Real-time sensing and monitoring in robotic gas metal arc welding

Abstract

A real-time monitoring system is developed for detecting abnormal conditions in robotic gas metal arc welding. The butt-joint test pieces with simulated large gaps are used to intentionally introduce step disturbance of welding conditions. During the welding process, the welding voltage and current signals are sampled and processed on-line to extract the characteristic information reflecting the process quality. After the first statistical processing, it is found that seven statistical parameters (the mean, standard deviation, coefficient of variance and kurtosis of welding voltage; the mean, coefficient of variance and kurtosis of welding current) show variations during the step disturbance. Through the second statistical processing of the means of the welding voltage for subgroups of continuous measurement, the statistical control chart is obtained, and an SPC (statistical process control)-based on-line identifying method is developed. Ten robotic welding experiments are conducted to verify the real-time monitoring system. It is found that the correct identification rates for normal and abnormal welding conditions are 100% and 95%, respectively.