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  1. B200 工学部/工学研究科
  2. B200a 雑誌掲載論文
  3. 学術雑誌

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
paper.pdf paper (1.7 MB)
Item type 学術雑誌論文 / Journal Article(1)
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

WEKO 91016

Wang, Zhi-Lei

Search repository
Adachi, Yoshitaka

× Adachi, Yoshitaka

WEKO 91017

Adachi, Yoshitaka

Search repository
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
著者版フラグ
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