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Real-Time Detection of Weld Defects for Automated Welding Process Base on Deep Neural Network

1
School of Mechanical Convergence Engineering, Hanyang University, Seoul 04763, Korea
2
Monisys co., Ltd, 775, Gyeongin-ro, Seoul 07299, Korea
*
Author to whom correspondence should be addressed.
Metals 2020, 10(3), 389; https://doi.org/10.3390/met10030389
Received: 5 February 2020 / Revised: 12 March 2020 / Accepted: 16 March 2020 / Published: 18 March 2020
(This article belongs to the Special Issue Quality Assessment and Process Management of Welded Joints)
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. View Full-Text
Keywords: gas metal arc welding; porosity; weld quality; detection; deep neural network gas metal arc welding; porosity; weld quality; detection; deep neural network
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MDPI and ACS Style

Shin, S.; Jin, C.; Yu, J.; Rhee, S. Real-Time Detection of Weld Defects for Automated Welding Process Base on Deep Neural Network. Metals 2020, 10, 389.

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