Abstract
Inspecting the blade by optical method is a meaningful work in manufacturing industry. 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 feature extraction. The moving least square surface is applied to create a geometric deviation, which is used to identify sparse points or excessive deviation points, in order to subdivide and cluster the point cloud. Then, the information entropy in k-neighbourhood is defined to distinguish density difference of blade point cloud. The objective is to smooth point-sampling surface meanwhile preserving high curvature feature. Furthermore, the computation method of single/multi section parameters is presented. Finally, two cases are carried out to demonstrate the feasibility and effectiveness.
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Li, Wl., Zhou, Lp., Xiong, Yl. (2013). Aviation Blade Inspection Based on Optical Measurement. In: Lee, J., Lee, M.C., Liu, H., Ryu, JH. (eds) Intelligent Robotics and Applications. ICIRA 2013. Lecture Notes in Computer Science(), vol 8103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40849-6_56
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DOI: https://doi.org/10.1007/978-3-642-40849-6_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40848-9
Online ISBN: 978-3-642-40849-6
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