A multi-scenario model fusion and verification method for digital twin machine tool

Abstract

High-fidelity digital twin modeling is the core of digital twin machine tool (DTMT) to achieve accurate mapping and deliver functional services. Model fusion is a key modeling technology to promote the integrity and system connectivity of DTMT. However, current model fusion lacks attention to the multi-scenario characteristics of DTMT, which hinders the effective application of DTMT. Therefore, this paper proposes a multi-scenario model fusion and verification method for DTMT to eliminate information islands, improve model collaboration and respond to dynamic application requirements. Firstly, an S3C2 architecture is proposed to guide the multi-scenario model fusion of DTMT. The S3C2 architecture helps clarify the structural relationships of multi-scenario models and mask their heterogeneity, thus enabling DTMT to fuse the right models at the right time and provide the desired digital twin service. In addition, the fusion mechanism with different topologies is also considered to support the information exchange in the multi-scenario model fusion process of DTMT. Then, a method combining SysML and π-calculus is proposed to describe the fusion behavior and verify the fusion process. Verifying the correctness of interactive behaviors and semantic consistency in the model fusion process is helpful to ensure the stability of the digital twin system and improve the utilization rate of resources. Finally, the effectiveness and operability of the proposed method is proved by a case study.