An architecture of an intelligent digital twin in a cyber-physical production system
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Abstract The role of a Digital Twin is increasingly discussed within the context of Cyber-Physical Production Systems. Accordingly, various architectures for the realization of Digital Twin use cases are conceptualized. There lacks, however, a clear, encompassing architecture covering necessary components of a Digital Twin to realize various use cases in an intelligent automation system. In this contribution, the added value of a Digital Twin in an intelligent automation system is highlighted and various existing definitions and architectures of the Digital Twin are discussed. Flowingly, an architecture for a Digital Twin and an architecture for an Intelligent Digital Twin and their required components are proposed, with which use cases such as plug and produce, self-x and predictive maintenance are enabled. In the opinion of the authors, a Digital Twin requires three main characteristics: synchronization with the real asset, active data acquisition from the real environment and the ability of simulation. In addition to all the characteristics of a Digital Twin, an Intelligent Digital Twin must also include the characteristics of Artificial Intelligence. The Intelligent Digital Twin can be used for the realization of the autonomous Cyber-Physical Production Systems. In order to realize the proposed architecture for a Digital Twin, several methods, namely the Anchor-Point-Method, a method for heterogeneous data acquisition and data integration as well as an agent-based method for the development of a co-simulation between Digital Twins were implemented and evaluated.
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The Intelligent DT (IDT) allows for the adaptation of existing models or adjustments based on the real asset's interactions with its environment.
“This allows for the adaptation of existing models or adjustments based on the real asset's interactions with its environment [103].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [103]
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Self-citation
- Authors
- Ashtari Talkhestani, B., Jung, T., Lindemann, B., Sahlab, N., Jazdi, N., Schloegl, W., & Weyrich, M.
- Affiliations
- Institute of Industrial Automation and Software Engineering , University of Stuttgart , Pfaffenwaldring 47 , Stuttgart , Germany
- University of Stuttgart (DE)
- Siemens AG , Digital Factory Division, Product Lifecycle Management, Digital Engineering Automation Designer , Gleiwitzerstraße 555 , Nuremberg , Germany
- Venue
- at - Automatisierungstechnik
- Publisher
- Walter de Gruyter GmbH
- Published
- 2019-09-01
- Volume
- 67
- Issue
- 9
- Pages
- 762-782
- ISSN
- 0178-2312 (ISSN-L)
- 2196-677X
- OpenAlex
- W2977510036
- Cited by
- 300
- References
- 38
- Citations by year
- 2026: 13 · 2025: 35 · 2024: 56 · 2023: 62 · 2022: 59 · 2021: 53 · 2020: 19 · 2019: 3
- FWCI
- 24.72
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- at - Automatisierungstechnik (publishedVersion, cc-by)
- 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, intelligent digital twins, cyber-physical production systems, digital twin architecture, intelligent automation, plug and produce, predictive maintenance, real-time synchronization, data acquisition, simulation, artificial intelligence, heterogeneous data integration, autonomous production systems
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