Development and analysis of machine learning-based hybrid optimization framework for cutting temperature-based energy consumption estimation in turning of titanium alloys

This study develops a machine learning-based hybrid optimization framework to predict energy consumption from cutting temperature in turning of Ti6Al4V alloy under different cooling environments. Due to the poor thermal conductivity of titanium alloys, heat accumulation in the cutting zone increases energy consumption and reduces process efficiency. Cutting temperatures were recorded in real time using a thermal camera, and energy consumption was measured with a power analyzer. Statistical features extracted from temperature data served as inputs for energy prediction. A hybrid optimization framework was developed by integrating feature selection methods, machine learning algorithms, and hyperparameter optimizers. Among six algorithms evaluated, CatBoost combined with SHAP-based feature selection and Optuna hyperparameter optimization achieved the best performance. The proposed hybrid model (CAT+SHAP+Optuna) provides a 91% improvement in Mean Squared Error compared to the benchmark model, demonstrating high accuracy in energy consumption estimation. This approach offers significant potential for enhancing energy efficiency and promoting sustainable manufacturing in the machining of difficult-to-cut materials. © 2026 Elsevier Ltd