Physics-based feature engineering framework for high-accuracy surface roughness prediction in Ti-6Al-4V dry machining

Abstract Purpose: Dry machining of Ti–6Al–4V presents significant challenges due to its low thermal conductivity, high strength, and strong adhesion at the tool–workpiece interface. These characteristics limit the accuracy of empirical surface-roughness models. This study aims to present and validate an AI-Optimized, Physics-Based Feature Engineering framework capable of predicting surface roughness with high fidelity. Methods: A Central Composite Design with 19 experiments was conducted by varying cutting speed, feed rate, and depth of cut. Surface roughness and cutting forces were measured, and physics-derived features such as specific cutting energy, strain rate, and shear stress were engineered. Multiple Machine Learning models, including boosting ensembles, neural networks, and support vector regression, were trained and evaluated through repeated cross-validation and benchmark comparisons. Results: The proposed Physics-Based framework demonstrated superior performance over classical methods. Specifically, the optimized XGBoost model achieved the highest predictive accuracy ( $$\varvec{R^2 = 0.98}$$ ), reducing the Mean Absolute Error (MAE) to 0.018 $$\mu$$ m compared to 0.23 $$\mu$$ m for Response Surface Methodology. The Neural Network also showed robust generalization ( $$\varvec{R^2 \approx 0.92}$$ ). The framework effectively captured complex thermomechanical behaviors characteristic of Ti–6Al–4V machining, delivering micrometric-precision predictions. Conclusion: The proposed framework provides a reliable tool for optimizing surface quality in dry machining of Ti–6Al–4V. By enabling precise prediction of roughness, it supports the selection of optimal cutting parameters for aerospace and biomedical components.