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.

There is a growing need to perform automated visual surface inspection in various manufacturing processes due to increased emphasis on quality control. A number of high-resolution three-dimensional metrology products are commercially available, but they are all very limited in their fields of view. The small field of view of the scanners makes inspection of relatively large parts a time-consuming operation, which has significant negative impacts on throughput. This article presents a two-stage inspection process in which a machine vision system, based on the photometric stereo principle, is used to detect potentially defective regions on parts over a much wider field of view than the one covered by the commercial products. The suspicious regions are then inspected using a high-resolution commercial three-dimensional surface measurement system, ignoring areas that are perceived to be defect free. Experimental tests on planar steel samples, having known surface defects, show that this approach is effective and it reduces the overall inspection time significantly.