Abstract
Digital twins present revolutionary potential in smart manufacturing and production. However, their current application in distributed control systems is minimal and largely unexplored. By applying digital twins to distributed control systems, distributed intelligent sensing and control systems may be achieved. These systems are fully automated and self-managing, making them a valuable asset.
In this paper, we provide a short literature review which establishes the definition, application, and implementation of digital twins in smart manufacturing and production. Based on this review, we propose their application in transforming distributed control systems into distributed intelligent and sensing control systems. We identify features of a digital twin which will be of greatest use in a distributed control system, and discuss our current research direction aimed at interfacing these control systems with digital twins.
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References
Ait-Alla, A., Kreutz, M., Rippel, D., Lütjen, M., Freitag, M.: Simulation-based analysis of the interaction of a physical and a digital twin in a cyber-physical production system. IFAC-PapersOnLine 52(13), 1331–1336 (2019). https://doi.org/10.1016/j.ifacol.2019.11.383
Azangoo, M., Taherkordi, A., Olaf Blech, J.: Digital twins for manufacturing using UML and behavioral specifications. In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2020-Sept, pp. 1035–1038 (2020). https://doi.org/10.1109/ETFA46521.2020.9212165
Biesinger, F., Meike, D., Kraß, B., Weyrich, M.: A digital twin for production planning based on cyber-physical systems: a case study for a cyber-physical system-based creation of a digital twin. Proc. CIRP 79, 355–360 (2019). https://doi.org/10.1016/j.procir.2019.02.087
Borangiu, T., Raileanu, S., Silisteanu, A., Anton, S., Anton, F.: Smart manufacturing control with cloud-embedded digital twins. In: 2020 24th International Conference on System Theory, Control and Computing, ICSTCC 2020—Proceedings pp. 915–920 (2020). https://doi.org/10.1109/ICSTCC50638.2020.9259684
He, R., Chen, G., Dong, C., Sun, S., Shen, X.: Data-driven digital twin technology for optimized control in process systems. ISA Trans. 95, 221–234 (2019). https://doi.org/10.1016/j.isatra.2019.05.011
Jazdi, N., Ashtari Talkhestani, B., Maschler, B., Weyrich, M.: Realization of AI-enhanced industrial automation systems using intelligent digital twins. Proc. CIRP 97, 396–400 (2020). https://doi.org/10.1016/j.procir.2020.05.257
Jeon, S.M., Schuesslbauer, S.: Digital twin application for production optimization. In: IEEE International Conference on Industrial Engineering and Engineering Management, 2020-Dec, pp. 542–545 (2020). https://doi.org/10.1109/IEEM45057.2020.9309874
Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W.: Digital twin in manufacturing: a categorical literature review and classification. IFAC-PapersOnLine 51(11), 1016–1022 (2018). https://doi.org/10.1016/j.ifacol.2018.08.474
Landolfi, G., Barni, A., Menato, S., Cavadini, F.A., Rovere, D., Dal Maso, G.: Design of a multi-sided platform supporting CPS deployment in the automation market. In: Proceedings—2018 IEEE Industrial Cyber-Physical Systems, ICPS 2018, pp. 684–689 (2018). https://doi.org/10.1109/ICPHYS.2018.8390790
Leng, J., Zhang, H., Yan, D., Liu, Q., Chen, X., Zhang, D.: Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop. J. Ambient Intelligence Humanized Comput. 10(3), 1155–1166 (2019). https://doi.org/10.1007/s12652-018-0881-5
Liu, M., Fang, S., Dong, H., Xu, C.: Review of digital twin about concepts, technologies, and industrial applications. J. Manuf. Syst., 1–16 (2020). https://doi.org/10.1016/j.jmsy.2020.06.017
Liu, Q., Leng, J., Yan, D., Zhang, D., Wei, L., Yu, A., Zhao, R., Zhang, H., Chen, X.: Digital twin-based designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system. J. Manuf. Syst., 1–13 (2020). https://doi.org/10.1016/j.jmsy.2020.04.012
Liu, Z., Chen, W., Zhang, C., Yang, C., Cheng, Q.: Intelligent scheduling of a feature-process-machine tool supernetwork based on digital twin workshop. J. Manuf. Syst., 0–1 (2020). https://doi.org/10.1016/j.jmsy.2020.07.016
Madni, A., Madni, C., Lucero, S.: Leveraging digital twin technology in model-based systems engineering. Systems 7(1), 7 (2019). https://doi.org/10.3390/systems7010007
Meier, N., Muller-Polyzou, R., Brach, L., Georgiadis, A.: Digital twin support for laser-based assembly assistance. In: Procedia CIRP, vol. 99, pp. 460–465. Elsevier B.V. (2021). https://doi.org/10.1016/j.procir.2021.03.066
Preuveneers, D., Joosen, W., Ilie-Zudor, E.: Robust digital twin compositions for industry 4.0 smart manufacturing systems. In: Proceedings—IEEE International Enterprise Distributed Object Computing Workshop, EDOCW, 2018-Oct, pp. 69–78 (2018). https://doi.org/10.1109/EDOCW.2018.00021
Qamsane, Y., Moyne, J., Toothman, M., Kovalenko, I., Balta, E.C., Faris, J., Tilbury, D.M., Barton, K.: A methodology to develop and implement digital twin solutions for manufacturing systems. IEEE Access 9, 44247–44265 (2021). https://doi.org/10.1109/ACCESS.2021.3065971
Qi, Q., Tao, F.: Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access 6, 3585–3593 (2018). https://doi.org/10.1109/ACCESS.2018.2793265
Schleich, B., Anwer, N., Mathieu, L., Wartzack, S.: Shaping the digital twin for design and production engineering. CIRP Ann.-Manuf. Technol. 66(1), 141–144 (2017). https://doi.org/10.1016/j.cirp.2017.04.040
Talkhestani, B.A., Braun, D., Schloegl, W., Weyrich, M.: Qualitative and quantitative evaluation of reconfiguring an automation system using digital twin. Proc. CIRP 93, 268–273 (2020). https://doi.org/10.1016/j.procir.2020.03.014
Uhlemann, T.H., Lehmann, C., Steinhilper, R.: The digital twin: realizing the cyber-physical production system for industry 4.0. Proc. CIRP 61, 335–340 (2017). https://doi.org/10.1016/j.procir.2016.11.152
Vachalek, J., Bartalsky, L., Rovny, O., Sismisova, D., Morhac, M., Loksik, M.: The digital twin of an industrial production line within the industry 4.0 concept. In: Proceedings of the 2017 21st International Conference on Process Control, PC 2017, pp. 258–262 (2017). https://doi.org/10.1109/PC.2017.7976223
Wu, C., Zhou, Y., Pereia Pessôa, M.V., Peng, Q., Tan, R.: Conceptual digital twin modeling based on an integrated five-dimensional framework and TRIZ function model. J. Manuf. Syst., 1–15 (2020). https://doi.org/10.1016/j.jmsy.2020.07.006
Xia, L., Lu, J., Zhang, H.: Research on construction method of digital twin workshop based on digital twin engine. In: Proceedings of 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2020, pp. 417–421 (2020). https://doi.org/10.1109/AEECA49918.2020.9213649
Yu-Ming, Q., Bing, X., San-Peng, D.: Research on intelligent manufacturing flexible production line system based on digital twin. In: Proceedings—2020 35th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2020, pp. 854–862 (2020). https://doi.org/10.1109/YAC51587.2020.9337500
Zhang, K., Qu, T., Zhou, D., Jiang, H., Lin, Y., Li, P., Guo, H., Liu, Y., Li, C., Huang, G.Q.: Digital twin-based opti-state control method for a synchronized production operation system. Robot. Comput.-Integr. Manuf. 63, 101, 892 (2020). https://doi.org/10.1016/j.rcim.2019.101892
Zhao, R., Yan, D., Liu, Q., Leng, J., Wan, J., Chen, X., Zhang, X.: Digital twin-driven cyber-physical system for autonomously controlling of micro punching system. IEEE Access 7, 9459–9469 (2019). https://doi.org/10.1109/ACCESS.2019.2891060
Zhuang, C., Miao, T., Liu, J., Xiong, H.: The connotation of digital twin, and the construction and application method of shop-floor digital twin. Robot. Comput.-Integr. Manuf. 68, 102, 075 (2021). https://doi.org/10.1016/j.rcim.2020.102075
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Lesage, J., Brennan, R. (2022). Digital Twins for Distributed Intelligent Sensing and Control Systems. In: Borangiu, T., Trentesaux, D., Leitão, P., Cardin, O., Joblot, L. (eds) Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future. SOHOMA 2021. Studies in Computational Intelligence, vol 1034. Springer, Cham. https://doi.org/10.1007/978-3-030-99108-1_9
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