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The architecture development of Industry 4.0 compliant smart machine tool system (SMTS)

Author

Listed:
  • Byeongwoo Jeon

    (Research Center for Smart Factory)

  • Joo-Sung Yoon

    (Korea Institute of Industrial Technology)

  • Jumyung Um

    (Kyung Hee University)

  • Suk-Hwan Suh

    (Research Center for Smart Factory)

Abstract

As part of the fourth industrial revolution, the movement to apply various enabling technologies under the name of Industry 4.0 is being promoted worldwide. Because of the wide range of applications and the capacity of manufacturing workpieces flexibly, machine tools are regarded as essential industrial elements. Hence, much research has been concerned with applying various enabling technologies such as cyber-physical systems to machine tools. To realize a machine tool suitable for Industry 4.0, development should be done in a systematic manner rather than the ad-hoc application of enabling technologies. In this paper, we propose a functional architecture for the Industry 4.0 version of machine tools, namely smart machine tool system. To reflect the voices of various stakeholders, stakeholder requirements are identified and transformed into design considerations. The design considerations are incorporated into the conceptual model and functional modeling, both of which are used to derive the functional architecture. The implementation procedure and an illustrative case study are presented for the application of the functional architecture.

Suggested Citation

  • Byeongwoo Jeon & Joo-Sung Yoon & Jumyung Um & Suk-Hwan Suh, 2020. "The architecture development of Industry 4.0 compliant smart machine tool system (SMTS)," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1837-1859, December.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01539-4
    DOI: 10.1007/s10845-020-01539-4
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    References listed on IDEAS

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    1. Mohamed Arezki Mellal & Edward J. Williams, 2016. "Parameter optimization of advanced machining processes using cuckoo optimization algorithm and hoopoe heuristic," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 927-942, October.
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    Cited by:

    1. Silvestro Vespoli & Guido Guizzi & Elisa Gebennini & Andrea Grassi, 2022. "A novel throughput control algorithm for semi-heterarchical industry 4.0 architecture," Annals of Operations Research, Springer, vol. 310(1), pages 201-221, March.
    2. Farzana Zahid & Awais Tanveer & Matthew M. Y. Kuo & Roopak Sinha, 2022. "A systematic mapping of semi-formal and formal methods in requirements engineering of industrial Cyber-Physical systems," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1603-1638, August.
    3. Kyu Tae Park & Sang Ho Lee & Sang Do Noh, 2022. "Information fusion and systematic logic library-generation methods for self-configuration of autonomous digital twin," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2409-2439, December.
    4. Kyu Tae Park & Jinho Yang & Sang Do Noh, 2021. "VREDI: virtual representation for a digital twin application in a work-center-level asset administration shell," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 501-544, February.
    5. Chi Ma & Hongquan Gui & Jialan Liu, 2023. "Self learning-empowered thermal error control method of precision machine tools based on digital twin," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 695-717, February.
    6. Benjamin Lutz & Dominik Kisskalt & Andreas Mayr & Daniel Regulin & Matteo Pantano & Jörg Franke, 2021. "In-situ identification of material batches using machine learning for machining operations," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1485-1495, June.

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