Inverse analysis of anisotropy of solid-liquid interfacial free energy based on machine learning
Inverse analysis of anisotropy of solid-liquid interfacial free energy based on machine learning
复制标题
基于机器学习的固液界面自由能各向异性反演分析
DOI：
10.1016/j.commatsci.2022.111294
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发表时间：
2022
影响因子：
3.3
通讯作者：
M. Ohno
中科院分区：
文献类型：
--
作者：
G. Kim;R. Yamada;T. Takaki;Y. Shibuta;M. Ohno
提出了一种基于机器学习的方法来逆分析固液界面自由能的各向异性参数。选择表征枝晶形态细节的界面形状分布（ISD）图作为卷积神经网络（CNN）的输入。通过定量相场模拟获得模型合金系统等温凝固过程中自由生长枝晶的 ISD 图，并将其用作 CNN 的训练和测试数据。两个各向异性参数的估计误差小于 5%，可以通过增加训练数据集的大小来进一步改善。
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.
等轴枝晶三维形貌的时不变性：相场研究
影响因子：
3.3
作者：
Yamada Ryo;Kudo Mikihiro;Kim Geunwoo;Takaki Tomohiro;Shibuta Yasushi;Ohno Munekazu
通讯作者：
Ohno Munekazu
枝晶臂的破碎和晶粒细化：使用同步辐射 X 射线进行原位观察
DOI：
--
发表时间：
2022
期刊：
影响因子：
--
作者：
Qi Xingyu;Shinagawa Tatsuya;Kishimoto Fuminao;Takanabe Kazuhiro;Hideyuki Yasuda,Taka Narumi,Ryoji Katsube
通讯作者：
Hideyuki Yasuda,Taka Narumi,Ryoji Katsube