DFA & GRA based Multi Objective Optimization During Hot Machining of AISI D3 Tool Steel using TNMG Insert

In this paper, an experimental investigation is carried out to optimize performance characteristics (surface roughness and material removal rate) simultaneously in hot machining operation using Grey relational analysis (GRA) and desirability function analysis (DFA). The experiments are conducted on CNC lathe with AISI D3 tool steel as work material and PVD coated TNMG-HM tool is inserted as a tool material which is based on Taguchi L18 orthogonal array design. The workpiece is heated using butane torch flame which is cost effective method compared to other heating techniques used in hot machining process. The influence of spindle speed, feed and depth of the cut were analyzed on the performance of surface roughness and material removal rate. The optimal turning parameters are determined by composite desirability index and grey relational grade. Analysis of variance (ANOVA) is used to determine the influence of parameters which significantly affect the responses simultaneously. From the study, it is concluded that the machining performance is significantly improved.