Composite material two-dimensional random pore generation method based on XCT image

Abstract

The invention relates to a composite material two-dimensional random pore generation method based on an XCT image, and the method comprises the following steps: collecting an initial state XCT image of a composite material, carrying out the preprocessing, and completing the manual marking of pores in a source image; according to the characteristic parameters, carrying out quantitative analysis on the pore areas of all the images; based on the quantitative analysis result, the circumscribed rectangles and intersection points of the pores are randomly determined; random inflection points are generated between the external intersection points, equidistant points are generated between the adjacent points, and multi-arc splicing contour generation is achieved; and after smoothing the sharp inflection points by using a polynomial fitting function, filling the internal region of the pore contour. According to the composite material two-dimensional random pore generation method based on the XCT image, more complex and changeable pore contours can be randomly generated, and the influence of pore structure parameters on the mechanical property of the material can be researched on the microscale.

Description

Technical Field

[0001] The invention relates to a method for generating pore profiles, in particular to a method for generating two-dimensional random pores in a composite material based on XCT images, and belongs to the technical field of computer vision. Background Art

[0002] Basalt fiber reinforced polymer (BFRP) is an advanced composite material system made by vacuum assisted resin transfer molding (VARTM) with basalt long fiber as reinforcement. With its low density and adjustable anisotropy, the industrial application of this material system has important strategic value in promoting the innovation of advanced manufacturing technology and realizing the lightweight upgrade of equipment.

[0003] X-ray Computed Tomography (XCT) is a non-destructive three-dimensional imaging technology based on the principle of X-ray transmission. It can achieve high spatial resolution characterization of the internal structure of materials through multi-angle projection data reconstruction. In the study of basalt fiber composites, XCT technology can non-destructively obtain full-scale micro-structural information from fiber bundle arrangement pattern to pore defect distribution, breaking through the dimensional limitations of traditional destructive detection methods. Through three-dimensional volume data reconstruction and image segmentation algorithms, the fiber-matrix interface characteristics and pore network topological parameters can be quantitatively analyzed, providing a high-precision digital image foundation for subsequent multi-scale structural modeling and performance correlation analysis. This technical system provides a key experimental characterization method for the study of composite material microstructure-performance correlation.

[0004] The ideal geometric modeling method mainly constructs the morphological characteristics of yarns in a parametric form, but the geometric models it generates often show obvious theoretical tendencies, which are manifested in the over-simplification of deformation patterns and the mechanical repetition of local topological features. This limitation stems from the mathematical constraints on the yarn deformation mechanism in the parametric modeling process - ideal functions are usually used to describe the macroscopic morphology of the yarn, but they fail to effectively characterize key physical mechanisms such as local compression deformation caused by yarn-yarn / yarn-matrix interactions during the manufacturing process. In order to break through this theoretical bottleneck, geometric modeling methods based on real physical mechanisms have gradually become the mainstream research paradigm. Its technical paths are mainly divided into two categories: one is to achieve the reverse reconstruction of the yarn microstructure based on XCT digital image processing technology, and the other is to forward predict the complex deformation behavior in the yarn forming process through multi-physical field coupling numerical simulation technology.

[0005] Although the existing research system has formed a relatively complete analytical framework for the characterization of yarn deformation mechanisms, it generally lacks systematic consideration of pore morphological characteristics. It should be pointed out that the nonlinear response and damage evolution path of the macroscopic mechanical properties of composite materials are essentially regulated by the synergistic effect of pore geometry, volume proportion and spatial distribution characteristics. There are two limitations in the current pore characterization research: first, the existing results focus on microscopic pore analysis, and the cross-scale coupling mechanism of the interaction between mesoscopic pores and yarn / matrix interface is obviously insufficient; second, conventional modeling methods often simplify pores into regular geometric shapes (such as rectangles, triangles or circles). This idealized treatment cannot characterize the asymmetric morphological characteristics and random distribution characteristics of pores caused by yarn extrusion in actual process (such as VARTM molding). Due to the shortcomings of the above-mentioned characterization technology, there is still a lack of systematic understanding of the quantitative mapping relationship between mesoscopic pore structure parameters and macroscopic mechanical response of materials.

[0006] Therefore, in order to solve the above problems, it is necessary to provide an innovative method for generating two-dimensional random pores in composite materials based on XCT images to overcome the above defects in the prior art. Summary of the invention

[0007] The purpose of the present invention is to provide a two-dimensional random pore generation method for composite materials based on XCT images, which can randomly generate more complex and changeable pore profiles and study the influence of pore structure parameters on the mechanical properties of materials at a microscopic scale.

[0008] To achieve the above object, the technical solution adopted by the present invention is: a method for generating two-dimensional random pores in a composite material based on XCT images, which comprises the following process steps: 1) Collect the initial state XCT image of the composite material and preprocess it, and complete the manual annotation of the pores in the source image; 2) Quantitatively analyze the pore areas of all images based on the characteristic parameters; 3) Based on the quantitative analysis results, randomly determine the pore's circumscribed rectangle and intersection points; 4) Generate random inflection points between circumscribed intersection points, and generate equally spaced points between adjacent points to achieve multi-arc splicing contour generation; 5) After smoothing the sharp inflection points using a polynomial fitting function, the inner area of ​​the pore contour is filled.

[0009] The method for generating two-dimensional random pores in a composite material based on XCT images of the present invention further comprises: the composite material is specifically a biaxial non-crimp warp knitted composite material.

[0010] The method for generating two-dimensional random pores in composite materials based on XCT images of the present invention is further as follows: the step 1) is specifically: 1-1), obtaining complete XCT source image data, and performing cropping and filtering on the XCT source image data; 1-2), based on the cropped and filtered XCT source image data, one image is extracted every ten images, and the characteristic areas such as warp, weft, matrix, and pores in the image are manually marked with different colors, and the connected areas with the same characteristics are painted with the same gray value; 1-3), use Boolean operations in the OpenCV library to extract manually annotated connected areas from the annotated images and establish a label dataset.

[0011] The method for generating two-dimensional random pores in composite materials based on XCT images of the present invention is further as follows: the step 2) is specifically: 2-1), using the width W and height H of the pore circumscribed rectangle as characteristic parameters to determine specific pore characteristics; 2-2), traverse the pore area in each image, calculate and record its circumscribed width W and height H, use the normal distribution to fit the width W and height H of all pores, and finally obtain the quantitative analysis results.

[0012] The method for generating two-dimensional random pores in composite materials based on XCT images of the present invention is further as follows: the step 3) is specifically: 3-1), randomly select the width W and height H of the circumscribed rectangle from the quantitative analysis results to determine the geometric characteristics of the pores; 3-2), locate the rectangle in the Cartesian coordinate system, and randomly select four intersection points on the four sides of the circumscribed rectangle, set as Ip1(x i1 ,y i1 ), Ip2(x i2 ,y i2 ), Ip3(x i3 ,y i3 ) and Ip4(x i4 ,y i4 ).

[0013] The method for generating two-dimensional random pores in composite materials based on XCT images of the present invention is further as follows: the step 4) is specifically: 4-1), four rectangular areas are formed between adjacent circumscribed intersection points; 4-2), redefine the ranges of the four rectangular areas respectively; 4-3), randomly generate multiple inflection points within the limited rectangular area; 4-4), determine the type of arc generated between the inflection point and the circumscribed intersection point or between the circumscribed intersection points; 4-5), generate equally spaced points between adjacent points; 4-6) Complete the generation of multi-arc splicing contour: Repeat steps 4-2) to 4-5) until all arc dividing points are calculated. The arc dividing points and the external connection points together constitute the outer contour features of the pore.

[0014] The method for generating two-dimensional random pores in composite materials based on XCT images of the present invention is further as follows: in the step 3-1), two adjacent intersection points (x in , y in ) and (x in+1， y in+1 ) forms a rectangle, the length and width of the rectangle are the difference between the horizontal coordinates and the vertical coordinates of adjacent intersection points, and the coordinates of the randomly generated inflection points are accurately defined by the following formula: min(x in , x in+1 )≤x≤max(x in ,x in+1 ) min(y in , y in+1 )≤y≤max(y in ,y in+1 ) The x coordinate range is from x in to x in+1 The y coordinate range is from the minimum value to the maximum value in y in to y in+1 The minimum to maximum value in .

[0015] The method for generating two-dimensional random pores in composite materials based on XCT images of the present invention is further as follows: in the step 3-3), the range of randomly generated inflection points follows the following formula: min(x in , x in+1 ) (1-a)≤x≤max(x in ,x in+1 )(1-b), 0 <a<1, 0<b<1,a> b min(y in , y in+1 ) (1-c)≤y≤max(y in ,y in+1 ) (1-d), 0 <c<1, 0<d<1,c> d; Among them, a, b, c, and d are coefficients determined to prevent excessive bending and mutation of the inflection point.

[0016] The method for generating two-dimensional random pores in composite materials based on XCT images of the present invention is also as follows: the step 5) is specifically: 5-1) Smooth the sharp turning points at the connection between regions: Use the polynomial curve fitting algorithm to globally reconstruct the discrete feature point set, eliminate the contour mutation points by establishing high-order differential continuity constraints, and finally form a smooth closed boundary that conforms to the actual pore geometry characteristics; 5-2) Fill the inner area of ​​the pore contour: store the coordinate values ​​of all pore contour points, and perform pixel-level filling operations on the topological closed area of ​​the pore contour geometric structure based on the polygon filling algorithm cv2.fillPoly of the OpenCV computer vision library.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. The method for generating two-dimensional random pores in composite materials based on XCT images of the present invention conducts in-depth research on the microstructure of biaxial non-crimp warp knitted composite materials based on XCT technology. By statistically analyzing the fine structural characteristics of yarns and pores, the microstructure of the material can be accurately described, and the influence of factors such as pore content, characteristic morphology, and distribution position on the mechanical properties of the microstructure can be analyzed, providing a scientific basis for further optimizing the preparation process and application of BNCFRP.

[0018] 2. Compared with the traditional pore modeling method that uses regular geometric shapes (circles, ellipses, polygons, etc.), the present invention effectively improves the morphological fidelity and spatial heterogeneous distribution characteristics of the generated pores by integrating the quantitative statistical characteristics of real pores; the generated synthetic pore images can not only provide data enhancement support for the initial defect semantic segmentation model based on deep learning, but also provide a high-fidelity initial defect model for composite material damage evolution simulation, which is helpful for in-depth research on the expansion path prediction of pore defects and the analysis of material failure mechanisms. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of the method for generating two-dimensional random pores in composite materials based on XCT images of the present invention.

[0020] Figure 2 This is a schematic diagram of the step 1) of the present invention after manually annotating the XCT image.

[0021] Figure 3 It is a schematic diagram of the qualitative description of pore characteristics in the present invention.

[0022] Figure 4 This is a schematic diagram of step 2) of the present invention after quantitative statistical fitting of pores in the XCT image.

[0023] Figure 5 It is a flow chart of step 4) of the present invention.

[0024] Figure 6This is a result display diagram of pores with different EI indices (aspect ratios) in an embodiment of the present invention, in order to verify the diversity of the pore generation results of this algorithm. DETAILED DESCRIPTION

[0025] Please refer to the instruction manual Figure 1 As shown, the present invention is a method for generating two-dimensional random pores in a composite material based on XCT images, which includes the following process steps: 1) Collect the initial state XCT image of the composite material and preprocess it, and complete the manual annotation of the pores in the source image.

[0026] In this embodiment, the composite material is specifically a biaxial non-crimp fabric-reinforced polymer composites (BNCFRP).

[0027] The specific steps of this step are: 1-1), obtain complete XCT source image data, and perform cropping and filtering on the XCT source image data. Specifically, the composite material sample is scanned using an XCT device to obtain complete XCT source data, which is then imported into the Avizo software for edge cropping, and the mean, median and non-local mean filters are used to reduce noise to ensure the quality of manual segmentation.

[0028] 1-2), based on the cropped and filtered XCT source image data, one image is extracted every ten images, and the characteristic areas such as warp, weft, matrix, and pores in the image are manually annotated with different colors. The connected areas with the same characteristics are painted with the same grayscale value. Finally, 138 images with a pixel length of 1431×1119 are obtained. The annotation results are shown in the attached figure. Figure 2 As shown in (a) and (b).

[0029] 1-3), use Boolean operations in the OpenCV library to extract manually annotated connected areas from the annotated images and establish a label dataset.

[0030] 2) Based on the characteristic parameters, the pore areas of all images are quantitatively analyzed.

[0031] The specific steps of this step are: 2-1), use the width W and height H of the pore circumscribed rectangle as characteristic parameters to determine the specific pore characteristics.

[0032] As attached Figure 3As shown in the figure, the pore features are composed of its contour and the closed pore area. The main geometric features can be described by the circumscribed rectangle with width W and height H. The pore contour must intersect with the rectangular frame at four intersections, thereby dividing the contour into four nonlinear curve segments. In addition, there is at least one inflection point between adjacent intersections, which further divides the nonlinear curve segment into shorter nonlinear curve segments, which have two states: concave and convex. In addition, these shorter nonlinear curve segments can be accurately approximated by a sufficient number of equally spaced points.

[0033] 2-2), traverse the pore area in each image in the label data set, calculate its circumscribed width W and height H and record them, use the normal distribution to fit the width W and height H of all pores, and finally get the quantitative analysis results. If the fitting value is higher, it means that the data fit is better, which means that the proportion of variability in the dependent variable that can be explained by the fitting model is higher. The fitting results are shown in the attached figure. Figure 4 As shown in (a) and (b).

[0034]

[0035] in, is a fitting function used to fit the statistical data of all pore widths W and heights H in the XCT image of the composite material. After the fitting is completed, this function can be used in combination with the pore generation algorithm to generate pseudo pores; e represents a natural constant, y 0, λ, μ, σ are constants, and their values ​​are determined by the fitting results of the fitting function on the statistical data of the external width W and height H.

[0036]

[0037] 3) Based on the quantitative analysis results, the pore's circumscribed rectangle and intersection points are randomly determined. The specific process is as follows: 3-1), randomly select the width W and height H of the circumscribed rectangle according to the quantitative analysis results of step 2) to determine the geometric characteristics of the pores.

[0038] 3-2), locate the rectangle in the Cartesian coordinate system, and randomly select four intersection points on the four sides of the circumscribed rectangle, set as Ip1(x i1 ,y i1 ), Ip2(x i2 ,y i2 ), Ip3(x i3 ,y i3 ) and Ip4(x i4 ,y i4 ), as attached Figure 5 As shown in (a) in .

[0039] 4) Generate random inflection points between circumscribed intersection points and generate equally spaced points between adjacent points to achieve multi-arc splicing contour generation.

[0040] The specific steps of this step are: 4-1), four rectangular areas are formed between adjacent circumscribed intersection points, that is, two adjacent intersection points (x in , y in ) and (x in+1， y in+1 ) forms a rectangle whose length and width are the difference between the horizontal and vertical coordinates of adjacent intersection points. The coordinates of the randomly generated inflection points can be accurately defined by the following formula: min(x in , x in+1 )≤x≤max(x in ,x in+1 ) min(y in , y in+1 )≤y≤max(y in ,y in+1 ) The x coordinate range is from x in to x in+1 The y coordinate range is from the minimum value to the maximum value in y in to y in+1 The minimum to maximum value in .

[0041] 4-2), redefine the range of the four rectangular areas respectively to avoid regional interference.

[0042] Randomly generating inflection points in a rectangular area may lead to interference areas, such as the intersection point and the ordinate or abscissa of the inflection point being parallel, resulting in unreasonable deformation of the contour. To this end, the range of randomly generated inflection points must be limited, following the following formula: min(x in , x in+1 ) (1-a)≤x≤max(x in ,x in+1 )(1-b), 0 <a<1, 0<b<1,a> b min(y in , y in+1 ) (1-c)≤y≤max(y in ,y in+1 ) (1-d), 0 <c<1, 0<d<1,c> d; Among them, a, b, c, and d are coefficients determined to prevent excessive bending and mutation of the inflection point.

[0043] 4-3), randomly generate multiple inflection points within the limited rectangular area.

[0044] In step 4-2), the normal range in which the inflection point can be generated is further limited, and an inflection point is randomly generated within the limited rectangular range. If the generated contour is required to have more complex features, more inflection points can be generated within the determined range according to the formula in step 4-2). In the present invention, the number of inflection points randomly generated between adjacent circumscribed intersection points is at most 1. The generated inflection point Fp1(x f1 , y f1 ) and Fp2(x f2 , y f2 ) as attached Figure 5 As shown in (b) in .

[0045] 4-4), determine the type of arc generated between the inflection point and the circumscribed intersection point or between the circumscribed intersection points.

[0046] As attached Figure 5 As shown in (c) in the figure, if a convex arc is to be generated between Ip1 and Fp1, the convex arc should be between the vector On the right side, connect the line segment Ip1Fp1 and draw a perpendicular bisector on the line segment. Calculate the angle between the line segment Ip1Fp1 and the two adjacent straight lines. In order to control the generated arc not to exceed the scope of the circumscribed rectangle, select the adjacent straight line with the smallest angle as a perpendicular line. The perpendicular line and the perpendicular bisector will intersect at point O1(x o1 , y o1 ). The radius of the convex arc can be calculated by the following formula:

[0047] Similarly, if you want to generate a concave arc between Fp1 and Ip2, the concave arc should be between vector On the left side, connect the line segment Fp1Ip2 and draw a perpendicular bisector on the line segment. Calculate the angle between the line segment Fp1Ip2 and the two adjacent straight lines. In order to control the generated arc not to exceed the scope of the circumscribed rectangle, select the adjacent straight line with the smallest angle as a perpendicular line. The perpendicular line and the perpendicular bisector will intersect at point O2 (x o2 , y o2 ). The radius of the concave arc can be calculated by the following formula:

[0048] Finally, through O1(x o1 , y o1 ) and O2(x o2 , y o2 ) is translated on the corresponding perpendicular bisector to control the smoothness of the generated arc, as shown in the attached figure. Figure 5 As shown in (d) in .

[0049] 4-5), generate equally spaced points between adjacent points.

[0050] The present invention takes a maximum of three equally divided points as an example. Figure 5 As shown in (e), using the translated O1'(x o1 ',y o1 ') and O2'(x o2 ', y o2 ') Establish a local coordinate system and obtain the equally divided angles as , , and Calculate the equally divided points E of all arcs based on the following formulas: spv (x esv , y esv ): .

[0051] 4-6) Complete the generation of multi-arc splicing contour: Repeat steps 4-2) to 4-5) until all arc dividing points are calculated. The arc dividing points and the external connection points together constitute the outer contour features of the pore.

[0052] 5) After smoothing the sharp inflection points using a polynomial fitting function, the inner area of ​​the pore contour is filled.

[0053] The specific steps of this step are: 5-1), smoothing the sharp inflection points between regions.

[0054] In the process of characterizing the pore contour based on equally spaced discrete points, the probabilistic occurrence of local curvature anomalies may lead to geometric discontinuity features of adjacent contour segments, thus forming sharp distortions that do not conform to the actual pore morphology. To solve this problem, the present invention uses a polynomial curve fitting algorithm to globally reconstruct the discrete feature point set, eliminates contour mutation points by establishing high-order differential continuity constraints, and finally forms a smooth closed boundary that conforms to the actual pore geometric characteristics. The morphological simulation effect is shown in the attached figure. Figure 5 This method significantly improves the physical rationality of the generated contour while maintaining the original topological distribution law.

[0055] 5-2), filling the inner area of ​​the pore outline.

[0056] The coordinate values ​​of all pore contour points are stored, and the polygon filling algorithm cv2.fillPoly based on the OpenCV computer vision library is used to perform pixel-level filling operations on the topological closed area of ​​the pore contour geometric structure. The implementation effect is shown in the attached figure. Figure 5 As shown in (h) in .

[0057] In order to verify the algorithm's ability to regulate pore morphology, this study, based on the EI parametric modeling framework, sets different aspect ratio thresholds to guide the geometric constraints in the pore generation process. Figure 6 As shown in (a) to (e) in the figure, the experimental results show that this method can effectively generate pore structures with multi-morphological characteristics, and its visual attributes such as contour curvature distribution and branch complexity all show significant diversity characteristics. Through morphological verification and comparative analysis, it is confirmed that the generated pores can well reproduce the defect distribution law of real materials at both the global topological structure and local geometric details level, indicating that the algorithm has good engineering applicability in modeling pore heterogeneity characteristics.

[0058] The above specific implementation methods are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for generating two-dimensional random pores in composite materials based on XCT images, characterized in that: The process steps include: 1) Collect the initial state XCT image of the composite material and preprocess it, and complete the manual annotation of the pores in the source image; 2) Quantitatively analyze the pore areas of all images based on the characteristic parameters; 3) Based on the quantitative analysis results, randomly determine the pore's circumscribed rectangle and intersection points; 4) Generate random inflection points between circumscribed intersection points, and generate equally spaced points between adjacent points to achieve multi-arc splicing contour generation; 5) After smoothing the sharp inflection points using a polynomial fitting function, the inner area of ​​the pore contour is filled.

2. The method for generating two-dimensional random pores in composite materials based on XCT images according to claim 1, characterized in that: The composite material is specifically a biaxial non-crimp warp knitted composite material.

3. The method for generating two-dimensional random pores in composite materials based on XCT images according to claim 1, characterized in that: The step 1) is specifically as follows: 1-1), obtaining complete XCT source image data, and performing cropping and filtering on the XCT source image data; 1-2), based on the cropped and filtered XCT source image data, one image is extracted every ten images, and the characteristic areas such as warp, weft, matrix, and pores in the image are manually marked with different colors, and the connected areas with the same characteristics are painted with the same gray value; 1-3), use Boolean operations in the OpenCV library to extract manually annotated connected areas from the annotated images and establish a label dataset.

4. The method for generating two-dimensional random pores in composite materials based on XCT images according to claim 1, characterized in that: The step 2) is specifically as follows: 2-1), using the width W and height H of the pore circumscribed rectangle as characteristic parameters to determine specific pore characteristics; 2-2), traverse the pore area in each image, calculate and record its circumscribed width W and height H, use the normal distribution to fit the width W and height H of all pores, and finally obtain the quantitative analysis results.

5. The method for generating two-dimensional random pores in composite materials based on XCT images according to claim 1, characterized in that: The step 3) is specifically as follows: 3-1), randomly select the width W and height H of the circumscribed rectangle from the quantitative analysis results to determine the geometric characteristics of the pores; 3-2), locate the rectangle in the Cartesian coordinate system, and randomly select four intersection points on the four sides of the circumscribed rectangle, set as Ip1(x i1 ,y i1 ), Ip2(x i2 ,y i2 ), Ip3(x i3 ,y i3 ) and Ip4 (x i4 ,y i4 ).

6. The method for generating two-dimensional random pores in composite materials based on XCT images according to claim 5, characterized in that: The step 4) is specifically as follows: 4-1), four rectangular areas are formed between adjacent circumscribed intersection points; 4-2), redefine the ranges of the four rectangular areas respectively; 4-3), randomly generate multiple inflection points within the limited rectangular area; 4-4), determine the type of arc generated between the inflection point and the circumscribed intersection point or between the circumscribed intersection points; 4-5), generate equally spaced points between adjacent points; 4-6) Complete the generation of multi-arc splicing contour: Repeat steps 4-2) to 4-5) until all arc dividing points are calculated. The arc dividing points and the external connection points together constitute the outer contour features of the pore.

7. The method for generating two-dimensional random pores in composite materials based on XCT images according to claim 6, characterized in that: In step 3-1), two adjacent intersection points (x in , y in ) and (x in+1， y in+1 ) forms a rectangle, the length and width of the rectangle are the difference between the horizontal coordinates and the vertical coordinates of adjacent intersection points, and the coordinates of the randomly generated inflection points are accurately defined by the following formula: min(x in , x in+1 )≤x≤max(x in ,x in+1 ) min(and in , and in+1 )≤y≤max(y in ,and in+1 ) The x coordinate range is from x in to x in+1 The y coordinate range is from the minimum value to the maximum value in y in to y in+1 The minimum to maximum value in .

8. The method for generating two-dimensional random pores in composite materials based on XCT images according to claim 6, characterized in that: In step 3-3), the range of randomly generated inflection points follows the following formula: min(x in , x in+1 )(1-a)≤x≤max(x in ,x in+1 )(1-b),0 min(y in , y in+1 )(1-c)≤y≤max(y in ,y in+1 ) (1-d),0 Among them, a, b, c, and d are coefficients determined to prevent excessive bending and mutation of the inflection point.

9. The method for generating two-dimensional random pores in composite materials based on XCT images according to claim 1, characterized in that: The step 5) is specifically as follows: 5-1) Smooth the sharp turning points at the connection between regions: Use the polynomial curve fitting algorithm to globally reconstruct the discrete feature point set, eliminate the contour mutation points by establishing high-order differential continuity constraints, and finally form a smooth closed boundary that conforms to the actual pore geometry characteristics; 5-2) Fill the inner area of ​​the pore contour: store the coordinate values ​​of all pore contour points, and perform pixel-level filling operations on the topological closed area of ​​the pore contour geometric structure based on the polygon filling algorithm cv2.fillPoly of the OpenCV computer vision library.

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