{"title":"关于 \"利用人工神经网络进行基于成分的铝合金选择 \"的评论","authors":"Russell Wanhill","doi":"10.1088/1361-651x/ad4573","DOIUrl":null,"url":null,"abstract":"This article comments on the article ‘Composition-based aluminum alloy selection using an artificial neural network previously published in this journal. It is shown that the input information of the modelling is much too limited and the selection procedure is simplistic and not applicable or relevant to the actual selection procedures for aerospace aluminum alloys. The modelling has been done without sufficient engineering knowledge (almost none) about the properties, selection criteria, alloy compositions and processing of aerospace structural aluminum alloys.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"3 1","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comment on ‘Composition-based aluminum alloy selection using an artificial neural network’\",\"authors\":\"Russell Wanhill\",\"doi\":\"10.1088/1361-651x/ad4573\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article comments on the article ‘Composition-based aluminum alloy selection using an artificial neural network previously published in this journal. It is shown that the input information of the modelling is much too limited and the selection procedure is simplistic and not applicable or relevant to the actual selection procedures for aerospace aluminum alloys. The modelling has been done without sufficient engineering knowledge (almost none) about the properties, selection criteria, alloy compositions and processing of aerospace structural aluminum alloys.\",\"PeriodicalId\":18648,\"journal\":{\"name\":\"Modelling and Simulation in Materials Science and Engineering\",\"volume\":\"3 1\",\"pages\":\"\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Modelling and Simulation in Materials Science and Engineering\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1088/1361-651x/ad4573\",\"RegionNum\":4,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Modelling and Simulation in Materials Science and Engineering","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1088/1361-651x/ad4573","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Comment on ‘Composition-based aluminum alloy selection using an artificial neural network’
This article comments on the article ‘Composition-based aluminum alloy selection using an artificial neural network previously published in this journal. It is shown that the input information of the modelling is much too limited and the selection procedure is simplistic and not applicable or relevant to the actual selection procedures for aerospace aluminum alloys. The modelling has been done without sufficient engineering knowledge (almost none) about the properties, selection criteria, alloy compositions and processing of aerospace structural aluminum alloys.
期刊介绍:
Serving the multidisciplinary materials community, the journal aims to publish new research work that advances the understanding and prediction of material behaviour at scales from atomistic to macroscopic through modelling and simulation.
Subject coverage:
Modelling and/or simulation across materials science that emphasizes fundamental materials issues advancing the understanding and prediction of material behaviour. Interdisciplinary research that tackles challenging and complex materials problems where the governing phenomena may span different scales of materials behaviour, with an emphasis on the development of quantitative approaches to explain and predict experimental observations. Material processing that advances the fundamental materials science and engineering underpinning the connection between processing and properties. Covering all classes of materials, and mechanical, microstructural, electronic, chemical, biological, and optical properties.