Neural network modeling for weld shape process of P-GMAW

Weld shape control is a fundamental issue in automatic welding. In this paper, a double side visual system is established for pulsed gas metal arc welding (P-GMAW), and both topside and backside weld pool images can be captured and stored continuously in real time. By analyzing the weld shape regulation with the molten metal volume, some topside weld pool characterized parameters (WPCPs) are proposed for determining penetration in butt welding of thin mild steel. Moreover, some BP network models are established to predict backside weld pool width with welding parameters and WPCPs as inputs.