Research on quality inspection for backside weld of tailored blank laser welding based on texture feature
In tailored blank laser welding, the quality of backside weld has a significant influence on mechanical properties of tailored blanks. The conventional method for the quality inspection of weld seams is based on the structured light deformation. However, the structured light deformation is not obvious in the backside weld which with dust disturbance, so the extraction of feature points is difficult only based on the structured light. A quality inspection system for backside weld of tailored blank laser welding was built and a new method based on combination of texture feature and structured light was proposed in this paper. In this method, the textural feature of the image was firstly analyzed, and then the image was segmented by the difference of the image texture feature between the weld region and the base metal region; finally, the zone of the weld were recognized based on voting strategy and the feature points were extracted. Experiments were conducted with this method, then the considered weld width was detected and other weld defects could also be detected. The experiment results showed that the proposed inspection system and method were effective for backside weld quality inspection of tailored blanks laser welding.
A high-throughput texture classification approach using a new descriptor
In this paper, we propose a simple construction approach (FR: features' value range) as a high performance texture descriptor. The FR works based on local textural information. We show the throughput of texture classification can be improved using the FR. In the classification process, the FR is considered as a pre-classifier and selects a few candidate categories for an input texture. Using the proposed approach, comparison time of the main classifier is reduced. To evaluate of the FR in different situations, some criteria have been proposed. To implement of the proposed approach, the texture descriptors such as local binary pattern (LBP), Haralick, and circular Gabor filter (CGF) are considered. The experimental results are done by implementation of the FR approach on the Scene-13, Outex and UIUC data sets. The results show the throughput of texture classifiers improve up to 14.85×.
Joint tracking and adaptive robotic welding using vision sensing of the weld joint geometry
An approach to the vision-guidance of welding robots and the in-process adjustment of welding conditions is presented. The implementation of a complete vision-guided adaptive robotic welding system is described. The vision-guided adaptive welding system described here has been used to track and weld a wide variety of test and production parts ranging in size from 1.6-mm (1/16-in.) sheet steel to 19.1-mm (3/4-in.) steel plate. Both conventional joint types, including square butt, lap, and V-groove, and special types, such as a multipass square butt submerged arc weld with pre-welded root passes or the axle joints were welded. Various welding procedures, such as GMA welding with a variety of shielding gases and submerged arc welding, have also been used.