An application of genetic algorithm for edge detection of molten pool in fixed pipe welding

Abstract

This paper presents a study on an application of genetic algorithm (GA) for edge detection of molten pool in fixed pipe welding. As circumferential butt-welded pipes are frequently used in power stations, offshore structures, and process industries, it is important to investigate the characteristic of the welding process. In pipe welding using constant arc current and welding speed, the bead width becomes wider as the circumferential welding of small-diameter pipes progresses. In order to avoid the errors and to maintain the uniform weld bead over the entire circumference of the pipe, the welding conditions should be controlled as the welding proceeds. This research studies the intelligent welding process of aluminum alloy pipe 6063S-T5 in fixed position using the AC welding machine. The monitoring system used an omnidirectional camera to monitor backside image of molten pool. A method of optimization for image processing algorithm using GA was proposed and has been implemented into a process to recognize the edge of molten pool. The result of detection, which is back bead width, was delivered into a fuzzy inference system to control welding speed. The experimental results show the effectiveness of the control system that is confirmed by a sound weld of the experimental results.