/www.scientific.net/ kem.504-506.231

Pozdnyakov, B. A. (2018). All-Russian Research Institute of Mechanization Flax Cultivation, I. V. Velikanova, and All-Russian Research Institute of Flax, "Strategic management of production modernization in the flax subcomplex in Russia. Экономика Сельскохозяйственных и Перерабатывающих Предприятий, (11), 39-42.

Pachernegg-Mair, L., Schaubeder, J. B., Brandberg, A., Thoman, M., Urstöger, G., Rüggeberg, M., ... Czibula, C. (2025). Ionic liquid treatment of flax fibers and the effects on morphology and mechanical properties. Materials Science & Engineering. A, Structural Materials: Properties, Microstructure and Processing, 942 (148675), 148675. doi:10.1016/j.msea.2025.148675

Patel, S., B. Karthikeyan, & P. V. Elumalai. (2025). Influence of graphite nano fillers on the properties of flax fiber-reinforced epoxy biocomposites. Journal of Environmental Nanotechnology, 14(3), 287-296. doi:10.13074/jent.2025.09.2521539

Koroleva, E. N. (2018). Lines for processing of industrial hemp into the single-type and staple hemps," Oil Crops Sci. Oil Crops Sci. Tech. Bull. All-Russ. Res. Insitute Oil Crops Name Pustovoit V S, 3(175), 85-91. DOI 10.25230/2412-608X-2018-3-175-85-91

Novikov, E. V. (2018). Development of small-sized technical line for oil flax processing basing on studying of fiber characteristics," Oil Crops Sci. Oil Crops Sci. Tech. Bull. All-Russ. Res. Insitute Oil Crops Name Pustovoit V S, 2(174), 83-89. . 41807

D. U. Shah, R. K. Nag, and M. J. Clifford, "Why do we observe significant differences between measured and 'back-calculated' properties of natural fibres?," Cellulose, vol. 23, no. 3, pp. 1481-1490, 2016.

J. Govilas, A. Chevallier, W. Akleh, J. Beaugrand, C. Clévy, and V. Placet, "Mechanical behavior and apparent stiffness of flax, hemp and nettle fibers under single fiber transverse compression tests," Compos. Part A Appl. Sci. Manuf., vol. 185, no. 108321, p. 108321, 2024.

Toll, S. (1998). Packing mechanics of fiber reinforcements. Polymer Engineering & Science, 38(8), 1337-1350. . 10304

Toll, S., & Månson, J.-A. E. (1994). Dynamics of a planar concentrated fiber suspension with non-hydrodynamic interaction. Journal of Rheology, 38(4), 985-997.

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.

Keywords Index

A

Advanced High-strength Steel Cost-Effective One-Shot Stamping of Steel-Based High-Performance Lightweight Multi-Material Structures

Automated Fibre Placement Iterative Un-Forming of Highly Aligned Discontinuous Preform/Material

B

Benchmark Exercise The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

C

C-smc The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Charge Process Influences on Plastificate and Fiber Microstructure Formation: An LFT-D Compression Molding Parameter Study

Clustering Influence of Fiber Orientation Clustering Methods in Virtual Process Chains of Discontinuous-Fiber Composite Components

Composites Process Influences on Plastificate and Fiber Microstructure Formation: An LFT-D Compression Molding Parameter Study

Compression Moulding The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Cure Kinetics Thermo-Chemical Modeling and Experimental Validation of Pultruded Glass Fiber Reinforced Composites

Curvilinear Fibers Topological Optimization of Shell Laminate Composites Parts Manufactured by the TFP Process

D

Deep Draw Wrinkling Behaviour of Engineering Fabrics in Diaphragm-Assisted Deep Drawing

Diaphragm Forming Wrinkling Behaviour of Engineering Fabrics in Diaphragm-Assisted Deep Drawing

Dp780 Cost-Effective One-Shot Stamping of Steel-Based High-Performance Lightweight Multi-Material Structures

E

Engineering Fabrics Wrinkling Behaviour of Engineering Fabrics in Diaphragm-Assisted Deep Drawing

F

Fiber Content Process Influences on Plastificate and Fiber Microstructure Formation: An LFT-D Compression Molding Parameter Study

Fiber Orientation Process Influences on Plastificate and Fiber Microstructure Formation: An LFT-D Compression Molding Parameter Study; Influence of Fiber Orientation Clustering Methods in Virtual Process Chains of Discontinuous-Fiber Composite Components

Fibre Tension Fibre Tension-Induced Stresses in Filament-Wound Composites

Filament Winding Fibre Tension-Induced Stresses in Filament-Wound Composites

Finite Element Modelling Iterative Un-Forming of Highly Aligned Discontinuous Preform/Material

Flax Rovings; Compaction Behavior; Mechanical Response; Experimental Investigation; Mesoscopic Scale Mechanical Response of Flax Rovings under Compaction: Experimental Investigation

Forming Process Iterative Un-Forming of Highly Aligned Discontinuous Preform/Material

Frtp Process Influences on Plastificate and Fiber Microstructure Formation: An LFT-D Compression Molding Parameter Study

G

Glass Fiber Composites Thermo-Chemical Modeling and Experimental Validation of Pultruded Glass Fiber Reinforced Composites

Glass Fibre Cost-Effective One-Shot Stamping of Steel-Based High-Performance Lightweight Multi-Material Structures

I

In-situ Preform Deformation Controlled Preform Consolidation for Manufacturing Difficult Resin System Composites

L

Life Cycle Assessment Environmental Impact Assessment of Thermoplastic Pultrusion of GF-PP Pre-Consolidated Tapes

Line-profile Features A CT-Based Methodology for Mesostructural Characterization of Unidirectional Continuous-Fiber Polymer Composites Using Line-Profile Descriptors

Liquid Composite Molding Controlled Preform Consolidation for Manufacturing Difficult Resin System Composites

Lofting Process Influences on Plastificate and Fiber Microstructure Formation: An LFT-D Compression Molding Parameter Study

Lubrication Wrinkling Behaviour of Engineering Fabrics in Diaphragm-Assisted Deep Drawing

M

Mesostructure Characterization A CT-Based Methodology for Mesostructural Characterization of Unidirectional Continuous-Fiber Polymer Composites Using Line-Profile Descriptors

Molding Simulation Influence of Fiber Orientation Clustering Methods in Virtual Process Chains of Discontinuous-Fiber Composite Components

Multi-material Structures Cost-Effective One-Shot Stamping of Steel-Based High-Performance Lightweight Multi-Material Structures

Multi-stage Consolidation Multi-Stage Consolidation Strategy to Improve Impregnation Quality in Thermoplastic UD Tapes

Multifunctional Resin Systems Controlled Preform Consolidation for Manufacturing Difficult Resin System Composites

P

Pa6 Cost-Effective One-Shot Stamping of Steel-Based High-Performance Lightweight Multi-Material Structures

Plastificate Process Influences on Plastificate and Fiber Microstructure Formation: An LFT-D Compression Molding Parameter Study

Process Optimization Multi-Stage Consolidation Strategy to Improve Impregnation Quality in Thermoplastic UD Tapes

Processing Process Influences on Plastificate and Fiber Microstructure Formation: An LFT-D Compression Molding Parameter Study

Pultrusion Thermo-Chemical Modeling and Experimental Validation of Pultruded Glass Fiber Reinforced Composites; Environmental Impact Assessment of Thermoplastic Pultrusion of GF-PP Pre-Consolidated Tapes

R

Residual Stress Fibre Tension-Induced Stresses in Filament-Wound Composites

S

Sheet Forming Cost-Effective One-Shot Stamping of Steel-Based High-Performance Lightweight Multi-Material Structures

Squeeze Flow Testing The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Stability Stability of Tubular Preforms from Multi-Layer Winding of Composite Tapes Prior to Thermoforming

Structural Simulation Influence of Fiber Orientation Clustering Methods in Virtual Process Chains of Discontinuous-Fiber Composite Components

Sustainability Environmental Impact Assessment of Thermoplastic Pultrusion of GF-PP Pre-Consolidated Tapes

T

Tape Winding Stability of Tubular Preforms from Multi-Layer Winding of Composite Tapes Prior to Thermoforming

Tfp Process Topological Optimization of Shell Laminate Composites Parts Manufactured by the TFP Process

Thermo-chemical Modeling Thermo-Chemical Modeling and Experimental Validation of Pultruded Glass Fiber Reinforced Composites

Thermoplastic Composites Multi-Stage Consolidation Strategy to Improve Impregnation Quality in Thermoplastic UD Tapes

Topology Optimization Topological Optimization of Shell Laminate Composites Parts Manufactured by the TFP Process

Tubular Composites Stability of Tubular Preforms from Multi-Layer Winding of Composite Tapes Prior to Thermoforming

U

Ud Tape Multi-Stage Consolidation Strategy to Improve Impregnation Quality in Thermoplastic UD Tapes

Unidirectional Continuous- Fiber Composites A CT-Based Methodology for Mesostructural Characterization of Unidirectional Continuous-Fiber Polymer Composites Using Line-Profile Descriptors

W

Wrinkling Wrinkling Behaviour of Engineering Fabrics in Diaphragm-Assisted Deep Drawing

X

X-ray Micro-computed Tomography A CT-Based Methodology for Mesostructural Characterization of Unidirectional Continuous-Fiber Polymer Composites Using Line-Profile Descriptors

Authors Index

A

Aitomaki, Y. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Ambuehl, K. Wrinkling Behaviour of Engineering Fabrics in Diaphragm-Assisted Deep Drawing

Astarita, A. Environmental Impact Assessment of Thermoplastic Pultrusion of GF-PP Pre-Consolidated Tapes

B

Banu, M. Thermo-Chemical Modeling and Experimental Validation of Pultruded Glass Fiber Reinforced Composites

Belnoue, J. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review; Iterative Un-Forming of Highly Aligned Discontinuous Preform/Material

Bieleman, G. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Binetruy, C. Topological Optimization of Shell Laminate Composites Parts Manufactured by the TFP Process; The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Biswas, P. Cost-Effective One-Shot Stamping of Steel-Based High-Performance Lightweight Multi-Material Structures

Blair, C. Wrinkling Behaviour of Engineering Fabrics in Diaphragm-Assisted Deep Drawing

C

Caglar, B. Multi-Stage Consolidation Strategy to Improve Impregnation Quality in Thermoplastic UD Tapes

Campos, R. Stability of Tubular Preforms from Multi-Layer Winding of Composite Tapes Prior to Thermoforming

Carlone, P. A CT-Based Methodology for Mesostructural Characterization of Unidirectional Continuous-Fiber Polymer Composites Using Line-Profile Descriptors

Cozzolino, E. Environmental Impact Assessment of Thermoplastic Pultrusion of GF-PP Pre-Consolidated Tapes

D

Ding, Z. Cost-Effective One-Shot Stamping of Steel-Based High-Performance Lightweight Multi-Material Structures

Duhovic, M. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Duriatti, D. Mechanical Response of Flax Rovings under Compaction: Experimental Investigation

E

El-Jakl, M. Multi-Stage Consolidation Strategy to Improve Impregnation Quality in Thermoplastic UD Tapes

Elgaby, N. Stability of Tubular Preforms from Multi-Layer Winding of Composite Tapes Prior to Thermoforming

Eryilmaz, O. Multi-Stage Consolidation Strategy to Improve Impregnation Quality in Thermoplastic UD Tapes

F

Feng, Y. Iterative Un-Forming of Highly Aligned Discontinuous Preform/Material

G

Ganapathy, M. Cost-Effective One-Shot Stamping of Steel-Based High-Performance Lightweight Multi-Material Structures

Gao, C. Cost-Effective One-Shot Stamping of Steel-Based High-Performance Lightweight Multi-Material Structures

Gebhard, A. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Gharat, S. Thermo-Chemical Modeling and Experimental Validation of Pultruded Glass Fiber Reinforced Composites

Gortner, F. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Greenhalgh, E. Controlled Preform Consolidation for Manufacturing Difficult Resin System Composites

Grouve, W. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

H

Hallett, S. Iterative Un-Forming of Highly Aligned Discontinuous Preform/Material

Hamerton, I. Iterative Un-Forming of Highly Aligned Discontinuous Preform/Material

Hamila, N. Topological Optimization of Shell Laminate Composites Parts Manufactured by the TFP Process; Stability of Tubular Preforms from Multi-Layer Winding of Composite Tapes Prior to Thermoforming

Harris, J. Thermo-Chemical Modeling and Experimental Validation of Pultruded Glass Fiber Reinforced Composites

Harrison, P. Wrinkling Behaviour of Engineering Fabrics in Diaphragm-Assisted Deep Drawing

Helbert, G. Stability of Tubular Preforms from Multi-Layer Winding of Composite Tapes Prior to Thermoforming

Henning, F. Process Influences on Plastificate and Fiber Microstructure Formation: An LFT-D Compression Molding Parameter Study

Hivet, A. Mechanical Response of Flax Rovings under Compaction: Experimental Investigation

Hivet, G. Mechanical Response of Flax Rovings under Compaction: Experimental Investigation

Hohberg, M. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

I

Imbsweiler, A. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Ivanov, D. Controlled Preform Consolidation for Manufacturing Difficult Resin System Composites; The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Ivens, J. Fibre Tension-Induced Stresses in Filament-Wound Composites; The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

J

Jakimow, J. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

K

Kapshammer, A. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Karakaya, N. Multi-Stage Consolidation Strategy to Improve Impregnation Quality in Thermoplastic UD Tapes

Kim, B. Iterative Un-Forming of Highly Aligned Discontinuous Preform/Material

Koptelov, A. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Krauß, C. Influence of Fiber Orientation Clustering Methods in Virtual Process Chains of Discontinuous-Fiber Composite Components

Kärger, L. Influence of Fiber Orientation Clustering Methods in Virtual Process Chains of Discontinuous-Fiber Composite Components; The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

L

Laine, B. Stability of Tubular Preforms from Multi-Layer Winding of Composite Tapes Prior to Thermoforming

Larquemin, T. Mechanical Response of Flax Rovings under Compaction: Experimental Investigation

Li, N. Cost-Effective One-Shot Stamping of Steel-Based High-Performance Lightweight Multi-Material Structures

Liu, J. Fibre Tension-Induced Stresses in Filament-Wound Composites

Lomov, S. Fibre Tension-Induced Stresses in Filament-Wound Composites; The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

M

Mahé, F. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Major, Z. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Meckes, L. Process Influences on Plastificate and Fiber Microstructure Formation: An LFT-D Compression Molding Parameter Study

Mele, R. A CT-Based Methodology for Mesostructural Characterization of Unidirectional Continuous-Fiber Polymer Composites Using Line-Profile Descriptors

Mohamed, M. Cost-Effective One-Shot Stamping of Steel-Based High-Performance Lightweight Multi-Material Structures

N

Neumeyer, T. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

O

Olma, M. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Ozkoc, G. Multi-Stage Consolidation Strategy to Improve Impregnation Quality in Thermoplastic UD Tapes

P

Palubiski, D. Controlled Preform Consolidation for Manufacturing Difficult Resin System Composites

Park, C. Fibre Tension-Induced Stresses in Filament-Wound Composites

Q

Qian, C. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

R

Ramantani, D. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Ramirez, L. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

S

Sanchez, A. Stability of Tubular Preforms from Multi-Layer Winding of Composite Tapes Prior to Thermoforming

Schelleis, C. Process Influences on Plastificate and Fiber Microstructure Formation: An LFT-D Compression Molding Parameter Study

Schommer, D. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Schreyer, L. Influence of Fiber Orientation Clustering Methods in Virtual Process Chains of Discontinuous-Fiber Composite Components; The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Shaffer, M. Controlled Preform Consolidation for Manufacturing Difficult Resin System Composites

Shirshova, N. Controlled Preform Consolidation for Manufacturing Difficult Resin System Composites

Simaafrookhteh, S. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review

Simon, J. Topological Optimization of Shell Laminate Composites Parts Manufactured by the TFP Process

Sio, P. A CT-Based Methodology for Mesostructural Characterization of Unidirectional Continuous-Fiber Polymer Composites Using Line-Profile Descriptors; Environmental Impact Assessment of Thermoplastic Pultrusion of GF-PP Pre-Consolidated Tapes

T

Tabatabaeian, A. Wrinkling Behaviour of Engineering Fabrics in Diaphragm-Assisted Deep Drawing

Taub, A. Thermo-Chemical Modeling and Experimental Validation of Pultruded Glass Fiber Reinforced Composites

Taylor, A. Cost-Effective One-Shot Stamping of Steel-Based High-Performance Lightweight Multi-Material Structures

Tucci, F. A CT-Based Methodology for Mesostructural Characterization of Unidirectional Continuous-Fiber Polymer Composites Using Line-Profile Descriptors; Environmental Impact Assessment of Thermoplastic Pultrusion of GF-PP Pre-Consolidated Tapes

V

Verma, R. Cost-Effective One-Shot Stamping of Steel-Based High-Performance Lightweight Multi-Material Structures

W

Wang, L. Thermo-Chemical Modeling and Experimental Validation of Pultruded Glass Fiber Reinforced Composites

Wu, H. Cost-Effective One-Shot Stamping of Steel-Based High-Performance Lightweight Multi-Material Structures

Y

Yaluff, L. Influence of Fiber Orientation Clustering Methods in Virtual Process Chains of Discontinuous-Fiber Composite Components

Yavuz, B. Iterative Un-Forming of Highly Aligned Discontinuous Preform/Material

Yu, P. Topological Optimization of Shell Laminate Composites Parts Manufactured by the TFP Process

Yuan, H. The First European Benchmark Exercise on Squeeze Flow Testing of High-Performance C-SMCs: Review; Iterative Un-Forming of Highly Aligned Discontinuous Preform/Material

Yuksel, O. Multi-Stage Consolidation Strategy to Improve Impregnation Quality in Thermoplastic UD Tapes

Z

Zidi, S. Mechanical Response of Flax Rovings under Compaction: Experimental Investigation

Zolali, A. Thermo-Chemical Modeling and Experimental Validation of Pultruded Glass Fiber Reinforced Composites