Artificial neural network optimisation of shielding gas flow rate in gas metal arc welding subjected to cross drafts when using alternating shielding gases

Stuart Campbell, F.H. Ley, Alexander Galloway, Norman McPherson

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This study implemented an iterative experimental approach in order to determine the shielding gas flow required to produce high quality welds in the gas metal arc welding (GMAW) process with alternating shielding gases when subjected to varying velocities of cross drafts. Thus determining the transitional zone where the weld quality deteriorates as a function of cross draft velocity.
An Artificial Neural Network (ANN) was developed using the experimental data that would predict the weld quality based primarily on shielding gas composition, alternating frequency and flowrate, and cross draft velocity, but also incorporated other important input parameters including voltage and current. A series of weld trials were conducted validate and test the robustness of the model generated. It was found that the alternating shielding gas process does not provide the same level of resistance to the adverse effects of cross drafts as a conventional argon/carbon dioxide mixture. The use of such a prediction tool is of benefit to industry in that it allows the adoption of a more efficient shielding gas flow rate, whilst removing the uncertainty of the resultant weld quality.
LanguageEnglish
Pages122-129
Number of pages7
JournalProceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
Volume229
Issue number1
Early online date20 Mar 2014
DOIs
Publication statusPublished - Jan 2015

Fingerprint

Gas metal arc welding
Shielding
Flow of gases
Welds
Gases
Flow rate
Neural networks
Argon
Carbon Dioxide
Carbon dioxide
Electric potential
Chemical analysis
Industry

Keywords

  • GMAW
  • shielding gas consumption
  • alternating shielding gases
  • improved efficiency
  • radiography
  • artificial neural networks
  • quality prediction

Cite this

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title = "Artificial neural network optimisation of shielding gas flow rate in gas metal arc welding subjected to cross drafts when using alternating shielding gases",
abstract = "This study implemented an iterative experimental approach in order to determine the shielding gas flow required to produce high quality welds in the gas metal arc welding (GMAW) process with alternating shielding gases when subjected to varying velocities of cross drafts. Thus determining the transitional zone where the weld quality deteriorates as a function of cross draft velocity.An Artificial Neural Network (ANN) was developed using the experimental data that would predict the weld quality based primarily on shielding gas composition, alternating frequency and flowrate, and cross draft velocity, but also incorporated other important input parameters including voltage and current. A series of weld trials were conducted validate and test the robustness of the model generated. It was found that the alternating shielding gas process does not provide the same level of resistance to the adverse effects of cross drafts as a conventional argon/carbon dioxide mixture. The use of such a prediction tool is of benefit to industry in that it allows the adoption of a more efficient shielding gas flow rate, whilst removing the uncertainty of the resultant weld quality.",
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