Abstract
In recent years, the new generation of information technology has been widely applied in manufacturing domain. Building intelligent workshop and achieving intelligent manufacturing have become the purposes of industry development. The current workshop service systems just fulfill the mapping between physical and digital layer, while have not completed the interconnection and interaction between physical world and information world. It is the emergence of digital twin that become one of the solution to this bottleneck. In this paper, a system framework of digital twin workshop is proposed. In the light of the framework, a model of digital twin workshop based on physical perception data is established. This model is divided into three parts, which including workshop physical model, digital model based on ontology, and virtual model. Moreover, the operation mechanism of digital twin workshop is described among the three models. Finally, a three-dimensional model for a production line is built, and the connection between digital and virtual layer is established and demonstrated.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tao, F., Zhang, M., Cheng, J., et al.: Digital twin workshop: a new paradigm for future workshop. Comput. Integr. Manuf. Syst. 23(1), 1–9 (2017). (in Chinese)
APRISO Digital twin: manufacturing excellence through virtual factory replication. http://www.apriso.com. Accessed 06 May 2014
Boschert, S., Rosen, R.: Digital twin—the simulation aspect. In: Hehenberger, P., Bradley, D. (eds.) Mechatronic Futures. Springer, Cham (2016)
Grieves, M., Vickers, J.: Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems. In: Kahlen, J., Flumerfelt, S., Alves, A. (eds.) Transdisciplinary Perspectives on Complex Systems. Springer, Cham (2017)
Tuegel, E., Ingraffea, A., Eason, T., et al.: Reengineering aircraft structural life prediction using a digital twin. Int. J. Aerosp. Eng. 2011, 1687–5966 (2011)
Hochhalter, J.: On the effects of modeling as-manufactured geometry: toward digital twin. Int. J. Aerosp. Eng. 2014(439278), 1–10 (2014)
Glaessgen, E., Stargel, D.: The digital twin paradigm for future NASA and U.S. air force vehicles. In: Proceedings of the 53rd Structures Dynamics and Materials Congerence, pp. 1–14. AIAA, Reston (2012)
Canedo, A.: Industrial IoT lifecycle via digital twins. In: 11th Ieee/acm/ifip International Conference on Hardware/Software Codesign and System Synthesis, p. 29. IEEE, Pittsburgh (2016)
SIEMENS The digital twin. https://www.siemens.com/customer-magazine/en/home/indus-try/digitalization-in-machine-building/the-digital-twin.html. Accessed 17 Nov 2015
Rosen, R., Wichert, G., Lo, G., et al.: About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine 48(3), 567–572 (2015)
Schluse, M., Rossmann, J.: From simulation to experimentable digital twins: simulation-based development and operation of complex technical systems. In: IEEE International Symposium on Systems Engineering, pp. 1–6. IEEE, Edinburgh (2016)
Du, L., Fang, Y., He, Y.: Manufacturing resource optimization deployment for manufactuing execution system. In: Second International Symposium on Intelligent Information Technology Application, pp. 234–238. IEEE, Shanghai (2008)
Luo, Y., Lin, Z., Fei, T., et al.: Key technologies of manufacturing capability modeling in cloud manufacturing mode. Comput. Integr. Manuf. Syst. 18(7), 1357–1367 (2012)
Studer, R., Benjamins, V., Fensel, D.: Knowledge engineering: principles and methods. Data Knowl. Eng. 25(1–2), 161–197 (1998)
Xu, W., Yu, J., Zhou, Z., et al.: Dynamic modeling of manufacturing equipment capability using condition Information in cloud manufacturing. J. Manuf. Sci. Eng. 137(4), 1–14 (2015)
Acknowledgment
This research is supported by National Natural Science Foundation of China (Grant Nos. 51305319 and 51475343), the International Science & Technology Cooperation Program, Hubei Technological Innovation Special Fund (Grant No. 2016AHB005), and the Fundamental Research Funds for the Central Universities (Grant No. 2017III5XZ).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Zhang, Q., Zhang, X., Xu, W., Liu, A., Zhou, Z., Pham, D.T. (2017). Modeling of Digital Twin Workshop Based on Perception Data. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10464. Springer, Cham. https://doi.org/10.1007/978-3-319-65298-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-65298-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-65297-9
Online ISBN: 978-3-319-65298-6
eBook Packages: Computer ScienceComputer Science (R0)