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公开(公告)号：EP4292722A1
公开(公告)日：2023-12-20
申请号：EP22784444.6
申请日：2022-03-15
IPC分类号： B21C37/08 , B21D5/01 , B21D39/20 , G05B19/418
摘要： A steel pipe out-of-roundness prediction method according to the present invention is a method of predicting out-of-roundness of a steel pipe after a pipe expanding step in a steel pipe manufacturing process including steps of a U-press step, an O-press step, and the pipe expanding step, the U-press step being a step of performing forming processing of a steel sheet to make the steel sheet into a formed body having a U-shaped cross section using a U-press tool, the O-press step being a step of performing forming processing of the formed body having the U-shaped cross section to form an open pipe, and the pipe expanding step being a step of performing forming processing by pipe expansion on a steel pipe obtained by joining ends of the open pipe in a width direction. The method includes a step of predicting the out-of-roundness information 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 or more operational parameters selected from the operational parameters of the U-press step and one or more operational parameters selected from the operational parameters of the O-press step, and an output data is out-of-roundness information of the steel pipe after the pipe expanding step.
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公开(公告)号：EP4272883A1
公开(公告)日：2023-11-08
申请号：EP22784443.8
申请日：2022-03-15
摘要： A steel pipe out-of-roundness prediction model generation method according to the present invention includes: executing a numerical computation in which an input data is an operational condition dataset including one or more operational parameters selected from operational parameters of a U-press step and one or more parameters selected from operational parameters of an O-press step, and an output data is a steel pipe out-of-roundness information after a pipe expanding step, with the execution of the calculation conducted a plurality of times while changing the operational condition dataset, and generating, by the execution of the 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; and then 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 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.
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