Materials Science Forum Vol. 1182

Abstract: Accurate yet computationally efficient simulation models are essential for the virtual design and optimization of thermoforming processes for fiber-reinforced composites. Selecting an appropriate material model remains challenging, particularly when balancing model fidelity against computational cost. In this work, a framework is developed to validate material models used in thermoforming simulations for fiber-reinforced composites. The framework evaluates model performance based on time-series data using covariance-based input-output statistics, without prior calibration. Two numerical studies of increasing complexity demonstrate the versatility of the approach. First, the framework is applied to one-dimensional rheological models, verifying its applicability to mechanical problems relevant to thermoforming simulation. These insights are then applied to complex finite element thermoforming simulations to assess the ability of isothermal material models to predict wrinkling behavior in comparison to a fully coupled thermomechanical reference model. A curvature-based method is introduced to quantitatively evaluate wrinkling severity relative to natural curvatures induced by the tool geometry. The results show that isothermal models are sufficient for short total process times with minor temperature-driven effects, whereas longer total process times with pronounced thermal effects require thermomechanical models to ensure accurate predictions. The findings offer practical guidance for selecting appropriate material models based on specific process conditions, as well as objective criteria for assessing model validity in virtual process design.

Abstract: Accurate prediction of forming behavior in dry textile reinforcements requires constitutive models that capture both in-plane and out-of-plane deformation mechanisms. This work presents the development and validation of advanced bending models for unidirectional non-crimp fabrics (UD-NCFs) that exhibit two distinct characteristics: side-dependent behavior arising from asymmetric stitching and glass fiber backing, and nonlinear behavior characterized by decreasing bending stiffness with increasing curvature. Based on cantilever bending tests with optical moment–curvature measurement, five mathematical formulations (piecewise linear, polynomial, power law, logarithmic, and exponential) used to describe the moment-curvature relation were systematically evaluated using the coefficient of determination R². The piecewise linear and logarithmic models achieved the highest accuracy, with R² values approaching unity across all fiber orientations and bending directions. These models were implemented in ABAQUS/Explicit via the VUGENS user subroutine and validated through virtual cantilever tests, demonstrating good agreement with experimental deflection curves within the standard deviation bands. Application to hemispherical forming simulations revealed significant differences in wrinkle prediction between linear and nonlinear models. While the classical linear model based on Peirce predicted a single pronounced wrinkle in fiber direction, the nonlinear models captured additional wrinkles in the transverse direction and wider wrinkle patterns in fiber direction. Side-dependent models exhibited slightly increased wrinkle amplitudes compared to non-side-dependent models, particularly in fiber direction. The developed framework allows for a more accurate virtual process design than the current state of the art for composite forming operations by accounting for the side-dependent and nonlinear bending characteristics of UD-NCF materials.

Abstract: The impregnation represents a crucial phase in liquid composite molding (LCM) processes. Researchers over the years have used various approaches for monitoring, based on smart weave, pressure sensors, dielectric. Among the LCM processes, the vacuum bag allows the use of visual systems for detecting the resin flow front. The integration of monitoring systems with controllers for automated management of process parameters leads to an improvement in the characteristics of the final manufactured component. In the present work, an AI-based system integrated with the control of a resin preheating system allows for improvement of the impregnation stage. A machine learning approach, based on the You Only Look Once (YOLO) algorithm, has been integrated with the visual monitoring system to detect and dynamically track the resin flow front in real time. The flow front position has been compared with the theoretical one, evaluated by using the Darcy’s law and based on the mismatch the controller suggests a proper in-time regulation of microwave power. The implemented system is capable of processing images through an AI-based algorithm and extracting the kinematic data of the flow front and integrating the information from the thermocouples and the visual system to control the microwave power.

Abstract: Aiming to minimize time, energy, and materials-consuming trial and error experimental analyses, a numerical modeling approach of vitrimer flow and cold spray deposition is proposed in this work. The characteristics of vitrimeric matrices were evaluated by elaborating data from previously performed differential scanning calorimetry and dynamic mechanical analysis. The pieces of information related to the transition temperatures and mechanical evolution after curing were exploited to feed the numerical models and to run sensitivity analyses. The flow model is based on prior evaluation of the dry reinforcement permeability at a micro- and meso-scale. The flow model has been implemented using a commercial simulative environment based on the control volumes approach. A single impacting particle was simulated in a finite element environment to analyze, in a focused way, the deposition mechanisms.The objective of this analysis is the integrated implementation of a numerical model for vitrimer flow through carbon fabric reinforcement in infusion processes and single particle deposition on vitrimer matrix composite substrates.

Abstract: Aligned discontinuous fibre reinforced composites (ADFRC) have demonstrated an improved formability for small to medium sized parts with complex geometries compared to the continuous fibre based prepreg due to their stretchability along the fibre direction. The process simulation tool developed for this class of materials so far mostly concerns their tensile behaviour along the fibre direction. However, neglecting other deformation modes like the in-plane shear in a forming simulation may pose risks for the correct prediction of formed shape. This study verified a strategy which adopts previously developed analytical micromechanical models for tensile and in-plane shear deformation of ADFRCs, in a finite element framework. The implementation is validated by comparing results from virtual shear tests against experiments at different temperatures. This was then followed by virtual forming experiments on a doubly curved geometry, in which the tensile and shear properties of the material were varied separately to study the effects of each deformation mechanism on the simulated forming behaviour of the material.

Abstract: Forming of dry textile preforms for liquid moulding processes becomes increasingly challenging for geometries exhibiting strong double curvature and aggressive tapering. While sequential draping can mitigate defects, it is often impractical for high-rate manufacturing and lacks robustness for dry fabrics with limited inter-ply tack. This paper investigates an alternative approach in which locally printed and cured or semi-cured resin patches are used to steer deformation during forming and suppress shear localisation. A numerical framework is developed to model patched preforms using a superposition-based material representation and is applied to an extreme “bow-tie” benchmark geometry. Initial simulations reveal severe shear-strain localisation leading to fibre-path instability. Various patching strategies are explored to identify the dominant drivers governing defect mitigation. The results demonstrate that appropriately placed and sufficiently stiff patches can significantly delocalise shear and eliminate multi-stripe deformation patterns. Based on these findings, key optimisation parameters and practical guidelines for patch placement and stiffness selection are formulated, providing a foundation for future automated optimisation of patch-assisted preform forming.

Abstract: Natural fiber-reinforced composites offer lightweight and sustainable alternatives for automotive and aerospace applications. However, similar to synthetic composite options, forming-induced defects such as wrinkles can reduce targeted performance. Predicting these defects is particularly challenging for natural fiber reinforcements due to the inherent variability in the fiber geometry and ensuing fabric properties. This study applies a machine learning approach to predict formability in flax woven fabrics (2×2 twill and biaxial non-crimp) using a glass fabric as a synthetic fabric reference. The fabrics’ shear, bending, tensile, and friction behaviors were experimentally characterized to capture forming-relevant mechanical properties. The fabrics were subsequently formed in single-, dual-, and triple-layer configurations over a square tool, followed by 3D scanning to quantify wrinkle distributions. Forming-induced surface deformations were transformed into grayscale maps, from which Haralick texture features were extracted. Combined with the fabric design parameters such as weave, orientation, number of layers, grammage, and thickness, the mechanical properties features were used to train linear regression models, reliably predicting select Haralick features, cross-validated using Monte Carlo simulations. Results showed that flax twill reinforcements exhibited the lowest formability, while the glass fabric formed smoothly, and biaxial non-woven fabric showed primarily localized folds. Increasing the fabric orientation from 0° to 45° improved forming performance for most woven reinforcements; but not the biaxial non-woven alternative. Linear regression models accurately predicted the defect severity via homogeneity (R² = 0.81) and dissimilarity (R² = 0.73) texture features, demonstrating that integrating texture-based image analysis with fabric parameters and mechanical properties provides a promising machine-learning-based framework for predicting surface quality upon forming of fabric-reinforced composites.

Abstract: Thermoforming of thermoplastic fiber-reinforced composites enables cost-effective production of complex, high-volume components, yet wrinkling and shear-induced thickness variations remain persistent challenges in compound-curvature geometries, often leading to nonuniform consolidation. This work presents a predictive virtual process simulation that integrates discrete mesoscopic finite element modeling with targeted blank design strategies to address these limitations. The approach, developed by the Sherwood Group and implemented in LS-DYNA, is applied to the thermoforming of a UHMWPE unidirectional cross-ply composite system (DSM® Dyneema® HB210). A thickness-stretch shell formulation (SHELL ELFORM25), coupled with a user-defined material model, is employed to simultaneously capture in-plane shear, through-thickness deformation, and frictional interactions during forming. A parametric study is conducted to evaluate the combined effects of tooling geometry and strategically introduced slits in the blank, including side-and corner-oriented configurations. The results demonstrate that the proposed formulation provides an effective balance between computational efficiency and predictive accuracy while explicitly reducing shear-induced thickening. Notably, corner-oriented slits at 45° to the fiber directions significantly reduced thickness variability and wrinkle severity compared to unmodified blanks and side-slit configurations. These findings highlight the novelty of integrating thickness-aware forming simulations with geometric blank modification as a robust pathway for achieving near-uniform thickness and improved preform quality in thermoformed composite parts.

Abstract: This work investigates the formation and control of residual deformations in composite L-shaped parts manufactured by autoclave curing. An experimental study is first conducted to characterize spring-in for different stacking sequences. Dimensional measurements are performed using stereo digital image correlation to quantify spring-in and to assess the presence of warpage along the flanges. A numerical study is then carried out to develop a simulation method for process-induced deformation prediction. The influence of the main mechanisms involved is analyzed, and a modeling strategy for the tool-part interaction is proposed. Finally, compensated mould design is addressed. The convergence of the optimization problem is investigated, and an optimized mold geometry is determined for each part considered in this study.