OPTIMIZATION OF MACHINING PARAMETERS IN TURNING TO STEEL USING GREY RELATIONAL ANALYSIS

The present study investigated the multi-response optimization of turning using nanofluids as coolants to determine the best parametric combination for surface roughness, flank wear, and material removal rate (MRR) by employing the Taguchi method and Grey relational analysis. Eighteen experimental runs were carried out using an orthogonal array of the Taguchi method within the defined experimental domain to derive and optimize the goal functions. The selected objective functions related to the turning process parameters included the volume fraction of nanoparticles (0.04%, 0.08%), cutting speed (110, 170, and 230 m/min), feed rate (0.125, 0.15, and 0.175 mm/rev), type of nanoparticles (MoS2, multi-walled carbon nanotubes (MWCNT), and SiO2), and depth of cut (0.3, 0.6, and 0.9 mm). The multi-response optimization problem was addressed using the Taguchi approach in conjunction with Grey relational analysis. The significance of the factors affecting the overall quality characteristics in the Minimum Quantity Lubrication (MQL) turning of AISI 4340 with nanofluid was quantitatively evaluated through Signal-to-Noise ratio (S/N) analysis and Analysis of Variance (ANOVA) to determine the contribution of each parameter to performance outcomes. The cutting speed was identified as the most significant parameter. Verification experiments were conducted to validate the optimal results. These findings demonstrated the effectiveness of the Taguchi technique and Grey relational analysis in continuously improving product quality in the manufacturing sector.