{"title":"Modelling Frictional noise using Artificial neural network and Regression: The case of steel on steel reciprocating sliding","authors":"M. Hanief, M. John","doi":"10.1504/ijmatei.2023.10048814","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":14033,"journal":{"name":"International Journal of Materials Engineering Innovation","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Materials Engineering Innovation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijmatei.2023.10048814","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Materials Science","Score":null,"Total":0}
期刊介绍：
IJMatEI is a multidisciplinary journal that will publish refereed high quality articles with special emphasis on research and development into recent advances in composites, ceramics, functionally graded materials, cellular materials and ecomaterials. IJMatEI fosters information exchange and discussion on all aspects of modern materials engineering, such as materials preparation and processing, relationships between structure (nano and micro) and properties (physical, chemical, mechanical, thermal, electrical and magnetic), as well as performance and technological applications for advanced industry.
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