A method for analyzing the degradation performance of bone ash tree characterization material defects

Abstract

The present application relates to the field of material damage mechanics, and particularly relates to a kind of ossification tree characterization material defect details degradation performance analysis method.Three-dimensional topographic data of material internal defects are obtained by micro-CT scanning, defect features are extracted and converted into ossification tree structure by combining image processing algorithm, and defect anisotropic tensor model is constructed;3D printing technology is used to accurately reproduce single defect sample, and stiffness degradation index equation is established by mechanical test and symbolic regression analysis, to quantify the influence of defect on material performance.The present application is based on K-means clustering and autoencoder network, generates defect self-encoding fingerprint through feature weight matrix and adjacency matrix, realizes clustering analysis of 6 kinds of defects such as ring and sickle shape;3D printing technology is used to accurately reproduce the original shape of the defect, to realize the preparation of single defect material.By symbolic regression, a one-dimensional bubble stiffness degradation index equation is proposed to quantify the influence of material defects on material degradation.

Description

Technical Field

[0001] This invention relates to the field of material damage mechanics, and specifically to a method for analyzing the degradation performance of materials by characterizing the details of material defects using ossification trees. Background Technology

[0002] Traditional material defect analysis primarily relies on the following methods: microscopic imaging techniques, such as SEM (scanning electron microscopy), TEM (transmission electron microscopy), and X-ray computed tomography (CT), to observe the morphology and distribution of defects; non-destructive testing techniques, such as ultrasonic testing, eddy current testing, and infrared thermography, to identify internal defects; computational simulations, such as finite element analysis (FEA) and phase-field methods, to simulate the defect evolution process; and statistical methods, based on Weibull distribution and fractal theory, to quantify the randomness and complexity of defects.

[0003] Traditional techniques have limitations. Modeling relies on Euclidean geometry simplification, leading to the loss of geometric details. Noise in CT images may be misidentified as defect branches during morphological processing, resulting in redundant nodes or incorrect connections in the skeleton. Fractal dimension calculation depends on box counting, which may ignore the local curvature features of the defect, resulting in a weak correlation between the fractal dimension and stress concentration areas. Skeletonization algorithms are sensitive to image noise and edge discontinuities, potentially distorting the topology of the ossified tree. Furthermore, the high irregularity of defect morphology contradicts traditional parametric modeling methods, resulting in a lack of a defect representation system that integrates multi-scale features, and the inability to precisely control individual defect variables during experimental verification. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a method for analyzing the deterioration performance of materials by characterizing the details of defects in ossification trees. This invention obtains three-dimensional morphological data of internal defects in materials through micro-CT scanning, extracts defect features by combining image processing algorithms and converts them into ossification tree structures, and constructs a defect heterogeneous tensor model. A single defect sample is accurately reproduced by 3D printing technology. Through mechanical experiments and symbolic regression analysis, a stiffness deterioration index equation is established to quantify the impact of defects on material properties.

[0005] The technical solution adopted is as follows:

[0006] A method for analyzing the degradation properties of materials by characterizing the details of defects in ossification trees, comprising the following steps:

[0007] (1) Use an X-ray three-dimensional microscope scanning stage to perform CT scans on the interior of the material;

[0008] (2) The threshold segmentation algorithm is used to screen defects and perform seed point region segmentation to extract material defects;

[0009] (3) Perform grayscale processing, erosion and dilation morphological processing on the image material to eliminate noise in the image and fill small holes;

[0010] (4) Construct a defect heterogeneous tensor model, and use the skeletonization algorithm to further describe the defects in the composite material, and skeletonize the defects into a tree;

[0011] (5) Treat the skeleton of the defect as the trunk or branches of the tree, and the connection points of the skeleton as the nodes of the tree to form a tree network; Based on K-means clustering and autoencoder network, generate the autoencode fingerprint of the defect through the feature weight matrix and the adjacency matrix to realize defect clustering analysis.

[0012] (6) For 3D printing of a single defect, a magnification formula is constructed based on the printing resolution and the minimum radius of curvature of the defect;

[0013] (7) Perform tensile tests on a single defect and finally analyze the deterioration performance.

[0014] Preferably, in step (1), the resolution is 20-32 μm, and the material is fixed on the scanning stage.

[0015] Preferably, a threshold segmentation algorithm is used to divide the pixels in the image into defective solid skeletons. Let the original image be I and the threshold be T, then the binary image B is calculated using the following formula:

[0016]

[0017] Here, (x,y) represents the pixel position in the image.

[0018] Preferably, seed point region segmentation involves selecting one or more seed points as initial regions, determining the criteria for whether adjacent pixels belong to the same region, and adding adjacent pixels that meet the similarity criteria to the current region until no new pixels can be added.

[0019] Let the initial set of seed points be S, and the initial state of region R be R0 = S. The similarity criterion can be defined as:

[0020]

[0021] Where: I(x,y) is the gray value of pixel (x,y); mean(R) k ) is region R k The average gray value; T is the similarity threshold;

[0022] The region expansion process can be represented as:

[0023] R k+1 =R k ∪{(x,y)∣C(x,y,R k)=1};

[0024] Until R k+1 =R k This means that no new pixels can be added, thus extracting the individual defect.

[0025] Preferably, the defect performance is transformed into node and edge representations of an ossified tree; by iteratively deleting pixels on the boundary of the target object until no more can be deleted, the skeleton of the image is obtained. The skeleton structure is obtained through a skeleton extraction algorithm. The defect skeleton tree image is then used to effectively distinguish the role of nodes in the network by introducing node cosine similarity. The K-means clustering algorithm is used to achieve clustering analysis of defects through degree centrality, as shown in the following expression:

[0026]

[0027] In the formula, V represents the node clustering formula; argmin is the cluster that minimizes a certain function by searching all clusters; n is the number of nodes; c i It refers to the degree centrality of nodes; μ c It is the center of the cluster, and it is the average value of all nodes; k i It represents the number of existing edges connected to node i.

[0028] Preferably, construct a defective heterogeneous tensor:

[0029] S=W[D,AR,F,C,r min ];

[0030] In the formula, D is the defect diameter, referring to the maximum size of the defect, usually the distance between one edge of the defect and another; AR is the defect width-to-length ratio, referring to the ratio of the longest vertical chord of the defect to its diameter; F is the defect fractal dimension, which can quantitatively describe highly irregular shapes and can be used to study defects with multi-level, multi-scale fractal characteristics; C is the degree centrality, used to describe the importance of nodes; r min It is the minimum radius of curvature.

[0031] Preferably, the defect heterogeneous tensors are as follows:

[0032]

[0033] These represent heterogeneous tensors representing ring-shaped, sickle-shaped, hump-shaped, V-shaped, conical, and elongated defects, respectively.

[0034] Preferably, when 3D printing a single defect, the defect is embedded in the sample model to form a bubble defect, which is then loaded into slicing software. The slicing software divides the 3D model into thin layers and sets the printing parameters to generate a two-dimensional image of each layer so that the subsequent light source can cure the resin layer by layer. Next, the resin is introduced into the resin tank of the printer to ensure that there are no impurities. Finally, printing begins. The printer projects the two-dimensional image of each slice onto the liquid resin through a projection system, exposes it to the light source, and triggers a photocuring reaction to solidify the layer. The printing platform gradually moves upward, and the slice image of the next layer is projected and cured, and so on, stacking layer by layer until the entire model is printed.

[0035] Preferably, the minimum print size is determined by combining the printer resolution, and the formula for the magnification factor M is:

[0036]

[0037] In the formula, r min Minimum radius of curvature; A is the print area size; P is the printer resolution.

[0038] Preferably, the influence of different defects on the mechanical properties of polymer materials is analyzed. By integrating and processing the experimental data, the degradation degree curves of different defects are determined using the chi-square probability density function χ². 2 (ε,b), exponential term e -dε The regression equation RDIE is established by the three terms, including the constant term k, and its expression is as follows:

[0039] R die =a·χ 2 (ε,b)+c·e -dε +k

[0040] In the formula, a is the probability density function coefficient of the chi-square distribution, b is the degree of freedom parameter of the chi-square distribution, c is the coefficient of the exponential function, d is the exponent of the exponential function, and k is the constant term;

[0041] χ 2 (ε,b) is the chi-square probability density function, and its mathematical expression is as follows:

[0042]

[0043] Γ(x) is the gamma function, and its mathematical expression is as follows:

[0044]

[0045] The stiffness degradation exponent equations for ring-shaped, sickle-shaped, hump-shaped, V-shaped, conical, and elongated defects are R, respectively. die :

[0046]

[0047] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0048] This invention extracts micron-level defect features through CT scanning and skeletonization algorithms, and combines them with a heterogeneous tensor model to achieve cross-scale mapping from microscopic morphology to macroscopic mechanical properties, successfully quantifying the influence of large-diameter defects on the overall stiffness of the sample.

[0049] Based on K-means clustering and autoencoder network, an autoencoder fingerprint of defects is generated through feature weight matrix and adjacency matrix to achieve cluster analysis of six types of defects, including ring and sickle shapes; through symbolic regression, a one-dimensional bubble stiffness degradation index equation is proposed to quantify the impact of material defects on material degradation.

[0050] The skeletonization algorithm generates an ossified tree structure, and a tensor model is constructed by combining five types of heterogeneous parameters, including diameter, aspect ratio, fractal dimension, degree centrality and minimum radius of curvature, to achieve multi-scale fusion representation of geometric morphology, topological network and mechanical properties.

[0051] It pioneered a similarity criterion for 3D printing technology, accurately replicating the original shape of defects and enabling the preparation of materials with single defects. Attached Figure Description

[0052] Figure 1 The flowchart of CT reconstruction, image feature extraction and bubble defect skeletonization of the present invention.

[0053] Figure 2 A schematic diagram of defect extraction and skeletonization in this invention.

[0054] Figure 3 A schematic diagram of the tree heterogeneous tensor parameters of this invention.

[0055] Figure 4 The present invention provides a fitting curve for the stiffness degradation index equation.

[0056] Figure 5 Overall flowchart of the invention. Detailed Implementation

[0057] The accompanying drawings are for illustrative purposes only. The present invention will be further described below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments are only preferred examples of the present invention and do not represent all the technical contents of the present invention.

[0058] Example 1

[0059] This invention proposes a method for characterizing material defects and analyzing deterioration properties using ossification trees, aiming to characterize and analyze internal defects and deterioration properties of composite materials. The method specifically includes the following steps:

[0060] Step S1: Obtain three-dimensional morphological data of internal defects in the material using micro-CT scanning. Place the data on an X-ray three-dimensional microscope scanning stage with a resolution of 32 μm. To ensure scanning accuracy, the carbon fiber should be kept as stationary as possible during the rotation of the sample stage. Test results are as follows. Figure 1 As shown (the image is a cross-section of the test material), the gray part is the solid carbon fiber material, and the black part is the irregular pore / crack structure. The porosity of the material is 2.14%.

[0061] Step S2: To avoid processing a large amount of CT data, we selected areas with a variety of defects for cropping studies, and selected local cubic regions of defects in the sample as the research object.

[0062] Step S3: Perform multi-step processing on the material defect image:

[0063] (1) Using a threshold segmentation algorithm, the pixels in the image are divided into foreground (defects) and background (solid skeleton). Let the original image be I and the threshold be T, then the binary image B can be calculated using the following formula:

[0064]

[0065] Here, (x,y) represents the pixel position in the image.

[0066] (2) Region growing is a segmentation method based on seed points. It forms regions by gradually expanding pixels that satisfy similarity criteria, thus extracting individual defects, as detailed below:

[0067] In an image, one or more seed points are selected as initial regions. A criterion is used to determine whether adjacent pixels belong to the same region (e.g., the difference in grayscale values ​​is less than a certain threshold). Adjacent pixels that satisfy the similarity criterion are added to the current region until no new pixels can be added. Let the initial set of seed points be S, and the initial state of region R be R0 = S. The similarity criterion can be defined as:

[0068]

[0069] Where: I(x,y) is the gray value of pixel (x,y). mean(R) k ) is region R k The average gray value. T is the similarity threshold. The region expansion process can be represented as:

[0070] R k+1 =R k ∪{(x,y)∣C(x,y,R k )=1};

[0071] Until R k+1 =R kThis means that no new pixels can be added, thus extracting the individual defect.

[0072] (3) Then, the extracted individual defect images are converted to grayscale. There are three commonly used methods for image grayscale conversion: maximum value method, average value method, and weighted average method. Here, based on importance and other indicators, the three components are weighted and averaged with different weights. Since the human eye is most sensitive to green and least sensitive to blue, a more reasonable grayscale image can be obtained by weighting and averaging the RGB components according to the following formula:

[0073] I(x,y)=0.299·R(x,y)+0.587·G(x,y)+0.114·B(x,y).

[0074] (4) The opening operation is achieved by combining the operations of erosion and expansion:

[0075]

[0076] in, This represents an erosion operation on an image. This represents the dilation operation on the image, where B is a structuring element.

[0077] The opening operation can effectively eliminate isolated noise points outside the defect area (such as burrs and stray defects smaller than the structuring element) while maintaining the geometric shape of the main area, meeting the conformity requirement, and further improving image quality.

[0078] Step S4: Using the skeleton algorithm of image processing, a new concept of "ossified tree" representation of defects is proposed. By utilizing the feature similarity between complex networks, fractals and defects, a "tree heterogeneous tensor" containing diameter, aspect ratio, fractal dimension, degree centrality and minimum radius of curvature is established, and the defect performance is transformed into node representation and edge representation of ossified tree.

[0079] After image preprocessing, we obtained a binary image of the material defect. After removing some points from the binary image, the remaining points still retain their original shape. By iteratively deleting pixels on the boundary of the target object until no more can be deleted, we obtain the skeleton of the image. The specific operation is as follows:

[0080] Let the neighboring pixels of the current pixel P1 be P2, P3...P9, arranged in a clockwise direction. Complete the following two stages:

[0081] Phase 1:

[0082] a) 2 ≤ B(P1) ≤ 6;

[0083] b) A(P1) = 1;

[0084] c) P2*P4*P6=0;

[0085] d) P4*P6*P8=0;

[0086] Where B(P1) is the number of non-zero neighboring pixels of P, and A(P1) is the number of times that the 8 neighboring pixels of P change from 0 to 1.

[0087] Pixels that meet the above conditions are marked as pixels to be deleted, and their pixel values ​​are set to 0.

[0088] Phase Two:

[0089] a) 2 ≤ B(P1) ≤ 6;

[0090] b) A(P1) = 1;

[0091] c) P2*P4*P8=0;

[0092] d) P2*P6*P8=0;

[0093] The two phases differ only in steps (c) and (d). In phase one, steps (c) and (d) remove points on the eastern and southern boundary lines, as well as the corner point in the northwest. In phase two, steps (c) and (d) remove points on the western and northern boundary lines, as well as the corner point in the southeast. For a binary image, the algorithm iterates between phases one and two until a certain phase has no more pixels removed, at which point the algorithm terminates.

[0094] Let B k Let (x, y) be the image after the k-th iteration, and M1 and M2 be the sets of labeled pixels in the two stages, respectively. Then:

[0095]

[0096] Let the input defect binary image be The skeleton structure is obtained through a skeleton extraction algorithm:

[0097] S = Skeleton(D)

[0098] Its mathematical expression is:

[0099] S = {p∈D | p is a skeleton pixel that satisfies the skeleton definition condition}

[0100] In the characterization of material defects, complex tree structures often contain a large number of irrelevant or redundant details and may even form loops, leading to data redundancy and increased computational complexity. Therefore, the MST algorithm is introduced to simplify the geometric representation of defects and break the loop structure in the network. The MST algorithm eliminates all loops by deleting redundant edges while preserving key topological features, thereby reducing redundant information. The specific formula is as follows:

[0101]

[0102] In the formula, argmin represents finding an optimal set of edges E' such that the sum of the weights of the selected edges is minimized; Let E represent the set of edges of the minimum spanning tree, which is the set of edges of the original graph. 0 A subset of; w(e) represents the weight of an edge e.

[0103] Transform the skeleton S into an acyclic tree structure: T = (V, E), where:

[0104] It is a set of nodes, corresponding to the endpoints, branch points, and key feature points of the skeleton;

[0105] Let be the set of edges, representing the skeleton connections;

[0106] Node V i The coordinates are Side e ij The Euclidean length is l ij =‖x i -x j ‖2.

[0107] Introducing cosine similarity effectively distinguishes the roles of nodes in a network, determining whether node A is the intersection of edges AB and AC, or located on edge BC. Cosine similarity provides a mathematical basis for precise node location determination by analyzing the angular relationships between vectors. Two key thresholds, 10° and 170°, are used to analyze whether AB and AC are collinear. If 10° < θ < 170°, then point A is a node; if 0° < θ < 10° or 170° < θ < 180°, then point A is not a node. The thresholds of 10° and 170° are chosen to effectively ensure the positional relationship of node A, and the threshold judgment has high precision and sensitivity. 10°, as the upper limit for collinearity judgment, accurately identifies nearly parallel vector relationships, ensuring that node A is considered to be on edge BC when the included angle is less than this value. 170°, as the lower limit, covers the case of reverse collinearity, preventing the omission of this special case. Setting these two thresholds avoids ambiguous judgments, ensuring clear boundaries while considering the geometric characteristics of practical application scenarios, making the algorithm more practical and flexible.

[0108]

[0109] In the formula, V AB ·V AC Represented as the dot product of edge vectors; |V AB ||V AC | represents the product of the magnitudes of the edge vectors.

[0110] The defect skeleton tree image is then used to perform cluster analysis of defects through the K-means clustering algorithm, based on degree centrality. The expression is as follows:

[0111]

[0112] In the formula, V represents the node clustering formula; argmin is the cluster that minimizes a certain function by searching all clusters; n is the number of nodes; c i It refers to the degree centrality of nodes; μ c It is the center of the cluster, and it is the average value of all nodes; k i It represents the number of existing edges connected to node i.

[0113] Defect heterogeneous tensors are constructed using parameters including, but not limited to, diameter, aspect ratio, fractal dimension, degree centrality, and minimum radius of curvature.

[0114] S=W[D,AR,F,C,r min ];

[0115] In the formula, D is the defect diameter, referring to the maximum size of the defect, usually the distance between one edge of the defect and another; AR is the defect width-to-length ratio, referring to the ratio of the longest vertical chord of the defect to its diameter; F is the defect fractal dimension, which can quantitatively describe highly irregular shapes and can be used to study defects with multi-level, multi-scale fractal characteristics; C is the degree centrality, used to describe the importance of nodes; r min It is the minimum radius of curvature.

[0116] 1) Node V i The local region diameter is defined as the neighborhood N(v) i Distance between the two furthest points within )

[0117]

[0118] Where N(v) i ) as v i The largest inscribed circle neighborhood centered at the given point.

[0119] 2) AR is the defect width-to-length ratio, which refers to the ratio of the longest vertical chord of the bubble to its diameter:

[0120]

[0121] Where H is the longest chord of the bubble in the vertical direction (i.e., the length of the minor axis), and D is the diameter of the bubble (i.e., the length of the major axis).

[0122] 3) Calculate the fractal dimension of the node neighborhood using box counting:

[0123]

[0124] Where N(δ) is the number of boxes in the grid-covered node region with side length δ.

[0125] 4) Due to the large number of nodes in the ossified tree network, degree centrality is calculated using the most important node:

[0126]

[0127] Among them, e ij Let be the edge between nodes i and j.

[0128] 5) The minimum radius of curvature is the skeleton curve at v i curvature k(v) at the point i The reciprocal of )

[0129]

[0130] Where y′ and y″ are the first and second derivatives, respectively.

[0131] Step S5: Treat the skeleton of the defect as the trunk or branches of a tree, and the connection points of the skeleton as the nodes of the tree, forming a tree-like network. The ossified tree representation method not only highly restores the shape features of the defect, but also enables heterogeneous representation of defect details through the adjacency matrix of the tree.

[0132] Parameter illustration as follows Figure 3 As shown, the various defect heterogeneous tensors are as follows:

[0133]

[0134] These represent heterogeneous tensors representing ring-shaped, sickle-shaped, hump-shaped, V-shaped, conical, and elongated defects, respectively.

[0135] Step S6: 3D printing a single defect and preparing the test material is accomplished using Digital Light Processing (DLP) technology in 3D printing. First, the reconstructed defect is embedded into the sample model to form a bubble defect. Then, it is loaded into slicing software, which divides the 3D model into thin layers and sets printing parameters, including layer height and curing time. Typically, the slice size is between tens and hundreds of micrometers, generating a two-dimensional image of each layer so that the subsequent light source can cure the resin layer by layer. Next, the resin is introduced into the printer's resin tank, ensuring it is free of impurities. Finally, printing begins. The printer projects the two-dimensional image of each slice onto the liquid resin through a projection system, exposing it to a light source to trigger a photocuring reaction, solidifying the layer. The printing platform gradually moves upwards, projecting and curing the image of the next slice, and so on, stacking layer by layer until the entire model is printed.

[0136] To investigate the impact of different types of bubble defects on the degradation performance of the matrix material, we defined a magnification factor when embedding defects within the matrix material. Based on a combination of Canny edge detection and contour tracking algorithms, we extracted the contour boundary from the defect cross-sectional contour image and determined the minimum radius of curvature r of the contour. min After performing calculations and marking their positions, we obtained the formula for determining the minimum print size using the printer resolution. The formula for the magnification factor M is:

[0137]

[0138] In the formula, r min Minimum radius of curvature; A is the print area size; P is the printer resolution.

[0139] Step S7: The test used a servo-controlled tensile testing machine (model AI-7000-MU1, maximum load 5KN). The relevant tests were conducted according to the test standards. The specimen length was 150mm, the clamping end width was 20mm, the width of the middle tensile section was 10mm, the total height was 6mm, and the mark distance was 90mm. The measured specimen was clamped in the tensile testing machine's fixtures, ensuring the fixtures were aligned with the mark distances. Tensile testing was performed at room temperature (20℃) at a speed of 1mm / min. When the load reached a certain level, the load on the defective part reached its limit, leading to fracture. Fracture data was measured, and the influence of different defects on the mechanical properties of the polymer material was analyzed. Through the integration and processing of the test data, the result analysis curve of the defective specimen during the tensile process was plotted, as shown in the figure. Based on the integration and processing of the test data, the chi-square probability density function χ² was determined for the deterioration degree curves of different defects. 2 (ε,b), exponential term e -dε The regression equation RDIE is established by the three terms, including the constant term k, and its expression is as follows:

[0140] R die =a·χ 2 (ε,b)+c·e -dε +k;

[0141] In the formula, a is the probability density function coefficient of the chi-square distribution, b is the degree of freedom parameter of the chi-square distribution, c is the coefficient of the exponential function, d is the exponent of the exponential function, and k is the constant term.

[0142] χ 2 (ε,b) is the chi-square probability density function, and its mathematical expression is as follows:

[0143]

[0144] Γ(x) is the gamma function, and its mathematical expression is as follows:

[0145]

[0146] The stiffness degradation exponent equations for ring-shaped, sickle-shaped, hump-shaped, V-shaped, conical, and elongated defects are R, respectively. die :

[0147]

[0148] Introducing Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²) 2 The fit was validated using the mean absolute percentage error (MAPE) and other metrics. The formulas for these metrics are as follows:

[0149]

[0150] In the formula, n is the amount of data. It is the actual value of the i-th data point, while σ i These are the corresponding fitted values. It is the average value of the data. The smaller the MSE, RMSE, and MAPE, the better the R value. 2 The closer the value is to 1, the better the fit. By combining the curves, we quantified the impact of a single material defect on material degradation.

[0151] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for analyzing the degradation performance of materials by characterizing the details of defects in ossification trees, characterized in that, Includes the following steps: (1) Use an X-ray three-dimensional microscope scanning stage to perform CT scans on the interior of the material; (2) A threshold segmentation algorithm is used to screen defects and perform seed point region segmentation to extract material defects; (3) Perform grayscale processing, erosion and dilation morphological processing on the material image to eliminate noise in the image and fill small holes; (4) Construct a defect heterogeneous tensor model, and use the skeletonization algorithm to further describe the defects in the composite material, skeletonizing the defects into a tree; wherein, the defect heterogeneous tensor is constructed as follows: ； In the formula, D is the defect diameter, referring to the maximum size of the defect, usually the distance between one edge of the defect and another; AR is the defect width-to-length ratio, referring to the ratio of the longest vertical chord of the defect to its diameter; F is the defect fractal dimension, quantitatively describing highly irregular shapes, used to study defects with multi-level, multi-scale fractal characteristics; C is the degree centrality, used to describe the importance of nodes; r min It is the minimum radius of curvature; (5) Treat the skeleton of the defect as the trunk or branches of the tree, and the connection points of the skeleton as the nodes of the tree to form a tree network; based on K-means clustering and autoencoder network, generate the autoencoder fingerprint of the defect through the feature weight matrix and the adjacency matrix to realize defect clustering analysis. (6) For 3D printing of a single defect, a magnification formula is constructed based on the printing resolution and the minimum radius of curvature of the defect; (7) Perform a tensile test on a single defect and finally analyze the deterioration performance.

2. The method for analyzing the degradation performance of ossified tree material defect details according to claim 1, characterized in that, In step (1), the resolution is 20-32 μm, and the material is fixed on the scanning stage.

3. The method for analyzing the degradation performance of ossified tree material defect details according to claim 1, characterized in that, Using a threshold segmentation algorithm, the pixels in the image are divided into defective solid skeletons. Let the threshold be N, then the binary image B is calculated using the following formula: ； Where (x,y) represents the pixel position in the image. I ( x , y ) is a pixel ( x , y The grayscale value of ).

4. The method for analyzing the degradation performance of ossified tree material defect details according to claim 1, characterized in that, Seed point region segmentation: Select one or more seed points as the initial region, determine the criteria for whether adjacent pixels belong to the same region, and add adjacent pixels that meet the similarity criteria to the current region until no new pixels are added. Let the initial seed point set be S, and the initial state of region R be R0=S. The similarity criterion is defined as: ； Where: I(x,y) is the gray value of pixel (x,y); mean(R) k ) is region R k The average gray value; T is the similarity threshold; The region expansion process is represented as: ； Until R k+1 =R k This means that no new pixels are added, thus allowing individual defects to be extracted.

5. The method for analyzing the degradation performance of ossified tree material defect details according to claim 1, characterized in that, The defective performance is transformed into node and edge representations of an ossified tree; The skeleton of the image is obtained by iteratively deleting pixels on the boundary of the target object until no more pixels can be deleted. The skeleton structure is then obtained through a skeleton extraction algorithm. The defect skeleton tree image is then processed by introducing node cosine similarity to effectively distinguish the roles of nodes in the network. Finally, K-means clustering is used to perform clustering analysis of defects based on degree centrality. The expression is as follows: ； ； In the formula, It represents the node clustering formula; arg min searches all clusters to find the cluster that minimizes a certain function; It is the number of nodes; It refers to the degree centrality of nodes; It is the center of the cluster, and is the average value of all nodes; It is an existing node The number of connected edges.

6. The method for analyzing the degradation performance of ossified tree material defect details according to claim 1, characterized in that... The defect heterogeneous tensors are shown below: ； These represent heterogeneous tensors representing ring-shaped, sickle-shaped, hump-shaped, V-shaped, conical, and elongated defects, respectively.

7. The method for analyzing the degradation performance of ossified tree material defect details according to claim 6, characterized in that, When 3D printing a single defect, the defect is embedded in the sample model to form a bubble defect. It is then loaded into the slicing software, which divides the 3D model into thin layers and sets the printing parameters to generate a two-dimensional image of each layer so that the subsequent light source can cure the resin layer by layer. Next, pour the resin into the printer's resin tank, ensuring there are no impurities. Finally, printing begins. The printer projects a two-dimensional image of each slice onto the liquid resin through a projection system, exposes it to a light source, triggers a photocuring reaction, and solidifies the layer. The printing platform gradually moves upwards, and the sliced ​​image of the next layer is projected and solidified. This process is repeated layer by layer until the entire model is printed.

8. The method for analyzing the degradation performance of ossified tree material defect details according to claim 7, characterized in that, Determine the minimum print size and magnification factor based on the printer resolution. The formula is: ； In the formula, Minimum radius of curvature; This refers to the size of the printing area; It refers to the printer resolution.

9. The method for analyzing the degradation performance of ossified tree material defect details according to claim 8, characterized in that, The effects of different defects on the mechanical properties of polymer materials were analyzed. By integrating and processing experimental data, analytical curves of defective specimens during tensile testing were generated. For the degradation degree curves of different defects, the probability density function of the chi-square distribution was determined. exponent term The regression equation R is established using the three terms, including the constant term k. die Its expression is: ； In the formula, a is the probability density function coefficient of the chi-square distribution, b is the degree of freedom parameter of the chi-square distribution, c is the coefficient of the exponential function, d is the exponent of the exponential function, and k is the constant term; Let be the probability density function of the chi-square distribution, and its mathematical expression is as follows: ； It is a gamma function; The stiffness degradation index equations for annular, sickle-shaped, hump-shaped, V-shaped, conical, and elongated defects are as follows: 。

Images