Inverse analysis of anisotropy of solid-liquid interfacial free energy based on machine learning
Kim, Geunwoo 1Yamada, Ryo 1Takaki, Tomohiro 2Shibuta, Yasushi 3Ohno, Munekazu1
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作者信息
1. Hokkaido Univ
2. Kyoto Inst Technol
3. Univ Tokyo
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Abstract
A machine leaning-based approach is proposed for the inverse analysis of the anisotropy parameters of solid -liquid interfacial free energy. The interface shape distribution (ISD) map, which characterizes the details of the dendrite morphology, was selected as the input of a convolutional neural network (CNN). The ISD maps for a free-growing dendrite during the isothermal solidification of a model alloy system were obtained by quantitative phase-field simulations and used as the training and test data for the CNN. Two anisotropy parameters were estimated with errors of less than 5%, which can be further improved by increasing the size of the training dataset.
NSTL
Elsevier