Digital Twin Driven End-Face Defect Control Method for Hot-Rolled Coil With Cloud-Edge Collaboration

End-face defect control (EF-DC) of the hot-rolled coil is crucial to the quality management. The core of EF-DC is to identify defects accurately, predict defects in advance, and improve production in time to prevent similar defects. The current research works on EF-DC are mainly at the defect recognition stage, which merely rely on the operating data in physical space. However, different types of defects exist in coils with different reasons, therefore it is difficult to accurately control defects in time only through the hot rolling operating data. In order to solve the above problems, first an EF-DC framework based on digital twin with cloud-edge collaboration is designed in this article. The computing tasks are reasonably allocated through cloud-edge collaboration to realize the timeliness of control. Second, the virtual model of hot-rolled coil is constructed from four aspects: geometry, physics, behavior, and rules. Through the analysis of the defect mechanism, the abnormal behavior events are obtained, and the mapping relationship between the defect and the behavior event is established to realize the defect traceability and control. Finally, the feasibility of the proposed method is verified by taking the edge scratch defect as an example.