Analysis of Multi Objective Response Variables for the Milling Characteristics of AI-Sic Particulate Composites using Taguchi based Grey Relational Analysis

In this paper, the Taguchi method and Grey correlation analysis are combined to optimize the response parameters of the end milling characteristics of stir casting LM25AI-SiC particle composite material. In order to obtain high-quality output and obtain the best combination of response parameters, experiments were conducted based on the experimental method designed by Taguchi. Using the input parameters % Vol of SiC, cutter speed, feed rate, depth of cut and machining time, the L27 orthogonal array was developed for machining characteristics such as flank wear, specific energy and surface roughness. Use multi-criteria optimization techniques (Grey Relational Analysis (GRA)) to analyze response data to optimize face milling characteristics. Multiple response performance standards are converted into a single response performance standard, which is the Grey Relational Grade (GRG). The grey relational grade of the response parameters of the main effect plot and the analysis of variance (ANOVA) is analyzed to obtain the optimal parameters.