Robotic Welding Systems with Vision-Sensing and Self-learning Neuron Control of Arc Welding Dynamic Process

Abstract

This paper addresses the vision sensing and neuron control techniques for real-time sensing and control of weld pool dynamics during robotic arc welding. Current teaching playback welding robots are not provided with this real-time function for sensing and control of the welding process. In our research, using composite filtering technology, a computer vision sensing system was established and clear weld pool images were captured during robotic-pulsed Gas Tungsten Arc Welding (GTAW). A corresponding image processing algorithm has been developed to pick up characteristic parameters of the weld pool in real-time. Furthermore, an ANN model of the weld pool dynamic process of robotic-pulsed GTAW was developed. Based on neuron self-learning PSD controller design, the real-time control of weld pool dynamics during the pulsed GTAW process has been realized in robotic systems.