{"title":"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","authors":"Prakash Kumar, Binay Kumar","doi":"10.1177/09544089241255931","DOIUrl":null,"url":null,"abstract":"This research delves into the tribological performance of hybrid aluminum metal matrix composites (HAMMCs) incorporating zirconium diboride (ZrB<jats:sub>2</jats:sub>) 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.","PeriodicalId":20552,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Synergistic approach to tribological characterization of hybrid aluminum metal matrix composites with ZrB2 and fly ash: Experimental and predictive insights\",\"authors\":\"Prakash Kumar, Binay Kumar\",\"doi\":\"10.1177/09544089241255931\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This research delves into the tribological performance of hybrid aluminum metal matrix composites (HAMMCs) incorporating zirconium diboride (ZrB<jats:sub>2</jats:sub>) 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.\",\"PeriodicalId\":20552,\"journal\":{\"name\":\"Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1177/09544089241255931\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/09544089241255931","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
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.
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.