Multi performance characteristics optimization of end milling parameters on surface quality and Micro-hardness of SS-304

Abstract

Taguchi and Grey Relational Analysis approaches are used in the present study for optimizing the end milling process parameters of stainless steel 304 (SS-304). The four key input machining parameters i.e., rate of feed presence/absence of coolant, speed is investigated experimentally. An orthogonal Taguchi L18 array was used to perform the trials, which consisted of 18 trials with two coolant levels, three levels for the remaining three parameters. Three performance features for improvement: surface roughness (SR), surface strain (SS), and microhardness (MH) are taken into consideration. Multi performance characteristics were improved using Taguchi with grey relational analysis (GRA), and the data was then analysed using mean response tables and variance analysis i.e., ANOVA. The optimal combination is obtained as A1B3C3D1 i.e., coolant on, feed rate 2500 mm/min, depth of cut 0.4 mm, and cutting speed 1500 rpm. Further, it is also observed that highest contributing parameter is speed with the percentage of 25.75. This approach detects several performances optimum improvements of 3.9615 percent through a confirmation test.