Multi-Objective Optimization of Wear Parameters for Aluminium Matrix Composites (413/B4C) using Grey Relational Analysis

• Published in: Materials today : proceedings Vol. 5; no. 2; pp. 7207 - 7216
• Main Authors: Udaya Prakash, J., Ananth, S., Sivakumar, G., Moorthy, T.V.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2018
• Subjects: AMCs; ANOVA; Grey Relational Analysis; Taguchi Technique; Wear; AMCs; ANOVA; Taguchi Technique; Wear; Grey Relational Analysis
• ISSN: 2214-7853; 2214-7853
• DOI: 10.1016/j.matpr.2017.11.387

Particle reinforced AMCs are increasingly used in various automobile and aerospace applications since, they exhibit isotropic properties. AMCs containing 413 Aluminium alloy as the matrix material and 3%, 6% & 9% boron carbide of average particle size 63 µm as reinforcement were fabricated using stir casting. Optical micrographs of the composites reveal that the reinforcement particles were uniformly distributed in the matrix. Dry sliding wear tests were conducted using a pin on disc wear testing machine to study the effect of sliding speed, sliding distance, load and reinforcement on the output parameters specific wear rate (SWR) and coefficient of friction (COF). Taguchi based Grey relational analysis is used to optimize the multi response wear behavior. Analysis of Variance (ANOVA) is used to find the percentage contribution of the parameters and that of their interactions. The wear studies reveal that the SWR and COF of the AMCs are greatly influenced by the sliding distance and sliding speed. SEM micrographs of the worn pins were analyzed to find the wear mechanisms. The present study suggests that by using correct amount of reinforcement particles, it is possible to obtain tailor made composites for a particular application. The present work provides useful insight to industrial composite manufacturers especially for automotive industries.