Power Consumption in Machining AISI 316L Stainless Steel Using Taguchi-ANOVA and Grey Relational Analysis

This paper presents a systematic experimental investigation into the optimisation of CNC turning parameters for AISI 316L austenitic stainless steel using the Taguchi L9 orthogonal array, Analysis of Variance (ANOVA), and Grey Relational Analysis (GRA). Three cutting conditions — dry, wet flood, and Minimum Quantity Lubrication (MQL) — were evaluated at three levels of cutting speed (80, 120, 160 m/min), feed rate (0.08, 0.12, 0.16 mm/rev), and depth of cut (0.3, 0.6, 0.9 mm) using a coated carbide insert (TiCN/Al₂O₃/TiN triple-layer CVD coating). Response variables include surface roughness (Ra), material removal rate (MRR), and spindle power consumption (Pc). Signal-to-Noise ratio analysis identified optimal parameters as cutting speed 160 m/min, feed rate 0.08 mm/rev, depth of cut 0.6 mm, with MQL lubrication. ANOVA revealed feed rate as the most influential factor for surface roughness with 42.3 percent contribution, followed by cutting speed (31.6 percent) and cutting fluid type (17.2 percent). GRA simultaneously optimised all three conflicting responses (Grey Relational Grade = 0.831 at optimal). MQL reduced surface roughness by 46.8 percent and power consumption by 22.4 percent compared to dry turning. Confirmation experiments validated predicted values within 4.3 percent error.