公开(公告)号：US20230321706A1

公开(公告)日：2023-10-12

申请号：US18043044

申请日：2021-08-16

Applicant: JFE STEEL CORPORATION

Inventor： Mayumi OJIMA ,  Yoshimasa FUNAKAWA

IPC: B21C51/00

CPC classification number: B21C51/00

Abstract: Provided is a steel strip that can provide accurate material information and a method of producing the same. A steel strip (9) comprises a medium that provides material information including a material distribution associating each position in a two-dimensional direction of a rolling direction and a transverse direction with a material characteristic value. The material information is predicted using a prediction model to which input data including a line output factor in a production line for the steel strip, a disturbance factor, and a component value of the steel strip being produced is input. The prediction model includes: a machine learning model that receives the input data as input and outputs a production condition factor and that is generated by machine learning; and a metallurgical model that receives the production condition factor as input and outputs the material characteristic value.

公开(公告)号：US20230323503A1

公开(公告)日：2023-10-12

申请号：US18043579

申请日：2021-08-16

Applicant: JFE STEEL CORPORATION

Inventor： Mayumi OJIMA ,  Yoshimasa FUNAKAWA

IPC: C21D11/00 ,  C21D9/46 ,  C21D8/02 ,  B21B37/76 ,  G05B13/02 ,  G05B13/04

CPC classification number: C21D11/00 ,  C21D9/46 ,  C21D8/0205 ,  C21D8/0263 ,  C21D8/0278 ,  B21B37/76 ,  G05B13/0265 ,  G05B13/048

Abstract: A material characteristic value prediction system that can predict material characteristic values with high accuracy is provided. Also provided is a method of manufacturing a metal sheet that can improve the product yield rate, by changing manufacturing conditions of subsequent processes. The material characteristic value prediction system (100) includes a material characteristic value predictor configured to acquire input data including line output factors in a metal sheet manufacturing line, disturbance factors, and component values of a metal sheet being manufactured, and predict material characteristic values of the manufactured metal sheet using a prediction model configured to take the input data as inputs, wherein the prediction model includes a machine learning model generated by machine learning and configured to take the input data as inputs and output production condition factors, and a metallurgical model configured to take the production condition factors as inputs and output the material characteristic values.