Explainable prediction of the mechanical properties of composites with CNNs
File(s)
Published version
Author(s)
Raaghav, Varun
Bikos, Dimitrios
Rago, Antonio
Toni, Francesca
Charalambides, Maria
Type
Conference Paper
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 focused 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
2025-10-25
Date Acceptance
2025-07-10
Citation
Frontiers in Artificial Intelligence and Applications, 2025, Volume 413: ECAI 2025, pp.5208-5215
ISBN
978-1-64368-631-8
ISSN
0922-6389
Publisher
IOS Press
Start Page
5208
End Page
5215
Journal / Book Title
Frontiers in Artificial Intelligence and Applications
Volume
Volume 413: ECAI 2025
Copyright Statement
© 2025 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
License URL
Identifier
10.3233/FAIA251455
Source
The 14th Conference on Prestigious Applications of Intelligent Systems (PAIS 2025)
Publication Status
Published
Start Date
2025-10-25
Finish Date
2025-10-30
Coverage Spatial
Bologna, Italy