Image analysis device, image analysis method, and program
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Solution Overview
Problem
Current methods for analyzing fiber bundle orientations in fiber-reinforced composite materials, such as those used in aircraft engine components, are limited in their ability to accurately assess multiple orientations and are impractical for product testing due to long imaging times and incompatibility with fiber bundles having non-circular cross sections.
Innovation Solution
An image analysis apparatus and method that utilizes a computer-based system to process X-ray CT images by binarization, overlapping area extraction, reference direction determination, Z-yarn removal, and fiber bundle orientation estimation, employing directional distance methods to accurately analyze fiber bundle orientations in three-dimensional images, even with curved surfaces and non-circular cross sections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional orientation analysis methods (PTL 1) are used, then the analysis process is simple, but only one direction can be obtained and multiple fiber bundle orientations cannot be analyzed
Solution Approach 1:
The analysis process is segmented into multiple independent modules: binarization unit, overlapping area extraction unit, reference direction determination unit, Z-yarn removal unit, and fiber bundle orientation estimation unit. Each module handles a specific aspect of the analysis, enabling the system to process multiple orientations simultaneously while maintaining manageable complexity.
Solution Approach 2:
The invention transitions from analyzing only in-plane orientations (2D) to analyzing three-dimensional orientations (3D) by determining reference directions and removing Z-yarns. This dimensional expansion allows the system to capture fiber bundle orientations in multiple directions, including those extending out of the image plane.
2Measurement precision
If high-definition X-ray CT imaging is performed to identify individual fibers, then measurement precision is improved, but imaging time becomes excessively long
Solution Approach 1:
The invention extracts and analyzes only the overlapping areas of fiber bundles rather than processing the entire high-definition image in detail. By focusing computational resources on specific regions of interest where fiber orientations need to be determined, the system achieves accurate orientation analysis without requiring excessively long imaging times.
Solution Approach 2:
The system performs partial analysis by extracting overlapping areas and determining orientations only where necessary, rather than analyzing every fiber in the entire volume. This partial action approach provides sufficient information for quality control without the time cost of complete high-definition imaging.
3Productivity
If conventional methods are used, then processing is fast, but they cannot handle fiber bundles with non-circular cross sections
Solution Approach 1:
The orientation analysis method is designed to be universal and applicable to fiber bundles with various cross-sectional shapes, not limited to circular sections. The method uses overlapping area extraction and reference direction determination that work equally well for rectangular, elliptical, or irregular cross sections, making it suitable for diverse composite materials.
Solution Approach 2:
The invention changes the analysis parameters from assuming circular cross sections to using shape-agnostic overlapping area measurements. By basing the orientation determination on the geometry of overlapping regions rather than predefined cross-sectional shapes, the system maintains processing speed while gaining versatility.
4Measurement precision
If detailed analysis of each fiber is performed, then measurement precision is improved, but device complexity and calculation time increase
Solution Approach 1:
The system extracts only the essential information needed for orientation analysis from the full image data. By focusing on overlapping areas and determining reference directions rather than analyzing every fiber individually, the system achieves accurate orientation measurement with reduced computational complexity.
Solution Approach 2:
The analysis is segmented into distinct functional units that process different aspects of the data independently. This modular approach allows precise orientation determination through coordinated operation of multiple specialized units rather than a single complex analysis system.
Data Source
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AI summary
[Object] An image analysis apparatus, image analysis method, and program capable of easily analyzing orientations of fiber bundles from a three-dimensional image of a CMC is proposed. [Solution] An image analysis apparatus for analyzing orientations of fiber bundles of X-yarns and Y-yarns from a three-dimensional image of a woven fabric made of fiber bundles of the X-yarns, the Y-yarns, and Z-yarns includes: a binarization unit that binarizes the three-dimensional image; an overlapping area extraction unit that extracts an overlapping area, in which the X-yarns and the Y-yarns perpendicularly and three-dimensionally intersect with each other, from the binarized image; a reference direction determination unit that averages an overlapping direction of each voxel included in the overlapping area and determines the averaged direction as a reference direction; a Z-yarn removal unit that removes the Z-yarns from the binarized image by applying a directional distance method on a reference plane perpendicular to the reference direction; and a fiber bundle orientation estimation unit that applies the directional distance method again to the image, from which the Z-yarns have been removed, on the reference plane and estimates the orientations of the fiber bundles of the X-yarns and the Y-yarns on the basis of a directional distance calculated upon the application.