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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:

Heterogeneous sensors-based feature optimisation and deep learning for tool wear prediction
Xiaoyang Zhang, Sheng Wang, Weidong Li, et al.
The International Journal of Advanced Manufacturing Technology (2021) Vol. 114, Iss. 9-10, pp. 2651-2675
Closed Access | Times Cited: 36

Showing 1-25 of 36 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: 199
Systematic review on tool breakage monitoring techniques in machining operations
Xuebing Li, Xianli Liu, Caixu Yue, et al.
International Journal of Machine Tools and Manufacture (2022) Vol. 176, pp. 103882-103882
Closed Access | Times Cited: 120
Milling tool wear prediction using multi-sensor feature fusion based on stacked sparse autoencoders
Zhaopeng He, Tielin Shi, Jianping Xuan
Measurement (2022) Vol. 190, pp. 110719-110719
Closed Access | Times Cited: 65
Application of sophisticated sensors to advance the monitoring of machining processes: analysis and holistic review
Sumanth Ratna Kandavalli, Aqib Mashood Khan, Asif Iqbal, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 125, Iss. 3-4, pp. 989-1014
Closed Access | Times Cited: 28
Robust Tool Wear Prediction using Multi-Sensor Fusion and Time-Domain Features for the Milling Process using Instance-based Domain Adaptation
Vivek Warke, Satish Kumar, Arunkumar Bongale, et al.
Knowledge-Based Systems (2024) Vol. 288, pp. 111454-111454
Closed Access | Times Cited: 10
Tool wear state recognition based on feature selection method with whitening variational mode decomposition
Xudong Wei, Xianli Liu, Caixu Yue, et al.
Robotics and Computer-Integrated Manufacturing (2022) Vol. 77, pp. 102344-102344
Closed Access | Times Cited: 30
Tool wear prediction using long short-term memory variants and hybrid feature selection techniques
Sameer Sayyad, Satish Kumar, Arunkumar Bongale, et al.
The International Journal of Advanced Manufacturing Technology (2022) Vol. 121, Iss. 9-10, pp. 6611-6633
Closed Access | Times Cited: 29
Online Monitoring of Sensor Calibration Status to Support Condition-Based Maintenance
Alexandre Martins, Inácio Fonseca, José Torres Farinha, et al.
Sensors (2023) Vol. 23, Iss. 5, pp. 2402-2402
Open Access | Times Cited: 16
Surface roughness and tool wear monitoring in turning processes through vibration analysis using PSD and GRMS
Roumaissa Bouchama, Mohamed Lamine Bouhalais, Abdelhakim Cherfia
The International Journal of Advanced Manufacturing Technology (2024) Vol. 130, Iss. 7-8, pp. 3537-3552
Closed Access | Times Cited: 5
Hybrid prognostics to estimate cutting inserts remaining useful life based on direct wear observation
Luca Bernini, Ugo Malguzzi, Paolo Albertelli, et al.
Mechanical Systems and Signal Processing (2024) Vol. 210, pp. 111163-111163
Open Access | Times Cited: 5
Mill condition monitoring based on instantaneous identification of specific force coefficients under variable cutting conditions
Luca Bernini, Paolo Albertelli, Michele Monno
Mechanical Systems and Signal Processing (2022) Vol. 185, pp. 109820-109820
Open Access | Times Cited: 24
Multi-sensor heterogeneous data-based online tool health monitoring in milling of IN718 superalloy using OGM (1, N) model and SVM
Mulpur Sarat Babu, Thella Babu Rao
Measurement (2022) Vol. 199, pp. 111501-111501
Closed Access | Times Cited: 21
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
A GAN-Based Multi-Sensor Data Augmentation Technique for CNC Machine Tool Wear Prediction
Yuechi Jiang, Benny Drescher, Guoguang Yuan
IEEE Access (2023) Vol. 11, pp. 95782-95795
Open Access | Times Cited: 9
Artificial intelligence for machining process monitoring
Hakkı Özgür Ünver, Ahmet Murat Özbayoğlu, Cem Söyleyici, et al.
Elsevier eBooks (2024), pp. 307-350
Closed Access | Times Cited: 2
Correlation Analysis of Vibration Signal Frequency with Tool Wear During the Milling Process on Martensitic Stainless Steel Material
Achmad Zaki Rahman, Khairul Jauhari, Mahfudz Al Huda, et al.
Arabian Journal for Science and Engineering (2023) Vol. 49, Iss. 8, pp. 10573-10586
Closed Access | Times Cited: 7
Milling cutter wear prediction method under variable working conditions based on LRCN
Changsen Yang, Jingtao Zhou, Enming Li, et al.
The International Journal of Advanced Manufacturing Technology (2022) Vol. 121, Iss. 3-4, pp. 2647-2661
Closed Access | Times Cited: 11
Visualization of relevant areas of milling tools for the classification of tool wear by machine learning methods
Björn Papenberg, Sebastian Hogreve, Kirsten Tracht
Procedia CIRP (2023) Vol. 118, pp. 525-530
Open Access | Times Cited: 5
Tool wear area estimation through in-process edge force coefficient in trochoidal milling of Inconel 718
Aash M. Shahl, Ankit Agarwal, Laine Mears
Manufacturing Letters (2023) Vol. 35, pp. 391-398
Closed Access | Times Cited: 5
Research progress on intelligent monitoring of tool condition based on deep learning
Dahu Cao, Wei Liu, Jimin Ge, et al.
The International Journal of Advanced Manufacturing Technology (2024) Vol. 134, Iss. 5-6, pp. 2129-2150
Closed Access | Times Cited: 1
Enhanced prediction accuracy in high-speed grinding of brittle materials using advanced machine learning techniques
Sangkyoung Lee, Zhuoxiao Chen, Yadan Luo, et al.
Journal of Intelligent Manufacturing (2024)
Closed Access | Times Cited: 1
Hybrid prognosis of drill-bits based on direct inspection
Luca Bernini, Ugo Malguzzi, Paolo Albertelli, et al.
Procedia Computer Science (2024) Vol. 232, pp. 201-210
Open Access | Times Cited: 1
A Review: Sensors Used in Tool Wear Monitoring and Prediction
Perin Ünal, Bilgin Umut Deveci, Ahmet Murat Özbayoğlu
Lecture notes in computer science (2022), pp. 193-205
Closed Access | Times Cited: 6
A comparative study of force models in monitoring the flank wear using the cutting force coefficients
Huan Luo, Zhao Zhang, Ming Luo, et al.
Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science (2022) Vol. 238, Iss. 13, pp. 6217-6230
Closed Access | Times Cited: 4
Tool wear state recognition for variable sensor combinations by deep forest with parameter adaptive fine-tuning
Xuejun Wang, Ning Li, Dengsheng Lu, et al.
Applied Soft Computing (2024), pp. 112629-112629
Closed Access
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