Explainable prediction of the mechanical properties of composites with CNNs
OA Location
Author(s)
Raaghav, Varun
Bikos, Dimitrios
Rago, Antonio
Toni, Francesca
Charalambides, Maria
Type
preprint
Abstract
Composites are amongst the most important materials manufactured today, as evidenced by their use in countless applications. In order to establish the suitability of composites in specific applications, finite element (FE) modelling, a numerical method based on partial differential equations, is the industry standard for assessing their mechanical properties. However, FE modelling is exceptionally costly from a computational viewpoint, a limitation which has led to efforts towards applying AI models to this task. However, in these approaches: the chosen model architectures were rudimentary, feed-forward neural networks giving limited accuracy; the studies focus on predicting elastic mechanical properties, without considering material strength limits; and the models lacked transparency, hindering trustworthiness by users. In this paper, we show that convolutional neural networks (CNNs) equipped with methods from explainable AI (XAI) can be successfully deployed to solve this problem. Our approach uses customised CNNs trained on a dataset we generate using transverse tension tests in FE modelling to predict composites' mechanical properties, i.e., Young's modulus and yield strength. We show empirically that our approach achieves high accuracy, outperforming a baseline, ResNet-34, in estimating the mechanical properties. We then use SHAP and Integrated Gradients, two post-hoc XAI methods, to explain the predictions, showing that the CNNs use the critical geometrical features that influence the composites' behaviour, thus allowing engineers to verify that the models are trustworthy by representing the science of composites.
Date Issued
2025-05-20
Citation
arXiv, 2025
Journal / Book Title
arXiv
Copyright Statement
© 2025 The Author(s). This preprint is is made available under a CC-BY 4.0 International license (https://creativecommons.org/licenses/by/4.0/)
License URL
Identifier
http://arxiv.org/abs/2505.14745v1
Subjects
cs.LG
cs.LG
cs.AI
I.2.1