This paper presents a systematic experimental investigation into the optimisation of CNC turning process parameters for AISI 4340 alloy steel using the Taguchi L9 orthogonal array design, Analysis of Variance (ANOVA), and Response Surface Methodology (RSM). Three cutting conditions — dry, flood coolant, and Minimum Quantity Lubrication (MQL) were evaluated at three levels of cutting speed (60, 90, 120 m/min), feed rate (0.10, 0.15, 0.20 mm/rev), and depth of cut (0.5, 1.0, 1.5 mm) using a coated carbide insert. Response variables measured include surface roughness (Ra), main cutting force (Fc), and flank tool wear (VB). Signal-to-Noise (S/N) ratio analysis identified the optimal parameter combination as cutting speed 120 m/min, feed rate 0.10 mm/rev, depth of cut 0.5 mm, and MQL lubrication. ANOVA revealed cutting speed as the most significant factor with 44.3 percent contribution, followed by feed rate (29.8 percent) and cutting fluid type (15.6 percent). RSM second-order models predicted all three responses with R² greater than 0.97. MQL application reduced surface roughness by 58.1 percent and tool wear by 53.1 percent compared to dry cutting. Confirmation experiments validated the predicted optimal values with errors below 3.5 percent.
Bamankar et al. (Mon,) studied this question.