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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:

ConvLSTM-Att: An Attention-Based Composite Deep Neural Network for Tool Wear Prediction
Renwang Li, Xiaolei Ye, Yang Fangqing, et al.
Machines (2023) Vol. 11, Iss. 2, pp. 297-297
Open Access | Times Cited: 12

Showing 12 citing articles:

Multi-sensor signal fusion for tool wear condition monitoring using denoising transformer auto-encoder Resnet
Hui Wang, Shuhui Wang, Weifang Sun, et al.
Journal of Manufacturing Processes (2024) Vol. 124, pp. 1054-1064
Closed Access | Times Cited: 10
Research on multi-signal milling tool wear prediction method based on GAF-ResNext
Yaonan Cheng, Mengda Lu, Xiaoyu Gai, et al.
Robotics and Computer-Integrated Manufacturing (2023) Vol. 85, pp. 102634-102634
Closed Access | Times Cited: 21
1DCNN-based prediction methods for subsequent settlement of subgrade with limited monitoring data
Senlin Xie, Anfeng Hu, Meihui Wang, et al.
European Journal of Environmental and Civil engineering (2025), pp. 1-26
Closed Access
Study of an ISSA-XGBoost model for milling tool wear prediction under variable working conditions
S. -L. Chen, Zengbin Yin, Lei Zheng, et al.
The International Journal of Advanced Manufacturing Technology (2024) Vol. 133, Iss. 5-6, pp. 2761-2774
Closed Access | Times Cited: 2
An Augmented AutoEncoder With Multi-Head Attention for Tool Wear Prediction in Smart Manufacturing
Chunping Dong, Jiaqiang Zhao
IEEE Access (2024) Vol. 12, pp. 79128-79137
Open Access | Times Cited: 1
A hybrid tool wear prediction model based on JDA
Hua Huang, Weiwei Yu, Jiajing Yao, et al.
Engineering Computations (2024) Vol. 41, Iss. 5, pp. 1121-1140
Open Access | Times Cited: 1
An intelligent monitoring system for robotic milling process based on transfer learning and digital twin
Zhaoju Zhu, Weiren Zhu, Jianwei Huang, et al.
Journal of Manufacturing Systems (2024) Vol. 78, pp. 433-443
Closed Access | Times Cited: 1
Predicting Tool Wear with ParaCRN-AMResNet: A Hybrid Deep Learning Approach
Lian Guo, Yongguo Wang
Machines (2024) Vol. 12, Iss. 5, pp. 341-341
Open Access
Tool wear prediction based on hybrid feature selection
Wanzhen Wang, Sze Song Ngu, Miaomiao Xin, et al.
(2024), pp. 41-41
Closed Access
Exploring the Processing Paradigm of Input Data for End-to-End Deep Learning in Tool Condition Monitoring
Chengguan Wang, Guangping Wang, Tao Wang, et al.
Sensors (2024) Vol. 24, Iss. 16, pp. 5300-5300
Open Access
A prediction method of tool wear distribution for ball-end milling under various postures based on WVEM-T
Xudong Wei, Xianli Liu, Changxia Liu, et al.
Journal of Manufacturing Systems (2024) Vol. 77, pp. 446-463
Closed Access
Intelligent Tool Wear Monitoring Method Using a Convolutional Neural Network and an Informer
Xingang Xie, Min Huang, Weiwei Sun, et al.
Lubricants (2023) Vol. 11, Iss. 9, pp. 389-389
Open Access | Times Cited: 1
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