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:

Prediction of ultimate tensile strength of friction stir welding joint using deep learning-based-multilayer perceptron and long short term memory networks
Ujjaval Modi, Shuja Ahmed, Akhand Rai
Welding International (2023) Vol. 37, Iss. 7, pp. 387-399
Closed Access | Times Cited: 6

Showing 6 citing articles:

DACNN based predicting tensile strength of friction stir welded aluminium alloy joints
T. L. Prakash, Abhinav Abhinav, G. Gnanakumar, et al.
International Journal of Machine Learning and Cybernetics (2025)
Closed Access

A sustainable gas metal arc welding operation: machine learning models-based experiment and optimization of welded carbon steels
An-Le Van, Trung-Thanh Nguyen, Dang Xuan Ba
Welding International (2025), pp. 1-19
Closed Access

Machine Learning for Modeling and Defect Detection of Friction Stir Welds: A Review
Abdelhakim Dorbane, Fouzi Harrou, Ying Sun, et al.
Journal of Failure Analysis and Prevention (2025)
Closed Access

A Review of Recent Developments in Friction Stir Welding for Various Industrial Applications
Shalok Bharti, Sudhir Kumar, Inderjeet Singh, et al.
Journal of Marine Science and Engineering (2023) Vol. 12, Iss. 1, pp. 71-71
Open Access | Times Cited: 12

Practical implications of FSW parameter optimization for AA5754-AA6061 alloys
Sankar Kumar Manickam, Ilamathi Palanivel
Matéria (Rio de Janeiro) (2024) Vol. 29, Iss. 4
Open Access | Times Cited: 1

The Application of Machine-Learning Technologies in the Design and Production of Composite-Material Structures
I. D. Shonichev, В С Тынченко, А. С. Бородулин, et al.
Polymer Science Series D (2024) Vol. 17, Iss. 4, pp. 928-933
Closed Access

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