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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:

Triboinformatics Approach for Friction and Wear Prediction of Al-Graphite Composites Using Machine Learning Methods
Md Syam Hasan, Amir Kordijazi, Pradeep K. Rohatgi, et al.
Journal of Tribology (2021) Vol. 144, Iss. 1
Closed Access | Times Cited: 78

Showing 1-25 of 78 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
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
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
Advances in machine learning-aided design of reinforced polymer composite and hybrid material systems
Christian Emeka Okafor, Sunday Iweriolor, Okwuchukwu Innocent Ani, et al.
Hybrid Advances (2023) Vol. 2, pp. 100026-100026
Open Access | Times Cited: 63
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
Methods, progresses, and opportunities of materials informatics
Chen Li, Kun Zheng
InfoMat (2023) Vol. 5, Iss. 8
Open Access | Times Cited: 28
Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning
I. S. N. V. R. Prasanth, Prabahar Jeevanandam, P. Selvaraju, et al.
Journal of Nanomaterials (2023) Vol. 2023, pp. 1-15
Open Access | Times Cited: 27
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
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
A Review of Application of Machine Learning in Design, Synthesis, and Characterization of Metal Matrix Composites: Current Status and Emerging Applications
Amir Kordijazi, Tian Zhao, Jun Zhang, et al.
JOM (2021) Vol. 73, Iss. 7, pp. 2060-2074
Closed Access | Times Cited: 51
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
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
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
Molecular dynamics simulation and machine learning prediction of tribological properties of graphene solid-liquid two-phase lubrication system
Feng Qiu, Hui Song, Zhiquan Yang, et al.
Tribology International (2024), pp. 110347-110347
Closed Access | Times Cited: 6
Effect of different types of reinforcement on tribological properties of aluminium metal matrix composites (MMCs) – A review of recent studies
Rajwinder Singh Gill, Partap Singh Samra, Amresh Kumar
Materials Today Proceedings (2021) Vol. 56, pp. 3094-3101
Closed Access | Times Cited: 36
Triboinformatics: machine learning algorithms and data topology methods for tribology
Md Syam Hasan, Michael Nosonovsky
Surface Innovations (2022) Vol. 10, Iss. 4-5, pp. 229-242
Closed Access | Times Cited: 23
Tribological performance study and prediction of copper coated by MoS2 based on GBRT method
Guoqing Wang, Yuling Ruan, Hongxing Wang, et al.
Tribology International (2022) Vol. 179, pp. 108149-108149
Closed Access | Times Cited: 23
Tribological properties and machine learning prediction of FeCoCrNiAlN high entropy coatings
Binyuan Jia, Qiang Wan, Long Yan, et al.
Surface and Coatings Technology (2023) Vol. 477, pp. 130341-130341
Closed Access | Times Cited: 16
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