Optimization of Multiple Performance Characteristics in Turning AI-SiCP Metal Matrix Composites Using Weighted Principal Component Analysis

This paper presents a new approach for optimizing the machining parameters on turning Metal Matrix Composite (AI-SiC-MMC). Optimization of machining process parameters was done by an analysis called Weighted Principal Component Analysis (WPCA), which is a useful tool for optimizing multi-response problems. Based on Taguchi's L₂₇ orthogonal array, turning experiments were conducted for aluminium silicon carbide particulate reinforced metal matrix composite (A356/10/SiC_p) using polycrystalline diamond (PCD) fine grade insert. The machining process parameters such as cutting speed, feed rate and depth of cut are optimized by multi response considerations namely surface roughness and specific power. The optimum combination of process parameters was studied by extracting more than one principal component and integrating into multi-response performance index (MPI). Finally, the analysis of variance (ANOVA) was used to find out the most influential parameters for multi response criteria. As a result, optimization of the complicated multiple performance characteristics can be greatly simplified through this approach.