Sustainable assessment of high-temperature solid particle erosion behaviour of Ti–6Al–5Zr–0.5Mo–0.2Si via experimental and data-driven approaches

High-temperature solid particle erosion (HTSPE) limits the operational life of titanium alloys used in aerospace and power generation systems. This study develops a machine learning–driven predictive framework for the Ti–6Al–5Zr–0.5Mo–0.2Si alloy to estimate and optimize its erosion rate under high-velocity particle impact. Experimental datasets were generated by varying particle velocity, impingement angle, and specimen surface temperature. Three ensemble learning models: Random Forest, Gradient Boosting, and XGBoost were trained to capture nonlinear interactions among these parameters. The Gradient Boosting model achieved the best performance with an R 2 of 0.9425 and an RMSE of 0.00012. Model predictions identified an optimal condition corresponding to a velocity of ~ 33 m/s, surface temperature of ~ 204 °C, and impact angle of 70°, yielding a minimum material removal rate of 0.000530 mg/g. The results demonstrate that data-driven modelling can effectively capture erosion trends within the investigated parameter range and provide useful predictive insights for identifying erosion-resistant operating conditions. This framework bridges data analytics and physical metallurgy, enabling predictive optimisation of titanium alloys subjected to combined thermal and mechanical loading.