Penetration/keyhole status prediction and model visualization based on deep learning algorithm in plasma arc welding-Reference-Cited by-同舟云学术

Penetration/keyhole status prediction and model visualization based on deep learning algorithm in plasma arc welding

Published:2021-08-31 Issue:11-12 Volume:117 Page:3577-3597
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Jia Chuan-Bao^ORCID,Liu Xin-Feng,Zhang Guo-Kai,Zhang Yong,Yu Chang-Hai,Wu Chuan-Song

Funder

National Natural Science Foundation of China

major scientific and technological innovation project of shandong province

Aeronautical Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00170-021-07903-9.pdf

Reference32 articles.

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2. Zhang YM, Zhang SB (1999) Observation of the keyhole during plasma arc welding. Weld J 78:53s–58s

3. Wu CS, Jia CB, Chen MA (2010) A control system for keyhole plasma arc welding of stainless steel plates with medium thickness. Weld J 89:225s–231s

4. Martikainen JK, Moisio TJI (1993) Investigation of the effect of welding parameters on weld quality of plasma-arc keyhole welding of structural-steels. Weld J 72:S329–S340

5. Zhang YM, Liu YC (2007) Control of dynamic keyhole welding process. Automatica 43:876–884. https://doi.org/10.1016/j.automatica.2006.11.008

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