Process Optimization and Wear Behavior of Red Mud Reinforced Aluminum Composites

Rajesh Shanmugavel,Pethuraj Manickaraj,Thirumalai Kumaran Sundaresan,Uthayakumar Marimuthu

doi:10.1155/2016/9082593

Rajesh Shanmugavel, Pethuraj Manickaraj + Show 2 more

Open Access

https://doi.org/10.1155/2016/9082593

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Abstract

This work presents the application of hybrid approach for optimizing the dry sliding wear behavior of red mud based aluminum metal matrix composites (MMCs). The essential input parameters are identified as applied load, sliding velocity, wt.% of reinforcement, and hardness of the counterpart material, whereas the output responses are specific wear rate and Coefficient of Friction (COF). The Grey Relational Analysis (GRA) is performed to optimize the multiple performance characteristics simultaneously. The Principle Component Analysis (PCA) and entropy methods are applied to evaluate the values of weights corresponding to each output response. The experimental result shows that the wt.% of reinforcements (Q=34.9%) followed by the sliding velocity (Q=34.5%) contributed more to affecting the dry sliding wear behavior. The optimized conditions are verified through the confirmation test, which exhibited an improvement in the grey relational grade of specific wear rate and COF by 0.3 and 0.034, respectively.

Highlights

Aluminum metal matrix composites (MMCs) created an interest to several industries, due to their high stiffness, specific strength, and superior wear resistance behavior, compared to unreinforced aluminum alloys
The optimization was performed using Grey Relational Analysis (GRA) and the results indicated that the sliding velocity was the most effective factor among the control parameters on dry sliding wear, followed by the reinforcement percentage, sliding distance, and contact stress
This study proposes GRA to optimize the dry sliding wear behavior of red mud reinforced aluminum composite

Summary

Introduction

Aluminum MMCs created an interest to several industries, due to their high stiffness, specific strength, and superior wear resistance behavior, compared to unreinforced aluminum alloys. Used GRA as performance index to study the behavior of electroless Ni-P coating with respect to friction and wear characteristics. Soy et al [7] studied the wear behaviors of A360 matrix reinforced with SiC and B4 C ceramic particles using Taguchi method They have concluded that the type of the material, applied load, and sliding speed exert a great effect on the specific wear rate, at 48.13%, 31.83%, and 8.77%, respectively. This study proposes GRA to optimize the dry sliding wear behavior of red mud reinforced aluminum composite. PCA approach explains the structure of variance-covariance by way of the linear combinations of each performance characteristic It is a statistical method which uses an orthogonal transformation to convert the correlated variables into linearly uncorrelated variables called principal components [11].

Result and Discussion

Conclusions