A Review of the Roles of Digital Twin in CPS-based Production Systems
Open accessAbstract
The Digital Twin (DT) is one of the main concepts associated to the Industry 4.0 wave. This term is more and more used in industry and research initiatives; however, the scientific literature does not provide a unique definition of this concept. The paper aims at analyzing the definitions of the DT concept in scientific literature, retracing it from the initial conceptualization in the aerospace field, to the most recent interpretations in the manufacturing domain and more specifically in Industry 4.0 and smart manufacturing research. DT provides virtual representations of systems along their lifecycle. Optimizations and decisions making would then rely on the same data that are updated in real-time with the physical system, through synchronization enabled by sensors. The paper also proposes the definition of DT for Industry 4.0 manufacturing, elaborated by the European H2020 project MAYA, as a contribution to the research discussion about DT concept.
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The novel manufacturing systems oriented towards the support of the Digital Twin concept are characterized by the need to integrate different flows of data originated by different manufacturing, control and management systems, and several enterprises processes and decision chains.
“From a software platform perspective, the novel manufacturing systems oriented towards the support of the Digital Twin concept are characterized by the need to integrate a) different flows of data originated by different manufacturing, control and management systems, and b) several enterprises processes and decision chains, [28].”
From Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models (2020) · cites this paper as [28]
Section 1 (Introduction; page unavailable) says data models can help integrate and share sensed data. Section 3.1 (The Digital Twin concept; page unavailable) describes a common representation of factory entities, including processes, and information used for management decisions. The copy does not establish the more specific claim that a software platform must integrate several enterprise processes and decision chains. (LLM, conf=0.82)
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Retracted
- Authors
- Negri, E., Fumagalli, L., & Macchi, M.
- Affiliations
- Politecnico di Milano (IT)
- Corresponding author
- Negri, Elisa
- Venue
- Procedia Manufacturing
- Publisher
- Elsevier BV
- Published
- 2017
- Volume
- 11
- Pages
- 939-948
- ISSN
- 2351-9789 (ISSN-L)
- OpenAlex
- W2755214325
- Cited by
- 1,669
- References
- 7
- Citations by year
- 2026: 195 · 2025: 224 · 2024: 220 · 2023: 244 · 2022: 229 · 2021: 230 · 2020: 191 · 2019: 107 · 2018: 29
- FWCI
- 98.02
- 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 › Engineering › Industrial and Manufacturing Engineering › Flexible and Reconfigurable Manufacturing Systems
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Applied Advanced Technologies
- Keywords
- digital twin, cyber-physical systems, Industry 4.0, smart manufacturing, digital twin definition, real-time data synchronization, sensors, manufacturing systems
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