An aerofoil blade scattered point cloud feature preserving simplification method and system

CN122115267APending Publication Date: 2026-05-29XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
View PDF 0 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
XI'AN UNIVERSITY OF ARCHITECTURE AND TECHNOLOGY
Filing Date
2026-04-17
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

Existing technologies face challenges in processing point cloud data of aircraft blades, including massive data volume, interference from metal reflection noise, and limitations of traditional sampling methods. These limitations lead to the easy loss, breakage, or significant geometric errors of weak edge features.

Method used

We employ large-scale neighborhood geometric feature extraction, intelligent feature region classification, and hierarchical adaptive sampling methods. We remove noise through hybrid filtering, establish spatial indexes using KD trees, calculate local covariance matrices and perform eigenvalue decomposition, set curvature thresholds for point cloud classification, and use full retention of feature point sets and voxel mesh filtering of non-feature point sets to achieve efficient compression and feature preservation of point clouds.

Benefits of technology

While preserving the key features of the blade, it effectively compresses the amount of point cloud data, smooths the reflective noise on the metal surface, and ensures the integrity and continuity of edge features, making it suitable for reverse modeling and quality inspection of precision aerospace components.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122115267A_ABST
    Figure CN122115267A_ABST
Patent Text Reader

Abstract

The application discloses an aviation blade scattered point cloud feature preserving simplification method and system, and belongs to the technical field of three-dimensional point cloud processing. The method comprises the following steps: reading an original point cloud and performing statistical and radius hybrid filtering denoising; a spatial index is established through a KD tree, a covariance matrix is constructed by adopting a >=100 large-scale neighborhood, surface curvature is obtained through eigenvalue decomposition; a 0.003-0.005 curvature threshold is set to divide feature and non-feature point sets, and false features are removed through spatial consistency verification; the feature point set is fully reserved, the non-feature point set is sparsified by adopting a voxel grid filtering, and a simplified point cloud model is obtained through fusion. The application can greatly compress data volume, inhibit metal reflection noise, continuously and completely retain weak edge features of a blade, has extremely low geometric error, and is suitable for blade reverse modeling, surface detection, digital twin and the like.
Need to check novelty before this filing date? Find Prior Art