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機械学習によるFe基合金のミクロ偏析生成挙動の予測

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Tetsu-to-Hagane 103(12) 711.pdf2.74 MBPDFView/Open
Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/75380

Title: 機械学習によるFe基合金のミクロ偏析生成挙動の予測
Other Titles: Prediction of Microsegregation Behavior in Fe-based Alloys Based on Machine Learning
Authors: 大野, 宗一1 Browse this author →KAKEN DB
木村, 大地2 Browse this author
松浦, 清隆3 Browse this author →KAKEN DB
Authors(alt): Ohno, Munekazu1
Kimura, Daichi2
Matsuura, Kiyotaka3
Keywords: microsegregation
deep learning
solidification
casting
simulation
Issue Date: 1-Dec-2017
Publisher: 日本鉄鋼協会
Journal Title: 鉄と鋼
Volume: 103
Issue: 12
Start Page: 711
End Page: 719
Publisher DOI: 10.2355/tetsutohagane.TETSU-2017-028
Abstract: A prediction method for microsegregation in Fe-based alloys was developed based on an approach of machine learning called Deep Learning. A set of model and algorithm of Deep Learning suitable for description of microsegregation was constructed by employing training data obtained by one-dimensional finite difference calculations for interdendritic microsegregation. It is shown that the developed method enables accurate prediction of the microsegregation behavior in Fe-based binary and ternary alloys with the solute atoms of C, Si, Mn, P and S. The present results demonstrate that Deep Learning offers a promising way of constructing an easy-to-use approach for prediction of microsegregation with high accuracy. Importantly, it is expected that the present method can be extended to describe effects of microstructural processes on microsegregation behavior.
Rights: 著作権は日本鉄鋼協会にある
Type: article
URI: http://hdl.handle.net/2115/75380
Appears in Collections:工学院・工学研究院 (Graduate School of Engineering / Faculty of Engineering) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 大野 宗一

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