Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations
Author
Abstract
Suggested Citation
DOI: 10.1007/s10845-019-01488-7
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Jeff Morgan & Garret E. O’Donnell, 2018. "Cyber physical process monitoring systems," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1317-1328, August.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Yanan Pan & Renke Kang & Zhigang Dong & Wenhao Du & Sen Yin & Yan Bao, 2022. "On-line prediction of ultrasonic elliptical vibration cutting surface roughness of tungsten heavy alloy based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 675-685, March.
- Ammar H. Elsheikh & Taher A. Shehabeldeen & Jianxin Zhou & Ezzat Showaib & Mohamed Abd Elaziz, 2021. "Prediction of laser cutting parameters for polymethylmethacrylate sheets using random vector functional link network integrated with equilibrium optimizer," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1377-1388, June.
- Shanmugasivam Pillai & Prahlad Vadakkepat, 2022. "Deep learning for machine health prognostics using Kernel-based feature transformation," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1665-1680, August.
- Raouf Zerrougui & Amel B. H. Adamou-Mitiche & Lahcene Mitiche, 2023. "A novel machine learning algorithm for interval systems approximation based on artificial neural network," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2171-2184, June.
- Chia-Yu Hsu & Wei-Chen Liu, 2021. "Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 823-836, March.
- Yuqing Zhou & Bintao Sun & Weifang Sun & Zhi Lei, 2022. "Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 247-258, January.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Kendrik Yan Hong Lim & Pai Zheng & Chun-Hsien Chen, 2020. "A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1313-1337, August.
- Shashi Bhushan Jha & Radu F. Babiceanu & Remzi Seker, 2020. "Formal modeling of cyber-physical resource scheduling in IIoT cloud environments," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1149-1164, June.
- Guodong Huang & Jie Chen & Yacob Khojasteh, 2021. "A cyber-physical system deployment based on pull strategies for one-of-a-kind production with limited resources," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 579-596, February.
- A. J. H. Redelinghuys & A. H. Basson & K. Kruger, 2020. "A six-layer architecture for the digital twin: a manufacturing case study implementation," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1383-1402, August.
More about this item
Keywords
Tool wear predicting; Multi-domain; Feature fusion; Convolutional neural network; Milling;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:31:y:2020:i:4:d:10.1007_s10845-019-01488-7. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.