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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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“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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A Comprehensive Survey on Digital Twin: Focusing on Security Threats and Requirements
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Automated data-driven creation of the Digital Twin of a brownfield plant
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Self-citationSimplexity testbed: A model-based digital twin testbed
Wang, T., Tan, C., Huang, L., Shi, Y., Yue, T., & Huang, Z. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103804 · Cited by 20
A Structure of Modelling Depths in Behavior Models for Digital Twins
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Self-citation
Metadata
- 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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