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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 26-50 of 78 citing articles:

Physics-Informed Machine Learning—An Emerging Trend in Tribology
Max Marian, Stephan Tremmel
Lubricants (2023) Vol. 11, Iss. 11, pp. 463-463
Open Access | Times Cited: 13
Data‐driven modeling for predicting tribo‐performance of graphene‐incorporated glass‐fabric reinforced epoxy composites using machine learning algorithms
Santosh Kumar, K. Sourabh K. Singh, Kalyan Kumar Singh
Polymer Composites (2022) Vol. 43, Iss. 9, pp. 6599-6610
Closed Access | Times Cited: 21
Wear Resistance Prediction of AlCoCrFeNi-X (Ti, Cu) High-Entropy Alloy Coatings Based on Machine Learning
Jiajie Kang, Yi Niu, Yong-kuan Zhou, et al.
Metals (2023) Vol. 13, Iss. 5, pp. 939-939
Open Access | Times Cited: 12
Wear evolution of graphite asperities on dry-sliding with the squamous textured SiC
Na Zhang, Xianghua Zhan, Yancong Liu, et al.
Tribology International (2024) Vol. 193, pp. 109390-109390
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
Prediction of friction coefficient of su-8 and its composite coatings using machine learning techniques
Anwaruddin Siddiqui Mohammed, Srihari Dodla, Jitendra Kumar Katiyar, et al.
Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology (2022) Vol. 237, Iss. 4, pp. 943-953
Closed Access | Times Cited: 16
Machine learning assisted optimization of tribological parameters of Al–Co–Cr–Fe–Ni high-entropy alloy
Saurabh Vashistha, Bashista Kumar Mahanta, Vivek K. Singh, et al.
Materials and Manufacturing Processes (2023) Vol. 38, Iss. 16, pp. 2093-2106
Closed Access | Times Cited: 9
Experimental Investigation of the Influence of Various Wear Parameters on the Tribological Characteristics of AZ91 Hybrid Composites and their Machine Learning Modelling
Dhanunjay Kumar Ammisetti, S. S. Harish Kruthiventi
Journal of Tribology (2024) Vol. 146, Iss. 5
Closed Access | Times Cited: 3
Experimental investigations with machine learning techniques for understanding of erosion wear in advanced aluminum nanocomposites
Chitti Babu Golla, R.N. Rao, Syed Ismail, et al.
Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering (2024)
Closed Access | Times Cited: 3
Prediction of Wear Rate of Glass-Filled PTFE Composites Based on Machine Learning Approaches
Abhijeet Deshpande, Atul Kulkarni, Namrata N. Wasatkar, et al.
Polymers (2024) Vol. 16, Iss. 18, pp. 2666-2666
Open Access | Times Cited: 3
An application of Artificial Neural Network (ANN) to predict the friction coefficient of nuclear grade graphite
Ashish Soni, Mohammad Yusuf, Mukarram Beg, et al.
Materials Today Proceedings (2022) Vol. 68, pp. 701-709
Closed Access | Times Cited: 15
Application of Triboinformatics Approach in Tribological Studies of Aluminum Alloys and Aluminum-Graphite Metal Matrix Composites
Md Syam Hasan, Amir Kordijazi, Pradeep K. Rohatgi, et al.
˜The œminerals, metals & materials series (2022), pp. 41-51
Closed Access | Times Cited: 14
Gradient nanostructured tungsten and the thermal shock response
Yan Jiang, Ping Huang, Zhiqiang Jiang, et al.
Tungsten (2023) Vol. 5, Iss. 4, pp. 548-557
Closed Access | Times Cited: 8
Recent Progress of Machine Learning Algorithms for the Oil and Lubricant Industry
Md Hafizur Rahman, Sadat Shahriar, Pradeep L. Menezes
Lubricants (2023) Vol. 11, Iss. 7, pp. 289-289
Open Access | Times Cited: 8
High-temperature dry sliding wear behavior of hybrid aluminum composite reinforced with ceria and graphene nanoparticles
Dinesh Kumar, Surjit Angra, Satnam Singh
Engineering Failure Analysis (2023) Vol. 151, pp. 107426-107426
Closed Access | Times Cited: 7
Prediction of maximum temperature of fluid-lubricated bearing based on machine learning algorithm
Hongbin Ji, Jianmei Wang, Wanru Zhang, et al.
International Communications in Heat and Mass Transfer (2023) Vol. 149, pp. 107109-107109
Closed Access | Times Cited: 7
Mapping Acoustic Frictional Properties of Self-Lubricating Epoxy-Coated Bearing Steel with Acoustic Emissions during Friction Test
V. Krishnamoorthy, Ashvita Anitha John, Shubrajit Bhaumik, et al.
Technologies (2024) Vol. 12, Iss. 3, pp. 30-30
Open Access | Times Cited: 2
Synergistic approach to tribological characterization of hybrid aluminum metal matrix composites with ZrB2 and fly ash: Experimental and predictive insights
Prakash Kumar, Binay Kumar
Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering (2024)
Closed Access | Times Cited: 2
Topological Data Analysis of Nanoscale Roughness in Brass Samples
Mikhail V. Zhukov, Md Syam Hasan, Pavel V. Nesterov, et al.
ACS Applied Materials & Interfaces (2021) Vol. 14, Iss. 1, pp. 2351-2359
Closed Access | Times Cited: 13
Application of machine learning to mechanical properties of copper-graphene composites
Milan Rohatgi, Amir Kordijazi
MRS Communications (2023) Vol. 13, Iss. 1, pp. 111-116
Closed Access | Times Cited: 5
A Machine Learning Approach for Real-Time Wheel-Rail Interface Friction Estimation
Morinoye O. Folorunso, Mick Watson, Alan Martín, et al.
Journal of Tribology (2023) Vol. 145, Iss. 9
Open Access | Times Cited: 5
Recent Progress on the Tribological Applications of Solid Lubricants
Changqing Miao, Jun Tang, Kang Yang, et al.
Journal of Tribology (2023) Vol. 146, Iss. 2
Closed Access | Times Cited: 5
Vibration Analysis Approach to Model Incremental Wear and Associated Sound in Multi-Contact Sliding Friction Mechanisms
Kanza Basit, Hamza Shams, Muhammad Khan, et al.
Journal of Tribology (2023) Vol. 145, Iss. 9
Open Access | Times Cited: 4
Prediction of Wear Rate in Al/SiC Metal Matrix Composites Using a Neurosymbolic Artificial Intelligence (NSAI)-Based Algorithm
Akshansh Mishra, Vijaykumar S. Jatti
Lubricants (2023) Vol. 11, Iss. 6, pp. 261-261
Open Access | Times Cited: 4
Data-driven design of brake pad composites for high-speed trains
Lingzhi Wu, Peng Zhang, Bin Xu, et al.
Journal of Materials Research and Technology (2023) Vol. 27, pp. 1058-1071
Open Access | Times Cited: 4

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