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

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/ad4573

Russell Wanhill

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引用次数: 0

摘要

这篇文章是对本刊之前发表的文章 "使用人工神经网络进行基于成分的铝合金选择 "的评论。文章指出，建模的输入信息过于有限，选择程序过于简单，不适用于航空铝合金的实际选择程序。建模时没有充分了解（几乎没有）航空结构铝合金的性能、选择标准、合金成分和加工工艺。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.

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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.