对 "利用人工神经网络选择基于成分的铝合金 "评论的回复

IF 1.9 4区材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY

Modelling and Simulation in Materials Science and Engineering Pub Date : 2024-05-08 DOI:10.1088/1361-651x/ad4574

Jaka Fajar Fatriansyah, Raihan Kenji Rizqillah, Iping Suhariadi, Andreas Federico, Ade Kurniawan

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引用次数: 0

摘要

本答复是针对我们题为《使用人工神经网络进行基于成分的铝合金选择》的论文所提出的意见。主要有六条意见，我们已认真处理了这些意见。这种机器学习（ML）建模只是开发更广泛的材料选择（或材料筛选）系统的一部分。通过集成 ML 系统，当然还可以考虑其他材料特性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Reply to comment on ‘Composition-based aluminum alloy selection using an artificial neural network’

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.

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来源期刊

Modelling and Simulation in Materials Science and Engineering 工程技术-材料科学：综合

CiteScore

3.30

自引率

5.60%

发文量

审稿时长

1.7 months

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.