Wear Performance Analysis of Aluminum Matrix Composites and Optimization of Process Parameters Using Statistical Techniques

Amir Hussain Idrisi, Abdel Hamid Ismail Mourad

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

This paper presents the wear behavior of gears manufactured using Al matrix composites (AMCs) reinforced with microparticles (with sizes of 40 µm and contents of 5 and 10 wt pct) and nanoparticles (with sizes of < 100 nm and contents of 1 and 2 wt pct) of SiC, fabricated using stir casting. Specially designed test rig was manufactured for determining the wear performance of these gears and investigated under different applied loads and experiment times. The composite prepared using 2 pct SiC nanoparticle reinforcements was superior to other compositions tested in terms of tribological applications. The effectiveness of nanoparticles compared to that of microparticles was analyzed statistically. Taguchi’s method was used for optimizing the wear parameters. Furthermore, the influence of the experiment time, applied load, and SiC content on the wear was investigated and a regression equation was developed for AMCs reinforced with micro- and nanoparticles. The “smaller is better” characteristic was selected as the objective of this model to analyze the wear resistance. The experiment time and applied load had the most significant effect, followed by the SiC content, in the case of microparticles, whereas for nanoparticles, the applied load was the least significant factor when compared to experiment time and SiC content.

Original languageEnglish
Pages (from-to)5395-5409
Number of pages15
JournalMetallurgical and Materials Transactions A: Physical Metallurgy and Materials Science
Volume50
Issue number11
DOIs
Publication statusPublished - Nov 1 2019

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Mechanics of Materials
  • Metals and Alloys

Fingerprint

Dive into the research topics of 'Wear Performance Analysis of Aluminum Matrix Composites and Optimization of Process Parameters Using Statistical Techniques'. Together they form a unique fingerprint.

Cite this