Multi Response Optimization of Surface Roughness and Tool Wear in Turning AL/SIC Particulate Metal Matrix Composites Using Taguchi Grey Relational Analysis

Metal matrix composites (MMCs) having aluminum (Al) in the matrix phase and particulates/particles of silicon carbide (SiCp) in reinforcement phase have been found in common use for making components in manufacturing industries with various operations viz., turning, milling, grinding, and drilling as well as number of non conventional machining processes. Recently the machinability study of composite machining has attracted researchers. Surface roughness (R_a) and tool flank wear (VB) are two important parameters are studied. Researchers’ employed numbers of conventional and soft computing based computational techniques for modelling and optimization machining process with are single/multiple optimization objective. In the present work, an attempt has been made to obtain optimum process parameters in turning Al/SiCp using carbide tool using Taguchi grey relational analysis. Cutting speed (v), feed rate (f) and depth of cut (d) are three machining parameters on which R_a and VB depends. In the present investigation Taguchi L₉ orthogonal array of experimental design were considered and grey relational analysis is used for obtaining optimal parameters of the multi response problem. The predicted result shows that the surface roughness and tool wear at optimum cutting conditions found as 4.08 μm and 0.72mm respectively, resulting the percentage of error as 1.03% for R_a and 5.5% for VB. The method does not require mathematical computation and can be applied easily for multi response problems.