Accurate Registration of Point Clouds of Damaged Aeroengine Blades

Abstract

Abstract This paper presents a novel method for aligning the scanned point clouds of damaged blades with their nominal computer-aided design (CAD) model. To inspect a damaged blade, the blade surface is scanned and the scan data in the form of a point cloud is compared with the nominal CAD model of the blade. To be able to compare the scanned point cloud and the CAD model, they must be brought to a common coordinate system via a registration algorithm. The geometric nonconformity between the scanned damaged blade and its nominal model stemmed from the damaged regions can affect the registration (alignment) outcome. The alignment errors then cause wrong inspection results. To prevent this from happening, the data points from the damaged regions have to be removed from the alignment calculations. The proposed registration method in this work can accurately and automatically eliminate the unreliable scanned data points of the damaged regions from the registration process. The main feature is a correspondence search technique based on the geometric properties of the local neighborhood of points. By combining the average curvature Hausdorff distance and average Euclidean Hausdorff distance, a metric is defined to locally measure the dissimilarities between the scan data and the nominal model and progressively remove the identified unreliable data points of the damaged regions with each iteration of the fine-tuned alignment algorithm. Implementation results have demonstrated that the proposed method is accurate and robust to noise with superior performance in comparison with the existing methods.