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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 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

Showing 17 citing articles:

A novel exponential model for tool remaining useful life prediction
Mingjian Sun, Kai Guo, Desheng Zhang, et al.
Journal of Manufacturing Systems (2024) Vol. 73, pp. 223-240
Closed Access | Times Cited: 25
Utilizing TGAN and ConSinGAN for Improved Tool Wear Prediction: A Comparative Study with ED-LSTM, GRU, and CNN Models
Milind Shah, Himanshu Borade, Vipul Dave, et al.
Electronics (2024) Vol. 13, Iss. 17, pp. 3484-3484
Open Access | Times Cited: 6
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 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
Artificial Intelligence and Machine Learning in Tribology: Selected Case Studies and Overall Potential
Raj Shah, Rudy Jaramillo, Garvin Thomas, et al.
Advanced Engineering Materials (2025)
Closed Access
Deep-learning-driven intelligent tool wear identification of high-precision machining with multi-scale CNN-BiLSTM-GCN
Zhicheng Xu, Baolong Zhang, Louis Luo Fan, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103234-103234
Closed Access
SE-U-Lite: Milling Tool Wear Segmentation Based on Lightweight U-Net Model With Squeeze-and-Excitation Module
Pengyue Zhao, Ziteng Li, Zhichao You, et al.
IEEE Transactions on Instrumentation and Measurement (2024) Vol. 73, pp. 1-8
Closed Access | Times Cited: 4
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
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
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
Early condition monitoring of circular saw blades with large diameter-to-thickness ratios under high-speed sawing of hard metals
Jinyou Kang, Jinsheng Zhang, Heng Zhang, et al.
Measurement (2024) Vol. 235, pp. 114933-114933
Closed Access | Times Cited: 3
A Self-Adaptive Physics-Informed Gated Recurrent Unit Neural Networks Model for Estimating the Lifetime of Li-ion Batteries
Mohammad AlShaikh Saleh, Alamera Nouran Alquennah, Ali Ghrayeb, et al.
(2024)
Open Access | Times Cited: 2
Recent Progress of Chatter Detection and Tool Wear Online Monitoring in Machining Process: A Review and Future Prospects
Feng-ze Qin, Huajun Cao, Guibao Tao, et al.
International Journal of Precision Engineering and Manufacturing-Green Technology (2024) Vol. 12, Iss. 2, pp. 719-748
Closed Access | Times Cited: 2
A Review of Physics-Based, Data-Driven, and Hybrid Models for Tool Wear Monitoring
Haoyuan Zhang, Shanglei Jiang, Daqing Gao, et al.
Machines (2024) Vol. 12, Iss. 12, pp. 833-833
Open Access | Times Cited: 1
A PINN-Based Lifetime Predictor for Oil-Impregnated Paper Insulation Degradation in Power Transformers Using Degree of Polymerization
Mohammad AlShaikh Saleh, Alamera Nouran Alquennah, Ali Ghrayeb, et al.
(2024), pp. 1-6
Closed Access
Cutting force prediction of longitudinal-torsional ultrasonic assisted milling based on synergistic prediction ensemble learning model: A case study on CFRP
Tianming Yan, Lida Zhu, Wentian Shi, et al.
Composites Part A Applied Science and Manufacturing (2024), pp. 108702-108702
Closed Access
A Review of Intelligentization System and Architecture for Ultra-Precision Machining Process
Minghua Pan, Guoqing Zhang, Wenqi Zhang, et al.
Processes (2024) Vol. 12, Iss. 12, pp. 2754-2754
Open Access
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