Accumulated plastic strain prediction of LPBF alloy under fatigue load based on CPFEM and incremental neural network

Abstract

In this study, the micromechanical response of a representative volume element (RVE) under cyclic loading was simulated using the crystal plasticity finite element method (CPFEM) to obtain the local stress-strain response and accumulated plastic strain. Based on the high-fidelity data generated by CPFEM, an incremental neural network (INN) model was constructed. The INN model takes the load ratio and the current accumulated plastic strain as inputs to predict the corresponding accumulated plastic strain increment for a given number of cycles. Compared with traditional fatigue prediction models, this model does not require presetting empirical equations. The results demonstrate that this incremental learning approach can effectively capture the nonlinear evolution of plastic strain with the number of cycles. The developed single-hidden-layer INN model accurately predicts the plastic strain accumulation process in laser powder bed fusion (LPBF) GH4169 (Inconel 718) under cyclic loading and achieves the highest prediction accuracy.