{"title":"Reply to comment on ‘Composition-based aluminum alloy selection using an artificial neural network’","authors":"Jaka Fajar Fatriansyah, Raihan Kenji Rizqillah, Iping Suhariadi, Andreas Federico, Ade Kurniawan","doi":"10.1088/1361-651x/ad4574","DOIUrl":null,"url":null,"abstract":"This reply is addressed to comments on our paper entitled ‘Composition-based Aluminum Alloy Selection Using an Artificial Neural Network.’ There are six main comments, and we addressed the comments carefully. This machine learning (ML) modeling is only part of the development of a broader material selection (or material screening) system. Consideration of other material properties can certainly be included through the integration of ML systems.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":null,"pages":null},"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/ad4574","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}
引用次数: 0
Abstract
This reply is addressed to comments on our paper entitled ‘Composition-based Aluminum Alloy Selection Using an Artificial Neural Network.’ There are six main comments, and we addressed the comments carefully. This machine learning (ML) modeling is only part of the development of a broader material selection (or material screening) system. Consideration of other material properties can certainly be included through the integration of ML systems.
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
