Abstract
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.
Highlights
Gas metal arc welding (GMAW) is a welding method in which metal is melted by generating an arc between a consumable electrode and a base metal
We developed a system for the monitoring and prediction of back-bead generation by acquiring a scalogram feature image using the Morlet wavelet transform (MWT) of the welding current during the short-circuit transfer mode of GMAW and applying the image data to the designed Convolution neural networks (CNNs) model
In the GMAW process, as the wire feed rate (WFR) increases, the rate of deposition increases and the arc heat applied to the base material increases thereby increasing the penetration depth
Summary
Gas metal arc welding (GMAW) is a welding method in which metal is melted by generating an arc between a consumable electrode and a base metal. Owing to its high metal deposition rate, GMAW is suitable for automatic welding and is widely applied in various industries, such as shipbuilding and automobile manufacturing [1]. In butt joint GMAW, the backbead is typically not uniformly generated on the back side of the welds because of differences in penetration depth or gaps between the workpieces of each welded section. Lack of backbead uniformity adversely affects the mechanical properties and weldability of the welded structure [2]. Generating a back-bead improves productivity by reducing the work required to repair the welds. The real-time prediction of back-bead generation is crucial to monitoring and minimizing changes in the back-bead shape
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