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

Author

Listed:
  • Shun Zhou
  • Feng Xiang
  • Hongjun Li
  • Chi Zhang
  • Xuerong Zhang

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.

Suggested Citation

  • Shun Zhou & Feng Xiang & Hongjun Li & Chi Zhang & Xuerong Zhang, 2023. "A data and model-driven predictive diagnosis framework towards hot-rolled coil defect," International Journal of Service and Computing Oriented Manufacturing, Inderscience Enterprises Ltd, vol. 4(2), pages 156-165.
    • Handle: RePEc:ids:ijscom:v:4:y:2023:i:2:p:156-165
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