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OpenAlex Citation Counts

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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:

Physics-informed hidden markov model for tool wear monitoring
Kunpeng Zhu, Xin Li, Shenshen Li, et al.
Journal of Manufacturing Systems (2023) Vol. 72, pp. 308-322
Closed Access | Times Cited: 12

Showing 12 citing articles:

Tool wear monitoring based on physics-informed Gaussian process regression
Mingjian Sun, Xianding Wang, Kai Guo, et al.
Journal of Manufacturing Systems (2024) Vol. 77, pp. 40-61
Closed Access | Times Cited: 5
A multi-feature dataset of coated end milling cutter tool wear whole life cycle
Na Li, Xiao Wang, Wanzhen Wang, et al.
Scientific Data (2025) Vol. 12, Iss. 1
Open Access
Research on multi-step ahead prediction method for tool wear based on MSTCN-SBiGRU-MHA
Jing Xue, Yaonan Cheng, Wenjie Zhai, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103219-103219
Closed Access
A novel algorithm for tool wear monitoring utilizing model and Knowledge-Guided Multi-Expert weighted adversarial deep transfer learning
Zhilie Gao, Ni Chen, Liang Li
Mechanical Systems and Signal Processing (2025) Vol. 228, pp. 112456-112456
Closed Access
Developing a deep learning-based uncertainty-aware tool wear prediction method using smartphone sensors for the turning process of Ti-6Al-4V
Gyeongho Kim, Sang Min Yang, Dong Min Kim, et al.
Journal of Manufacturing Systems (2024) Vol. 76, pp. 133-157
Closed Access | Times Cited: 3
Gated recurrent unit and temporal convolutional network with soft thresholding and attention mechanism for tool wear prediction
Binglin Li, Jun Li, Xingsheng Wu, et al.
Measurement (2024) Vol. 240, pp. 115546-115546
Closed Access | Times Cited: 3
Leveraging artificial intelligence for real-time indirect tool condition monitoring: From theoretical and technological progress to industrial applications
Delin Liu, Zhanqiang Liu, Bing Wang, et al.
International Journal of Machine Tools and Manufacture (2024) Vol. 202, pp. 104209-104209
Closed Access | Times Cited: 3
An innovative Multisource Lightweight Adaptive Replayed Online Deep Transfer Learning algorithm for tool wear monitoring
Zhilie Gao, Ni Chen, Yinfei Yang, et al.
Journal of Manufacturing Processes (2024) Vol. 124, pp. 261-281
Closed Access | Times Cited: 2
An innovative multisource multibranch metric ensemble deep transfer learning algorithm for tool wear monitoring
Zhilie Gao, Ni Chen, Yingfei Yang, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102659-102659
Closed Access | Times Cited: 2
A Tcn-Bigru Network with Soft Thresholding and Attention Mechanism for the Tool Wear Prediction
Binglin Li, Jun Li, Xingsheng Wu, et al.
(2024)
Closed Access | Times Cited: 1
Interpretable tool wear monitoring: Architecture with large-scale CNN and adaptive EMD
Yi Sun, Hong‐Liang Song, Hongli Gao, et al.
Journal of Manufacturing Systems (2024) Vol. 78, pp. 294-307
Closed Access | Times Cited: 1
An intelligent monitoring system for robotic milling process based on transfer learning and digital twin
Zhaoju Zhu, Weiren Zhu, Jianwei Huang, et al.
Journal of Manufacturing Systems (2024) Vol. 78, pp. 433-443
Closed Access
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