Automated Fiber Defect Detection via Image Normalization
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Solution Overview
Problem
Current methods for detecting defects in fiber composite materials during automated fiber laying processes, especially in large-scale components like aircraft wings, are inefficient due to manual inspection limitations and inadequate image analysis from metrological detection systems, leading to potential manufacturing deviations and structural weaknesses.
Innovation Solution
An image-based defect detection method using an image recording device with normalization techniques, such as adaptive histogram equalization and morphological expansion, to generate accurate digital image data for identifying defects in fiber materials deposited on a tool, combined with machine learning for error categorization and quality assurance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to detect defects in fiber composite materials, then inspection can be performed with simple equipment, but inspection time increases significantly and detection accuracy is limited by operator experience
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated optical image-based detection system. The system uses image recording devices to capture fiber material surfaces and automated image processing algorithms to detect defects, eliminating dependence on operator experience and significantly reducing inspection time while improving detection accuracy and consistency.
Solution Approach 2:
The system enables self-inspection of the fiber composite material through automated image capture and processing. The detection system independently analyzes the fiber material without requiring human operators to manually examine each component, allowing continuous automated inspection during the manufacturing process.
2Productivity
If image-based detection systems are used to automate defect detection, then inspection time is reduced, but image data variations due to surface tilting or curvature cause false positives and negatives
Solution Approach 1:
The system performs preliminary normalization of image data before defect analysis. By applying normalization techniques that compensate for surface tilting and curvature effects prior to defect detection, the system eliminates the cause of false positives and negatives, ensuring accurate detection results while maintaining high inspection efficiency.
Solution Approach 2:
The patent transforms image data parameters through normalization processing. The system adjusts brightness, contrast, and geometric parameters of captured images to compensate for surface variations, converting variable image conditions into standardized data that enables reliable automated defect detection without sacrificing inspection speed.
3Reliability
If comprehensive defect detection is performed on all fiber material surfaces, then manufacturing deviations are detected early, but false detections increase due to surface variations
Solution Approach 1:
The system applies preliminary normalization to distinguish between normal surface variations and actual defects before comprehensive analysis. By pre-processing images to account for surface tilting and curvature, the system enables thorough defect detection across all fiber material surfaces while minimizing false detections, thereby maintaining high component quality without sacrificing detection reliability.
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
This approach significantly improves defect detection accuracy by equalizing image data variations caused by surface tilting or curvature, reducing false positives/negatives, and enabling real-time correction of manufacturing errors, thus enhancing the quality and efficiency of fiber composite component production.
Implementation Method 1
the fiber material surface of the fiber materials deposited on the tool is recorded using an image-based recording device
Implementation Method 2
normalization techniques, such as adaptive histogram equalization and morphological expansion, to generate accurate digital image data
Data Source
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AI summary
The invention relates to a method for detecting defects in fiber materials (15) of a fiber composite material deposited on a tool (16) for the production of a fiber composite component, wherein the method comprises the following steps: - capturing a fiber material surface of the fiber material deposited on the tool and generating digital image data of the fiber material surface by means of at least one image-based recording device (19); - normalizing the image data by increasing the variance of a frequency distribution of color-, contrast- and/or brightness-related image features of the digital image data and generating normalized image data by means of an image processing unit; - detecting defects in the deposited fiber material by image analysis of the normalized image data by means of an evaluation unit.