Multi-Objective Optimization of Dry Sliding Wear Parameters of Aluminium Matrix Composites (AA7068/TiC) using Grey Relational Analysis
Syed Altaf Hussain1, Naresh Poppathi2, Md. Alamgir3
1Syed Altaf Hussain*, Department of Mechanical Engineering, Rajeev Gandhi Memorial College of Engineering & Technology, Nandyal, India.
2Naresh Poppathi, Department of Mechanical Engineering, GATES Institute of Technology, Gooty, India.
3Md. Alamgir, Department of Mechanical Engineering, Rajeev Gandhi Memorial College of Engineering & Technology, Nandyal, India.
Manuscript received on February 12, 2022. | Revised Manuscript received on February 19, 2022. | Manuscript published on March 30, 2022. | PP: 12-18 | Volume-11, Issue-4, March 2022 | Retrieval Number: 100.1/ijitee.C97860311422 | DOI: 10.35940/ijitee.C9786.0311422
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Metal matrix composites are supplanting conventional materials due to their prevalent properties like high strength of weight ratio, high specific stiffness, high fracture toughness, high thermal stability and wear resistance etc. In this investigation, Al-TiC composites consist of TiC particles of an average size 4μm whose wt% of reinforcement varied from 2 to 10 wt% in steps of 2 wt%, composites have been prepared using the stir casting technique. Dry-sliding wear experiments have been performed on pin-on-disc apparatus according to Taguchi’s L25 in the design of experiments. The parameters considered are wt% of TiC, rotational speed (Nr), load (P) and sliding velocity (Vs). Optimum combinations of parameters have been identified based on grey relational grade (GRG) to solve the wear response of AA7068/TiC MMCs. Also, analysis of variance (ANOVA) is applied to recognize the main factors affecting the wear response. Confirmation experiments with optimum conditions show that the results were nearer to the anticipated outcomes.
Keywords: AA7068/TiC MMCs, Grey Relational Analysis, Taguchi Design, ANOVA, Optimization.
Scope of the Article: Discrete Optimization