Multi-objective optimization of surface roughness and cutting forces in high-speed turning of Inconel 718 using Taguchi grey relational analysis (TGRA)

In this paper, a new effective approach, Taguchi grey relational analysis has been applied to experimental results in order to optimize the high-speed turning of Inconel 718 with consideration to multiple performance measures. The approach combines the orthogonal array design of experiments with grey relational analysis. Grey relational theory is adopted to determine the best process parameters that give lower magnitude of cutting forces as well as surface roughness. The response table and the grey relational grade graph for each level of the machining parameters have been established. The parameters: cutting speed, 475?m/min; feed rate, 0.10?mm/rev; depth of cut, 0.50?mm; and CW2 edge geometry have highest grey relational grade and therefore are the optimum parameter values producing better turning performance in terms of cutting forces and surface roughness. Depth of cut shows statistical significance on overall turning performance at 95% confidence interval.