Theory-inspired machine learning for stress–strain curve prediction of short fiber-reinforced composites with unseen design space

Abstract

Computational analysis of composites is well established; however, the process is time-consuming, owing to the disparate length scales and nonlinearities involved. Therefore, the use of machine learning models to predict the mechanical properties of short fiber-reinforced composites (SFRCs) is promising when large amounts of data are lacking. However, predicting data for an unseen design space remains challenging. The use of existing theoretical models for training is a viable solution to address this issue. This paper proposes a theory-guided machine learning (TGML) framework through training deep neural networks, based on the finite element analysis dataset that considers the cohesive effect of the interface, to predict the nonlinear mechanical responses of SFRCs. Our results demonstrate that incorporating the Halpin-Tsai theory with machine learning improves the predictive performance of the model in an unseen design space. Moreover, inspired by this theory, we propose a theory-inspired two-phase machine learning (TPML) approach to further improve predictive performance. Our results indicate that TGML and TPML capture more information from the data and thus enhance predictive performance. The proposed method can be adapted to the analyses of other composites with nonlinear mechanical behaviors.