open access

https://hdl.handle.net/2115/92149

Inverse analysis of anisotropy of solid-liquid interfacial free energy based on machine learning

英語

Machine learning

2022-05

Elsevier

Computational materials science

207

111294

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.

学術雑誌論文

Accepted Manuscript

Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

©2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

0927-0256