Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism
Xingwei Xu, Jianwen Wang, Bingfu Zhong, et al.
Measurement (2021) Vol. 177, pp. 109254-109254
Closed Access | Times Cited: 115

Showing 1-25 of 115 citing articles:

A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges
Vahid Nasir, Farrokh Sassani
The International Journal of Advanced Manufacturing Technology (2021) Vol. 115, Iss. 9-10, pp. 2683-2709
Closed Access | Times Cited: 201
Intelligent tool wear monitoring and multi-step prediction based on deep learning model
Minghui Cheng, Jiao Li, Pei Yan, et al.
Journal of Manufacturing Systems (2021) Vol. 62, pp. 286-300
Closed Access | Times Cited: 127
Machine learning and artificial intelligence in CNC machine tools, A review
Mohsen Soori, Behrooz Arezoo, Roza Dastres
Sustainable Manufacturing and Service Economics (2023) Vol. 2, pp. 100009-100009
Open Access | Times Cited: 96
A novel multi-scale CNN and attention mechanism method with multi-sensor signal for remaining useful life prediction
Xingwei Xu, Xiang Li, Weiwei Ming, et al.
Computers & Industrial Engineering (2022) Vol. 169, pp. 108204-108204
Closed Access | Times Cited: 80
Multiscale inverted residual convolutional neural network for intelligent diagnosis of bearings under variable load condition
Wenlei Zhao, Zhijian Wang, Wenan Cai, et al.
Measurement (2021) Vol. 188, pp. 110511-110511
Closed Access | Times Cited: 82
MS-SSPCANet: A powerful deep learning framework for tool wear prediction
Jian Duan, Cheng Hu, Xiaobin Zhan, et al.
Robotics and Computer-Integrated Manufacturing (2022) Vol. 78, pp. 102391-102391
Closed Access | Times Cited: 44
Accurate estimation of tool wear levels during milling, drilling and turning operations by designing novel hyperparameter tuned models based on LightGBM and stacking
Jawad Mahmood, Ghulam-e Mustafa, Muhammad Ali
Measurement (2022) Vol. 190, pp. 110722-110722
Closed Access | Times Cited: 40
Experimental investigations and prediction of WEDMed surface of nitinol SMA using SinGAN and DenseNet deep learning model
Vinay Vakharia, Jay Vora, Sakshum Khanna, et al.
Journal of Materials Research and Technology (2022) Vol. 18, pp. 325-337
Open Access | Times Cited: 39
Online quality inspection of resistance spot welding for automotive production lines
Wei Dai, Dayong Li, Yongjia Zheng, et al.
Journal of Manufacturing Systems (2022) Vol. 63, pp. 354-369
Closed Access | Times Cited: 39
A novel adversarial domain adaptation transfer learning method for tool wear state prediction
Kai Li, Ming-Song Chen, Y.C. Lin, et al.
Knowledge-Based Systems (2022) Vol. 254, pp. 109537-109537
Closed Access | Times Cited: 37
Cutting tool wear state recognition based on a channel-space attention mechanism
Rongyi Li, Peining Wei, Xianli Liu, et al.
Journal of Manufacturing Systems (2023) Vol. 69, pp. 135-149
Closed Access | Times Cited: 31
A milling tool wear monitoring method with sensing generalization capability
Runqiong Wang, Qinghua Song, Yezhen Peng, et al.
Journal of Manufacturing Systems (2023) Vol. 68, pp. 25-41
Closed Access | Times Cited: 28
Data-driven prediction of tool wear using Bayesian regularized artificial neural networks
Tam T. Truong, Jay Airao, Faramarz Hojati, et al.
Measurement (2024) Vol. 238, pp. 115303-115303
Open Access | Times Cited: 14
Research on tool wear state identification method driven by multi-source information fusion and multi-dimension attention mechanism
Peining Wei, Rongyi Li, Xianli Liu, et al.
Robotics and Computer-Integrated Manufacturing (2024) Vol. 88, pp. 102741-102741
Closed Access | Times Cited: 9
A comprehensive review of minimum quantity lubrication (MQL) machining technology and cutting performance
Donghui Li, Tao Zhang, Tao Zheng, et al.
The International Journal of Advanced Manufacturing Technology (2024) Vol. 133, Iss. 5-6, pp. 2681-2707
Closed Access | Times Cited: 9
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 method based on symmetrized dot pattern and multi-covariance Gaussian process regression
Chuandong Zhang, Wei Wang, Hai Li
Measurement (2021) Vol. 189, pp. 110466-110466
Closed Access | Times Cited: 40
Tool wear prediction based on domain adversarial adaptation and channel attention multiscale convolutional long short-term memory network
Wen Hou, Hong Guo, Lei Luo, et al.
Journal of Manufacturing Processes (2022) Vol. 84, pp. 1339-1361
Closed Access | Times Cited: 30
Bayesian-based uncertainty-aware tool-wear prediction model in end-milling process of titanium alloy
Gyeongho Kim, Sang Min Yang, Dong Min Kim, et al.
Applied Soft Computing (2023) Vol. 148, pp. 110922-110922
Open Access | Times Cited: 20
A frequency-spatial hybrid attention mechanism improved tool wear state recognition method guided by structure and process parameters
Xuwei Lai, Kai Zhang, Qing Zheng, et al.
Measurement (2023) Vol. 214, pp. 112833-112833
Closed Access | Times Cited: 19
Synergetic application of an E-tongue, E-nose and E-eye combined with CNN models and an attention mechanism to detect the origin of black pepper
Shoucheng Wang, Qing Zhang, Chuanzheng Liu, et al.
Sensors and Actuators A Physical (2023) Vol. 357, pp. 114417-114417
Closed Access | Times Cited: 19
A hybrid-driven probabilistic state space model for tool wear monitoring
Zhipeng Ma, Ming Zhao, Xuebin Dai, et al.
Mechanical Systems and Signal Processing (2023) Vol. 200, pp. 110599-110599
Closed Access | Times Cited: 19
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
An Improved ResNet-1d with Channel Attention for Tool Wear Monitor in Smart Manufacturing
Liang Dong, Chensheng Wang, Guang Yang, et al.
Sensors (2023) Vol. 23, Iss. 3, pp. 1240-1240
Open Access | Times Cited: 16
Multi-sensor signal fusion for tool wear condition monitoring using denoising transformer auto-encoder Resnet
Hui Wang, Shuhui Wang, Weifang Sun, et al.
Journal of Manufacturing Processes (2024) Vol. 124, pp. 1054-1064
Closed Access | Times Cited: 7
Page 1 - Next Page

Scroll to top