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.

Nondestructive test (NDT) technology is required in the gas metal arc (GMA) welding process to secure weld robustness and to monitor the welding quality in real-time. In this study, a laser vision sensor (LVS) is designed and fabricated, and an image processing algorithm is developed and implemented to extract precise laser lines on tested welds. A camera calibration method based on a gyro sensor is used to cope with the complex motion of the welding robot. Data are obtained based on GMA welding experiments at various welding conditions for the estimation of quality prediction models. Deep neural network (DNN) models are developed based on external bead shapes and welding conditions to predict the internal bead shapes and the tensile strengths of welded joints.

The battery performance of electric vehicles depends on the density and capacity of the battery; thus, the battery cells must be assembled in as many layers as possible. Electric vehicle batteries are typically composed of several cells which form modules connected by busbars, with dozens of modules manufactured as battery packs. The ultrasonic metal welding (UMW) technology is applied to such multilayered foil welding. This study analyzed UMW to ensure the weldability of multilayered Cu foils and a Ni-plated Cu strip in lithium-ion battery cells through various approaches. In UMW, the effect of the alignment on weld production and quality were examined through the energy and mechanical performance of the weld by conducting comparative experiments on the alignment of the horn and anvil. Additionally, the effects of UMW process parameters, such as the welding pressure, amplitude, and welding time, were statistically analyzed. The weldability evaluation and characteristic analysis were performed based on these variables. Furthermore, the cross-sectional shapes and microstructure behavior of the Ni layers were analyzed based on the weld quality.

An efficient nondestructive testing method of resistance spot weld quality is essential in evaluating the weld quality of all welded joints in the automotive components of a car body production line. This study proposes a quality prediction algorithm for resistance spot welding that can predict the geometrical and physical properties of a spot-welded joint and evaluate weld quality based on quality acceptance criteria. To this end, four statistical models that predict the main geometrical and physical properties of a spot-welded joint, including tensile shear strength, indentation depth, expulsion occurrence, and failure mode, were estimated based on material information, dynamic resistance, and electrode displacement signals. The significance of the estimated models was then verified through an analysis of variance. The prediction accuracies of the models were 94.3%, 93.4%, 97.5%, and 85.0% for the tensile shear strength, indentation depth, expulsion occurrence, and failure modes, respectively. A weld quality evaluation methodology that can predict the properties of a spot-welded joint and evaluate the overall quality requirements based on authorized welding standards was proposed using the four statistical models.

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 back-bead 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.