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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:

Tool wear identification and prediction method based on stack sparse self-coding network
Yiyuan Qin, Xianli Liu, Caixu Yue, et al.
Journal of Manufacturing Systems (2023) Vol. 68, pp. 72-84
Closed Access | Times Cited: 55

Showing 1-25 of 55 citing articles:

Remaining useful life prediction via a deep adaptive transformer framework enhanced by graph attention network
Pengfei Liang, Ying Li, Bin Wang, et al.
International Journal of Fatigue (2023) Vol. 174, pp. 107722-107722
Closed Access | Times Cited: 36
Intelligent tool wear monitoring based on multi-channel hybrid information and deep transfer learning
Pengfei Zhang, Dong Gao, Dongbo Hong, et al.
Journal of Manufacturing Systems (2023) Vol. 69, pp. 31-47
Closed Access | Times Cited: 28
Multi-scale one-dimensional convolution tool wear monitoring based on multi-model fusion learning skills
Wei Ma, Xianli Liu, Caixu Yue, et al.
Journal of Manufacturing Systems (2023) Vol. 70, pp. 69-98
Closed Access | Times Cited: 19
Cross-domain tool wear condition monitoring via residual attention hybrid adaptation network
Zhiwen Huang, Weidong Li, Jianmin Zhu, et al.
Journal of Manufacturing Systems (2023) Vol. 72, pp. 406-423
Closed Access | Times Cited: 17
Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm
Zhou Wen-jun, Xiaoping Xiao, Zisheng Li, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 4, pp. 046112-046112
Closed Access | Times Cited: 5
A tool wear monitoring method based on data-driven and physical output
Yiyuan Qin, Xianli Liu, Caixu Yue, et al.
Robotics and Computer-Integrated Manufacturing (2024) Vol. 91, pp. 102820-102820
Closed Access | Times Cited: 5
Process planning of parameter intelligent adjustment for batch machining based on historical data segmented modeling
Juan Lu, Shiying Tu, Ying Li, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 145, pp. 110180-110180
Closed Access
Ball-end tool wear monitoring and multi-step forecasting with multi-modal information under variable cutting conditions
Yanpeng Hao, Lida Zhu, Jinsheng Wang, et al.
Journal of Manufacturing Systems (2024) Vol. 76, pp. 234-258
Closed Access | Times Cited: 4
Intelligent monitoring system for production lines in smart factories: A hybrid method integrating Transformer and Kalman filter
Xuzhou Fang, Qinghua Song, Zhenyang Li, et al.
Journal of Manufacturing Systems (2025) Vol. 79, pp. 27-47
Closed Access
Intelligent wireless tool wear monitoring system based on chucked tool condition monitoring ring and deep learning
Ni Chen, Zhan Liu, Zhongling Xue, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103176-103176
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
Tool wear monitoring by estimating milling torque and calculating the wear energy coefficient under variable depth of cut conditions
Defeng Peng, Hongkun Li, Bin Sun, et al.
ISA Transactions (2025)
Closed Access
Semi-supervised prediction of milling cutter wear based on an empirical formula for cutting force and wear
Wujun Yu, Hongfei Zhan, Junhe Yu, et al.
The International Journal of Advanced Manufacturing Technology (2025)
Closed Access
Precise measurement of geometric and physical quantities in cutting tools inspection and condition monitoring: A review
Wenqi Wang, Wei Liu, Yang Zhang, et al.
Chinese Journal of Aeronautics (2023) Vol. 37, Iss. 4, pp. 23-53
Open Access | Times Cited: 12
Experimental prediction model for the running-in state of a friction system based on chaotic characteristics and BP neural network
Cong Ding, Shiqing Feng, Zhizhao Qiao, et al.
Tribology International (2023) Vol. 188, pp. 108846-108846
Closed Access | Times Cited: 10
Tool Wear Prediction Based on LSTM and Deep Residual Network
Chun Fang, Yikang Gong, Xibo Ming, et al.
International Journal of Pattern Recognition and Artificial Intelligence (2024) Vol. 38, Iss. 05
Closed Access | Times Cited: 3
Hybrid physics data-driven model-based fusion framework for machining tool wear prediction
Tianhong Gao, Haiping Zhu, Jun Wu, et al.
The International Journal of Advanced Manufacturing Technology (2024) Vol. 132, Iss. 3-4, pp. 1481-1496
Closed Access | Times Cited: 3
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
Tool Wear Prediction Model Using Multi-Channel 1D Convolutional Neural Network and Temporal Convolutional Network
Min Huang, Xingang Xie, Weiwei Sun, et al.
Lubricants (2024) Vol. 12, Iss. 2, pp. 36-36
Open Access | Times Cited: 3
Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning
Shen-Yung Lin, Chia-Jen Hsieh
Applied Sciences (2024) Vol. 14, Iss. 9, pp. 3811-3811
Open 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
Study on tool wear state recognition algorithm based on spindle vibration signals collected by homemade tool condition monitoring ring
Zhongling Xue, Liang Li, Youling Wu, et al.
Measurement (2023) Vol. 223, pp. 113787-113787
Closed Access | Times Cited: 8
Research on tap breakage monitoring method for tapping process based on SSAELSTM fusion network
Ting Chen, Jianming Zheng, Chao Peng, et al.
Measurement (2024) Vol. 236, pp. 115076-115076
Closed Access | Times Cited: 2
Multi-source online transfer learning based on hybrid physics-data model for cross-condition tool health monitoring
Biyao Qiang, Kaining Shi, Junxue Ren, et al.
Journal of Manufacturing Systems (2024) Vol. 77, pp. 1-17
Closed Access | Times Cited: 2
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