Physics‐Embedded Machine Learning for Fatigue Cumulative Damage Prediction - Gao, Zhiyuan; Jiang, Xiaomo; Guo, Yifan; Cui, Mingqing; Wang, Shengbo (Fatigue & fracture of engineering materials & structures, 2025)
ABSTRACT Fatigue damage accumulation is critical to the safety and reliability of mechanical structures, yet accurate prediction remains challenging, especially under small‐sample conditions. This study proposes an innovative physics‐embedded machine learning (ML) framework to enhance residual fatigue damage prediction by integrating the Manson–Halford (MH) physical model with data‐driven algorithms. The framework employs a dual‐regressor approach: One regressor embeds the MH model to predict the interaction coefficient, while the other is purely data driven to directly predict residual fatigue damage, with a customized loss function enforcing physical consistency between the two outputs. A compiled dataset of 14 materials demonstrates the framework's superiority over six baseline ML models. Notably, the model retains high accuracy even with 30% fewer training data, showcasing its robustness in data‐scarce scenarios. By harmonizing physical mechanisms with ML, this work provides a generalizable and efficient strategy for fatigue damage prediction. Summary A novel physics‐embedded ML framework for predicting fatigue damage was proposed. A customized loss function was applied to embed physical mechanism. The advantage of the model in small sample prediction was validated.