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Gas metal arc welding

About: Gas metal arc welding is a research topic. Over the lifetime, 11706 publications have been published within this topic receiving 109555 citations. The topic is also known as: metal active gas welding & GMAW.


Papers
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Dissertation
01 Jan 1989

34 citations

Patent
15 Mar 2005
TL;DR: Welders and methods for short-circuit arc welding a workpiece using a modified series arc welding configuration with two electrodes via a sequence of welding cycles, in which each cycle includes an arc condition a shortcircuit condition, wherein one or both electrode currents are selectively reversed during a reverse boost portion of the welding cycle to transfer molten metal from the second electrode to the first electrode prior to a short circuit condition as discussed by the authors.
Abstract: Welders and methods are presented for short-circuit arc welding a workpiece using a modified series arc welding configuration with two electrodes via a sequence of welding cycles, in which each cycle includes an arc condition a short-circuit condition, wherein one or both electrode currents are selectively reversed during a reverse boost portion of the welding cycle to transfer molten metal from the second electrode to the first electrode prior to a short-circuit condition of a subsequent welding cycle.

34 citations

Journal ArticleDOI
TL;DR: In this paper, a self-consistent three-dimensional gas metal arc welding (GMAW) modeling tool is used to analyze energy flow in an aluminum GMAW process.

34 citations

Journal ArticleDOI
TL;DR: In this paper, a two-dimensional axisymmetric numerical model from McKelliget et al. was adopted to describe the heat transfer and fluid flow in the gas tungsten arc (GTA) to predict the basic energy source properties of nitrogen GTA.

34 citations

Journal ArticleDOI
18 Mar 2020
TL;DR: In this article, a deep neural network (DNN)-based non-destructive testing method that can detect and predict porosity defects in real-time, based on welding voltage signal, without requiring additional device in gas metal arc welding (GMAW) process.
Abstract: In the process of welding zinc-coated steel, zinc vapor causes serious porosity defects. The porosity defect is an important indicator of the quality of welds and degrades the durability and productivity of the weld. Therefore, this study proposes a deep neural network (DNN)-based non-destructive testing method that can detect and predict porosity defects in real-time, based on welding voltage signal, without requiring additional device in gas metal arc welding (GMAW) process. To this end, a galvannealed hot-rolled high-strength steel sheet applied to automotive parts was used to measure process signals in real-time. Then, feature variables were extracted through preprocessing, and correlation between the feature variables and weld porosity was analyzed. The proposed DNN based framework outperformed the artificial neural network (ANN) model by 15% or more. Finally, an experiment was conducted by using the developed porosity detection and prediction system to evaluate its field application.

34 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023195
2022351
2021292
2020385
2019330
2018346