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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:

Identification of cutting tool wear condition in turning using self-organizing map trained with imbalanced data
Lucas Costa Brito, Márcio Bacci da Silva, Marcus Antônio Viana Duarte
Journal of Intelligent Manufacturing (2020) Vol. 32, Iss. 1, pp. 127-140
Closed Access | Times Cited: 55

Showing 1-25 of 55 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
Systematic review on tool breakage monitoring techniques in machining operations
Xuebing Li, Xianli Liu, Caixu Yue, et al.
International Journal of Machine Tools and Manufacture (2022) Vol. 176, pp. 103882-103882
Closed Access | Times Cited: 120
A State-of-the-art Review on the Intelligent Tool Holders in Machining
Qinglong An, Jie Yang, Junli Li, et al.
Intelligent and sustainable manufacturing (2024) Vol. 1, Iss. 1, pp. 10002-10002
Open Access | Times Cited: 25
AI for tribology: Present and future
Nian Yin, Pufan Yang, Songkai Liu, et al.
Friction (2024) Vol. 12, Iss. 6, pp. 1060-1097
Open Access | Times Cited: 17
MS-SSPCANet: A powerful deep learning framework for tool wear prediction
Jian Duan, Cheng Hu, Xiaobin Zhan, et al.
Robotics and Computer-Integrated Manufacturing (2022) Vol. 78, pp. 102391-102391
Closed Access | Times Cited: 44
Implementation of Visual Clustering Strategy in Self-Organizing Map for Wear Studies Samples Printed Using FDM
Latchoumi Thamarai Pugazhendhi, K. Raja, Balamurugan Karnan
Traitement du signal (2022) Vol. 39, Iss. 2, pp. 531-539
Open Access | Times Cited: 41
Cutting tool wear state recognition based on a channel-space attention mechanism
Rongyi Li, Peining Wei, Xianli Liu, et al.
Journal of Manufacturing Systems (2023) Vol. 69, pp. 135-149
Closed Access | Times Cited: 31
Surface defect detection methods for industrial products with imbalanced samples: A review of progress in the 2020s
Dongxu Bai, Gongfa Li, Du Jiang, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 130, pp. 107697-107697
Closed Access | Times Cited: 24
Review of advances in tool condition monitoring techniques in the milling process
T. Mohanraj, E S Kirubakaran, Dinesh Kumar Madheswaran, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 9, pp. 092002-092002
Closed Access | Times Cited: 10
A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques
Lorenzo Colantonio, Lucas Equeter, Pierre Dehombreux, et al.
Machines (2021) Vol. 9, Iss. 12, pp. 351-351
Open Access | Times Cited: 51
Multi-condition identification in milling Ti-6Al-4V thin-walled parts based on sensor fusion
Runqiong Wang, Qinghua Song, Zhanqiang Liu, et al.
Mechanical Systems and Signal Processing (2021) Vol. 164, pp. 108264-108264
Closed Access | Times Cited: 50
Milling cutter fault diagnosis using unsupervised learning on small data: A robust and autonomous framework
Abhishek D. Patange, Rohan Soman, Sujit S. Pardeshi, et al.
Eksploatacja i Niezawodnosc - Maintenance and Reliability (2024) Vol. 26, Iss. 1
Open Access | Times Cited: 6
A multi-target predictive model for predicting tool wear and surface roughness
Guohao Song, Jianhua Zhang, Yingshang Ge, et al.
Expert Systems with Applications (2024) Vol. 251, pp. 123779-123779
Closed Access | Times Cited: 6
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
Tool Wear State Identification Method with Variable Cutting Parameters Based on Multi-Source Unsupervised Domain Adaptation
Zhigang Cai, Wangyang Li, Jianxin Song, et al.
Sensors (2025) Vol. 25, Iss. 6, pp. 1742-1742
Open Access
An imbalanced data learning approach for tool wear monitoring based on data augmentation
Bowen Zhang, Xianli Liu, Caixu Yue, et al.
Journal of Intelligent Manufacturing (2023)
Closed Access | Times Cited: 13
A State-of-the-art Review on the Intelligent Tool Holders in Machining
Qinglong An, Jie Yang, Junli Li, et al.
Intelligent and sustainable manufacturing (2024) Vol. 1, Iss. 1, pp. 10002-10002
Open Access | Times Cited: 4
Development of multi-sensor data fusion and in-process expert system for monitoring precision in thin wall lens barrel turning
Ke-Er Tang, Yin-Chung Huang, Chun‐Wei Liu
Mechanical Systems and Signal Processing (2024) Vol. 210, pp. 111195-111195
Closed Access | Times Cited: 4
Comparative analysis of different machine vision algorithms for tool wear measurement during machining
Mayur A. Makhesana, Prashant J. Bagga, Kaushik M. Patel, et al.
Journal of Intelligent Manufacturing (2024)
Closed Access | Times Cited: 4
Cross working conditions manufacturing process monitoring using deep convolutional adversarial discriminative domain adaptation network
Bohao Li, Hongxing Liu, Nan Ma, et al.
Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture (2025)
Closed Access
A semisupervised autoencoder-based method for anomaly detection in cutting tools
Shixu Sun, Yingchao Liu, Xiao Hu, et al.
Journal of Manufacturing Processes (2023) Vol. 93, pp. 315-327
Closed Access | Times Cited: 10
Advancements in Tool Wear Monitoring in Turning Operations: Digital Image Processing and AI Techniques
Kushagra Agrawal, Amlana Panda, Ashok Kumar Sahoo
Journal of Physics Conference Series (2024) Vol. 2818, Iss. 1, pp. 012040-012040
Open Access | Times Cited: 3
Gaussian mixture model for tool condition monitoring
Debasish Mishra, Krishna R. Pattipati, George M. Bollas
Journal of Manufacturing Processes (2024) Vol. 131, pp. 1001-1013
Closed Access | Times Cited: 3
Gaussian process regression model incorporated with tool wear mechanism
Dehua Li, Yingguang Li, Changqing Liu
Chinese Journal of Aeronautics (2021) Vol. 35, Iss. 10, pp. 393-400
Closed Access | Times Cited: 23
A systematic review of artificial intelligence in the detection of cutting tool breakage in machining operations
Wenchao Xiao, Jianghua Huang -, Baoyu Wang, et al.
Measurement (2022) Vol. 190, pp. 110748-110748
Closed Access | Times Cited: 16
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