Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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 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

Showing 1-25 of 93 citing articles:

Tool condition monitoring techniques in milling process — a review
T. Mohanraj, S. Shankar, Rajasekar Rathanasamy, et al.
Journal of Materials Research and Technology (2019) Vol. 9, Iss. 1, pp. 1032-1042
Open Access | Times Cited: 309
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
Development of tool condition monitoring system in end milling process using wavelet features and Hoelder’s exponent with machine learning algorithms
T. Mohanraj, Jayanthi Yerchuru, H. Anjana Krishnan, et al.
Measurement (2020) Vol. 173, pp. 108671-108671
Closed Access | Times Cited: 137
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
Application of measurement systems in tool condition monitoring of Milling: A review of measurement science approach
Danil Yurievich Pimenov, Munish Kumar Gupta, Leonardo Rosa Ribeiro da Silva, et al.
Measurement (2022) Vol. 199, pp. 111503-111503
Open Access | Times Cited: 96
Machine learning and artificial intelligence in CNC machine tools, A review
Mohsen Soori, Behrooz Arezoo, Roza Dastres
Sustainable Manufacturing and Service Economics (2023) Vol. 2, pp. 100009-100009
Open Access | Times Cited: 96
A novel ensemble deep learning model for cutting tool wear monitoring using audio sensors
Zhixiong Li, Xihao Liu, Atilla İncecik, et al.
Journal of Manufacturing Processes (2022) Vol. 79, pp. 233-249
Closed Access | Times Cited: 73
Review of Intelligence for Additive and Subtractive Manufacturing: Current Status and Future Prospects
M. Rahman, Tanveer Saleh, Muhammad P. Jahan, et al.
Micromachines (2023) Vol. 14, Iss. 3, pp. 508-508
Open Access | Times Cited: 49
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
Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140
Mustafa Kuntoğlu, Abdullah Aslan, H. Saĝlam, et al.
Sensors (2020) Vol. 20, Iss. 16, pp. 4377-4377
Open Access | Times Cited: 103
Tool Wear Condition Monitoring Method Based on Deep Learning with Force Signals
Yaping Zhang, Xiaozhi Qi, Tao Wang, et al.
Sensors (2023) Vol. 23, Iss. 10, pp. 4595-4595
Open Access | Times Cited: 31
CNN-based in situ tool wear detection: A study on model training and data augmentation in turning inserts
Alberto García-Pérez, Amina Ziegenbein, Eric Schmidt, et al.
Journal of Manufacturing Systems (2023) Vol. 68, pp. 85-98
Closed Access | Times Cited: 29
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: 24
Cutting tool prognostics enabled by hybrid CNN-LSTM with transfer learning
Mohamed Marei, Weidong Li
The International Journal of Advanced Manufacturing Technology (2021) Vol. 118, Iss. 3-4, pp. 817-836
Closed Access | Times Cited: 51
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
Application of machine vision for tool condition monitoring and tool performance optimization–a review
Tiyamike Banda, Ali Akhavan Farid, Chuan Li, et al.
The International Journal of Advanced Manufacturing Technology (2022) Vol. 121, Iss. 11-12, pp. 7057-7086
Closed Access | Times Cited: 28
A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring
Jiayu Ou, Hongkun Li, Gangjin Huang, et al.
Sensors (2020) Vol. 20, Iss. 10, pp. 2878-2878
Open Access | Times Cited: 41
Application of Bayesian Family Classifiers for Cutting Tool Inserts Health Monitoring on CNC Milling
Abhishek D. Patange, R. Jegadeeshwaran
International Journal of Prognostics and Health Management (2021) Vol. 11, Iss. 2
Open Access | Times Cited: 33
Digital Twin-Driven Tool Wear Monitoring and Predicting Method for the Turning Process
Kejia Zhuang, Zhenchuan Shi, Yaobing Sun, et al.
Symmetry (2021) Vol. 13, Iss. 8, pp. 1438-1438
Open Access | Times Cited: 32
Applications of artificial neural networks in machining processes: a comprehensive review
Sirin Chakraborty, Shankar Chakraborty
International Journal on Interactive Design and Manufacturing (IJIDeM) (2024) Vol. 18, Iss. 4, pp. 1917-1948
Closed Access | Times Cited: 4
Notifying Type-2 Error and Segregating Undefined Conditions in Health Monitoring of Milling Cutter: A Statistical and Deep Learning Approach
Aditya Sanju, Abhishek D. Patange, Aditya M. Rahalkar, et al.
Journal of Vibration Engineering & Technologies (2025) Vol. 13, Iss. 1
Closed Access
Online monitoring and multi-objective optimisation of technological parameters in high-speed milling process
Dung Hoang Tien, Quy Tran Duc, Thien Nguyen Van, et al.
The International Journal of Advanced Manufacturing Technology (2021) Vol. 112, Iss. 9-10, pp. 2461-2483
Closed Access | Times Cited: 29
A novel approach of combined edge detection and segmentation for tool wear measurement in machining
Prashant J. Bagga, Mayur A. Makhesana, Kaushik M. Patel
Production Engineering (2021) Vol. 15, Iss. 3-4, pp. 519-533
Closed Access | Times Cited: 29
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
Tool and Workpiece Condition Classification Using Empirical Mode Decomposition (EMD) with Hilbert–Huang Transform (HHT) of Vibration Signals and Machine Learning Models
Isaac O. Olalere, Oludolapo Akanni Olanrewaju
Applied Sciences (2023) Vol. 13, Iss. 4, pp. 2248-2248
Open Access | Times Cited: 12
Page 1 - Next Page

Scroll to top