Blade contour error evaluation method based on K nearest neighbor iterative nearest grid algorithm
A technology of blade profile and evaluation method, applied in computing, computer control, image data processing and other directions, can solve the problems of high memory requirements, inability to accurately reflect the real contour error of the workpiece surface, and low calculation efficiency.
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment 1
[0084] Such as figure 1 As shown, a blade surface profile error evaluation method based on the K-nearest neighbor iterative nearest grid algorithm includes the following steps:
[0085] S1. Set the machining tolerance of the workpiece as τ, and triangulate the CAD surface of the blade with the discrete precision of ω·τ (0 Where t represents the identification number of each part of the blade, Indicates the number of discrete points of this part of the profile.
[0086] S2: if image 3 As shown, the REVO five-axis measurement system is used to obtain the measurement points of the entire blade surface, and the measurement point cloud data set is recorded as no t Indicates the number of measuring points in this part, N=∑ t no t . k represents the number of iterations, the initial situation k=0, i=1...n t .
[0087] S3: Calculate the positions of all current measurement points for each part of the blade to the nearest point on the triangular patch and the correspo...
specific Embodiment approach
[0129] The specific implementation of the blade contour error evaluation method based on the K-nearest neighbor iterative nearest grid algorithm comprises the following steps:
[0130] Step 1. Mark the four parts CC, CV, LE, and TE of the blade CAD surface of the compressor as t=1, 2, 3, 4 respectively, and the machining tolerance of the workpiece τ=0.01mm, take ω=0.1, and The discrete precision of 0.1τ triangulates the CAD surface of the blade, and the discrete point cloud set that constitutes the triangular surface is The amount of discrete point cloud data in each part is A total of 154966. The discrete point cloud that will make up the triangular patch Stored according to the k-d tree structure, the set of triangular faces of each part is The quantity is m 1 =69531, m 2 =75043, m 3 =59688, m 4 =102331, a total of 306593 pieces. The REVO five-axis measurement system is used to obtain the measurement points of the entire blade surface, and the measurement point c...
PUM
Login to View More Abstract
Description
Claims
Application Information
Login to View More 


