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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:

A review of machine learning (ML) and explainable artificial intelligence (XAI) methods in additive manufacturing (3D Printing)
Jeewanthi Ukwaththa, Sumudu Herath, D.P.P. Meddage
Materials Today Communications (2024) Vol. 41, pp. 110294-110294
Open Access | Times Cited: 15

Showing 15 citing articles:

Selecting the most suitable 3D printing technology for custom manufacturing using fuzzy decision-making methodology
Betül Yıldırım, Ertuğrul Ayyıldız
International Journal on Interactive Design and Manufacturing (IJIDeM) (2025)
Closed Access
An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior
Fanlong Zeng, Jintao Wang, Chaoyan Zeng
PLoS ONE (2025) Vol. 20, Iss. 3, pp. e0316287-e0316287
Open Access
Reviewing Additive Manufacturing Techniques: Material Trends and Weight Optimization Possibilities Through Innovative Printing Patterns
A. Ramos, Virginia García-Angel, Miriam Siqueiros-Hernández, et al.
Materials (2025) Vol. 18, Iss. 6, pp. 1377-1377
Open Access
Extreme heat prediction through deep learning and explainable AI
Fatima Shafiq, Amna Zafar, Muhammad Usman Ghani Khan, et al.
PLoS ONE (2025) Vol. 20, Iss. 3, pp. e0316367-e0316367
Open Access
Uncovering water conservation patterns in semi-arid regions through hydrological simulation and deep learning
Rui Zhang, Qichao Zhao, Mingyue Liu, et al.
PLoS ONE (2025) Vol. 20, Iss. 3, pp. e0319540-e0319540
Open Access
Artificial Intelligence in Fault Diagnosis and Signal Processing
Andrés Bustillo, Athanasios Karlis
Applied Sciences (2025) Vol. 15, Iss. 7, pp. 3922-3922
Open Access
An interpretable deep learning model for the accurate prediction of mean fragmentation size in blasting operations
Baoqian Huan, Xianglong Li, Jian-Guo Wang, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Transforming Additive Manufacturing with Artificial Intelligence: A Review of Current and Future Trends
Shyam S. Pancholi, Munish Kumar Gupta, Marian Bartoszuk, et al.
Archives of Computational Methods in Engineering (2025)
Closed Access
Explainable Artificial Intelligence Visions on Incident Duration Using eXtreme Gradient Boosting and SHapley Additive exPlanations
Khaled Hamad, Emran Alotaibi, Lubna Obaid, et al.
Multimodal Transportation (2025), pp. 100209-100209
Open Access
Computer Science Integrations with Laser Processing for Advanced Solutions
Serguei P. Murzin
Photonics (2024) Vol. 11, Iss. 11, pp. 1082-1082
Open Access | Times Cited: 2
Artificial Intelligence-Driven Innovations in Laser Processing of Metallic Materials
Serguei P. Murzin
Metals (2024) Vol. 14, Iss. 12, pp. 1458-1458
Open Access | Times Cited: 2
Machine learning prediction of web-crippling strength in cold-formed steel beams with staggered slotted perforations
Perampalam Gatheeshgar, R.S.S. Ranasinghe, Lenganji Simwanda, et al.
Structures (2024) Vol. 71, pp. 108079-108079
Closed Access | Times Cited: 2
Prediction of surface roughness of tempered steel AISI 1060 under effective cooling using super learner machine learning
Firi Ziyad, Habtamu Alemayehu, Desalegn Wogaso, et al.
The International Journal of Advanced Manufacturing Technology (2024)
Closed Access
Explainable artificial intelligence with fusion-based transfer learning on adverse weather conditions detection using complex data for autonomous vehicles
Khaled Tarmissi, Hanan Abdullah Mengash, Noha Negm, et al.
AIMS Mathematics (2024) Vol. 9, Iss. 12, pp. 35678-35701
Open Access
Predicting Flexural Properties of 3D-Printed Composites: A Small Dataset Analysis Using Multiple Machine Learning Models
Hamza Qayyum, Khubaib Saqib, Ghulam Hussain, et al.
Materials Today Communications (2024), pp. 111135-111135
Closed Access
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