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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 1-25 of 66 citing articles:

Tool wear identification and prediction method based on stack sparse self-coding network
Yiyuan Qin, Xianli Liu, Caixu Yue, et al.
Journal of Manufacturing Systems (2023) Vol. 68, pp. 72-84
Closed Access | Times Cited: 55
Remaining useful life prediction via a deep adaptive transformer framework enhanced by graph attention network
Pengfei Liang, Ying Li, Bin Wang, et al.
International Journal of Fatigue (2023) Vol. 174, pp. 107722-107722
Closed Access | Times Cited: 36
Integrating physics-informed recurrent Gaussian process regression into instance transfer for predicting tool wear in milling process
Biyao Qiang, Kaining Shi, Ning Liu, et al.
Journal of Manufacturing Systems (2023) Vol. 68, pp. 42-55
Closed Access | Times Cited: 32
A milling tool wear predicting method with processing generalization capability
Mingjian Sun, Yunlong Han, Kai Guo, et al.
Journal of Manufacturing Processes (2024) Vol. 120, pp. 975-1001
Closed Access | Times Cited: 11
Robust Tool Wear Prediction using Multi-Sensor Fusion and Time-Domain Features for the Milling Process using Instance-based Domain Adaptation
Vivek Warke, Satish Kumar, Arunkumar Bongale, et al.
Knowledge-Based Systems (2024) Vol. 288, pp. 111454-111454
Closed Access | Times Cited: 10
Improving milling tool wear prediction through a hybrid NCA-SMA-GRU deep learning model
Zhongyuan Che, Chong Peng, T. Warren Liao, et al.
Expert Systems with Applications (2024) Vol. 255, pp. 124556-124556
Closed Access | Times Cited: 7
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
Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm
Zhou Wen-jun, Xiaoping Xiao, Zisheng Li, et al.
Measurement Science and Technology (2024) Vol. 35, Iss. 4, pp. 046112-046112
Closed Access | Times Cited: 5
Identification of tool wear status using multi-sensor signals and improved gated recurrent unit
Zisheng Li, Xiaoping Xiao, Zhou Wen-jun, et al.
The International Journal of Advanced Manufacturing Technology (2025)
Closed Access
Advances in Acoustic Emission Monitoring for Grinding of Hard and Brittle Materials
Zhiqi Fan, Chengwei Kang, Xuliang Li, et al.
Journal of Materials Research and Technology (2025)
Open Access
Multi-source information fusion deep self-attention reinforcement learning framework for multi-label compound fault recognition
Zisheng Wang, Jianping Xuan, Tielin Shi
Mechanism and Machine Theory (2022) Vol. 179, pp. 105090-105090
Closed Access | Times Cited: 24
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
A meta-learning method for smart manufacturing: Tool wear prediction using hybrid information under various operating conditions
Xuandong Mo, Xiaofeng Hu, Andong Sun, et al.
Robotics and Computer-Integrated Manufacturing (2024) Vol. 91, pp. 102846-102846
Closed Access | Times Cited: 4
An unsupervised dual-regression domain adversarial adaption network for tool wear prediction in multi-working conditions
Yumeng Zhu, Yanyang Zi, Jing Xu, et al.
Measurement (2022) Vol. 200, pp. 111644-111644
Closed Access | Times Cited: 21
Tool wear monitoring based on the combination of machine vision and acoustic emission
Meiliang Chen, Mengdan Li, Linfeng Zhao, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 125, Iss. 7-8, pp. 3881-3897
Closed Access | Times Cited: 12
Tool Condition Monitoring Methods Applicable in the Metalworking Process
Melvin Alexis Lara de León, Jakub Kolařík, Radek Byrtus, et al.
Archives of Computational Methods in Engineering (2023) Vol. 31, Iss. 1, pp. 221-242
Open Access | Times Cited: 12
Multiple color representation and fusion for diabetes mellitus diagnosis based on back tongue images
Nannan Zhang, Zhixing Jiang, Jinxing Li, et al.
Computers in Biology and Medicine (2023) Vol. 155, pp. 106652-106652
Closed Access | Times Cited: 10
Multi-condition wear prediction and assessment of milling cutters based on linear discriminant analysis and ensemble methods
Honggen Zhou, Shangshang Gao, Yang Xie, et al.
Measurement (2023) Vol. 216, pp. 112900-112900
Closed Access | Times Cited: 10
Hybrid physics data-driven model-based fusion framework for machining tool wear prediction
Tianhong Gao, Haiping Zhu, Jun Wu, et al.
The International Journal of Advanced Manufacturing Technology (2024) Vol. 132, Iss. 3-4, pp. 1481-1496
Closed Access | Times Cited: 3
Unlocking Predictive Capability and Enhancing Sensing Performances of Plasmonic Hydrogen Sensors via Phase Space Reconstruction and Convolutional Neural Networks
Xiangxin Lin, Mingyu Cheng, Xinyi Chen, et al.
ACS Sensors (2024) Vol. 9, Iss. 8, pp. 3877-3888
Closed Access | Times Cited: 3
Milling wear prediction using an artificial neural network model
Her-Terng Yau, Ping‐Huan Kuo, Song-Wei Hong
Engineering Applications of Artificial Intelligence (2024) Vol. 135, pp. 108686-108686
Closed Access | Times Cited: 3
Deep transfer residual variational autoencoder with multi-sensors fusion for tool condition monitoring in impeller machining
Jiayu Ou, Hongkun Li, Bo Liu, et al.
Measurement (2022) Vol. 204, pp. 112028-112028
Closed Access | Times Cited: 16
The Prediction of Abrasion Resistance of Mortars Modified with Granite Powder and Fly Ash Using Artificial Neural Networks
Sławomir Czarnecki, Adrian Chajec, Seweryn Malazdrewicz, et al.
Applied Sciences (2023) Vol. 13, Iss. 6, pp. 4011-4011
Open Access | Times Cited: 9
A tool wear prediction and monitoring method based on machining power signals
Qi Wang, Xi Chen, Qinglong An, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 129, Iss. 11-12, pp. 5387-5401
Open Access | Times Cited: 9
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
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