Issue |
Manufacturing Rev.
Volume 7, 2020
|
|
---|---|---|
Article Number | 16 | |
Number of page(s) | 8 | |
DOI | https://doi.org/10.1051/mfreview/2020013 | |
Published online | 05 May 2020 |
Research Article
Multi objective optimization of wear behaviour of Aluminum MMCs using Grey-Taguchi method
Mechanical Engineering Department, University College of Engineering, Anna University, Ramanathapuram 623 513, Tamilnadu, India
* e-mail: vpmuthu2001@yahoo.com
Received:
15
December
2019
Accepted:
18
March
2020
In recent years, metal matrix composite (MMCs) have been receiving worldwide attention on account of their superior strength-to-weight ratio and stiffness. Among the several classes of composite materials, Aluminium matrix ceramic reinforcement composites have attracted increasing attention due to their unique properties such as better specific strength, specific stiffness, wear resistance, excellent corrosion resistance, high elastic modulus and light weight. The aim of the present investigation is to optimize the dry sliding wear parameters of Aluminum LM25 matrix reinforced with silicon carbide (SiC) (5 wt.%) and Copper (Cu) (3 wt.%) using Taguchi based grey relational analysis. In this work, the composite is prepared using stir casting method. The specimens are prepared according to ASTM standard. Using pin-on-disc apparatus, wear tests are conducted as per Taguchi's L9 orthogonal array and optimum wear parameters are identified with an objective to minimise the wear rate and coefficient of friction based on the grey relational grade. The effect of parameters on the wear rate and coefficient of friction was determined using Analysis of variance (ANOVA). Finally, the experimental results were verified using confirmation tests and the SEM analysis was carried out to study the wear mechanism.
Key words: Aluminium–metal matrix composite / optimization / grey relational analysis / ANOVA
© P. Muthu, Published by EDP Sciences 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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