期刊
IEEE ACCESS
卷 8, 期 -, 页码 153123-153133出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3016725
关键词
Force; Mathematical model; Strain; Neural networks; Machine learning; Temperature; Steel; Hot rolling; machine learning; gradient boosting; neural network
资金
- National Research Foundation of Korea (NRF) - Korea Government (MSIT) [2017R1E1A1A03070105]
- Institute for the Information and Communications Technology Promotion (IITP) - Korea Government (MSIP) [Artificial Intelligence Graduate School Program (POSTECH)] [2019-0-01906]
- Institute for the Information and Communications Technology Promotion (IITP) - Korea Government (MSIP) [Information Technology Research Center (ITRC) Support Program] [IITP-2018-0-01441]
Steelmaking requires precise calculation at several steps of the manufacturing processes. We focus on the hot rolling process using Steckel mills, almost the end step in steel coil manufacturing. The rolling process is a type of plastic working in which a slab passes between two rolls and is stretched to reach the target thickness. It is necessary to predetermine the exact rolling force to obtain a coil with an accurate thickness after the rolling process. First, we introduced a machine learning model for calculating the rolling force, which can be used in-line in real plants. However, a direct calculation of the rolling force can cause stability problems, because the model output directly affects the process. In order to avoid such a problem, we determined a special temperature of the coil by inverse calculation of the classical mechanical model of hot rolling and set it as the model output value. As learning models, deep neural networks (DNN) and gradient boosting-based decision tree models were used. We preprocessed the collected process history data and added artificial features to the model input by creating physical variables used in the classical models. Moreover, to supplement the black-box nature of DNN, feature importance was analyzed from the decision tree model, and utilization and interpretation of each feature in the process are presented. Thus, our methods take advantage of both the classical mathematical model and the deep neural network model.
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