Tetsu-to-Hagane
Online ISSN : 1883-2954
Print ISSN : 0021-1575
ISSN-L : 0021-1575
Regular Article
Prediction of Microsegregation Based on Machine Learning and Its Extension to a Macrosegregation Simulation
Munekazu OhnoDaichi KimuraKiyotaka Matsuura
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JOURNAL OPEN ACCESS FULL-TEXT HTML

2017 Volume 103 Issue 12 Pages 720-729

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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.

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© 2017 The Iron and Steel Institute of Japan

This article is licensed under a Creative Commons [Attribution-NonCommercial-NoDerivatives 4.0 International] license.
https://creativecommons.org/licenses/by-nc-nd/4.0/
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