Explainable Prediction of the Mechanical Properties of Composites with CNNs

TL;DR

This paper demonstrates that customized CNNs combined with explainable AI techniques can accurately predict and interpret the mechanical properties of composites, offering a trustworthy alternative to traditional finite element modeling.

Contribution

The study introduces a CNN-based approach with explainability methods for predicting composite properties, surpassing previous models in accuracy and transparency.

Findings

CNNs outperform baseline models like ResNet-34 in accuracy

Explainability methods reveal geometrical features influencing predictions

The approach reduces computational costs compared to FE modeling

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.