Enhancing friction and wear performance in hybrid aluminum composites through grey relational analysis

Abstract

Hybrid aluminum metal matrix composites (AMMCs) include qualities including being lightweight, very effective, and highly resistant to wear, and corrosion. The process used in this study to maximize friction and minimize wear of hybrid AMMCs involves applying grey relational analysis (GRA) to optimize the process features. Stir-casting was used to create hybrid AMMCs of the Al-6061 alloy reinforced with graphene (Gr) and silicon carbide (SiC). The experiment used Taguchi's L27 design, and three process parameters—normal load, sliding distance, and sliding speed—were combined with GRA to produce the maximum coefficient of friction and lowest wear rate. Al-6Gr-6SiC obtained the maximum hardness of 128 VHN and 253 MPa as tensile strength, further coefficient of friction, and wear were analyzed for the same composite., respectively. The ideal mixture of graphene (6 weight percent) and silicon carbide (6 weight percent) as reinforcement in Al-6061 alloy obtained the optimum value of frictional coefficient (0.29766) and wear (0.002588 mm3/m), respectively.