Plate Shape Prediction Based on Data-Driven in Roll Quenching Process

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dc.contributor.authorHu, L.
dc.contributor.authorChen, L.
dc.contributor.authorHu, J.
dc.contributor.authorWu, M.
dc.contributor.authorPedrycz, W.
dc.contributor.authorHirota, K.
dc.date.accessioned2024-05-19T14:33:50Z
dc.date.available2024-05-19T14:33:50Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description2023 China Automation Congress, CAC 2023 -- 17 November 2023 through 19 November 2023 -- -- 198194en_US
dc.description.abstractIn 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.en_US
dc.identifier.doi10.1109/CAC59555.2023.10451294
dc.identifier.endpage6102en_US
dc.identifier.isbn9798350303759
dc.identifier.scopus2-s2.0-85189292197en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage6097en_US
dc.identifier.urihttps://doi.org/10.1109/CAC59555.2023.10451294
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4348
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 2023 China Automation Congress, CAC 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectDefect Typesen_US
dc.subjectFlatnessen_US
dc.subjectGbdten_US
dc.subjectRoll Quenchingen_US
dc.subjectShape Predictionen_US
dc.titlePlate Shape Prediction Based on Data-Driven in Roll Quenching Processen_US
dc.typeConference Objecten_US

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