Exploration of materials fatigue influence factors using interpretable machine learning

Abstract

Data‐driven fatigue strength predictions are gaining popularity. Nevertheless, many machine learning models lack trustworthiness due to their limited decision‐making transparency which often hinders their practical application. In this investigation, we assess the expressiveness of the model‐agnostic explainable AI method known as SHapley Additive exPlanations (SHAP) for data‐driven fatigue strength prediction. Our study demonstrates that the SHAP feature sensitivity analysis underpins known physical relations from materials processing and fatigue theory. This even applies in view of the high‐dimensional, cross‐correlated fatigue feature space and despite data heterogeneity (different steels, component designs, and loads). For instance, SHAP indicates a fatigue strength increase with higher solid solution‐strengthening element concentrations, such as chromium and nickel. SHAP identifies correlations rather than causality. Thus, data science and domain knowledge should be closely linked during the SHAP assessment. If this is satisfied, plausible causal relations can be inferred, and spurious ones arising from confounding variables can be discarded.