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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:

Recent Advances on Machine Learning Applications in Machining Processes
Francesco Aggogeri, Nicola Pellegrini, Franco Luis Tagliani
Applied Sciences (2021) Vol. 11, Iss. 18, pp. 8764-8764
Open Access | Times Cited: 39

Showing 1-25 of 39 citing articles:

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: 45
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
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: 53
AI-based optimisation of total machining performance: A review
Katrin Ullrich, Magnus von Elling, Kevin Gutzeit, et al.
CIRP journal of manufacturing science and technology (2024) Vol. 50, pp. 40-54
Open Access | Times Cited: 13
Investigation of Fused Filament Fabrication-Based Manufacturing of ABS-Al Composite Structures: Prediction by Machine Learning and Optimization
Nishant Ranjan, Raman Kumar, Ranvijay Kumar, et al.
Journal of Materials Engineering and Performance (2022) Vol. 32, Iss. 10, pp. 4555-4574
Closed Access | Times Cited: 32
Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotors
Iñigo Aldekoa, Ander del Olmo, Leonardo Sastoque Pinilla, et al.
Mechanical Systems and Signal Processing (2023) Vol. 204, pp. 110773-110773
Open Access | Times Cited: 18
Chatter Identification in Milling of Titanium Alloy Using Machine Learning Approaches with Non-Linear Features of Cutting Force and Vibration Signatures
Viswajith S. Nair, K. Rameshkumar, S. Saravanamurugan
International Journal of Prognostics and Health Management (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 5
Comparison of machine learning algorithms for dynamic performance assessment in complex shapes manufacturing of hybrid particle-reinforced composite
Muhammad Asad Ali, Nadeem Ahmad Mufti, Muhammad Sana, et al.
Expert Systems with Applications (2025) Vol. 275, pp. 127022-127022
Closed Access
Identification of Material Layer During Helical Milling Process on Aerospace Stacks Using Spindle Power Data
Sughosh Deshpande, Anna Carla Araújo, Pierre Lagarrigue
(2025)
Closed Access
Application of Machine Learning to the Prediction of Surface Roughness in the Milling Process on the Basis of Sensor Signals
Katarzyna Antosz, Edward Kozłowski, Jarosław Sęp, et al.
Materials (2025) Vol. 18, Iss. 1, pp. 148-148
Open Access
Multi-agent evolution reinforcement learning method for machining parameters optimization based on bootstrap aggregating graph attention network simulated environment
Weiye Li, Songping He, Xinyong Mao, et al.
Journal of Manufacturing Systems (2023) Vol. 67, pp. 424-438
Closed Access | Times Cited: 12
Bionic digital brain realizing the digital twin-cutting process
Jielin Chen, Shuang Li, Xiaolong Leng, et al.
Robotics and Computer-Integrated Manufacturing (2023) Vol. 84, pp. 102591-102591
Closed Access | Times Cited: 12
Fx-Net and PureNet: Convolutional Neural Network architecture for discrimination of Chronic Obstructive Pulmonary Disease from smokers and healthy subjects through electronic nose signals
Cries Avian, Muhammad Izzuddin Mahali, Nur Achmad Sulistyo Putro, et al.
Computers in Biology and Medicine (2022) Vol. 148, pp. 105913-105913
Closed Access | Times Cited: 17
Optimization of the SAG Grinding Process Using Statistical Analysis and Machine Learning: A Case Study of the Chilean Copper Mining Industry
Manuel Saldaña, Edelmira D. Gálvez, Alessandro Navarra, et al.
Materials (2023) Vol. 16, Iss. 8, pp. 3220-3220
Open Access | Times Cited: 9
Tool wear classification in precision machining using distance metrics and unsupervised machine learning
Debasish Mishra, Utsav Awasthi, Krishna R. Pattipati, et al.
Journal of Intelligent Manufacturing (2023)
Closed Access | Times Cited: 9
Machine learning models and machinability analysis for comparison of various cooling and lubricating mediums during milling of Hardox 400 steel
Abdullah Aslan
Tribology International (2024) Vol. 198, pp. 109860-109860
Closed Access | Times Cited: 3
Explainable machine learning for enhancing predictive accuracy of cutting forces in hard turning processes
Abdelhakim Dorbane, Fouzi Harrou, Ying Sun, et al.
The International Journal of Advanced Manufacturing Technology (2024)
Closed Access | Times Cited: 3
Artificial intelligence and its relevance in mechanical engineering from Industry 4.0 perspective
P. K. Ambadekar, Sarita Ambadekar, Chandrashekhar Choudhari, et al.
Australian Journal of Mechanical Engineering (2023), pp. 1-21
Closed Access | Times Cited: 7
Multi-Objective Electric Discharge Machining Process Parameters Optimization of Inconel 718 by using Machine Learning Techniques.
Kebebew Alemu Tufa, Gulam Mohammed Sayeed Ahmed, Habtamu Beri Tufa, et al.
Research Square (Research Square) (2024)
Open Access | Times Cited: 2
Machine Learning Sequential Methodology for Robot Inverse Kinematic Modelling
Franco Luis Tagliani, Nicola Pellegrini, Francesco Aggogeri
Applied Sciences (2022) Vol. 12, Iss. 19, pp. 9417-9417
Open Access | Times Cited: 8
Intelligent prediction using AI-based modeling and optimization of surface roughness in Al7049 end milling with coconut oil under minimum quantity lubrication
G. Lavanya, K. Sundaramurthy, V. Subburam, et al.
Journal of Mechanical Science and Technology (2024) Vol. 38, Iss. 4, pp. 2005-2014
Closed Access | Times Cited: 1
Prediction of the cutting forces in milling operation based on multi-objective optimization of the time series analysis parameters
Masoud Pour, Mohsen Fallah
Machining Science and Technology (2024) Vol. 28, Iss. 4, pp. 597-625
Closed Access | Times Cited: 1
Electrical discharge machining of hybrid metal matrix composites: a comprehensive review
Satesh Sah, Santanu Sardar, Ashim Guha, et al.
The International Journal of Advanced Manufacturing Technology (2024)
Closed Access | Times Cited: 1
Prediction of Thrust Force and Torque for High-Speed Drilling of AL6061 with TMPTO-Based Bio-Lubricants Using Machine Learning
Pramod S. Kathmore, Bhanudas Bachchhav, Somnath Nandi, et al.
Lubricants (2023) Vol. 11, Iss. 9, pp. 356-356
Open Access | Times Cited: 3
Hydrogen Cooling of Turbo Aggregates and the Problem of Rotor Shafts Materials Degradation Evaluation
Alexander Balitskii, А. М. Syrotyuk, Maria R. Havrilyuk, et al.
Energies (2023) Vol. 16, Iss. 23, pp. 7851-7851
Open Access | Times Cited: 2
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