OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

DT-CEPA: A digital twin-driven contour error prediction approach for machine tools based on hybrid modeling and sparse time series
Shuai Ji, Hepeng Ni, Tianliang Hu, et al.
Robotics and Computer-Integrated Manufacturing (2024) Vol. 88, pp. 102738-102738
Closed Access | Times Cited: 6

Showing 6 citing articles:

Digital twin technology in modern machining: A comprehensive review of research on machining errors
Xiangfu Fu, Hyo-Sook Song, Shuo Li, et al.
Journal of Manufacturing Systems (2025) Vol. 79, pp. 134-161
Open Access | Times Cited: 1

Digital twin of dynamics for parallel kinematic machine with distributed force/position interaction
Fangyan Zheng, Xinghui Han, Lin Hua, et al.
Journal of Manufacturing Systems (2025) Vol. 80, pp. 70-88
Closed Access

Uncertainty-Aware Self-Attention Model for Time Series Prediction with Missing Values
Jiabao Li, Chengjun Wang, Wenhang Su, et al.
Fractal and Fractional (2025) Vol. 9, Iss. 3, pp. 181-181
Open Access

Digital twins for engineering structures—An Industry 4.0 perspective
Johannes Wimmer, Thomas Braml
Structural Concrete (2024)
Open Access | Times Cited: 1

Positioning error compensation method for industrial robots based on stacked ensemble learning
Qizhi Chen, Chengrui Zhang, Wei Ma, et al.
Research Square (Research Square) (2024)
Open Access

Positioning error compensation method for industrial robots based on stacked ensemble learning
Qizhi Chen, Chengrui Zhang, Wei Ma, et al.
The International Journal of Advanced Manufacturing Technology (2024)
Closed Access

Digital twin-driven virtual commissioning for robotic machining enhanced by machine learning
Hepeng Ni, Tianliang Hu, Jian Xin Deng, et al.
Robotics and Computer-Integrated Manufacturing (2024) Vol. 93, pp. 102908-102908
Closed Access

Page 1

Scroll to top