An Octree-Based Two-Step Method of Surface Defects Detection for Remanufacture

Accurate and quick detection has a significant bearing on overall productivity of remanufacture. 3D scanning technologies have been widely applied in defects detection by comparing the damaged model with the nominal model. In this process, a huge amount of point cloud data is required to ensure detection accuracy whereas resulting in large storage space and long processing time of detection. This paper proposed an efficient two-step method based on octree to detect defects accurately and quickly for remanufacturing. In this method, the damaged point cloud and the nominal point cloud are first registered. Then a two-step detection approach is developed to extract the surface defects, coarse detection and detailed extraction, where the octree method is applied to create an effective topology of discrete points and perform the Boolean operation for defects extraction. In coarse detection, rough location and size information of the defects are acquired from the whole point cloud data. Based on coarse detected boundary box containing defects, the detailed extraction step is applied to extract corresponding defects shape accurately. The feasibility of proposed method was validated by using a case to detect defects of a damaged turbine blade and the detection results can be used to generate restoration tool path. The results show that the proposed method outperforms state-of-art defects detection methods, which can reduce time by 74.03% and reduce error by 36.86%, respectively.