IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i2p1441-d1033113.html
   My bibliography  Save this article

Intelligent Retrofitting Paradigm for Conventional Machines towards the Digital Triplet Hierarchy

Author

Listed:
  • Hassan Alimam

    (Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, 60131 Ancona, Italy)

  • Giovanni Mazzuto

    (Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, 60131 Ancona, Italy)

  • Marco Ortenzi

    (Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, 60131 Ancona, Italy)

  • Filippo Emanuele Ciarapica

    (Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, 60131 Ancona, Italy)

  • Maurizio Bevilacqua

    (Department of Industrial Engineering and Mathematical Science, Università Politecnica delle Marche, 60131 Ancona, Italy)

Abstract

Industry 4.0 is evolving through technological advancements, leveraging information technology to enhance industry with digitalisation and intelligent activities. Whereas Industry 5.0 is the Age of Augmentation, striving to concentrate on human-centricity, sustainability, and resilience of the intelligent factories and synergetic industry. The crucial enhancer for the improvements accomplished by digital transformation is the notion of ‘digital triplet D3’, which is an augmentation of the digital twin with artificial intelligence, human ingenuity, and experience. digital triplet D3 encompasses intelligent activities based on human awareness and the convergence among cyberspace, physical space, and humans, in which Implementing useful reference hierarchy is a crucial part of instigating Industry 5.0 into a reality. This paper depicts a digital triplet which discloses the potency of retrofitting a conventional drilling machine. This hierarchy included the perceptive level for complex decision-making by deploying machine learning based on human ingenuity and creativity, the concatenated level for controlling the physical system’s behaviour predictions and emulation, the observing level is the iterative observation of the actual behaviour of the physical system using real-time data, and the duplicating level visualises and emulates virtual features through physical tasks. The accomplishment demonstrated the viability of the hierarchy in imitating the real-time functionality of the physical system in cyberspace, an immaculate performance of this paradigm. The digital triplet’s complexity was diminished through the interaction among facile digital twins, intelligent activities, and human awareness. The performance parameters of the digital triplet D3 paradigm for retrofitting were eventually confirmed through appraising, anomaly analysis, and real-time monitoring.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1441-:d:1033113
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/2/1441/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/2/1441/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dimitris Mourtzis & John Angelopoulos & Nikos Panopoulos, 2022. "A Literature Review of the Challenges and Opportunities of the Transition from Industry 4.0 to Society 5.0," Energies, MDPI, vol. 15(17), pages 1-29, August.
    2. Marcello Fera & Raffaele Abbate & Mario Caterino & Pasquale Manco & Roberto Macchiaroli & Marta Rinaldi, 2020. "Economic and Environmental Sustainability for Aircrafts Service Life," Sustainability, MDPI, vol. 12(23), pages 1-17, December.
    3. Michael W. Grieves, 2005. "Product lifecycle management: the new paradigm for enterprises," International Journal of Product Development, Inderscience Enterprises Ltd, vol. 2(1/2), pages 71-84.
    4. Benjamin James Ralph & Marcel Sorger & Karin Hartl & Andreas Schwarz-Gsaxner & Florian Messner & Martin Stockinger, 2022. "Transformation of a rolling mill aggregate to a cyber physical production system: from sensor retrofitting to machine learning," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 493-518, February.
    5. 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.
    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. Hazrathosseini, Arman & Moradi Afrapoli, Ali, 2023. "The advent of digital twins in surface mining: Its time has finally arrived," Resources Policy, Elsevier, vol. 80(C).
    2. Maria Mercanti-Guérin, 2021. "From Perceived Creativity To Status Quo Bias The Case Of Digital Twins In The Home," Post-Print hal-03450262, HAL.
    3. 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.
    4. Chen, Ziyue & Huang, Lizhen, 2021. "Digital twins for information-sharing in remanufacturing supply chain: A review," Energy, Elsevier, vol. 220(C).
    5. 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.
    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. Pengcheng Ni & Zhiyuan Ye & Can Cao & Zhimin Guo & Jian Zhao & Xing He, 2023. "Cooperative Game-Based Collaborative Optimal Regulation-Assisted Digital Twins for Wide-Area Distributed Energy," Energies, MDPI, vol. 16(6), pages 1-17, March.
    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. 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.
    10. Wang, Jinrui & Zhang, Zongzhen & Liu, Zhiliang & Han, Baokun & Bao, Huaiqian & Ji, Shanshan, 2023. "Digital twin aided adversarial transfer learning method for domain adaptation fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    11. Hsing-Chun Hung & Yuh-Wen Chen, 2023. "Striving to Achieve United Nations Sustainable Development Goals of Taiwanese SMEs by Adopting Industry 4.0," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    12. 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.
    13. Fabio Di Carlo & Giovanni Mazzuto & Maurizio Bevilacqua & Filippo Emanuele Ciarapica, 2021. "Retrofitting a Process Plant in an Industry 4.0 Perspective for Improving Safety and Maintenance Performance," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
    14. Chris Turner & John Oyekan & Wolfgang Garn & Cian Duggan & Khaled Abdou, 2022. "Industry 5.0 and the Circular Economy: Utilizing LCA with Intelligent Products," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    15. Verdouw, Cor & Tekinerdogan, Bedir & Beulens, Adrie & Wolfert, Sjaak, 2021. "Digital twins in smart farming," Agricultural Systems, Elsevier, vol. 189(C).
    16. Dianyou Yu & Zheng He, 2022. "Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(1), pages 1-36, May.
    17. 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).
    18. M J Schniederjans & A M Schniederjans & D G Schniederjans, 2009. "Operations research methodology life cycle trend phases as recorded in journal articles," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(7), pages 881-894, July.
    19. Padmanathan Kasinathan & Rishi Pugazhendhi & Rajvikram Madurai Elavarasan & Vigna Kumaran Ramachandaramurthy & Vinoth Ramanathan & Senthilkumar Subramanian & Sachin Kumar & Kamalakannan Nandhagopal & , 2022. "Realization of Sustainable Development Goals with Disruptive Technologies by Integrating Industry 5.0, Society 5.0, Smart Cities and Villages," Sustainability, MDPI, vol. 14(22), pages 1-31, November.
    20. Federica Acerbi & Claudio Sassanelli & Sergio Terzi & Marco Taisch, 2021. "A Systematic Literature Review on Data and Information Required for Circular Manufacturing Strategies Adoption," Sustainability, MDPI, vol. 13(4), pages 1-26, February.

    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:gam:jsusta:v:15:y:2023:i:2:p:1441-:d:1033113. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.