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A modular factory testbed for the rapid reconfiguration of manufacturing systems

Author

Listed:
  • D.-Y. Kim

    (Ulsan National Institute of Science and Technology)

  • J.-W. Park

    (Ulsan National Institute of Science and Technology)

  • S. Baek

    (Ulsan National Institute of Science and Technology)

  • K.-B. Park

    (Ulsan National Institute of Science and Technology)

  • H.-R. Kim

    (Ulsan National Institute of Science and Technology)

  • J.-I. Park

    (Ulsan National Institute of Science and Technology)

  • H.-S. Kim

    (Ulsan National Institute of Science and Technology)

  • B.-B. Kim

    (Ulsan National Institute of Science and Technology)

  • H.-Y. Oh

    (Ulsan National Institute of Science and Technology)

  • K. Namgung

    (Ulsan National Institute of Science and Technology)

  • W. Baek

    (Ulsan National Institute of Science and Technology)

Abstract

The recent manufacturing trend toward mass customization and further personalization of products requires factories to be smarter than ever before in order to: (1) quickly respond to customer requirements, (2) resiliently retool machinery and adjust operational parameters for unforeseen system failures and product quality problems, and (3) retrofit old systems with upcoming new technologies. Furthermore, product lifecycles are becoming shorter due to unbounded and unpredictable customer requirements, thereby requiring reconfigurable and versatile manufacturing systems that underpin the basic building blocks of smart factories. This study introduces a modular factory testbed, emphasizing transformability and modularity under a distributed shop-floor control architecture. The main technologies and methods, being developed and verified through the testbed, are presented from the four aspects of rapid factory transformation: self-layout recognition, rapid workstation and robot reprogramming, inter-layer information sharing, and configurable software for shop-floor monitoring.

Suggested Citation

  • D.-Y. Kim & J.-W. Park & S. Baek & K.-B. Park & H.-R. Kim & J.-I. Park & H.-S. Kim & B.-B. Kim & H.-Y. Oh & K. Namgung & W. Baek, 2020. "A modular factory testbed for the rapid reconfiguration of manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 661-680, March.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:3:d:10.1007_s10845-019-01471-2
    DOI: 10.1007/s10845-019-01471-2
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    References listed on IDEAS

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    1. Ricardo Jardim-Goncalves & Antonio Grilo & Keith Popplewell, 2016. "Novel strategies for global manufacturing systems interoperability," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 1-9, February.
    2. Yassine Qamsane & Abdelouahed Tajer & Alexandre Philippot, 2017. "A synthesis approach to distributed supervisory control design for manufacturing systems with Grafcet implementation," International Journal of Production Research, Taylor & Francis Journals, vol. 55(15), pages 4283-4303, August.
    3. Amro M. Farid, 2017. "Measures of reconfigurability and its key characteristics in intelligent manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 353-369, February.
    4. Mingzhou Liu & Jing Ma & Ling Lin & Maogen Ge & Qiang Wang & Conghu Liu, 2017. "Intelligent assembly system for mechanical products and key technology based on internet of things," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 271-299, February.
    5. Chuang Wang & Pingyu Jiang, 2018. "Manifold learning based rescheduling decision mechanism for recessive disturbances in RFID-driven job shops," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1485-1500, October.
    6. Olivier Cardin & Damien Trentesaux & André Thomas & Pierre Castagna & Thierry Berger & Hind Bril El-Haouzi, 2017. "Coupling predictive scheduling and reactive control in manufacturing hybrid control architectures: state of the art and future challenges," Journal of Intelligent Manufacturing, Springer, vol. 28(7), pages 1503-1517, October.
    7. Bruccoleri, Manfredi & Pasek, Zbigniew J. & Koren, Yoram, 2006. "Operation management in reconfigurable manufacturing systems: Reconfiguration for error handling," International Journal of Production Economics, Elsevier, vol. 100(1), pages 87-100, March.
    8. Sihan Huang & Guoxin Wang & Xiwen Shang & Yan Yan, 2018. "Reconfiguration point decision method based on dynamic complexity for reconfigurable manufacturing system (RMS)," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1031-1043, June.
    9. Alfred Theorin & Kristofer Bengtsson & Julien Provost & Michael Lieder & Charlotta Johnsson & Thomas Lundholm & Bengt Lennartson, 2017. "An event-driven manufacturing information system architecture for Industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 55(5), pages 1297-1311, March.
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    Cited by:

    1. Giovanni Boschetti & Matteo Bottin & Maurizio Faccio & Riccardo Minto, 2021. "Multi-robot multi-operator collaborative assembly systems: a performance evaluation model," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1455-1470, June.
    2. 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.
    3. 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.
    4. Gautam Dutta & Ravinder Kumar & Rahul Sindhwani & Rajesh Kr. Singh, 2021. "Digitalization priorities of quality control processes for SMEs: a conceptual study in perspective of Industry 4.0 adoption," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1679-1698, August.
    5. Wei Nie & Katharina Vita & Tariq Masood, 2024. "An ontology for defining and characterizing demonstration environments," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3501-3521, October.

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