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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 Condition Monitoring of Single-Point Dresser Using Acoustic Emission and Neural Networks Models
Cesar Henrique Rossinoli Martins, Paulo Aguiar, Arminio Frech, et al.
IEEE Transactions on Instrumentation and Measurement (2014) Vol. 63, Iss. 3, pp. 667-679
Closed Access | Times Cited: 85

Showing 26-50 of 85 citing articles:

An Intelligent Milling Tool Wear Monitoring Methodology Based on Convolutional Neural Network with Derived Wavelet Frames Coefficient
Xincheng Cao, Binqiang Chen, Bin Yao, et al.
Applied Sciences (2019) Vol. 9, Iss. 18, pp. 3912-3912
Open Access | Times Cited: 35
Monitoring of through-feed centreless grinding processes with acoustic emission signals
Everaldo Araújo Dias, Flávio Bonato Pereira, Sérgio Luiz Moni Ribeiro Filho, et al.
Measurement (2016) Vol. 94, pp. 71-79
Closed Access | Times Cited: 33
Depth of dressing optimization in CBN wheels of different friabilities using acoustic emission (AE) technique
Felipe Aparecido Alexandre, José Cláudio Lopes, Lucas de Martini Fernandes, et al.
The International Journal of Advanced Manufacturing Technology (2020) Vol. 106, Iss. 11-12, pp. 5225-5240
Closed Access | Times Cited: 29
An efficient short-time Fourier transform algorithm for grinding wheel condition monitoring through acoustic emission
Wenderson Nascimento Lopes, Pedro Oliveira, Paulo Roberto de Aguiar, et al.
The International Journal of Advanced Manufacturing Technology (2021) Vol. 113, Iss. 1-2, pp. 585-603
Closed Access | Times Cited: 26
Tool condition prognostics using logistic regression with penalization and manifold regularization
Jianbo Yu
Applied Soft Computing (2017) Vol. 64, pp. 454-467
Closed Access | Times Cited: 32
Digital signal processing of acoustic emission signals using power spectral density and counts statistic applied to single‐point dressing operation
Wenderson Nascimento Lopes, Fábio Isaac Ferreira, Felipe Aparecido Alexandre, et al.
IET Science Measurement & Technology (2017) Vol. 11, Iss. 5, pp. 631-636
Open Access | Times Cited: 29
Grinding Wheel Loading Evaluation by Using Acoustic Emission Signals and Digital Image Processing
Chien-Sheng Liu, Yang-Jiun Ou
Sensors (2020) Vol. 20, Iss. 15, pp. 4092-4092
Open Access | Times Cited: 24
Development of Lightweight RBF-DRNN and Automated Framework for CNC Tool-Wear Prediction
Sheng-Min Chiu, Yi‐Chung Chen, Cheng-Ju Kuo, et al.
IEEE Transactions on Instrumentation and Measurement (2022) Vol. 71, pp. 1-11
Closed Access | Times Cited: 16
Monitoring Single-point Dressers Using Fuzzy Models
Hugo Ivan Chang Miranda, C.A. Rocha, Pedro Oliveira, et al.
Procedia CIRP (2015) Vol. 33, pp. 281-286
Open Access | Times Cited: 25
Grinding wheel condition prediction with discrete hidden Markov model using acoustic emission signature
P. Sachin Krishnan, K. Rameshkumar
Materials Today Proceedings (2020) Vol. 46, pp. 9168-9175
Closed Access | Times Cited: 21
Optimization of Thermal-Wave Radar Thermography by Transverse Heat Flow Suppression Technique for Accurate Defect Detection of CFRP Laminates
Fei Wang, Junyan Liu, Boyuan Dong, et al.
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-10
Closed Access | Times Cited: 20
A Sequence-to-Sequence Model With Attention and Monotonicity Loss for Tool Wear Monitoring and Prediction
Gang Wang, Feng Zhang
IEEE Transactions on Instrumentation and Measurement (2021) Vol. 70, pp. 1-11
Closed Access | Times Cited: 18
Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 2—Neural Networks and K-Nearest Neighbor Classifier Approach
Pedro Oliveira, Doriana M. D’Addona, Paulo Roberto de Aguiar, et al.
Sensors (2018) Vol. 18, Iss. 12, pp. 4453-4453
Open Access | Times Cited: 22
Low-Cost Piezoelectric Transducer for Ceramic Grinding Monitoring
Martin Antonio Aulestia Viera, Paulo Roberto de Aguiar, Pedro Oliveira, et al.
IEEE Sensors Journal (2019) Vol. 19, Iss. 17, pp. 7605-7612
Closed Access | Times Cited: 21
A decision-making tool based on decision trees for roughness prediction in face milling
Juan J. Rodríguez, Guillem Quintana, Andrés Bustillo, et al.
International Journal of Computer Integrated Manufacturing (2016) Vol. 30, Iss. 9, pp. 943-957
Closed Access | Times Cited: 20
Identification of MOSFET Working State Based on the Stress Wave and Deep Learning
Yun Bai, Songyuan Liu, Yunze He, et al.
IEEE Transactions on Instrumentation and Measurement (2022) Vol. 71, pp. 1-9
Closed Access | Times Cited: 11
Monitoring the cutting condition of structurally distinct aluminum oxide grinding wheels using acoustic emission signals and the Hinkley criterion
Wenderson Nascimento Lopes, Paulo Roberto de Aguiar, Zaqueu R. Fernando Antônio, et al.
The International Journal of Advanced Manufacturing Technology (2024) Vol. 131, Iss. 3-4, pp. 1071-1079
Open Access | Times Cited: 2
Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 1—PZT Diaphragm Transducer Response and EMI Sensing Technique
Pedro Oliveira, Doriana M. D’Addona, Paulo Roberto de Aguiar, et al.
Sensors (2018) Vol. 18, Iss. 12, pp. 4455-4455
Open Access | Times Cited: 19
Evaluation of transducer signature selections on machine learning performance in cutting tool wear prognosis
I.-Chun Sun, Ren-Chi Cheng, Kuo‐Shen Chen
The International Journal of Advanced Manufacturing Technology (2022) Vol. 119, Iss. 9-10, pp. 6451-6468
Closed Access | Times Cited: 11
Machine condition monitoring in FDM based on electret microphone, SVM, and neural networks
Thiago Glissoi Lopes, Paulo Roberto de Aguiar, Paulo Monteiro de Carvalho Monson, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 129, Iss. 3-4, pp. 1769-1786
Closed Access | Times Cited: 6
A Time–Frequency Acoustic Emission-Based Technique to Assess Workpiece Surface Quality in Ceramic Grinding with PZT Transducer
Martin Antonio Aulestia Viera, Paulo Roberto de Aguiar, Pedro Oliveira, et al.
Sensors (2019) Vol. 19, Iss. 18, pp. 3913-3913
Open Access | Times Cited: 17
Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network
Yiting Li, Qingsheng Xie, Haisong Huang, et al.
Symmetry (2019) Vol. 11, Iss. 6, pp. 809-809
Open Access | Times Cited: 16
Prediction of Dressing in Grinding Operation via Neural Networks
Doriana M. D’Addona, D. Matarazzo, Roberto Teti, et al.
Procedia CIRP (2017) Vol. 62, pp. 305-310
Open Access | Times Cited: 16
Evaluation of grinding wheel loading phenomena by using acoustic emission signals
Chien-Sheng Liu, Yu-An Li
The International Journal of Advanced Manufacturing Technology (2018) Vol. 99, Iss. 5-8, pp. 1109-1117
Closed Access | Times Cited: 16
Ultrasound Characterization of Bone Demineralization Using a Support Vector Machine
Max Denis, Leighton Wan, Mostafa Fatemi, et al.
Ultrasound in Medicine & Biology (2017) Vol. 44, Iss. 3, pp. 714-725
Open Access | Times Cited: 15

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