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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:

Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review
Danil Yurievich Pimenov, Andrés Bustillo, Szymon Wojciechowski, et al.
Journal of Intelligent Manufacturing (2022) Vol. 34, Iss. 5, pp. 2079-2121
Closed Access | Times Cited: 224

Showing 1-25 of 224 citing articles:

Machine learning and artificial intelligence in CNC machine tools, A review
Mohsen Soori, Behrooz Arezoo, Roza Dastres
Sustainable Manufacturing and Service Economics (2023) Vol. 2, pp. 100009-100009
Open Access | Times Cited: 104
Application of measurement systems in tool condition monitoring of Milling: A review of measurement science approach
Danil Yurievich Pimenov, Munish Kumar Gupta, Leonardo Rosa Ribeiro da Silva, et al.
Measurement (2022) Vol. 199, pp. 111503-111503
Open Access | Times Cited: 97
Chatter detection in milling processes—a review on signal processing and condition classification
John Henry Navarro-Devia, Yun Chen, Dzung Viet Dao, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 125, Iss. 9-10, pp. 3943-3980
Open Access | Times Cited: 48
AI in precision agriculture: A review of technologies for sustainable farming practices
Adebunmi Okechukwu Adewusi, Onyeka Franca Asuzu, Temidayo Olorunsogo, et al.
World Journal of Advanced Research and Reviews (2024), Iss. 1, pp. 2276-2285
Open Access | Times Cited: 39
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
Parallel structure of crayfish optimization with arithmetic optimization for classifying the friction behaviour of Ti-6Al-4V alloy for complex machinery applications
Sumika Chauhan, Govind Vashishtha, Munish Kumar Gupta, et al.
Knowledge-Based Systems (2024) Vol. 286, pp. 111389-111389
Closed Access | Times Cited: 19
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
Digital accessibility in the era of artificial intelligence—Bibliometric analysis and systematic review
Khansa Chemnad, Achraf Othman
Frontiers in Artificial Intelligence (2024) Vol. 7
Open Access | Times Cited: 17
Data-driven prediction of tool wear using Bayesian regularized artificial neural networks
Tam T. Truong, Jay Airao, Faramarz Hojati, et al.
Measurement (2024) Vol. 238, pp. 115303-115303
Open Access | Times Cited: 16
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
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
Tool wear prediction in face milling of stainless steel using singular generative adversarial network and LSTM deep learning models
Milind Shah, Vinay Vakharia, Rakesh Chaudhari, et al.
The International Journal of Advanced Manufacturing Technology (2022) Vol. 121, Iss. 1-2, pp. 723-736
Open Access | Times Cited: 67
Machine-Learning-Based Methods for Acoustic Emission Testing: A Review
Giuseppe Ciaburro, Gino Iannace
Applied Sciences (2022) Vol. 12, Iss. 20, pp. 10476-10476
Open Access | Times Cited: 56
Prediction of Surface Roughness Using Machine Learning Approach in MQL Turning of AISI 304 Steel by Varying Nanoparticle Size in the Cutting Fluid
Vineet Dubey, Anuj Kumar Sharma, Danil Yurievich Pimenov
Lubricants (2022) Vol. 10, Iss. 5, pp. 81-81
Open Access | Times Cited: 54
Advance monitoring of hole machining operations via intelligent measurement systems: A critical review and future trends
Rüstem Binali, Mustafa Kuntoğlu, Danil Yurievich Pimenov, et al.
Measurement (2022) Vol. 201, pp. 111757-111757
Open Access | Times Cited: 49
A Comparative Review of Thermocouple and Infrared Radiation Temperature Measurement Methods during the Machining of Metals
Emilios Leonidas, Sabino Ayvar-Soberanis, Hatim Laalej, et al.
Sensors (2022) Vol. 22, Iss. 13, pp. 4693-4693
Open Access | Times Cited: 42
Load Forecasting with Machine Learning and Deep Learning Methods
Moisés Cordeiro-Costas, Daniel Villanueva, Pablo Eguía, et al.
Applied Sciences (2023) Vol. 13, Iss. 13, pp. 7933-7933
Open Access | Times Cited: 41
An efficient IoT-Artificial intelligence-based disease prediction using lightweight CNN in healthcare system
Areej Malibari
Measurement Sensors (2023) Vol. 26, pp. 100695-100695
Open Access | Times Cited: 38
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
Intelligent tool wear monitoring based on multi-channel hybrid information and deep transfer learning
Pengfei Zhang, Dong Gao, Dongbo Hong, et al.
Journal of Manufacturing Systems (2023) Vol. 69, pp. 31-47
Closed Access | Times Cited: 29
Integration of Deep Learning into the IoT: A Survey of Techniques and Challenges for Real-World Applications
Abdussalam Elhanashi, Pierpaolo Dini, Sergio Saponara, et al.
Electronics (2023) Vol. 12, Iss. 24, pp. 4925-4925
Open Access | Times Cited: 29
A novel online tool condition monitoring method for milling titanium alloy with consideration of tool wear law
Bo Qin, Yongqing Wang, Kuo Liu, et al.
Mechanical Systems and Signal Processing (2023) Vol. 199, pp. 110467-110467
Closed Access | Times Cited: 26
Development of Deep Belief Network for Tool Faults Recognition
Archana P. Kale, Revati M. Wahul, Abhishek D. Patange, et al.
Sensors (2023) Vol. 23, Iss. 4, pp. 1872-1872
Open Access | Times Cited: 24
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
Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application
Shuguo Hu, Changhe Li, Benkai Li, et al.
Intelligent and sustainable manufacturing (2024) Vol. 1, Iss. 1, pp. 10007-10007
Open Access | Times Cited: 15
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