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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:

Prediction of milling force based on spindle current signal by neural networks
Defeng Peng, Hongkun Li, Yuebang Dai, et al.
Measurement (2022) Vol. 205, pp. 112153-112153
Closed Access | Times Cited: 22

Showing 22 citing articles:

Analysis of Cutting Forces and Geometric Surface Structures in the Milling of NiTi Alloy
Małgorzata Kowalczyk
Materials (2024) Vol. 17, Iss. 2, pp. 488-488
Open Access | Times Cited: 6
Online tool wear prediction based on cutting force coefficients identification using neural network
Guicheng Wang, Min Wang, Peng Gao, et al.
The International Journal of Advanced Manufacturing Technology (2025)
Closed Access
Prediction of milled surface characteristics of carbon fiber-reinforced polyetheretherketone using an optimized machine learning model by gazelle optimizer
Wajdi Rajhi, Ahmed Mohamed Mahmoud Ibrahim, Abdel‐Hamid I. Mourad, et al.
Measurement (2023) Vol. 222, pp. 113627-113627
Closed Access | Times Cited: 15
Prediction of bidirectional milling forces based on spindle current signals by using deep learning algorithms
Guochao Li, Ru Jiang, Li Sun, et al.
Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering (2025)
Closed Access
Causal inference dynamic modeling for real-time surface roughness monitoring in the milling process
Kunhong Chen, Hongguang Liu, Jun Zhang, et al.
Mechanical Systems and Signal Processing (2025) Vol. 229, pp. 112551-112551
Closed Access
Intelligent monitoring of milling tool wear based on milling force coefficients by prediction of instantaneous milling forces
Defeng Peng, Hongkun Li
Mechanical Systems and Signal Processing (2023) Vol. 208, pp. 111033-111033
Closed Access | Times Cited: 12
Fast extraction of cutter-workpiece engagement for milling force prediction in multi-axis machining
Xing Zhang, Xiaoqian Wang, Pengfei Zhang, et al.
Measurement (2024) Vol. 231, pp. 114490-114490
Closed Access | Times Cited: 3
Mechanism-informed friction-dynamics coupling GRU neural network for real-time cutting force prediction
Yinghao Cheng, Yingguang Li, Qiyang Zhuang, et al.
Mechanical Systems and Signal Processing (2024) Vol. 221, pp. 111749-111749
Closed Access | Times Cited: 3
Investigation on eXtreme Gradient Boosting for cutting force prediction in milling
Thomas Heitz, Ning He, Addi Ait‐Mlouk, et al.
Journal of Intelligent Manufacturing (2023)
Closed Access | Times Cited: 4
A review on error generation and control in efficient precision machining of thin-walled parts
Zhao Yiyang, Jian Mao, Gang Liu, et al.
The International Journal of Advanced Manufacturing Technology (2024)
Closed Access | Times Cited: 1
Research on the diffusion kernel density estimation method for modeling the cutting force spectrum and program load spectrum considering multiple manufacturing conditions
Baobao Qi, Chuanhai Chen, Zhifeng Liu, et al.
Journal of Manufacturing Processes (2024) Vol. 127, pp. 140-159
Closed Access | Times Cited: 1
Tool breakage monitoring driven by the real-time predicted spindle cutting torque using spindle servo signals
Yinghao Cheng, Yingguang Li, Guangxu Li, et al.
Robotics and Computer-Integrated Manufacturing (2024) Vol. 92, pp. 102888-102888
Closed Access | Times Cited: 1
Research on the mapping relationship between load current and cutting force of CNC machine tools
Xin Li, he wanlin, Hongjie Zhao, et al.
(2024), pp. 40-40
Closed Access
An Interval Prediction Method for Quantifying the Uncertainties of Gate Lifting Force Under Sediment Deposition
Yuqi Zhang, Ying Tie, Jingran Xiong, et al.
(2024)
Closed Access
Tool wear feature extraction in BTA deep hole drilling process based on maximum probability multi-synchrosqueezing transform of spindle current signal
Chao Peng, Jianming Zheng, Ting Chen, et al.
Measurement (2024), pp. 115780-115780
Closed Access
Study on Self-Learning System for Milling Chatter Feature Based on Hybrid Preprocessing Model and Improved Convolutional Clustering
Pengcheng Guo, Sijie Cai, Shuixuan Chen, et al.
The International Journal of Acoustics and Vibration (2024) Vol. 29, Iss. 3, pp. 316-327
Open Access
Research on milling cutter wear monitoring based on self-learning feature boundary model
Xuchen Hou, Wei Xia, Xianli Liu, et al.
The International Journal of Advanced Manufacturing Technology (2024) Vol. 135, Iss. 3-4, pp. 1789-1807
Closed Access
Methodology and Experimental Verification for Predicting the Remaining Useful Life of Milling Cutters Based on Hybrid CNN-LSTM-Attention-PSA
Meilu Zhu, Ji Zhang, Lingfan Bu, et al.
Machines (2024) Vol. 12, Iss. 11, pp. 752-752
Open Access
Improved random forest for titanium alloy milling force prediction based on finite element-driven
Hong Bian, Congfu Fang
Journal of the Brazilian Society of Mechanical Sciences and Engineering (2024) Vol. 46, Iss. 12
Closed Access
Physics-Guided Deep Network for Milling Dynamics Prediction
Kunpeng Zhu, Jun Li
Engineering (2024)
Open Access
Real-time milling force monitoring based on a parallel deep learning model with dual-channel vibration fusion
Kunhong Chen, Wanhua Zhao, Xing Zhang
The International Journal of Advanced Manufacturing Technology (2023) Vol. 126, Iss. 5-6, pp. 2545-2565
Open Access | Times Cited: 1
Hardware implementation of Monte Carlo and RSM method for the optimization of cutting force during turning of NiTi shape memory alloy
Małgorzata Kowalczyk
Materials research proceedings (2023) Vol. 28, pp. 1275-1284
Open Access
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