FMU-supported simulation for CPS Digital Twin
Open accessAbstract
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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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 measuredIn 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 measuredThe 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
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Self-citationRole of Ontologies for CPS Implementation in Manufacturing
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Self-citation
Flagged first, then by citations
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Cited by
Sorted by citations
Digital twin: values, challenges and enablers from a modeling perspective
Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952 · see citing works
Review of digital twin applications in manufacturing
Cimino, C., Negri, E., & Fumagalli, L. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.103130 · Cited by 874 · see citing works
Self-citationDigital twin for integration of design-manufacturing-maintenance: an overview
Fu, Y., Zhu, G., Zhu, M., & Xuan, F. (2022) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-022-00760-x · Cited by 98 · see citing works
Framework for simulation software selection
Fumagalli, L., Polenghi, A., Negri, E., & Roda, I. (2019) · Journal of Simulation · doi:10.1080/17477778.2019.1598782 · Cited by 33 · see citing works
Self-citation
- 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
- diamond
- doi.org
- Licence
- 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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