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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:

Tribo-informatics approach to predict wear and friction coefficient of Mg/Si3N4 composites using machine learning techniques
Mahammod Babar Pasha, R.N. Rao, Syed Ismail, et al.
Tribology International (2024) Vol. 196, pp. 109696-109696
Closed Access | Times Cited: 15

Showing 15 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
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: 6
Establishing high temperature tribological performance and wear mechanism map of engineered in-situ TiB2 reinforced Mg-RE metal matrix composites
S.K. Sahoo, М. Р. Рамеш, Saubhagya Kumar Panigrahi
Tribology International (2024) Vol. 201, pp. 110189-110189
Closed Access | Times Cited: 4
Multi-objective optimization of wear and friction behavior of hybrid Magnesium-graphene-silicon nitride composite under dry sliding conditions
V. Preethi, V. Kavimani, P. M. Gopal
Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology (2025)
Closed Access
Tribological properties study and prediction of QBe2 beryllium bronze and 7075-T6 aluminum alloys based on machine learning under mixed lubrication
Z.Y. Li, Lijie Qiao, Jiaqi Li, et al.
Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology (2025)
Closed Access
Comprehensive review of Mg-based alloys: Mechanical, Chemical and Biological properties for Prosthetic and Orthopedic applications
C. Pranaya Joshi, R.N. Rao, Chitti Babu Golla, et al.
Journal of Materials Research and Technology (2025)
Open Access
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
The regression analysis of dry - wet wear outcomes and materials properties of biodegradable MgCu and MgZn, made by P/M, using machine learning models
Rukiye Tekin Ünver, Cihan Bayraktar, Bilge Demir
Applied Physics A (2025) Vol. 131, Iss. 4
Open Access
Development of Hybrid Aluminum Nanocomposites for Automotive Applications: An In-Depth Analysis Using Experimental Approaches and Predictive Machine Learning Techniques
Chitti Babu Golla, R. Narasimha Rao, Syed Ismail, et al.
Journal of Materials Research and Technology (2025)
Open Access
Investigations on Microstructure, Mechanical, and Wear Properties, with Strengthening Mechanisms of Al6061-CuO Composites
Sriteja Mantha, Gonal Basavaraja Veeresh Kumar, R. Pramod, et al.
Journal of Manufacturing and Materials Processing (2024) Vol. 8, Iss. 6, pp. 245-245
Open Access | Times Cited: 3
Physics-Informed machine learning for tribological properties prediction of S32750/CFRPEEK tribopair under seawater lubrication via PISSA-CNN-LSTM
Fanglong Yin, Hao Luo, Songlin Nie, et al.
Tribology International (2024) Vol. 199, pp. 109965-109965
Closed Access | Times Cited: 1
Triboinformatics approach for prediction of high-stress abrasive wear and coefficient of friction in Al/TiC nanocomposites using machine learning techniques
Chitti Babu Golla, R. Narasimha Rao, Syed Ismail
Journal of Tribology (2024) Vol. 147, Iss. 2
Closed Access | Times Cited: 1
Multi-response optimisation of wear parameters of B4C reinforced Al-Fe-Si composites: Using Taguchi-grey relational analysis
Bharat Kumar Talluri, R.N. Rao
Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering (2024)
Closed Access
Data-Driven Prediction of COF in Wet Friction Components: A Model Development and Interpretability Analysis
Jianpeng Wu, Peng Zhao, Jiahao Cui, et al.
Journal of Tribology (2024) Vol. 147, Iss. 7
Closed Access
Tribo-informatics empowered research on triboelectrification
Nian Yin, Zhangli Hou, Xin Wang, et al.
Friction (2024) Vol. 13, Iss. 2, pp. 9441045-9441045
Open Access
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