- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | DING Guofu, LIU Mingyuan, XIE Jiaxiang, ZHANG Jian, ZHANG Haizhu, ZHENG Qing. Collaborative Computing Method for Highly Available Operation of Digital Twin Manufacturing Equipment[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20230074
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In digital twin technology, the operation of complex models and production logic consumes a large number of resources. Meanwhile, differences in hardware capabilities and user requirements make it difficult to ensure simulation accuracy and real-time performance, reducing system availability. To address this issue, a real-time synchronous computing framework for digital twin manufacturing equipment that collaboratively processed visualization and logic computation was proposed. Firstly, a digital model of the equipment was constructed based on multidimensional workshop information. According to hardware capabilities and users’ personalized computational needs, a configurable and adaptive system environment mapping method was introduced to adjust simulation fidelity, ensuring the real-time and correct operation of the twin equipment. The process was illustrated by using lighting environment mapping as an example. Secondly, a simulation-based motion logic solving algorithm for a six-degree-of-freedom (6-DOF) manipulator was presented, which used the rendering frame time as the simulation clock advancement step to ensure accurate model motion and synchronization between visualization and computation. The algorithm was generalized for application to other multi-body equipment. Finally, a web-based digital twin workshop modeling and simulation cloud platform was designed and developed. A 6-DOF manipulator and a specific bogie frame processing workshop were used as application cases, and the proposed method was validated. The results show that with the adaptive reduction of mapping fidelity, simulation response speed is increased by 45%, while GPU and CPU resource utilization is effectively reduced. It proves that the method can achieve reasonable resource allocation and efficient system computation while reducing error accumulation, making it a highly available real-time collaborative computing method.
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