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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:

Convolutional and generative adversarial neural networks in manufacturing
Andrew Kusiak
International Journal of Production Research (2019) Vol. 58, Iss. 5, pp. 1594-1604
Closed Access | Times Cited: 108

Showing 26-50 of 108 citing articles:

Cloud-VAE: Variational autoencoder with concepts embedded
Yue Liu, Zitu Liu, Shuang Li, et al.
Pattern Recognition (2023) Vol. 140, pp. 109530-109530
Closed Access | Times Cited: 14
Machine learning for control of (bio)chemical manufacturing systems
Andreas Himmel, Janine Matschek, Rudolph Kok, et al.
Elsevier eBooks (2024), pp. 181-240
Closed Access | Times Cited: 4
Understanding determinants of GenAI usage and its effect on SCM performance using dynamic capability view
Hemlata Gangwar, Mohammad Shameem, Sarjita Tusharbhai Patel, et al.
Industrial Management & Data Systems (2025)
Closed Access
Analysing the artificial intelligence of e-marketing adoption in the b2b enterprise market
Khalid Thaher Amayreh, Mohammad Ali Alqudah, Firas Rashed Wahsheh, et al.
Data & Metadata (2025) Vol. 4, pp. 667-667
Closed Access
In-Situ Defect Detection of Metal Additive Manufacturing: An Integrated Framework
Davide Cannizzaro, Antonio Giuseppe Varrella, Stefano Paradiso, et al.
IEEE Transactions on Emerging Topics in Computing (2021) Vol. 10, Iss. 1, pp. 74-86
Closed Access | Times Cited: 29
Deposition angle prediction of Fused Deposition Modeling process using ensemble machine learning
Nishtha Hooda, Jasgurpreet Singh Chohan, Ruchika Gupta, et al.
ISA Transactions (2021) Vol. 116, pp. 121-128
Closed Access | Times Cited: 27
Daydreaming factories
Aydin Nassehi, Marcello Colledani, Botond Kádár, et al.
CIRP Annals (2022) Vol. 71, Iss. 2, pp. 671-692
Open Access | Times Cited: 19
Opportunities for Generative Artificial Intelligence to Accelerate Deployment of Human-Supervised Autonomous Robots
Jason M. Gregory, Satyandra K. Gupta
Proceedings of the AAAI Symposium Series (2024) Vol. 2, Iss. 1, pp. 177-181
Open Access | Times Cited: 3
Indirect cutting tool wear classification using deep learning and chip colour analysis
Luca Pagani, Paolo Parenti, Salvatore Cataldo, et al.
The International Journal of Advanced Manufacturing Technology (2020) Vol. 111, Iss. 3-4, pp. 1099-1114
Open Access | Times Cited: 31
An online visual measurement method for workpiece dimension based on deep learning
Xuebing Li, Yan Yang, Yingxin Ye, et al.
Measurement (2021) Vol. 185, pp. 110032-110032
Closed Access | Times Cited: 26
(Re)-discovering simulation as a critical element of OM/SCM research: call for research
Steven A. Melnyk, Matthias Thürer, Constantin Blome, et al.
International Journal of Operations & Production Management (2023) Vol. 44, Iss. 7, pp. 1376-1389
Open Access | Times Cited: 9
Synergistic Integration of Digital Twins and Neural Networks for Advancing Optimization in the Construction Industry: A Comprehensive Review
Alexey Borovkov, Khristina Maksudovna Vafaeva, Nikolai Vatin, et al.
Construction Materials and Products (2024) Vol. 7, Iss. 4, pp. 7-7
Closed Access | Times Cited: 3
A knowledge augmented image deblurring method with deep learning for in-situ quality detection of yarn production
Chuqiao Xu, Junliang Wang, Jing Tao, et al.
International Journal of Production Research (2022) Vol. 61, Iss. 13, pp. 4220-4236
Closed Access | Times Cited: 14
Small Sample Reliability Assessment With Online Time-Series Data Based on a Worm Wasserstein Generative Adversarial Network Learning Method
Bo Sun, Zeyu Wu, Qiang Feng, et al.
IEEE Transactions on Industrial Informatics (2022) Vol. 19, Iss. 2, pp. 1207-1216
Closed Access | Times Cited: 13
Deep Neural Networks, Cellular Automata and Petri Nets: Useful Hybrids for Smart Manufacturing
Olena Kaikova, Vagan Terziyan
Procedia Computer Science (2024) Vol. 232, pp. 2334-2346
Open Access | Times Cited: 2
Proceedings of the Cardiff University Engineering Research Conference 2023

(2024)
Open Access | Times Cited: 2
Generative artificial intelligence in smart manufacturing
Andrew Kusiak
Journal of Intelligent Manufacturing (2024)
Open Access | Times Cited: 2
Evaluation of production line expansion efficiency using computer simulation
Roksana Poloczek, B. Oleksiak
Production Engineering Archives (2024) Vol. 30, Iss. 4, pp. 520-527
Open Access | Times Cited: 2
CAD-based data augmentation and transfer learning empowers part classification in manufacturing
Patrick Ruediger-Flore, Moritz Glatt, Marco Hussong, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 125, Iss. 11-12, pp. 5605-5618
Open Access | Times Cited: 6
Hardware Trojan Detection Method Based on Dual Discriminator Assisted Conditional Generation Adversarial Network
Wenjing Tang, Jing Su, Yuchan Gao
Journal of Electronic Testing (2023) Vol. 39, Iss. 2, pp. 129-140
Open Access | Times Cited: 6
Resilient manufacturing
Andrew Kusiak
Journal of Intelligent Manufacturing (2019) Vol. 31, Iss. 2, pp. 269-269
Closed Access | Times Cited: 19
Conditional StyleGAN modelling and analysis for a machining digital twin
Е. В. Зотов, Ashutosh Tiwari, Visakan Kadirkamanathan
Integrated Computer-Aided Engineering (2021) Vol. 28, Iss. 4, pp. 399-415
Open Access | Times Cited: 14
A GAN-Enhanced Ensemble Model for Energy Consumption Forecasting in Large Commercial Buildings
Danlan Wu, Kyeon Hur, Zhifeng Xiao
IEEE Access (2021) Vol. 9, pp. 158820-158830
Open Access | Times Cited: 14
Taxonomy of generative adversarial networks for digital immunity of Industry 4.0 systems
Vagan Terziyan, Svitlana Gryshko, Mariia Golovianko
Procedia Computer Science (2021) Vol. 180, pp. 676-685
Open Access | Times Cited: 13
On enhancing prediction abilities of vision-based metallic surface defect classification through adversarial training
Vikanksh Nath, Chiranjoy Chattopadhyay, K. A. Desai
Engineering Applications of Artificial Intelligence (2022) Vol. 117, pp. 105553-105553
Closed Access | Times Cited: 9

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