Digital twin-driven assembly accuracy prediction method for high performance precision assembly of complex products

Abstract

The high performance precision assembly (HPPA) of complex products such as aerospace, aircraft and high-end machine tool has demanding requirements for assembly accuracy. Achieving the accurate prediction of assembly accuracy for these complex products before assembling is the premise of improving the assembly quality and performance, and also has always been a challenge. Existing assembly accuracy prediction methods focus on acquiring the assembly deviation based on CAD model and manufacturing errors of parts, but rarely involve the multidimensional error coupling of parts and the influencing factors in the assembly process, which inevitably cause a certain gap between the prediction result and the actual condition, affecting the reliability of the prediction result. To address the above problems, this paper presents a digital twin (DT)-driven assembly accuracy prediction method for the HPPA of complex products. Firstly, this paper introduces the methodology overview and proposes an overall framework for DT-driven assembly accuracy prediction. Secondly, three key enabling technologies realizing the DT-driven assembly accuracy prediction, including the construction of part digital twin model, the generation of DT-based assembly process model, and assembly deviation propagation and accuracy analysis are introduced in detail. Finally, an application implementation of a prototype system and a case study involving a simplified satellite structure panel assembly process are used to verify the effectiveness and feasibility of the proposed method.