Pingjiang Wang

A novel seam measuring method of complex tight butt joint for laser welding

Seam measuring of complex tight butt joint without misalignment for laser welding is a challenging problem. The allowable seam gap width and misalignment in laser welding process are less than 0.1 mm for thin sheet around 2 mm thickness. Current measuring methods or sensors can not stably and accurately extract 3D seam position and seam gap width, due to narrow seam gap and too little misalignment of the butt joint. Furthermore, almost all sensors can not provide seam surface normal vector directly along with seam position and seam gap width. A novel seam measuring method, integrating the 2D machine vision and 3D structured light vision techniques, is proposed. By projecting a unique pattern of three structured light stripes onto the seam surface, 3D seam position, seam gap width and seam surface normal vector can be accurately acquired from a single image, using the 2D/3D vision information fusion approach. Experimental results demonstrate that the proposed seam measuring method is capable of extracting the integrated seam features of tight butt joint without misalignment stably and accurately.

Simple surface reconstruction algorithm with 3D points in parallel lines

Referring to 3D measurement methods like grating projection measurement, which make 3D output data arranged in parallel lines, the topic brings forward an algorithm to reconstruct surface that represented in triangle geometry model more efficiently, easily and automatically than those for 3D scattered points do. The algorithm select pairs of matched points from every 2 neighbor lines dynamically, each point from one line. The line made up of a pair of matched points is called skeleton line. Those skeleton lines can be expected as parallel as possible. Then the 4 end points in 2 neighbor Skeleton lines can construct 1 quadrangle, also 2 good-characteristic triangles as elements of triangle geometry model. Also the method is improved to avoid surface gap problems.

Image merging method based on characteristic objects' distributing statisic feature

An algorithm for merging images has been proposed based on statistic principle of region geometric shape in the paper. The Algorithm is of high precision and speed for solving images conjoint when there is rare points feature on those images. For enhancing the region geometric shape features we described an Image Difference Dynamic Binary Method firstly. And then the merging principle of images on which there is little points feature. Last the process and steps of merging image are described in all details.