Convolutional Twin Point Network Blade Contour Stitching System Based on Multiple Spatial Similarity

A blade profile and stitching system technology, applied to the details of image stitching, image analysis, image enhancement, etc., can solve the problems of blade error, point cloud density inconsistency, increase the difficulty of rotation and translation invariance features, and achieve feasibility sex good effect

A blade profile and stitching system technology, applied to the details of image stitching, image analysis, image enhancement, etc., can solve the problems of blade error, point cloud density inconsistency, increase the difficulty of rotation and translation invariance features, and achieve feasibility sex good effect

CN112991187BActive Publication Date: 2021-07-27SICHUAN UNIV

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  • Convolutional Twin Point Network Blade Contour Stitching System Based on Multiple Spatial Similarity
  • Convolutional Twin Point Network Blade Contour Stitching System Based on Multiple Spatial Similarity
  • Convolutional Twin Point Network Blade Contour Stitching System Based on Multiple Spatial Similarity

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Embodiment Construction

[0024] The multi-spatial similarity-based convolutional twin point network leaf contour stitching system provided in this embodiment includes a data collection module, a convolutional twin point network, and a data stitching module.

[0025] 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 (Sx, Sy and Sz) 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 change due to rotation and translation is called a rigid body transformation. The profile data of the blade B 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, Y={...

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Abstract

The invention discloses a convolutional twin point network leaf profile splicing system based on multi-space similarity, 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, and a network module Including feature extraction module, matching matrix module, attention mechanism module and singular value decomposition module; feature extraction module uses edge convolution network structure to extract high-dimensional spatial features in source point cloud and target point cloud, and then uses high-dimensional spatial features and The coordinate space calculates the feature space matching matrix and the coordinate space matching matrix respectively, and then handles the conflict between the feature space matching matrix and the coordinate space matching matrix through the attention mechanism to obtain the final matching matrix, and calculates the source point cloud through the final matching matrix The corresponding relationship with the midpoint of the target point cloud, and finally the rigid body transformation is obtained through singular value decomposition, and the optimal rigid body transformation is obtained based on multiple iterations.

Description

technical field [0001] The invention relates to the field of blade profile detection, in particular to a multi-space similarity-based convolution twin point network blade profile splicing system. 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 focus ...

Claims

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Application Information

Patent Timeline
27 Jul 2021
Publication
CN112991187B
IPC
G06T3/40; G06T7/00; G06T7/13; G06K9/46; G06K9/62
CPC
G06T3/4038; G06T7/0004; G06T7/13; G06T2200/32; G06T2207/20081; G06T2207/20084; G06T2207/30164; G06T2207/10028
Inventors
谢罗峰; 朱杨洋