基于数控机床实测坐标的薄壁结构件阵列孔原位重建方法
By fusing measured coordinates from CNC machine tools with line laser scanning data, and combining normal-consistent clustering and periodic priors for array holes, a weighted Poisson reconstruction with normal constraints is adopted to solve the problem of high-precision in-situ reconstruction of thin-walled array hole structures. This generates a three-dimensional mesh model that preserves sharp edges, which is suitable for inspection and error analysis in the aerospace field.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
- Filing Date
- 2026-05-10
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies for machining thin-walled array hole structures on CNC machine tools suffer from high subjectivity, low efficiency, and inability to quantify errors. General point cloud processing algorithms lack specificity, resulting in large deviations between the reconstructed model and the ideal geometric structure, and making it difficult to distinguish between the real wall surface and machining defects.
The method integrates measured coordinates from CNC machine tools with line laser scanning data, combines normal consistency clustering and dynamic angle thresholding to remove noise, performs point cloud normalization based on the periodic prior of array holes, and uses weighted Poisson reconstruction with normal constraints to generate a normalized 3D mesh model.
It achieves in-situ reconstruction of array holes in thin-walled structural components with high precision and strong noise resistance, and generates a three-dimensional mesh model that retains the sharp edges of the thin walls, which is suitable for high-precision inspection and processing error analysis in the aerospace field.
Smart Images
Figure CN122176241B_ABST