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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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
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