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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:

Deep Spatial-Temporal Feature Extraction and Lightweight Feature Fusion for Tool Condition Monitoring
Yufeng Li, Shilong Wang, He Yan, et al.
IEEE Transactions on Industrial Electronics (2021) Vol. 69, Iss. 7, pp. 7349-7359
Closed Access | Times Cited: 50

Showing 1-25 of 50 citing articles:

Physical model-based tool wear and breakage monitoring in milling process
Xing Zhang, Yang Gao, Zhuocheng Guo, et al.
Mechanical Systems and Signal Processing (2022) Vol. 184, pp. 109641-109641
Closed Access | Times Cited: 53
On-line tool wear monitoring under variable milling conditions based on a condition-adaptive hidden semi-Markov model (CAHSMM)
Shichao Yan, Liang Sui, Siqi Wang, et al.
Mechanical Systems and Signal Processing (2023) Vol. 200, pp. 110644-110644
Closed Access | Times Cited: 26
Review of advances in tool condition monitoring techniques in the milling process
T. Mohanraj, E S Kirubakaran, Dinesh Kumar Madheswaran, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 9, pp. 092002-092002
Closed Access | Times Cited: 10
A hybrid-driven probabilistic state space model for tool wear monitoring
Zhipeng Ma, Ming Zhao, Xuebin Dai, et al.
Mechanical Systems and Signal Processing (2023) Vol. 200, pp. 110599-110599
Closed Access | Times Cited: 20
Physics-Informed Deep Learning for Tool Wear Monitoring
Kunpeng Zhu, Hao Guo, Si Li, et al.
IEEE Transactions on Industrial Informatics (2023) Vol. 20, Iss. 1, pp. 524-533
Closed Access | Times Cited: 17
Self-adaptive fusion of local-temporal features for tool condition monitoring: A human experience free model
Runqiong Wang, Qinghua Song, Yezhen Peng, et al.
Mechanical Systems and Signal Processing (2023) Vol. 195, pp. 110310-110310
Closed Access | Times Cited: 16
Nonlinear Slow-varying Dynamics-assisted Temporal Graph Transformer Network for Remaining Useful Life Prediction
Zhan Gao, Weixiong Jiang, Jun Wu, et al.
Reliability Engineering & System Safety (2024) Vol. 248, pp. 110162-110162
Closed Access | Times Cited: 6
Prediction of milling force based on spindle current signal by neural networks
Defeng Peng, Hongkun Li, Yuebang Dai, et al.
Measurement (2022) Vol. 205, pp. 112153-112153
Closed Access | Times Cited: 23
Tool wear monitoring based on the combination of machine vision and acoustic emission
Meiliang Chen, Mengdan Li, Linfeng Zhao, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 125, Iss. 7-8, pp. 3881-3897
Closed Access | Times Cited: 15
An Intelligent Multiscale Spatiotemporal Fusion Network Model for TCM
Yu Quan, Changfu Liu, Zhuang Yuan, et al.
IEEE Sensors Journal (2023) Vol. 23, Iss. 7, pp. 6628-6637
Closed Access | Times Cited: 14
Intelligent monitoring of milling tool wear based on milling force coefficients by prediction of instantaneous milling forces
Defeng Peng, Hongkun Li
Mechanical Systems and Signal Processing (2023) Vol. 208, pp. 111033-111033
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
Spatio-temporal information analytics based performance-driven industrial process monitoring framework with cloud–edge-device collaboration
Chi Zhang, Jie Dong, Kaixiang Peng, et al.
Journal of Manufacturing Processes (2024) Vol. 110, pp. 224-237
Closed Access | Times Cited: 5
Tool condition monitoring in micro milling of brittle materials
Zheng Gong, Dehong Huo
Precision Engineering (2024) Vol. 87, pp. 11-22
Open Access | Times Cited: 5
Hybrid Data Augmentation Combining Screening-Based MCGAN and Manual Transformation for Few-Shot Tool Wear State Recognition
Yu Quan, Changfu Liu, Zhuang Yuan, et al.
IEEE Sensors Journal (2024) Vol. 24, Iss. 8, pp. 12186-12196
Closed Access | Times Cited: 5
A task-cooperative drilling monitoring method based on heterogeneous multi-task learning: Task adaptive fusion guided by domain knowledge
Jing Qin, Qinghua Song, Runqiong Wang, et al.
Mechanical Systems and Signal Processing (2025) Vol. 225, pp. 112299-112299
Closed Access
Toward high-performance machining of thin-walled parts: Fusion of parallel spatial–temporal information in high-speed milling for monitoring tool wear
Yezhen Peng, Qinghua Song, Runqiong Wang, et al.
Measurement (2025), pp. 116899-116899
Closed Access
An Adaptive Parallel Feature Learning and Hybrid Feature Fusion-Based Deep Learning Approach for Machining Condition Monitoring
Bufan Liu, Chun‐Hsien Chen, Pai Zheng, et al.
IEEE Transactions on Cybernetics (2022) Vol. 53, Iss. 12, pp. 7584-7595
Closed Access | Times Cited: 20
Feature fusion-based food protein subcellular prediction for drug composition
Haewon Byeon, Mohammad Shabaz, Janjhyam Venkata Naga Ramesh, et al.
Food Chemistry (2024) Vol. 454, pp. 139747-139747
Closed Access | Times Cited: 4
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: 4
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
A physics-informed machine learning model for surface roughness prediction in milling operations
Pengcheng Wu, Haicong Dai, Yufeng Li, et al.
The International Journal of Advanced Manufacturing Technology (2022) Vol. 123, Iss. 11-12, pp. 4065-4076
Closed Access | Times Cited: 18
Deep transfer residual variational autoencoder with multi-sensors fusion for tool condition monitoring in impeller machining
Jiayu Ou, Hongkun Li, Bo Liu, et al.
Measurement (2022) Vol. 204, pp. 112028-112028
Closed Access | Times Cited: 16
A multivariate monitoring method based on kernel principal component analysis and dual control chart
Liang Liu, Jianchang Liu, Honghai Wang, et al.
Journal of Process Control (2023) Vol. 127, pp. 102994-102994
Closed Access | Times Cited: 10
Tool wear classification in precision machining using distance metrics and unsupervised machine learning
Debasish Mishra, Utsav Awasthi, Krishna R. Pattipati, et al.
Journal of Intelligent Manufacturing (2023)
Closed Access | Times Cited: 10
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