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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 condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions
Guoxiao Zheng, Weifang Sun, Hao Zhang, et al.
Eksploatacja i Niezawodnosc - Maintenance and Reliability (2021) Vol. 23, Iss. 4, pp. 612-618
Open Access | Times Cited: 18

Showing 18 citing articles:

A new tool wear condition monitoring method based on deep learning under small samples
Yuqing Zhou, Gaofeng Zhi, Wei Chen, et al.
Measurement (2021) Vol. 189, pp. 110622-110622
Closed Access | Times Cited: 112
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
Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis
Yunus Emre Karabacak
Eksploatacja i Niezawodnosc - Maintenance and Reliability (2023) Vol. 25, Iss. 3
Open Access | Times Cited: 14
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
Research on cutting tool edge geometry design based on SVR-PSO
Yimin Jiang, Wei Huang, Yu Tian, et al.
The International Journal of Advanced Manufacturing Technology (2024) Vol. 131, Iss. 9-10, pp. 5047-5059
Open Access | Times Cited: 2
A Generalized Multi-Stage Deep Machine Learning Framework for Tool Wear Level Prediction in Milling Operations
Mahmoud Hassan, Ayman Mohamed, Helmi Attia
Procedia CIRP (2024) Vol. 126, pp. 441-446
Open Access | Times Cited: 2
Prediction of tool wear during micro-milling Inconel 718 based on long short-term memory network
Xiaohong Lü, Fanmao Zeng, Kai Xv, et al.
Precision Engineering (2023) Vol. 86, pp. 195-202
Closed Access | Times Cited: 6
A multi-input parallel convolutional attention network for tool wear monitoring
Qiang Liu, Dingkun Li, Jing Ma, et al.
International Journal of Computer Integrated Manufacturing (2023) Vol. 38, Iss. 1, pp. 23-39
Closed Access | Times Cited: 6
Development of tool wear condition on-line monitoring method for impeller milling based on new data processing approach and DAE-BP-ANN-integrated modeling
Zheng Zou, Xu Gao, Si-Cong Lei, et al.
Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture (2023) Vol. 238, Iss. 1-2, pp. 124-136
Closed Access | Times Cited: 4
Remaining useful life prediction with insufficient degradation data based on deep learning approach
Yi Lyu, Yijie Jiang, Qichen Zhang, et al.
Eksploatacja i Niezawodnosc - Maintenance and Reliability (2021) Vol. 23, Iss. 4, pp. 745-756
Open Access | Times Cited: 6
Multi-feature spatial distribution alignment enhanced domain adaptive method for tool condition monitoring
Z. K. Hei, Bintao Sun, Gaonghai Wang, et al.
Eksploatacja i Niezawodnosc - Maintenance and Reliability (2023) Vol. 25, Iss. 4
Open Access | Times Cited: 2
A machine learning method for soil conditioning automated decision-making of EPBM : hybrid GBDT and Random Forest Algorithm
Lin Lin, Hao Guo, Yancheng Lv, et al.
Eksploatacja i Niezawodnosc - Maintenance and Reliability (2022) Vol. 24, Iss. 2, pp. 237-247
Open Access | Times Cited: 4
1DCNN-BiGRU network for surface roughness level detection
Zengren Pan, Yanhui Liu, Zhiwei Li, et al.
Surface Topography Metrology and Properties (2022) Vol. 10, Iss. 4, pp. 044005-044005
Closed Access | Times Cited: 3
A novel NMF-DiCCA deep learning method and its application in wind turbine blade icing failure identification
Chuangyan Yang, Peng Li, Xun Lang, et al.
Eksploatacja i Niezawodnosc - Maintenance and Reliability (2024) Vol. 26, Iss. 4
Open Access
Vibration analysis during AZ31 magnesium alloy milling with the use of different toolholder types
Jarosław Korpysa, Ireneusz Zagórski
Eksploatacja i Niezawodnosc - Maintenance and Reliability (2022) Vol. 24, Iss. 3, pp. 489-501
Open Access | Times Cited: 1
Tool Remaining Useful Life Prediction Method Based on Multi-Sensor Fusion under Variable Working Conditions
Qingqing Huang, Chun-Yan Qian, Chao Li, et al.
Machines (2022) Vol. 10, Iss. 10, pp. 884-884
Open Access | Times Cited: 1
Saw Blade Wear Identification Based on Data Enhancement and Feature Fusion
C.Y. Wang, Xiangjiang Wang, Chao Zeng
IEEE Access (2023) Vol. 11, pp. 123677-123687
Open Access
Health Indicator Construction for Milling Tool Wear Monitoring With Multi-sensor Fusion
Shuhao Zhang, Bin Zhang, Congying Deng
2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS) (2023), pp. 1-5
Closed Access
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