Machine Learning-Assisted Sensitivity Analysis for Stochastic Fatigue Life Modeling of Metals

Predicting fatigue life with precision requires more than isolated evaluations of mechanical properties; it requires an integrated approach that captures the interdependencies between various parameters, including elastic modulus, tensile strength, yield strength, and strain-hardening exponent. Neglecting these correlations in sensitivity analyses can compromise prediction accuracy and physical interpretability. In this study, we introduce a dependency-aware sensitivity analysis framework, assisted by machine learning-based surrogate models, to evaluate the contributions of these mechanical properties to fatigue life variability. Tensile strength emerged as the most influential parameter, with significant second-order interactions, particularly between tensile and yield strength, highlighting the central role of coupled effects in fatigue mechanisms. By addressing these interdependencies, the proposed approach improves the reliability of fatigue life predictions and offers a solid foundation for the optimization of metallic components subjected to cyclic stresses.