The digital twin of an industrial production line within the industry 4.0 concept
Abstract
This article presents the digital twin concept, which is an augmented manufacturing project created in close collaboration by SOVA Digital and the Institute of Automation, Measurement and Applied Informatics (ÚAMAI), of the Faculty of Mechanical Engineering, Slovak University of Technology in Bratislava with the support of SIEMENS. The project is a technological concept focusing on the continuous optimization of production processes, proactive maintenance, and continuous processing of process data. This project is the basis for further work to promote the concept of Industry 4.0. for the needs of the industry subjects within Slovakia. Its basic goal is to support the existing production structures within the automotive industry and the most efficient use of resources by augmented production and planning strategies, such as the digital twin presented here.
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In the reviewed articles, DT was also used to evaluate the performances of a cloud-based DT, mainly aimed at verifying the speed with which data are updated in a cloud environment.
“- 8) Finally, the DT was also used to evaluate the 'Performances of a cloud-based DT' , that are mainly aimed at verifying the speed with which the data are updated in a cloud environment [74,75].”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [75]
The dataset description is explicitly mentioned in only nineteen reviewed articles.
“The dataset description is showed only in nineteen articles: in four cases a description is done using Unified Modeling Language (UML) [25,51,63,73], in one case only with the Sequence Diagram (SD) [75].”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [75]
The only reviewed articles that actually perform control of the real system are ad-hoc systems, meaning they used open protocols and programmable boards.
“The only articles that actually perform a control of the real system (i.e. in Table 1, column 'Control of the real system from the digital twin') are ' ad-hoc systems ' [43,74,75], meaning that they used open protocols and programmable boards.”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [75]
In one reviewed article, the dataset description is done only with a Sequence Diagram (SD).
“in one case only with the Sequence Diagram (SD) [75]”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [75]
Cites
Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014) · Business & Information Systems Engineering · doi:10.1007/s12599-014-0334-4 · Cited by 4,276 · see citing works
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Digital Twin in Industry: State-of-the-Art
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2873186 · Cited by 4,391 · see citing works
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 · see citing works
Digital twin: values, challenges and enablers from a modeling perspective
Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952 · see citing works
Enabling technologies and tools for digital twin
Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Wang, L., & Nee, A. Y. C. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2019.10.001 · Cited by 1,579 · see citing works
A survey on digital twin: definitions, characteristics, applications, and design implications
Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019) · IEEE Access · doi:10.1109/access.2019.2953499 · Cited by 1,480 · see citing works
Digital twin: origin to future
Singh, M., Fuenmayor, E., Hinchy, E., Qiao, Y., Murray, N., & Devine, D. (2021) · Applied System Innovation · doi:10.3390/asi4020036 · Cited by 1,038 · see citing works
Review of digital twin applications in manufacturing
Cimino, C., Negri, E., & Fumagalli, L. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.103130 · Cited by 874 · see citing works
Rathore, M. M., Shah, S. A., Shukla, D., Bentafat, E., & Bakiras, S. (2021) · IEEE Access · doi:10.1109/access.2021.3060863 · Cited by 616 · see citing works
A Survey on Digital Twin for Industrial Internet of Things: Applications, Technologies and Tools
Xu, H., Wu, J., Pan, Q., Guan, X., & Guizani, M. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3297395 · Cited by 304 · see citing works
Human Digital Twin for Fitness Management
Barricelli, B. R., Casiraghi, E., Gliozzo, J., Petrini, A., & Valtolina, S. (2020) · IEEE Access · doi:10.1109/access.2020.2971576 · Cited by 259 · see citing works
Warke, V., Kumar, S., Bongale, A., & Kotecha, K. (2021) · Sustainability · doi:10.3390/su131810139 · Cited by 103 · see citing works
Survey on digital twins for Internet of Vehicles: Fundamentals, challenges, and opportunities
Guo, J., Bilal, M., Qiu, Y., Qian, C., Xu, X., & Raymond Choo, K.-K. (2024) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.05.023 · Cited by 99 · see citing works
Digital twin for integration of design-manufacturing-maintenance: an overview
Fu, Y., Zhu, G., Zhu, M., & Xuan, F. (2022) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-022-00760-x · Cited by 98 · see citing works
Stavropoulos, P., & Mourtzis, D. (2022) · Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology · doi:10.1016/b978-0-12-823657-4.00010-5 · Cited by 80 · see citing works
- Authors
- Vachalek, J., Bartalsky, L., Rovny, O., Sismisova, D., Morhac, M., & Loksik, M.
- Venue
- 2017 21st International Conference on Process Control (PC)
- Publisher
- IEEE
- Published
- 2017-06
- Pages
- 258-262
- OpenAlex
- W2735797103
- Cited by
- 339
- References
- 1
- Citations by year
- 2026: 12 · 2025: 34 · 2024: 46 · 2023: 45 · 2022: 59 · 2021: 53 · 2020: 47 · 2019: 29 · 2018: 14
- FWCI
- 36.16
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Process Control (publishedVersion)
- Funders
- Kultúrna a Edukacná Grantová Agentúra MŠVVaŠ SR
- Vedecká Grantová Agentúra MŠVVaŠ SR a SAV
- 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, Industry 4.0, industrial production lines, automotive industry, production process optimization, proactive maintenance, process data processing, production planning, resource efficiency
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