Convolutional Twin Point Network Blade Contour Stitching System Based on Multi-scale Feature Fusion

A technology of multi-scale features and leaf outlines, applied to the details of image stitching, image enhancement, image analysis, etc., can solve the problems of leaf error, difficult to find point correspondence, inconsistent point cloud density, etc., and achieve good feasibility Effect

CN112990373BActive Publication Date: 2021-08-03SICHUAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Publication Date
2021-08-03

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Abstract

The invention discloses a convolutional twin point network blade profile splicing system based on multi-scale feature fusion, including a data acquisition module, a convolutional twin point network and a data splicing module. The convolutional twin point network includes a network module that iterates several times, The network module includes a feature extraction module, a feature space matching module and a singular value decomposition module; the feature extraction module uses the edge convolution network structure of the improved pyramid structure to extract the high-dimensional spatial features in the source point cloud and the target point cloud, and then utilizes The high-dimensional spatial features calculate the feature space matching matrix, and use the feature space matching matrix to calculate the correspondence between the points in the two point cloud data (source point cloud and target point cloud), and finally obtain the two point cloud data ( The rigid body transformation between the source point cloud and the target point cloud), and the optimal rigid body transformation is solved according to multiple iterations. The experimental results show the feasibility and good practical application prospects of this method.
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Description

technical field

[0001] The invention relates to the field of blade profile detection, in particular to a convolution twin point network blade profile splicing system based on multi-scale feature fusion. Background technique

[0002] Blades are known as the jewel in the crown of modern industry and are widely used in aero engines, steam turbines and wind turbines. To ensure perfect and stable aerodynamic performance at high speeds, blades require extremely high dimensional accuracy and surface integrity. Accurate measurement of blade profile is an important means to guide blade production. However, thin-walled, twisted and mirror-like free-form surfaces increase the difficulty of blade surface measurement. At present, the acquisition of blade profile is done by three-coordinate measurement, which is a high-precision and easy-to-implement method. However, the efficiency of three-coordinate measurement is low, which hinders the production efficiency of blades. The increased...

Examples

Embodiment Construction

[0025] The convolutional twin point network blade contour stitching system based on multi-scale feature fusion provided in this embodiment includes a data acquisition module, a convolutional twin point network, and a data stitching module.

[0026] The data collection module is used to collect point cloud data of the blade B contour under different viewing angles, specifically using a line laser profiler A equipped with a four-axis measurement system, such as figure 1 As shown, the four-axis measurement system includes three translation axes and one rotation axis. The line laser profiler A is installed on the translation axis and is moved by the translation axis. The blade B is installed on the rotation axis. This occurs due to the rotation and translation. The change of becomes the rigid body transformation. The blade B profile data includes the source point cloud data X of the field of view 1, X={x 1 ,x 2 ,...,x i ,...,x n} and field of view 2 target point cloud data Y, ...