Knowledge-based Digital Twin Model Evolution Management Method for Mechanical Products

With the rapid increase of multi-source heterogeneous data throughout the lifecycle of mechanical products, digital twin and parallel system are considered to be important methods for deep integration of product data and realizing intelligent manufacturing systems. Due to their dynamic mapping characteristics, model evolution and version management has become a fundamental problem to be solved, as well as an application basis of knowledge automation. In this context, we analyzed the characteristics of mechanical product digital twin, and proposed a knowledge-based digital twin model evolution and version management method, which takes the advantage of mechanical hierarchy and continuously optimizes the model structure while meeting the traceability requirements. Finally, the feasibility and effectiveness of the proposed method are verified by taking the digital twin model of a helicopter and its transmission system main reducer clutch as an example.