LAMINATE STRUCTURE, CABLE AND TUBE
    2.
    发明公开

    公开(公告)号:US20240296971A1

    公开(公告)日:2024-09-05

    申请号:US18646595

    申请日:2024-04-25

    申请人: Proterial, Ltd.

    摘要: A laminate structure includes a first layer as a substrate and a second layer provided on the first layer. The second layer is composed of a rubber composition including a rubber component, first fine particles for providing a surface with irregularity, and second fine particles for shielding UV-C light. When performing Raman mapping analysis on a first peak derived from oscillation of the second fine particles in Raman scattering spectrum obtained by Raman scattering measurement of the second layer, the second layer includes a region where an intensity of the first peak is greater in an area where the first fine particles are not present than an area where the first fine particles are present.

    MATERIAL DATA PROCESSING DEVICE AND MATERIAL DATA PROCESSING METHOD

    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.