@inproceedings{d4680028c7a64a53aaf178b6178c5b8b,
title = "Plate Shape Prediction Based on Data-Driven in Roll Quenching Process",
abstract = "In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect types and flatness of the plate, providing theoretical support for setting process parameters in roller quenching production. First, the parameters of the quenching process are analyzed to identify their characteristics. Then, the K-Means clustering algorithm and correlation analysis are employed to process the quenching process parameters. A gradient boosting decision tree (GBDT) model is used to predict the defect types and flatness of the steel plates. Finally, industrial production data is utilized for experimental validation. The obtained experimental results verify the reliability of the proposed method.",
keywords = "Defect types, Flatness, GBDT, Roll quenching, Shape prediction",
author = "Liu Hu and Luefeng Chen and Jie Hu and Min Wu and Witold Pedrycz and Kaoru Hirota",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 China Automation Congress, CAC 2023 ; Conference date: 17-11-2023 Through 19-11-2023",
year = "2023",
doi = "10.1109/CAC59555.2023.10451294",
language = "English",
series = "Proceedings - 2023 China Automation Congress, CAC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6097--6102",
booktitle = "Proceedings - 2023 China Automation Congress, CAC 2023",
address = "United States",
}