Machine vision-based automatic focusing method for robot laser welding system

Abstract

Background Defocus distance is a critical parameter in laser welding, especially when encountering changes in the contour of the welding surface. This paper proposed an automated focusing method to address the challenging issue of accurately adjusting the defocus distance. Methods The proposed method involves several steps. Firstly, a clarity evaluation function based on the Kirsch operator is employed to calculate real-time image clarity of the welding surface captured by the machine vision system. Next, an improved Canny edge detection algorithm is applied to identify the edge contours of the welding surface, from which their central points are extracted. Finally, automatic focusing is achieved by employing a variable step-size hill-climbing algorithm to search for the focal plane. Results To verify the applicability of the automatic focusing method proposed for welding the solder ring, a robot laser welding system was designed and constructed. Experimental results show that the positioning error of the robot after automatic focusing is within ±0.4 mm. The average time required for a single automatic focusing process is 16.27 s. These results demonstrated the successful accomplishment of automatic adjustment and control of the focal length. Conclusions The machine vision-based automatic focusing method proposed in this paper enhances the consistency of the robot’s position after automatic focusing in robot laser welding systems. It elevates the level of automation in the welding process and provides an efficient solution for accurately adjusting the welding focal distance during the laser welding process.