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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:

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

Showing 22 citing articles:

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
Cross-domain tool wear condition monitoring via residual attention hybrid adaptation network
Zhiwen Huang, Weidong Li, Jianmin Zhu, et al.
Journal of Manufacturing Systems (2023) Vol. 72, pp. 406-423
Closed Access | Times Cited: 17
Image-based machine learning model for tool wear estimation in milling Inconel 718
Tam T. Truong, Jay Airao, Saman Fattahi, et al.
Wear (2025), pp. 205865-205865
Open 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
Semi-supervised prediction of milling cutter wear based on an empirical formula for cutting force and wear
Wujun Yu, Hongfei Zhan, Junhe Yu, et al.
The International Journal of Advanced Manufacturing Technology (2025)
Closed Access
Leveraging artificial intelligence for real-time indirect tool condition monitoring: From theoretical and technological progress to industrial applications
Delin Liu, Zhanqiang Liu, Bing Wang, et al.
International Journal of Machine Tools and Manufacture (2024) Vol. 202, pp. 104209-104209
Closed Access | Times Cited: 3
Semi-supervised multi-source meta-domain generalization method for tool wear state prediction under varying cutting conditions
Wangyang Li, Hongya Fu, Yue Zhuo, et al.
Journal of Manufacturing Systems (2023) Vol. 71, pp. 323-341
Closed Access | Times Cited: 8
Machining surface roughness detection by adaptive deep fusion capsule network with low illumination and noise robustness
Zhiwen Huang, Qiang Zhang, Jiajie Shao, et al.
Measurement Science and Technology (2023) Vol. 35, Iss. 1, pp. 015037-015037
Closed Access | Times Cited: 8
Research on tap breakage monitoring method for tapping process based on SSAELSTM fusion network
Ting Chen, Jianming Zheng, Chao Peng, et al.
Measurement (2024) Vol. 236, pp. 115076-115076
Closed Access | Times Cited: 2
An innovative Multisource Lightweight Adaptive Replayed Online Deep Transfer Learning algorithm for tool wear monitoring
Zhilie Gao, Ni Chen, Yinfei Yang, et al.
Journal of Manufacturing Processes (2024) Vol. 124, pp. 261-281
Closed Access | Times Cited: 2
Denoising Diffusion Probabilistic Model Enhanced Tool Condition Monitoring Method Under Imbalanced Conditions
Yu Fu, Meipeng Zhong, Junfeng Huang, et al.
Measurement Science and Technology (2024) Vol. 36, Iss. 1, pp. 015018-015018
Closed Access | Times Cited: 2
Research on multi-source information fusion tool wear monitoring based on MKW-GPR model
Ruitao Peng, Zelin Xiao, Yuanyuan Peng, et al.
Measurement (2024), pp. 116055-116055
Closed Access | Times Cited: 2
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
A deep transfer learning model for online monitoring of surface roughness in milling with variable parameters
Kai Zhou, Pingfa Feng, Feng Feng, et al.
Computers in Industry (2024) Vol. 164, pp. 104199-104199
Closed Access | Times Cited: 1
Modelling of kerf width and surface roughness using vibration signals in laser beam machining of stainless steel using design of experiments
K. Venkata Rao, L. Suvarna Raju, Gamini Suresh, et al.
Optics & Laser Technology (2023) Vol. 169, pp. 110146-110146
Closed Access | Times Cited: 4
Label propagation-based unsupervised domain adaptation for intelligent fault diagnosis
Huanjie Wang, Yuan Li, Xiwei Bai, et al.
Journal of Intelligent Manufacturing (2023) Vol. 35, Iss. 7, pp. 3131-3148
Closed Access | Times Cited: 3
A New Semi-supervised Tool-wear Monitoring Method using Unreliable Pseudo-Labels
Yi Sun, Jigang He, Hongli Gao, et al.
Measurement (2023) Vol. 226, pp. 113991-113991
Closed Access | Times Cited: 3
Methodology and Experimental Verification for Predicting the Remaining Useful Life of Milling Cutters Based on Hybrid CNN-LSTM-Attention-PSA
Meilu Zhu, Ji Zhang, Lingfan Bu, et al.
Machines (2024) Vol. 12, Iss. 11, pp. 752-752
Open Access
The pin tool wear identification with vibration signal of friction stir lap welding based on a new pin tool wear division model
Yuning Wang, Siyu Zhao, Peng Zhang, et al.
Measurement (2024) Vol. 242, pp. 116131-116131
Closed Access
Based on domain adversarial neural network with multiple loss collaborative optimization for milling tool wear state monitoring under different machining conditions
Qiang Liu, Jiaqi Liu, Xianli Liu, et al.
Precision Engineering (2024) Vol. 91, pp. 692-706
Closed Access
Variable universe fuzzy inference decided neural network feed-forward compensation for pid control of motor position
Long Li, Yidan Zhu, Zhiwen Huang, et al.
Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science (2024)
Closed Access
Applying Feature Transformation-Based Domain Confusion to Neural Network for the Denoising of Dispersion Spectrograms
Weibin Song, Shichuan Yuan, Ming Cheng, et al.
Seismological Research Letters (2023) Vol. 95, Iss. 1, pp. 378-396
Closed Access | Times Cited: 1
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