采用协同方法表征含 ZrB2 和粉煤灰的混合铝金属基复合材料的摩擦学特性:实验和预测见解

IF 2.3 4区 工程技术 Q2 ENGINEERING, MECHANICAL
Prakash Kumar, Binay Kumar
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引用次数: 0

摘要

本研究探讨了加入二硼化锆(ZrB2)颗粒和粉煤灰作为增强剂的混合铝金属基复合材料(HAMMC)的摩擦学性能。该研究采用线性往复磨损试验来研究在环境温度和高温条件下干式滑动磨损对这些 HAMMC 的影响。磨损机理通过场发射扫描电子显微镜进行分析。采用遗传算法对磨损试验参数、摩擦系数(COF)和磨损率进行了优化。此外,还采用了人工神经网络(ANN)和多元线性回归分析来制定磨损预测模型,估算各种测试条件下的具体磨损率和 COF。人工神经网络预测值与实验值的偏差在 0% 至 1.39% 之间,表明该模型在了解和预测 HAMMC 研究中的磨损行为方面非常有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Synergistic approach to tribological characterization of hybrid aluminum metal matrix composites with ZrB2 and fly ash: Experimental and predictive insights
This research delves into the tribological performance of hybrid aluminum metal matrix composites (HAMMCs) incorporating zirconium diboride (ZrB2) particles and fly ash as reinforcing agents. The study employs a linear reciprocating wear test to investigate the impact of dry sliding wear on these HAMMCs under ambient and elevated temperatures. Wear mechanisms are discerned through field emission scanning electron microscopy. Optimization of wear test parameters, coefficient of friction (COF), and wear rate is achieved using the genetic algorithm. Additionally, artificial neural network (ANN) and multiple linear regression analysis are employed to formulate a predictive model for wear, estimating specific wear rate and COF under various testing conditions. The ANN predictions exhibit a deviation ranging from 0% to 1.39% from the experimental values, indicating the model's effectiveness in understanding and predicting wear behavior in the study of HAMMC.
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来源期刊
CiteScore
3.80
自引率
16.70%
发文量
370
审稿时长
6 months
期刊介绍: The Journal of Process Mechanical Engineering publishes high-quality, peer-reviewed papers covering a broad area of mechanical engineering activities associated with the design and operation of process equipment.
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