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

A steel pipe out-of-roundness prediction method, the method being a method 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 into an open pipe by a plurality of times of pressing by 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 method comprising
• a step of predicting the out-of-roundness of the steel pipe after the pipe expanding step by using an out-of-roundness prediction model having been trained by machine learning, the out-of-roundness prediction model for which
• an input data is data including one operational parameter or two or more operational parameters selected from the operational parameters of the end bending step and one operational parameter or two or more operational parameters selected from the operational parameters of the press bending step, and
• an output data is steel pipe out-of-roundness information after the pipe expanding step.

A steel pipe out-of-roundness prediction model generation method, the method being a method of generating a steel pipe 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 by 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 an out-of-roundness prediction model generating step of
• acquiring a plurality of pieces of training data in which one piece or two or more pieces of operational performance data selected from the operational performance data of the end bending step, and one piece or two or more pieces of operational performance data selected from the operational performance data of the press bending step are input performance data, and performance data of the out-of-roundness of the steel pipe after the pipe expanding step in the steel pipe manufacturing process using the input performance data is output performance data, and
• generating an out-of-roundness prediction model by machine learning using the acquired plurality of pieces of training data.

A steel pipe out-of-roundness prediction device, the device being a device of predicting a steel pipe out-of-roundness 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 by 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:
• an operational parameter acquisition unit that acquires one operational parameter or two or more operational parameters selected from the operational parameters of the end bending step and one operational parameter or two or more operational parameters selected from the operational parameters of the press bending step;
• and
• an out-of-roundness prediction unit that predicts out-of-roundness information of the steel pipe after the pipe expanding step by inputting the operational parameter acquired by the operational parameter acquisition unit to an out-of-roundness prediction model having been trained by machine learning, the out-of-roundness prediction model for which
• an input data is data including one operational parameter or two or more operational parameters selected from the operational parameters of the end bending step and one operational parameter or two or more operational parameters selected from the operational parameters of the press bending step, and
• an output data is out-of-roundness information of the steel pipe after the pipe expanding step.