Aircraft Fairing Skin Repair Using Wing Point Cloud Registration
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Solution Overview
Problem
Current fairing skin repair methods for aircraft are labor-intensive and inefficient, with low accuracy due to manual adjustments and lack of precise measurement techniques.
Innovation Solution
A fairing skin repair method based on measured wing data using point cloud registration and processing techniques, including denoising, voxel grid filtering, feature descriptor calculation, clustering, and iterative closest point algorithms to accurately match and repair skin surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual comparison, marking and final comparison methods are used for skin repair, then the process can be completed with simple equipment, but the labor intensity increases and efficiency decreases
Solution Approach 1:
The patent replaces the manual mechanical comparison and marking process with a computer-aided system that uses point cloud data processing, histogram feature descriptors, and automated algorithms to determine skin repair allowances, eliminating the need for manual measurement and calculation
Solution Approach 2:
The system enables the skin repair process to be self-sufficient by automatically processing point cloud data, calculating repair allowances, and generating repair schemes without requiring continuous manual intervention or subjective judgment
2Manufacturing precision
If manual adjustment methods are used for skin repair allowance, then the equipment complexity remains low, but the accuracy of skin repair cannot be guaranteed
Solution Approach 1:
The patent replaces manual measurement and adjustment methods with a computer-aided system that processes point cloud data through voxel grid filtering, normal calculation, and histogram feature descriptor analysis to automatically determine precise repair allowances, significantly improving measurement accuracy
Solution Approach 2:
The system creates a digital copy of the skin surface through point cloud data acquisition, allowing for precise virtual measurement, analysis, and planning of repair operations before actual execution, thereby ensuring high accuracy without complex physical measurement devices
3Manufacturing precision
If computer-aided point cloud processing methods are used for skin repair, then the accuracy and efficiency are improved, but the device complexity increases
Solution Approach 1:
The patent segments the complex point cloud processing task into distinct modules: data acquisition, denoising, voxel grid filtering, normal calculation, histogram feature descriptor computation, and repair allowance determination. This modular approach manages complexity while maintaining high precision
Solution Approach 2:
The system introduces histogram feature descriptors as an intermediary representation that bridges the raw point cloud data and the final repair decisions, enabling accurate comparison and matching without requiring direct complex processing of the original point cloud data
Data Source
AI summary
A fairing skin repair method based on measured wing data includes fairing skin registration. Data set P1 through denoising and filtering wing point cloud data is reorganized to obtain a key point set P. A histogram feature descriptor in a normal direction of any key point in set P and a skin point cloud data Q is calculated. Euclidean distance between feature descriptors of two points is calculated through K-nearest neighbor algorithm, and points with high similarity are added into a set M. A clustering is performed on set M using a Hough voting algorithm to obtain a local point cloud set P′ in set P. The method includes fairing skin repair. The boundary line of the point frame is projected onto Q, and a distance between a projection line on the point cloud and the boundary line is calculated to obtain an amount of skin to be repaired.