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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:

Investigated iterative convergences of neural network for prediction turning tool wear
Wen-Yang Chang, Sheng-Jhih Wu, Jia-Wei Hsu
The International Journal of Advanced Manufacturing Technology (2020) Vol. 106, Iss. 7-8, pp. 2939-2948
Closed Access | Times Cited: 22

Showing 22 citing articles:

A new optimized predictive model based on political optimizer for eco-friendly MQL-turning of AISI 4340 alloy with nano-lubricants
Ammar H. Elsheikh, Mohamed Abd Elaziz, Sudhansu Ranjan Das, et al.
Journal of Manufacturing Processes (2021) Vol. 67, pp. 562-578
Closed Access | Times Cited: 132
A new tool wear condition monitoring method based on deep learning under small samples
Yuqing Zhou, Gaofeng Zhi, Wei Chen, et al.
Measurement (2021) Vol. 189, pp. 110622-110622
Closed Access | Times Cited: 112
Tool Condition Monitoring for High-Performance Machining Systems—A Review
Ayman Mohamed, Mahmoud Hassan, Rachid M’Saoubi, et al.
Sensors (2022) Vol. 22, Iss. 6, pp. 2206-2206
Open Access | Times Cited: 89
Digitalisation and servitisation of machine tools in the era of Industry 4.0: a review
Chao Liu, Pai Zheng, Xun Xu
International Journal of Production Research (2021) Vol. 61, Iss. 12, pp. 4069-4101
Open Access | Times Cited: 89
Tool Wear Condition Monitoring Method Based on Deep Learning with Force Signals
Yaping Zhang, Xiaozhi Qi, Tao Wang, et al.
Sensors (2023) Vol. 23, Iss. 10, pp. 4595-4595
Open Access | Times Cited: 31
AI-based optimisation of total machining performance: A review
Katrin Ullrich, Magnus von Elling, Kevin Gutzeit, et al.
CIRP journal of manufacturing science and technology (2024) Vol. 50, pp. 40-54
Open Access | Times Cited: 13
A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques
Lorenzo Colantonio, Lucas Equeter, Pierre Dehombreux, et al.
Machines (2021) Vol. 9, Iss. 12, pp. 351-351
Open Access | Times Cited: 52
Cutting tool prognostics enabled by hybrid CNN-LSTM with transfer learning
Mohamed Marei, Weidong Li
The International Journal of Advanced Manufacturing Technology (2021) Vol. 118, Iss. 3-4, pp. 817-836
Closed Access | Times Cited: 51
Recent Advances on Machine Learning Applications in Machining Processes
Francesco Aggogeri, Nicola Pellegrini, Franco Luis Tagliani
Applied Sciences (2021) Vol. 11, Iss. 18, pp. 8764-8764
Open Access | Times Cited: 41
Pepper leaf disease recognition based on enhanced lightweight convolutional neural networks
Min Dai, Wenjing Sun, Lixing Wang, et al.
Frontiers in Plant Science (2023) Vol. 14
Open Access | Times Cited: 19
Cross-domain adaptation network based on attention mechanism for tool wear prediction
Jianliang He, Yuxin Sun, Chen Yin, et al.
Journal of Intelligent Manufacturing (2022) Vol. 34, Iss. 8, pp. 3365-3387
Closed Access | Times Cited: 23
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
Unsupervised online prediction of tool wear values using force model coefficients in milling
Dou Jianming, Shengjie Jiao, Chuangwen Xu, et al.
The International Journal of Advanced Manufacturing Technology (2020) Vol. 109, Iss. 3-4, pp. 1153-1166
Closed Access | Times Cited: 24
Estimation and Optimization of Tool Wear in Conventional Turning of 709M40 Alloy Steel Using Support Vector Machine (SVM) with Bayesian Optimization
Mahdi S. Alajmi, Abdullah M. Almeshal
Materials (2021) Vol. 14, Iss. 14, pp. 3773-3773
Open Access | Times Cited: 23
Reliability analysis of the cutting tool in plasma-assisted turning and prediction of machining characteristics
Thella Babu Rao
Australian Journal of Mechanical Engineering (2020) Vol. 20, Iss. 4, pp. 1020-1034
Closed Access | Times Cited: 13
Tool life prediction via SMB-enabled monitor based on BPNN coupling algorithms for sustainable manufacturing
Wen-Yang Chang, Bo-Yao Hsu
Artificial intelligence for engineering design analysis and manufacturing (2023) Vol. 37
Closed Access | Times Cited: 4
Online Measurement of Wear Depth Based on Displacement Signal of Vertical Tester
Hongju Li, Ying Liu, Haoran Liao, et al.
Tribology International (2024) Vol. 200, pp. 110179-110179
Closed Access | Times Cited: 1
Error Fusion of Hybrid Neural Networks for Mechanical Condition Dynamic Prediction
Wentao Zhang, Yucheng Liu, Shaohui Zhang, et al.
Sensors (2021) Vol. 21, Iss. 12, pp. 4043-4043
Open Access | Times Cited: 5
A Study on Multivariable Optimization in Precision Manufacturing Using MOPSONNS
Zhaopeng He, Tielin Shi, Jianping Xuan, et al.
International Journal of Precision Engineering and Manufacturing (2020) Vol. 21, Iss. 11, pp. 2011-2026
Closed Access | Times Cited: 2
Construction of rail wear and crack initiation prediction model combined with BP neural network
Yichao Wu, Shuying Wang
(2022), pp. 137-141
Closed Access | Times Cited: 1
Establishing a Model for Predicting Material Wear and Crack Initiation Based on BP Neural Network: Taking Ceramic Materials as an Example
Xiuru Wang, Shenghui Wang, Jiamin Mai, et al.
(2023), pp. 567-571
Closed Access
A two-stage approach for modeling inverse S-shaped wear processes of cutting tools
Renyan Jiang
2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) (2021), pp. 1-5
Closed Access
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