主催: The Institute of Systems, Control and Information Engineers
会議名: 2022国際フレキシブル・オートメーション・シンポジウム
開催地: Hiyoshi Campus, Keio University, Yokohama, Japan
開催日: 2022/07/03 - 2022/07/07
p. 144-149
Surface defect detection is a essential feedback for quality control in manufacturing processes. Existing methods used for defect detection are mainly based on image-based approaches that is subject to the disturbances in light exposure or surface reflectivity, especially for the textured surfaces. In this paper, a new defect detection method is presented to detect surface defects using laser scanning point cloud data, which contain the full profiles of the surface in 3D space. The developed method converted the 3D point cloud data to surface normal vectors to normalize the scale of the geometry, and then extracted defect-induced geometric features from the surface normal via Gabor filter. The feasibility of the method has been tested in case studies for detecting different types of defects on the textured surfaces through simulations. The results show the effectiveness and robustness of the proposed method, and demonstrated improved performance compared to the conventional method based on Region Growing Segmentation algorithm.
