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

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.

Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed

“…Deep learning can obtain high-dimensional image features from the input data, fit the features and finally utilize the learning method of weights to increase the accuracy of classification prediction. However, while analyzing welds of industrial steel plates, deep learning cannot learn due to the lack of complete datasets; in addition, current research primarily focuses on improving the weld recognition ability; however, a complete automatic detection system has never been built, increasing the difficulty in actual industrial applications [45]. Image processing is the basis for effective weld detection.…”

Weld Defect Detection and Image Defect Recognition Using Deep Learning Technology

“…Deep learning can obtain high-dimensional image features from the input data, fit the features, and finally, utilize the learning method of weights to increase the accuracy of classification prediction. However, while analyzing welds of industrial steel plates, deep learning cannot learn due to the lack of complete datasets; in addition, current research mostly focuses on improving the weld recognition ability; however, a complete automatic detection system is never built, increasing the difficulty in actual industrial applications [25].…”

Real-Time Weld Gap Monitoring and Quality Control Algorithm during Weaving Flux-Cored Arc Welding Using Deep Learning

“…Recently, research on welding condition monitoring using artificial intelligence and camera vision devices has gained increasing attention because of the increasing demand for manufacturing intelligence, cost reduction, efficiency, and quality. This research can be classified into three areas of application: weld defect prediction [10][11][12], weld bead shape prediction [13,14], and weld seam tracking [15][16][17]. For instance, Zhang et al [10] developed a convolutional neural network (CNN) algorithm based on a multi-sensor system, including an auxiliary illumination (AI) visual sensor system, UVV band visual sensor system, spectrometer, and two photodiodes to detect three different welding defects during highpower disk laser welding.…”

“…Yu et al [11] proposed a deep neural network (DNN)-based quality assessment method based on a spectrometer in the laser beam welding (LBW) process. Shin et al [12] proposed a DNN-based nondestructive testing method for the detection and prediction of porosity defects in real time based on welding voltage signals. Nagesh and Datta [13] used back-propagation neural networks to associate welding process variables with the features of bead geometry and penetration.…”