The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Modeling of Cloud-Based Digital Twins for Smart Manufacturing with MT Connect
Open accessAbstract
The common modeling of digital twins uses an information model to describe the physical machines. The integration of digital twins into productive cyber-physical cloud manufacturing (CPCM) systems imposes strong demands such as reducing overhead and saving resources. In this paper, we develop and investigate a new method for building cloud-based digital twins (CBDT), which can be adapted to the CPCM platform. Our method helps reduce computing resources in the information processing center for efficient interactions between human users and physical machines. We introduce a knowledge resource center (KRC) built on a cloud server for information intensive applications. An information model for one type of 3D printers is designed and integrated into the core of the KRC as a shared resource. Several experiments are conducted and the results show that the CBDT has an excellent performance compared to existing methods.
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“this is done with the creation of a system architecture for the virtualization of manufacturing machines (VMM) [27]”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [27]
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“For this aspect, the articles considered can be divided into two different categories: (i) those that effectively use a cloud and build a cloud-based DT application, marked in the table as [27,34,37,43,47,48,54,56,62,67,74-76], (ii) those that construct the DT application in a virtual space with the use of a software highlighting the possibility to build it also in a cloud”
From Review of digital twin applications in manufacturing (2019) · cites this paper as [27]
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Cites
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From cloud computing to cloud manufacturing
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About 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
Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing
Tao, F., & Zhang, M. (2017) · IEEE Access · doi:10.1109/access.2017.2756069 · Cited by 1,397
Current status and advancement of cyber-physical systems in manufacturing
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Cloud manufacturing: a new manufacturing paradigm
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Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation
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Cloud manufacturing: Strategic vision and state-of-the-art
Wu, D., Greer, M. J., Rosen, D. W., & Schaefer, D. (2013) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2013.04.008 · Cited by 556
Digital Twin Data Modeling with AutomationML and a Communication Methodology for Data Exchange
Schroeder, G. N., Steinmetz, C., Pereira, C. E., & Espíndola, D. B. (2016) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2016.11.115 · Cited by 495
Cyber-physical manufacturing cloud: Architecture, virtualization, communication, and testbed
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Characterising the Digital Twin: A systematic literature review
Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020) · CIRP Journal of Manufacturing Science and Technology · doi:10.1016/j.cirpj.2020.02.002 · Cited by 2,422
Lu, Y., Liu, C., Wang, K. I., Huang, H., & Xu, X. W. (2019) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101837 · Cited by 1,753
Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects
Mihai, S., Yaqoob, M., Hưng, Đ. V., Davis, W., Towakel, P., Raza, M., Karamanoglu, M., Barn, B. S., Shetve, D., Prasad, R. V., et al. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3208773 · Cited by 1,195
Digital Twin: Generalization, characterization and implementation
VanDerHorn, E., & Mahadevan, S. (2021) · Decision Support Systems · doi:10.1016/j.dss.2021.113524 · Cited by 808
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
Metadata
- Authors
- Hu, L., Nguyen, N.-T., Tao, W., Leu, M. C., Liu, X. F., Shahriar, M. R., & Al Sunny, S. M. N.
- Affiliations
- Missouri University of Science and Technology (US)
- University of Arkansas at Fayetteville (US)
- Corresponding author
- Tao, Wenjin
- Venue
- Procedia Manufacturing
- Publisher
- Elsevier BV
- Published
- 2018
- Volume
- 26
- Pages
- 1193-1203
- ISSN
- 2351-9789 (ISSN-L)
- OpenAlex
- W2886143408
- Cited by
- 219
- References
- 20
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 22 · 2023: 37 · 2022: 53 · 2021: 56 · 2020: 17 · 2019: 15 · 2018: 1
- FWCI
- 18.39
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Manufacturing (publishedVersion, cc-by-nc-nd)
- Journal of the Arkansas Academy of Science (submittedVersion, cc-by-nc-nd)
- Funders
- National Science Foundation (1551448)
- Division of Civil, Mechanical and Manufacturing Innovation
- 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
- smart manufacturing, information model, 3D printer, resource efficiency, human-machine interaction, cloud computing
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