Study on identifying GMAW process deviations by means of optical and electrical process data using ANN
2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), pp. 1596–1601
Abstract
In this study, different process deviations of gas metal arc welding were classified using transient optical and electrical process data by means of artificial neural networks. The examined process deviations varied from massive process disturbances due to insufficient shielding gas coverage to a slight shift of the process operating point due to a mispositioned welding torch. It was found that good classification results can be achieved even with simple, statistical process features. In addition, the classification results derived from the photodiodes used, achieved better results compared to welding current and voltage alone. However, the best results were reached when optical and electrical process data were used together.
Authors 6
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Affiliation as printed
Welding and Joining Institute (ISF), RWTH Aachen University Pontstr. 49, Aachen, Germany
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Affiliation as printed
Welding and Joining Institute (ISF), RWTH Aachen University Pontstr. 49, Aachen, Germany
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Affiliation as printed
Welding and Joining Institute (ISF), RWTH Aachen University Pontstr. 49, Aachen, Germany
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Leibniz Institute for Plasma Science and Technology
Affiliation as printed
Leibniz Institute for Plasma Science and Technology Felix-Hausdorff-Str. 2, Greifswald, Germany
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Affiliation as printed
Welding and Joining Institute (ISF), RWTH Aachen University Pontstr. 49, Aachen, Germany
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Leibniz Institute for Plasma Science and Technology
Affiliation as printed
Leibniz Institute for Plasma Science and Technology Felix-Hausdorff-Str. 2, Greifswald, Germany
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