Effect of Process Parameters on Robotic GMAW Bead Area Estimation

Abstract

In this paper, a methodology for understanding the relationships between process parameters and the bead area geometry are presented. The objective of the first part of this study is to find the optimal bead area geometry in the Gas Metal Arc Welding (GMAW) process. A radial basis function (RBF) neural network is used for the prediction of the cross sectional area of the welding bead using a three level factorial design of experiments for the training of the neural network.