{"title":"焊接金属成分与微观结构和性能相关联的数值模型评述","authors":"Glyn Evans, Marie Quintana, S. S. Babu","doi":"10.29391/2024.103.012","DOIUrl":null,"url":null,"abstract":"There has been a steady rise in the use of computational tools to model or describe weld microstructure and properties from data sets containing a wide range of inputs and outputs measured by experiments. The tools range in sophistication from simple two-factor correlations in spreadsheets to artificial neural networks.","PeriodicalId":23681,"journal":{"name":"Welding Journal","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comments on the Numerical Models for Correlating Weld Metal Composition to Microstructure and Properties\",\"authors\":\"Glyn Evans, Marie Quintana, S. S. Babu\",\"doi\":\"10.29391/2024.103.012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"There has been a steady rise in the use of computational tools to model or describe weld microstructure and properties from data sets containing a wide range of inputs and outputs measured by experiments. The tools range in sophistication from simple two-factor correlations in spreadsheets to artificial neural networks.\",\"PeriodicalId\":23681,\"journal\":{\"name\":\"Welding Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Welding Journal\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.29391/2024.103.012\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"METALLURGY & METALLURGICAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Welding Journal","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.29391/2024.103.012","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"METALLURGY & METALLURGICAL ENGINEERING","Score":null,"Total":0}
Comments on the Numerical Models for Correlating Weld Metal Composition to Microstructure and Properties
There has been a steady rise in the use of computational tools to model or describe weld microstructure and properties from data sets containing a wide range of inputs and outputs measured by experiments. The tools range in sophistication from simple two-factor correlations in spreadsheets to artificial neural networks.
期刊介绍:
The Welding Journal has been published continually since 1922 — an unmatched link to all issues and advancements concerning metal fabrication and construction.
Each month the Welding Journal delivers news of the welding and metal fabricating industry. Stay informed on the latest products, trends, technology and events via in-depth articles, full-color photos and illustrations, and timely, cost-saving advice. Also featured are articles and supplements on related activities, such as testing and inspection, maintenance and repair, design, training, personal safety, and brazing and soldering.