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 control and manufacturing method comprising:

• 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, wherein
• the predicting uses an out-of-roundness prediction model trained by machine learning, the out-of-roundness prediction model for which:
• input data includes one or more operational parameters of the end bending step and one or more operational parameters of the press bending step, wherein the input data further includes a pipe expansion rate, a number of pipe expanding dies, and a pipe expanding die diameter, selected from operational parameters of the pipe expanding step, and
• output data is steel pipe out-of-roundness information after the pipe expanding step, and
• the predicting is performed before starting a reconfiguration target step which is selected from a plurality of forming processing steps constituting the steel pipe manufacturing process;
• reconfiguring one or more operational parameters of the reconfiguration target step, or one or more operational parameters of a forming processing step on a downstream side of the reconfiguration target step, so as to reduce the steel pipe out-of-roundness after the pipe expanding step;
• and
• manufacturing the steel pipe using the reconfigured one or more operational parameters.