Plate Shape Prediction Based on Data-Driven in Roll Quenching Process
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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. © 2023 IEEE.
Açıklama
2023 China Automation Congress, CAC 2023 -- 17 November 2023 through 19 November 2023 -- -- 198194
Anahtar Kelimeler
Defect Types, Flatness, Gbdt, Roll Quenching, Shape Prediction
Kaynak
Proceedings - 2023 China Automation Congress, CAC 2023
WoS Q Değeri
Scopus Q Değeri
N/A
