Smart Machine Learning Assisted Surface Optimization in Ultra-Precision Machining for Difficult-to-Cut Alloys

Surface integrity critically governs the performance, durability, and reliability of high-value components machined from difficult-to-cut alloys. This work presents an intelligent, data-driven framework for predicting and optimizing ultra-precision machining outcomes—spanning both surface topography and subsurface states—using machine learning. Target variables include arithmetic roughness Ra, peak-to-valley height Rz, residual stress \(\sigma_{rs}\) , and subsurface microhardness change ΔHV. A physics-aware data pipeline integrates an orthogonal experimental plan with rigorously curated literature measurements, harmonizing units and metrology, encoding categorical factors with low-leakage schemes, and applying robust statistical and mechanistic filters to remove artifacts. Features are standardized to stabilize optimization, with response transformations used where beneficial but reported in native units for interpretability. Models are trained with stratified splits and strict leakage control and coupled with uncertainty quantification and multi-objective optimization to navigate trade-offs among roughness, stress, and microhardness. Validation through machining trials on Ti-6Al-4 V and Inconel 718 demonstrates predictive accuracy, computational efficiency, and practical utility, yielding superior control over surface integrity under unseen conditions. The proposed approach offers an adaptive, scalable pathway for surface quality enhancement in advanced manufacturing, enabling rapid, physics-consistent decision-making for aerospace, biomedical, and precision tooling applications.