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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:

Milling tool wear prediction using multi-sensor feature fusion based on stacked sparse autoencoders
Zhaopeng He, Tielin Shi, Jianping Xuan
Measurement (2022) Vol. 190, pp. 110719-110719
Closed Access | Times Cited: 66

Showing 26-50 of 66 citing articles:

A Novel Multivariate Cutting Force-Based Tool Wear Monitoring Method Using One-Dimensional Convolutional Neural Network
Yang Xu, Rui Yuan, Yong Lv, et al.
Sensors (2022) Vol. 22, Iss. 21, pp. 8343-8343
Open Access | Times Cited: 15
Tool Wear Monitoring in Milling Based on Fine-Grained Image Classification of Machined Surface Images
Jing Yang, Jian Duan, Tianxiang Li, et al.
Sensors (2022) Vol. 22, Iss. 21, pp. 8416-8416
Open Access | Times Cited: 14
Tool life prognostics in CNC turning of AISI 4140 steel using neural network based on computer vision
Prashant J. Bagga, Mayur A. Makhesana, Pranav P. Darji, et al.
The International Journal of Advanced Manufacturing Technology (2022) Vol. 123, Iss. 9-10, pp. 3553-3570
Open Access | Times Cited: 12
Wear Parameter Diagnostics of Industrial Milling Machine with Support Vector Regression
Teemu Mäkiaho, Henri Vainio, Kari Koskinen
Machines (2023) Vol. 11, Iss. 3, pp. 395-395
Open Access | Times Cited: 7
An end-to-end deep learning approach for tool wear condition monitoring
Lin Ma, Nan Zhang, Jiawei Zhao, et al.
The International Journal of Advanced Manufacturing Technology (2024) Vol. 133, Iss. 5-6, pp. 2907-2920
Closed Access | Times Cited: 2
Bayesian neural networks modeling for tool wear prediction in milling Al 6061 T6 under MQL conditions
Jay Airao, Abhishek Gupta, Chandrakant K. Nirala, et al.
The International Journal of Advanced Manufacturing Technology (2024) Vol. 135, Iss. 5-6, pp. 2777-2788
Closed Access | Times Cited: 2
A dual knowledge embedded hybrid model based on augmented data and improved loss function for tool wear monitoring
Xuzhou Fang, Qinghua Song, Jing Qin, et al.
Robotics and Computer-Integrated Manufacturing (2024) Vol. 92, pp. 102901-102901
Closed Access | Times Cited: 2
A novel method of health indicator construction and remaining useful life prediction based on deep learning
Xianbiao Zhan, Zixuan Liu, Hao Yan, et al.
Eksploatacja i Niezawodnosc - Maintenance and Reliability (2023) Vol. 25, Iss. 4
Open Access | Times Cited: 5
Combining adaptive time-series feature window and stacked bidirectional LSTM for predicting tool remaining useful life without failure data
Weili Kong, Hai Li
The International Journal of Advanced Manufacturing Technology (2022) Vol. 121, Iss. 11-12, pp. 7509-7526
Closed Access | Times Cited: 8
Machining process condition monitoring based on ordinal pattern analysis and image matching
Yazhou Li, Wei Dai, Junjun Dong, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 125, Iss. 7-8, pp. 3329-3347
Closed Access | Times Cited: 4
Tool wear monitoring strategy during micro-milling of TC4 alloy based on a fusion model of recursive feature elimination-bayesian optimization-extreme gradient boosting
Hongfei Wang, Qingshun Bai, Jianduo Zhang, et al.
Journal of Materials Research and Technology (2024) Vol. 31, pp. 398-411
Open Access | Times Cited: 1
Intelligent Recognition of Tool Wear with Artificial Intelligence Agent
Jiaming Gao, Han Qiao, Yilei Zhang
Coatings (2024) Vol. 14, Iss. 7, pp. 827-827
Open Access | Times Cited: 1
Physics-informed inhomogeneous wear identification of end mills by online monitoring data
Guochao Li, S. Xu, Ru Jiang, et al.
Journal of Manufacturing Processes (2024) Vol. 132, pp. 759-771
Closed Access | Times Cited: 1
An intelligent surface roughness prediction method based on automatic feature extraction and adaptive data fusion
Xun Zhang, Sibao Wang, Fangrui Gao, et al.
Autonomous Intelligent Systems (2024) Vol. 4, Iss. 1
Open Access | Times Cited: 1
A Real-Time Deep Machine Learning Approach for Sudden Tool Failure Prediction and Prevention in Machining Processes
Mahmoud Hassan, A. Sadek, Helmi Attia
Sensors (2023) Vol. 23, Iss. 8, pp. 3894-3894
Open Access | Times Cited: 4
A new complex system fault detection method based on belief rule base for unreliable interval values
Guohui Zhou, Erkai Zhao, Ruohan Yang, et al.
Measurement Science and Technology (2023) Vol. 34, Iss. 8, pp. 084007-084007
Closed Access | Times Cited: 4
Composite Fault Diagnosis of Rolling Bearings: A Feature Selection Approach Based on the Causal Feature Network
K. Gao, Zongning Wu, Chongchong Yu, et al.
Applied Sciences (2023) Vol. 13, Iss. 16, pp. 9089-9089
Open Access | Times Cited: 4
Exploring the ViDiDetect Tool for Automated Defect Detection in Manufacturing with Machine Vision
Mateusz Dziubek, Jacek Rysiński, Daniel Jancarczyk
Applied Sciences (2023) Vol. 13, Iss. 19, pp. 11098-11098
Open Access | Times Cited: 4
A hybrid network capturing multisource feature correlations for tool remaining useful life prediction
Shihao Wu, Yang Li, Weiguang Li, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 125, Iss. 5-6, pp. 2815-2831
Closed Access | Times Cited: 3
Monitoring Built-Up Edge, Chipping, Thermal Cracking, and Plastic Deformation of Milling Cutter Inserts through Spindle Vibration Signals
Keshav H. Jatakar, Varsha Shah, Rüstem Binali, et al.
Machines (2023) Vol. 11, Iss. 8, pp. 790-790
Open Access | Times Cited: 3
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 Novel Multisensor Orthogonal Attention Fusion Network for Multibolt Looseness State Recognition Under Small Sample
Feng Liu, Zhousuo Zhang, Xu Chen, et al.
IEEE Transactions on Instrumentation and Measurement (2022) Vol. 71, pp. 1-17
Closed Access | Times Cited: 4
Welding Instructors' Perspectives on Using AI Technology in Welding Training
Tina Lassiter, Chelsea Collier, Kenneth R. Fleischmann, et al.
Proceedings of the Association for Information Science and Technology (2023) Vol. 60, Iss. 1, pp. 233-243
Closed Access | Times Cited: 2
Contrastive Feature Learning for Fault Detection and Diagnostics in Railway Applications
Katharina Rombach, Gabriel Michau, Kajan Ratnasabapathy, et al.
SSRN Electronic Journal (2022)
Closed Access | Times Cited: 3
Identification of Milling Cutter Wear State under Variable Working Conditions Based on Optimized SDP
Hao Chang, Feng Gao, Yan Li, et al.
Applied Sciences (2024) Vol. 14, Iss. 10, pp. 4314-4314
Open Access

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