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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:

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 51-75 of 78 citing articles:

Prediction of Tribological Properties of UHMWPE/SiC Polymer Composites Using Machine Learning Techniques
Abdul Jawad Mohammed, Anwaruddin Siddiqui Mohammed, Mohammed Abdul Samad
Polymers (2023) Vol. 15, Iss. 20, pp. 4057-4057
Open Access | Times Cited: 4
On the use of machine learning for predicting femtosecond laser grooves in tribological applications
Luis Moles, Iñigo Llavori, A. Aginagalde, et al.
Tribology International (2024) Vol. 200, pp. 110067-110067
Open Access | Times Cited: 1
Synthesis of ZnO/TiO2-Based Hydrophobic Antimicrobial Coatings for Steel and Their Roughness, Wetting, and Tribological Characterization
Md Syam Hasan, Filip Zemajtis, Michael Nosonovsky, et al.
Journal of Tribology (2022) Vol. 144, Iss. 8
Closed Access | Times Cited: 6
Small-Dataset Machine Learning for Wear Prediction of Laser Powder Bed Fusion Fabricated Steel
Yi Zhu, Zijun Yuan, M. M. Khonsari, et al.
Journal of Tribology (2023) Vol. 145, Iss. 9
Closed Access | Times Cited: 3
Study on the Tribological Behavior and the Interaction between Friction and Oxidation of Graphite Reinforced by Impregnated Phosphate at High Temperatures
Hao Cheng, Siyang Gao, Deli Duan, et al.
Materials (2023) Vol. 16, Iss. 9, pp. 3517-3517
Open Access | Times Cited: 3
A Semantic Annotation Pipeline towards the Generation of Knowledge Graphs in Tribology
Patricia Kügler, Max Marian, Rene Dorsch, et al.
Lubricants (2022) Vol. 10, Iss. 2, pp. 18-18
Open Access | Times Cited: 5
An Approach of Data Science for the Prediction of Wear Behaviour of Hypereutectoid Steel
Poornima Hulipalled, Veerabhadrappa Algur, V. Lokesha
Journal of Bio- and Tribo-Corrosion (2022) Vol. 8, Iss. 3
Closed Access | Times Cited: 5
Process optimization for enhanced tribological properties of Al/MWCNT composites produced by powder metallurgy using artificial neural networks
Türker Türkoğlu, Sare Çelik
Surface Topography Metrology and Properties (2021) Vol. 9, Iss. 4, pp. 045032-045032
Closed Access | Times Cited: 6
Data-Driven Model of the Distribution Lubrication on Water-Lubricated Bearing Under Severe Operating Conditions
Wu Ouyang, Qilin Liu, Xingxin Liang, et al.
Journal of Tribology (2023) Vol. 146, Iss. 1
Closed Access | Times Cited: 2
Mathematical and artificial neural network model in composite electrode assisted electrical discharge coating
U. Elaiyarasan, V. Satheeshkumar, C. Senthilkumar, et al.
Surface Topography Metrology and Properties (2022) Vol. 10, Iss. 2, pp. 025004-025004
Closed Access | Times Cited: 3
Prediction of Coefficient of Friction and Wear Rate of Stellite 6 Coatings Manufactured by LMD Using Machine Learning
Ricardo-Antonio Cázares-Vázquez, Viridiana Humarán-Sarmiento, Angel‐Iván García‐Moreno
Lecture notes in networks and systems (2024), pp. 17-25
Closed Access
Characterization of the friction-induced attractor trajectories during the running-in process in ball-on-disk tribosystem
Yuting Wang, Guodong Sun, Haisheng Wang, et al.
Industrial Lubrication and Tribology (2024) Vol. 76, Iss. 3, pp. 431-440
Closed Access
Prediction of the Tribological Properties of Graphite-Filled Ptfe Composites Based on Machine Learning Algorithms
Huifeng Ning, Wenwen WANG, Gui Gao, et al.
(2024)
Closed Access
Adhesive wear characteristics of mono and hybrid CF/Ep composite with nano-HAP filler
Divya GURKAR SOMASHEKAR, Naveena BETTAHALLI ESWAREGOWDA, SURESHA BHEEMAPPA
Journal of Metals Materials and Minerals (2024) Vol. 34, Iss. 3, pp. 2040-2040
Open Access
An integrated knowledge and data model for adaptive diagnosis of lubricant conditions
Shuo Wang, Zhidong Han, Weijun Hui, et al.
Tribology International (2024) Vol. 199, pp. 109914-109914
Closed Access
Integration of machine learning prediction and optimization for determination of the coefficient of friction of textured UHMWPE surfaces
Huihui Feng, Jing Liu, Ron A.J. van Ostayen, et al.
Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology (2024)
Closed Access
Mechanical Properties and Tribological Study of Bottom Pouring Stir-Cast A356 Alloy Reinforced with Graphite Solid Lubricant Extracted from Corn Stover
V. S. S. Venkatesh, Pandu R. Vundavilli
Lubricants (2024) Vol. 12, Iss. 10, pp. 341-341
Open Access
Machine learning‐based investigations of the effect of surface texture geometry on the wear behaviour of UHMWPE bearings in hip joint implants
Vipin Kumar, Ravi Prakash Tewari, Anubhav Rawat
Biosurface and Biotribology (2024)
Open Access
Experimental Investigation and Machine Learning Modeling of Tribological Characteristics of AZ31/B4C/GNPs Hybrid Composites
Dhanunjay Kumar Ammisetti, Bharat Kumar Chigilipalli, Baburao Gaddala, et al.
Crystals (2024) Vol. 14, Iss. 12, pp. 1007-1007
Open Access
Erosion wear performance of titania filled ramie-epoxy composites: A data driven optimization study using supervised machine learning approach
Sourav Kumar Mahapatra, Alok Satapathy
Journal of Elastomers & Plastics (2024)
Closed Access
Calculation and prediction of sliding energy barriers by first-principles combined with machine learning
Yuan Niu, Yun Wang, Minjuan He, et al.
Ceramics International (2023) Vol. 49, Iss. 15, pp. 24752-24761
Closed Access | Times Cited: 1
Modelling of performance parameters of phenolic base resins Non-Asbestos Organic (NAO) friction material in brake pad using machine learning algorithms
Danishtah Quamar, Chiranjit Sarkar
Tribology International (2023) Vol. 191, pp. 109188-109188
Closed Access | Times Cited: 1
Machine Learning-based Investigation of Wear and Frictional Behavior in Graphite-reinforced Aluminum Nanocomposites
Sathishkumar Arumugam, Sachin Kumar, S Pramod, et al.
NanoWorld Journal (2023) Vol. 9
Open Access | Times Cited: 1
Rheology of concentrated suspension of fibers with load dependent friction coefficient
Monsurul Khan, Rishabh V. More, Arash Alizad Banaei, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 2
Multi-source sensor data and worn surface topography for tribo-informatics research
Yufei Ma, Ke He, Nian Yin, et al.
Research Square (Research Square) (2022)
Open Access | Times Cited: 1

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