Blockchain-based collaborative evolution method for digital twin ontology model of mechanical products

被引：0
|
作者：
Zheng M. [1 ]
Tian L. [1 ]
机构：
[1] Department of Mechanical Engineering, Tsinghua University, Beijing
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2023年 / 29卷 / 06期
关键词：
blockchain; collaborative modeling; digital twin; mechanical products; model evolution; ontology model;
D O I：
10.13196/j.cims.2023.06.001
中图分类号：
学科分类号：
摘要：
The digital twin of mechanical products is a dynamic mapping developed by multiple stakeholders in the entire lifecycle,and the synchronization of its model evolution has become an important technical problem that needs to be solved urgently.Considering the multi-source heterogeneous evolution content and the trust-variable collaboration background,a blockchain-based collaborative evolution method for the digital twin ontology model of mechanical products was proposed.The evolution information was converted into standard exchangeable data through ontology-based representation and encapsulation technology.On this basis,the block structure was designed,and the consensus algorithm was used to realize the synchronous update of the model at each node in the authorized network.Through blockchain-based distributed storage,the modeling operations were mutually trusted and traceable.In this method,conflict identification and lightweight publishing were also supported.The evolution process of a clutch in the helicopter was taken as an example to verify the feasibility and effect of the proposed method. © 2023 CIMS. All rights reserved.
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页码：1781 / 1794
页数：13
相关论文
共 33 条
  • [1] TAO F, QI Q L., Make more digital twins[J], Nature, 573, pp. 490-491, (2019)
  • [2] GRIEVES M., Digital twin: Manufacturing excellence through virtual factory replication(2014), (2017)
  • [3] GLAESSGEN E, STARGEL D., The digital twin paradigm for future NASA and US air force vehicles, Proceedings of the 53rd ALAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference-Special Session on the Digital Twin, (2012)
  • [4] TAO F, ZHANG M, LIU Y S, Et al., Digital twin driven prognostics and health management for complex equipment [J], CIRP Annals, 67, 1, pp. 169-172, (2018)
  • [5] LIU Jianhua, XIONG Hui, Et al., Connotation, architecture and trends of product digital twin[J], Computer Integrated Manufacturing Systems, 23, 4, pp. 753-768, (2017)
  • [6] SCHLUSE M, ATORF L., ROSSMANN J., Experimentable digital twins for model-based systems engineering and simulation-based development, Proceedings of 2017 Annual IEEE International Systems Conference (SysCon), pp. 1-8, (2017)
  • [7] ULLAH A M M S., Modeling and simulation of complex manufacturing phenomena using sensor signals from the perspective of Industry 4.0, Advanced Engineering Informatics, 39, pp. 1-13, (2019)
  • [8] SCHROEDER G N, CPEREIRA C E, Et al., Digital twin data modeling with automationml and a communication methodology for data exchange[J], IFAC-PapersOnLine, 49, 30, pp. 12-17, (2016)
  • [9] NAFORS D, GONG L, Et al., Application of a hybrid digital twin concept for factory layout planning [J], Smart and Sustainable Manufacturing Systems, 4, 2, (2020)
  • [10] SINGH S, SHEHAB E, HIGGINS N, Et al., Data management for developing digital twin ontology model, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 235, 14, pp. 2323-2337, (2021)
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