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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:

Multi-sensor heterogeneous data-based online tool health monitoring in milling of IN718 superalloy using OGM (1, N) model and SVM
Mulpur Sarat Babu, Thella Babu Rao
Measurement (2022) Vol. 199, pp. 111501-111501
Closed Access | Times Cited: 21

Showing 21 citing articles:

Application of metaheuristic optimization based support vector machine for milling cutter health monitoring
Naman S. Bajaj, Abhishek D. Patange, R. Jegadeeshwaran, et al.
Intelligent Systems with Applications (2023) Vol. 18, pp. 200196-200196
Open Access | Times Cited: 37
Tool wear condition monitoring across machining processes based on feature transfer by deep adversarial domain confusion network
Zhiwen Huang, Jiajie Shao, Jianmin Zhu, et al.
Journal of Intelligent Manufacturing (2023) Vol. 35, Iss. 3, pp. 1079-1105
Closed Access | Times Cited: 22
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 State-of-the-Art Review on Cutting Tool Materials and Coatings in Enhancing the Tool Performance in Machining the Superior Nickel-Based Superalloys
Amal A. Seenath, Ahmed A. D. Sarhan
Arabian Journal for Science and Engineering (2024) Vol. 49, Iss. 8, pp. 10203-10236
Closed Access | Times Cited: 6
An in-process machined surface roughness classification using an ensemble learning algorithm based on extracted automated features from real-time surface images in milling process
Mulpur Sarat Babu
International Journal on Interactive Design and Manufacturing (IJIDeM) (2024) Vol. 18, Iss. 7, pp. 4499-4511
Closed Access | Times Cited: 5
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
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
Application of Recurrence Quantification Analysis and Gaussian Mixture Model to Diagnosis Robotic Grinding Process Condition
Kuan-Jung Chung, Chi Hou, Kai-An Yang, et al.
International Journal of Precision Engineering and Manufacturing (2025)
Closed Access
Tool Wear State Identification Based on SVM Optimized by the Improved Northern Goshawk Optimization
Jiaqi Wang, Zhong Xiang, Xiao Cheng, et al.
Sensors (2023) Vol. 23, Iss. 20, pp. 8591-8591
Open Access | Times Cited: 10
Early condition monitoring of circular saw blades with large diameter-to-thickness ratios under high-speed sawing of hard metals
Jinyou Kang, Jinsheng Zhang, Heng Zhang, et al.
Measurement (2024) Vol. 235, pp. 114933-114933
Closed Access | Times Cited: 3
An in-process tool wear assessment using Bayesian optimized machine learning algorithm
Mulpur Sarat Babu, Thella Babu Rao
International Journal on Interactive Design and Manufacturing (IJIDeM) (2023) Vol. 17, Iss. 4, pp. 1823-1845
Closed Access | Times Cited: 9
Research on Tool Remaining Life Prediction Method Based on CNN-LSTM-PSO
Shuo Wang, Zhenliang Yu, G. Xu, et al.
IEEE Access (2023) Vol. 11, pp. 80448-80464
Open Access | Times Cited: 9
Development of an In-Process Cutting Tool Life Prediction System Using Bidirectional Long Short-Term Memory Network
Mulpur Sarat Babu, Thella Babu Rao
Journal of Failure Analysis and Prevention (2023) Vol. 23, Iss. 2, pp. 837-845
Closed Access | Times Cited: 4
Real-time cutting tool condition assessment and stochastic tool life predictive models for tool reliability estimation by in-process cutting tool vibration monitoring
Mulpur Sarat Babu, Thella Babu Rao
International Journal on Interactive Design and Manufacturing (IJIDeM) (2022) Vol. 17, Iss. 3, pp. 1237-1253
Closed Access | Times Cited: 5
A PCA-Integrated OGM (1, N) Predictive Model for In-Process Tool Wear Prediction Based on Continuous Monitoring of Multi-Sensorial Information
Sarat Babu Mulpur, Thella Babu Rao
Journal of Failure Analysis and Prevention (2022) Vol. 22, Iss. 6, pp. 2199-2208
Closed Access | Times Cited: 4
Milling tool condition monitoring for difficult-to-cut materials based on NCAE and IGWO-SVM
Siqi Wang, Shichao Yan, Yuwen Sun
The International Journal of Advanced Manufacturing Technology (2023) Vol. 129, Iss. 3-4, pp. 1355-1374
Closed Access | Times Cited: 2
A Study on Lathe Machining Job Classification Using Deep Learning Techniques
Yong Hwan Bae, Ho-Chan Kim
Journal of the Korean Society of Manufacturing Process Engineers (2024) Vol. 23, Iss. 6, pp. 13-20
Closed Access
Triboinformatic Machine Learning Model for Frictional Behavior and Wear Volume Loss Prediction of SS316L Alloy Clad with WC/NiCrBSi
Lakshmi Manasa Birada, Vyshnavi Pullela, Thella Babu Rao
Journal of Bio- and Tribo-Corrosion (2024) Vol. 10, Iss. 3
Closed Access
Exploring the Processing Paradigm of Input Data for End-to-End Deep Learning in Tool Condition Monitoring
Chengguan Wang, Guangping Wang, Tao Wang, et al.
Sensors (2024) Vol. 24, Iss. 16, pp. 5300-5300
Open Access
A Review of Physics-Based, Data-Driven, and Hybrid Models for Tool Wear Monitoring
Haoyuan Zhang, Shanglei Jiang, Daqing Gao, et al.
Machines (2024) Vol. 12, Iss. 12, pp. 833-833
Open Access
Cluster analysis approach for identifying optimal cutting parameters in end milling of aluminum alloy 7136 for improved surface roughness
Aurel Mihail Țîțu, Constantin OPREAN, Alina Bianca Pop
ANNALS OF THE ACADEMY OF ROMANIAN SCIENTISTS Series on ENGINEERING SCIENCES (2023) Vol. 15, Iss. 1, pp. 124-137
Open Access
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