{"title":"Research on alloy composition-process-wear properties of medium manganese steel based on machine learning","authors":"Yafeng Ji, Shimin Ma, Wen Peng, Zhihui Cai","doi":"10.1016/j.triboint.2024.110164","DOIUrl":null,"url":null,"abstract":"To reveal the complex relationship between the wear properties of medium manganese steel and related parameters, the effects of silicon elements and heat treatment temperature on the wear properties of medium manganese steel under different impact energies were discussed. The microstructure, hardness, impact toughness, and wear resistance were analyzed by various tests. Based on machine learning, a comprehensive framework linking the alloy composition, heat treatment, and wear characteristics of Fe-Mn-Al-C medium manganese steel was developed. After experimental analysis and verification, silicon-containing steel shows the best wear resistance under high impact load at 700 °C heat treatment temperature. In addition, the IPSO-SVM model effectively predicted the impact wear performance, reaching an R value of 0.97 during testing.","PeriodicalId":23238,"journal":{"name":"Tribology International","volume":null,"pages":null},"PeriodicalIF":6.1000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tribology International","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.triboint.2024.110164","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
To reveal the complex relationship between the wear properties of medium manganese steel and related parameters, the effects of silicon elements and heat treatment temperature on the wear properties of medium manganese steel under different impact energies were discussed. The microstructure, hardness, impact toughness, and wear resistance were analyzed by various tests. Based on machine learning, a comprehensive framework linking the alloy composition, heat treatment, and wear characteristics of Fe-Mn-Al-C medium manganese steel was developed. After experimental analysis and verification, silicon-containing steel shows the best wear resistance under high impact load at 700 °C heat treatment temperature. In addition, the IPSO-SVM model effectively predicted the impact wear performance, reaching an R value of 0.97 during testing.
为了揭示中锰钢磨损性能与相关参数之间的复杂关系，讨论了硅元素和热处理温度对不同冲击能量下中锰钢磨损性能的影响。通过各种试验分析了中锰钢的显微组织、硬度、冲击韧性和耐磨性。基于机器学习，建立了一个将中锰钢的合金成分、热处理和磨损特性联系起来的综合框架。经过实验分析和验证，含硅钢在 700 °C 热处理温度下的高冲击载荷下表现出最佳的耐磨性。此外，IPSO-SVM 模型有效地预测了冲击磨损性能，测试期间的 R 值达到 0.97。
期刊介绍：
Tribology is the science of rubbing surfaces and contributes to every facet of our everyday life, from live cell friction to engine lubrication and seismology. As such tribology is truly multidisciplinary and this extraordinary breadth of scientific interest is reflected in the scope of Tribology International.
Tribology International seeks to publish original research papers of the highest scientific quality to provide an archival resource for scientists from all backgrounds. Written contributions are invited reporting experimental and modelling studies both in established areas of tribology and emerging fields. Scientific topics include the physics or chemistry of tribo-surfaces, bio-tribology, surface engineering and materials, contact mechanics, nano-tribology, lubricants and hydrodynamic lubrication.
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