| CPC B21C 37/08 (2013.01) [B21C 37/06 (2013.01); G01N 3/00 (2013.01); G01N 3/08 (2013.01); G01N 2203/0016 (2013.01); G01N 2203/0067 (2013.01); G01N 2203/0075 (2013.01); G01N 2203/0216 (2013.01); G01N 2203/0274 (2013.01); G01N 2203/0298 (2013.01); G05B 13/027 (2013.01); G05B 19/41875 (2013.01); G05B 2219/32193 (2013.01); G06F 30/10 (2020.01); G06F 30/27 (2020.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01)] | 8 Claims |

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1. A steel pipe manufacturing method comprising:
performing machine learning of a plurality of learning data that include, as an input datum, a previous steel pipe manufacturing characteristic including a steel pipe shape after steel pipe forming, a steel pipe strength characteristic after steel pipe forming, and a pipe-making strain during steel pipe forming and, as an output datum for the input datum, a previous collapse strength of a steel pipe after steel pipe forming, to generate a steel pipe collapse strength prediction model that predicts a steel pipe collapse strength after steel pipe forming;
forming a steel pipe;
predicting a collapse strength of the formed steel pipe by inputting, into the steel pipe collapse strength prediction model, a steel pipe manufacturing characteristic including a steel pipe shape of the formed steel pipe, a steel pipe strength characteristic of the formed steel pipe, and a pipe-making strain during steel pipe forming, to predict a steel pipe collapse strength of the formed steel pipe; and
assigning the predicted steel pipe collapse strength to the formed steel pipe.
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