Real-time monitoring of high-power disk laser welding statuses based on deep learning framework-Reference-Cited by-同舟云学术

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Real-time monitoring of high-power disk laser welding statuses based on deep learning framework

Published:2019-06-11 Issue:4 Volume:31 Page:799-814
ISSN:0956-5515
Container-title:Journal of Intelligent Manufacturing
language:en
Short-container-title:J Intell Manuf

Author:

Zhang Yanxi,You Deyong,Gao Xiangdong,Wang Congyi,Li Yangjin,Gao Perry P.

Funder

National Natural Science Foundation of China

The Science and Technology Planning Project of Guangzhou

Natural Science Foundation of Guangdong Province

Innovation Team Project, Department of Education of Guangdong Province

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Link

http://link.springer.com/content/pdf/10.1007/s10845-019-01477-w.pdf

Reference33 articles.

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2. Atabaki, M. M., Yazdian, N., Ma, J., & Kovacevic, R. (2016). High power laser welding of thick steel plates in a horizontal butt joint configuration. Optics & Laser Technology,83, 1–12.

3. Charte, D., Charte, F., García, S., Jesus, M. J., & Herrera, F. (2018). A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines. Information Fusion,44, 78–96.

4. Cho, J. H., & Na, S. J. (2007). Theoretical analysis of keyhole dynamics in polarized laser drilling. Journal of Physics D: Applied Physics,40, 7638–7647.

5. Dos Santos, E. M., Sabourin, R., & Maupin, P. (2009). Overfitting cautious selection of classifier ensembles with genetic algorithms. Information Fusion,10, 150–162.

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