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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:

Triboinformatic modeling of dry friction and wear of aluminum base alloys using machine learning algorithms
Md Syam Hasan, Amir Kordijazi, Pradeep K. Rohatgi, et al.
Tribology International (2021) Vol. 161, pp. 107065-107065
Closed Access | Times Cited: 121

Showing 1-25 of 121 citing articles:

Current Trends and Applications of Machine Learning in Tribology—A Review
Max Marian, Stephan Tremmel
Lubricants (2021) Vol. 9, Iss. 9, pp. 86-86
Open Access | Times Cited: 144
Contact mechanics in tribological and contact damage-related problems: A review
Biao Li, Peidong Li, Runhua Zhou, et al.
Tribology International (2022) Vol. 171, pp. 107534-107534
Closed Access | Times Cited: 96
Analysis of the friction and wear of graphene reinforced aluminum metal matrix composites using machine learning models
Md Syam Hasan, Tien Yin Wong, Pradeep K. Rohatgi, et al.
Tribology International (2022) Vol. 170, pp. 107527-107527
Closed Access | Times Cited: 72
AI for tribology: Present and future
Nian Yin, Pufan Yang, Songkai Liu, et al.
Friction (2024) Vol. 12, Iss. 6, pp. 1060-1097
Open Access | Times Cited: 18
Machine learning models of the transition from solid to liquid lubricated friction and wear in aluminum-graphite composites
Md Syam Hasan, Amir Kordijazi, Pradeep K. Rohatgi, et al.
Tribology International (2021) Vol. 165, pp. 107326-107326
Closed Access | Times Cited: 60
Tribo-informatics approaches in tribology research: A review
Nian Yin, Zhiguo Xing, Ke He, et al.
Friction (2022) Vol. 11, Iss. 1, pp. 1-22
Open Access | Times Cited: 57
The Role of Machine Learning in Tribology: A Systematic Review
Uma Maheshwera Reddy Paturi, Sai Teja Palakurthy, N.S. Reddy
Archives of Computational Methods in Engineering (2022) Vol. 30, Iss. 2, pp. 1345-1397
Closed Access | Times Cited: 57
Predicting EHL film thickness parameters by machine learning approaches
Max Marian, Jonas Mursak, Marcel Bartz, et al.
Friction (2022) Vol. 11, Iss. 6, pp. 992-1013
Open Access | Times Cited: 40
A review of recent advances and applications of machine learning in tribology
Abhishek T. Sose, Soumil Y. Joshi, Lakshmi Kumar Kunche, et al.
Physical Chemistry Chemical Physics (2023) Vol. 25, Iss. 6, pp. 4408-4443
Closed Access | Times Cited: 30
Tribo-informatics evaluation of dry sliding friction of silanized jute filler reinforced epoxy composites using machine learning techniques
Vijay Kumar Mahakur, Sumit Bhowmik, Promod Kumar Patowari
Tribology International (2023) Vol. 183, pp. 108388-108388
Closed Access | Times Cited: 25
Machine learning-based prediction of friction torque and friction coefficient in statically loaded radial journal bearings
Hasan Baş, Yunus Emre Karabacak
Tribology International (2023) Vol. 186, pp. 108592-108592
Closed Access | Times Cited: 24
A modified artificial neural network to predict the tribological properties of Al-SiC nanocomposites fabricated by accumulative roll bonding process
I.M.R. Najjar, AM Sadoun, Ahmed Mohamed Mahmoud Ibrahim, et al.
Journal of Composite Materials (2023) Vol. 57, Iss. 21, pp. 3433-3445
Closed Access | Times Cited: 23
Machine Learning Design for High-Entropy Alloys: Models and Algorithms
S. Liu, Chao Yang
Metals (2024) Vol. 14, Iss. 2, pp. 235-235
Open Access | Times Cited: 15
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
Development of machine learning models for the prediction of erosion wear of hybrid composites
Sourav Kumar Mahapatra, Alok Satapathy
Polymer Composites (2024) Vol. 45, Iss. 9, pp. 7950-7966
Closed Access | Times Cited: 11
Modeling and classifying the in-operando effects of wear and metal contaminations of lubricating oil on diesel engine: A machine learning approach
Mohammad Rahimi, Mohammad-Reza Pourramezan, Abbas Rohani
Expert Systems with Applications (2022) Vol. 203, pp. 117494-117494
Closed Access | Times Cited: 30
Triboinformatic modeling of the friction force and friction coefficient in a cam-follower contact using machine learning algorithms
Hasan Baş, Yunus Emre Karabacak
Tribology International (2023) Vol. 181, pp. 108336-108336
Closed Access | Times Cited: 16
Physics-Informed Neural Network (PINN) for Solving Frictional Contact Temperature and Inversely Evaluating Relevant Input Parameters
Yichun Xia, Yonggang Meng
Lubricants (2024) Vol. 12, Iss. 2, pp. 62-62
Open Access | Times Cited: 7
Low-dimensional dynamical models of structures with uncertain boundaries via a hybrid knowledge- and data-driven approach
Chao Chen, Yilong Wang, Bo Fang, et al.
Mechanical Systems and Signal Processing (2024) Vol. 223, pp. 111876-111876
Closed Access | Times Cited: 7
Atomic-Scale Insights Into Graphene/Fullerene Tribological Mechanisms and Machine Learning Prediction of Properties
Feng Qiu, Hui Song, Weimin Feng, et al.
Journal of Tribology (2024) Vol. 146, Iss. 6
Closed Access | Times Cited: 6
Analysis and prediction of erosion behavior of epoxy composites using statistical and machine learning techniques
Sourav Kumar Mahapatra, Alok Satapathy
Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering (2024)
Closed Access | Times Cited: 6
Predictive analytics of wear performance in high entropy alloy coatings through machine learning
S. Sivaraman, N. Radhika
Physica Scripta (2024) Vol. 99, Iss. 7, pp. 076014-076014
Closed Access | Times Cited: 6
Clay minerals regulating the performance of tribo-composites: A review
Kun Yu, Xi Shang, Liangjie Fu, et al.
Deleted Journal (2024) Vol. 1, Iss. 2, pp. 220-240
Open Access | Times Cited: 6
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
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