IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v34y2023i8d10.1007_s10845-022-02028-6.html
   My bibliography  Save this article

A digital shadow framework using distributed system concepts

Author

Listed:
  • Ayman AboElHassan

    (Polytechnique Montréal
    Cairo University)

  • Soumaya Yacout

    (Polytechnique Montréal)

Abstract

Digital twin (DT) is a research topic that gained momentum in the Industry 4.0 era. The goal of DT is to create a virtual real-time intelligent system that is a typical twin of the physical system. DT provides analysis, prognosis, planning, and rapid response when needed. Digital shadow (DS) is an artifact concept of DT that provides a real-time replica of the physical system. Information in a DS is passed in one direction only, from the physical system to the virtual one. While in DT, the information goes in both directions. The definition and roles of DS and DT are overlapping. However, DS can be defined as the main component of a DT system. In this paper, DS roles are specified. Based on these roles, A DS framework architecture is proposed, and the communication system between its components. The proposed framework design is built using distributed system concepts such as event-driven architecture, microservices, and containerization. These concepts are well defined and utilized in the software engineering domain. The originality of the proposed framework is the definition of a systematic approach for designing and integrating digital models (DMs) from different vendors and domains. An experiment is designed to prove the framework’s ability to shadow a physical system in real-time. Multiple DMs are implemented and deployed on the proposed DS framework. These DMs are used to shadow a natural gas compressor system. Experimental results prove the practicality of our proposed DS framework to operate in real-time.

Suggested Citation

  • Ayman AboElHassan & Soumaya Yacout, 2023. "A digital shadow framework using distributed system concepts," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3579-3598, December.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:8:d:10.1007_s10845-022-02028-6
    DOI: 10.1007/s10845-022-02028-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-022-02028-6
    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/s10845-022-02028-6?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. Xin Tong & Qiang Liu & Shiwei Pi & Yao Xiao, 2020. "Real-time machining data application and service based on IMT digital twin," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1113-1132, June.
    2. Martí de Castro-Cros & Manel Velasco & Cecilio Angulo, 2021. "Machine-Learning-Based Condition Assessment of Gas Turbines—A Review," Energies, MDPI, vol. 14(24), pages 1-27, December.
    3. A. J. H. Redelinghuys & A. H. Basson & K. Kruger, 2020. "A six-layer architecture for the digital twin: a manufacturing case study implementation," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1383-1402, August.
    4. Konstantinos Mykoniatis & Gregory A. Harris, 2021. "A digital twin emulator of a modular production system using a data-driven hybrid modeling and simulation approach," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1899-1911, October.
    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. 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.
    2. Ahmed Ktari & Mohamed El Mansori, 2022. "Digital twin of functional gating system in 3D printed molds for sand casting using a neural network," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 897-909, March.
    3. PengYu Wang & Wen-An Yang & YouPeng You, 2023. "A cyber-physical prototype system in augmented reality using RGB-D camera for CNC machining simulation," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3637-3658, December.
    4. Aniket Nagargoje & Pavan Kumar Kankar & Prashant Kumar Jain & Puneet Tandon, 2023. "Application of artificial intelligence techniques in incremental forming: a state-of-the-art review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 985-1002, March.
    5. Hassan Alimam & Giovanni Mazzuto & Marco Ortenzi & Filippo Emanuele Ciarapica & Maurizio Bevilacqua, 2023. "Intelligent Retrofitting Paradigm for Conventional Machines towards the Digital Triplet Hierarchy," Sustainability, MDPI, vol. 15(2), pages 1-30, January.
    6. Zander, Bennet & Lange, Kerstin & Haasis, Hans-Dietrich, 2021. "Designing the data supply chain of a smart construction factory," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 41-62, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    7. Zengya Zhao & Sibao Wang & Zehua Wang & Shilong Wang & Chi Ma & Bo Yang, 2022. "Surface roughness stabilization method based on digital twin-driven machining parameters self-adaption adjustment: a case study in five-axis machining," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 943-952, April.
    8. Jyrki Savolainen & Michele Urbani, 2021. "Maintenance optimization for a multi-unit system with digital twin simulation," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1953-1973, October.
    9. Jacek Czyżewicz & Piotr Jaskólski & Paweł Ziemiański & Marian Piwowarski & Mateusz Bortkiewicz & Krzysztof Laszuk & Ireneusz Galara & Marta Pawłowska & Karol Cybulski, 2022. "Towards Designing an Innovative Industrial Fan: Developing Regression and Neural Models Based on Remote Mass Measurements," Energies, MDPI, vol. 15(7), pages 1-19, March.
    10. Cheng, Xianda & Zheng, Haoran & Yang, Qian & Zheng, Peiying & Dong, Wei, 2023. "Surrogate model-based real-time gas path fault diagnosis for gas turbines under transient conditions," Energy, Elsevier, vol. 278(PA).
    11. Vivek Warke & Satish Kumar & Arunkumar Bongale & Ketan Kotecha, 2021. "Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis," Sustainability, MDPI, vol. 13(18), pages 1-49, September.
    12. Pan, Yanghua & Zhong, Ray Y. & Qu, Ting & Ding, Liqiang & Zhang, Jun, 2024. "Multi-level digital twin-driven kitting-synchronized optimization for production logistics system," International Journal of Production Economics, Elsevier, vol. 271(C).
    13. Saporiti, Nicolò & Cannas, Violetta Giada & Pozzi, Rossella & Rossi, Tommaso, 2023. "Challenges and countermeasures for digital twin implementation in manufacturing plants: A Delphi study," International Journal of Production Economics, Elsevier, vol. 261(C).
    14. Bettiol, Marco & Capestro, Mauro & Di Maria, Eleonora & Ganau, Roberto, 2024. "Is this time different?: how Industry 4.0 affects firms' labor productivity," LSE Research Online Documents on Economics 124545, London School of Economics and Political Science, LSE Library.
    15. 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.
    16. Benno Gerlach & Simon Zarnitz & Benjamin Nitsche & Frank Straube, 2021. "Digital Supply Chain Twins—Conceptual Clarification, Use Cases and Benefits," Logistics, MDPI, vol. 5(4), pages 1-24, December.
    17. Guo Zhou & Kai Zhou & Jing Zhang & Meng Yuan & Xiaohao Wang & Pingfa Feng & Min Zhang & Feng Feng, 2024. "Digital modeling-driven chatter suppression for thin-walled part manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 289-305, January.
    18. Marco Bettiol & Mauro Capestro & Eleonora Di Maria & Roberto Ganau, 2024. "Is this time different? How Industry 4.0 affects firms’ labor productivity," Small Business Economics, Springer, vol. 62(4), pages 1449-1467, April.
    19. Weifei Hu & Jinyi Shao & Qing Jiao & Chuxuan Wang & Jin Cheng & Zhenyu Liu & Jianrong Tan, 2023. "A new differentiable architecture search method for optimizing convolutional neural networks in the digital twin of intelligent robotic grasping," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2943-2961, October.
    20. Kaishu Xia & Thorsten Wuest & Ramy Harik, 2023. "Automated manufacturability analysis in smart manufacturing systems: a signature mapping method for product-centered digital twins," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3069-3090, October.

    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:34:y:2023:i:8:d:10.1007_s10845-022-02028-6. 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.