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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:

Tool wear monitoring using an online, automatic and low cost system based on local texture
María Teresa Garcí­a-Ordás, Enrique Alegre, Rocío Aláiz-Rodríguez, et al.
Mechanical Systems and Signal Processing (2018) Vol. 112, pp. 98-112
Open Access | Times Cited: 86

Showing 1-25 of 86 citing articles:

Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review
Danil Yurievich Pimenov, Andrés Bustillo, Szymon Wojciechowski, et al.
Journal of Intelligent Manufacturing (2022) Vol. 34, Iss. 5, pp. 2079-2121
Closed Access | Times Cited: 220
Machine vision based condition monitoring and fault diagnosis of machine tools using information from machined surface texture: A review
Yuekai Liu, Liang Guo, Hongli Gao, et al.
Mechanical Systems and Signal Processing (2021) Vol. 164, pp. 108068-108068
Closed Access | Times Cited: 117
Automatic Identification of Tool Wear Based on Convolutional Neural Network in Face Milling Process
Xuefeng Wu, Yahui Liu, Xianliang Zhou, et al.
Sensors (2019) Vol. 19, Iss. 18, pp. 3817-3817
Open Access | Times Cited: 127
Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision
Pauline Ong, Woon Kiow Lee, Raymond Jit Hoo Lau
The International Journal of Advanced Manufacturing Technology (2019) Vol. 104, Iss. 1-4, pp. 1369-1379
Closed Access | Times Cited: 93
Digitalisation and servitisation of machine tools in the era of Industry 4.0: a review
Chao Liu, Pai Zheng, Xun Xu
International Journal of Production Research (2021) Vol. 61, Iss. 12, pp. 4069-4101
Open Access | Times Cited: 88
Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth
Andrés Bustillo, Danil Yurievich Pimenov, Mozammel Mia, et al.
Journal of Intelligent Manufacturing (2020) Vol. 32, Iss. 3, pp. 895-912
Open Access | Times Cited: 76
Indirect monitoring of machining characteristics via advanced sensor systems: a critical review
Mehmet Erdi Korkmaz, Munish Kumar Gupta, Zhixiong Li, et al.
The International Journal of Advanced Manufacturing Technology (2022) Vol. 120, Iss. 11-12, pp. 7043-7078
Closed Access | Times Cited: 63
Tool wear estimation and life prognostics in milling: Model extension and generalization
Yu Zhang, Kunpeng Zhu, Xianyin Duan, et al.
Mechanical Systems and Signal Processing (2021) Vol. 155, pp. 107617-107617
Closed Access | Times Cited: 59
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
Laser ultrasonics and machine learning for automatic defect detection in metallic components
Gaolong Lv, Shifeng Guo, Dan Chen, et al.
NDT & E International (2022) Vol. 133, pp. 102752-102752
Open Access | Times Cited: 44
Online tool wear monitoring by super-resolution based machine vision
Kunpeng Zhu, Hao Guo, Si Li, et al.
Computers in Industry (2022) Vol. 144, pp. 103782-103782
Closed Access | Times Cited: 42
A novel adversarial domain adaptation transfer learning method for tool wear state prediction
Kai Li, Ming-Song Chen, Y.C. Lin, et al.
Knowledge-Based Systems (2022) Vol. 254, pp. 109537-109537
Closed Access | Times Cited: 37
Machine Learning in Production – Potentials, Challenges and Exemplary Applications
Andreas Mayr, Dominik Kißkalt, Moritz Meiners, et al.
Procedia CIRP (2019) Vol. 86, pp. 49-54
Open Access | Times Cited: 66
Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors
Jitesh Ranjan, Karali Patra, Tibor Szalay, et al.
Sensors (2020) Vol. 20, Iss. 3, pp. 885-885
Open Access | Times Cited: 65
On-line milling cutter wear monitoring in a wide field-of-view camera
Zhichao You, Hongli Gao, Liang Guo, et al.
Wear (2020) Vol. 460-461, pp. 203479-203479
Closed Access | Times Cited: 53
Tool wear state prediction based on feature-based transfer learning
Jianbo Li, Juan Lu, Chaoyi Chen, et al.
The International Journal of Advanced Manufacturing Technology (2021) Vol. 113, Iss. 11-12, pp. 3283-3301
Closed Access | Times Cited: 47
Application of machine vision method in tool wear monitoring
Ruitao Peng, Jiachen Liu, Xiuli Fu, et al.
The International Journal of Advanced Manufacturing Technology (2021) Vol. 116, Iss. 3-4, pp. 1357-1372
Closed Access | Times Cited: 47
Automatic Identification of Tool Wear Based on Thermography and a Convolutional Neural Network during the Turning Process
Nika Brili, Mirko Ficko, Simon Klančnik
Sensors (2021) Vol. 21, Iss. 5, pp. 1917-1917
Open Access | Times Cited: 41
Deep Learning Approach for Vibration Signals Applications
Han-Yun Chen, Ching‐Hung Lee
Sensors (2021) Vol. 21, Iss. 11, pp. 3929-3929
Open Access | Times Cited: 40
Machine vision based adaptive online condition monitoring for milling cutter under spindle rotation
Zhichao You, Hongli Gao, Liang Guo, et al.
Mechanical Systems and Signal Processing (2022) Vol. 171, pp. 108904-108904
Closed Access | Times Cited: 33
Intelligent prediction of wear location and mechanism using image identification based on improved Faster R-CNN model
Miao Wang, Lei Yang, Zhibin Zhao, et al.
Tribology International (2022) Vol. 169, pp. 107466-107466
Closed Access | Times Cited: 33
State-of-the-art review of applications of image processing techniques for tool condition monitoring on conventional machining processes
Danil Yurievich Pimenov, Leonardo Rosa Ribeiro da Silva, Ali Erçetin, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 130, Iss. 1-2, pp. 57-85
Open Access | Times Cited: 21
Electrical discharge machining of ceramic nanocomposites: sublimation phenomena and adaptive control
Sergey N. Grigoriev, М. П. Козочкин, A. N. Porvatov, et al.
Heliyon (2019) Vol. 5, Iss. 10, pp. e02629-e02629
Open Access | Times Cited: 44
Measurement of Micro Burr and Slot Widths through Image Processing: Comparison of Manual and Automated Measurements in Micro-Milling
Fatih Akkoyun, Ali Erçetin, Kubilay Aslantaş, et al.
Sensors (2021) Vol. 21, Iss. 13, pp. 4432-4432
Open Access | Times Cited: 40
A vision-based fusion method for defect detection of milling cutter spiral cutting edge
Tongjia Zhang, Chengrui Zhang, Yanjie Wang, et al.
Measurement (2021) Vol. 177, pp. 109248-109248
Closed Access | Times Cited: 36
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