IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v36y2025i7d10.1007_s10845-024-02488-y.html

Web tension AI modeling and reconstruction for digital twin of roll-to-roll system

Author

Listed:
  • Anton Nailevich Gafurov

    (Korea Institute of Machinery and Materials
    University of Science and Technology)

  • Jaeyoung Kim

    (Korea Institute of Machinery and Materials)

  • Inyoung Kim

    (Korea Institute of Machinery and Materials
    University of Science and Technology)

  • Taik-Min Lee

    (Korea Institute of Machinery and Materials
    University of Science and Technology)

Abstract

Digital twins (DT) are gaining attention as an emerging technology in Smart manufacturing systems. These DTs comprise various units that enable simulation, monitoring, and prediction of the manufacturing process. This study introduces a predictive model for web tension and a tension reconstruction algorithm for the DT of the roll-to-roll (R2R) system. The observed web tension signals from tension sensors decomposed into a mean component, a sinusoidal wave, and a random noise. Utilizing deep neural networks, the predictive model integrated various sub-models to forecast statistical (mean, standard deviation) and frequency domain (main frequency, signal-to-noise ratio) features of the web tension signal. Through fivefold cross-validation, 23 model architectures were optimized, with selected architectures ranging from 16-32-32-1 to 16-32-64-32-1 nodes per layer. Overall, R2 scores on the test set ranged from approximately 52 to 100%. The proposed reconstruction algorithm generated tension signals from the model’s predictions that closely resemble the original tension signals, indicating credible reconstructions. The proposed predictive model and reconstruction algorithm were integrated into the DT of the R2R system, offering a valuable tool for monitoring and optimizing the R2R process.

Suggested Citation

  • Anton Nailevich Gafurov & Jaeyoung Kim & Inyoung Kim & Taik-Min Lee, 2025. "Web tension AI modeling and reconstruction for digital twin of roll-to-roll system," Journal of Intelligent Manufacturing, Springer, vol. 36(7), pages 4977-4995, October.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:7:d:10.1007_s10845-024-02488-y
    DOI: 10.1007/s10845-024-02488-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-024-02488-y
    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/s10845-024-02488-y?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Ercan Oztemel & Samet Gursev, 2020. "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 127-182, January.
    Full references (including those not matched with items on IDEAS)

    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. Dinko Herman BOIKANYO, 2024. "Utilization Of 4ir Technologies To Enhance Strategic Intelligence And Dynamic Capabilities For A Sustainable Competitive Advantage," Business Excellence and Management, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 14(1), pages 5-24, March.
    2. Shuting Wang & Jie Meng & Yuanlong Xie & Liquan Jiang & Han Ding & Xinyu Shao, 2023. "Reference training system for intelligent manufacturing talent education: platform construction and curriculum development," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1125-1164, March.
    3. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2025. "Critical analysis of the impact of artificial intelligence integration with cutting-edge technologies for production systems," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 61-93, January.
    4. Christoph March & Ina Schieferdecker, 2021. "Technological Sovereignty as Ability, Not Autarky," CESifo Working Paper Series 9139, CESifo.
    5. Esther Calderon-Monge & Domingo Ribeiro-Soriano, 2024. "The role of digitalization in business and management: a systematic literature review," Review of Managerial Science, Springer, vol. 18(2), pages 449-491, February.
    6. Pompeu Casanovas & Louis de Koker & Mustafa Hashmi, 2022. "Law, Socio-Legal Governance, the Internet of Things, and Industry 4.0: A Middle-Out/Inside-Out Approach," J, MDPI, vol. 5(1), pages 1-28, January.
    7. Anna Kwiotkowska & Radosław Wolniak & Bożena Gajdzik & Magdalena Gębczyńska, 2022. "Configurational Paths of Leadership Competency Shortages and 4.0 Leadership Effectiveness: An fs/QCA Study," Sustainability, MDPI, vol. 14(5), pages 1-21, February.
    8. Masoud Zafarzadeh & Magnus Wiktorsson & Jannicke Baalsrud Hauge, 2021. "A Systematic Review on Technologies for Data-Driven Production Logistics: Their Role from a Holistic and Value Creation Perspective," Logistics, MDPI, vol. 5(2), pages 1-32, April.
    9. Özköse, Hakan & Güney, Gül, 2023. "The effects of industry 4.0 on productivity: A scientific mapping study," Technology in Society, Elsevier, vol. 75(C).
    10. S. G. Yapa & G. D. N Perera & P. Jayasekara, 2025. "Employee Engagement in Smart Factories: Research Gaps and a Nomological Framework for the Sri Lankan Apparel Sector," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(9), pages 4538-4549, September.
    11. Iñigo Flores Ituarte & Suraj Panicker & Hari P. N. Nagarajan & Eric Coatanea & David W. Rosen, 2023. "Optimisation-driven design to explore and exploit the process–structure–property–performance linkages in digital manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 219-241, January.
    12. Qinglan Liu & Adriana Hofmann Trevisan & Miying Yang & Janaina Mascarenhas, 2022. "A framework of digital technologies for the circular economy: Digital functions and mechanisms," Business Strategy and the Environment, Wiley Blackwell, vol. 31(5), pages 2171-2192, July.
    13. Liangjie Xia & Yongwan Bai & Sanjoy Ghose & Juanjuan Qin, 2022. "Differential game analysis of carbon emissions reduction and promotion in a sustainable supply chain considering social preferences," Annals of Operations Research, Springer, vol. 310(1), pages 257-292, March.
    14. John Mugambwa Serumaga-Zake & John Andrew van der Poll, 2021. "Addressing the Impact of Fourth Industrial Revolution on South African Manufacturing Small and Medium Enterprises (SMEs)," Sustainability, MDPI, vol. 13(21), pages 1-31, October.
    15. Mahdi Mokhtarzadeh & Jorge Rodríguez-Echeverría & Ivana Semanjski & Sidharta Gautama, 2025. "Hybrid intelligence failure analysis for industry 4.0: a literature review and future prospective," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2309-2334, April.
    16. 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.
    17. Fadi Shehab Shiyyab & Abdallah Bader Alzoubi & Qais Mohammad Obidat & Hashem Alshurafat, 2023. "The Impact of Artificial Intelligence Disclosure on Financial Performance," IJFS, MDPI, vol. 11(3), pages 1-25, September.
    18. Zhaoyuan He & Paul Turner, 2021. "A Systematic Review on Technologies and Industry 4.0 in the Forest Supply Chain: A Framework Identifying Challenges and Opportunities," Logistics, MDPI, vol. 5(4), pages 1-22, December.
    19. Ibrahim Yousif & Liam Burns & Fadi El Kalach & Ramy Harik, 2025. "Leveraging computer vision towards high-efficiency autonomous industrial facilities," Journal of Intelligent Manufacturing, Springer, vol. 36(5), pages 2983-3008, June.
    20. Andres Bustillo & Roberto Reis & Alisson R. Machado & Danil Yu. Pimenov, 2022. "Improving the accuracy of machine-learning models with data from machine test repetitions," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 203-221, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:36:y:2025:i:7:d:10.1007_s10845-024-02488-y. 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.