Fatigue Life Prediction Under Multiaxial Loading Using Machine Learning and Dependency‐Aware Sensitivity Analysis

ABSTRACT Accurate prediction of fatigue life under multiaxial loading conditions remains challenging due to complex stress–strain interactions. In this study, we integrate machine‐learning (ML) regression with variance‐based sensitivity analysis (SA) to predict multiaxial fatigue life in CuZn37 brass and to identify the dominant mechanical factors influencing fatigue damage. Several surrogate models were evaluated, with the Gaussian Process model achieving the highest accuracy ( R 2 = 0.991) while maintaining robust generalization across loading paths. Gradient Boosting, Random Forest, and Penalized Spline Regression models also demonstrated strong predictive capabilities. Importantly, the SA explicitly accounted for statistical dependencies among input parameters, revealing that normal strain–stress interactions account for over 40% of the total variance in fatigue life. In contrast, shear‐related parameters exhibited secondary, compensatory effects. These results highlight the importance of capturing parameter dependencies in fatigue modeling and demonstrate that ML‐based surrogates can help provide both high‐fidelity predictions and physical insights under complex multiaxial loading conditions.