Automated Composite Placement Anomaly Detection
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Solution Overview
Problem
Current methods for inspecting the automatic placement of composite materials in ATL machines are prone to human error and do not provide an automatic record of inspection data, leading to potential quality issues and increased repair costs.
Innovation Solution
A computer vision-based system integrated into ATL machines, utilizing AI algorithms to analyze images captured by cameras, detects anomalies such as material shortages or placement errors during the manufacturing phase.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual visual inspection by operator is used, then device complexity is reduced, but measurement precision and reliability deteriorate due to human error
Solution Approach 1:
The patent replaces the manual visual inspection system with an automated computer vision system. Cameras capture images of the composite material placement, and image processing algorithms automatically analyze these images to detect anomalies such as missing layers, misplaced materials, or placement defects. This substitution of mechanical/manual inspection with optical and computational systems eliminates human error while maintaining acceptable system complexity.
Solution Approach 2:
The system creates digital copies (images) of the composite material placement surfaces and analyzes these copies rather than requiring direct manual inspection. The captured images serve as representations of the physical structure, allowing automated algorithms to detect anomalies in the copied data, which then reflects the actual placement quality.
2Loss of information
If manual visual inspection is used, then loss of information is reduced, but productivity and manufacturing precision deteriorate
Solution Approach 1:
The automated vision system operates continuously during the manufacturing process, capturing images at multiple stages without interrupting production. The system maintains continuous monitoring and automatically records all inspection data, eliminating the discontinuous nature of manual inspection and ensuring no information is lost while maintaining high productivity.
Solution Approach 2:
The system automatically captures, processes, and records inspection data without requiring operator intervention. The image processing algorithms autonomously analyze the captured images and generate inspection reports, making the inspection process self-service oriented and eliminating the bottleneck of manual data collection while maintaining complete information records.
3Manufacturing precision
If NDT systems are applied in final assembly line, then manufacturing precision is improved, but loss of time and loss of substance increase due to late defect detection
Solution Approach 1:
The vision system performs inspection during the manufacturing process itself, before the composite materials are fully cured and before final assembly. By detecting placement anomalies in advance while the structure is still accessible and uncured, the system enables timely corrections without waiting for final assembly or NDT stages, thereby preventing time loss associated with late defect detection and costly repairs.
Solution Approach 2:
The system proactively detects and reports placement defects during manufacturing, allowing corrective actions to be taken before defects can propagate to final assembly. This preliminary detection and reporting mechanism prevents the need for time-consuming repairs later by addressing issues at their source during the fabrication stage.
Data Source
AI summary
A system for inspecting structural elements during their manufacturing capable of detecting anomalies in the automatic placement of composite materials through computer vision, comprising an image capture module that can be integrated into an ATL machine extracting data from the images, from which an artificial vision module obtains information on the anomalies detected with computer vision, information that a human-machine interface module automatically translates into a language understandable to humans. By avoiding the need to manually enter data for the inspection, human errors are avoided, and the information obtained for each inspection can be automatically saved for record storage. The system is designed to inspect large structural element compounds, such as aircraft wings.