A study on seam tracking in robotic GMA welding process

Abstract

Abstract In recent years, an image processing and sensor technology for welding automation, intelligence and information with rapid development of welding automation have become an inevitable trend. Owing to the features of mass information, high reliability and applicability in weld inspection for robotic arc welding, the sensor-based weld seam detection has been widely applied and gradually become a hot topic. Furthermore, the robotic arc welding can use its own machine vision system to obtain workpiece positioning information, weld space position, bead geometry and weld seam tracking. The most important task in the machine vision system is how to process the collected visual information to accurately locate the center of the weld and achieve weld tracking. During the welding process, the images taken are often unclear and the features are not obvious due to the interference of arc, splash and smoke. Therefore, it is necessary to carefully design the image acquisition process and the image processing algorithm to complete the weld inspection. In this study, the robotic GMA(Gas Metal Arc) welding has been applied to bead-on-plate welding with vertical position. Thermal images of two different angles (0°, 30°) based on infrared camera through experiments might be obtained for developing an optimal image processing algorithm which was applied in seam tracking. In image processing procedure, the first step is image pre-processing including noise removal, contrast enhancement and binarization. The obtained image adopts multi-scale edge detection method, which can extract complex image edges well, then segment the image, accurately locate the weld part, perform image size measurement and centerline extraction, and finally convert the image information of the weld into the world calibration. The calculated data is then fed to a controller which controls the welding electrode movement. Use of machine vision system has eliminated the requirement to pre-feed the workpiece data for the robot to detect the dimension and position of the workpieces. The developed algorithm can detect weld image effectively, and extract the edge and the center line of the weld successfully