Analysis of the current wave in the arc phase of metal transfer in the GMAW dynamic feeding process

Originally developed for application in dissimilar joints, the dynamic feeding technology applied to the GMAW process is gaining popularity with applications in wire arc additive manufacturing (WAAM). Given the growing importance of additive manufacturing (AM) in industrial production, it is essenti...

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Bibliographic Details
Published inInternational journal of advanced manufacturing technology Vol. 134; no. 7-8; pp. 3293 - 3310
Main Authors Silva, Régis Henrique Gonçalves e, Pereira, Alex Sandro, Galeazzi, Daniel, Marques, Cleber
Format Journal Article
LanguageEnglish
Published London Springer London 01.10.2024
Springer Nature B.V
Subjects
Additive manufacturing
Advanced manufacturing technologies
Artificial intelligence
CAE) and Design
Computer-Aided Engineering (CAD
Engineering
Gas metal arc welding
Geometry
Industrial and Production Engineering
Manufacturing
Mechanical Engineering
Media Management
Open source software
Original Article
Parameterization
Plant layout
Steel
Technology assessment
Waveforms
Welding parameters
Wire
Open-source
Dynamic feeding
CMT
Metal transfer
WAAM
MIG-AD
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Summary:Originally developed for application in dissimilar joints, the dynamic feeding technology applied to the GMAW process is gaining popularity with applications in wire arc additive manufacturing (WAAM). Given the growing importance of additive manufacturing (AM) in industrial production, it is essential to better understand the main tool of WAAM technology. Acknowledging the limitations imposed on current waveform parameterization in commercial welding sources, this work used a proprietary welding source, named MIG-AD, which allows full control of parameters through dedicated hardware and open-source software. This study focuses on the impact of arc phase parameters on metal transfer, namely arc pulse time, arc pulse current level, and arc base current level. The results show the impact of altering each parameter on droplet formation, wire retraction, detachment frequency, and metal bridge elongation, as well as the stability and quality of the weld bead. This research offers valuable insights for optimizing welding parameters, aiming for greater control and process customization, making it a viable alternative for advanced research in WAAM, cladding, computational simulation, and artificial intelligence applied to welding.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-024-14303-2