A Physics‐Informed Neural Network for High‐Accuracy Multiaxial‐Fatigue Life Prediction of Aluminum Alloys With Mechanical Interpretation

ABSTRACT Accurate prediction of multiaxial fatigue life is crucial for structural integrity. This study proposes a physics‐informed neural network for multiaxial fatigue life prediction of aluminum alloys, addressing the limitations of purely data‐driven methods in capturing loading path characteristics, while ensuring physical consistency. The model integrates a parallel CNN‐LSTM architecture to extract spatial–temporal features from loading paths, while a multi‐head attention mechanism fuses these with material properties. The physical laws (higher tensile and yield strengths enhance fatigue life) are embedded via loss function. Compared with other purely data‐driven models, the proposed model achieves superior generalization ( R 2 = 0.873, with 97.3% of predictions within 3 times error band on the life prediction) and significant reduction in model parameters. Furthermore, SHAP and Grad‐CAM provide interpretability by quantifying feature contributions and visualizing critical path regions. Analysis reveals peak shear stresses and rapid stress change rates dominate predictions, and tensile strength is the most critical factor among material properties.