Digital thread-driven cloud-fog-edge collaborative disturbance mitigation mechanism for adaptive production in digital twin discrete manufacturing workshop

The collaborative capacity among various computing layers within a digital twin workshop directly impacts the handling of disturbances in discrete manufacturing processes. However, the intricate nature of the cross-space (physical and virtual space) and cross-layer (cloud, fog, and edge) interactions within a digital twin system poses challenges to construct a comprehensive data model and interoperable data flow, leading to a fragmented state of computing layers. To tackle these challenges, this study presents a digital thread-driven theory and method for cloud-fog-edge collaboration in digital twin discrete manufacturing workshops. Firstly, the five-dimensions production information that cross spaces and layers is concatenated and integrated by constructing digital thread models. Subsequently, a disturbance response model is conducted based on these digital thread models to detect, track, and evaluate disturbances. Furthermore, corresponding cloud-fog-edge collaboration strategies are devised for different types and severity of disturbances, facilitating the dynamic production tasks adjustments. Finally, a case study to discuss the performance of the proposed method regarding the maturity of the digital thread model, disturbance response capability, and production self-adaptive adjustment capability. The results demonstrate that the proposed method effectively mitigates makespan and machine idle time under gradual and abrupt disturbances, and saves 46.22% of computing processing time.