Surface defect detection of aluminum alloy welds with 3D depth image and 2D gray image

Abstract

Towards the trend and demand of automatic welding inspection in industry, a composite vision system enabling simultaneous 3D-depth and 2D-gray imaging of the bead surface is constructed to detect typical surface defects of aluminum alloy weld beads. In this vision system, the structured laser light is responsible for obtaining 3D-depth image of the bead surface; meanwhile, the multi-angle illuminations are used to capture gray images. Then, four methods are proposed to extract the weld bead boundaries according to its different characters shown in the 3D depth images and 2D gray images. In the 3D depth image, the extraction algorithms of defects such as collapse, undercut, burning-through, excessive reinforcement, surface porosity, spatter, and poor forming are studied. In multi-angle gray images, the extraction algorithm of defects such as cracks and surface blackening is also proposed and studied.