Steel pipe out-of-roundness prediction model generation method, steel pipe out-of-roundness prediction method, steel pipe out-of-roundness control method, steel pipe manufacturing method, and steel pipe out-of-roundness prediction device

A steel pipe out-of-roundness prediction model generation method, the method being a method of generating an out-of-roundness prediction model that predicts out-of-roundness of a steel pipe after a pipe expanding step in a steel pipe manufacturing process, the steel pipe manufacturing process including:

• an end bending step of applying end bending processing on an end of a steel sheet in a width direction;
• a press bending step of performing forming processing on a steel sheet that has undergone the end bending processing to form the steel sheet into an open pipe by a plurality of times of pressing using a punch;
• and the pipe expanding step being a step of performing forming processing by pipe expansion on the steel pipe obtained by joining ends of the open pipe,
• the steel pipe out-of-roundness prediction model generation method comprising:
• a basic data acquisition step of executing a numerical computation in which an input data is an operational condition dataset including one operational parameter or two or more operational parameters selected from operational parameters of the end bending step and one operational parameter or two or more operational parameters selected from operational parameters of the press bending step, and an output data is a steel pipe out-of-roundness after the pipe expanding step, the execution of the numerical computation conducted a plurality of times while changing the operational condition dataset, and generating, by this numerical computation, a plurality of pairs of data of the operational condition data set and the steel pipe out-of-roundness data after the pipe expanding step corresponding to the operational condition dataset, offline as training data;
• and
• an out-of-roundness prediction model generation step of generating an out-of-roundness prediction model for which an input data is the operational condition dataset, and an output data is the out-of-roundness of the steel pipe after the pipe expanding step, the generation of the out-of-roundness prediction model performed offline by machine learning using the plurality of pairs of training data generated in the basic data acquisition step.

A steel pipe out-of-roundness prediction device, the device being a device of predicting out-of-roundness of a steel pipe after a pipe expanding step in a steel pipe manufacturing process, the steel pipe manufacturing process including:

• an end bending step of applying end bending processing on an end of a steel sheet in a width direction;
• a press bending step of performing forming processing on a steel sheet that has undergone the end bending processing to form the steel sheet into an open pipe by a plurality of times of pressing using a punch;
• and the pipe expanding step being a step of performing forming processing by pipe expansion on the steel pipe obtained by joining ends of the open pipe,
• the steel pipe out-of-roundness prediction device comprising:
• a basic data acquisition unit that
• executes a numerical computation in which an input data is an operational condition dataset including one operational parameter or two or more operational parameters selected from operational parameters of the end bending step and one operational parameter or two or more operational parameters selected from operational parameters of the press bending step, and an output data is a steel pipe out-of-roundness information after the pipe expanding step, the execution of the numerical computation conducted a plurality of times while changing the operational condition dataset, and
• generates, by this numerical computation, a plurality of pairs of data of the operational condition data set and the steel pipe out-of-roundness information data after the pipe expanding step corresponding to the operational condition dataset, as training data;
• an out-of-roundness prediction model generation unit that generates an out-of-roundness prediction model for which an input data is the operational condition dataset, and an output data is the out-of-roundness information of the steel pipe after the pipe expanding step, the generation of the out-of-roundness prediction model performed by machine learning using the plurality of pairs of training data generated by the basic data acquisition unit;
• an operational parameter acquisition unit that acquires online an operational condition dataset to be set as an operational condition of the steel pipe manufacturing process;
• and
• an out-of-roundness prediction unit that predicts online the steel pipe out-of-roundness information after the pipe expanding step corresponding to the operational condition dataset acquired by the operational parameter acquisition unit, using the out-of-roundness prediction model generated by the out-of-roundness prediction model generation unit.