Taguchi based grey relational analysis for multi-performance optimization of slab milling process parameters

Abstract

Taguchi based grey relational analysis is used to optimize multi-performance characteristic of CNC slab milling process parameters for machining ASTM A572 grade 50 high strength low alloy (HSLA) steel plates. Four process parameters i.e. cutting fluid, cutting speed, feed and depth of cut each at three levels except the cutting fluid at two levels were considered. Experiments were conducted as per L18 orthogonal array of Taguchi method. The surface roughness (SR) and material removal rate (MRR) were considered as the multi-performance characteristics. Grey relational analysis was used to determine grey relational grade which indicated the multi-performance characteristics of the process. Subsequently, Taguchi response table method and ANOVA were used for data analysis. Confirmation test was conducted to ensure validity of the test result. Results revealed that combination of factors and their levels A1B2C3D3 i.e. the machining done in the absence of cutting fluid, at a cutting speed of 2300 r.p.m. with a feed of 300 mm/min. and depth of cut of 0.23 mm yielded the optimum multi-performance characteristics of the slab milling process. Further, the results of ANOVA indicated that all four machining significantly affected the multi-performance with maximum contribution from feed (30.68%) followed by depth of cut (25.93%), cutting fluid (20.13%) and cutting speed (10.58%). It is also observed that the multi performance characteristics of slab milling process can be improved effectively through this approach.