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Property prediction and properties-to-microstructure inverse analysis of steels by a machine-learning approach
http://hdl.handle.net/2237/00030151
a0411280-7289-4e6d-8350-b34c6a9d5e5e
名前 / ファイル | ライセンス | アクション | |
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2019-05-09 | |||||
タイトル | ||||||
タイトル | Property prediction and properties-to-microstructure inverse analysis of steels by a machine-learning approach | |||||
著者 |
Wang, Zhi-Lei
× Wang, Zhi-Lei× Adachi, Yoshitaka |
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権利 | ||||||
権利情報 | © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | 3D microstructural analysis | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Property prediction | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Inverse analysis | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Machine learning | |||||
キーワード | ||||||
主題Scheme | Other | |||||
主題 | Steels | |||||
抄録 | ||||||
内容記述 | The design of new materials with useful properties is becoming increasingly important. Machine-learning tools Materials Genome Integration System Phase and Property Analysis (MIPHA) and rMIPHA (based on the R programming environment) have been independently developed to accelerate the process of materials discovery via a data-driven materials research approach. In the present work, MIPHA and rMIPHA are applied to steel, where machine-learning-based 2D/3D microstructural analysis, direct analysis of property predictions, and properties-to-microstructure inverse analysis were conducted. The results demonstrate that the prediction models deliver satisfactory performance. The inverse exploration of microstructures related to desired target properties (e.g., stress–strain curve, tensile strength, and total elongation) was realized. MIPHA and rMIPHA are still under improvement. The microstructure-to-processing inverse analysis is expected to be realized in the future. | |||||
内容記述タイプ | Abstract | |||||
内容記述 | ||||||
内容記述 | ファイル公開:2021-01-28 | |||||
内容記述タイプ | Other | |||||
出版者 | ||||||
出版者 | Elsevier | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源タイプresource | http://purl.org/coar/resource_type/c_6501 | |||||
タイプ | journal article | |||||
DOI | ||||||
関連識別子 | ||||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1016/j.msea.2018.12.049 | |||||
ISSN(print) | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 0921-5093 | |||||
書誌情報 |
Materials Science and Engineering: A 巻 744, p. 661-670, 発行日 2019-01-28 |
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著者版フラグ | ||||||
値 | author |