1. Proceedings
  2. I3M
  3. 2021
  4. EMSS
  5. 10.46354/i3m.2021.emss.021

A Multi-level Heterogeneous Model data Framework for Intelligent Factory Digital-Twin Systems

  • a Zheng Li,
  • b Yusheng Kong ,
  • c Lei Ren
  • a  China Academy of Information and Communications Technology, 52 Huayuan North Road, Haidian District, Beijing, 100089, China
  • b,c School of Automation Science and Electrical Engineering, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100089, China
Cite as
Li Z., Kong Y., Ren L. (2021). A Multi-level Heterogeneous Model data Framework for Intelligent Factory Digital-Twin Systems. Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 152-157. DOI: https://doi.org/10.46354/i3m.2021.emss.021

Abstract

By using physical models and continuously updated sensor data, digital-twin can map industrial physical space entities to cyber space model system to realize the whole life cycle simulation and evaluation of complex industrial entities, which is an important key technology to promote industry 4.0. The management of complex heterogeneous models of digital-twin systems in an intelligent factory is facing severe challenges. This paper presents a multi-level heterogeneous model data framework for intelligent factory digital-twin systems. The multi-level integration framework is established for all levels of unit equipment, production lines, workshops and factories, as well as cross-domain product design, manufacturing, operation and maintenance. The paper presents an industrial model data management framework for multi-level digital-twin systems, and designs an data interoperability mechanism for cross-domain heterogeneous models based on knowledge ontology semantic networks. The proposed framework can provide an important theoretical framework for the management of complex model systems of digital-twin systems in intelligent factory.

References

  1. Grieves, & Michael, W. . (2005). Product lifecycle management: the new paradigm for enterprises. International Journal of Product Development, 2(1/2), 71.
  2. Ren, L. , Zhang, L. , Wang, L. , F Tao, & Chai, X. . (2014). Cloud manufacturing: key characteristics and applications. International Journal of Computer Integrated Manufacturing.
  3. Ren, L. , Meng, Z. , Wang, X. , Zhang, L. , & Yang, L. T. . (2020). A data-driven approach of product quality prediction for complex production systems. IEEE Transactions on Industrial Informatics, PP(99), 1-1.
  4. Ren, L. , Laili, Y. , Li, X. , & Wang, X. . (2019). Coding-based large-scale task assignment for industrial edge intelligence. IEEE Transactions on Network Science and Engineering, PP(99), 1-1.
  5. Grieves, M. . (2011). Virtually Perfect: Driving Innovative and Lean Products through Product Lifecycle Management. 
  6. Grieves, M. , & Vickers, J. . (2017). Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. Springer International Publishing.
  7. Shafto, M. , Conroy, M. , Doyle, R. , Glaessgen, E. , & Wang, L. . (2010). Modeling, Simulation, Information Technology and Processing Roadmap.
  8. Tuegel, E. J. , Ingraffea, A. R. , Eason, T. G. , & Spottswood, S. M. . (2011). Reengineering aircraft structural life prediction using a digital twin. International Journal of Aerospace Engineering, 2011(1687-5966).
  9. Li, C. , Mahadevan, S. , You, L. , Choze, S. , & Wang, L. . (2017). Dynamic bayesian network for aircraft wing health monitoring digital twin. AIAA Journal, 55(3), 1-12.
  10. Wang, J. , Xiang, Y. , & Zhengwen, H. E. . (2019). Models and implementation of digital twin based spacecraft system engineering. Computer Integrated Manufacturing Systems.
  11. Zhang, L. , Zhang, X. S. , Song, X. , Li, B. H. , & Liu, Y. . (2013). Model engineering for complex system simulation. Journal of System Simulation.
  12. Zeigler, B. P. , & Lin, Z. . (2015). Service-Oriented Model Engineering and Simulation for System of Systems Engineering. Springer International Publishing.
  13. Zhang, L. , Zhang, X. S. , Song, X. , Li, B. H. , & Liu, Y. . (2013). Model engineering for complex system simulation. Journal of System Simulation.
  14. Zhang, L et al. (2020). Model maturity towards modeling and simulation: Concepts, index system framework and evaluation method. International Journal of Modeling, Simulation, and Scientific Computing, 11(03), pp. 21.
  15. Tao, F. , Cheng, Y. , Cheng J. F. et al.(2017). Theory and technology of Information Physics fusion in digital twin Workshop .Computer Integrated Manufacturing Systems.
  16. Zhao, H. , Liu, J. , Xiong, H. , Zhuang, C. , Miao, T. , & Liu, J. , et al. (2019). 3d visualization real-time monitoring method for digital twin workshop. Computer Integrated Manufacturing Systems.
  17. G.L. Knapp et al. (2017). Building blocks for a digital twin of additive manufacturing. Acta Materialia, 135pp. 390-399.
  18. Pedro Daniel Urbina Coronado et al. (2018). Part data integration in the Shop Floor Digital Twin: Mobile and cloud technologies to enable a manufacturing execution system. Journal of Manufacturing Systems.
  19. Qingguang Tao et al. (2018). A cloud-based experimental platform for networked industrial control systems. International Journal of Modeling, Simulation, and Scientific Computing, 9(4).
  20. Chenglong Ge and Yuanchang Zhu and Yanqiang Di. (2018). Equipment remaining useful life prediction oriented symbiotic simulation driven by real-time degradation data. International Journal of Modeling, Simulation, and Scientific Computing, 9(2).
  21. Ren, L. , Liu, Y. , X Wang, J Lu, & Deen, M. J. . (2020). Cloud-edge based lightweight temporal convolutional networks for remaining useful life prediction in iiot. IEEE Internet of Things Journal, PP(99), 1-1.
  22. Ren, L., Dong, J., Wang, X., Meng, Z., Zhao, L., & Deen, M. J. (2020). A data-driven auto-cnn-lstm prediction model for lithium-ion battery remaining useful life. IEEE Transactions on Industrial Informatics, 17(5), 3478-3487.
  23. Ren, Lei, Cui, Jin, &Sun, Yaqiang, et al. (2017). Multi-bearing remaining useful life collaborative prediction: a deep learning approach. Journal of Manufacturing Systems.
  24. Ren, L. , Meng, Z. , Wang, X. , Lu, R. , & Yang, L. T. . (2020). A wide-deep-sequence model-based quality prediction method in industrial process analysis. IEEE Transactions on Neural Networks and Learning Systems, PP(99), 1-11.