A method and apparatus for characterizing the uniformity of a hetero ceramic matrix composite component

Abstract

The application provides a kind of heteromorphic ceramic matrix composite component uniformity characterization method and equipment, the method comprises: CT tomogram sequence is carried out image foreground segmentation, and the foreground profile image sequence of composite component is obtained;CT tomogram sequence is segmented based on the foreground profile image sequence, and the pore defect image sequence is obtained;Based on the pore defect image sequence, three-dimensional space is segmented, and the average gray value of each block obtained after segmentation is obtained;Average gray value is replaced into the corresponding block of foreground image sequence to carry out three-dimensional reconstruction, and the visualization characterization of the uniformity of composite material component is completed.The application solves the problem that the material uniformity characterization method in the prior art has larger error, and cannot accurately quantify the uniformity distribution difference of the heteromorphic component.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of metamaterial design, and particularly relates to a uniformity characterization method and equipment for a special-shaped ceramic matrix composite component. BACKGROUND

[0002] The ceramic matrix composite material is a composite material with new performance composed of a ceramic material such as silicon carbide or silicon nitride as a matrix and other materials with different properties, has a series of advantages such as high strength, high temperature resistance, oxidation resistance, strong fracture toughness and good thermal conductivity, is widely used in many aspects such as aerospace, automobile, electronics and electricity, and chemical and biomedical engineering industries, and serves in various environments, and is a new generation of high-temperature structural material with broad application prospect.

[0003] However, due to the complex and various preparation processes and the anisotropy of the material itself, defects such as pores, delamination and cracks are inevitably generated in the preparation process, which leads to uneven local density distribution and weakens the local area, and affects the reliability in the service process of the structure, so the defect detection and structural uniformity characterization are of great significance. At present, most researches mainly focus on the mechanical properties, tensile strength, thermal stability, oxidation resistance and other performances, and the research on the influence of the defects such as pores generated in the preparation process of the special-shaped ceramic matrix composite material on the structural uniformity is relatively less. Therefore, it is necessary to establish a method for effectively characterizing the density uniformity of the ceramic matrix composite material without damaging the material, and to improve the preparation process through the characterization results to reduce the influence of the pore defects generated in the preparation on the material uniformity.

[0004] Most of the existing technical researches are based on the whole component as the research object, and the researched components are mostly regular components, or the uniformity of the sample is usually characterized by porosity, and the calculation of the porosity usually introduces a large error, and cannot accurately quantify and characterize the uniformity distribution difference of the special-shaped component. SUMMARY

[0005] In view of the above analysis, the application aims to provide a uniformity characterization method and equipment for a special-shaped ceramic matrix composite component, to solve the problem that the material uniformity characterization method in the prior art has a large error and cannot accurately quantify and characterize the uniformity distribution difference of the special-shaped component.

[0006] The purpose of the application is mainly realized by the following technical scheme:

[0007] On one hand, the application provides a uniformity characterization method for a special-shaped ceramic matrix composite component, which comprises the following steps:

[0008] performing image foreground segmentation on the CT tomographic image sequence to obtain a foreground contour image sequence of the composite material component;

[0009] performing a void defect segmentation on the CT tomographic image sequence based on the foreground contour image sequence to obtain a void defect image sequence;

[0010] performing a three-dimensional spatial segmentation based on the void defect image sequence, and obtaining average gray values of each block after the segmentation;

[0011] replacing the average gray values into corresponding blocks in the foreground image sequence to perform a three-dimensional reconstruction, and completing a visual representation of the uniformity of the composite material member.

[0012] Further, the method further comprises obtaining a voxel number and a total gray value of each block, and obtaining the average gray value of each block based on the voxel number and the total gray value.

[0013] Further, the three-dimensional spatial segmentation based on the void defect image sequence is performed by a pixel labeling method, and the method comprises:

[0014] performing a region division based on a contour of each image in the void defect image sequence;

[0015] calculating a pixel labeling value of each pixel point based on coordinate information of the pixel point in the void defect image sequence, and dividing pixel points with the same pixel labeling value into the same block.

[0016] Further, the pixel labeling value of each pixel point is calculated by the following formula:

[0017]

[0018] wherein k (i,j) represents the pixel labeling value of the pixel point (i, j), m , n respectively represent a block number of the void defect image and a block number in the tomographic sequence direction, l represents a length of the block in the region division direction, N represents a number of the tomographic image of the composite material member, i represents a coordinate of a target pixel in the block direction, and j represents a coordinate of the void defect image sequence, i.e., the jth image, represents a rounding operation.

[0019] Further, the image foreground segmentation on the CT tomographic image sequence is performed by an improved image foreground segmentation algorithm, and the method comprises:

[0020] obtaining an initial foreground contour of each image in the CT tomographic image sequence by edge detection;

[0021] constructing an energy function, wherein the energy function comprises an internal energy for controlling smoothness and elasticity of a contour line and an external energy for attracting the initial foreground contour line to a target edge;

[0022] The positions of the initial foreground contours in each image are iteratively adjusted to minimize the energy function, so as to obtain the image foreground contour conforming to the edge of the material component.

[0023] Further, the initial foreground contour of the CT tomographic image sequence is obtained through edge detection, comprising:

[0024] The CT tomographic image is smoothed through a Gaussian filter;

[0025] The gradient intensity and direction of each pixel point in the image are calculated by using a Sobel operator, so as to obtain a corresponding gradient image;

[0026] The gradient image is scanned based on a non-maximum suppression algorithm, and the points with the maximum local gradient are retained to remove non-edge pixels;

[0027] The edge pixel points are determined based on preset high and low gradient thresholds, comprising: the points with gradient values greater than the high threshold are determined as strong edges and retained; the pixel points with gradient values between the two thresholds are determined as weak edges; the weak edges connected with the strong edges are determined as edge pixel points and retained, and the weak edge pixel points not connected with the strong edges are determined as noise together with the pixel points less than the low threshold and removed;

[0028] The detected edges are tracked to identify and connect the broken edges, so as to form a complete edge contour;

[0029] The CT tomographic image including the edge contour is processed through mathematical morphological closing operation, and the image hole part is filled, so as to obtain the initial foreground contour.

[0030] Further, the replacing of the average gray value into the corresponding block in the foreground image sequence for three-dimensional reconstruction comprises:

[0031] The average gray value of each block is replaced into the foreground contour image sequence;

[0032] The average gray value of each block is projected to each block with corresponding color and transparency by using a ray casting volume rendering algorithm, so as to complete the three-dimensional reconstruction of the CT tomographic image sequence.

[0033] Further, it further comprises:

[0034] The average gray value of each block of the composite material component is used to represent the density distribution of the sample piece;

[0035] The average gray value of each block is statistically analyzed and a broken line graph is drawn, so as to complete the digital representation of the material uniformity of the composite material component based on the density distribution.

[0036] Further, using an adaptive threshold segmentation method, the CT tomographic image sequence of the composite material component is segmented for the pore defect, comprising:

[0037] Within the corresponding foreground contour, each image in the CT tomographic image sequence is smoothed to remove noise;

[0038] For each pixel point in each image, the weighted average value of the pixel values of the neighborhood pixel points is calculated to obtain a local threshold value;

[0039] The local threshold value is used for binaryzation processing of each pixel point to complete the pore defect segmentation.

[0040] On the other hand, a computer device is also disclosed, comprising at least one processor, and at least one memory in communication connection with the processor;

[0041] The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to realize the uniformity characterization method of the special-shaped ceramic matrix composite material component.

[0042] The beneficial effects of the technical solution are:

[0043] 1. The improved image foreground segmentation algorithm is used to automatically obtain accurate composite material component foreground contour images, and based on the foreground contour images, block division is performed by pixel labeling method, the average gray value of each block is calculated, and the average gray value is used to realize the visual and digital characterization of the uniformity of the composite material component, which greatly improves the accuracy of the uniformity distribution characterization of the material component.

[0044] 2. The present application can simultaneously quantify the uniformity of the whole and local of the sample, and can represent the uniformity difference of each part of the component through three-dimensional space block division data; the uniformity characterization of the special-shaped component is realized, and the space block division method can process regular components and irregular components, thereby realizing the uniformity characterization of the special-shaped component. BRIEF DESCRIPTION OF DRAWINGS

[0045] The accompanying drawings are only used for the purpose of illustrating specific embodiments and are not considered as limiting the present application, and in the whole drawings, the same reference signs represent the same parts;

[0046] Figure 1 The flow chart of the uniformity characterization method of the special-shaped ceramic matrix composite material component of the embodiment of the present application;

[0047] Figure 2 The shape schematic diagram of the composite material component of the embodiment of the present application;

[0048] Figure 3Fig. 1 is a schematic diagram of foreground image segmentation of an embodiment of the present application, Figure 3 Fig. 1(a) is a CT slice image, Figure 3 Fig. 1(b) is a foreground image obtained by edge detection, Figure 3 Fig. 1(c) is an initial foreground image after filling holes, Figure 3 Fig. 1(d) is a final foreground image after iterative adjustment of the initial foreground image;

[0049] Figure 4 Fig. 2 is a schematic diagram of adaptive threshold segmentation of an embodiment of the present application, Figure 4 Fig. 2(a) is a partial CT slice image, Figure 4 Fig. 2(b) is a void defect image after adaptive threshold segmentation;

[0050] Figure 5 Fig. 3 is a schematic diagram of region division of an embodiment of the present application;

[0051] Figure 6 Fig. 4 is a schematic diagram of block division of an embodiment of the present application; Figure 6 Fig. 4(a) is a schematic diagram of block division along the image block direction, Figure 6 Fig. 4(b) is a schematic diagram of block division along the image sequence direction;

[0052] Figure 7 Fig. 5 is a schematic diagram of an image after replacement of the average gray value of an embodiment of the present application;

[0053] Figure 8 Fig. 6 is a schematic diagram of visualization of material uniformity after three-dimensional reconstruction of an embodiment of the present application;

[0054] Figure 9 Fig. 7 is a schematic diagram of the relationship between the density of a sample and the gray value of an X-ray image of an embodiment of the present application;

[0055] Figure 10 Fig. 8 is a schematic diagram of digital representation of material uniformity of an embodiment of the present application, Figure 10 Fig. 8(a) is a schematic diagram of representation of the average gray value of each block in region ①, Figure 10 Fig. 8(b) is a schematic diagram of representation of the average gray value of each block in region ② and ③, Figure 10 Fig. 8(c) is a schematic diagram of representation of the average gray value of each block in region ④, Figure 10 Fig. 8(d) is a schematic diagram of representation of the average gray value of each block in region ⑤. DETAILED DESCRIPTION

[0056] The preferred embodiments of the present application will be described in detail below with reference to the drawings, wherein the drawings form a part of this application and are used to illustrate the principles of the present application, and are not intended to limit the scope of the present application.

[0057] An embodiment of the present application provides a uniformity characterization method of a special ceramic matrix composite component, the special ceramic matrix composite component refers to a composite component with complex structure and irregular shape, and the special ceramic matrix composite component usually has multiple deformation parts, and the deformation parts such as bending are more prone to have porosity defects due to the influence of a processing process, and therefore, accurate characterization of the uniformity of the special ceramic matrix composite component is a necessary process for improving a ceramic matrix composite preparation process.

[0058] As shown in Figure 1 , the uniformity characterization method comprises the following steps:

[0059] Step S1: performing image foreground segmentation on the CT tomographic image sequence to obtain a foreground contour image sequence of the composite component.

[0060] The CT tomographic image sequence can be obtained by scanning a sample to be measured by using a cone beam CT scanning system of a high-resolution microfocus industrial CT device.

[0061] In particular, the present application takes a carbon fiber reinforced ceramic matrix composite special component as an example, and the specific structure is a "U" shape, as shown in Figure 2 . Preferably, the embodiment performs image foreground segmentation on the CT tomographic image sequence by using an improved image foreground segmentation algorithm, and specifically comprises the following steps:

[0062] obtaining an initial foreground contour of each image in the CT tomographic image sequence by edge detection;

[0063] constructing an energy function, the energy function comprising internal energy for controlling the smoothness and elasticity of the contour line and external energy for attracting the initial foreground contour line to move towards the target edge;

[0064] iteratively adjusting the position of the initial foreground contour line in each image to minimize the energy function, and obtaining an image foreground contour conforming to the edge of the composite component.

[0065] Further, the initial foreground contour of the CT tomographic image sequence is obtained by edge detection, and the method comprises the following steps:

[0066] performing smoothing processing on the CT tomographic image by using a Gaussian filter;

[0067] calculating the gradient intensity and direction of each pixel point in the image by using a Sobel operator to obtain a corresponding gradient image;

[0068] based on a non-maximum suppression algorithm, scanning the gradient image, and retaining a point with the maximum local gradient to remove non-edge pixels;

[0069] The edge pixel points are determined based on preset high and low gradient thresholds. For a point with a gradient value greater than the high threshold, the point is determined as a strong edge and is reserved. For a pixel point with a gradient value between the two thresholds, the pixel point is determined as a weak edge. For a weak edge connected with the strong edge, the weak edge is determined as an edge pixel point and is reserved. For a weak edge pixel point not connected with the strong edge, the weak edge pixel point and a pixel point less than the low threshold are jointly determined as noise and are removed. The gradient thresholds can be adaptively set according to the gradient of the actual image to be processed.

[0070] The detected edges are tracked to identify and connect the broken edges, so as to form a complete edge contour.

[0071] The CT tomographic image including the edge contour is processed by the mathematical morphological closing operation, a hole part of the image is filled, and the initial foreground contour is obtained.

[0072] That is, the embodiment is improved by the active contour method combined with multiple algorithms, and adaptive segmentation of the foreground of a special-shaped component image is realized. First, a method for adaptively obtaining an initial contour curve is established by combining Canny edge detection with mathematical morphological closing operation, and initial contour curve setting is realized according to the required foreground contour of the image to be segmented.

[0073] A certain image of a CT tomographic image sequence is shown in Fig. 1(a), and the foreground contour of the tomographic image is preliminarily obtained by Canny edge detection, as shown in Fig. 1(b). Then, the tomographic image is processed by using mathematical morphological closing operation, a hole part of the image is filled, and the initial contour curve is obtained after the foreground contour is filled, as shown in Fig. 1(c). Figure 3 Figure 3 A certain image of a CT tomographic image sequence is shown in Fig. 1(a), and the foreground contour of the tomographic image is preliminarily obtained by Canny edge detection, as shown in Fig. 1(b). Then, the tomographic image is processed by using mathematical morphological closing operation, a hole part of the image is filled, and the initial contour curve is obtained after the foreground contour is filled, as shown in Fig. 1(c). Figure 3

[0074] After the initial contour curve is obtained, the active contour method is used to segment an accurate foreground contour image. The basic principle of the active contour method is to construct an energy function, drive the energy function to the minimum value, make the contour curve close to the image foreground edge, and segment the required foreground.

[0075] More specifically, for a CT tomographic image I(x,y), a C(S) = C(x(s),y(s)) is constructed as an evolution curve in the image, where s is used to uniformly represent the coordinates x and y of the curve.

[0076] The energy function is defined as:

[0077] J snake =J int +J ext +J cons ;

[0078] J int ​​is the internal energy term related to the information inside the contour curve, which makes the contour curve keep continuity and smoothness during evolution; J ext is the external energy term related to the image information, also called image force, under the action of which the contour curve approaches the edge of the target; J cons is the constraint term, which provides constraint for the evolution of the curve and makes the segmentation result more accurate, and can be ignored.

[0079] The internal energy term related to the information inside the contour curve is defined as follows:

[0080]

[0081] Wherein, J coutin is the energy continuity term; a(s) is the elastic coefficient, which controls the extension of the contour curve to the target and keeps continuity; J smooth is the smooth energy term; β(s) is the rigidity coefficient, which controls the concave-convex degree of the contour curve with the shape of the target and keeps the smoothness of the contour curve.

[0082] The external energy term is determined by the information of the image, and a scalar representation representing the global features of the image can be selected. Considering the gradient jump at the edge of the target, the embodiment defines the external energy term by the gradient of the image, and the expression is as follows:

[0083]

[0084] The external energy drives the contour curve to converge to the target, and the role of the coefficient γ(s) is to adjust the step of convergence, and ∇I is the gradient information of the image.

[0085] Therefore, the energy function of the embodiment is represented as:

[0086]

[0087] By solving the minimum value of the above formula, the contour curve can be converged at the maximum gradient point of the image, and the maximum gradient of the image is generally obtained at the edge of the target, so solving the minimum value of the above formula can detect the edge of the target.

[0088] That is, the embodiment generates deformation of the initial contour curve under the action of the internal energy and the external energy by the active contour method, the external energy attracts the initial contour curve to move to the edge of the object foreground, and the internal energy keeps the smoothness of the initial contour curve, when the energy reaches the minimum value, the initial contour curve moves to the edge of the image foreground, and the final foreground contour image is obtained. As shown in Figure 3 (d) of FIG. 4

[0089] The embodiment improves the image foreground segmentation algorithm, accurately segments the foreground contour image of the tomographic image, and is used for subsequent three-dimensional spatial blocking and average gray value calculation, thereby improving the accuracy of the gray value calculation and the accuracy of the uniformity representation of the composite material component

[0090] Step S2: performing a pore defect segmentation on the CT tomographic image sequence based on the foreground contour image sequence, to obtain a pore defect image sequence;

[0091] Specifically, the adaptive threshold segmentation method can be used to perform the pore defect segmentation on the CT tomographic image sequence of the composite material component, including:

[0092] In the corresponding foreground contour, each image in the CT tomographic image sequence is subjected to a smoothing processing to remove noise;

[0093] For each pixel point in each image, a weighted average value of pixel values of the neighborhood pixel points is calculated to obtain a local threshold value;

[0094] Each pixel point is subjected to a binaryzation processing by using the local threshold value, to complete the pore defect segmentation.

[0095] The pixel points with a pixel value greater than or equal to a threshold value are marked as 1, representing the image foreground, i.e., the pore defect; and the pixel points with a pixel value less than the threshold value are marked as 0, representing the background pixel.

[0096] That is, for the problem of uneven pore density distribution of the special-shaped composite material component, the adaptive threshold segmentation method is used to perform the pore defect segmentation. The adaptive threshold segmentation is to determine the threshold value of each pixel according to the local characteristics of the image. The segmentation threshold value at each pixel position is determined by the distribution of the neighborhood pixels, and the threshold value with the best segmentation effect can be calculated according to the gray distribution characteristics of the image. For example Figure 4 As shown in (a) of FIG. 6, the CT image of the bending part of the “U” shaped structure component is taken as an example, and the adaptive threshold segmentation result is shown in (b) of FIG. 6. Figure 4

[0097] Step S3: performing a three-dimensional spatial blocking based on the pore defect image sequence, and obtaining the average gray values of each block after the blocking;

[0098] Specifically, the pixel marking method can be used to perform the three-dimensional spatial blocking based on the pore defect image sequence, including:

[0099] Based on the image contour in the pore defect image sequence, the region is divided;

[0100] ​Based on the coordinate information of each pixel point in the different regions of the pore defect image sequence, the pixel label value of each pixel point is calculated, and the pixel points with the same pixel label value are divided into the same block.

[0101] More specifically, the CT tomographic image of the U-shaped sample used in the embodiment is as shown in Fig. 1(a). Considering the structural shape characteristics, in order to facilitate the analysis of the uniformity of the pore distribution in different parts, the images in the pore defect image sequence are divided into five regions as shown in Fig. 1(b), which are two side edge parts, two curved parts and a horizontal part of the component, for subsequent block division. Figure 4 Figure 5

[0102] Further, the three-dimensional space is divided into blocks for each region of the pore defect image, as shown in Fig. 1(a) and Fig. 1(b), and the calculation of the pixel label value of each pixel point can be represented by the following formula: Figure 6 Figure 6

[0103]

[0104] wherein k (i,j) represents the pixel label value of the pixel point (i, j), m , n are respectively the number of blocks on the pore defect image and the number of blocks in the direction of the tomographic sequence, l is the length of the block direction of each region, N is the number of tomographic images of the composite component, i is the coordinate of the target pixel in the block direction, j represents the coordinate of the pore defect image sequence direction, i.e. the jth image, represents the rounding operation.

[0105] Further, assuming that the number of voxels of the block labeled k in the pore defect image sequence is mk , and the total gray value is gk , then the average gray value of the block corresponding to the corresponding pixel label value is represented as:

[0106] G k = g k / m k .

[0107] The accurate average gray value of each block calculated in the embodiment can be used for subsequent visualization and digital characterization of the uniformity of the composite component, thereby improving the accuracy and intuitiveness of the material uniformity characterization.

[0108] Step S4: replacing the average gray value into the corresponding block of the foreground image sequence to perform three-dimensional reconstruction, thereby completing the visualization and characterization of the uniformity of the composite component.

[0109] ​​​​Specifically, the embodiment realizes the visual representation of the material uniformity by reconstructing the sequence of tomographic images in three dimensions and representing the average gray value in two dimensions to the three-dimensional space. In order to reconstruct in three dimensions, first, the average gray value of each block is replaced into the sequence of foreground profile images according to the corresponding pixel marker value, and the replacement result is shown in Figure 7 Then, the sequence of images after replacement is reconstructed in three dimensions using volume rendering algorithm. Volume rendering is a direct display based on volume data, which directly projects pixels to the display plane with certain color and transparency. The embodiment adopts ray casting volume rendering algorithm, and the result is shown in Figure 8 Different gray values are mapped to different colors after three-dimensional reconstruction. In the CT tomographic image after three-dimensional reconstruction, the part with large gray value is the high-density area, and the part with small gray value is the low-density area. Therefore, the density distribution of the composite material can be reflected by the distribution of colors, and the uniformity of the composite material can be intuitively represented.

[0110] Further, the embodiment also represents the density distribution of the sample by the average gray value of each block of the composite material component; the average gray value of each block is statistically analyzed and a broken line graph is drawn to complete the digital representation of the material uniformity of the composite material component based on the density distribution.

[0111] Specifically, the CT tomographic image reflects the absorption degree of the sample to X-ray through different gray values. When the thickness of the sample is not much different, the difference in the density of the sample is reflected in the difference in brightness on the X-ray image. Therefore, the CT tomographic image can represent the size of the material density according to the size of the pixel gray value of the tomographic image. The relationship between the X-ray image gray value and the sample with different densities is shown in Figure 9 For the high-density part, more X-rays are absorbed and less X-rays are transmitted, which is represented as white on the CT tomographic image; for the medium-density part, the absorption of X-rays is reduced and the transmission of X-rays is increased, which is represented as gray on the image; for the low-density part, less X-rays are absorbed and more X-rays are transmitted, which is represented as black on the image. The density distribution of the sample is represented by calculating the average gray value of each block of the composite material component. The distribution of the gray value is used as the basis for evaluating the material uniformity. The digital representation of the material uniformity can be realized by statistically analyzing the average gray value of each block and drawing a broken line graph.

[0112] The embodiment statistically analyzes the average gray value of each block calculated for each region. Each part is divided into 16 blocks along the Y-axis direction (the direction of the sequence of CT tomographic images), region ① is divided into 5 blocks along the X-axis direction, and regions ④ and ⑤ are divided into 8 blocks along the Z-axis direction. The results of the average gray value of each block of each part are shown in Figure 10 (a), (b), (c) and (d) of Figure 10It can be seen that the uniformity of the five regions in the Y direction (the length direction of the component) shows the difference in the grayscale value distribution of each part. The high and low grayscale values ​​in the figure can represent the high and low density of each part, and then the uniformity distribution difference of each part can be characterized. The area with abnormal density can be located according to the data for generating process analysis and optimization, thereby improving the uniformity and reliability of composite components.

[0113] In another aspect, a computer device is provided, comprising at least one processor and at least one memory communicatively connected to the processor;

[0114] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the aforementioned method for characterizing the uniformity of a special-shaped ceramic-based composite material component.

[0115] In summary, the method for characterizing the uniformity of a special-shaped ceramic-based composite material component proposed in the present invention automatically obtains an accurate foreground contour image of the composite material component through an improved image foreground segmentation algorithm, and divides the foreground contour image into blocks using a pixel labeling method, calculates the grayscale average value of each block, and uses the average grayscale value to achieve a visual and digital characterization of the uniformity of the composite material component, greatly improving the accuracy of the uniformity distribution characterization of various parts of the material component. In addition, the present invention can simultaneously quantitatively characterize the overall and local uniformity of the sample, and by dividing the data into three-dimensional spatial blocks, it can characterize the uniformity differences of various parts of the component; it realizes the uniformity characterization of special-shaped components, and the spatial block division method can process both regular components and irregular components, thereby achieving the uniformity characterization of special-shaped components.

[0116] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0117] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for characterizing the uniformity of a special-shaped ceramic matrix composite component, characterized in that: include: Performing image foreground segmentation on a CT tomographic image sequence to obtain a foreground contour image sequence of the composite material component; wherein performing image foreground segmentation on the CT tomographic image sequence using an improved image foreground segmentation algorithm includes: obtaining an initial foreground contour of each image in the CT tomographic image sequence through edge detection; constructing an energy function, wherein the energy function includes internal energy for controlling the smoothness and elasticity of the contour line and external energy for attracting the initial foreground contour line to move toward the target edge; iteratively adjusting the position of the initial foreground contour line in each image to minimize the energy function to obtain an image foreground contour line that conforms to the edge of the material component; The method of obtaining the initial foreground contour of the CT image sequence by edge detection includes: smoothing the CT image by a Gaussian filter; calculating the gradient intensity and direction of each pixel in the image by using a Sobel operator to obtain a corresponding gradient image; scanning the gradient image based on a non-maximum suppression algorithm, retaining points with the largest local gradient, and removing non-edge pixels; determining edge pixels based on preset high and low gradient thresholds, determining points with gradient values ​​greater than the high threshold as strong edges and retaining them; determining pixels with gradient values ​​between the two thresholds as weak edges; determining weak edges connected to strong edges as edge pixels and retaining them, and determining weak edge pixels not connected to strong edges as noise together with pixels with values ​​less than the low threshold and removing them; tracking the detected edges to identify and connect broken edges to form a complete edge contour; processing the CT image including the edge contour by a mathematical morphological closing operation to fill the holes in the image and obtain the initial foreground contour; The CT tomographic image sequence is subjected to pore defect segmentation based on the foreground contour image sequence to obtain a pore defect image sequence; wherein the pore defect segmentation is performed on the CT tomographic image sequence using an adaptive threshold segmentation method, including: smoothing each image in the CT tomographic image sequence within the corresponding foreground contour to remove noise; for each pixel point in each image, calculating a weighted average of the pixel values ​​of its neighboring pixels to obtain a local threshold; and binarizing each pixel point using the local threshold to complete the pore defect segmentation; Performing three-dimensional spatial blocking based on the pore defect image sequence and obtaining an average grayscale value of each block obtained after the blocking; wherein the three-dimensional spatial blocking based on the pore defect image sequence is performed using a pixel labeling method, including: performing regional division based on the contours of each image in the pore defect image sequence; calculating a pixel label value of each pixel point based on coordinate information of each pixel point in different regions corresponding to the pore defect image sequence, and dividing pixels with the same pixel label value into the same block; The average grayscale value is replaced by the corresponding block in the foreground contour image sequence for three-dimensional reconstruction to complete the visual representation of the uniformity of the composite material component; including: replacing the average grayscale value of each block with the foreground contour image sequence; using a ray casting volume rendering algorithm to project the average grayscale value corresponding to each block to each block with corresponding color and transparency to complete the three-dimensional reconstruction of the CT tomographic image sequence.

2. The method for characterizing the uniformity of a special-shaped ceramic matrix composite component according to claim 1, characterized in that: The method further includes obtaining the number of voxels and the total grayscale value of each block; and obtaining the average grayscale value of each block based on the number of voxels and the total grayscale value.

3. The method for characterizing the uniformity of a special-shaped ceramic matrix composite component according to claim 1, wherein: The pixel mark value of each pixel is calculated by the following formula: ； ； ； in, Represents the pixel label value of pixel (i, j), 、 are the number of blocks on the pore defect image and the number of blocks in the fault sequence direction, is the length of each region in the block direction, is the number of tomographic images of composite material components, is the coordinate of the target pixel in the block direction, Represents the coordinates of the pore defect image sequence direction, i.e. the jth image, Indicates a rounding operation.

4. The method for characterizing the uniformity of a special-shaped ceramic matrix composite component according to claim 2, wherein: Also includes: The average gray value of each block of the composite material component is used to characterize the density distribution of the sample; The average grayscale value of each block is statistically analyzed and a broken line graph is drawn to complete the digital characterization of the material uniformity of the composite material component based on density distribution.

5. A computer device, characterized in that: comprising at least one processor, and at least one memory communicatively connected to the processor; The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the uniformity characterization method of the special-shaped ceramic matrix composite component according to any one of claims 1 to 4.

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