Optimization of Surface Roughness in the CNC Milling of AISI D2 Tool Steel Using the Taguchi Method

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In this study, the milling behavior of AISI D2 cold work tool steel was experimentally investigated on a CNC vertical machining center. The aim was to optimize surface roughness through the Taguchi L18 orthogonal array design, considering the effects of cooling method, spindle speed, feed rate, and depth of cut. A total of 18 experiments were conducted under both wet and dry machining conditions. The results revealed that the use of coolant generally led to higher roughness values compared to dry machining. Statistical analysis using signal-to-noise (S/N) ratios and analysis of variance (ANOVA) showed that coolant application had the highest impact on surface roughness with a contribution rate of 47.04%, followed by spindle speed (27.99%), feed rate (8.05%), and depth of cut (4.66%). Regression models were developed to predict surface roughness, achieving a high correlation with experimental values (R² = 0.90). Confirmation experiments verified the optimization, with the optimal parameter combination (dry machining, 4000 rpm, 750 mm/min feed rate, and 0.10 mm depth of cut) reducing the average surface roughness from 1.08 μm to 0.37 μm, corresponding to a 65.7% improvement. Additionally, quality loss analysis indicated that losses were minimized by nearly 88.3% under optimized conditions. The results indicate that surface roughness values obtained under dry machining conditions were lower than those achieved using coolant in the milling of AISI D2 tool steel.