Optimization of turning process parameters of SS-321 using taguchi based grey relational analysis

Abstract

Tool wear, high surface roughness & MRR (Material Removal Rate) are the major problems associated with turning of SS-321. The turning parameters such as feed rate, depth of cut and cutting speed have the major effects on surface roughness, MRR, and cutting forces. An increase in magnitude of cutting forces leads to increase in the power requirements and simultaneously it decreases efficiency, which is the great concern in current scenario. In this turning work on SS-321, Taguchi based L27 orthogonal array was designed and experiments were performed accordingly. The data analysis was done by Taguchi, ANOVA and Grey relational analysis. The optimized results and improvements by using this method were also validated by a verification test. Results revealed that turning process with parameters such that feed rate, depth of cut and cutting speed at 0.3 mm/rev, 0.3 mm and 120 m/min respectively yields the optimum multi-performance characteristics. However, it was determined by ANOVA (Analysis of Variances) that depth of cut is a process parameter that affect most the multi-function features with percentage contribution of 64.86% followed by feed (16.84%) and speed (10.20%). Under these circumstances, the optimum parameters set seems to favorably affect the average values of all the three response parameters (surface roughness, cutting forces, MRR) so that the average values of output characteristics arrive at optimum level.