Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems
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Nine primary studies [4,8,10,16,23,24,31,40,49] cover edge-Cloud orchestration for data processing (not necessarily data quality management).
“Nine primary studies [4,8,10,16,23,24,31,40,49] cover edge-Cloud orchestration for data processing (not necessarily data quality management).”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [24]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Digital twin-driven product design, manufacturing and service with big data
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0233-1 · Cited by 2,920 · see citing works
The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles
Glaessgen, E., & Stargel, D. (2012) · 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference<BR>20th AIAA/ASME/AHS Adaptive Structures Conference<BR>14th AIAA · doi:10.2514/6.2012-1818 · Cited by 2,404 · cited 1× · see citing works
“, to mirror the life of its corresponding twin [50].”
BackgroundEnabling 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
Digital twin driven prognostics and health management for complex equipment
Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. C. (2018) · CIRP Annals · doi:10.1016/j.cirp.2018.04.055 · Cited by 1,000 · see citing works
Tao, F., & Qi, Q. (2019) · Nature · doi:10.1038/d41586-019-02849-1 · Cited by 799 · see citing works
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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 AI-driven digital twins in Industry 4.0: smart manufacturing and advanced robotics
Huang, Z., Shen, Y., Li, J., Fey, M., & Brecher, C. (2021) · Sensors · doi:10.3390/s21196340 · Cited by 418 · see citing works
Warke, V., Kumar, S., Bongale, A., & Kotecha, K. (2021) · Sustainability · doi:10.3390/su131810139 · Cited by 103 · see citing works
A Systematic Review of Data Quality in CPS and IoT for Industry 4.0
Goknil, A., Nguyen, P., Sen, S., Politaki, D., Niavis, H., Pedersen, K. J., Suyuthi, A., Anand, A., & Ziegenbein, A. (2023) · ACM Computing Surveys · doi:10.1145/3593043 · Cited by 68 · see citing works
Sparse attention-driven quality prediction for production process optimization in digital twins
Yin, Y., Wang, L., Thai Hoang, D., Wang, W., & Niyato, D. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3448256 · Cited by 14 · see citing works
Development of Digital Twin for FDM Printer With Preventive Cyber-Attack and Control Algorithms
Hazrat Ali, M., Waqar Malik, A., Jyeniskhan, N., Arif Mahmood, M., Shehab, E., & Liou, F. (2024) · IEEE Access · doi:10.1109/access.2024.3516827 · Cited by 12 · see citing works
- Authors
- Liu, C., Le Roux, L., Körner, C., Tabaste, O., Lacan, F., & Bigot, S.
- Affiliations
- Cardiff University (GB)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Corresponding author
- Liu, Chao
- Venue
- Journal of Manufacturing Systems
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 62
- Pages
- 857-874
- ISSN
- 0278-6125 (ISSN-L)
- OpenAlex
- W3032039544
- Cited by
- 252
- References
- 6
- Citations by year
- 2026: 45 · 2025: 42 · 2024: 50 · 2023: 42 · 2022: 34 · 2021: 34 · 2020: 5
- FWCI
- 17.43
- Type
- journal article
- Language
- en
- Open access
- green
- orca.cardiff.ac.uk
- Locations
- Journal of Manufacturing Systems (publishedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion, cc-by-nc-nd)
- Funders
- Horizon 2020
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Mechanical Engineering › Additive Manufacturing Materials and Processes
- Physical Sciences › Engineering › Automotive Engineering › Additive Manufacturing and 3D Printing Technologies
- Keywords
- digital twin, metal additive manufacturing, collaborative data management, product data model, product lifecycle data, deep learning, process monitoring, process optimization
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