IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v32y2021i7d10.1007_s10845-021-01748-5.html
   My bibliography  Save this article

A smart process controller framework for Industry 4.0 settings

Author

Listed:
  • Yuval Cohen

    (Afeka Tel-Aviv College of Engineering)

  • Gonen Singer

    (Bar-Ilan University)

Abstract

This paper presents a smart supervisory framework for a single process controller, designed for Industry 4.0 shop floors. This digitization of a full supervisory suite for a single process controller enables self-awareness, self-diagnosis, self-prognosis, and self-healing (by definition, these "self" elements are missing from other supervisory frameworks diagnosing numerous controllers in parallel). The proposed framework is aligned with the concept of a Cyber Physical System (CPS), since its implementation generates a rich cyber physical entity of the controlled process. This CPS entity can either be considered as the process digital twin, or can provide a solid basis for generating it. Finally, the framework includes the main characteristics of Industry 4.0, such as advanced use of Artificial Intelligence (AI) and big data analysis. The framework is based on four modules: (1) Control and Awareness module—performing both continuous process control and adjustments, as well as machine learning (ML) and statistical process control (SPC) for identifying abnormalities that require further diagnosis; (2) Process -diagnosis module—performing continual (recurrent) analysis of the process state and trends; (3) Prognosis and Healing module—performing prognosis and automated intervention via parameter changes, re-configurations, and automated maintenance; (4) External Interaction Platform—an interactive module for interfacing with experts, presenting them with the process analysis information and obtaining feedback from them as part of a learning process. Using an implementation showcase to illustrate the methodological framework’s applicability, we demonstrate its real-world potential. The proposed framework could serve as a guide for implementing smart process control and maintenance systems in Industry 4.0 shop floors. It could also provide a firm basis for comparison with future suggested frameworks. Future research directions could include pursuing improvements to the proposed process control framework and validating the framework by case studies of its implementation.

Suggested Citation

  • Yuval Cohen & Gonen Singer, 2021. "A smart process controller framework for Industry 4.0 settings," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1975-1995, October.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:7:d:10.1007_s10845-021-01748-5
    DOI: 10.1007/s10845-021-01748-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01748-5
    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/s10845-021-01748-5?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. Bokrantz, Jon & Skoogh, Anders & Berlin, Cecilia & Stahre, Johan, 2017. "Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030," International Journal of Production Economics, Elsevier, vol. 191(C), pages 154-169.
    2. 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.
    3. Marcelo Bacher & Irad Ben-Gal, 2017. "Ensemble-Bayesian SPC: Multi-mode process monitoring for novelty detection," IISE Transactions, Taylor & Francis Journals, vol. 49(11), pages 1014-1030, November.
    4. Mahesh Mani & Brandon M. Lane & M. Alkan Donmez & Shaw C. Feng & Shawn P. Moylan, 2017. "A review on measurement science needs for real-time control of additive manufacturing metal powder bed fusion processes," International Journal of Production Research, Taylor & Francis Journals, vol. 55(5), pages 1400-1418, March.
    5. Nitesh Khilwani & J. A. Harding, 2016. "Managing corporate memory on the semantic web," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 101-118, February.
    6. Alexandre Moeuf & Robert Pellerin & Samir Lamouri & Simon Tamayo-Giraldo & Rodolphe Barbaray, 2018. "The industrial management of SMEs in the era of Industry 4.0," International Journal of Production Research, Taylor & Francis Journals, vol. 56(3), pages 1118-1136, February.
    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. Mohamed Ismail & Noha A. Mostafa & Ahmed El-assal, 2022. "Quality monitoring in multistage manufacturing systems by using machine learning techniques," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2471-2486, December.

    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. Peter Chhim & Ratna Babu Chinnam & Noureddin Sadawi, 2019. "Product design and manufacturing process based ontology for manufacturing knowledge reuse," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 905-916, February.
    2. Gillani, Fatima & Chatha, Kamran Ali & Sadiq Jajja, Muhammad Shakeel & Farooq, Sami, 2020. "Implementation of digital manufacturing technologies: Antecedents and consequences," International Journal of Production Economics, Elsevier, vol. 229(C).
    3. Gábor Szabó-Szentgróti & Bence Végvári & József Varga, 2021. "Impact of Industry 4.0 and Digitization on Labor Market for 2030-Verification of Keynes’ Prediction," Sustainability, MDPI, vol. 13(14), pages 1-19, July.
    4. Sony, Michael & Naik, Subhash, 2020. "Industry 4.0 integration with socio-technical systems theory: A systematic review and proposed theoretical model," Technology in Society, Elsevier, vol. 61(C).
    5. Zahoor, Nadia & Zopiatis, Anastasios & Adomako, Samuel & Lamprinakos, Grigorios, 2023. "The micro-foundations of digitally transforming SMEs: How digital literacy and technology interact with managerial attributes," Journal of Business Research, Elsevier, vol. 159(C).
    6. Anhang Chen & Huiqin Zhang & Yuxiang Zhang & Junwei Zhao, 2024. "Manufacturers’ digital transformation under carbon cap-and-trade policy: investment strategy and environmental impact," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    7. Marić, Josip & Opazo-Basáez, Marco & Vlačić, Božidar & Dabić, Marina, 2023. "Innovation management of three-dimensional printing (3DP) technology: Disclosing insights from existing literature and determining future research streams," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    8. Durga Prasad Penumuru & Sreekumar Muthuswamy & Premkumar Karumbu, 2020. "Identification and classification of materials using machine vision and machine learning in the context of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1229-1241, June.
    9. Christoph Markmann & Alexander Spickermann & Heiko A. von der Gracht & Alexander Brem, 2021. "Improving the question formulation in Delphi‐like surveys: Analysis of the effects of abstract language and amount of information on response behavior," Futures & Foresight Science, John Wiley & Sons, vol. 3(1), March.
    10. Bianco, Débora & Bueno, Adauto & Godinho Filho, Moacir & Latan, Hengky & Miller Devós Ganga, Gilberto & Frank, Alejandro G. & Chiappetta Jabbour, Charbel Jose, 2023. "The role of Industry 4.0 in developing resilience for manufacturing companies during COVID-19," International Journal of Production Economics, Elsevier, vol. 256(C).
    11. Yüksel, Hilmi, 2020. "An empirical evaluation of industry 4.0 applications of companies in Turkey: The case of a developing country," Technology in Society, Elsevier, vol. 63(C).
    12. Hossein Heirani & Naser Bagheri Moghaddam & Sina Labbafi & Seyedali Sina, 2022. "A Business Model for Developing Distributed Photovoltaic Systems in Iran," Sustainability, MDPI, vol. 14(18), pages 1-21, September.
    13. Vitkauskaitė, Elena & Varaniūtė, Viktorija & Bouwman, Harry, 2019. "Evaluating SMEs Readiness to Transform to IoT-Based Business Models," 30th European Regional ITS Conference, Helsinki 2019 205220, International Telecommunications Society (ITS).
    14. Szymon Cyfert & Waldemar Glabiszewski & Maciej Zastempowski, 2021. "Impact of Management Tools Supporting Industry 4.0 on the Importance of CSR during COVID-19. Generation Z," Energies, MDPI, vol. 14(6), pages 1-13, March.
    15. Bokrantz, Jon & Skoogh, Anders & Berlin, Cecilia & Wuest, Thorsten & Stahre, Johan, 2020. "Smart Maintenance: a research agenda for industrial maintenance management," International Journal of Production Economics, Elsevier, vol. 224(C).
    16. Juan Pablo Usuga Cadavid & Samir Lamouri & Bernard Grabot & Robert Pellerin & Arnaud Fortin, 2020. "Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1531-1558, August.
    17. Federica Costa & Alberto Portioli-Staudacher, 2021. "Labor flexibility integration in workload control in Industry 4.0 era," Operations Management Research, Springer, vol. 14(3), pages 420-433, December.
    18. Miguel Baritto & Md Mashum Billal & S. M. Muntasir Nasim & Rumana Afroz Sultana & Mohammad Arani & Ahmed Jawad Qureshi, 2020. "Supporting Tool for The Transition of Existing Small and Medium Enterprises Towards Industry 4.0," Papers 2010.12038, arXiv.org.
    19. Henrik Saabye & Thomas Borup Kristensen & Brian Vejrum Wæhrens, 2020. "Real-Time Data Utilization Barriers to Improving Production Performance: An In-depth Case Study Linking Lean Management and Industry 4.0 from a Learning Organization Perspective," Sustainability, MDPI, vol. 12(21), pages 1-21, October.
    20. Andreas Felsberger & Gerald Reiner, 2020. "Sustainable Industry 4.0 in Production and Operations Management: A Systematic Literature Review," Sustainability, MDPI, vol. 12(19), pages 1-39, September.

    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:32:y:2021:i:7:d:10.1007_s10845-021-01748-5. 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.