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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:

Predicting the quality of a machined workpiece with a variational autoencoder approach
Antoine Proteau, Antoine Tahan, Ryad Zemouri, et al.
Journal of Intelligent Manufacturing (2021) Vol. 34, Iss. 2, pp. 719-737
Closed Access | Times Cited: 20

Showing 20 citing articles:

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: 220
Hydrogenerator early fault detection: Sparse Dictionary Learning jointly with the Variational Autoencoder
Ryad Zemouri, Rony Ibrahim, Antoine Tahan
Engineering Applications of Artificial Intelligence (2023) Vol. 120, pp. 105859-105859
Closed Access | Times Cited: 22
Stacked encoded cascade error feedback deep extreme learning machine network for manufacturing order completion time
Waqar Ahmed Khan, Mahmoud Masoud, Abdelrahman E. E. Eltoukhy, et al.
Journal of Intelligent Manufacturing (2024)
Closed Access | Times Cited: 6
Attention mechanism-guided residual convolution variational autoencoder for bearing fault diagnosis under noisy environments
Xiaoan Yan, Yanyu Lü, Ying Liu, et al.
Measurement Science and Technology (2023) Vol. 34, Iss. 12, pp. 125046-125046
Closed Access | Times Cited: 11
Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing
Patrick Link, Miltiadis Poursanidis, Jochen Schmid, et al.
Journal of Intelligent Manufacturing (2022) Vol. 33, Iss. 7, pp. 2129-2142
Open Access | Times Cited: 18
Recent Research and Applications in Variational Autoencoders for Industrial Prognosis and Health Management: A Survey
Ryad Zemouri, Mélanie Lévesque, Étienne Boucher, et al.
2022 Prognostics and Health Management Conference (PHM-2022 London) (2022), pp. 193-203
Closed Access | Times Cited: 16
Non-invasive Detection of Rotor Inter-turn Short Circuit of a Hydrogenerator Using AI-Based Variational Autoencoder
Rony Ibrahim, Ryad Zemouri, Bachir Kedjar, et al.
IEEE Transactions on Industry Applications (2023), pp. 1-10
Closed Access | Times Cited: 9
Production quality prediction of cross-specification products using dynamic deep transfer learning network
Pei Wang, Tao Wang, Sheng Yang, et al.
Journal of Intelligent Manufacturing (2023) Vol. 35, Iss. 6, pp. 2567-2592
Closed Access | Times Cited: 8
Multiscale variational autoencoder regressor for production prediction and energy saving of industrial processes
Yongming Han, Yue Wang, Zhiwei Chen, et al.
Chemical Engineering Science (2023) Vol. 284, pp. 119529-119529
Closed Access | Times Cited: 7
Quality control in multistage machining processes based on a machining error propagation event-knowledge graph
Haoliang Shi, Pingyu Jiang
Advances in Manufacturing (2024) Vol. 12, Iss. 4, pp. 679-697
Closed Access | Times Cited: 2
Anomaly Detection for Large Hydrogenerators Using the Variational Autoencoder Based on Vibration Signals
Rony Ibrahim, Ryad Zemouri, Antoine Tahan, et al.
2022 International Conference on Electrical Machines (ICEM) (2022), pp. 1609-1615
Closed Access | Times Cited: 8
Towards AI driven surface roughness evaluation in manufacturing: a prospective study
Sourish Ghosh, Ricardo Knoblauch, Mohamed El Mansori, et al.
Journal of Intelligent Manufacturing (2024)
Open Access | Times Cited: 1
Generating Manufacturing Distributions for Sampling-based Tolerance Analysis using Deep Learning Models
Paul Schaechtl, Martin Roth, J. Brau, et al.
Procedia CIRP (2024) Vol. 129, pp. 103-108
Open Access | Times Cited: 1
Fault Detection Based on Vibration Measurements and Variational Autoencoder-Desirability Function
Rony Ibrahim, Ryad Zemouri, Antoine Tahan, et al.
IEEE Open Journal of Industry Applications (2024) Vol. 5, pp. 106-116
Open Access | Times Cited: 1
Remote Monitoring for Surface Roughness Based on Vibration and Spindle Power
Leibo Wu, Kaiguo Fan, Wen Le
Arabian Journal for Science and Engineering (2022) Vol. 48, Iss. 3, pp. 2617-2631
Closed Access | Times Cited: 5
Study on surface morphology and residual stress in inclined milling of titanium alloy TC11
Yanxuan Song, Hongxu Chen, Yiheng Tang, et al.
The International Journal of Advanced Manufacturing Technology (2022) Vol. 122, Iss. 7-8, pp. 3411-3423
Closed Access | Times Cited: 3
Multivariate quality prediction of thin-walled parts machining using multi-task parallel deep transfer learning
Pei Wang, Pengde Huang, Haizhen Tao, et al.
International Journal of Production Research (2024), pp. 1-32
Closed Access
Tool health monitoring in lathe turning process by artificial intelligence techniques — a review
CG Gavina, K. Hemalatha, KJ Ranganath, et al.
Concurrent Engineering (2024)
Closed Access
CNC Machining Quality Prediction Using Variational Autoencoder: A Novel Industrial 2 TB Dataset
Antoine Proteau, Ryad Zemouri, Antoine Tahan, et al.
2022 Prognostics and Health Management Conference (PHM-2022 London) (2022), pp. 360-367
Closed Access | Times Cited: 2
Bearing Fault Diagnosis Based on Auto-Encoder
Huijun Han, Jin Mao, Yahui Cui
(2023) Vol. abs 1412 3555, pp. 361-365
Closed Access
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