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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.

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Showing 23 citing articles:

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 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
Infrastructure monitoring and quality diagnosis in CNC machining: A review
Myrsini Ntemi, Spyridon Paraschos, Αναστάσιος Καρακώστας, et al.
CIRP journal of manufacturing science and technology (2022) Vol. 38, pp. 631-649
Open Access | Times Cited: 31
Tool wear mechanism, monitoring and remaining useful life (RUL) technology based on big data: a review
Zhou Yang, Changfu Liu, Xinli Yu, et al.
SN Applied Sciences (2022) Vol. 4, Iss. 8
Open Access | Times Cited: 28
Comparative Analysis of the Optimized KNN, SVM, and Ensemble DT Models Using Bayesian Optimization for Predicting Pedestrian Fatalities: An Advance towards Realizing the Sustainable Safety of Pedestrians
Lei Yang, Mahdi Aghaabbasi, Mujahid Ali, et al.
Sustainability (2022) Vol. 14, Iss. 17, pp. 10467-10467
Open Access | Times Cited: 27
A Supervised Machine Learning Model for Tool Condition Monitoring in Smart Manufacturing
S. Ganeshkumar, T. Deepika, Anandakumar Haldorai
Defence Science Journal (2022) Vol. 72, Iss. 5, pp. 712-720
Open Access | Times Cited: 27
Novel Framework for Quality Control in Vibration Monitoring of CNC Machining
Georgia Apostolou, Myrsini Ntemi, Spyridon Paraschos, et al.
Sensors (2024) Vol. 24, Iss. 1, pp. 307-307
Open Access | Times Cited: 5
Research on tool wear classification of milling 508III steel based on chip spectrum feature
Rui Guan, Yaonan Cheng, Shilong Zhou, et al.
The International Journal of Advanced Manufacturing Technology (2024) Vol. 133, Iss. 3-4, pp. 1531-1547
Closed Access | Times Cited: 3
Comparative analysis of different machine learning algorithms in prediction of cutting force using hybrid nanofluid enriched cutting fluid in turning operation
Vishal Kumar, Vineet Dubey, Anuj Kumar Sharma
Materials Today Proceedings (2023)
Closed Access | Times Cited: 7
Research on cutting tool edge geometry design based on SVR-PSO
Yimin Jiang, Wei Huang, Yu Tian, et al.
The International Journal of Advanced Manufacturing Technology (2024) Vol. 131, Iss. 9-10, pp. 5047-5059
Open Access | Times Cited: 2
FEM-supported machine learning for residual stress and cutting force analysis in micro end milling of aluminum alloys
M. K. Sharma, Hamzah Ali Alkhazaleh, Shavan Askar, et al.
International Journal of Mechanics and Materials in Design (2024) Vol. 20, Iss. 5, pp. 1077-1098
Closed Access | Times Cited: 2
Multispectral UAV and satellite images for digital soil modeling with gradient descent boosting and artificial neural network
Turgay Dindaroğlu, Miraç Kılıç, Elif Günal, et al.
Earth Science Informatics (2022) Vol. 15, Iss. 4, pp. 2239-2263
Closed Access | Times Cited: 11
Machine Learning Approach: Prediction of Surface Roughness in Dry Turning Inconel 625
A. S. Rajesh, M. S. Prabhuswamy, M. Rudra Naik
Advances in Materials Science and Engineering (2022) Vol. 2022, pp. 1-7
Open Access | Times Cited: 10
Research on intelligent tool condition monitoring based on data-driven: a review
Yaonan Cheng, Rui Guan, Yingbo Jin, et al.
Journal of Mechanical Science and Technology (2023) Vol. 37, Iss. 7, pp. 3721-3738
Closed Access | Times Cited: 5
Predicting tool life and sound pressure levels in dry turning using machine learning models
Alex Fernandes de Souza, Filipe Alves Neto Verri, Paulo Henrique da Silva Campos, et al.
The International Journal of Advanced Manufacturing Technology (2024) Vol. 135, Iss. 7-8, pp. 3777-3793
Closed Access | Times Cited: 1
MATWI: A Multimodal Automatic Tool Wear Inspection Dataset and Baseline Algorithms
L De Pauw, Thomas L. Jacobs, Toon Goedemé
Lecture notes in computer science (2023), pp. 255-269
Closed Access | Times Cited: 3
Gun Life Prediction Model Based on Bayesian Optimization CNN-LSTM
Min Wang, Lu Xikun, Zhou Yidi
Integrated ferroelectrics (2022) Vol. 228, Iss. 1, pp. 107-116
Closed Access | Times Cited: 4
Tool Wear Monitoring Based on Transfer Learning and Improved Deep Residual Network
Nan Zhang, Jiawei Zhao, Lin Ma, et al.
IEEE Access (2022) Vol. 10, pp. 119546-119557
Open Access | Times Cited: 4
Automatic crimping state classification in X-ray images of crimped tension clamp
Huitu Shao, Jiliang Lv, Jintao Wang, et al.
Nondestructive Testing And Evaluation (2024), pp. 1-17
Closed Access
A CNN-LSTM-PSO tool wear prediction method based on multi-channel feature fusion
Shuo Wang, Zhenliang YU, Yongqi GUO, et al.
Mechanical Engineering Science (2022) Vol. 4, Iss. 2, pp. 39-39
Open Access | Times Cited: 1
Classification of Acceleration Signal in Machining Process for Surface Roughness Identification using Tuned Support Vector Mechanics
Norlida Jamil, Cucuk Nur Rosyidi, Ahmad Razlan Yusoff
Research Square (Research Square) (2023)
Open Access
Prediction of the cutting tool wear during dry hard turning of AISI D2 steel by using models based on Learning process and GA polyfit
Khaled Djellouli, Kamel Haddouche, Mostefa Belarbi, et al.
The Journal of Engineering and Exact Sciences (2023) Vol. 9, Iss. 12, pp. 18297-18297
Open Access
A two-stage approach for modeling inverse S-shaped wear processes of cutting tools
Renyan Jiang
2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) (2021), pp. 1-5
Closed Access
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