Abstract: A steel pipe out-of-roundness prediction model generation method includes: executing a numerical computation in which an input data is an operational condition dataset including one or more operational parameters of an end bending step and one or more operational parameters of a press bending step, and an output data is a steel pipe out-of-roundness after a pipe expanding step, the numerical computation conducted a plurality of times while changing the operational condition dataset, and generating 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, offline as training data; and generating a 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 model performed offline by machine learning using the plurality of pairs of training data.

Abstract: A steel pipe out-of-roundness prediction method of predicting out-of-roundness of a steel pipe after a pipe expanding step in a steel pipe manufacturing process including: an end bending step; a press bending step; and the pipe expanding step, the steel pipe out-of-roundness prediction method includes 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.