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 0.020Formula: see textmm 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 textrpm, Formula: see text Formula: see textmm/min, and Formula: see text Formula: see textmm, yielding Formula: see textm, Formula: see textm, and MRR =48Formula: see textmm 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.
Truong et al. (Thu,) studied this question.