Article: A data and model-driven predictive diagnosis framework towards hot-rolled coil defect Journal: International Journal of Service and Computing Oriented Manufacturing (IJSCOM) 2023 Vol.4 No.2 pp.156 - 165 Abstract: The quality defect is one of the important indicators of hot-rolled coil quality. In order to realise real-time prediction of quality defect and timely control, a data and model-driven predictive diagnosis framework towards hot-rolled coil defect is proposed. Firstly, build a digital twin model from four aspects: geometry, physics, behaviour and rule. On this basis, combined with expert knowledge, deep learning and historical data, a predictive diagnostic model for hot-rolled coil defect was constructed. Then, the data-driven defect diagnosis method is used to realise the prediction of defects, and the model-driven result verification method is used to verify the prediction results. Finally, the accuracy of the result is verified by consistency judgement to improve the defect predictive diagnostic model, thereby improving the accuracy of prediction. Inderscience Publishers - linking academia, business and industry through research

Title: A data and model-driven predictive diagnosis framework towards hot-rolled coil defect

Authors: Shun Zhou; Feng Xiang; Hongjun Li; Chi Zhang; Xuerong Zhang

Addresses: Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China ' Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, Hubei 430081, China ' School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, Hubei 430073, China ' School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, Hubei 430073, China ' Wuhan Iron & Steel Co., Ltd., No. 999,Youyi Avenue, Qingshan District, Wuhan, Hubei 430080, China

Abstract: The quality defect is one of the important indicators of hot-rolled coil quality. In order to realise real-time prediction of quality defect and timely control, a data and model-driven predictive diagnosis framework towards hot-rolled coil defect is proposed. Firstly, build a digital twin model from four aspects: geometry, physics, behaviour and rule. On this basis, combined with expert knowledge, deep learning and historical data, a predictive diagnostic model for hot-rolled coil defect was constructed. Then, the data-driven defect diagnosis method is used to realise the prediction of defects, and the model-driven result verification method is used to verify the prediction results. Finally, the accuracy of the result is verified by consistency judgement to improve the defect predictive diagnostic model, thereby improving the accuracy of prediction.

Keywords: deep learning; digital twin; hot-rolled coil defect; predictive diagnosis.

DOI: 10.1504/IJSCOM.2023.131577

International Journal of Service and Computing Oriented Manufacturing, 2023 Vol.4 No.2, pp.156 - 165

Received: 28 Jan 2022
Accepted: 01 Mar 2022
Published online: 19 Jun 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: A data and model-driven predictive diagnosis framework towards hot-rolled coil defect

Keep up-to-date