Stress field prediction in fiber-reinforced composite materials using a deep learning approach

Abstract

Stress analysis is an important step in the design of material systems, and finite element methods (FEM) are a standard approach of performing computational analysis of stresses in complex material systems. The significant cost associated with multi-scale FEM analysis motivates replacement of FEM with a significantly faster data-driven machine learning based approach. In this study, we consider the application of deep learning tools to local stress field prediction in a fiber-reinforced matrix composite material system as an efficient alternative to FEM. The first challenge is to predict stress field maps for composite cross-sections with a fixed number of fibers and varying spatial configurations. Specifically, a mapping between the spatial arrangement of fibers and the corresponding von Mises stress field is achieved by using a convolutional neural network (CNN), specifically a U-Net architecture. The CNN is trained using data with the same number of fibers as the target systems. A robustness analysis uses different initializations of the training samples to find the evolution of the prediction accuracy with increasing number of training samples. Systems with a larger number of fibers typically require a finer finite element mesh discretization, leading to an increase in the computational cost. Thus, the secondary goal here is to predict the stress field for systems with larger number of fibers using CNNs that are pretrained on data from relatively cheaper systems with smaller fiber number.