Process Optimization and Wear Behavior of Red Mud Reinforced Aluminum Composites

IF 1.5 Q3 ENGINEERING, MECHANICAL
R. Shanmugavel, Thirumalai Kumaran Sundaresan, Uthayakumar Marimuthu, Pethuraj Manickaraj
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引用次数: 11

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 (%) followed by the sliding velocity (%) 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.
赤泥增强铝复合材料工艺优化及磨损性能研究
本文介绍了混合方法在赤泥基铝金属基复合材料(MMCs)干滑动磨损性能优化中的应用。基本的输入参数被确定为施加载荷、滑动速度、wt.%的增强和对应材料的硬度,而输出响应是特定磨损率和摩擦系数(COF)。通过灰色关联分析(GRA)对多个性能特征同时进行优化。应用主成分分析(PCA)和熵值法对每个输出响应对应的权重值进行评估。试验结果表明,对干滑动磨损性能影响最大的是增强剂wt %,其次是滑动速度%。通过验证试验验证了优化后的工艺条件,比磨损率和COF的灰色关联度分别提高了0.3和0.034。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Advances in Tribology
Advances in Tribology ENGINEERING, MECHANICAL-
CiteScore
5.00
自引率
0.00%
发文量
1
审稿时长
13 weeks
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