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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:

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

Showing 1-25 of 121 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
Systematic review on tool breakage monitoring techniques in machining operations
Xuebing Li, Xianli Liu, Caixu Yue, et al.
International Journal of Machine Tools and Manufacture (2022) Vol. 176, pp. 103882-103882
Closed Access | Times Cited: 126
Tool Condition Monitoring for High-Performance Machining Systems—A Review
Ayman Mohamed, Mahmoud Hassan, Rachid M’Saoubi, et al.
Sensors (2022) Vol. 22, Iss. 6, pp. 2206-2206
Open Access | Times Cited: 89
Prediction and classification of tool wear and its state in sustainable machining of Bohler steel with different machine learning models
Mehmet Erdi Korkmaz, Munish Kumar Gupta, Mustafa Kuntoğlu, et al.
Measurement (2023) Vol. 223, pp. 113825-113825
Closed Access | Times Cited: 50
A State-of-the-art Review on the Intelligent Tool Holders in Machining
Qinglong An, Jie Yang, Junli Li, et al.
Intelligent and sustainable manufacturing (2024) Vol. 1, Iss. 1, pp. 10002-10002
Open Access | Times Cited: 29
AI for tribology: Present and future
Nian Yin, Pufan Yang, Songkai Liu, et al.
Friction (2024) Vol. 12, Iss. 6, pp. 1060-1097
Open Access | Times Cited: 18
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 | Times Cited: 2
Tribo-informatics approaches in tribology research: A review
Nian Yin, Zhiguo Xing, Ke He, et al.
Friction (2022) Vol. 11, Iss. 1, pp. 1-22
Open Access | Times Cited: 57
Prediction of wear performance of ZK60 / CeO2 composites using machine learning models
Fatih Aydın, Rafet Durgut, Mustafa Mustu, et al.
Tribology International (2022) Vol. 177, pp. 107945-107945
Closed Access | Times Cited: 50
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
Measurement and prediction of wear volume of the tool in nonlinear degradation process based on multi-sensor information fusion
Kangping Gao, Xinxin Xu, Shengjie Jiao
Engineering Failure Analysis (2022) Vol. 136, pp. 106164-106164
Closed Access | Times Cited: 39
A milling tool wear monitoring method with sensing generalization capability
Runqiong Wang, Qinghua Song, Yezhen Peng, et al.
Journal of Manufacturing Systems (2023) Vol. 68, pp. 25-41
Closed Access | Times Cited: 29
Tool wear prediction in milling CFRP with different fiber orientations based on multi-channel 1DCNN-LSTM
Bohao Li, Zhenghui Lu, Xiaoliang Jin, et al.
Journal of Intelligent Manufacturing (2023) Vol. 35, Iss. 6, pp. 2547-2566
Closed Access | Times Cited: 28
State-of-the-art review of applications of image processing techniques for tool condition monitoring on conventional machining processes
Danil Yurievich Pimenov, Leonardo Rosa Ribeiro da Silva, Ali Erçetin, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 130, Iss. 1-2, pp. 57-85
Open Access | Times Cited: 24
Deep learning based multi-source heterogeneous information fusion framework for online monitoring of surface quality in milling process
Xiaofeng Wang, Jihong Yan
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108043-108043
Closed 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: 9
Cutting temperature field online reconstruction using temporal convolution and deep learning networks
Yang Zheng, Zengbin Yin
International Journal of Heat and Mass Transfer (2025) Vol. 241, pp. 126766-126766
Closed Access | Times Cited: 1
Wear resistance and tribological properties of GNPs and MWCNT reinforced AlSi18CuNiMg alloys produced by stir casting
Muhammet Emre Turan, Fatih Aydın, Yavuz Sun, et al.
Tribology International (2021) Vol. 164, pp. 107201-107201
Closed Access | Times Cited: 45
Tool wear prediction method based on symmetrized dot pattern and multi-covariance Gaussian process regression
Chuandong Zhang, Wei Wang, Hai Li
Measurement (2021) Vol. 189, pp. 110466-110466
Closed Access | Times Cited: 43
Tool wear prediction based on domain adversarial adaptation and channel attention multiscale convolutional long short-term memory network
Wen Hou, Hong Guo, Lei Luo, et al.
Journal of Manufacturing Processes (2022) Vol. 84, pp. 1339-1361
Closed Access | Times Cited: 34
Prediction and evaluation of surface roughness with hybrid kernel extreme learning machine and monitored tool wear
Minghui Cheng, Li Jiao, Pei Yan, et al.
Journal of Manufacturing Processes (2022) Vol. 84, pp. 1541-1556
Closed Access | Times Cited: 32
Infrastructure monitoring and quality diagnosis in CNC machining: A review
Myrsini Ntemi, Spyridon Paraschos, Αναστάσιος Καρακώστας, et al.
CIRP journal of manufacturing science and technology (2022) Vol. 38, pp. 631-649
Open Access | Times Cited: 31
Tool wear mechanism, monitoring and remaining useful life (RUL) technology based on big data: a review
Zhou Yang, Changfu Liu, Xinli Yu, et al.
SN Applied Sciences (2022) Vol. 4, Iss. 8
Open Access | Times Cited: 28
Bayesian-based uncertainty-aware tool-wear prediction model in end-milling process of titanium alloy
Gyeongho Kim, Sang Min Yang, Dong Min Kim, et al.
Applied Soft Computing (2023) Vol. 148, pp. 110922-110922
Open Access | Times Cited: 21
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