No 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.

In this study, lap joint experiments were conducted using galvanized high-strength steel, SGAFH 590 FB 2.3 mmt, which was applied to automotive chassis components in the gas metal arc welding (GMAW) process. Zinc residues were confirmed using a semi-quantitative energy dispersive X-ray spectroscopy (EDS) analysis of the porosity in the weld. In addition, a tensile shear test was performed to evaluate the weldability. Furthermore, the effect of porosity defects, such as blowholes and pits generated in the weld, on the tensile shear strength was experimentally verified by comparing the porosity at the weld section of the tensile test specimen with that measured through radiographic testing.

Many lightweight materials, including aluminum alloy, magnesium alloy, and plastic, have been used for automotives. Aluminum alloy—the most commonly utilized lightweight metal—has poor resistance spot weldability owing to its inherent properties, which demand the development of welding solutions. Various welding techniques are utilized to improve the resistance spot weldability of aluminum alloy, including DeltaSpot welding. However, the technological development for welding dissimilar metals (aluminum alloy and steel) required for vehicle body assembly is still in its nascent stages. This study proposes DeltaSpot welding (a resistance spot welding process with spooling process tapes) using the alloy combination of 6000 series aluminum alloy (Al 6K32) and 440 MPa grade steel (SGARC 440). The welding characteristics of the main process parameters in DeltaSpot welding were analyzed and the weldability of the combination of the aluminum alloy, Al 6K32, and 440 MPa grade steel was evaluated. In addition, the characteristics of the intermetallic compound layer between the 440 MPa grade steel and Al 6K32 sheets were identified via scanning electron microscopy/energy dispersive X-ray spectroscopy (SEM-EDS).

Creating and consistently maintaining the weld shape during gas metal arc welding (GMAW) is vital for ensuring and maintaining the specified weld quality. However, the back-bead is often not uniformly generated owing to the change that occurs in the narrow gap between the base metals during butt joint GMAW, which substantially influences weldability. Automating the GMAW process requires the capability of real-time weld quality monitoring and diagnosis. In this study, we developed a convolutional neural network-based backbead prediction model. Specifically, scalogram feature image data were acquired by performing Morlet wavelet transform on the welding current measured in the short-circuit transform mode of the GMAW process. The acquired scalogram feature image data were then analyzed and used to develop labeled weld quality training data for the convolutional neural network model. The model predictions were compared with welding data acquired through additional experiments to validate the proposed prediction model. The prediction accuracy was approximately 93.5%, indicating that the findings of this study could serve as a foundation for the future development of automated welding systems. INDEX TERMS Gas metal arc welding, back-bead monitoring, automated weld quality control, supervised deep learning, time-frequency analysis