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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:

Interpretable ensemble machine learning framework to predict wear rate of modified ZA-27 alloy
Poornima Hulipalled, Veerabhadrappa Algur, V. Lokesha, et al.
Tribology International (2023) Vol. 188, pp. 108783-108783
Closed Access | Times Cited: 12

Showing 12 citing articles:

Tribo-informatics analysis of in-situ TiC reinforced ZA27 alloy: Microstructural insights and wear performance modeling by machine learning
Khursheed Ahmad Sheikh, Mohammad Mohsin Khan
Tribology International (2024) Vol. 199, pp. 110021-110021
Closed Access | Times Cited: 6
Employing ensemble machine learning techniques for predicting the thermohydraulic performance of double pipe heat exchanger with and without turbulators
S. Sammil, M. Sridharan
Thermal Science and Engineering Progress (2023) Vol. 47, pp. 102337-102337
Closed Access | Times Cited: 15
Optimizing wear resistance of ZA-27 alloy through analytical modelling: A study on the effect of titanium carbide content, load, and speed
Khursheed Ahmad Sheikh, Mohammad Mohsin Khan, Abhijit Dey
Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology (2024) Vol. 238, Iss. 10, pp. 1273-1297
Closed Access | Times Cited: 5
Predictive Modeling of Abrasive Wear in In-Situ TiC Reinforced ZA37 Alloy: A Machine Learning Approach
Khursheed Ahmad Sheikh, Mohammad Mohsin Khan
Tribology International (2024), pp. 110291-110291
Closed Access | Times Cited: 5
Advancing tunnel equipment maintenance through data-driven predictive strategies in underground infrastructure
Xiaoping Zou, Jie Zeng, Gongxing Yan, et al.
Computers and Geotechnics (2024) Vol. 173, pp. 106532-106532
Closed Access | Times Cited: 4
Comparative Study of Wear Behaviour of ZA37 Alloy, ZA37/SiC Composite, and Grey Cast Iron under Lubricated Conditions: Predictive Modeling by Machine Learning
Khursheed Ahmad Sheikh, Mohammad Mohsin Khan, Mohd Nadeem Bhat
Tribology International (2025), pp. 110623-110623
Closed Access
Machine learning-assisted prediction of mechanical properties in WC-based composites with multicomponent alloy binders
Hui Ren, Kaiyue Wang, Kai Xu, et al.
Composites Part B Engineering (2025), pp. 112389-112389
Closed Access
Recent Advances in Zinc-Based Metal Matrix Composites: Fabrication, Properties, and Future Prospects
Khursheed Ahmad Sheikh, Mohammad Mohsin Khan
Journal of Alloys and Metallurgical Systems (2025), pp. 100173-100173
Open Access
A new wear-resistant component system of copper-based composite materials: Modified sepiolite-complex ceramic powder
Jiaqi Wu, Zhuan Li, Guoyuan Wen, et al.
Ceramics International (2024) Vol. 50, Iss. 11, pp. 18559-18568
Closed Access | Times Cited: 2
A statistical and neural network approach to investigate the tribological behavior of ZA-27 alloy
Poornima Hulipalled, Veerabhadrappa Algur, V. Lokesha, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 6, pp. 5855-5870
Closed Access | Times Cited: 1
Research on alloy composition-process-wear properties of medium manganese steel based on machine learning
Yafeng Ji, Shimin Ma, Peng Wen, et al.
Tribology International (2024) Vol. 200, pp. 110164-110164
Closed Access
Predicting wear resistance of high-carbon Cr-V alloy steel based on machine learning
Shuaiwu Tong, Shizhong Wei, Yuan Liu, et al.
Materials Today Communications (2024) Vol. 40, pp. 110231-110231
Closed Access
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