Interpretable Machine Learning for Mechanical Property Prediction of 5Cr-0.5Mo Steel: SHAP Explainability, Multi-Model Comparison, and Uncertainty Quantification

Abstract

5Cr-0.5Mo ferritic steels are widely used in high-temperature power-plant components. Although artificial neural network (ANN) models have shown good performance in predicting tensile properties, they provide limited insight into predictions and generally do not quantify the uncertainty. In this study, three tree-based machine learning models—Random Forest (RF), XGBoost (XGB), and Gradient Boosting (GB)—were developed using 36 unique alloy grade–temperature observations from a validated NIMS 5Cr-0.5Mo tensile dataset. The model performance was evaluated using leave-one-grade-out (LOGO) cross-validation, with pooled out-of-fold (OOF) predictions used to assess the overall performance. SHapley Additive exPlanations (SHAP) were used to examine feature contributions, whereas Gaussian Process Regression (GPR) was evaluated as a proof-of-concept for uncertainty quantification of yield strength (YS). GB showed the strongest performance for ultimate tensile strength (UTS) and reduction in area (RA), achieving pooled OOF R2 values of 0.9698 and 0.9570, respectively. RF achieved corresponding R2 values of 0.9406 and 0.9488, respectively. SHAP identified the test temperature as the most influential feature across all four properties, whereas the Cr content and austenite grain size contributed significantly to the strength predictions. For YS, the GPR achieved complete empirical coverage of the 95% predictive intervals, although the relatively large mean interval width indicated conservative uncertainty estimates. Given the limited dataset and feature correlations, the SHAP results should be regarded as exploratory, rather than mechanistic. Overall, this study demonstrates the potential of interpretable, uncertainty-aware ML for small alloy datasets, while emphasizing the need for larger, compositionally diverse datasets and independent validation.