Multi-objective optimization of wear parameters of hybrid composites (LM6/B 4 C/fly ash) using grey relational analysis
Charles Sarala Rubi, Jayavelu Udaya Prakash, Sunder Jebarose Juliyana, Sachin Salunkhe, Robert Cep, Emad Abouel Nasr, Ranvir Singh Panwar, Ranvir Singh Panwar, Ranvir Singh Panwar
TL;DR
This paper uses grey relational analysis to optimize wear parameters in hybrid aluminum composites for better performance in automotive applications.
Contribution
The study introduces grey relational analysis for optimizing wear parameters in LM6/B4C/fly ash hybrid composites.
Findings
Optimal wear parameters include sliding speed of 1.5 m/s, sliding distance of 500 m, load of 30 N, and 9% reinforcement.
Wear rate decreases with increased reinforcement particles and hardness.
Confirmation studies show minimal error margins and good agreement with results.
Abstract
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…
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Taxonomy
TopicsAluminum Alloys Composites Properties · Advanced Machining and Optimization Techniques · Surface Treatment and Coatings