Multi-response optimization and effect of process parameters during machining of inconel X-750 using grey relational analysis and analysis of variance

In machining, there is a significant effect of process parameters on the machining performance. It is a known fact that increasing the cutting speed results into generation of additional temperature at cutting zone and further cause the work hardening effect in workpiece material. Consequently, this phenomena results into a rapid tool wear and high cutting forces which causes to deteriorate the surface finish. The process parameters has independent as well as combined effects on surface quality, cutting forces and electrical energy consumption. Besides, the increase in feed increases the area of tool travel which causes to generate more friction and heat therefore, a rapid tool wear takes place and again results into a deteriorated surface finish. The tool geometry, tool coating and lubrication environment plays a vital role in producing a surface quality. Therefore, in order to analyze and compare the effect, the tools with negative and positive rake angle were employed for turning Inconel X-750 under dry and MQL-based sustainable composite hybrid nanofluid lubrication environment. In this way four parameters viz. cutting speed, feed, type of tool geometry and machining environment were chosen as process parameters. The response variables were influenced due to interaction between parameters as well as their independent effects. In current study, with two factors three levels and two factors two levels, the Taguchi mixed level design of experiment was employed for the analysis. Further, thirty six number of experiments were performed with four conditions. The effect of process parameters on the response variables were analysed by using main effect plot and analysis of variance (ANOVA) by employing a statistical software "Minitab 21". Besides, a study was carried out to identify the optimal cutting condition among all experiments by employing the GRA method. The experiments were ranked in the increasing order of their grey relational grade (GRG), which is an average of the grey relational coefficient. The experiment, for which the highest GRG obtained, was considered as an optimal cutting condition. Accordingly, the optimum cutting condition was found to be at the highest level of cutting speed 140 m/min and feed 0.125 mm/rev, while turning with tool having positive rake angle under dry environment.