A study on prediction of bead height in robotic arc welding using a neural network

Abstract

This paper presents development of an intelligent algorithm to understand relationships between process parameters and bead height, and to predict process parameters on bead height through a neural network and multiple regression methods for robotic multi-pass welding process. Using a series of robotic arc welding, additional multi-pass butt welds were carried out in order to verify the performance of the neural network estimator and multiple regression methods as well as to select the most suitable model. The results show that not only the proposed models can predict the bead height with reasonable accuracy and guarantee the uniform weld quality, but also a neural network model could be better than the empirical models (linear and curvilinear equations).