On detailed deviation zone evaluation of scanned surfaces for automatic detection of defected regions

Abstract

Online inspection and surface characterization including 3D defect detection of surfaces using 3D scanning and coordinate metrology data has crucial applications in today’s Industry 4.0. Although 3D vision-based metrology methods are superior to 2D in providing spatial information, its processing remains challenge. A novel automated Detailed Deviation Zone Evaluation method is proposed in this paper in which 3D unorganized PC is converted into 2D grayscale image such that the intensity variation of image is proportional to the surface topography. This developed image is addressed as “skin Image” of the scanned surface. The considered point clouds include only XYZ-coordinates. No prior knowledge of defects, and no training set is required. The methodology is fully implemented, and verified by inspecting point clouds of several workpieces with predefined defects. The experimental results show high efficiency of the developed methodology in defect detection and 3D-feature identification irrespective of shape and size.