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Cited by 48Digital Transformation in IndustryFlexible and Reconfigurable Manufacturing SystemsManufacturing Process and Optimization

A Connective Framework to Support the Lifecycle of Cyber–Physical Production Systems

Open access
Harrison, Robert, Vera, Daniel A., Ahmad, Bilal·2021·Proceedings of the IEEE
DOI 10.1109/jproc.2020.3046525
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Abstract

The potential benefits of the adoption of cyber-physical production systems (CPPSs) and their significant role in enabling smart manufacturing is well recognized today. However, it is less clear how such CPPS can be most effectively and consistently engineered and maintained throughout their lifecycle due to the existing divide in the information technology (IT) and operational technology (OT) landscape and ad hoc integration practices that result in inconsistent data and data models at various levels of manufacturing processes. The work presented in this article addresses this problem by envisioning a connective framework to support the engineering of CPPS through the use of a set of digital twins consistent with the real system throughout its lifecycle, not just used in the design and deployment phases. A review of the latest perspectives on using digital integration frameworks, methods, and solutions for lifecycle engineering of CPPS is provided in this article. This article demonstrates how a suitable framework, named SIMPLE, can be realized to effectively address the lack of consistent data models throughout the engineering lifecycle, including implementation details and example cases developed by the authors at the Warwick Manufacturing Group (WMG) in selected industrial sectors. Consideration is given to supporting cyber-to-physical systems' connectivity and extendable engineering toolsets, forming the basis for multidisciplinary digital engineering environments. Key discussion points include the role and importance of effective integration of IT and OT, suitable frameworks for integration and collaboration.

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  • Implementations of digital twins are limited due to a lack of standards and recognized interoperability, especially in the manufacturing domain.

    “The authors of [77] discuss that implementations of DTs are limited due to a lack of standards and recognized interoperability, especially in the manufacturing domain.”

    From Digital Twin Technology Challenges and Applications: A Comprehensive Review (2022) · cites this paper as [77]

    In “CPPS and Emerging Digital Twins” (page not provided), the paper calls integration of the data-sharing entities needed for high-fidelity digital twins a major challenge in manufacturing. It discusses digital-twin standards and a manufacturing framework, but does not say that a lack of standards or recognized interoperability limits implementations. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cites

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Research works printed but not linked: 28

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Cited by

Sorted by citations

Authors
Harrison, R., Vera, D. A., & Ahmad, B.
Affiliations
University of Warwick (GB)
ORCID
Venue
Proceedings of the IEEE
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2021-04
Volume
109
Issue
4
Pages
568-581
ISSN
  • 0018-9219 (ISSN-L)
  • 1558-2256
DOI
10.1109/jproc.2020.3046525
OpenAlex
W3129095565
Cited by
48
References
1
Citations by year
2026: 7 · 2025: 6 · 2024: 13 · 2023: 8 · 2022: 8 · 2021: 6
FWCI
4.53
Type
journal article
Language
en
Open access
Licence
Locations
  • Proceedings of the IEEE (publishedVersion, cc-by)
  • Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
Funders
  • Engineering and Physical Sciences Research Council (EP/K018191/1)
  • Innovate UK
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 › Manufacturing Process and Optimization
Keywords
cyber-physical production systems, digital twin, IT-OT integration, smart manufacturing, data model

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