Burn-through prediction and weld depth estimation by deep learning model monitoring the molten pool in gas metal arc welding with gap fluctuation

In a single bevel GMAW (gas metal arc welding) with gap fluctuation, a deep learning model was constructed using the monitoring image during the welding to predict the welding quality. We utilized Python and the library Keras and created a CNN (Convolutional neural network) model using the top surfa...

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Bibliographic Details
Published in:Journal of manufacturing processes 2021-01, Vol.61, p.590-600
Main Authors: Nomura, Kazufumi, Fukushima, Koki, Matsumura, Takumi, Asai, Satoru
Format: Article
Language:English
Subjects:
Camera
CNN
Deep learning
Gap
Penetration depth
Single bevel groove MAG welding
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Summary:In a single bevel GMAW (gas metal arc welding) with gap fluctuation, a deep learning model was constructed using the monitoring image during the welding to predict the welding quality. We utilized Python and the library Keras and created a CNN (Convolutional neural network) model using the top surface image including the molten pool as an input. The classification model was used to predict the burn-through, and the regression model was used to estimate the penetration depth. As a result, the excessive penetration and burn-through could be predicted in advance and more than 95 % of estimated results of penetration depth were less 1 mm error for stepped and tapered sample shapes.
ISSN:1526-6125
2212-4616
DOI:10.1016/j.jmapro.2020.10.019