IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v31y2020i8d10.1007_s10845-020-01539-4.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01539-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-020-01539-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mellal, Mohamed Arezki & Zio, Enrico, 2020. "System reliability-redundancy optimization with cold-standby strategy by an enhanced nest cuckoo optimization algorithm," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    2. Neeraj Kumar Bhoi & Harpreet Singh & Saurabh Pratap & Pramod K. Jain, 2022. "Chemical reaction optimization algorithm for machining parameter of abrasive water jet cutting," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 350-363, March.
    3. N. A. Fountas & R. Benhadj-Djilali & C. I. Stergiou & N. M. Vaxevanidis, 2019. "An integrated framework for optimizing sculptured surface CNC tool paths based on direct software object evaluation and viral intelligence," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1581-1599, April.
    4. Mohamed Arezki Mellal & Enrico Zio, 2019. "An adaptive cuckoo optimization algorithm for system design optimization under failure dependencies," Journal of Risk and Reliability, , vol. 233(6), pages 1099-1105, December.
    5. Elango Natarajan & Varadaraju Kaviarasan & Wei Hong Lim & Sew Sun Tiang & S. Parasuraman & Sangeetha Elango, 2020. "Non-dominated sorting modified teaching–learning-based optimization for multi-objective machining of polytetrafluoroethylene (PTFE)," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 911-935, April.
    6. R. Venkata Rao & Dhiraj P. Rai & J. Balic, 2019. "Multi-objective optimization of abrasive waterjet machining process using Jaya algorithm and PROMETHEE Method," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2101-2127, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01539-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.