Digital twin for cutting tool: Modeling, application and service strategy
Integrity signals
Registry
Cites
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Smart manufacturing technologies, such as optimization of key manufacturing features and production framework improvements, benefit significantly from Digital Twin integration.
“Additionally, smart manufacturing technologies, such as the optimization of key manufacturing features and production framework improvements, benefit significantly from Digital Twin integration [108,109].”
From Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review (2025) · cites this paper as [108]
Cites
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
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 · 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
Flagged first, then by citations
Cited by
Sorted by citations
Warke, V., Kumar, S., Bongale, A., & Kotecha, K. (2021) · Sustainability · doi:10.3390/su131810139 · Cited by 103 · see citing works
Analysis of Digital Twin Applications in Energy Efficiency: A Systematic Review
Ba, L., Tangour, F., El Abbassi, I., & Absi, R. (2025) · Sustainability · doi:10.3390/su17083560 · Cited by 96 · see citing works
- Authors
- Xie, Y., Lian, K., Liu, Q., Zhang, C., & Liu, H.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Zhang, Chaoyong
- Liu, Hongqi
- Venue
- Journal of Manufacturing Systems
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 58
- Pages
- 305-312
- ISSN
- 0278-6125 (ISSN-L)
- OpenAlex
- W3049463917
- Cited by
- 145
- References
- 3
- Citations by year
- 2026: 17 · 2025: 26 · 2024: 36 · 2023: 27 · 2022: 21 · 2021: 16 · 2020: 2
- FWCI
- 10.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Manufacturing Systems (publishedVersion)
- Funders
- Project of International Cooperation and Exchanges NSFC (51861165202)
- National Natural Science Foundation of China (51705263, 51805330)
- 111 Project of China (B16019)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Engineering Technology and Methodologies
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Applied Advanced Technologies
- Keywords
- digital twin, cutting tools, data fusion, digitalization of manufacturing, smart manufacturing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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