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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:

Physical model-based tool wear and breakage monitoring in milling process
Xing Zhang, Yang Gao, Zhuocheng Guo, et al.
Mechanical Systems and Signal Processing (2022) Vol. 184, pp. 109641-109641
Closed Access | Times Cited: 52

Showing 1-25 of 52 citing articles:

Study on developing predicted system model of cutting-edge trajectory for micro-milling process based on tool runout error, chip thickness and force signal
Yao Sun, Yirong Sun, Yiming Huang, et al.
Mechanical Systems and Signal Processing (2025) Vol. 228, pp. 112410-112410
Closed Access | Times Cited: 6
Integrating physics-informed recurrent Gaussian process regression into instance transfer for predicting tool wear in milling process
Biyao Qiang, Kaining Shi, Ning Liu, et al.
Journal of Manufacturing Systems (2023) Vol. 68, pp. 42-55
Closed Access | Times Cited: 33
On-line tool wear monitoring under variable milling conditions based on a condition-adaptive hidden semi-Markov model (CAHSMM)
Shichao Yan, Liang Sui, Siqi Wang, et al.
Mechanical Systems and Signal Processing (2023) Vol. 200, pp. 110644-110644
Closed Access | Times Cited: 26
Position-dependent milling process monitoring and surface roughness prediction for complex thin-walled blade component
Zequan Yao, Jingyuan Shen, Ming Wu, et al.
Mechanical Systems and Signal Processing (2023) Vol. 198, pp. 110439-110439
Closed Access | Times Cited: 20
A hybrid-driven probabilistic state space model for tool wear monitoring
Zhipeng Ma, Ming Zhao, Xuebin Dai, et al.
Mechanical Systems and Signal Processing (2023) Vol. 200, pp. 110599-110599
Closed Access | Times Cited: 20
ACWGAN-GP for milling tool breakage monitoring with imbalanced data
Xuebing Li, Caixu Yue, Xianli Liu, et al.
Robotics and Computer-Integrated Manufacturing (2023) Vol. 85, pp. 102624-102624
Closed Access | Times Cited: 20
Tool Wear Prediction Based on Multi-Information Fusion and Genetic Algorithm-Optimized Gaussian Process Regression in Milling
Zhiwen Huang, Jiajie Shao, Weicheng Guo, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-16
Open Access | Times Cited: 18
Self-adaptive fusion of local-temporal features for tool condition monitoring: A human experience free model
Runqiong Wang, Qinghua Song, Yezhen Peng, et al.
Mechanical Systems and Signal Processing (2023) Vol. 195, pp. 110310-110310
Closed Access | Times Cited: 16
Meta-learning-based approach for tool condition monitoring in multi-condition small sample scenarios
Bowen Zhang, Xianli Liu, Caixu Yue, et al.
Mechanical Systems and Signal Processing (2024) Vol. 216, pp. 111444-111444
Closed Access | Times Cited: 6
Mill condition monitoring based on instantaneous identification of specific force coefficients under variable cutting conditions
Luca Bernini, Paolo Albertelli, Michele Monno
Mechanical Systems and Signal Processing (2022) Vol. 185, pp. 109820-109820
Open Access | Times Cited: 24
Analysis of wear mechanism and sawing performance of carbide and PCD circular saw blades in machining hard aluminum alloy
Jinyou Kang, Jinsheng Zhang, Kaida Wang, et al.
International Journal of Refractory Metals and Hard Materials (2023) Vol. 116, pp. 106362-106362
Closed Access | Times Cited: 13
A tool wear condition monitoring method for non-specific sensing signals
Yezhen Peng, Qinghua Song, Runqiong Wang, et al.
International Journal of Mechanical Sciences (2023) Vol. 263, pp. 108769-108769
Closed Access | Times Cited: 13
A machine learning algorithm based cutting tool wear assessment using multi-sensorial data
Mulpur Sarat Babu
International Journal on Interactive Design and Manufacturing (IJIDeM) (2025)
Closed Access
Sensorless tool wear estimation by using the artificial intelligence (AI) tools from the currents of motors generating linear motions
Mustafa Demetgül, Anand D. Darji, İbrahim N. Tansel, et al.
The International Journal of Advanced Manufacturing Technology (2025)
Closed Access
A novel algorithm for tool wear monitoring utilizing model and Knowledge-Guided Multi-Expert weighted adversarial deep transfer learning
Zhilie Gao, Ni Chen, Liang Li
Mechanical Systems and Signal Processing (2025) Vol. 228, pp. 112456-112456
Closed Access
Research on multi-step ahead prediction method for tool wear based on MSTCN-SBiGRU-MHA
Jing Xue, Yaonan Cheng, Wenjie Zhai, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103219-103219
Closed Access
Deep-learning-driven intelligent tool wear identification of high-precision machining with multi-scale CNN-BiLSTM-GCN
Zhicheng Xu, Baolong Zhang, Louis Luo Fan, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103234-103234
Closed Access
Real-time reliability analysis of micro-milling processes considering the effects of tool wear
Pengfei Ding, Xianzhen Huang, Yuxiong Li, et al.
Mechanical Systems and Signal Processing (2023) Vol. 200, pp. 110582-110582
Closed Access | Times Cited: 12
Physics-informed hidden markov model for tool wear monitoring
Kunpeng Zhu, Xin Li, Shenshen Li, et al.
Journal of Manufacturing Systems (2023) Vol. 72, pp. 308-322
Closed Access | Times Cited: 12
A domain adversarial graph convolutional network for intelligent monitoring of tool wear in machine tools
Kai Li, Zhou-Long Li, Xianshi Jia, et al.
Computers & Industrial Engineering (2023) Vol. 187, pp. 109795-109795
Closed Access | Times Cited: 11
Physics-informed Gaussian process for tool wear prediction
Kunpeng Zhu, Cheng-Yi Huang, Si Li, et al.
ISA Transactions (2023) Vol. 143, pp. 548-556
Closed Access | Times Cited: 10
A tool wear prediction and monitoring method based on machining power signals
Qi Wang, Xi Chen, Qinglong An, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 129, Iss. 11-12, pp. 5387-5401
Open Access | Times Cited: 9
Tooth-wise monitoring of the asymmetrical tool wear in micro-milling based on the chip thickness reconstruction and cutting force signal
Tongshun Liu, Jingze Song, Kedong Zhang, et al.
Mechanical Systems and Signal Processing (2023) Vol. 208, pp. 111004-111004
Closed Access | Times Cited: 9
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
Leveraging artificial intelligence for real-time indirect tool condition monitoring: From theoretical and technological progress to industrial applications
Delin Liu, Zhanqiang Liu, Bing Wang, et al.
International Journal of Machine Tools and Manufacture (2024) Vol. 202, pp. 104209-104209
Closed Access | Times Cited: 3
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