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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 estimation and life prognostics in milling: Model extension and generalization
Yu Zhang, Kunpeng Zhu, Xianyin Duan, et al.
Mechanical Systems and Signal Processing (2021) Vol. 155, pp. 107617-107617
Closed Access | Times Cited: 59

Showing 1-25 of 59 citing articles:

Data-model linkage prediction of tool remaining useful life based on deep feature fusion and Wiener process
Xuebing Li, Xianli Liu, Caixu Yue, et al.
Journal of Manufacturing Systems (2024) Vol. 73, pp. 19-38
Closed Access | Times Cited: 19
Interpretable deep learning approach for tool wear monitoring in high-speed milling
Hao Guo, Yu Zhang, Kunpeng Zhu
Computers in Industry (2022) Vol. 138, pp. 103638-103638
Closed Access | Times Cited: 44
DP2Net: A discontinuous physical property-constrained single-source domain generalization network for tool wear state recognition
Xuwei Lai, Kai Zhang, Qing Zheng, et al.
Mechanical Systems and Signal Processing (2024) Vol. 215, pp. 111421-111421
Closed Access | Times Cited: 12
Investigation of the tool flank wear influence on cutter-workpiece engagement and cutting force in micro milling processes
Shuaishuai Gao, Xianyin Duan, Kunpeng Zhu, et al.
Mechanical Systems and Signal Processing (2024) Vol. 209, pp. 111104-111104
Closed Access | Times Cited: 9
Physics based models for characterization of machining performance – A critical review
Helmi Attia, A. Sadek, Yusuf Altintaş, et al.
CIRP journal of manufacturing science and technology (2024) Vol. 51, pp. 161-189
Open Access | Times Cited: 9
Improving milling tool wear prediction through a hybrid NCA-SMA-GRU deep learning model
Zhongyuan Che, Chong Peng, T. Warren Liao, et al.
Expert Systems with Applications (2024) Vol. 255, pp. 124556-124556
Closed Access | Times Cited: 8
Modeling and Comprehensive Mechanism Analysis of Torus Milling Cutter Wear in Multi-axis Milling of Ni-based Superalloy Using the Active Cutting Edge Segment Change Technique
Michał Gdula, G. Mrówka-Nowotnik, Andrzej Nowotnik
Tribology International (2025) Vol. 204, pp. 110509-110509
Closed Access | Times Cited: 1
A data-driven approach for tool wear recognition and quantitative prediction based on radar map feature fusion
Xuebing Li, Xianli Liu, Caixu Yue, et al.
Measurement (2021) Vol. 185, pp. 110072-110072
Closed Access | Times Cited: 53
Application of machine vision method in tool wear monitoring
Ruitao Peng, Jiachen Liu, Xiuli Fu, et al.
The International Journal of Advanced Manufacturing Technology (2021) Vol. 116, Iss. 3-4, pp. 1357-1372
Closed Access | Times Cited: 47
Machine vision based adaptive online condition monitoring for milling cutter under spindle rotation
Zhichao You, Hongli Gao, Liang Guo, et al.
Mechanical Systems and Signal Processing (2022) Vol. 171, pp. 108904-108904
Closed Access | Times Cited: 33
Artificial intelligence enabled smart machining and machine tools
Yu Sung Chuo, Jiwoong Lee, Chang Hyeon Mun, et al.
Journal of Mechanical Science and Technology (2022) Vol. 36, Iss. 1, pp. 1-23
Closed Access | Times Cited: 28
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
Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotors
Iñigo Aldekoa, Ander del Olmo, Leonardo Sastoque Pinilla, et al.
Mechanical Systems and Signal Processing (2023) Vol. 204, pp. 110773-110773
Open Access | Times Cited: 20
A frequency-spatial hybrid attention mechanism improved tool wear state recognition method guided by structure and process parameters
Xuwei Lai, Kai Zhang, Qing Zheng, et al.
Measurement (2023) Vol. 214, pp. 112833-112833
Closed Access | Times Cited: 19
Monitoring and forecasting of tool wear based on measurements of vibration accelerations during cast iron milling
Paweł Twardowski, Jakub Czyżycki, Agata Felusiak, et al.
Journal of Manufacturing Processes (2023) Vol. 95, pp. 342-350
Closed Access | Times Cited: 16
Tool wear monitoring based on multi-kernel Gaussian process regression and Stacked Multilayer Denoising AutoEncoders
Guohao Song, Jianhua Zhang, Kangyi Zhu, et al.
Mechanical Systems and Signal Processing (2022) Vol. 186, pp. 109851-109851
Closed Access | Times Cited: 25
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: 13
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
Tool wear prediction based on XGBoost feature selection combined with PSO-BP network
Lin Zhang, Yankun Fan, Jinling Tan, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Multi-task dual-level adversarial transfer learning boosted RUL estimation of CNC milling tools
Pei Wang, Jinrui Liu, Jingshuai Qi, et al.
Knowledge-Based Systems (2025), pp. 113152-113152
Closed Access
Recent Trends, Developments, and Emerging Technologies towards Sustainable Intelligent Machining: A Critical Review, Perspectives and Future Directions
Muhammad Asif Khan, Hang Shen, Chunlin Zhou, et al.
Sustainability (2023) Vol. 15, Iss. 10, pp. 8298-8298
Open Access | Times Cited: 12
A Physics-Assisted Online Learning Method for Tool Wear Prediction
Dezhi Yuan, Ting Luo, Dan Zhang, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-11
Closed Access | Times Cited: 10
Time-varying dynamic modeling of micro-milling considering tool wear and the process parameter identification
Pengfei Ding, Xianzhen Huang, Yuxiong Li
Mechanics Based Design of Structures and Machines (2024) Vol. 52, Iss. 11, pp. 8658-8684
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
Predicting the quality of a machined workpiece with a variational autoencoder approach
Antoine Proteau, Antoine Tahan, Ryad Zemouri, et al.
Journal of Intelligent Manufacturing (2021) Vol. 34, Iss. 2, pp. 719-737
Closed Access | Times Cited: 20
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