Laser-Line Defect Visualization in Automated Fiber Placement
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Solution Overview
Problem
Current AFP manufacturing processes face challenges in detecting defects like gaps, overlaps, missing tows, twisted tows, puckers, or foreign object debris, which are time-consuming and prone to human error, reducing production efficiency and increasing costs due to manual inspection and low contrast visibility.
Innovation Solution
An in-process automated fiber placement inspection system (IAMIS) that uses a defect visualization system with a laser projection system to indicate defect locations on a part, enabling rapid, accurate, and automated defect detection and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to detect defects in AFP manufacturing, then defect detection can be performed, but production time increases significantly (20-70 percent of total production time) and human error increases
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated optical inspection system using cameras and image processing algorithms. The system captures images of the composite layup and automatically analyzes them to detect defects such as gaps, overlaps, missing tows, and FOD, eliminating the need for manual visual inspection and thereby maintaining high detection accuracy while significantly improving production rate
Solution Approach 2:
The inspection system operates autonomously during the AFP manufacturing process, automatically detecting and reporting defects without requiring operator intervention. The system processes images and generates defect reports independently, enabling continuous production with real-time quality monitoring
2Measurement precision
If manual visual inspection is used, then defect identification can be performed, but it is subject to human error and operator skill dependency
Solution Approach 1:
The patent replaces human visual inspection with an automated optical detection system that uses cameras to capture images and computer vision algorithms to identify defects. This substitution eliminates variability in operator skill and experience, providing consistent and reliable defect detection across different inspectors and production batches
Solution Approach 2:
The system provides immediate feedback by automatically detecting defects and generating reports in real-time during the manufacturing process. The automated detection algorithms consistently apply the same detection criteria, ensuring reliable and repeatable inspection results without the variability inherent in manual inspection
3Difficulty of detecting and measuring
If conventional inspection methods are used, then defect detection is possible, but the low contrast between substrate and incoming tows makes visual identification difficult
Solution Approach 1:
The patent replaces conventional visual inspection methods with an automated optical detection system that uses specialized cameras and image processing techniques. The system enhances image quality through computational methods to overcome low contrast conditions, enabling reliable detection of defects even when visual identification would be difficult
Solution Approach 2:
The system changes the detection parameters by using multiple imaging modes and advanced image processing algorithms that can detect defects based on variations in optical properties, texture, and pattern rather than relying solely on high contrast. This allows the system to identify defects in low-contrast conditions where conventional visual inspection would fail
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances production efficiency by reducing manual inspection time, improving defect detection accuracy, and minimizing human error, allowing for continuous production with real-time defect visualization and repair.
Implementation Method 1
projecting one or more laser lines onto the part to indicate the location of at least one defect
Data Source
AI summary
In a method of visualizing one or more defects on a part comprises, defect data representative of the one or more defects on the part is obtained by an in-process automated fiber placement manufacturing inspection system. The data is then received by a visualization module. A location on the part is determined for each defect of the one or more defects. One or more laser lines are projected onto the part to indicate the location of at least one defect of the one or more defects. The method can be implemented by a system that uses a laser projection system to project the laser lines on the part and a user device running a visualization app that interfaces with the main software for the in-process automated fiber placement manufacturing inspection system.