AR Component Inspection With CAD Latching Validation
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Solution Overview
Problem
Existing aircraft inspection methods require human intervention for component orientation and positioning, leading to inefficiencies and potential delays due to human limitations and labor intensity, especially in remote worksites with less expertise.
Innovation Solution
A self-validating augmented reality system using a trained latching model that superimposes a CAD image onto a real-world image, determining latching through a neural network, and providing real-time graphical feedback for accurate component inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human experts perform visual inspection of aircraft components, then inspection reliability is improved, but labor intensity and time consumption increase significantly
Solution Approach 1:
The inspection system performs self-validation by automatically comparing captured component images against CAD models and inspection criteria, eliminating the need for continuous human expert intervention while maintaining inspection reliability through automated defect detection and validation mechanisms
Solution Approach 2:
The patent replaces the mechanical human inspection process with an automated computer vision system that uses image processing, pattern recognition, and comparison algorithms to detect defects, thereby improving efficiency while maintaining reliability through consistent automated evaluation
2Measurement precision
If human experts perform visual inspection, then accurate judgment is achieved, but human limitations on perception and concentration reduce overall accuracy
Solution Approach 1:
The system performs self-validation by automatically verifying inspection results against predefined criteria and CAD models, eliminating human perceptual limitations and concentration variability while maintaining high accuracy through consistent automated evaluation
Solution Approach 2:
The system implements automated feedback mechanisms that continuously validate inspection results by comparing captured images against reference models and inspection standards, ensuring high measurement precision through iterative verification without human intervention
3Adaptability or versatility
If automated inspection aids are deployed at remote worksites with less expertise, then accessibility is improved, but inspection capability decreases due to lack of expert knowledge
Solution Approach 1:
The inspection system performs self-validation by automatically comparing captured images against embedded CAD models and inspection criteria, enabling remote worksites to conduct reliable inspections without requiring local expert knowledge, thus improving accessibility while maintaining capability
Solution Approach 2:
The system incorporates universal inspection capabilities that can be deployed across multiple worksite locations with varying expertise levels, using standardized comparison algorithms and reference models to maintain consistent inspection reliability regardless of location or local expertise
4Manufacturing precision
If manual orientation and positioning of components is performed during inspection, then proper alignment is achieved, but time consumption and labor intensity increase
Solution Approach 1:
The system replaces manual orientation and positioning with automated image processing and pattern recognition algorithms that automatically detect component alignment and orientation from captured images, achieving precise alignment verification without time-consuming manual intervention
Solution Approach 2:
The inspection system performs self-validation by automatically analyzing component orientation and alignment from captured images against reference models, eliminating the need for manual positioning while maintaining manufacturing precision through automated geometric comparison
Data Source
AI summary
A system and method for self-validating augmented reality inspection of a component are provided, including receiving an image of the component in a real world environment, determining a virtual camera pose relative to a CAD model of the component in a CAD environment that is substantially identical to the camera pose relative to the component in the world space, generating a CAD image at the virtual camera pose, removing background from the CAD image, generating a composite image by superimposing the masked CAD image on the image of the component in the real world environment, providing the composite image to a trained latching model to thereby make a latching determination of whether the CAD image is latched to the captured image of the component in the real world environment, where the latching model is trained on a dataset including ground truth composite image examples of latched and unlatched components.