A Numerical Study on the Influence of Strain Rate in Finite-Discrete Element Simulation of the Perforation Behaviour of Woven Composites

Abstract

Predicting the perforation limit of composite laminates is an important design aspect and is a complex task due to the multi-mode failure mechanism and complex material constitutive behaviour required. This requires high-fidelity numerical models for a better understanding of the physics of the perforation event. This work presents a numerical study on the perforation behaviour of a satin-weave S2-glass/epoxy composite subjected to low-velocity impact. A novel strain-rate-dependent finite-discrete element model (FDEM) is presented and validated by comparison with experimental data for impacts at several energies higher and lower than their perforation limit. The strain rate sensitivity was included in the model by developing a novel user-defined material model, which had a rate-dependent bilinear traction separation cohesive behaviour, implemented using a VUSDFLD subroutine in Abaqus/Explicit. The capability of the model in predicting the perforation limit of the composite was investigated by developing rate-sensitive and insensitive models. The results showed that taking the strain rate into account leads to more accurate predictions of the perforation limit and damage morphology of the laminate subjected to impacts at different energies. The experimental penetration threshold of 89 J was estimated as 79 J by the strain-rate-sensitive models, which was more accurate compared to 52 J predicted by the strain-rate-insensitive model. Additionally, the coupling between interlaminar and intralaminar failure modes in the models led to a more accurate prediction of the delamination area when considering the rate sensitivity.

A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions

“…Figure 1 Shows the workflow of the presented method in comparison with a typical FE simulation of the same problem. The FE-CNN surrogate model, if provided with accurate training data, provides fast and reliable predictions of this complex computational problem without the need to do complex and time-consuming FE simulations [50][51][52].…”

Machine Learning-Assisted Characterization of Pore-Induced Variability in Mechanical Response of Additively Manufactured Components

“…FEM has proven to be a valuable method for analysing the mechanical behaviour and performance of materials such as metals [20,[56][57][58] and composites [59][60][61][62] under mechanical loads; therefore, it was used here to generate the dataset for the ML model. Figure 1a shows the 3D CAD model of the compression-shear specimen that was used to perform FEM using the Abaqus/Standard commercial solver [63].…”

Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks

“…The potential of deep learning to uncover intricate patterns and spatial dependencies within complex datasets has enabled end-to-end field prediction outputs such as damage, stress, and strain from image datasets of material microstructures [3][4][5][6] or heterogeneous geometries [7][8][9][10]. Data-driven models trained using computer vision and semantic segmentation techniques [11] have utilized datasets with both paired [12,13] and unpaired [14,15] images from physics-informed simulation approaches like molecular dynamics (MD) [6,14] and finite element method (FEM) [16][17][18][19]. Machine learning models, like the ones presented in this work, offer enhanced computational efficiency and reduced simulation time [17] complementing the precision of physics-informed simulations.…”