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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:

Ensemble learning with support vector machines algorithm for surface roughness prediction in longitudinal vibratory ultrasound-assisted grinding
Mingli Zhao, Boxi Xue, Bohan Li, et al.
Precision Engineering (2024) Vol. 88, pp. 382-400
Closed Access | Times Cited: 11

Showing 11 citing articles:

A review of artificial intelligent methods for machined surface roughness prediction
Huguang Yang, Han Zheng, Taohong Zhang
Tribology International (2024) Vol. 199, pp. 109935-109935
Closed Access | Times Cited: 12
Adaptive process parameters decision-making in robotic grinding based on meta-reinforcement learning
Jie Pan, Fan Chen, Dan Han, et al.
Journal of Manufacturing Processes (2025) Vol. 137, pp. 376-396
Closed Access
Machine learning prediction of surface roughness in sustainable machining of AISI H11 tool steel
A. Balasuadhakar, S. Thirumalai Kumaran, M. Uthayakumar
Smart Materials in Manufacturing (2025) Vol. 3, pp. 100075-100075
Open Access
Fundamental Study on Precision Ultrasonic Vibration Polishing
Hiroyuki Nakano, Peerapong Kasuriya, Hidenari Kanai, et al.
Key engineering materials (2025) Vol. 1008, pp. 43-48
Closed Access
Predictive Modelling of Surface Roughness in Grinding Operations Using Machine Learning Techniques
Maya M. Charde, Trupti P. Najan, Lenka Čepová, et al.
MANUFACTURING TECHNOLOGY (2025)
Open Access
Geometric feature extraction in nanofiber membrane image based on convolution neural network for surface roughness prediction
Dong Hee Kang, Na Kyong Kim, Wonoh Lee, et al.
Heliyon (2024) Vol. 10, Iss. 15, pp. e35358-e35358
Open Access | Times Cited: 2
Surface microstructure evolution analysis of Inconel 718 during ultrasonic vibration-assisted grinding using FEM
Nianwei Xu, Zhigang Dong, Renke Kang, et al.
Journal of Manufacturing Processes (2024) Vol. 127, pp. 631-644
Closed Access | Times Cited: 2
Analytical Approach for Forecasting the Load Capacity of the EN AW-7075-T6 Aluminum Alloy Joints Created Using RFSSW Technology
R. Kluz, Magdalena Bucior, Andrzej Kubit, et al.
Materials (2024) Vol. 17, Iss. 7, pp. 1529-1529
Open Access | Times Cited: 1
An Optimization Method for Short-Term Power Load Forecasting Based on IPSO-SVM
Yaguang Shi, Shaopeng Wang, Haoran Huang, et al.
(2024) Vol. 14, pp. 758-761
Closed Access
Adaptive Hybrid Prediction Model for Adapting to Data Distribution Shifts in Machining Quality Prediction
Feng Li, Xu Yang, Jie Gao, et al.
Measurement Science and Technology (2024) Vol. 36, Iss. 1, pp. 016022-016022
Closed Access
Advances in low-damage grinding technology for hard and brittle optical materials
Haiyan Zhu, Xiaojing Li, Ping Hu, et al.
(2024), pp. 2-2
Closed Access
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