A case study of blade inspection based on optical scanning method

Abstract

Blades play an important role in aviation engine, gas turbine and jet engine. Inspecting the blade by optical method is a meaningful work in manufacturing industry. During optical inspecting process, one common problem encountered is that the scanned point cloud is large scale and noisy. In this paper, we present a systematic introduction of simplification, smoothing and parameter extraction with respect to point-sampled blades. First, the moving least square surface is applied to create a geometric deviation, which is used to subdivide and cluster the point cloud. Then, the information entropy in k-neighbourhood is defined to smooth point-sampled surface, meanwhile preserving high curvature feature. Furthermore, the computation method of single/multi section parameters is presented, and test experiments are performed in iCloud3D Blade V1.0. Experimental results demonstrate the feasibility and effectiveness of the proposed method.