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.
Highlights
Automobile manufacturers have increased the application of galvannealed hot-rolled high-strength steel sheets to improve crash stability and corrosion resistance
Ultrasonic inspection and radiographic inspection are applied to production lines as non-destructive testing methods, their application is limited because they are difficult to apply in real-time and their cost is too high to apply all the automotive parts
The relationship between the welding signals and porosity in arcinwelding is difficult to express the welding signals and porosity arc welding is difficult to express in a sample mathematical formula. To deal with this complex relationship, this study proposes an an in a sample mathematical formula. To deal with this complex relationship, this study proposes algorithm for predicting porosity defects in welds, using an deep neural network (DNN), that are currently algorithm for predicting porosity defects in welds, using an artificial neural network (ANN) and a DNN, that are currently widely usedused in machine learning
Summary
Automobile manufacturers have increased the application of galvannealed hot-rolled high-strength steel sheets to improve crash stability and corrosion resistance. The application rate of highly corrosion-resistant galvannealed steel sheets is increasing to prevent the corrosion Porosity defects, such as blowholes or pits in welds are a serious problem, caused by zinc vapor formed due to heat energy during the welding of zinc-coated steel. This zinc vapor remains trapped in the weld, resulting in the formation of porosity and weld defects [1,2,3,4]. Ultrasonic inspection and radiographic inspection are applied to production lines as non-destructive testing methods, their application is limited because they are difficult to apply in real-time and their cost is too high to apply all the automotive parts
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