Title: Multi-objective optimisation of wear process parameters of 413/fly ash composites using grey relational analysis

Authors: J. Udaya Prakash; S. Jebarose Juliyana; R. Rajesh; A. Divya Sadhana

Addresses: Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India ' Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India ' Department of Mechanical Engineering, Rohini College of Engineering and Technology, Kanyakumari, Tamil Nadu, India ' Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India

Abstract: Composite materials are made by combining the qualities of two or more distinct materials of comparable attributes. Metal matrix composites have emerged as remarkable materials due to their superior characteristics when compared to other matrix composites. The most promising lightweight materials are composed of aluminium alloys, which are utilised in the marine, aerospace, and automotive industries, but their use is limited due to their average strength and moderate wear resistance. In terms of wear resistance, AMCs beat their monolithic counterparts that are unreinforced. Stir casting was used to produce aluminium matrix composites made of 413 aluminium alloys with particulate fly ash reinforcements weighing 3%, 6%, and 9%. Wear tests were conducted in pin-on-disc wear tester following ASTM Standard G99-05 guidelines. The goal is to explore the effects of sliding speed, load, sliding distance, and reinforcing weight percentage on coefficient of friction and specific wear rate using ANOVA and grey relational analysis. Sliding distance (18.48%), load (17.39%) are the parameters which have extreme significance on composites' GRG followed by sliding speed (10.33%) and reinforcement (4.35%). By using grey relational analysis, wear behaviour can be predicted successfully.

Keywords: composites; wear; grey relational analysis; GRA; design of experiments; DOEs; ANOVA.

DOI: 10.1504/IJENM.2023.134578

International Journal of Enterprise Network Management, 2023 Vol.14 No.4, pp.334 - 347

Received: 13 Nov 2021
Accepted: 27 Jan 2022
Published online: 30 Oct 2023 *

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