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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 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: 58

Showing 1-25 of 58 citing articles:

Intelligent monitoring system for production lines in smart factories: A hybrid method integrating Transformer and Kalman filter
Xuzhou Fang, Qinghua Song, Zhenyang Li, et al.
Journal of Manufacturing Systems (2025) Vol. 79, pp. 27-47
Closed Access | Times Cited: 2
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: 41
Intelligent tool wear monitoring based on multi-channel hybrid information and deep transfer learning
Pengfei Zhang, Dong Gao, Dongbo Hong, et al.
Journal of Manufacturing Systems (2023) Vol. 69, pp. 31-47
Closed Access | Times Cited: 29
Multi-scale one-dimensional convolution tool wear monitoring based on multi-model fusion learning skills
Wei Ma, Xianli Liu, Caixu Yue, et al.
Journal of Manufacturing Systems (2023) Vol. 70, pp. 69-98
Closed Access | Times Cited: 19
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: 19
A tool wear monitoring method based on data-driven and physical output
Yiyuan Qin, Xianli Liu, Caixu Yue, et al.
Robotics and Computer-Integrated Manufacturing (2024) Vol. 91, pp. 102820-102820
Closed Access | Times Cited: 6
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
Developing a deep learning-based uncertainty-aware tool wear prediction method using smartphone sensors for the turning process of Ti-6Al-4V
Gyeongho Kim, Sang Min Yang, Dong Min Kim, et al.
Journal of Manufacturing Systems (2024) Vol. 76, pp. 133-157
Closed Access | Times Cited: 5
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: 5
Experimental prediction model for the running-in state of a friction system based on chaotic characteristics and BP neural network
Cong Ding, Shiqing Feng, Zhizhao Qiao, et al.
Tribology International (2023) Vol. 188, pp. 108846-108846
Closed Access | Times Cited: 12
Precise measurement of geometric and physical quantities in cutting tools inspection and condition monitoring: A review
Wenqi Wang, Wei Liu, Yang Zhang, et al.
Chinese Journal of Aeronautics (2023) Vol. 37, Iss. 4, pp. 23-53
Open Access | Times Cited: 12
Tool Wear Prediction Model Using Multi-Channel 1D Convolutional Neural Network and Temporal Convolutional Network
Min Huang, Xingang Xie, Weiwei Sun, et al.
Lubricants (2024) Vol. 12, Iss. 2, pp. 36-36
Open Access | Times Cited: 4
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: 4
Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning
Shen-Yung Lin, Chia-Jen Hsieh
Applied Sciences (2024) Vol. 14, Iss. 9, pp. 3811-3811
Open Access | Times Cited: 4
Ball-end tool wear monitoring and multi-step forecasting with multi-modal information under variable cutting conditions
Yanpeng Hao, Lida Zhu, Jinsheng Wang, et al.
Journal of Manufacturing Systems (2024) Vol. 76, pp. 234-258
Closed Access | Times Cited: 4
Multi-source online transfer learning based on hybrid physics-data model for cross-condition tool health monitoring
Biyao Qiang, Kaining Shi, Junxue Ren, et al.
Journal of Manufacturing Systems (2024) Vol. 77, pp. 1-17
Closed Access | Times Cited: 4
Process planning of parameter intelligent adjustment for batch machining based on historical data segmented modeling
Juan Lu, Shiying Tu, Ying Li, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 145, pp. 110180-110180
Closed Access
Intelligent wireless tool wear monitoring system based on chucked tool condition monitoring ring and deep learning
Ni Chen, Zhan Liu, Zhongling Xue, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103176-103176
Closed Access
A novel algorithm for tool wear monitoring utilizing model and Knowledge-Guided Multi-Expert weighted adversarial deep transfer learning
Zhilie Gao, Ni Chen, Liang Li
Mechanical Systems and Signal Processing (2025) Vol. 228, pp. 112456-112456
Closed Access
Tool wear monitoring by estimating milling torque and calculating the wear energy coefficient under variable depth of cut conditions
Defeng Peng, Hongkun Li, Bin Sun, et al.
ISA Transactions (2025)
Closed Access
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
Tool wear prediction based on multisensor data fusion and machine learning
Tanner Jones, Yang Cao
The International Journal of Advanced Manufacturing Technology (2025)
Open Access
An intelligent tool wear monitoring model based on knowledge-data-driven physical-informed neural network for digital twin milling
Xuzhou Fang, Qinghua Song, Xiaojuan Wang, et al.
Mechanical Systems and Signal Processing (2025) Vol. 232, pp. 112736-112736
Closed Access
Towards understanding wear mechanisms of Parallel Ultrasonic Vibration-assisted Cutting
Wei Cai, Yi Long, Hongxiang Yin, et al.
Wear (2025), pp. 206092-206092
Closed Access
Investigation of an Optimized Linear Regression Model with Nonlinear Error Compensation for Tool Wear Prediction
Lester Shen, Baorui Du, He Fan, et al.
Machines (2025) Vol. 13, Iss. 5, pp. 355-355
Open Access
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