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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Engineering / Faculty of Engineering >
Peer-reviewed Journal Articles, etc >
機械学習によるFe基合金のミクロ偏析生成挙動の予測
| 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)
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Submitter: 大野 宗一
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