Robust inspection technique for detection of flatness defects of oil pans

Abstract

This paper discusses two different methods for the detection of flatness defects present on the mounting surfaces of oil pans using laser-scanned point clouds. The first method involves registration, which is a widely used method in the field of 3D data inspection: scanned point clouds are registered with CAD data and the iterative closest point (ICP) algorithm is used for further comparison. The second method is our proposed method, a simple yet effective method for measuring the flatness of an oil pan mounting surface. The process is based on the construction of a reference plane on the scanned surface. The oil pan mounting surface is scanned by a 3D laser scanner, obtaining point cloud data that is then further processed to reduce noise. Using this processed data, a reference plane parallel to the direction of the mounting surface is defined at the mean position of the mounting surface. The direction of the reference plane is determined by the normal vector of the mounting surface. Construction of the reference plane is carried out by the singular value decomposition (SVD) technique. The deviation of the surface from the reference plane is measured by calculating the error distance between the points of the surface to the reference plane using the least-squares method.