Optimization in Dry Sliding Wear Test of Al 2219 – SiCp Composite Using Taguchi Based Grey Relational Analysis

uminium metal matrix composites (AMMC) reinforced with SiC particulate are one of the familiar materials which are widely used in automobile, mining, etc. due to their superior properties such as high stiffness, low density, good corrosion resistance etc. this paper presents an effective approach for the optimization of process parameters during dry sliding wear test with multiple response characteristics based on Taguchis method with grey relational analysis. Taguchis L9 orthogonal array has been used for experimentation. The process parameters such as weight fraction of reinforcement, precipitation temperature, load and disc speeds are optimized with multiple responses such as wear and coefficient of friction. Response tables, grey relational grade and ANOVA are used for the analysis and conclude that all the process parameters influence the wear performance of the composite at significant level but the level of influence differs significantly with respect to the size of the reinforcement particle in the composite. Precipitation temperature and disc speed have less influence for lower size particle whereas they become significant for higher size particle.