Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (10): 1525-1534.

Wear Resistance Prediction Model for Magnesium Metal Composite by Response Surface Methodology using Central Composite Design

Prem Sagara,* and Amit Handab

1. aI.K. Gujral Punjab Technical University, Kapurthala, India;
bDepartment of Mechanical Engineering, I.K. Gujral Punjab Technical University, Kapurthala, India
• Submitted on  ;  Revised on  ; Accepted on
• Contact: * E-mail address: jasujaprems@gmail.com
• About author:Prem Sagar is an associate research fellow at I.K. Gujral, Ptu, Punjab, India. His research interests include the development of magnesium-based composite materials via friction stir processing.
Dr. Amit Handa is an associate professor at I.K. Gujral, Ptu, Punjab, India. His research interests include friction stir welding of materials.

Abstract: Recently, friction stir processing (FSP) has emerged as a pioneering approach for the manufacture of composites with enhanced mechanical and tribological properties. The present study examines the impact of process parameters such as tool rotation speed and FSP pass number on the AZ61A/TiC magnesium metal composite for responses such as hardness and wear resistance. To minimize number of experiments, the design of experiments (DOE) was configured according to the response surface methodology (RSM) using central composite design (CCD). Analysis of variance (ANOVA) was conducted to develop a mathematical and empirical model for studying the relationship between tool rotation and number of passes for responses such as microhardness and wear resistance. Microhardness was checked on the Vickers microhardness testing machine, and tribological behavior was examined on the pin-on-disc tribotester. Wear tracks were analyzed via scanning electron microscopy (SEM). The responses were predicted using validated mathematical model, and contour plots were generated to study the interaction and influence of process parameters. Finally, the findings suggested that both selected parameters are significant and largely influence the responses.