A study on development of a new algorithm for predicting the process variables in GMA welding processes

Gas Metal Arc (GMA) welding is extensively employed in the metal industries to weld a variety of ferrous and non-ferrous metals because of its potential for increasing the productivity and quality of welding which is controlled by the process parameters. The objective of this paper is to develop the algorithm that enables the determination of process variables from the optimized bead geometry for robotic GMA welding. It depends on the inversion of empirical equations derived from multiple regression analysis of the relationships between the process variables and the bead dimensions using the least square method. The method directly determines those variables which will give the desired set of bead geometry. This avoids the need to iterate with a succession of guesses employed Finite Element Method (FEM). These results suggest that process variable from experimental equation for robotic GMA welding may be employed to monitor and control the bead geometry in real time.