Prediction Model for Back-Bead Monitoring During Gas Metal Arc Welding Using Supervised Deep Learning
Creating and consistently maintaining the weld shape during gas metal arc welding (GMAW) is vital for ensuring and maintaining the specified weld quality. However, the back-bead is often not uniformly generated owing to the change that occurs in the narrow gap between the base metals during butt joi...
Main Authors: | Chengnan Jin, Seungmin Shin, Jiyoung Yu, Sehun Rhee |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9272996/ |
Similar Items
-
Real-Time Detection of Weld Defects for Automated Welding Process Base on Deep Neural Network
by: Seungmin Shin, et al.
Published: (2020-03-01) -
Real-Time Weld Gap Monitoring and Quality Control Algorithm during Weaving Flux-Cored Arc Welding Using Deep Learning
by: Chengnan Jin, et al.
Published: (2021-07-01) -
Influence of Inclination of Welding Torch on Weld Bead during Pulsed-GMAW Process
by: Ping Yao, et al.
Published: (2020-06-01) -
Determining the Degree of Admixing Rate of the Base Material and the Melting Efficiency in Single-Bead Surface Welds Using Different Methods, Including New Approaches
by: Matija Zorc, et al.
Published: (2019-05-01) -
Influence of the Longitudinal Magnetic Field on the Formation of the Bead in Narrow Gap Gas Tungsten Arc Welding
by: Xiaoxia Jian, et al.
Published: (2020-10-01)