Prediction of weld bead geometry and penetration in shielded metal-arc welding using artificial neural networks

Abstract

Bead geometry (bead height and width) and penetration (depth and area) are important physical characteristics of a weldment. Several welding parameters seem to affect the bead geometry and penetration. It was observed that high arc-travel rate or low arc-power normally produced poor fusion. Higher electrode feed rate produced higher bead width making the bead flatter. Current, voltage and arc-travel rate influence the depth of penetration. The other factors that influence the penetration are heat conductivity, arc-length and arc-force. Longer arc-length produces shallower penetration. Too small arc-length may also give rise to poor penetration, if the arc-power is very low. Use of artificial neural networks to model the shielded metal-arc welding process is explored in this paper. Back-propagation neural networks are used to associate the welding process variables with the features of the bead geometry and penetration. These networks have achieved good agreement with the training data and have yielded satisfactory generalisation. A neural network could be effectively implemented for estimating the weld bead and penetration geometric parameters. The results of these experiments show a small error percentage difference between the estimated and experimental values.