Stringer Glue-Spraying Trajectory Extraction With 3D Point Clouds
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Solution Overview
Problem
The traditional manual gluing process between aircraft stringers and skins is time-consuming and prone to deviations due to size differences, making it difficult to achieve accurate and efficient automatic glue-spraying and quality inspection.
Innovation Solution
A method utilizing high-precision 3D point cloud data to extract the glue-spraying trajectory and inspect the glue quality, involving data collection, feature point extraction, minimum spanning tree construction, optimization of the trajectory for a glue-spraying robot, and post-spraying defect detection and correction using line laser scanning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual glue-spraying is performed between stringer and skin, then the process can be completed with simple equipment, but the time and labor consumption increases significantly
Solution Approach 1:
The patent replaces manual mechanical operations with automated systems including a glue-spraying robot equipped with force sensors, vision systems, and automated control. The robot automatically positions and applies adhesive based on pre-extracted trajectories from 3D point cloud data, eliminating manual labor while maintaining manufacturing capability.
Solution Approach 2:
The system performs self-positioning and self-adjustment through integrated sensors and feedback mechanisms. The glue-spraying robot autonomously navigates the assembly, detects position deviations in real-time, and compensates for trajectory offsets without human intervention, enabling continuous automated operation.
2Device complexity
If manual glue-spraying is used, then equipment complexity is low, but manufacturing precision deteriorates due to trajectory offset with large skins
Solution Approach 1:
The patent transitions from 2D trajectory planning to 3D point cloud-based trajectory extraction. By utilizing three-dimensional spatial data from laser scanning, the system accurately represents the complex geometry of large skin assemblies and calculates precise glue-spraying paths that account for surface curvature and position variations in all three dimensions.
Solution Approach 2:
The system implements real-time feedback through force sensors and vision systems that monitor the robot's position and the assembly's geometry during glue-spraying. This feedback enables dynamic trajectory adjustment to compensate for deviations caused by large skin sizes and positioning variations, maintaining high manufacturing precision throughout the process.
3Device complexity
If manual inspection is performed after glue-spraying, then simple equipment can be used, but time consumption increases and inspection accuracy is limited
Solution Approach 1:
The patent replaces manual visual inspection with automated optical inspection systems including line lasers and cameras. These systems automatically scan the glued assembly, capture images of the adhesive application, and analyze the data to detect defects such as missing glue, excessive glue, or improper adhesion, dramatically reducing inspection time while improving accuracy.
Solution Approach 2:
The system introduces intermediate measurement devices (line lasers, cameras, and image processing algorithms) between the glue-spraying process and the final quality assessment. These intermediaries capture and analyze the glue application in real-time, providing objective quantitative data about adhesive quality without requiring manual intervention.
4Productivity
If automated glue-spraying is implemented, then productivity improves, but device complexity increases due to need for 3D scanning and trajectory extraction systems
Solution Approach 1:
The patent integrates multiple functions into a single automated glue-spraying robot platform. The robot combines 3D scanning capabilities, point cloud processing, trajectory extraction, force-controlled adhesive application, and real-time inspection functions. This multi-functional integration achieves high productivity while managing system complexity through consolidation rather than separate independent systems.
Solution Approach 2:
The system merges the scanning subsystem, processing subsystem, and execution subsystem into an integrated automated gluing system. The 3D laser scanner, point cloud processing software, trajectory calculation algorithms, and robot controller work as a unified system, sharing data and coordination protocols, which reduces overall system complexity compared to separate independent systems.
Data Source
AI summary
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.