Feature extraction and tracking of a weld joint for adaptive robotic welding

Abstract

Recent advances in automation and sensor technology have enabled the use of industrial robots for complex tasks that require intelligent decision making. Vision sensors have been the most successfully used sensor in many high value industrial applications. Over the recent years, weld seam tracking has been a topic of interest, as most of the existing robotic welding systems operate on basis of pre-programmed instructions. Such automated systems are incapable of adapting to unexpected variations in the seam trajectory or part fit-up. Applications such as tungsten inert gas (TIG) welding of aerospace components require high tolerances and needs intelligent decision making. Such decision making procedure has to be based on the weld groove geometry at any instance. In this study, a novel algorithm along with an automated system was developed for estimating the joint profile and path tracking of a three dimensional (3D) weld groove. A real-time position based closed-loop system was developed with a six axis industrial robot and a laser triangulation based sensor. The system was capable of finding the 3D weld joint pro¿le and position in real-time, and make intelligent decisions accordingly. Raw data from a vision sensor was processed through a novel algorithm to obtain X and Z co-ordinates at an accuracy of 8.3μm and 43μm respectively at an acquisition speed of 2.5 profiles per second. The algorithm was also capable of measuring the weld gaps with an accuracy of 28μm. Finally, the developed system was successfully used for three dimensional seam tracking, and demonstrates an accuracy of ±0.5mm at a tracking a speed of 2mm/s.

Laser Scanning and Parametrization of Weld Grooves with Reflective Surfaces

“…This can be advantageous if access to raw image data is not possible, when commercial line laser sensors are used. Some methods using triangulated points have been also previously reported (e.g., [ 13 ]), however the methods did not include strong data noise caused by the reflections of a laser.…”

“…A simple way to detect the corners is to identify points where the direction of the profile changes by using the gradient. This was done in [13], where the authors applied the technique to V-grooves (or U-grooves). A similar feature extraction technique for V-grooves using averaged slope between four consequent points was presented in [14], where the least-square method was additionally used to improve the location of corner points.…”

Prediction of Bead Geometry with Changing Welding Speed Using Artificial Neural Network

“…Extracting the bead feature values is still a difficult task. The traditional methods to extract the turning points are template matching and gradient calculation [ 21 , 22 ]. In this paper, a polynomial function is introduced to extract the turning points.…”

Intelligent Guidance Programming of Welding Robot for 3D Curved Welding Seam

“…In recent years, the extraction methods of 3D spatial welds have also been studied. Aiming at the characteristics of high adaptability and intelligent decision making in the welding application of aerospace parts tungsten inert gas (TIG), Manorathna [19] et al developed an algorithm for joint contour estimation and path tracking for the groove of 3D welding parts. It used 6-axis industrial robot and sensor laser 3-angle ranging principle to form a closed-loop system to get the contour and position of 3D welded joint in real time and make intelligent decision.…”