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2023 | OriginalPaper | Chapter

Transformation of Conventional Manufacturing and Service Systems into a Cyber-Physical Environment: Review of Potential Solutions

Author : Tamás Bányai

Published in: Vehicle and Automotive Engineering 4

Publisher: Springer International Publishing

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Abstract

The Fourth Industrial Revolution offers new potentials to increase the efficiency, availability, sustainability and transparency of manufacturing systems. The Industry 4.0 technologies are widely used in the field of purchasing, production, distribution and reverse processes to enhance the efficiency from a technological and logistics point of view. The application of these new technologies leads to the transformation of the conventional manufacturing and service systems into cyber-physical systems, where the design and operation of the new, globalised, interconnected and hyper-connected systems and supply chains require new optimisation approaches. Within the frame of this article, the author focuses on the potential of these mentioned Industry 4.0 technologies from a digitalisation point of view. After a systematic literature review, this paper introduces some potential ways to transform conventional manufacturing and related service systems into cyber-physical environment in the field of manufacturing processes in the automotive industry, hyper-connected collection and distribution systems in city logistics and switch pool packaging logistics in Industry 4.0 era, where smart sensors, intelligent tools, gentelligent products, digital twin solutions and edge computing support the transformation.

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Zhou, J., Li, P., Zhou, Y., Wang, B., Zang, J., Meng, L.: Toward new-generation intelligent manufacturing. Engineering 4(1), 11–20 (2018). https://doi.org/10.1016/j.eng.2018.01.002CrossRef

Negri, E., Fumagalli, L., Macchi, M.: A review of the roles of digital twin in cps-based production systems. Procedia Manufact. 11, 939–948 (2017). https://doi.org/10.1016/j.promfg.2017.07.198CrossRef

Tao, F., Zhang, M.: Digital twin shop-floor: A new shop-floor paradigm towards smart manufacturing. IEEE Access 5, 20418–20427 (2017). https://doi.org/10.1109/ACCESS.2017.2756069CrossRef

Bányai, Á.: Energy consumption-based maintenance policy optimization. Energies 14(18), 5674 (2021). https://doi.org/10.3390/en14185674CrossRef

Cimino, C., Negri, E., Fumagalli, L.: Review of digital twin applications in manufacturing. Comput. Ind. 113, 103130 (2019). https://doi.org/10.1016/j.compind.2019.103130CrossRef

Qian, F., Zhong, W., Du, W.: Fundamental theories and key technologies for smart and optimal manufacturing in the process industry. Engineering 3(2), 154–160 (2017). https://doi.org/10.1016/J.ENG.2017.02.011CrossRef

Uhlemann, T.H.-J., Lehmann, C., Steinhilper, R.: The digital twin: Realising the cyber-physical production system for industry 4.0. Procedia CIRP 61, 335–340 (2017). https://doi.org/10.1016/j.procir.2016.11.152CrossRef

Uhlemann, T.H.-J., Schock, C., Lehmann, C., Freiberger, S., Steinhilper, R.: The digital twin: Demonstrating the potential of real time data acquisition in production systems. Procedia Manufact. 9, 113–120 (2017). https://doi.org/10.1016/j.promfg.2017.04.043CrossRef

Frazzon, E.M., Hartmann, J., Makuschewitz, T., Scholz-Reiter, B.: Towards socio-cyber-physical systems in production networks. Procedia CIRP 7, 49–54 (2013). https://doi.org/10.1016/j.procir.2013.05.009CrossRef

10.

Nikolakis, N., Alexopoulos, K., Xanthakis, E., Chryssolouris, G.: The digital twin implementation for linking the virtual representation of human-based production tasks to their physical counterpart in the factory-floor. Int. J. Comput. Integr. Manuf. 32(1), 1–12 (2019). https://doi.org/10.1080/0951192X.2018.1529430CrossRef

11.

Liu, C., Cao, S., Tse, W., Xu, X.: Augmented reality-assisted intelligent window for cyber-physical machine tools. J. Manuf. Syst. 44, 280–286 (2017). https://doi.org/10.1016/j.jmsy.2017.04.008CrossRef

12.

Glistau, E., Trojahn, S., Bányai, Á.: Logistics 4.0: Smart infrastructure. Multi. Sci. 11(5), 215–224 (2021). https://doi.org/10.35925/j.multi.2021.5.22CrossRef

13.

Leng, J., et al.: Digital twin-driven rapid reconfiguration of the automated manufacturing system via an open architecture model. Robot. Comput. Integr. Manuf. 63, 101895 (2020). https://doi.org/10.1016/j.rcim.2019.101895CrossRef

14.

Chen, J., et al.: CPS modeling of CNC machine tool work processes using an instruction-domain based approach. Engineering 1(2), 247–260 (2015). https://doi.org/10.15302/J-ENG-2015054CrossRef

15.

Trstenjak, M., Cosic, P.: Process planning in Industry 4.0 environment. Procedia Manufact. 11, 1744–1750 (2017). https://doi.org/10.1016/j.promfg.2017.07.303CrossRef

16.

Weyer, S., Meyer, T., Ohmer, M., Gorecky, D., Zühlke, D.: Future modeling and simulation of CPS-based factories: An example from the automotive industry. IFAC-PapersOnLine 49(31), 97–102 (2016). https://doi.org/10.1016/j.ifacol.2016.12.168CrossRef

17.

Liu, Q., et al.: Digital twin-based designing of the configuration, motion, control, and optimisation model of a flow-type smart manufacturing system. J. Manuf. Syst. 58, 52–64 (2021). https://doi.org/10.1016/j.jmsy.2020.04.012CrossRef

18.

Lanza, G., Haefner, B., Kraemer, A.: Optimisation of selective assembly and adaptive manufacturing by means of cyber-physical system based matching. CIRP Ann. Manuf. Technol. 64(1), 399–402 (2015). https://doi.org/10.1016/j.cirp.2015.04.123CrossRef

19.

Ghahramani, M., Qiao, Y., Zhou, M., Hagan, A., Sweeney, J.: AI-based modeling and data-driven evaluation for smart manufacturing processes. IEEE/CAA J. Automatica Sinica 7(4), 1026–1037 (2020). https://doi.org/10.1109/JAS.2020.1003114CrossRef

20.

Liang, Y.C., Lu, X., Li, W.D., Wang, S.: Cyber physical system and big data enabled energy efficient machining optimisation. J. Clean. Prod. 187, 46–62 (2018). https://doi.org/10.1016/j.jclepro.2018.03.149CrossRef

21.

Kota, L.: Optimisation of the supplier selection problem using discrete firefly algorithm. Adv. Logistic Syst. Theory Pract. 6(1), 117–126 (2012)

22.

Veres, P., Illés, B., Landschützer, C.: Supply chain optimization in automotive industry: A comparative analysis of evolutionary and swarming heuristics. In: Jármai, K., Bolló, B. (eds.) VAE 2018. LNME, pp. 666–676. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75677-6_57CrossRef

23.

Wan, J., Yin, B., Li, D., Celesti, A., Tao, F., Hua, Q.: An ontology-based resource reconfiguration method for manufacturing cyber-physical systems. IEEE/ASME Trans. Mechatron. 23(6), 2537–2546 (2018). https://doi.org/10.1109/TMECH.2018.2814784CrossRef

24.

Jiang, Z., Jin, Y., Mingcheng, E., Li, Q.: Distributed dynamic scheduling for cyber-physical production systems based on a multi-agent system. IEEE Access 6, 1855–1869 (2017). https://doi.org/10.1109/ACCESS.2017.2780321CrossRef

25.

Veres, P.: The importance of clustering in logistic systems. Rezanie i Instrumenty v Tekhnologicheskih Sistemah 94(1), 11–18 (2021). https://doi.org/10.20998/2078-7405.2021.94.02MathSciNetCrossRef

26.

Isaksson, A.J., Harjunkoski, I., Sand, G.: The impact of digitalisation on the future of control and operations. Comput. Chem. Eng. 114, 122–129 (2018). https://doi.org/10.1016/j.compchemeng.2017.10.037CrossRef

27.

Ma, S., Zhang, Y., Liu, Y., Yang, H., Lv, J., Ren, S.: Data-driven sustainable intelligent manufacturing based on demand response for energy-intensive industries. J. Clean. Prod. 274, 123155 (2020). https://doi.org/10.1016/j.jclepro.2020.123155CrossRef

28.

Tang, H., Li, D., Wang, S., Dong, Z.: CASOA: An architecture for agent-based manufacturing system in the context of Industry 4.0. IEEE Access 6, 12746–12754 (2017). https://doi.org/10.1109/ACCESS.2017.2758160CrossRef

29.

Ochoa, S.F., Fortino, G., Di Fatta, G.: Cyber-physical systems, internet of things and big data. Futur. Gener. Comput. Syst. 75, 82–84 (2017). https://doi.org/10.1016/j.future.2017.05.040CrossRef

30.

Ivanov, D., Dolgui, A., Das, A., Sokolov, B.: Digital supply chain twins: Managing the ripple effect, resilience, and disruption risks by data-driven optimization, simulation, and visibility. In: Ivanov, D., Dolgui, A., Sokolov, B. (eds.) Handbook of Ripple Effects in the Supply Chain. ISORMS, vol. 276, pp. 309–332. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14302-2_15CrossRef

31.

Huber, S., Wiemer, H., Schneider, D., Ihlenfeldt, S.: DMME: Data mining methodology for engineering applications - A holistic extension to the CRISP-DM model. Procedia CIRP 79, 403–408 (2019). https://doi.org/10.1016/j.procir.2019.02.106CrossRef

32.

Prinz, C., Kreggenfeld, N., Kuhlenkötter, B.: Lean meets Industrie 4.0 - A practical approach to interlink the method world and cyber-physical world. Procedia Manufact. 23, 21–26 (2018). https://doi.org/10.1016/j.promfg.2018.03.155CrossRef

33.

Bányai, Á., et al.: Smart cyber-physical manufacturing: Extended and real-time optimisation of logistics resources in matrix production. Appl. Sci.-Basel 9(7), 1287 (2019). https://doi.org/10.3390/app9071287CrossRef

34.

Bányai, T.: Optimisation of material supply in smart manufacturing environment: A metaheuristic approach for matrix production. Machines 9(10), 220 (2021). https://doi.org/10.3390/machines9100220CrossRef

35.

Akkad, M.Z., Bányai, T.: Multi-objective approach for optimisation of city logistics considering energy efficiency. Sustainability 12(18), 7366 (2020). https://doi.org/10.3390/su12187366CrossRef

36.

Upasani, K., Bakshi, M., Pandhare, V., Lad, B.K.: Distributed maintenance planning in manufacturing industries. Comput. Ind. Eng. 108, 1–14 (2017). https://doi.org/10.1016/j.cie.2017.03.027CrossRef

Title: Transformation of Conventional Manufacturing and Service Systems into a Cyber-Physical Environment: Review of Potential Solutions
Author: Tamás Bányai
Publisher: Springer International Publishing
Book: Vehicle and Automotive Engineering 4
Print ISBN: 978-3-031-15210-8

Electronic ISBN: 978-3-031-15211-5

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-3-031-15211-5_38

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