Applying Statistical Models to Optimize the Weld Bead Geometry in the Vertical Oscillation Arc Narrow Gap All-Position GMAW

Vertical oscillation arc welding for narrow gap gas metal arc welding (NG-GMAW) has a relatively simple structure, and it is widely used in all-position pipeline field welding. However, it has some shortcomings, such as incomplete fusion defects on the sidewall and interlayer. Aiming at resolving th...

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Bibliographic Details
Published in:Applied sciences 2023-06, Vol.13 (11), p.6801
Main Authors: Liu, Hongsheng, Xue, Ruilei, Zhou, Jianping, Bao, Yang, Xu, Yan
Format: Article
Language:English
Subjects:
all-position welding
Analysis
Experiments
Geometry
incomplete fusion defects
Interlayers
Lasers
Mathematical models
Metallography
Methods
Morphology
Neural networks
NG-GMAW
Response surface methodology
RSM
Statistical analysis
statistical model
Statistical models
Variance analysis
Vertical orientation
vertical oscillation arc
Vertical oscillations
Welding
Welding parameters
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Summary:Vertical oscillation arc welding for narrow gap gas metal arc welding (NG-GMAW) has a relatively simple structure, and it is widely used in all-position pipeline field welding. However, it has some shortcomings, such as incomplete fusion defects on the sidewall and interlayer. Aiming at resolving these shortcomings, a mathematical model is proposed to obtain appropriate welding parameters in different positions. In this model, the response surface methodology (RSM) based on the central composite design (CCD) was developed to study the interactions between welding parameters and the weld bead geometry. Then the analysis of variance (ANOVA) was used to evaluate the accuracy and significance of the proposed model. Finally, experiments were carried out in flat, vertical, and overhead positions to obtain the optimal parameters. The macroscopic metallography of the transversal section of the weld bead under the optimizing welding parameters showed that the weld beads were free of defects in the sidewall and interlayers.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13116801