Weld shape and size generally determine the quality of gas metal arc welding. Auto parts manufacturers prescribe the size and shape of the weld because they can indicate the mechanical properties of the weld. It is impossible to evaluate the quality of all welds through destruction inspection. Therefore, research on welding quality inspection using laser vision sensors as a non-destructive inspection method is underway. Although the external profile of the weld can be measured using a laser vision sensor, studies to predict the weld strength are insufficient. In this study, an artificial neural network (ANN) model was developed to predict the welding strength of the lap-fillet weld of an aluminum alloy. Input date for weld size was obtained in two ways. In the first method, a bead profile was acquired using a laser vision sensor, whereas the size of the weld was obtained through the acquired bead profile. In the second method, the size of the weld was obtained directly from cross-section analysis. The output data on the strength of the weld was obtained through a tensile shear test. Two models for predicting the tensile shear strength based on ANN were developed. By predicting the tensile strength of both models, the average error rate was within 10%, but the prediction accuracy using the laser vision sensor was better than that of the cross-sectional method.
When the gas metal arc welding (GMAW) process is applied in the shipbuilding and heavy equipment industries, it is important to increase the amount of welding on the weld without defects. Application of the tandem GMAW process can reduce the man-hours increased owing to multipass and increase productivity. In shipbuilding and heavy equipment industry sites, the weldments are long and large; thus, a carriage-type bed is generally used. Accordingly, the overall lengths of the welding cables in the welding system are increased; thus, arc voltage drop occurs owing to the load voltages generated in the welding cables, and a robust bead cannot be obtained owing to spatters caused by short circuits. In this study, the voltage drop caused by the increase in cable lengths was compensated using a developed welding machine. By compensating for the voltage drop, it was possible to obtain a good quality bead by reducing the occurrence of spatter caused by short circuit. As a result of performing one pass welding using the asynchronous tandem GMAW process and the developed welding machine, it was possible to secure sufficient amount of welding required in the field and to obtain a sound bead appearance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.