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機械学習によるミクロ偏析予測とマクロ偏析シミュレーションへの拡張
Title: | 機械学習によるミクロ偏析予測とマクロ偏析シミュレーションへの拡張 |
Other Titles: | Prediction of Microsegregation Based on Machine Learning and Its Extension to a Macrosegregation Simulation |
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 | macrosegregation | deep learning | phase-field simulation | solidification |
Issue Date: | 1-Dec-2017 |
Publisher: | 日本鉄鋼協会 |
Journal Title: | 鉄と鋼 |
Volume: | 103 |
Issue: | 12 |
Start Page: | 720 |
End Page: | 729 |
Publisher DOI: | 10.2355/tetsutohagane.TETSU-2017-040 |
Abstract: | An approach of machine learning called Deep Learning is utilized for construction of a prediction method of microsegregation behavior in Fe-based binary alloys with solute atoms of C, Si, Mn and P. Training data for the machine learning are obtained by quantitative phase-field simulations for directional solidification. Therefore, effects of microstructural evolutions on the microsegregation behavior are taken into account in the present method. Importantly, this method can be coupled with a macrosegregation model. The simulation result of the macrosegregation model is quite different from those obtained by a conventional macrosegregation model with the Scheil model and a model with a prediction method constructed from the training data of one-dimensional finite difference calculations for the microsegregation. This fact highlights the importance of accurate description of microsegregation behavior in prediction of macrosegregation. |
Rights: | 著作権は日本鉄鋼協会にある |
Type: | article |
URI: | http://hdl.handle.net/2115/75379 |
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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