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Cited by 100Digital Transformation in IndustryFlexible and Reconfigurable Manufacturing SystemsImpact of AI and Big Data on Business and Society

A framework to design smart manufacturing systems for Industry 5.0 based on the human-automation symbiosis

Open access
Peruzzini, Margherita, Prati, Elisa, Pellicciari, Marcello·2024·International Journal of Computer Integrated Manufacturing
DOI 10.1080/0951192x.2023.2257634
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Abstract

The concept of Industry 5.0 (I5.0) promotes the human-centricity as the core value behind the evolution of smart manufacturing systems (SMSs), based on a novel use of digital technologies in the design and management of modern industrial systems to take up the socio-technical challenges. In this context, the paper proposes a Smart Manufacturing Systems Design (SMSD) framework enabling I5.0, based on the human-automation symbiosis. Thanks to an ‘Augmented Digital Twin’ (ADT) able to integrate and digitize all the entities of the factory (i.e. machines, robots, environments, interfaces, people), AI-driven applications can be built to support the user domain and make people and machines co-evolve thanks to a systematic data sharing between physical and digital assets (e.g. digital twin, virtual mock-ups, human-machine interfaces), optimizing factory productivity and workers wellbeing. In this framework, machines and humans can both generate knowledge and learn from each other, generating a virtuous co-evolution, supporting the understanding of the human-machine interplay and the creation of an effective collaboration between people and SMSs. The framework was conceived and validated involving four industrial companies, belonging to diverse sectors, interested in overcoming the current limits of I4.0 lines by including the human factors for future SMS management.

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  • The proposed system is an ideal candidate for ensuring optimal resource usage in industrial automation and smart manufacturing systems.

    “As these systems depend on sensor data to make intelligent decisions, the proposed system is an ideal candidate for ensuring optimal resource usage [61].”

    From Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive Maintenance Framework (2024) · cites this paper as [61]

    The Conclusions (p. 17) say the proposed framework supports greater efficiency and productivity, and the human-automation symbiosis table (p. 13) lists process optimization. The paper does not establish that the framework ensures optimal resource usage, so the citing sentence overstates what it supports. (LLM, conf=0.82)

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Research works printed but not linked: 4

  1. Breque, M., L. De Nul, and A. Petridis 2021. 'Industry 5.0: Towards a Sustainable, Human-Centric and Resilient European Industry, EC DG for Research and Innovation.' https://doi.org/10.2777/308407. · DOI
  2. Krupitzer, C., S. Müller, V. Lesch, M. Züfle, J. Edinger, A. Lemken, D. Schäfer, S. Kounev, and C. Becker. 2020. 'A Survey on Human Machine Interaction in Industry 4.0.' HumanComputer Interaction 1 (1): 1-45. https://doi.org/10.1145/ 1122445.1122456.
  3. Renda, A., S. Schwaag Serger, D. Tataj, A. Morlet, D. Isaksson, F. Martins, M. Mir Roca, C. Hidalgo, A. Huang, S. DixsonDeclève, P.-A. Balland, F. Bria, C. Charveriat, K. Dunlop, and E. Giovannini. 2022. 'Industry 5.0, a Transformative Vision for Europe - Governing Systemic Transformations Towards a Sustainable Industry, EC DG for Research and Innovation.' https://doi.org/10.2777/17322. · DOI
  4. Tzafestas, S. 2006. 'Concerning Human-Automation Symbiosis in the Society and the Nature.' International Journal of Factory Automation, Robotics and Soft Computing 1 (3): 6-24. van der Valk, H., H. Haße, F. Möller, M. Arbter, J. L. Henning, and B. Otto. 2020. 'A Taxonomy of Digital Twins.' In Proceedings of 26th Americas Conference on Information Systems , edited by B. Anderson, J. Thatcher, and R. Meservy pp. 1-10.

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Authors
Peruzzini, M., Prati, E., & Pellicciari, M.
Affiliations
University of Modena and Reggio Emilia (IT)
Corresponding author
Peruzzini, Margherita
ORCID
Venue
International Journal of Computer Integrated Manufacturing
Publisher
Informa UK Limited
Published
2023-09-20
Volume
37
Issue
10-11
Pages
1426-1443
ISSN
  • 0951-192X (ISSN-L)
  • 1362-3052
DOI
10.1080/0951192x.2023.2257634
OpenAlex
W4386892322
Cited by
100
References
24
Citations by year
2026: 34 · 2025: 49 · 2024: 16 · 2023: 1
FWCI
13.28
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Computer Integrated Manufacturing (publishedVersion, cc-by)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by)
  • IRIS UNIMORE (University of Modena and Reggio Emilia) (submittedVersion, other-oa)
Funders
H2020 Industrial Leadership (958303)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Flexible and Reconfigurable Manufacturing Systems
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
Keywords
Industry 5.0, smart manufacturing systems, human-machine collaboration, human-centric manufacturing, employee well-being, human-machine interaction, design framework

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