EXPERIMENTAL–NUMERICAL ANALYSIS AND Ga OPTIMIZATION OF SURFACE ROUGHNESS IN POINT GRINDING OF Ti–6Al–4V USING ELECTROPLATED CBN WHEELS

This study presents an integrated experimental–numerical investigation of surface roughness formation in point grinding of Ti–6Al–4V using a single-layer electroplated CBN wheel. A full-factorial dataset covering variations in feed rate F, spindle speed S, and cutting width [Formula: see text] was used to construct nonlinear power-law regression models for [Formula: see text], [Formula: see text], and MRR. The models exhibited high predictive accuracy with [Formula: see text] for [Formula: see text] and [Formula: see text] for [Formula: see text], and statistical analysis confirmed that [Formula: see text] is the dominant parameter, contributing over 70% of the total variance in [Formula: see text] and more than 80% in [Formula: see text]. Experimental results showed that increasing [Formula: see text] from 0.005 to 0.020[Formula: see text]mm increased [Formula: see text] by 169% and Rz by 330%, whereas variations in feed rate and spindle speed produced comparatively smaller changes. A geometric simulation of electroplated grain protrusion captured the same monotonic trends and provided additional insight into the role of grain–workpiece engagement. A multi-objective optimization combining the regression models with a genetic algorithm was performed to simultaneously minimize [Formula: see text] and [Formula: see text] while maximizing MRR. The resulting Pareto front revealed a balanced operating point at [Formula: see text] [Formula: see text]rpm, [Formula: see text] [Formula: see text]mm/min, and [Formula: see text] [Formula: see text]mm, yielding [Formula: see text]m, [Formula: see text]m, and MRR =48[Formula: see text]mm 3 /min. The findings demonstrate the strong nonlinear influence of cutting width on surface generation and provide quantitative guidance for parameter selection in precision point grinding of titanium alloys.