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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Digital twins are used with the creation of a system architecture for the virtualization of manufacturing machines (VMM).
“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]
The reviewed articles can be divided into two categories by cloud usage: those that effectively use a cloud and build a cloud-based DT application, and those that construct the DT application in a virtual space with software highlighting the possibility to build it also in a cloud-based environment.
“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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Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts
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- 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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