Multi-Response Optimization of Milling Parameters of AISI D2 Steel Using Response Surface Methodology and Desirability Function

This study investigates multi-objective optimization of end-milling parameters for AISI D2 cold-worked tool steel using GC1130-coated carbide inserts under wet machining, focusing on cutting speed and feed rate per tooth values beyond manufacturer recommendations. The objective was to identify parameter settings that minimize surface roughness while maximizing cutting tool life—two performance criteria that often conflict in practice. A full-factorial design of experiments was implemented, varying the cutting speed (220–310 m/min) and feed rate (0.06–0.25 mm/tooth). Response Surface Methodology (RSM) was used to develop predictive models, and a desirability function approach (DFA) was applied to perform multi-response optimization under three weighting schemes. The statistical models showed strong reliability, with R² values of 81.09% for surface roughness and 95.02% for tool life. The optimal settings—220 m/min cutting speed and 0.25 mm/tooth feed—resulted in a tool life of 11.03 min and surface roughness of 0.587 µm. This yielded the highest desirability index (D = 0.8706) under tool-life-prioritized weighting, outperforming other cases by up to 10.69%. These findings offer a practical balance between quality and durability, especially for applications where tool wear is a limiting factor.