3D Woven Fibrous Structure Characterization from Distorted Tomography
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Solution Overview
Problem
Existing methods struggle to accurately characterize the deformation and displacement of yarns in woven fibrous structures during the shaping process, which affects the mechanical strength and stress behavior of composite parts, particularly in aircraft engine components, due to difficulties in visualizing and correcting distortions in X-ray tomography images.
Innovation Solution
A characterization method that decomposes the transformation of woven fibrous structures into secondary transformations, using digital image correlation algorithms to compare one-dimensional profiles with a simplified weave model, simplifying the characterization process and enabling precise displacement analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If X-ray tomography is used to visualize the three-dimensional arrangement of yarns, then the volume of the part is covered and microstructure is accessed, but the distortions and periodic patterns in the images make it difficult to identify weaving planes and correct geometric transformations
Solution Approach 1:
The patent applies segmentation by dividing the complex three-dimensional image analysis into multiple two-dimensional cross-sectional analyses. By slicing the volume into sequential 2D sections and analyzing each independently, the method overcomes the difficulty of visualizing and correcting distortions in the full 3D image, while maintaining measurement precision of yarn displacements.
Solution Approach 2:
The patent introduces an intermediary computational model that represents the expected geometric transformation of the weaving pattern. This virtual model serves as a reference to compare against actual image data, enabling the identification and correction of distortions caused by shaping processes without directly analyzing the complex distorted 3D image structure.
2Reliability
If manual analysis of tomographic images is performed to identify yarn displacements, then quality control can be carried out, but the process is time-consuming and difficult due to image distortions
Solution Approach 1:
The patent replaces manual visual analysis with an automated computational method. Digital image processing algorithms automatically identify yarn positions, calculate displacements, and compare them against the weaving pattern model, eliminating the time-consuming manual analysis while maintaining or improving reliability through systematic error reduction.
Solution Approach 2:
The patent creates a virtual copy or model of the expected weaving pattern and uses this digital representation to automatically compare against actual image data. This copying approach enables rapid automated analysis without requiring manual interpretation of distorted images, significantly reducing analysis time while maintaining reliability.
3Shape
If the fibrous blank is shaped to conform to the mold, then the desired part geometry is achieved, but substantial changes occur in the arrangement of yarns including shifts and slippage that affect mechanical strength
Solution Approach 1:
The patent performs preliminary characterization of yarn positions and displacements during the shaping process itself, before final densification. By measuring and analyzing yarn arrangement changes during shaping, the method enables early detection of potential manufacturing precision issues that could affect mechanical strength, allowing for process optimization.
Solution Approach 2:
The patent implements a feedback mechanism where the measured yarn displacements and positions during shaping are compared against the original weaving pattern and design specifications. This feedback information can be used to adjust shaping parameters or identify sections requiring additional quality control, thereby maintaining manufacturing precision while achieving the desired part shape.
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
Enables accurate characterization of deformations in woven fibrous structures, allowing for improved quality control and prediction of mechanical properties in composite parts by iteratively aligning images with a simplified model, enhancing manufacturing processes.
Implementation Method 1
This experimental method exploits the differential absorption of X-rays by different materials to reconstruct, through computation, a three-dimensional image of the part being studied
Data Source
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AI summary
The invention relates to a method for characterising from a volume image a fibrous structure having a three-dimensional weaving between a plurality of warp yarns extending in a first direction and a plurality of weft yarns extending in a second direction perpendicular to the first, the method comprising: a first filter processing (E10) of the volume image in a third direction perpendicular to the first and second directions so as to attenuate the periodic patterns in the third direction, obtaining (E20) a two-dimensional image corresponding to an intermediate plane in the third direction of the filtered volume image, a second filter processing (E31, E41) of the two-dimensional image in the first or second direction so as to attenuate the periodic patterns, obtaining (E32, E33) a one-dimensional profile representing the positions of the weft or warp columns and corresponding to an intermediate line in the first or second direction of the filtered two-dimensional image, and comparing (E33, E43) the one-dimensional profile with a reference profile.