Comparison of multiple regression and back-propagation neural network approaches in modelling top bead height of multipass gas metal arc welds

Abstract

With the advanced developments and automation of the welding process, the use of process optimisation techniques has increased. The objective of the present paper is to describe process optimisation techniques for the gas metal arc (GMA) welding process, based on experimental results generated by the process. Back-propagation (BP) neural network and multiple regression methods are employed to study relationships between process parameters and top bead height for robotic multipass welding process, and to select a suitable model that provides the weld final configuration and properties as output and employs the process parameters as input. The process parameters, namely pass number, arc current, welding voltage and welding speed are optimised to produce the required top bead height. These techniques have achieved good agreement with the experimental data and yielded satisfactory results. Also, the BP neural network that was developed was compared to the empirical equations for predicting top bead height through additional experiments, and it was evident that the BP neural network was considerably more accurate than multiple regression techniques.