Predicting the non-linear response of composite materials using deep recurrent convolutional neural networks

This paper presents a novel framework to predict the full non-linear response of composite materials using Deep Recurrent Convolutional Neural (DCRN) Networks. The framework is based on a Representative Volume Element (RVE) database populated by sampling the composite design space in terms of layups, defects/variabilities and loading conditions. Several sources of material non-linearity are included in these models such as matrix damage, delamination, fibre failure and shear non-linearity. A DCRN Network architecture is proposed which combines convolutional layers, for spatial features detection, with Long/Short Term Memory layers, for material loading history dependencies. The models' database consists of images of each RVE model, representing the layup and variables such as the presence of wrinkles, gaps or voids, and the homogenised 3D stress/strain curves. DCRN Networks are trained to predict the full 3D stress response of a laminated composite using the information from the RVE models database. Two formulations for the DCRN Network are studied, a pointwise prediction formulation and a time-marching prediction formulation. The two formulations are compared based on accuracy and robustness. The results show that both approaches can accurately predict the non-linear response of laminated composites.