The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
An architecture of an Intelligent Digital Twin in a Cyber-Physical Production System
Open accessAbstract
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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Cites
Cyber Physical Systems: Design Challenges
Lee, E. A. (2008) · IEEE International Symposium on Real-Time Distributed Computing (ISORC) · doi:10.1109/isorc.2008.25 · Cited by 3,638
Digital Twin in manufacturing: A categorical literature review and classification
Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2018.08.474 · Cited by 3,612 · cited 2×
“In [9] several definitions ofDigital Twins are compared and evaluated.”
BackgroundMethodAbout The Importance of Autonomy and Digital Twins for the Future of Manufacturing
Rosen, R., von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2015.06.141 · Cited by 1,474 · cited 1×
“During the mission, theground-basedmodelwasused to simulate alternatives, where the available flight datawas used to reflect the flight conditions as accurately as possible and to assist astronauts in orbit in critical situations [6, 7].”
MethodDigital Twin—The Simulation Aspect
Boschert, S., & Rosen, R. (2016) · Mechatronic Futures · doi:10.1007/978-3-319-32156-1_5 · Cited by 1,158
Haag, S., & Anderl, R. (2018) · Manufacturing Letters · doi:10.1016/j.mfglet.2018.02.006 · Cited by 559
Datenaustausch in der Anlagenplanung mit AutomationML
Draht, R. (2010) · Springer eBooks · doi:10.1007/978-3-642-04674-2 · Cited by 140
Predictive Analytics Model for Power Consumption in Manufacturing
Shin, S., Woo, J., & Rachuri, S. (2014) · Procedia CIRP · doi:10.1016/j.procir.2014.06.036 · Cited by 139
Modellbasierte virtuelle Produktentwicklung
Eigner, M., Roubanov, D., & Zafirov, R. (2014) · Springer eBooks · doi:10.1007/978-3-662-43816-9 · Cited by 134
Consistency check to synchronize the Digital Twin of manufacturing automation based on anchor points
Talkhestani, B. A., Jazdi, N., Schloegl, W., & Weyrich, M. (2018) · Procedia CIRP · doi:10.1016/j.procir.2018.03.166 · Cited by 119
Self-citationRibeiro, L., & Bjorkman, M. (2017) · IEEE Systems Journal · doi:10.1109/jsyst.2017.2771139 · Cited by 101
Research works printed but not linked: 3
- M. Schleipen and O. Sauer, “Use of dynamic product and process information in a production monitoring and control system by means of CAEX and OPC UA”, Proc. 3rd Int. Conf. Chang. Agil. Reconfigurable Virtual Prod., pp. 662–671, 2009.
- F. Bellalouna, “Integrationsplattform für eine interdisziplinäre Entwicklung mechatronischer Produkte, Dissertation”, Fakultät für Maschinenbau der Ruhr-Universität Bochum (2009).
- S. Feldmann and B. Vogel-Heuser, “Änderungsszenarien in der Automatisierungstechnik–Herausforderungen und interdisziplinäre Auswirkungen”, Eng. von der Anforderung bis zum Betr., vol. 3, p. 95, 2013.
Cited by
Sorted by citations
Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models
Minerva, R., Lee, G. M., & Crespi, N. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2020.2998530 · Cited by 812
Digital Twin for maintenance: A literature review
Errandonea, I., Beltrán, S., & Arrizabalaga, S. (2020) · Computers in Industry · doi:10.1016/j.compind.2020.103316 · Cited by 819
Digital Twin: A Comprehensive Survey of Security Threats
Alcaraz, C., & López, J. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3171465 · Cited by 364
Digital Twin in Aerospace Industry: A Gentle Introduction
Li, L., Aslam, S., Wileman, A., & Perinpanayagam, S. (2021) · IEEE Access · doi:10.1109/access.2021.3136458 · Cited by 316
Delgado, J. M. D., & Oyedele, L. O. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101332 · Cited by 272
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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