Dry sliding wear characterization of TIG embedded composite coatings using Taguchi based grey relational analysis approach

Many tribological coatings working under high temperature environment require combination of low wear rates and friction coefficients, which presents a significant challenge to the tribology community. This study presents an approach based on the Taguchi design with grey relational analysis (GRA) fo...

Full description

Saved in:
Bibliographic Details
Main Authors: Bello, K. A., Maleque, Md. Abdul, Adebisi, A. A.
Format: Conference or Workshop Item
Language:English
Published: 2016
Subjects:
Online Access:http://irep.iium.edu.my/51881/11/51881.pdf
http://irep.iium.edu.my/51881/
http://mjiit.utm.my/research-tribology/files/2016/08/MJTS016-proceedings-book.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Islam Antarabangsa Malaysia
Language: English
Description
Summary:Many tribological coatings working under high temperature environment require combination of low wear rates and friction coefficients, which presents a significant challenge to the tribology community. This study presents an approach based on the Taguchi design with grey relational analysis (GRA) for optimizing the tungsten inert gas (TIG) torch process parameters with consideration of multiple performance characteristics in order to minimize the wear and friction characteristics of TIG embedded composite coatings simultaneously. Taguchi based grey relational analysis with L-16 orthogonal array is employed to analyze the effects of the welding current, welding speed, welding voltage and argon gas flow rate (AFR) on the multiple response characteristics. A grey relational grade from the GRA is used as the performance index to determine the optimal process parameters for the coating system. The experimental results indicate that welding current parameter has the most significant contribution in controlling the wear and friction characteristics. Finally, confirmatory experiment was conducted to ensure the validity of the predicted GRA optimal parameter model.