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 sens...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.展开更多
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 elast...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.展开更多
文摘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.
基金We acknowledge Ho Chi Minh City University of Technology(HCMUT),VNU-HCM for supporting this study.
文摘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.
