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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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Citations
Cites
A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems
Lee, J., Bagheri, B., & Kao, H.-A. (2015) · Manufacturing Letters · doi:10.1016/j.mfglet.2014.12.001 · Cited by 5,113
From cloud computing to cloud manufacturing
Xu, X. W. (2011) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2011.07.002 · Cited by 1,806
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
Wang, L., Törngren, M., & Onori, M. (2015) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2015.04.008 · Cited by 948
Cloud manufacturing: a new manufacturing paradigm
Li, Z., Luo, Y., Tao, F., Li, B. H., Ren, L., Zhang, X., Guo, H., Cheng, Y., Hu, A., & Liu, Y. (2012) · Enterprise Information Systems · doi:10.1080/17517575.2012.683812 · Cited by 810
Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation
Wu, D., Rosen, D. W., Wang, L., & Schaefer, D. (2014) · Computer-Aided Design · doi:10.1016/j.cad.2014.07.006 · Cited by 614
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
Liu, X. F., Shahriar, M. R., Sunny, S. M. N. A., Leu, M. C., & Hu, L. (2017) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2017.04.004 · Cited by 154
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Zhang, Q., & Li, G. (2022) · International Journal of Distributed Systems and Technologies · doi:10.4018/ijdst.307953
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Wang, G., Fang, H., & Zhang, S. (2022) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc55942.2022.10004088
Timofeev, A. V., Titov, A. B., Kolesnikov, A. M., & Antonova, A. K. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-32719-3_58
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Barabanova, T., Сергеевна, П. Р., & Blinova, O. (2023) · E3S Web of Conferences · doi:10.1051/e3sconf/202341002050
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Rajesh, D. (2023) · Research Square · doi:10.21203/rs.3.rs-2084034/v1
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Othman, A. H. A., Alkhafaji, M. A., Hussam, R., Haroon, N. H., Majeed, M. G., & Al-Shaikhli, T. R. (2023) · Annual International Conference on Emerging Research Areas and International Conference on Microelectronics, Communications and Renewable Energy · doi:10.1109/aicera/icis59538.2023.10420394
Gao, H., & Guo, L. H. (2024) · SAE International Journal of Connected and Automated Vehicles · doi:10.4271/12-07-03-0022
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القصير, ن. أ., & أرحيم, ع. ع. (2024) · Al-Ghary Journal of Economic and Administrative Sciences · doi:10.36325/ghjec.v20i00.17092
Evaluation of the Educators' Perspectives on Pedagogy
Jawad, I. A., Hamid, N. K., Hassan, S. I., Al-Qaisy, S. A., & Wassif, S. J. D. (2024) · International Conference on Emerging Research in Computational Science (ICERCS) · doi:10.1109/icercs63125.2024.10895334
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Qasim, M. N., Hussain, F. H. T., Hameed, N. H., Rajab, A. B., & Al-Taee, M. M. (2024) · International Conference on Emerging Research in Computational Science (ICERCS) · doi:10.1109/icercs63125.2024.10894863
Optimization of Manufacturing Parameters Using Digital Twin Simulation
Kanangnanon, T., Fongsamut, C., Kongon, B., & Jitngernmadan, P. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-03722-0_13
Cloud Manufacturing and Focus on Future Trends and Directions in Health Care Applications
Prasad, T. R., & Kakaraparthi, P. (2025) · doi:10.1002/9781394389070.ch13
Blueprints of Tomorrow: The Promise of Digital Twins in Today’s World
Kumar, R., Garg, M., Soni, P., Paliwal, M., & Hadi, T. H. (2025) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815324242125010010
Cloud Manufacturing and Intelligent Network Importance in Healthcare Applications
Prasad, T. R., & Kakaraparthi, P. (2025) · doi:10.1002/9781394315635.ch12
Artificial intelligence tools for preventive maintenance in gold processing mills: A review
Ruvimbo, V. M., Mupona, M. I., & Museka, D. (2025) · i-manager’s Journal on Future Engineering and Technology · doi:10.26634/jfet.21.1.22261
Development of a Digital Twin Robot System for Railway Tunnel Diagnostic Vehicle
Du, Z.-N., Cao, G.-Z., Hu, H., Lei, Y., & Du, Y. (2025) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-xian66756.2025.11427624
Delissen, T., Klausner, L. D., Lackner, M., Priebe, T., & Thurnay, L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-34304-8_5
Paek, S. W., Noh, E., Jeon, Y., Kim, N., Sim, Y., Jun, M. B., & Lee, S. W. (2026) · Manufacturing Letters · doi:10.1016/j.mfglet.2026.08.042
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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