Abstract This study presents a machine learning (ML)-based approach to predict surface roughness, R a during dry grinding of Ti-6Al-4 V alloy using Decision Tree, Random Forest, Gradient Boosting, Extreme Gradient Boosting (XGBoost), Linear Regression, and Polynomial Regression models. Experiments were conducted with Aluminium Oxide (Al₂O₃) and Silicon Carbide (SiC) wheels at varying feed rates (0.2–0.9 mm/rev) and depths of cut (0.02–0.08 mm). Results showed that Al₂O₃ consistently produced lower R a values.Increased feed and depth of cut led to rougher surfaces. XGBoost algorithm achieved the highest prediction accuracy (R² = 0.90), effectively capturing nonlinear dependencies. Feature importance analysis identified feed rate as the most influential factor (Importance Score > 0.85), followed by depth of cut and wheel type. These findings demonstrate the potential of ML, particularly XGBoost, for optimizing grinding parameters and enhancing surface quality in Ti-6Al-4 V machining.
Reddy et al. (Mon,) studied this question.