Interpretable ensemble machine learning framework to predict wear rate of modified ZA-27 alloy

Abstract

This study investigates the impact of adding manganese (Mn) to ZA-27 alloy on microstructure and tribological properties. The Mn content varied from 0.2% to 1%. Volumetric wear rates were measured under different operating conditions. XRD and SEM were employed for phase identification and surface analysis. Ensemble Machine Learning (EML) regression models, including bagging, decision trees, random forest, ada boost, gradient boosting, and extreme gradient boost, were used to predict wear properties. Results indicate that the lowest wear rate occurred at 0.5% Mn content. Different wear mechanisms were observed for varying Mn contents. Among the EML models, extreme gradient boost showed superior performance with R2 values of 0.999 and 0.985 in training and testing, respectively.