Precise seam tracking in robotic welding by an improved image processing approach

The intelligent welding robots have been widely developed and applied into the industrial production lines to improve the manufacturing efficiency. The main and important factor to improve the robotic welding performance, quality, and intelligence is the seam recognition accuracy. Higher industrial production and demands of higher precision in gas metal arc welding (GMAW) process have increased robot use. Vision feedback in robotic welding provides flexibility, productivity, and precision and promotes vaster range of applicability. In this case, machine vision techniques that are widely used to identify and locate the welding seam allow planning an industrial robot path. Previous investigations have proposed various approaches to address the precise tracking of welding seam curve. In majority of cases, the simultaneous uses of laser-based and optical lighting approaches to determine the location of the seam were suggested. However, complex seams remain a big challenge in robotic welding, and no definite way has been proposed to produce a precise robot path. This paper aims to develop a comprehensive robot trajectory extraction method for complex welding seam through processing of noisy images and laser data. Since the structured light on laser basis and stereo vision propose more possibility in specifying the place of the seam, this study considers a two-source processing for GMAW. A new improved edge detection algorithm is also proposed to enclose the weld zone and determine the seam and the pool parameters. To evaluate the performance of this method, experimenting different types of work pieces under random welding conditions are carried out, and the results are verified. A considerable improvement in speed and detection of error between the measured and real seam coordinates is reported.