Cited by 89Digital Transformation in IndustryModeling and Simulation SystemsFlexible and Reconfigurable Manufacturing Systems

FMU-supported simulation for CPS Digital Twin

Open access
Negri, Elisa, Fumagalli, Luca, Cimino, Chiara, Macchi, Marco·2019·Procedia Manufacturing

Abstract

Manufacturing companies are experiencing the fourth industrial revolution characterised by the introduction of new technologies into production equipment, such as the Cyber Physical Systems and the Digital Twin simulations. Companies are then challenged by the digitization of products and production systems information, which leads to new potentials for digital continuity – i.e. information available and continuously updated for the decision makers – along the lifecycles. A semantic data model, that structures and stores physical and operational data from the field, can support the digital continuity to be used in production system simulations in a Digital Twin paradigm. This work proposes to model specific aspects and behaviours of the production system separately from the core simulation, in order to flexibly decide whether to activate the replica of the specific behaviours only when needed. The modules interact with the main simulation run through standard interfaces, allowing an easy reusability of the single modules also in different simulation environments.

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Crossref · 10.1016/j.promfg.2018.12.033

Cites

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  • Other articles introduce digital twins for maintenance purposes.

    “Other articles introduce the DT for 'Maintenance' purposes [48,54-59]”

    From Review of digital twin applications in manufacturing (2019) · cites this paper as [54]

    The Conclusions (page not provided) identify maintenance planning as a possible future application of the Digital Twin simulation. The paper introduces and tests its simulation approach for manufacturing equipment and energy consumption, not for maintenance purposes. (LLM, conf=0.86)

    Experimental — accuracy not yet measured
  • In the reviewed articles, DT was used for maintenance purposes, including reconditioning of old machines.

    “- 5) Other articles introduce the DT for 'Maintenance' purposes [48,54-59], such as reconditioning of old machines [53];”

    From Review of digital twin applications in manufacturing (2019) · cites this paper as [54]

    The Conclusions (page unavailable) identify maintenance planning and failure prevention as possible future applications of Digital Twin simulation. The paper’s demonstrated application instead models equipment states and energy consumption, so it does not show a Digital Twin being used for maintenance. Reconditioning is attached to a different citation, [53], in the citing sentence and is not attributed to this source. (LLM, conf=0.86)

    Experimental — accuracy not yet measured
  • The reviewed articles can be divided into two categories by cloud usage: those that effectively use a cloud and build a cloud-based DT application, and those that construct the DT application in a virtual space with software highlighting the possibility to build it also in a cloud-based environment.

    “For this aspect, the articles considered can be divided into two different categories: (i) those that effectively use a cloud and build a cloud-based DT application, marked in the table as [27,34,37,43,47,48,54,56,62,67,74-76], (ii) those that construct the DT application in a virtual space with the use of a software highlighting the possibility to build it also in a cloud”

    From Review of digital twin applications in manufacturing (2019) · cites this paper as [54]

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Cites

Flagged first, then by citations

Research works printed but not linked: 12

  1. T. Gabor, L. Belzner, M. Kiermeier, M. T. Beck, and A. Neitz, "A Simulation-Based Architecture for Smart Cyber-Physical Systems," in Autonomic Computing (ICAC), 2016 IEEE International Conference on, 2016, pp. 374-379.
  2. G. N. Schroeder, C. Steinmetz, C. E. Pereira, and E. D. B., "Digital Twin Data Modeling with AutomationML and a Communication Methodology for Data Exchange," in IFAC-PapersOnLine, 2016, vol. 49, no. 30, pp. 12-17.
  3. M. Abramovici, J. C. Gobel, and H. B. Dang, "Semantic data management for the development and continuous reconfiguration of smart products and systems," CIRP Ann. -Manuf. Technol., vol. 65, pp. 185-188, 2016.
  4. E. Negri, L. Fumagalli, and M. Macchi, "A review of the roles of Digital Twin in CPS-based production systems," Procedia Manuf., vol. 11, no. June, pp. 939-948, 2017.
  5. J. Lee and E. Lapira, "Predictive factories: the next transformation," Manuf. Leadersh. J., vol. 20, no. 1, pp. 13-24, 2013.
  6. S. Yun, J. Park, and W. Kim, "Data-centric Middleware based Digital Twin Platform for Dependable Cyber-Physical Systems," in Ubiquitous and Future Networks (ICUFN), 2017 Ninth International Conference on, 2017, pp. 922-926.
  7. B. Wang and J. S. Baras, "HybridSim : A Modeling and Co-simulation Toolchain for Cyber-Physical Systems," in 17th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, 2013, pp. 33-40.
  8. Y. Seow, S. Rahimifard, and E. Woolley, "Simulation of energy consumption in the manufacture of a product," Int. J. Comput. Integr. Manuf., vol. 26, no. 7, pp. 663-680, 2013.
  9. G. Wiederhold and M. Genesereth, "The conceptual basis for mediation services," IEEE Expert. Syst. their Appl., vol. 12, no. 5, pp. 38-47, 1997.
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  11. A. Maka, R. Cupek, and J. Rosner, "OPC UA Object Oriented Model for Public Transportation System," in UKSim 5th European Symposium on Coputer Modeling and Simulation, 2011, pp. 311-316.
  12. S. Weyer, T. Meyer, M. Ohmer, S. Dominic, D. Gorecky, and D. Zuehlke, "Future Modeling and Simulation of CPS-based Factories : an Example from the Automotive Industry," in IFAC-PapersOnLine 49-31, 2016, pp. 97-102.

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Authors
Negri, E., Fumagalli, L., Cimino, C., & Macchi, M.
Affiliations
Politecnico di Milano (IT)
Corresponding author
Negri, Elisa
Venue
Procedia Manufacturing
Publisher
Elsevier BV
Published
2019
Volume
28
Pages
201-206
ISSN
2351-9789 (ISSN-L)
OpenAlex
W2914193262
Cited by
89
References
5
Citations by year
2026: 3 · 2025: 6 · 2024: 15 · 2023: 14 · 2022: 18 · 2021: 13 · 2020: 12 · 2019: 8
FWCI
9.41
Type
journal article
Language
en
Open access
Locations
  • Procedia Manufacturing (publishedVersion, cc-by-nc-nd)
  • Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion, other-oa)
Funders
European Commission (678556, HORIZON2020)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Modeling and Simulation Systems
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Flexible and Reconfigurable Manufacturing Systems
Keywords
digital twin, cyber-physical systems, production system simulation, semantic data model, digital continuity, Fourth Industrial Revolution

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