Alternative predictive approach for low-cycle fatigue life based on machine learning and energy-based modeling

• The study introduces a hybrid ML/E model combining machine learning with an energy-based physical model. • The model offers a unified prediction for the LCF life of anisotropic Mg alloys. • It enhances prediction accuracy utilizing several ML strategies: loop learning, ε t−1 feature, optimum ANN architecture, and modified morrow model. • It outperforms the conventional coffin-manson model in both predictability and generalizability. Mg alloys are extremely valuable in the automotive and aerospace industries because of their lightweight properties and excellent machinability. The applications in these industries necessitate the accurate prediction of fatigue life under cyclic loading. However, this is challenging for many wrought Mg alloys owing to their pronounced plastic anisotropy. Conventional predictive methods such as the Coffin-Manson equation require manual parameter adjustment for different conditions, thus limiting their applicability. Accordingly, a novel predictive model for low-cycle fatigue (LCF) life that combines machine learning (ML) with an energy-based physical model, referred to as the hybrid ML/E model, is proposed herein. The hybrid ML/E model leverages a substantial hysteresis-loop dataset generated from LCF tests on a rolled AZ31 Mg alloy to effectively predict fatigue life. The proposed approach addresses the inherent challenges of small fatigue datasets, hysteresis-loop perception, and algorithm selection. The hybrid ML/E model demonstrates superior predictive accuracy and robustness in various loading directions, based on validation against conventional methods. The integration of ML and physical principles offers a unified framework for the LCF life prediction of anisotropic materials and represents a significant advancement for industrial applications.