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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:

A tool wear monitoring and prediction system based on multiscale deep learning models and fog computing
Huihui Qiao, Tao Wang, Peng Wang
The International Journal of Advanced Manufacturing Technology (2020) Vol. 108, Iss. 7-8, pp. 2367-2384
Closed Access | Times Cited: 60

Showing 1-25 of 60 citing articles:

A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges
Vahid Nasir, Farrokh Sassani
The International Journal of Advanced Manufacturing Technology (2021) Vol. 115, Iss. 9-10, pp. 2683-2709
Closed Access | Times Cited: 210
Intelligent tool wear monitoring and multi-step prediction based on deep learning model
Minghui Cheng, Jiao Li, Pei Yan, et al.
Journal of Manufacturing Systems (2021) Vol. 62, pp. 286-300
Closed Access | Times Cited: 131
Research on tool wear prediction based on temperature signals and deep learning
Zhaopeng He, Tielin Shi, Jianping Xuan, et al.
Wear (2021) Vol. 478-479, pp. 203902-203902
Closed Access | Times Cited: 121
At the Confluence of Artificial Intelligence and Edge Computing in IoT-Based Applications: A Review and New Perspectives
Amira Bourechak, Ouarda Zedadra, Mohamed Nadjib Kouahla, et al.
Sensors (2023) Vol. 23, Iss. 3, pp. 1639-1639
Open Access | Times Cited: 108
Milling tool wear prediction using multi-sensor feature fusion based on stacked sparse autoencoders
Zhaopeng He, Tielin Shi, Jianping Xuan
Measurement (2022) Vol. 190, pp. 110719-110719
Closed Access | Times Cited: 70
Tool wear identification and prediction method based on stack sparse self-coding network
Yiyuan Qin, Xianli Liu, Caixu Yue, et al.
Journal of Manufacturing Systems (2023) Vol. 68, pp. 72-84
Closed Access | Times Cited: 58
Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network
Xianli Liu, Shaoyang Liu, Xuebing Li, et al.
Journal of Manufacturing Systems (2021) Vol. 60, pp. 608-619
Closed Access | Times Cited: 95
A Hybrid Attention-Based Paralleled Deep Learning model for tool wear prediction
Jian Duan, Xi Zhang, Tielin Shi
Expert Systems with Applications (2022) Vol. 211, pp. 118548-118548
Closed Access | Times Cited: 68
Accurate estimation of tool wear levels during milling, drilling and turning operations by designing novel hyperparameter tuned models based on LightGBM and stacking
Jawad Mahmood, Ghulam-e Mustafa, Muhammad Ali
Measurement (2022) Vol. 190, pp. 110722-110722
Closed Access | Times Cited: 40
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: 9
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
Multi-scale one-dimensional convolution tool wear monitoring based on multi-model fusion learning skills
Wei Ma, Xianli Liu, Caixu Yue, et al.
Journal of Manufacturing Systems (2023) Vol. 70, pp. 69-98
Closed Access | Times Cited: 19
Surface roughness and tool wear monitoring in turning processes through vibration analysis using PSD and GRMS
Roumaissa Bouchama, Mohamed Lamine Bouhalais, Abdelhakim Cherfia
The International Journal of Advanced Manufacturing Technology (2024) Vol. 130, Iss. 7-8, pp. 3537-3552
Closed Access | Times Cited: 7
Augmentation of Decision Tree Model Through Hyper-Parameters Tuning for Monitoring of Cutting Tool Faults Based on Vibration Signatures
Abhishek D. Patange, Sujit S. Pardeshi, R. Jegadeeshwaran, et al.
Journal of Vibration Engineering & Technologies (2022) Vol. 11, Iss. 8, pp. 3759-3777
Closed Access | Times Cited: 28
A Supervised Machine Learning Model for Tool Condition Monitoring in Smart Manufacturing
S. Ganeshkumar, T. Deepika, Anandakumar Haldorai
Defence Science Journal (2022) Vol. 72, Iss. 5, pp. 712-720
Open Access | Times Cited: 27
Prediction of health monitoring with deep learning using edge computing
Piyush Gupta, Ajay Veer Chouhan, Mohammed Abdul Wajeed, et al.
Measurement Sensors (2022) Vol. 25, pp. 100604-100604
Open Access | Times Cited: 25
Remaining useful life prediction of rolling bearing under limited data based on adaptive time-series feature window and multi-step ahead strategy
Weili Kong, Hai Li
Applied Soft Computing (2022) Vol. 129, pp. 109630-109630
Closed Access | Times Cited: 23
Health Monitoring of Milling Tool Inserts Using CNN Architectures Trained by Vibration Spectrograms
S. S. Patil, Sujit S. Pardeshi, Abhishek D. Patange
Computer Modeling in Engineering & Sciences (2023) Vol. 136, Iss. 1, pp. 177-199
Open Access | Times Cited: 14
Intrinsic and post-hoc XAI approaches for fingerprint identification and response prediction in smart manufacturing processes
Abhilash Puthanveettil Madathil, Xichun Luo, Qi Liu, et al.
Journal of Intelligent Manufacturing (2024) Vol. 35, Iss. 8, pp. 4159-4180
Open Access | Times Cited: 5
Tool wear monitoring for cavity milling based on vibration singularity analysis and stacked LSTM
Kaile Ma, Guofeng Wang, Kai Yang, et al.
The International Journal of Advanced Manufacturing Technology (2022) Vol. 120, Iss. 5-6, pp. 4023-4039
Closed Access | Times Cited: 22
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
Deep Learning Real-Time Bit-Wear Model Approves to be Robust and Transferable in Hard Drilling Applications
Guodong Zhan, Abdulwahab Aljohar, Yazeed Qahtani, et al.
All Days (2024)
Closed Access | Times Cited: 4
An imbalance data quality monitoring based on SMOTE-XGBOOST supported by edge computing
Yan Han, Zhe Wei, Guotian Huang
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 4
Tool wear monitoring for robotic milling based on multi-dimensional stacked sparse autoencoders and bidirectional LSTM networks with singularity features
Chang’an Zhou, Kaixing Zhang, Jiawei Xu, et al.
The International Journal of Advanced Manufacturing Technology (2025)
Closed Access
Role of IoT, Machine Learning, and Artificial Intelligence in Machine Tool Logistics
Balaji Gopalan, G. S. Vijaya, Abhinav Tiwary, et al.
Advances in logistics, operations, and management science book series (2025), pp. 369-398
Closed Access
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