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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:

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network
Xianli Liu, Shaoyang Liu, Xuebing Li, et al.
Journal of Manufacturing Systems (2021) Vol. 60, pp. 608-619
Closed Access | Times Cited: 95

Showing 1-25 of 95 citing articles:

Intelligent tool wear monitoring and multi-step prediction based on deep learning model
Minghui Cheng, Jiao Li, Pei Yan, et al.
Journal of Manufacturing Systems (2021) Vol. 62, pp. 286-300
Closed Access | Times Cited: 131
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: 58
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 | Times Cited: 2
Measurement and prediction of wear volume of the tool in nonlinear degradation process based on multi-sensor information fusion
Kangping Gao, Xinxin Xu, Shengjie Jiao
Engineering Failure Analysis (2022) Vol. 136, pp. 106164-106164
Closed Access | Times Cited: 39
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: 29
Tool wear condition monitoring across machining processes based on feature transfer by deep adversarial domain confusion network
Zhiwen Huang, Jiajie Shao, Jianmin Zhu, et al.
Journal of Intelligent Manufacturing (2023) Vol. 35, Iss. 3, pp. 1079-1105
Closed Access | Times Cited: 24
A milling tool wear predicting method with processing generalization capability
Mingjian Sun, Yunlong Han, Kai Guo, et al.
Journal of Manufacturing Processes (2024) Vol. 120, pp. 975-1001
Closed Access | Times Cited: 12
A novel adaptive deep transfer learning method towards thermal error modeling of electric spindles under variable conditions
Xiaojuan Ma, Jiewu Leng, Zhuyun Chen, et al.
Journal of Manufacturing Systems (2024) Vol. 74, pp. 112-128
Closed Access | Times Cited: 11
Remaining Useful Life Prediction via Improved CNN, GRU and Residual Attention Mechanism With Soft Thresholding
Lijie Zhang, Bin Wang, Xiaoming Yuan, et al.
IEEE Sensors Journal (2022) Vol. 22, Iss. 15, pp. 15178-15190
Closed Access | Times Cited: 35
Prediction and evaluation of surface roughness with hybrid kernel extreme learning machine and monitored tool wear
Minghui Cheng, Li Jiao, Pei Yan, et al.
Journal of Manufacturing Processes (2022) Vol. 84, pp. 1541-1556
Closed Access | Times Cited: 32
A novel deep learning method with partly explainable: Intelligent milling tool wear prediction model based on transformer informed physics
Caihua Hao, Xinyong Mao, Tao Ma, et al.
Advanced Engineering Informatics (2023) Vol. 57, pp. 102106-102106
Closed Access | Times Cited: 20
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
Reliability assessment of multi-state weighted k-out-of-n man-machine systems considering dependent machine deterioration and human fatigue
Haiyang Che, Shengkui Zeng, Yingzhi Zhao, et al.
Reliability Engineering & System Safety (2024) Vol. 246, pp. 110048-110048
Closed Access | Times Cited: 6
Tool Condition Monitoring in the Milling Process Using Deep Learning and Reinforcement Learning
K. Devarajan, T. Mohanraj, Pavan Pradeep, et al.
Journal of Sensor and Actuator Networks (2024) Vol. 13, Iss. 4, pp. 42-42
Open Access | Times Cited: 6
Cutting tool wear monitoring based on a smart toolholder with embedded force and vibration sensors and an improved residual network
Pengfei Zhang, Dong Gao, Yong Lü, et al.
Measurement (2022) Vol. 199, pp. 111520-111520
Closed Access | Times Cited: 27
A tool wear condition monitoring method for non-specific sensing signals
Yezhen Peng, Qinghua Song, Runqiong Wang, et al.
International Journal of Mechanical Sciences (2023) Vol. 263, pp. 108769-108769
Closed Access | Times Cited: 16
Hierarchical temporal transformer network for tool wear state recognition
Zhongling Xue, Ni Chen, Youling Wu, et al.
Advanced Engineering Informatics (2023) Vol. 58, pp. 102218-102218
Closed Access | Times Cited: 14
Tool wear prediction based on parallel dual-channel adaptive feature fusion
Jinfei Yang, Jinxin Wu, Xianwang Li, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 128, Iss. 1-2, pp. 145-165
Closed Access | Times Cited: 13
Real-time reliability analysis of micro-milling processes considering the effects of tool wear
Pengfei Ding, Xianzhen Huang, Yuxiong Li, et al.
Mechanical Systems and Signal Processing (2023) Vol. 200, pp. 110582-110582
Closed Access | Times Cited: 13
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: 13
A novel method based on deep transfer learning for tool wear state prediction under cross-dataset
Yifan Wang, Jie Gao, Wei Wang, et al.
The International Journal of Advanced Manufacturing Technology (2024) Vol. 131, Iss. 1, pp. 171-182
Closed Access | Times Cited: 5
Computer numerical control machine tool wear monitoring through a data-driven approach
Fawzi Gougam, Adel Afia, Mohamed Abdessamed Ait Chikh, et al.
Advances in Mechanical Engineering (2024) Vol. 16, Iss. 2
Open Access | Times Cited: 5
Auxiliary input-enhanced siamese neural network: A robust tool wear prediction framework with improved feature extraction and generalization ability
Chenghan Wang, Bin Shen
Mechanical Systems and Signal Processing (2024) Vol. 211, pp. 111243-111243
Closed Access | Times Cited: 5
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: 5
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: 5
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