公开(公告)号：US20240142951A1

公开(公告)日：2024-05-02

申请号：US18385755

申请日：2023-10-31

申请人： Proterial, Ltd.

发明人： Asami OYA ,  Makoto ONO

IPC分类号： G05B19/418

CPC分类号： G05B19/4183 ,  G05B19/41885

摘要： A material data processing device using a computer is provided with a regression model creation processing unit that performs machine learning using, out of process data, composition data, characteristics data, and microstructure data, two or more data including the structure data, and creates a regression model representing a correlation between respective data, an estimation processing unit that estimates, by using the regression model, the process data, the composition data, the characteristics data, or the microstructure data, having been used for machine learning, wherein the microstructure data includes a feature amount based on a magnetization temperature dependence during heating, and a feature amount based on a magnetization temperature dependence during cooling; and a temperature type selection means that selects use of either one or both of the feature amount based on the magnetization temperature dependence during heating or the feature amount based on the magnetization temperature dependence during cooling as the microstructure data to be used for the machine learning. The material data processing method includes performing the machine learning, and creating the regression model, selecting the use of either one or both of the feature amount based on the magnetization temperature dependence during heating or the feature amount based on the magnetization temperature dependence during cooling as the microstructure data to be used for the machine learning.

公开(公告)号：US20240203538A1

公开(公告)日：2024-06-20

申请号：US18514527

申请日：2023-11-20

申请人： Proterial, Ltd.

发明人： Asami OYA ,  Makoto ONO ,  Tomohito MAKI

IPC分类号： G16C20/70

CPC分类号： G16C20/70

摘要： A material data processing device is provided with a regression model creation processing unit that performs machine learning using, out of process data including information on manufacturing conditions for manufacturing individual samples, composition data including information on composition of the individual samples, characteristics data including information on characteristics of the individual samples, and microstructure data including information on microstructure of the individual samples, two or more data including the microstructure data, and creates a regression model representing a correlation between respective data; and an estimation processing unit that estimates, by using the regression model, the process data, the composition data, the characteristics data, or the microstructure data, having been used for the machine learning, wherein the microstructure data includes a feature amount difference that is a difference between a feature amount during heating as a feature amount based on a magnetization temperature dependence during heating and a feature amount during cooling as a feature amount based on a magnetization temperature dependence during cooling. A material data processing method includes performing machine learning to create the regression model, and estimating, by using the regression model, the process data, the composition data, the characteristics data, or the microstructure data.