Automatic burr detection on surfaces of revolution based on adaptive 3D scanning

Abstract

This paper describes how to automatically extract the presence and location of geometrical irregularities on a surface of revolution. To this end a partial 3D scan of the workpiece under consideration is acquired by structured light ranging. The application we focus on is the detection and removal of burrs on industrial workpieces. Cylindrical metallic objects cause a strong specular reflection in every direction. These highlights are compensated for in the projected patterns, hence 'adaptive 3D scanning'. The triangular mesh produced is then used to identify the axis and generatrix of the corresponding surface of revolution. The search space for finding this axis is four dimensional: a valid choice of parameters is two orientation angles (as in spherical coordinates) and the 2D intersection point with the plane spanned by two out of three axis of the local coordinate system. For finding the axis we test the circularity of the planar intersections of the mesh in different directions, using statistical estimation methods to deal with noise. Finally the 'ideal' generatrix derived from the scan data is compared to the real surface topology. The difference identifies the burr. The algorithm is demonstrated on a metal wheel that has burrs on both sides. Visual servoing of a robotic arm based on this detection is work in progress.