Multi-objective optimization of wear parameters of hybrid composites (LM6/B4C/fly ash) using grey relational analysis

Aluminium based composites with hybrid reinforcement hold significant potential to replace Al-alloys in a variety of automotive sectors where cheap cost, a significant ratio of strength to weight, and better wear resistance are required.

Stir casting was utilized to make aluminium matrix composites (AMCs) with 3%, 6%, and 9% of B4C/Fly ash particles. The wear was examined with various Sliding Speed, S (1 m/s, 1.5 m/s and 2 m/s), Sliding Distance, D (500 m, 1000 m and 1500 m), applied load, L (15 N, 30N and 45 N) and reinforcement %, R (3, 6 and 9%). Grey Relational Analysis was used to optimise the wear variables. Taguchi's L27 Orthogonal array (OA) was selected for this statistical approach in order to analyse responses like Specific wear rate (SWR) and Coefficient of Friction (CoF). Furthermore, analysis of variance (ANOVA) was utilized to investigate the influence of input parameters on wear behavior by choosing "smaller is better" feature.

Based on this study, the optimal values of S - 1.5 m/s, D - 500 m, L - 30 N, and R% - 9 wt% Hybrid (4.5% Fly ash and 4.5% B4C) are found to yield the lowest SWR and CoF. Wear rate of composite decreased with an increase in reinforcement particles. Increase in hardness was also the reason for decrease in wear rate. The responses have a narrow margin of error, according to confirmation studies. There exists a good agreement between them.

The research on LM6/B4C/fly ash composite fabrication using Grey Relational Analysis (GRA) has significantly contributed to the development of high-performance materials for wear-related applications. Through the optimization of wear parameters, GRA allows for the improvement of wear resistance, strength, and sustainability.