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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:

Intelligent tool wear monitoring based on parallel residual and stacked bidirectional long short-term memory network
Xianli Liu, Shaoyang Liu, Xuebing Li, et al.
Journal of Manufacturing Systems (2021) Vol. 60, pp. 608-619
Closed Access | Times Cited: 95

Showing 26-50 of 95 citing articles:

A new method based on a WOA-optimized support vector machine to predict the tool wear
Yaonan Cheng, Xiaoyu Gai, Yingbo Jin, et al.
The International Journal of Advanced Manufacturing Technology (2022) Vol. 121, Iss. 9-10, pp. 6439-6452
Closed Access | Times Cited: 22
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
A task-cooperative drilling monitoring method based on heterogeneous multi-task learning: Task adaptive fusion guided by domain knowledge
Jing Qin, Qinghua Song, Runqiong Wang, et al.
Mechanical Systems and Signal Processing (2025) Vol. 225, pp. 112299-112299
Closed Access
Parallel Residual Stacked Bidirectional Long Short-Term Memory Network Optimized with Chameleon Swarm Optimization Algorithm for Time-Series Sensor Data
V. Shanmuganathan, A. Suresh
IETE Journal of Research (2025), pp. 1-10
Closed Access
Tool wear monitoring for robotic milling based on multi-dimensional stacked sparse autoencoders and bidirectional LSTM networks with singularity features
Chang’an Zhou, Kaixing Zhang, Jiawei Xu, et al.
The International Journal of Advanced Manufacturing Technology (2025)
Closed Access
Swin-fusion: An adaptive multi-source information fusion framework for enhanced tool wear monitoring
Kailin Hou, Rongyi Li, Xianli Liu, et al.
Journal of Manufacturing Systems (2025) Vol. 79, pp. 435-454
Closed Access
Robot milling tool wear monitoring based on variational autoencoder with multidomain depth features
Jiawei Xu, Jianguo Chen, Lei Yang, et al.
Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering (2025)
Closed Access
A Spatial–Temporal Adaptive Graph Convolutional Network with Multi-Sensor Signals for Tool Wear Prediction
Yu Xia, Guangji Zheng, Ye Li, et al.
Applied Sciences (2025) Vol. 15, Iss. 4, pp. 2058-2058
Open Access
High-speed machining of hardened steel during moldand die production: a critical review toward an Industry 5.0 environment
Jonny Herwan, German Herrera-Granados, Ichiro Ogura, et al.
The International Journal of Advanced Manufacturing Technology (2025)
Closed Access
Multi-dimensional tool wear detection using Non-Subsampled Shearlet Transform and enhanced pulse coupled neural network in optical microscopy reconstruction
Lei Li, Bing Li, Xiang Wei, et al.
Optics & Laser Technology (2025) Vol. 187, pp. 112849-112849
Closed Access
Digital twin-based tool wear monitoring of micro-milling process
Ziming Wang, Zhiyi Zhang, Yuchen Zhao, et al.
Research Square (Research Square) (2025)
Closed Access
Methods to improve wear prediction accuracy of nickel-based superalloy milling cutters under variable process conditions
Liang Xi Xie, Bingran Li, Zixuan Wang, et al.
The International Journal of Advanced Manufacturing Technology (2025)
Closed Access
Tool Wear Prediction Based on LSTM and Deep Residual Network
Chun Fang, Yikang Gong, Xibo Ming, et al.
International Journal of Pattern Recognition and Artificial Intelligence (2024) Vol. 38, Iss. 05
Closed Access | Times Cited: 3
Wear monitoring of micro-milling tools based on Improved Siamese Neural Network
Gang Zheng, Xinfeng Lv, Xu Zhang, et al.
Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science (2024) Vol. 238, Iss. 17, pp. 8715-8729
Closed Access | Times Cited: 3
Deep transfer learning for tool condition monitoring under different processing conditions
Yongqing Wang, Mengmeng Niu, Kuo Liu, et al.
The International Journal of Advanced Manufacturing Technology (2024) Vol. 133, Iss. 1-2, pp. 507-519
Closed Access | Times Cited: 3
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
Gated recurrent unit and temporal convolutional network with soft thresholding and attention mechanism for tool wear prediction
Binglin Li, Jun Li, Xingsheng Wu, et al.
Measurement (2024) Vol. 240, pp. 115546-115546
Closed Access | Times Cited: 3
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: 3
Real-time polymer flow state monitoring during fused filament fabrication based on acoustic emission
Hao Li, Zhonghua Yu, Feng Li, et al.
Journal of Manufacturing Systems (2022) Vol. 62, pp. 628-635
Closed Access | Times Cited: 16
Tool wear prediction in turning using workpiece surface profile images and deep learning neural networks
Meng Lip Lim, Mohd Naqib Derani, Mani Maran Ratnam, et al.
The International Journal of Advanced Manufacturing Technology (2022) Vol. 120, Iss. 11-12, pp. 8045-8062
Closed Access | Times Cited: 14
Robust tool wear monitoring system development by sensors and feature fusion
Yu‐Ru Lin, Ching‐Hung Lee, Ming‐Chyuan Lu
Asian Journal of Control (2022) Vol. 24, Iss. 3, pp. 1005-1021
Closed Access | Times Cited: 13
Tool Wear Monitoring Using Improved Dragonfly Optimization Algorithm and Deep Belief Network
Leo Gertrude David, Raj Kumar Patra, Przemysław Falkowski‐Gilski, et al.
Applied Sciences (2022) Vol. 12, Iss. 16, pp. 8130-8130
Open Access | Times Cited: 13
Cutting tool remaining useful life prediction based on robust empirical mode decomposition and Capsule-BiLSTM network
Liangshi Sun, Chengying Zhao, Xianzhen Huang, et al.
Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science (2023) Vol. 237, Iss. 14, pp. 3308-3323
Closed Access | Times Cited: 7
Physics-Guided Deep Learning Method for Tool Condition Monitoring in Smart Machining System
Shenshen Li, Xin Lin, Hu Shi, et al.
IEEE/ASME Transactions on Mechatronics (2023) Vol. 29, Iss. 3, pp. 2327-2337
Closed Access | Times Cited: 7
A multi-input parallel convolutional attention network for tool wear monitoring
Qiang Liu, Dingkun Li, Jing Ma, et al.
International Journal of Computer Integrated Manufacturing (2023) Vol. 38, Iss. 1, pp. 23-39
Closed Access | Times Cited: 7

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