Dynamic estimation of joint penetration by deep learning from weld pool image-Reference-Cited by-同舟云学术

Dynamic estimation of joint penetration by deep learning from weld pool image

Published:2021-03-21 Issue:4 Volume:26 Page:279-285
ISSN:1362-1718
Container-title:Science and Technology of Welding and Joining
language:en
Short-container-title:Science and Technology of Welding and Joining

Author:

Cheng Yongchao¹²,Chen Shujun¹,Xiao Jun¹^ORCID,Zhang YuMing²

Affiliation:

1. Welding Research Institute and Engineering Research Center of Advanced Manufacturing Technology for Automotive Components-Ministry of Education, Beijing University of Technology, Beijing, People’s Republic of China

2. Institute for Sustainable Manufacturing, University of Kentucky, Lexington, KY, USA

Funder

Beijing Municipal Natural Science Foundation

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Condensed Matter Physics,General Materials Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/13621718.2021.1896141

Reference27 articles.

1. Increasing deposition height stability in robotic GTA additive manufacturing based on arc voltage sensing and control

2. Arc voltage sensor for monitoring of penetration in TIG welds

3. 3D Reconstruction of Weld Pool Surface in Pulsed GMAW by Passive Biprism Stereo Vision

4. Deposition height detection and feature point extraction in robotic GTA-based additive manufacturing using passive vision sensing

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