Embedding Deep Neural Network in Enhanced Schapery Theory for Progressive Failure Analysis of Fiber Reinforced Laminates

Abstract Computational progressive failure analysis (PFA) of carbon fiber reinforced polymer composites (CFRP) is of vital importance in the verification and validation process of the structural integrity and damage tolerance of modern lightweight aeronautical structures. Enhanced Schapery Theory (EST) has been developed and applied to predict the damage pattern and load-bearing capacity of various composite structures. In this paper, EST is enhanced by a deep neural network (DNN) model, which enables fast and accurate predictions of matrix cracking angles under arbitrary stress states of any composite laminate. The DNN model is trained by TensorFlow based on data generated by a damage initiation criterion, which originates from the Mohr-Coulomb failure theory. The EST-DNN model is applied to open-hole tension/compression (OHT/OHC) problems. The results from the EST-DNN model are obtained with no loss in accuracy. The results presented combine the efficient and accurate predicting capabilities brought by machine learning tools and the robustness and user-friendliness of the EST finite element model.