Multi response optimization of process parameters using grey relational analysis for milling of hardened Custom 465 steel

Abstract

Abstract This study focused on optimizing the process parameters during end milling operation of hardened Custom 465 steel with multi-response criteria based on orthogonal Taguchi matrix with grey relational analysis. Nine experimental tests based on L9 orthogonal network of the Taguchi method have been carried out using Titanium aluminium nitride (TiAlN) coated carbide inserts under a dry environment. Cutting speed, feed rate and depth of cut are optimized by taking various multiple performance characteristics such as cutting force, temperature, surface roughness and material removal rate. Grey relational analysis is a method for analyzing the relationship between sequences using less data with multiple factors and is considered helpful to statistical regression analysis. Based on the grey system theory, the grey relational analysis can be used to solve complicated interdependence of parameters among multiple performance characteristics effectively. A grey relational grade (GRG) is determined from the grey analysis. Optimum levels of parameters have been identified based on the values of grey relational grade to solve the end milling process with multiple performance characteristics. In addition, analysis of variance (ANOVA) is also applied to identify the most significant factor influencing the machinability. Finally, comparisons were made between the experimental results and the predicted model developed. Experimental results have shown that the machining performance in the milling process can be improved effectively with this approach.