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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 26-50 of 86 citing articles:

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
A Study of Dimensionality Reduction in GLCM Feature-Based Classification of Machined Surface Images
Ganesha Prasad, Vijay Srinivas Gaddale, Raghavendra C. Kamath, et al.
Arabian Journal for Science and Engineering (2023) Vol. 49, Iss. 2, pp. 1531-1553
Open Access | Times Cited: 15
Hybrid prognostics to estimate cutting inserts remaining useful life based on direct wear observation
Luca Bernini, Ugo Malguzzi, Paolo Albertelli, et al.
Mechanical Systems and Signal Processing (2024) Vol. 210, pp. 111163-111163
Open Access | Times Cited: 5
Hybrid Data Augmentation Combining Screening-Based MCGAN and Manual Transformation for Few-Shot Tool Wear State Recognition
Yu Quan, Changfu Liu, Zhuang Yuan, et al.
IEEE Sensors Journal (2024) Vol. 24, Iss. 8, pp. 12186-12196
Closed Access | Times Cited: 4
A visual analytical method for evaluating tool flank wear volumes of micro-milling cutters with AKAZE features matching: A preliminary study
Yu Zhang, Shuaishuai Gao, Xianyin Duan, et al.
Wear (2025) Vol. 564-565, pp. 205739-205739
Closed Access
Prediction of cutting tool wear during a turning process using artificial intelligence techniques
Mohsen Marani, Mohammadjavad Zeinali, Jules Kouam, et al.
The International Journal of Advanced Manufacturing Technology (2020) Vol. 111, Iss. 1-2, pp. 505-515
Closed Access | Times Cited: 37
Indirect cutting tool wear classification using deep learning and chip colour analysis
Luca Pagani, Paolo Parenti, Salvatore Cataldo, et al.
The International Journal of Advanced Manufacturing Technology (2020) Vol. 111, Iss. 3-4, pp. 1099-1114
Open Access | Times Cited: 32
A Qualitative Tool Condition Monitoring Framework Using Convolution Neural Network and Transfer Learning
Harshavardhan Mamledesai, Mario A. Soriano, Rafiq Ahmad
Applied Sciences (2020) Vol. 10, Iss. 20, pp. 7298-7298
Open Access | Times Cited: 32
A novel method for predicting delamination of carbon fiber reinforced plastic (CFRP) based on multi-sensor data
Jiacheng Cui, Wei Liu, Yang Zhang, et al.
Mechanical Systems and Signal Processing (2021) Vol. 157, pp. 107708-107708
Closed Access | Times Cited: 27
A novel hybrid model integrating residual structure and bi-directional long short-term memory network for tool wear monitoring
Ning Zhang, Enping Chen, Yukang Wu, et al.
The International Journal of Advanced Manufacturing Technology (2022) Vol. 120, Iss. 9-10, pp. 6707-6722
Closed Access | Times Cited: 20
Multiple Activation Functions and Data Augmentation-Based Lightweight Network for In Situ Tool Condition Monitoring
Zhichao You, Hongli Gao, Shichao Li, et al.
IEEE Transactions on Industrial Electronics (2022) Vol. 69, Iss. 12, pp. 13656-13664
Closed Access | Times Cited: 19
ConvLSTM-Att: An Attention-Based Composite Deep Neural Network for Tool Wear Prediction
Renwang Li, Xiaolei Ye, Yang Fangqing, et al.
Machines (2023) Vol. 11, Iss. 2, pp. 297-297
Open Access | Times Cited: 12
Precise measurement of geometric and physical quantities in cutting tools inspection and condition monitoring: A review
Wenqi Wang, Wei Liu, Yang Zhang, et al.
Chinese Journal of Aeronautics (2023) Vol. 37, Iss. 4, pp. 23-53
Open Access | Times Cited: 12
An intrinsic timescale decomposition-based kernel extreme learning machine method to detect tool wear conditions in the milling process
Zhi Lei, Yuqing Zhou, Bintao Sun, et al.
The International Journal of Advanced Manufacturing Technology (2019) Vol. 106, Iss. 3-4, pp. 1203-1212
Closed Access | Times Cited: 34
Intelligent recognition of milling cutter wear state with cutting parameter independence based on deep learning of spindle current clutter signal
Kaiyu Song, Min Wang, Liming Liu, et al.
The International Journal of Advanced Manufacturing Technology (2020) Vol. 109, Iss. 3-4, pp. 929-942
Closed Access | Times Cited: 27
Simple machine learning allied with data-driven methods for monitoring tool wear in machining processes
Adalto de Farias, SERGIO LUIS RABELO DE ALMEIDA, Sérgio Delijaicov, et al.
The International Journal of Advanced Manufacturing Technology (2020) Vol. 109, Iss. 9-12, pp. 2491-2501
Closed Access | Times Cited: 27
Detection of accelerated tool wear in turning
Sebastian Bombiński, Joanna Kossakowska, Krzysztof Jemielniak
Mechanical Systems and Signal Processing (2021) Vol. 162, pp. 108021-108021
Open Access | Times Cited: 24
Dissociation artificial neural network for tool wear estimation in CNC milling
Shi Yuen Wong, Joon Huang Chuah, Hwa Jen Yap, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 125, Iss. 1-2, pp. 887-901
Closed Access | Times Cited: 10
Review of Image Processing Methods for Surface and Tool Condition Assessments in Machining
Ali Erçetin, Oğuzhan Der, Fatih Akkoyun, et al.
Journal of Manufacturing and Materials Processing (2024) Vol. 8, Iss. 6, pp. 244-244
Open Access | Times Cited: 3
Research on automatic monitoring method of face milling cutter wear based on dynamic image sequence
Aoping Qin, Liang Guo, Zhichao You, et al.
The International Journal of Advanced Manufacturing Technology (2020) Vol. 110, Iss. 11-12, pp. 3365-3376
Closed Access | Times Cited: 21
Automatically Designing Network-Based Deep Transfer Learning Architectures Based on Genetic Algorithm for In-Situ Tool Condition Monitoring
Yuekai Liu, Yaoxiang Yu, Liang Guo, et al.
IEEE Transactions on Industrial Electronics (2021) Vol. 69, Iss. 9, pp. 9483-9493
Closed Access | Times Cited: 18
Estimation of Flank Wear in Turning of Nimonic C263 Super Alloy Based on Novel MSER Algorithm and Deep Patten Network
R. M. Bommi, C. Ezilarasan, M. P. Sudeshkumar, et al.
Russian Journal of Nondestructive Testing (2022) Vol. 58, Iss. 2, pp. 140-156
Closed Access | Times Cited: 13
Tool Wear Monitoring Using Improved Dragonfly Optimization Algorithm and Deep Belief Network
Leo Gertrude David, Raj Kumar Patra, Przemysław Falkowski‐Gilski, et al.
Applied Sciences (2022) Vol. 12, Iss. 16, pp. 8130-8130
Open Access | Times Cited: 13
Recent Progress of Chatter Detection and Tool Wear Online Monitoring in Machining Process: A Review and Future Prospects
Feng-ze Qin, Huajun Cao, Guibao Tao, et al.
International Journal of Precision Engineering and Manufacturing-Green Technology (2024) Vol. 12, Iss. 2, pp. 719-748
Closed Access | Times Cited: 2
Evaluation of Deep Learning for Semantic Image Segmentation in Tool Condition Monitoring
Benjamin Lutz, Dominik Kißkalt, Daniel Regulin, et al.
2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (2019), pp. 2008-2013
Closed Access | Times Cited: 21

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