Method for automatic glue-spraying of stringer and inspection of glue-spraying quality

Abstract

A method for automatic glue-spraying of stringers and inspection of glue-spraying quality based on measured data. Three-dimensional (3D) point cloud data of a stringer-skin assembly is collected by 3D laser scanner, and then processed by denoising and sampling. Feature points of an intersection line of a site to be glued of the stringer-skin assembly are extracted by K-means clustering method based on Gaussian mapping, and a minimum spanning tree is constructed based on a set of the extracted feature points. A connected region is established to obtain an initial feature intersection line of the string-skin assembly, which is optimized by random sample consensus algorithm to remove redundant small branch structures to obtain the actual glue-spraying trajectory. The quality of the glue sprayed on the stringer-skin assembly is inspected by line laser to determine positions of the defects, which are then subjected to secondary glue-spraying.

Claims

1. A method for automatic glue-spraying of a stringer and inspection of glue-spraying quality, comprising: (1) collecting data of a 3D point cloud of an assembly of a stringer and a skin and pre- processing the collected data of the 3D point cloud prior to glue-spraying; (2) extracting feature points of an intersection line of a site to be glued of the stringer and the skin; building a minimum spanning tree; and connecting the feature points to obtain an initial feature intersection line between the stringer and the skin; wherein step (2) comprises: (2.1) extracting the feature points of the intersection line of the site to be glued of the stringer and the skin based on extraction of feature points from a point cloud data model; wherein in step (2.1), the feature points of the intersection line of the site to be glued of the stringer and the skin are extracted by a K-means clustering method based on Gaussian mapping through steps of: randomly selecting a 3D point in the 3D point cloud as a target point performing a K-nearest neighbor search on the target point subjecting a unit normal vector of a triangle set composed of the target point and its neighbor points to Gaussian mapping; selecting silhouette coefficient as a clustering validity index to determine an optimal number of clusters; and obtaining the feature points in the 3D point cloud model as the feature points of the intersection line of the site to be glued of the stringer and the skin according to clustering distribution of different patches; and (2.2) building the minimum spanning tree based on the extracted feature points; and connecting the feature points according to the minimum spanning tree; (3) optimizing the initial feature intersection line, and transforming the optimized feature intersection line to a coordinate system of an end effector of a glue-spraying robot to obtain an actual glue-spraying trajectory; wherein step (3) comprises: (3.1) optimizing the initial feature intersection line between the stringer and the skin to remove redundant small branch structures; and (3.2) transforming the optimized feature intersection line to the coordinate system of the end effector of the glue-spraying robot to obtain the actual glue-spraying trajectory according to a determined calibration relationship; (4) after the glue-spraying, collecting point cloud data of a glue sprayed on the stringer and the skin; calculating size information of the glue and determining a position of defects on the glue based on the point cloud data of the glue; and performing secondary glue-spraying on the defects of the glue.

2. The method of claim 1, wherein in step (1), the data of the 3D point cloud is collected using a 3D laser scanner, and the collected data of the 3D point cloud comprises position data of the site to be glued of the stringer and the skin.

3. The method of claim 1, wherein in step (3.1), the redundant small branch structures in the initial feature intersection line are removed through a random sample consensus algorithm.

4. The method of claim 1, wherein step (4) comprises: collecting point cloud data of the stringer and the skin after glue-spraying, wherein the collected point cloud data comprises data of structures of the stringer and skin and the glue sprayed thereon; subjecting the point cloud data of the stringer and the skin to straight-line fitting using the random sample consensus algorithm to obtain a fitting straight line; projecting the point cloud data of the glue sprayed on the stringer and the skin onto the fitting straight line by orthogonal projection to calculate the size information of the glue; determining positions of defects on the glue according to the size information of the glue and the position information obtained by line laser scanning; and performing secondary glue-spraying on the defects of the glue.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) This disclosure will be further described below with reference to the accompanying drawings. Obviously, depicted in the drawings are merely some embodiments of this disclosure, and other embodiments made by those skilled in the art based on the content of the disclosure without sparing any creative effort should fall within the scope of the disclosure.

(2) FIG. 1 is a flow chart of a method for automatic glue-spraying of a stringer and inspection of glue-spraying quality based on measured data according to an embodiment of this disclosure.

(3) FIG. 2 schematically shows three-dimensional point cloud data of a stringer and skin assembly according to an embodiment of this disclosure.

(4) FIG. 3 schematically illustrates a projection principle of point cloud of the glue sprayed on the stringer and skin assembly according to an embodiment of this disclosure.

(5) FIG. 4 schematically shows a calculation principle of size information of the glue sprayed on the stringer and skin assembly according to an embodiment of this disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

(6) This disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. It is obvious that provided below are merely some embodiments of this disclosure, which are not intended to limit the disclosure. Other embodiments made by those skilled in the art based on the content disclosed herein without sparing any creative effort should fall within the scope of the disclosure.

(7) Referring to FIGS. 1-4, this disclosure provides a method for automatic glue-spraying of a stringer and inspection of glue-spraying quality. The process of the method is schematically shown in FIG. 1, and is specifically described as follows.

(8) (1) Data Collection and Pre-Processing

(9) Three-dimensional point cloud data of a stringer and skin assembly is collected using a 3D laser scanner, and then the collected 3D point cloud data is subjected to denoising and sampling.

(10) (2) Extraction of Feature Intersection Line Between Stringer and Skin

(11) Feature points of the intersection line of the site to be glued of the stringer and skin are extracted by a K-means clustering method based on Gaussian mapping and then used to build a minimum spanning tree. A connected region is established through the minimum spanning tree to obtain the feature intersection line.

(12) (3) Generation of Actual Glue-Spraying Trajectory

(13) The feature intersection line is optimized and then transformed to a coordinate system of an end effector of a glue-spraying robot to obtain the glue-spraying trajectory.

(14) (4) Inspection of Glue-Spraying Quality

(15) Point cloud data of the glue sprayed on the stringer and skin assembly is collected by line laser and then used to calculate the size information of the glue at each position, so as to determine the position of the defects. Secondary glue-spraying is performed on the above defects.

(16) In step (1), the 3D point cloud data of the stringer and skin assembly is collected by a 3D laser scanner, and then subjected to denoising and sampling.

(17) In step (2), the feature intersection line between stringer and skin is extracted as follows.

(18) (2.1) The feature points of the intersection line of the site to be glued of the stringer and the skin are extracted using a K-Means clustering method based on Gaussian mapping.

(19) The step (2.1) is specifically performed as follows.

(20) (2.1.1) A 3D point in the 3D point cloud is selected as a target point, and then a K-nearest neighbor search is performed on the target point.

(21) (2.1.2) A unit normal vector of a triangle set composed of the target point and its neighbor points is subjected to Gaussian mapping.

(22) (2.1.3) The silhouette coefficient is selected as a clustering validity index to determine an optimal number of clusters.

(23) (2.1.4) The feature points in the 3D point cloud model are obtained as the feature points of the intersection line of the site to be glued of the stringer and the skin according to clustering distribution of different patches.

(24) (2.2) The minimum spanning tree is built based on a set of the extracted feature points, and then the feature points are connected according to the minimum spanning tree to obtain an initial feature intersection line.

(25) In step (3), the generation of the actual glue-spraying trajectory is specifically described as follows.

(26) (3.1) The initial feature intersection line between the stringer and the skin is optimized through a random sample consensus algorithm to remove the redundant small branch structures.

(27) (3.2) The optimized feature intersection line is transformed to the coordinate system of the end effector of the glue-spraying robot to obtain the actual glue-spraying trajectory according to a determined calibration relationship.

(28) In step (4), the glue-spraying quality is inspected as follows.

(29) (4.1) The point cloud data of the glue-sprayed stringer and skin assembly is collected by two-dimensional line laser, where the point cloud data includes data of the structure of the stringer and skin assembly and data of the glue sprayed thereon.

(30) (4.2) The point cloud data of the stringer and skin assembly is subjected to straight-line fitting using the random sample consensus algorithm to obtain a fitting straight line, onto which the point cloud data of the glue sprayed on the stringer and skin assembly is projected by orthogonal projection to calculate the size information of the glue.

(31) (4.3) Positions of defects on the glue are determined according to the size information of the glue and the position information obtained by line laser scanning, and the defects on the glue are subjected to secondary glue-spraying.

(32) Specifically, the method provided herein for automatic glue-spraying of a stringer and inspection of glue-spraying quality has the following advantages.

(33) In the automatic glue-spraying method provided herein for stringers based on measured data, a three-dimensional point cloud data model is employed to obtain a spraying trajectory, and then the quality of the glue on the stringer and skin assembly is inspected by line laser, so as to repair the defects in the glue. As a consequence, the method provided herein solves the problem in the prior art that it is difficult to automatically apply glue on stringers, and improves the glue-spraying efficiency, achieving a stable and efficient glue-spraying for stringers based on measured data.

(34) Various aspects of the disclosure are described herein with reference to the accompanying drawings, in which some embodiments of the disclosure are shown. The embodiments are not necessarily defined to include all aspects of the invention. It should be understood that the various concepts and embodiments introduced above, as well as those described in more detail below, can be implemented in any one of many optional ways. In addition, some aspects disclosed herein can be implemented alone or in any appropriate combination with other aspects disclosed in the disclosure.

(35) Described above are preferred embodiments of the disclosure, which are merely illustrative of the disclosure and are not intended to limit the disclosure. Obviously, any changes, modifications and replacements made by those of ordinary skill in the art without departing from the spirit of the disclosure should fall within the scope of the disclosure defined by the appended claims.