Abstract
Tandem arc welding is a guarantor for high efficiency and cost saving since the quantity of wire which is deposited in the welding is approximated 30% greater that in conventional welding. The welding process is now being successfully applied in many industries. However, in the case of tandem arc welding, good quality and high productivity should depend on the welding parameters. Therefore, an intelligent algorithms for the automatic tandem arc welding process has been necessarily required. In this study, a predictive model based on the neural network by using the data acquired during tandem gas metal arc (GMA) welding process has been developed. To verify the reliability of the developed predictive model, a mutual comparison with the surface of the top-bead width obtained from actual experiments has been analyzed.
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