A step-driven framework of digital twin model for product assembly precision based on polychromatic sets

The integration of digital twin technology into the assembly process of complex precision mechanical products has become a significant and feasible means to improve product assembly quality and consistency by performing dynamic assembly precision prediction and henceforth assembly process optimization. Most current research predominantly focuses on modeling the actual machining error of components and their subsequent error propagation, with limited attention given to the methods driving the precision models of digital twins in the product assembly process. In this paper, an assembly operation-driven framework for synchronizing digital twin models dedicated to product assembly precision prediction is proposed based on the polychromic sets theory. Firstly, taking the assembly feature as the core of digital twins for assembly precision, homogenous coordinate transformation matrix is adopted to establish connections between assembly hierarchy objects and conduct assembly deviation propagation calculations. Secondly, the association relationship among product parts, assembly features, and assembly feature pairs is constructed in the form of the polychromatic sets matrix. Further, by linking the assembly sequence, a general enabling framework that can be used for automatic inference in constructing and updating assembly precision prediction models for the assembly process is established. Finally, a data model associated with the instantiation of the assembly precision prediction model is provided, and the assembly process of the high-pressure compressor rotor is taken as a case study to verify the effectiveness of the framework model in practical applications.