Hybrid combination of Taguchi-GRA-PCA for optimization of wear behavior in AA6063/SiCp matrix composite

Abstract

The present work examined is centered on creation of aluminum alloy AA6063/SiCp metal matrix composites by liquid metallurgy stir casting route and optimization of wear properties by utilizing Taguchi-based GRA integrated with PCA approach. The AMCs are created in three diverse wt. % (3.5 wt %, 7 wt % and 10.5 wt %) of SiC reinforcement particles. The exploratory runs to examine the wear performance are executed as per L9 Taguchi plan to acquire the wear information in a controlled way. The wear loss data in terms of height loss are acquired using pin-on-disc tribometer attached with LVDT arrangement. Impact of three control factors, load (N), sliding distance (m) and wt. % of SiC (%) on the performance characteristics such as wear rate, frictional force and specific wear rate in unlubricated dry sliding conditions is inspected to obtain the optimum level of the process parameters. Analysis of variance (ANOVA) test was performed to analyze the impact of three control factors on wear rate, frictional force and specific wear rate. Experimental analysis revealed that the wear behavior is enhanced under optimum experimental states. Optical microscopic examinations of the worn out samples is also conducted to describe the wear mechanism of the matrix composites.

Keywords

• Wear
• AA6063
• AMC: Aluminum matrix composite
• Taguchi
• GRA: Grey relational analysis
• PCA: Principal component analysis