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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:

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

Showing 1-25 of 96 citing articles:

Chatter detection in milling processes—a review on signal processing and condition classification
John Henry Navarro-Devia, Yun Chen, Dzung Viet Dao, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 125, Iss. 9-10, pp. 3943-3980
Open Access | Times Cited: 45
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: 18
Multitype chatter detection via multichannelinternal and external signals in robotic milling
Kenan Deng, Lijun Yang, Yong Lü, et al.
Measurement (2024) Vol. 229, pp. 114417-114417
Closed Access | Times Cited: 16
Machine learning and IoT-based approach for tool condition monitoring: A review and future prospects
Minh‐Quang Tran, Hoang-Phuong Doan, Viet Q. Vu, et al.
Measurement (2022) Vol. 207, pp. 112351-112351
Closed Access | Times Cited: 63
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: 32
A novel online tool condition monitoring method for milling titanium alloy with consideration of tool wear law
Bo Qin, Yongqing Wang, Kuo Liu, et al.
Mechanical Systems and Signal Processing (2023) Vol. 199, pp. 110467-110467
Closed Access | Times Cited: 25
A new intelligent approach of surface roughness measurement in sustainable machining of AM-316L stainless steel with deep learning models
Nimel Sworna Ross, Peter Madindwa Mashinini, C. Sherin Shibi, et al.
Measurement (2024) Vol. 230, pp. 114515-114515
Closed Access | Times Cited: 12
A review of the use of cryogenic coolant during machining titanium alloys
Tharmalingam Sivarupan, Michael Bermingham, Chi‐Ho Ng, et al.
Sustainable materials and technologies (2024) Vol. 40, pp. e00946-e00946
Closed Access | Times Cited: 11
Intelligent milling tool wear estimation based on machine learning algorithms
Yunus Emre Karabacak
Journal of Mechanical Science and Technology (2024) Vol. 38, Iss. 2, pp. 835-850
Closed Access | Times Cited: 7
Prediction of surface roughness in turning using vibration features selected by largest Lyapunov exponent based ICEEMDAN decomposition
Vikrant Guleria, Vivek Kumar, Pradeep Kumar Singh
Measurement (2022) Vol. 202, pp. 111812-111812
Closed Access | Times Cited: 28
Digital Twin-Driven Tool Condition Monitoring for the Milling Process
Sriraamshanjiev Natarajan, T. Mohanraj, G. Sakthivel, et al.
Sensors (2023) Vol. 23, Iss. 12, pp. 5431-5431
Open Access | Times Cited: 20
Online monitoring model of micro-milling force incorporating tool wear prediction process
Pengfei Ding, Xianzhen Huang, Chengying Zhao, et al.
Expert Systems with Applications (2023) Vol. 223, pp. 119886-119886
Closed Access | Times Cited: 19
Position-dependent milling process monitoring and surface roughness prediction for complex thin-walled blade component
Zequan Yao, Jingyuan Shen, Ming Wu, et al.
Mechanical Systems and Signal Processing (2023) Vol. 198, pp. 110439-110439
Closed Access | Times Cited: 19
Tool Wear Prediction Based on Multi-Information Fusion and Genetic Algorithm-Optimized Gaussian Process Regression in Milling
Zhiwen Huang, Jiajie Shao, Weicheng Guo, et al.
IEEE Transactions on Instrumentation and Measurement (2023) Vol. 72, pp. 1-16
Open Access | Times Cited: 18
Influence of tool wear on chip-like burr formation during micro-milling, and image processing based measurement of inwardly-deflected burrs
Suman Saha, Sainul Islam Ansary, Sankha Deb, et al.
Wear (2023) Vol. 530-531, pp. 205024-205024
Closed Access | Times Cited: 17
Self-adaptive fusion of local-temporal features for tool condition monitoring: A human experience free model
Runqiong Wang, Qinghua Song, Yezhen Peng, et al.
Mechanical Systems and Signal Processing (2023) Vol. 195, pp. 110310-110310
Closed Access | Times Cited: 16
Machine-Learning- and Internet-of-Things-Driven Techniques for Monitoring Tool Wear in Machining Process: A Comprehensive Review
Sudhan Kasiviswanathan, G. Sakthivel, T. Mohanraj, et al.
Journal of Sensor and Actuator Networks (2024) Vol. 13, Iss. 5, pp. 53-53
Open Access | Times Cited: 5
Hybrid machine learning-enabled multi-information fusion for indirect measurement of tool flank wear in milling
Zhiwen Huang, Jiajie Shao, Weicheng Guo, et al.
Measurement (2022) Vol. 206, pp. 112255-112255
Closed Access | Times Cited: 22
The Use of a Radial Basis Function Neural Network and Fuzzy Modelling in the Assessment of Surface Roughness in the MDF Milling Process
Krzysztof Szwajka, Joanna Zielińska-Szwajka, Tomasz Trzepieciński
Materials (2023) Vol. 16, Iss. 15, pp. 5292-5292
Open Access | Times Cited: 15
Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis
Yunus Emre Karabacak
Eksploatacja i Niezawodnosc - Maintenance and Reliability (2023) Vol. 25, Iss. 3
Open Access | Times Cited: 14
A tool wear condition monitoring method for non-specific sensing signals
Yezhen Peng, Qinghua Song, Runqiong Wang, et al.
International Journal of Mechanical Sciences (2023) Vol. 263, pp. 108769-108769
Closed Access | Times Cited: 13
Predictive modelling and optimization for machinability indicators in cleaner milling of PH13-8Mo using sustainable cutting environments
Hakan Yurtkuran, Mustafa Günay
Journal of the Brazilian Society of Mechanical Sciences and Engineering (2024) Vol. 46, Iss. 5
Closed Access | Times Cited: 4
Learning More with Less Data in Manufacturing: The Case of Turning Tool Wear Assessment through Active and Transfer Learning
Alexios Papacharalampopoulos, Kosmas Alexopoulos, Paolo Catti, et al.
Processes (2024) Vol. 12, Iss. 6, pp. 1262-1262
Open Access | Times Cited: 4
Enhanced permeation mechanism and tribological assessment of ultrasonic vibration nanolubricants grinding CFRP
Teng Gao, Jixin Liu, Xiaofeng Sun, et al.
Tribology International (2025) Vol. 204, pp. 110494-110494
Closed Access
A machine learning algorithm based cutting tool wear assessment using multi-sensorial data
Mulpur Sarat Babu
International Journal on Interactive Design and Manufacturing (IJIDeM) (2025)
Closed Access
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