Interlaminar shear performance evaluation method, device and equipment considering pore type of special-shaped ceramic matrix composite material, medium and product

Abstract

The invention discloses an interlayer shear performance evaluation method and device considering the pore type of a special-shaped ceramic matrix composite, equipment, a medium and a product, and the method comprises the steps: segmenting pores in a CT image through employing adaptive filtering, gray scale transformation and Otsu dual-threshold segmentation algorithms, and determining a pore segmentation image; carrying out quantitative description on distribution characteristics of pores with different scale forms in the pore segmentation image, determining a relationship between each pore type and pore box dimension in the pore segmentation image, and further determining detailed representation of the pores; performing short beam shear test on the special-shaped ceramic matrix composite test piece, and determining a linear fitting relationship between detailed characterization of pores and interlayer shear strength of the short beam shear test; the short beam shear test interlaminar shear strength of the to-be-tested special-shaped ceramic matrix composite material test piece is predicted. According to the method, the interlayer shear performance of the to-be-tested special-shaped ceramic matrix composite material test piece can be effectively evaluated based on detailed characterization of the pores.

Description

Technical Field

[0001] The present application relates to the technical field of shear performance testing of ceramic matrix composite materials, and in particular to an evaluation method, device, equipment, medium and product for interlaminar shear performance of special-shaped ceramic matrix composite materials taking into account the pore type. Background Art

[0002] Carbon fiber-reinforced silicon carbide composites (CFRCs), a type of fiber-reinforced ceramic matrix composites (CMCs), are one of the most promising structural materials. Due to their excellent properties, such as low density, high-temperature resistance, and oxygen tolerance, CFRCs have been widely used in the aerospace field, including thermal protection systems for spacecraft and thermal components in aircraft engines. However, due to the complexity of the CMC manufacturing process and its inherent anisotropy, poor structural uniformity can be a problem when preparing large and complex components. Structural heterogeneity in CMCs can significantly impact material performance, and thus the safety and service life of components. Intralaminar heterogeneity in woven fabric-reinforced composites can affect their mechanical properties, particularly the bending and transverse shear properties, which are closely related to buckling performance. Manufacturing defects in CMCs primarily include porosity, delamination, inclusions, cracks, ply layup errors, and density inequality. Porosity is the most common and unavoidable defect, with its morphology exhibiting significant randomness and complexity. In terms of distribution and morphology, the pores in carbon fiber-reinforced silicon carbide composites are primarily small, isolated pores and large, interconnected pores (interlaminar spalling), which affect the mechanical properties of the material. Isolated pores are spatially dispersed individual pores, making them difficult to detect and locate. Networks of interconnected pores are typically found between several layers. Furthermore, these pores can cause stress concentration and generate matrix cracks.

[0003] Currently, most studies are based on simple pore characterization to study the damage evolution of regular-shaped ceramic matrix composite specimens under mechanical testing. However, there is little research on the pores in large or irregular-shaped composite components, especially carbon fiber reinforced silicon carbide composites. Currently, the overall porosity of ceramic matrix composite specimens is a pore characteristic parameter widely used to evaluate the impact on material properties, while there are few studies on the detailed characterization of pores in irregular-shaped ceramic matrix composites. Detailed characterization of pores can provide a data basis for evaluating their impact on the performance of irregular-shaped ceramic matrix composites. However, the law of the influence of pores on mechanical properties in ceramic matrix composites has not yet been established. Therefore, it is of great significance to study the detailed characterization of pores and their impact on the mechanical properties of materials. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, equipment, medium and product for evaluating the interlaminar shear performance of special-shaped ceramic-based composite materials taking into account the pore type, which can solve the problem that traditional technology cannot effectively evaluate the shear performance of special-shaped ceramic-based composite materials through detailed characterization of pores.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for evaluating the interlaminar shear performance of a special-shaped ceramic matrix composite material taking into account the pore type, comprising:

[0007] Based on X-ray tomography technology, CT images of special-shaped ceramic matrix composite specimens are obtained;

[0008] Adaptive filtering, grayscale transformation, and Otsu double-threshold segmentation algorithm are used to segment the pores in the CT image to determine a pore segmentation image;

[0009] quantitatively describing the distribution characteristics of pores of different scales in the pore segmentation image based on the fractal dimension and a preset fractal dimension threshold, and determining the relationship between each pore type and the pore box dimension in the pore segmentation image;

[0010] Based on the relationship between each pore type and the pore box dimension, the pore box dimension is used as a detailed characterization of the pores; the detailed characterization of the pores includes each pore type;

[0011] A short beam shear test is performed on the special-shaped ceramic matrix composite specimen using an electronic universal testing machine to determine the linear fitting relationship between the detailed characterization of the pores and the interlaminar shear strength of the short beam shear test;

[0012] Based on the linear fitting relationship, the interlaminar shear strength of the short beam shear test of the special-shaped ceramic matrix composite material specimen to be tested is predicted, and the interlaminar shear performance evaluation of the special-shaped ceramic matrix composite material specimen to be tested is completed.

[0013] In a second aspect, the present application provides an evaluation device for interlaminar shear performance of a special-shaped ceramic matrix composite material taking into account the pore type, comprising:

[0014] An acquisition module, used for acquiring CT images of special-shaped ceramic matrix composite specimens based on X-ray tomography technology;

[0015] a segmentation module, configured to segment the pores in the CT image using adaptive filtering, grayscale transformation, and Otsu's double-threshold segmentation algorithm to determine a pore segmentation image;

[0016] a classification module for quantitatively describing the distribution characteristics of pores of different scales in the pore segmentation image based on the fractal dimension and a preset fractal dimension threshold, and determining the relationship between each pore type and the pore box dimension in the pore segmentation image;

[0017] determining a characterization module for using the pore box dimension as a detailed characterization of the pores based on a relationship between each pore type and the pore box dimension; wherein the detailed characterization of the pores includes each pore type;

[0018] An experimental module, for conducting a short beam shear test on the special-shaped ceramic matrix composite specimen based on an electronic universal testing machine, and determining a linear fitting relationship between a detailed characterization of the pores and the interlaminar shear strength of the short beam shear test;

[0019] The evaluation module is used to predict the interlaminar shear strength of the short beam shear test of the special-shaped ceramic matrix composite material specimen to be tested based on the linear fitting relationship, and complete the interlaminar shear performance evaluation of the special-shaped ceramic matrix composite material specimen to be tested.

[0020] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for evaluating the interlaminar shear properties of special-shaped ceramic-based composite materials taking into account the pore type.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for evaluating the interlaminar shear performance of special-shaped ceramic-based composite materials considering the pore type.

[0022] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements the above-mentioned method for evaluating the interlaminar shear performance of special-shaped ceramic matrix composite materials considering the pore type.

[0023] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0024] The present application obtains a CT image of a special-shaped ceramic matrix composite material specimen, and uses adaptive filtering, grayscale transformation, and Otsu's double-threshold segmentation algorithm to segment the pores in the CT image and determine a pore segmentation image. The pore segmentation image segmented in this way is more accurate and contains detailed information about the pore shape, providing effective data support for the subsequent identification of pore types. Then, based on the fractal dimension and the preset fractal dimension threshold, the distribution characteristics of pores of different scales in the pore segmentation image are quantitatively described, and the relationship between each pore type and the pore box dimension in the pore segmentation image is determined. Based on the relationship between each pore type and the pore box dimension, the pore box dimension is used as a detailed representation of the pores. In other words, the pore box dimension is used to represent the pore type based on the relationship between each pore type and the pore box dimension, thereby achieving a detailed representation of the pores by the pore box dimension. Finally, based on an electronic universal testing machine, a short beam shear test was carried out on the special-shaped ceramic-based composite material specimen to determine the linear fitting relationship between the detailed characterization of the pores and the interlaminar shear strength of the short beam shear test. Based on the linear fitting relationship, the interlaminar shear strength of the short beam shear test of the special-shaped ceramic-based composite material specimen to be tested was predicted. That is, this application realizes an accurate and effective evaluation of the interlaminar shear performance of the special-shaped ceramic-based composite material specimen to be tested based on the detailed characterization of the pores. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 Schematic diagram of a flow chart for an evaluation method of interlaminar shear properties of special-shaped ceramic matrix composites considering pore types;

[0027] Figure 2 A flow chart of a specific evaluation method for interlaminar shear performance considering pore types in special-shaped ceramic matrix composites;

[0028] Figure 3 Schematic diagram of cone-beam CT scanning system;

[0029] Figure 4 This is a diagram of a special-shaped ceramic-based component;

[0030] Figure 5 The figure is a schematic diagram of the three-dimensional reconstruction of the special-shaped ceramic matrix composite material specimen and its three-directional tomographic imaging; Figure 5 (a) is the three-dimensional reconstruction of the special-shaped ceramic matrix composite specimen; Figure 5(b) is a schematic diagram of three-directional tomographic imaging of a special-shaped ceramic matrix composite specimen;

[0031] Figure 6 This is the pore segmentation flow chart;

[0032] Figure 7 Schematic diagram of pore segmentation results using different segmentation algorithms; Figure 7 (a) in the figure is the original CT image; Figure 7 (b) is the CT image after global threshold segmentation; Figure 7 (c) is the CT image after Otsu double threshold segmentation;

[0033] Figure 8 The division diagram for special-shaped C / SiC composite components; Figure 8 (a) is the XY plane division diagram; Figure 8 (b) is a schematic diagram of the No. 3 plate area;

[0034] Figure 9 This is the three-dimensional pore image of the flat plate in the middle of the U-shaped component; Figure 9 (a) is a three-dimensional pore image; Figure 9 (b) is the volume porosity distribution result diagram;

[0035] Figure 10 The three-dimensional pore images of regions A, B, and C; Figure 10 (a) is a schematic diagram of the plane porosity of area A; Figure 10 (b) is a schematic diagram of the plane porosity of area B; Figure 10 (c) is a schematic diagram of the plane porosity of the C region;

[0036] Figure 11 Schematic diagram of the pore box dimension of the pore image in area B;

[0037] Figure 12 Three representative two-dimensional images of pore defects; Figure 12 (a) is a two-dimensional image of isolated pore defects; Figure 12 (b) is a two-dimensional image of mixed pore defects; Figure 12 (c) is a two-dimensional image of large-area connected pore defects;

[0038] Figure 13 The distribution characteristics of isolated and connected pores parallel to the stacking direction of the carbon cloth;

[0039] Figure 14 is the pixel connectivity labeling map in the two-dimensional pore image; Figure 14 (a) is the pixel connectivity labeling map of the pore area; Figure 14 (b) Maximum and minimum pixel area markings;

[0040] Figure 15 The distribution curve of the pore box dimension and the maximum length of the pores in region B;

[0041] Figure 16 This is the box dimension distribution statistics corresponding to the regional pores when the box dimension threshold is set to 0.5;

[0042] Figure 17 The box dimension distribution statistics corresponding to pores of different scales;

[0043] Figure 18 is the relationship between the average box dimension and the average planar porosity of 30 regions;

[0044] Figure 19 This is a schematic diagram of the shape of the standard test piece for the short beam method;

[0045] Figure 20 This is the loading diagram for the short beam shear test;

[0046] Figure 21 Schematic diagram of the failure mode of the short beam shear specimen; Figure 21 (a) is a schematic diagram of single-layer separation and destruction; Figure 21 (b) is a schematic diagram of multi-layer separation failure;

[0047] Figure 22 3D pore reconstruction images of 1#, 2#, 3#, 4# and 5# short beam shear specimens; Figure 22 (a) is the 3D pore reconstruction image of the 1# short beam shear specimen; Figure 22 (b) is the 3D pore reconstruction image of the 2# short beam shear specimen; Figure 22 (c) is the 3D pore reconstruction image of the 3# short beam shear specimen; Figure 22 (d) is the 3D pore reconstruction image of the 4# short beam shear specimen; Figure 22 (e) is the 3D pore reconstruction image of the 5# short beam shear specimen;

[0048] Figure 23 This is the result analysis of 1#, 2#, 3#, 4# and 5# short beam shear specimens. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0050] Fiber-reinforced ceramic matrix composites (CMCs) have been widely used in the aerospace field due to their excellent performance. However, due to the anisotropy and manufacturing complexity of CMCs, porosity is inevitable during the manufacturing process. Porosity is a key factor contributing to structural heterogeneity in materials, which in turn affects mechanical properties and compromises component safety. Conventional porosity metrics cannot fully characterize pore characteristics. To accurately evaluate the impact of heterogeneous porosity on the performance of composites or components, a digital evaluation model for the shear performance of C / SiC composites in special-shaped components based on multiple characterizations of heterogeneous porosity was established. An improved fractal dimension method for X-ray CT images was proposed. The improved box count dimension showed a strong correlation with multi-pore characteristics. In C / SiC composites, pores ranging from micrometers to millimeters were digitally characterized and accurately identified using box count dimensions. Results show that the evaluation model can quantitatively assess the impact of heterogeneous porosity on shear performance. Pores with non-uniform connectivity significantly degrade performance. This invention provides a robust data foundation and applicable methods for mechanical property evaluation and optimized design of fiber-reinforced porous carbon fibers and other similar structural composites.

[0051] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0052] like Figure 1 As shown, the present application provides an evaluation method for interlaminar shear performance of special-shaped ceramic matrix composite materials considering the pore type, including:

[0053] Step 11: Obtain a CT image of the special-shaped ceramic matrix composite specimen based on X-ray tomography technology;

[0054] Step 12: Adaptive filtering, grayscale transformation, and Otsu double-threshold segmentation algorithm are used to segment the pores in the CT image to determine a pore segmentation image.

[0055] In some embodiments, step 12 specifically includes: filtering the CT image using adaptive filtering to determine a filtered image; performing grayscale transformation on the filtered image to determine an enhanced image; and segmenting the pores in the enhanced image based on the Otsu double threshold segmentation algorithm to determine a pore segmentation image.

[0056] Step 13: Based on the fractal dimension and the preset fractal dimension threshold, quantitatively describe the distribution characteristics of pores of different scales in the pore segmentation image, and determine the relationship between each pore type and the pore box dimension in the pore segmentation image.

[0057] In some embodiments, step 13 specifically includes steps 21 to 23.

[0058] Step 21: Based on the fractal dimension, quantitatively describe the distribution characteristics of pores of different scales in the pore segmentation image, and determine the pore box dimension of each pore;

[0059] Step 22: Determine the pore type corresponding to each pore and the pore box dimension corresponding to each pore type based on the pore box dimension of each pore and a preset fractal dimension threshold; the pore types include isolated pores, mixed pores, and large-area connected pores;

[0060] Step 23: According to the pore type corresponding to each pore and the pore box dimension corresponding to each pore type, determine the relationship between each pore type and the pore box dimension.

[0061] In some embodiments, after step 23, it also includes: determining the porosity based on the pore area and pore length of each pore; determining the pore length corresponding to each pore type, and the relationship between porosity and pore box dimension based on the porosity, the pore types, and the relationship between the pore type and the pore box dimension; determining the relationship between pore length and pore box dimension according to the pore length corresponding to each pore type and the relationship between the pore box dimension and the pore type; based on the relationship between the porosity and pore box dimension, and the relationship between the pore length and pore box dimension, using the pore box dimension as a detailed characterization of the pores; the detailed characterization of the pores also includes porosity and pore length.

[0062] Step 14: Based on the relationship between each pore type and the pore box dimension, the pore box dimension is used as a detailed representation of the pores; the detailed representation of the pores includes each pore type.

[0063] Step 15: Perform a short beam shear test on the special-shaped ceramic matrix composite specimen using an electronic universal testing machine to determine the linear fitting relationship between the detailed characterization of the pores and the interlaminar shear strength of the short beam shear test.

[0064] In some embodiments, step 15 specifically includes: obtaining the maximum load, width and thickness on the special-shaped ceramic-based composite material specimen during the short beam shear test, and determining the interlaminar shear strength of the short beam shear test; based on the least squares method, the interlaminar shear strength of the short beam shear test and the pore box dimension, determining the linear fitting relationship between the pore box dimension and the interlaminar shear strength of the short beam shear test.

[0065] In some embodiments, the interlaminar shear strength of the short beam shear test is: Among them, τ M is the interlaminar shear strength; F is the maximum load; b is the width of the special-shaped ceramic matrix composite specimen; h is the thickness of the special-shaped ceramic matrix composite specimen.

[0066] Step 16: Based on the linear fitting relationship, the interlaminar shear strength of the short beam shear test of the special-shaped ceramic matrix composite material specimen to be tested is predicted to complete the interlaminar shear performance evaluation of the special-shaped ceramic matrix composite material specimen to be tested.

[0067] Carbon fiber-reinforced silicon carbide composites (C / SiC composites) are a type of fiber-reinforced ceramic matrix composites (CMCs). This application aims to characterize the pore characteristics of heterogeneous C / SiC composite components, specifically, to provide detailed characterization of the pores and provide data support for evaluating the effects of different pore shapes and distributions on the mechanical properties of the material. In practical applications, this application proposes a method for evaluating the interlaminar shear performance of heterogeneous ceramic matrix composites, taking into account the pore type. The specific steps are as follows.

[0068] Step 1: Nondestructive testing of C / SiC composite components: Figure 3 As shown in FIG, the X-ray tomography technology is used to obtain two-dimensional tomographic sequence images of special-shaped ceramic matrix composite components, namely CT images. Special-shaped ceramic matrix components such as Figure 4 shown.

[0069] When scanning with a cone-beam CT scanning system, X-rays pass through the component, and the flat-panel detector receives the projected X-ray intensity. During the scanning process, the stage rotates 360° and scans every 0.5°. Each scan will produce a projection image. After a certain reconstruction algorithm, the projection image can be reconstructed into a tomographic image at different positions of the sample to be tested. Within the effective scanning area, the interval between each tomographic image is the same as the scanning resolution, and the interval between tomographic images is 0.018mm. The data format is RAW format, which contains the three-dimensional grayscale information inside the sample to be tested, the reconstructed two-dimensional tomographic image and the three-dimensional volume data image of the sample to be tested, such as Figure 5 shown.

[0070] Step 2: C / SiC composite material pore segmentation: Adaptive filtering, grayscale transformation and Otsu double threshold algorithm are used to accurately segment the pores of C / SiC composite material CT image. The image processing process is as follows Figure 6 shown.

[0071] Carbon fiber reinforced ceramic matrix composites are composed of multiple phases, mainly including carbon fiber, ceramic matrix and internal defects. Considering the multi-phase characteristics of C / SiC materials, adaptive filtering is used to filter the acquired CT images to remove artifact noise, etc., thereby improving the recognition of internal defects in the material. Adaptive filtering is an adaptive filtering process based on the local neighborhood estimation statistics of each pixel, which can better retain the edge information of the image. The grayscale transformation method is used to enhance the image, which enhances the grayscale contrast of the tomographic image and makes the edge information of the pores and the material clearer. Considering the multi-phase characteristics of C / SiC composite materials, it is difficult to accurately segment pore defects using a single global threshold. In order to achieve accurate segmentation of pore defects in C / SiC composite materials, this application adopts the Otsu dual threshold algorithm for pore defect segmentation.

[0072] like Figure 7 The following are the pore segmentation results of the global Otsu threshold and Otsu double threshold segmentation algorithms. Figure 7 The segmentation results of the red box in the middle show that the global Otsu threshold algorithm is significantly over-segmented. The Otsu dual-threshold algorithm used in this application can accurately segment the pores in the tomographic image and include detailed information about the pore shape, providing effective data support for subsequent identification of pore defects.

[0073] Step 3: Pore identification based on pore fractal dimension and geometric features: The pores in plain-woven ceramic matrix composites are irregular in shape, and their distribution is also quite random. Geometric parameters such as area, perimeter, and equivalent radius cannot fully characterize structural characteristics such as pore contour and distribution. Fractal theory can be used to quantitatively assess the complexity of pore structures. There are many methods for calculating fractal dimension, including similarity dimension, capacity dimension, correlation dimension, information dimension, and box dimension. Among them, box dimension is the most widely used in various fractal theory fields due to its simple principle and convenient calculation. This paper uses box dimension to quantitatively describe the pore distribution characteristics of different scale morphological features in C / SiC composite components.

[0074] The definition of box dimension is as follows: Let A be a bounded set in n-dimensional space, let N r (A) represents the minimum number of boxes that cover A with a set whose diameter does not exceed r. The fractal dimension is represented by D, and its existence condition is that the limit of formula (1) exists.

[0075]

[0076] The grid counting method is a commonly used box dimension determination method. The specific calculation process is as follows: Assume that a picture containing the target object T (i.e. Figure 7The two-dimensional digital image of the pores in the image is I, whose size is M×N (where M is length, N is width, and M>N), and it is divided into several rectangular blocks (i.e., boxes) with an integer a as the side length, and ensure that M 1 / 3 ≤a≤M / 2. Let r = a / M, and then count the number of "valid boxes". The so-called "valid box" is any pixel that contains the target object T. All are counted and recorded as N(r). N(r) is the number of valid boxes when the side length is r. By changing the value of a within a given range, a set of aN(r) or rN(r) data can be obtained, which satisfies the formula:

[0077]

[0078] Taking the logarithm of both sides of the above equation, and since M is a constant, the dimension of the box D is:

[0079]

[0080] Where N(a) is the number of boxes when the side length is a. The scatter plot of loga and logN(a) is drawn, and the slope of the fitted line obtained by linear fitting using the least squares method is the box dimension D.

[0081] Considering the characteristics of pore defects in C / SiC composite materials, this application adopts an improved box dimension to count the number of pixels belonging to the target object in each box n. n x(n), calculate the number of target pixels P under the corresponding size box n The coefficient of variation of x(n) is CV (CV = standard deviation / mean), and the box dimension D is:

[0082]

[0083] Wherein, CV(a) is the coefficient of variation of the number of target pixels in the box when the size is a.

[0084] The standard deviation values ​​for regions of materials with different porosities are small, and the differences are not significant. The difference in the mean leads to different CV values. According to formula (4), the smaller the porosity, the smaller the mean, the larger the coefficient of variation (CV), and the larger the fractal dimension. Generally speaking, regions containing large, connected pores have a higher porosity than isolated pore defects, so the fractal dimension of these regions is relatively small.

[0085] The XY plane of the special-shaped C / SiC composite component is as follows Figure 8 As shown in (a) in . Figure 8 As shown in (b), the No. 3 flat plate area in the component is cut out, and the pore characteristics of the C / SiC composite material are characterized and analyzed using the XZ plane tomography image of the No. 3 area.

[0086] like Figure 9 As shown in (a), the three-dimensional pore image of the middle plate part of the U-shaped component is displayed, and the pore area is equally divided into 30 space units. Figure 9 As shown in (b), the porosity distribution results of 30 space units in the U-shaped component are shown. Figure 8 and Figure 9 As shown, three regions are selected for calculation and analysis of pore fractal dimension and geometric parameters. The plane porosity of the image containing pores in each spatial unit is calculated to characterize the pore content of the spatial unit. Assuming the number of pore images is m, the number of pore pixels in each tomographic image is N i , the number of pixels in each tomographic image is N. The plane porosity of an image is equal to (N i / N)*100%. The regions with average planar porosity of 3.99%, 7.76%, and 11.82% are named A, B, and C, respectively. Figure 10 shown.

[0087] like Figure 11 As shown in the figure, the three-dimensional pores appear as a gradual process between connected pores and isolated pores in the two-dimensional tomography, which is reflected by the fluctuation of the box dimension value. Figure 11 The analysis shown in Figure 12 The pores shown are primarily classified into three types: isolated pores, mixed pores, and large-area connected pores. Based on the analysis results of these three regions, the fractal dimension threshold for pore classification was initially set to 0.5. The box dimension of the isolated pore image is greater than 0.5, while the box dimensions of the mixed pore and large-area connected pore images are both less than 0.5. The box dimension of the fully connected pore image is relatively low. Therefore, the pore type can be determined based on the box dimension of the 2D pore segmentation image.

[0088] The microscopic properties of a material determine its macroscopic properties. Quantitative characterization of internal pores can provide a numerical basis for the establishment of pore defect simulation models and the impact of different pores on material properties. Figure 13 The two-dimensional distribution characteristics of isolated pores and connected pores in the parallel direction of the carbon cloth composite of C / SiC composite material are shown. This application uses the principle of pixel connectivity to mark the connectivity of pores in the two-dimensional pore segmentation image. Considering the pore distribution characteristics, the geometric dimension information such as pore area S and outer rectangle length L is calculated based on the results of the marked pixels to quantitatively characterize the pore distribution characteristics. In addition, considering the pore distribution characteristics, such as Figure 9 As shown, this application uses the pixel 8 connectivity principle to quantitatively analyze the pore distribution characteristics.

[0089] like Figure 14As shown in (a) in the figure, the connectivity of pixels is used to mark different pores. According to the connectivity marking results, the pixel area and the length of the pore outer rectangle are calculated. Figure 14 As shown in (b), according to the size of the pore pixel area, the largest and smallest pores in the two-dimensional image are selected, and the length of the outer rectangle of the corresponding pore is calculated. Let the number of pixels marking the connected area be S i , the pixel area S is equal to S i , the horizontal width of the rectangle outside the pore is pixel W i , height is pixel H i , the pore length is W i and H i The CT image resolution is 18 μm / pixel.

[0090] In order to further verify the accuracy of box dimension recognition, the area and length of the pores in the two-dimensional tomography of regions A, B, and C were calculated and analyzed. The maximum area of ​​the pore is defined as S max , the maximum length of the pore is defined as L max .

[0091] In order to explore the correlation between fractal dimension and the relationship between them, the Pearson correlation coefficient calculation method is used to calculate the correlation coefficient. The correlation coefficient is the amount of linear correlation between the research variables, and the value range is [-1,1]. The closer it is to 1, the more relevant it is. max and L max The correlation coefficients are 0.93, 0.89 and 0.76 respectively, showing a significant positive correlation. max The correlation coefficients are 0.69, 0.62 and 0.62 respectively. max The correlation coefficients are 0.75, 0.76 and 0.76 respectively. By comparing the correlation coefficients, we found that L max The correlation between and the box dimension is strong, and by selecting the pore length to establish the corresponding relationship between them, the scale information corresponding to different types of pores can be obtained. Figure 15 As shown in the figure, the red marked points are pore images with box dimension less than 0.5. With the initial box dimension threshold of 0.5, the L images with box dimension greater than 0.5 and not greater than 0.5 are counted by multi-factor box plot. max Information such as Figure 16 As shown in the statistical graph, it can be seen that the L max Most of the L values ​​of pore tomography images are concentrated below 500 μm and with a box dimension below 0.5. maxThe pore sizes of regions A, B, and C are concentrated above 500 μm. According to formula (5), the accuracy of identifying isolated and connected pores by box dimension is calculated. When the box dimension threshold is set to 0.5, the pore sizes of regions A, B, and C are L max The distribution is as follows.

[0092]

[0093] In formula (5), K is the total number of pore images, H is the number of misidentified pores, and P is the accuracy rate of pore identification.

[0094] When the box dimension threshold for pore identification is set to 0.5, the average recognition rate of the three regions is 76.00%, the connected pores are 92.21%, and the isolated pores are 58.77%. The low recognition rate of isolated pores indicates that the box dimension recognition threshold needs to be adjusted. The pore size is divided into two groups: no less than 500μm and less than 500μm. The pore box dimension is statistically analyzed using a box plot, as shown in Figure 2. Figure 17 As shown. Figure 17 Based on the statistical results, the box dimension recognition threshold was adjusted to 0.6. With a box dimension threshold of 0.6 for pore recognition, the average recognition rate for the three regions was 87.18%, the connected pore recognition rate was 89.57%, and the isolated pore recognition rate was 75.16%. Adjusting the recognition threshold to 0.6 improved overall recognition accuracy and enabled accurate classification of isolated and connected pores.

[0095] As shown in Table 1, for different regions L max The pore types that can be identified by the box dimension are further verified, and the correlation between the box dimension and the pore type is obtained.

[0096] Table 1 Box dimension range and scale information of three pore types

[0097]

[0098]

[0099] In order to explore the relationship between pore box dimension and porosity, the least squares method was used to linearly fit the average pore box dimension and average surface porosity of 30 regions. The box dimension and plane porosity were basically linearly fitted, and the fitting degree (R 2 ) is 0.6795, and the numerical correlation coefficient (Pearson, sr) is -0.8243. There is a strong negative correlation between the two, such as Figure 18 As shown in Figure 2, the average pore box dimension varies with the average planar porosity. It can be seen that as the planar porosity increases, the average pore box dimension per unit area decreases. This result demonstrates that the box dimension not only reflects pore scale information but also regional porosity. The smaller the box dimension, the larger the pore size, the more uneven the distribution, and the greater the porosity.

[0100] Step 4: 2D C / SiC composite short beam shear test.

[0101] Since the interlaminar shear strength of two-dimensional reinforced composite materials is much lower than the tensile strength of the material, even if tensile stress or compressive stress is applied to the composite material, shear stress will inevitably be generated in the direction deviating from the fiber axis. Therefore, the interlaminar shear performance is also an important indicator of the performance of 2D C / SiC composite materials. Figure 2 The “U”-shaped component was used to cut the test specimen to test the shear properties of the C / SiC composite material.

[0102] The short beam shear test was conducted on an Instron 5982 electronic universal testing machine. During the experiment, the load and corresponding displacement data on the specimen were collected in real time by the load sensor and displacement sensor equipped in the universal testing machine in conjunction with the dynamic data acquisition system. At the same time, in order to ensure the reliability of the experimental results, the number of specimens of the same specification under the same loading conditions was determined to be 5, and experimental tests were carried out. The standard short beam shear specimen is a rectangular body with a thickness of 2.5 mm and a cross-sectional width of 10 mm. The appearance and dimensions of the specimen are as follows: Figure 19 shown.

[0103] The reference standard used to perform the short beam shear test was ASTM D 2344. The loading method was three-point bending with a span of 16 mm. The loading rate during the test was 0.5 mm / min. The load and displacement during the test were recorded at a sampling frequency of 20 Hz. Figure 20 shown.

[0104] like Figure 21 As shown, according to the number of separation layers, separation failure can be divided into Figure 21 The monolayer separation failure in (a) and Figure 21 The multilayer separation failure in (b) is shown in Figure 1. The CT image contains two-dimensional tomographic sequence images of different faults corresponding to single-layer separation failure and multilayer separation failure. The shear strength of the five specimens was calculated according to formula (6).

[0105] Interlaminar shear strength of short beam shear test:

[0106]

[0107] Among them, τ M is the interlaminar shear strength, in megapascals (MPa); F is the failure load or maximum load, in Newtons (N); b is the specimen width, in millimeters (mm); and h is the specimen thickness, in millimeters (mm).

[0108] Step 5: Shear performance evaluation based on pore fractal dimension.

[0109] The pore box dimensions and porosity were calculated for the five short beam shear test specimens, as shown in the following example: Figure 22 As shown, specimen 1#, with the highest porosity, and specimen 4#, with the lowest porosity, correspond to the maximum and minimum values ​​of interlaminar shear strength, respectively. Specimens 2#, 3#, and 5# exhibit similar porosity and box dimensions. Specimens 2#, 3#, and 5# have essentially the same porosity, but differ in box dimension and shear strength. Specimen 1# exhibits a higher number of connected pores, while specimen 4# exhibits a higher number of isolated pores. This suggests that interlaminar connected pores with a pore size greater than 500 μm significantly influence the interlaminar shear performance of the material.

[0110] like Figure 23 As shown, the box dimension and shear strength are linearly fitted, and the fitting degree (R 2 ) was 0.9259, and the numerical correlation coefficient (Pearson, sr) was 0.9623. Analysis of short beam shear test results shows that as the number of connected pores increases, the pore distribution becomes more complex and heterogeneous. The smaller the box dimension, the lower the shear strength of the C / SiC composite. The study found that the box dimension not only quantitatively characterizes the complexity of the pore distribution but also provides a digital evaluation of the shear performance. In summary, the complexity and heterogeneity of the pore distribution can reduce interlaminar shear performance.

[0111] Pore ​​identification based on pore fractal dimension and geometric features: A single pore fractal dimension can reflect multiple key characteristic parameters of pores, such as area, porosity, and scale, and exhibits a positive correlation. Based on the fractal dimension and pore scale information of different tomographic images, the scale information of pores with different morphological characteristics is obtained. A correlation between fractal dimension and pore type is established, enabling accurate identification of pores across multiple scales and types, providing a universal quantitative basis for subsequent material performance optimization and process improvement.

[0112] Shear performance evaluation based on pore fractal dimension: The linear fitting relationship between pore box dimension and shear performance of C / SiC composite material was obtained by least square method, which showed a linear fit and the fitting degree (R 2 ) is 0.9259. This method can be used to evaluate the performance of shear specimens by the pore box dimension without damaging the specimens, and it is proved that the box dimension can effectively reflect the shear strength of the material.

[0113] The present application also provides an evaluation device for interlaminar shear performance of a special-shaped ceramic matrix composite material taking into account the pore type, comprising:

[0114] The acquisition module is used to acquire CT images of special-shaped ceramic matrix composite material specimens based on X-ray tomography technology.

[0115] The segmentation module is used to segment the pores in the CT image by using adaptive filtering, grayscale transformation and Otsu double threshold segmentation algorithm to determine a pore segmentation image.

[0116] The classification module is used to quantitatively describe the distribution characteristics of pores of different scales in the pore segmentation image based on the fractal dimension and a preset fractal dimension threshold, and to determine the relationship between each pore type and the pore box dimension in the pore segmentation image.

[0117] A characterization module is determined, which is used to use the pore box dimension as a detailed characterization of the pores based on the relationship between each pore type and the pore box dimension; the detailed characterization of the pores includes each pore type;.

[0118] The experimental module is used to perform a short beam shear test on the special-shaped ceramic matrix composite material specimen based on an electronic universal testing machine to determine the linear fitting relationship between the detailed characterization of the pores and the interlaminar shear strength of the short beam shear test.

[0119] The evaluation module is used to predict the interlaminar shear strength of the short beam shear test of the special-shaped ceramic matrix composite material specimen to be tested based on the linear fitting relationship, and complete the interlaminar shear performance evaluation of the special-shaped ceramic matrix composite material specimen to be tested.

[0120] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the computer program.

[0121] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the above method when executed by a processor.

[0122] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the above method when executed by a processor.

[0123] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0124] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRdM), magnetic random access memory (MRdM), ferroelectric random access memory (FRdM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RdM) or external cache memory, etc. By way of illustration and not limitation, RdM may be in various forms, such as static random access memory (SRdM) or dynamic random access memory (DRdM).

[0125] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0126] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0127] This application uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for evaluating the interlaminar shear performance of a special-shaped ceramic matrix composite material considering the pore type, characterized in that: include: Based on X-ray tomography technology, CT images of special-shaped ceramic matrix composite specimens are obtained; Adaptive filtering, grayscale transformation, and Otsu double-threshold segmentation algorithm are used to segment the pores in the CT image to determine a pore segmentation image; quantitatively describing the distribution characteristics of pores of different scales in the pore segmentation image based on the fractal dimension and a preset fractal dimension threshold, and determining the relationship between each pore type and the pore box dimension in the pore segmentation image; Based on the relationship between each pore type and the pore box dimension, the pore box dimension is used as a detailed representation of the pores; the detailed representation of the pores includes each pore type; A short beam shear test is performed on the special-shaped ceramic matrix composite specimen using an electronic universal testing machine to determine the linear fitting relationship between the detailed characterization of the pores and the interlaminar shear strength of the short beam shear test; Based on the linear fitting relationship, the interlaminar shear strength of the short beam shear test of the special-shaped ceramic matrix composite material specimen to be tested is predicted, and the interlaminar shear performance evaluation of the special-shaped ceramic matrix composite material specimen to be tested is completed.

2. The method for evaluating interlaminar shear performance of special-shaped ceramic matrix composite materials considering pore types according to claim 1, characterized in that: Adaptive filtering, grayscale transformation, and Otsu's double-threshold segmentation algorithm are used to segment the pores in the CT image to determine a pore segmentation image, specifically including: Performing filtering on the CT image using adaptive filtering to determine a filtered image; Performing grayscale transformation on the filtered image to determine an enhanced image; The pores in the enhanced image are segmented based on the Otsu double-threshold segmentation algorithm to determine a pore segmentation image.

3. The method for evaluating interlaminar shear performance of special-shaped ceramic matrix composite materials considering pore types according to claim 1, characterized in that: According to the fractal dimension and the preset fractal dimension threshold, the distribution characteristics of pores of different scales in the pore segmentation image are quantitatively described, and the relationship between each pore type and the pore box dimension in the pore segmentation image is determined, specifically including: Based on the fractal dimension, quantitatively describe the distribution characteristics of pores of different scales in the pore segmentation image, and determine the pore box dimension of each pore; Determining the pore type corresponding to each pore and the pore box dimension corresponding to each pore type based on the pore box dimension of each pore and a preset fractal dimension threshold; the pore types include isolated pores, mixed pores, and large-area connected pores; According to the pore type corresponding to each pore and the pore box dimension corresponding to each pore type, the relationship between each pore type and the pore box dimension is determined.

4. The method for evaluating interlaminar shear performance of special-shaped ceramic matrix composite materials considering pore types according to claim 3, characterized in that: After determining the relationship between each pore type and the pore box dimension according to the pore type corresponding to each pore and the pore box dimension corresponding to each pore type, the following steps are also included: Determining porosity based on a pore area of ​​each pore and an area of ​​the CT image; wherein the pore area is determined based on segmenting the pores; Determining the pore length corresponding to each pore type and the relationship between the porosity and the pore box dimension based on the porosity, the pore types, and the relationship between the pore types and the pore box dimension; Determining the relationship between the pore length and the pore box dimension according to the pore length corresponding to each pore type and the relationship between the pore box dimension and the pore type; Based on the relationship between the porosity and the pore box dimension, and the relationship between the pore length and the pore box dimension, the pore box dimension is used as a detailed characterization of the pores; the detailed characterization of the pores also includes porosity and pore length.

5. The method for evaluating interlaminar shear performance of special-shaped ceramic matrix composite materials considering pore types according to claim 1, characterized in that: A short beam shear test was conducted on the special-shaped ceramic matrix composite specimen using an electronic universal testing machine to determine the linear fitting relationship between the detailed characterization of the pores and the interlaminar shear strength of the short beam shear test, specifically including: Obtain the maximum load, width, and thickness of the special-shaped ceramic matrix composite specimen during the short beam shear test, and determine the interlaminar shear strength of the short beam shear test; Based on the least square method, the interlaminar shear strength of the short beam shear test and the pore box dimension, a linear fitting relationship between the pore box dimension and the interlaminar shear strength of the short beam shear test is determined.

6. The method for evaluating interlaminar shear performance of special-shaped ceramic matrix composite materials considering pore types according to claim 1, characterized in that: The interlaminar shear strength of the short beam shear test is: Among them, τ M is the interlaminar shear strength; F is the maximum load; b is the width of the special-shaped ceramic matrix composite specimen; h is the thickness of the special-shaped ceramic matrix composite specimen.

7. An evaluation device for interlaminar shear performance considering the pore type of a special-shaped ceramic matrix composite material, characterized in that: include: An acquisition module, used for acquiring CT images of special-shaped ceramic matrix composite specimens based on X-ray tomography technology; a segmentation module, configured to segment the pores in the CT image using adaptive filtering, grayscale transformation, and Otsu's double-threshold segmentation algorithm to determine a pore segmentation image; a classification module for quantitatively describing the distribution characteristics of pores of different scales in the pore segmentation image based on the fractal dimension and a preset fractal dimension threshold, and determining the relationship between each pore type and the pore box dimension in the pore segmentation image; determining a characterization module for using the pore box dimension as a detailed characterization of the pores based on a relationship between the pore types and the pore box dimension; the detailed characterization of the pores including the pore types and the pore lengths; An experimental module, for conducting a short beam shear test on the special-shaped ceramic matrix composite specimen based on an electronic universal testing machine, and determining a linear fitting relationship between a detailed characterization of the pores and the interlaminar shear strength of the short beam shear test; The evaluation module is used to predict the interlaminar shear strength of the short beam shear test of the special-shaped ceramic matrix composite material specimen to be tested based on the linear fitting relationship, and complete the interlaminar shear performance evaluation of the special-shaped ceramic matrix composite material specimen to be tested.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for evaluating the interlaminar shear properties of special-shaped ceramic matrix composite materials taking into account the pore type according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating the interlaminar shear performance of a special-shaped ceramic matrix composite material taking into account the pore type according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for evaluating the interlaminar shear performance of a special-shaped ceramic matrix composite material taking into account the pore type according to any one of claims 1 to 6 is implemented.

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