Abstract
In submerged arc welding (SAW), selecting appropriate values for process variables is essential in order to control
bead size and quality. Also, condition must be selected that will ensure a predictable weld bead, which is critical for
obtaining high quality. In this investigation, mathematical models (based on multi-regression method) have been
developed and side by side prediction through artificial neural networks has been made. A comparative study has also
been done. Based on multi-regressions and a neural network, the mathematical models have been derived from
extensive experiments with different welding parameters and complex geometrical features. Graphic displays also
represent the resulting solution on bead geometry that can be employed to probe the model further. The developed
systems enable to put in the desired weld dimensions and select the optimal welding parameters. The experimental
results have proved the capability of the developed system to select the welding parameters in SAW process
according to complex external and internal geometry features of the substrate.