Method for evaluating particle quality of particle reinforced composite material

Abstract

The invention provides a particle quality evaluation method for a particle reinforced composite material, and relates to the technical field of composite material performance analysis, and the method comprises the following steps: obtaining multi-dimensional image data of a to-be-evaluated particle reinforced composite material, and extracting morphological metrology parameters of reinforced phase particles and central position coordinates of damage in the material; constructing a geometric model of reinforced phase particles according to the morphometric parameters, and introducing a threshold factor k to evaluate the particle quality of the particle reinforced composite material; wherein the evaluation of the particle quality of the particle reinforced composite material comprises the evaluation of a particle damage state and / or particle distribution uniformity. Particles are approximately expressed on the basis of a geometric model, a judgment criterion is set by introducing a threshold factor, fracture and debonding damage are automatically classified and counted, meanwhile, distribution uniformity is evaluated on the basis of the interaction distance between the particles, and multi-scale full-automatic quantitative analysis of the material microstructure is achieved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of composite material performance analysis, and particularly relates to a particle quality evaluation method for a particle reinforced composite material. BACKGROUND

[0002] Particle reinforced metal matrix composites (PRMMC) significantly improve the specific strength, specific stiffness, wear resistance and high temperature performance of materials by introducing hard ceramic particles such as SiC, B4C and Al2O3 into metal matrices such as aluminum, titanium and magnesium, and have become indispensable key materials in the fields of aerospace, automobile industry and advanced equipment manufacturing. The preparation processes of PRMMC are various, including powder metallurgy, stir casting, squeeze casting, spray deposition and in-situ synthesis, etc. However, no matter which process is used, the final mechanical properties (such as strength, stiffness, toughness and fatigue life) of the material are highly dependent on the fine control of the microstructure. Pores, the number, size, morphology, spatial distribution of reinforcing particles, the interface bonding state and the existence of second phase particles jointly determine the load transfer efficiency, stress distribution and damage initiation and propagation behavior. The uniformity of particle distribution directly affects the stress dispersion and crack propagation path, and uniform distribution can effectively improve the strength and fracture toughness of the material, while particle agglomeration leads to stress concentration, induces micro-cracks and reduces fatigue life. In addition, the introduction of ceramic particles changes the failure mode. The multi-axial stress constraint at the crack front due to the difference in elastic modulus and plastic deformation between the particles and the matrix leads to damage behaviors such as particle fracture, debonding or matrix micro-cracking, which are crucial for understanding material failure.

[0003] Existing microstructure characterization techniques, such as optical microscopy (OM) observation, scanning electron microscopy (SEM) and X-CT, can clearly reveal the pore morphology, particle size and matrix structure of PRMMC, and commercial image processing software (such as ImageJ, Olympus Stream) can also quantify basic parameters such as porosity, particle area fraction and particle size distribution. However, existing technologies have significant shortcomings in the precise quantitative evaluation of particle spatial distribution uniformity and the systematic statistics of damage mechanisms. Traditional methods rely on manual statistics, which are low in efficiency, strong in subjectivity, difficult to handle large data, and lack standardized tools to accurately quantify particle distribution uniformity and damage interaction. This limits the in-depth understanding and optimization of PRMMC performance, and increases the cost of process trial and error.

[0004] Therefore, it is necessary to provide a particle quality evaluation method for a particle reinforced composite material to quantitatively evaluate particle damage and distribution uniformity behavior, and provide a scientific basis for material design and process optimization. SUMMARY

[0005] Therefore, the application provides a particle quality evaluation method of a particle reinforced composite material, which can solve the problem of low efficiency of the evaluation method in the prior art.

[0006] To solve the above problems, the application provides a particle quality evaluation method of a particle reinforced composite material, comprising the following steps:

[0007] Step 1): obtaining multi-dimensional image data of the particle reinforced composite material to be evaluated, and extracting morphometric parameters of the reinforcing phase particles based on the multi-dimensional image data;

[0008] Step 2): constructing a geometric model of the reinforcing phase particles according to the morphometric parameters, and introducing a threshold factor k to evaluate the particle quality of the particle reinforced composite material;

[0009] The evaluation of the particle quality of the particle reinforced composite material comprises evaluation of a particle damage state and / or uniformity of particle distribution.

[0010] Further, in the step 1), the multi-dimensional image data is one of three-dimensional X-ray computed tomography (CT) data, two-dimensional scanning electron microscope (SEM) image data and two-dimensional optical microscope (OM) image data.

[0011] Further, in the step 1), the morphometric parameters comprise center coordinates of the reinforcing phase particles, scale parameters in each principal axis direction, and area or volume.

[0012] Further, when the multi-dimensional image data is three-dimensional X-ray computed tomography (CT) data, the geometric model is an ellipsoid model.

[0013] The specific geometric equation of the ellipsoid model is as follows:

[0014]

[0015] where (x, y, z) is the three-dimensional space coordinates of any point on the surface of the ellipsoid;

[0016] (xc, yc, zc): is the coordinate of the center of the ellipsoid;

[0017] a is the half-axis length of the ellipsoid in the x-axis direction;

[0018] b is the half-axis length of the ellipsoid in the y-axis direction;

[0019] c is the half-axis length of the ellipsoid in the z-axis direction;

[0020] When the multi-dimensional image data is two-dimensional scanning electron microscope (SEM) image data or two-dimensional optical microscope (OM) image data, the geometric model is an ellipse model.

[0021] The specific geometric equation of the elliptical model is as follows:

[0022]

[0023] where (x, y) are the two-dimensional plane coordinates of any point on the ellipse;

[0024] (xc, yc): are the coordinates of the center of the ellipse;

[0025] a is the semi-axis length of the ellipsoid in the x-axis direction;

[0026] b is the semi-axis length of the ellipsoid in the y-axis direction.

[0027] Furthermore, the evaluation of the particle damage state includes the following steps:

[0028] Calculate the Euclidean distance dij between the damage center and the center of the reinforcement particle;

[0029] Calculate the distance sj between the intersection point of the line connecting the damage center and the center of the reinforcement particle on the surface of the geometric model and the center of the reinforcement particle;

[0030] If dij < k × sj, it is determined that the reinforcement particle is related to the damage;

[0031] Determine the damage type according to the number of particles related to the same damage.

[0032] Furthermore, the determination of the damage type according to the number of particles related to the same damage includes:

[0033] If the number of particles related to the damage is 0, it is determined as matrix damage;

[0034] If the number of particles related to the damage is 1, it is determined as particle debonding damage;

[0035] If the number of particles related to the damage is greater than or equal to 2, it is determined as particle fracture damage.

[0036] Furthermore, the evaluation of the particle damage also includes:

[0037] Calculate the total volume or total area of the particles corresponding to the particle debonding damage and the particle fracture damage, and calculate their respective percentages of the total volume or total area of all the reinforcement particles to obtain the debonding ratio and fracture ratio of the particle-reinforced composite material.

[0038] Furthermore, the evaluation of the particle distribution uniformity includes the following steps:

[0039] Traverse all the reinforcement particles and calculate the distance d between the centers of particle i and particle j ij ;

[0040] Calculate the distances si and sj between the centers of particle i and particle j and the surface of the geometric model in the direction of the line connecting the centers of particle i and particle j respectively;

[0041] If dij < k×(si + sj), it is determined that there is an agglomeration tendency between particle i and particle j.

[0042] Furthermore, the evaluation of the particle distribution uniformity further includes:

[0043] Statistically calculate the total volume or total area of all particles determined to have an agglomeration tendency, and calculate the percentage of it in the total volume or total area of all reinforcing phase particles, so as to obtain the proportion of agglomerated particles in the particle reinforced composite material.

[0044] Furthermore, the threshold factor k is 1.0 - 1.5.

[0045] The method for evaluating the particle quality of a particle reinforced composite material provided by the present invention has the following beneficial effects:

[0046] The evaluation method of the present invention includes the following steps: obtaining multi-dimensional image data of the particle reinforced composite material to be evaluated, extracting the morphometric parameters of the reinforcing phase particles based on the multi-dimensional image data; constructing a geometric model of the reinforcing phase particles according to the morphometric parameters, and introducing a threshold factor k to evaluate the particle quality of the particle reinforced composite material; wherein, the evaluation of the particle quality of the particle reinforced composite material includes the evaluation of the particle damage state and / or the particle distribution uniformity. It should be noted that this method approximately represents particles based on a geometric model, sets discrimination criteria by introducing a threshold factor, automatically classifies and statistically calculates fracture and debonding damages, and simultaneously evaluates the distribution uniformity based on the interaction distance between particles, realizing multi-scale full-automatic quantitative analysis of the material microstructure. The present invention can accurately evaluate the mechanical properties of the composite material, provide data support for the optimization of the preparation process, significantly reduce the experimental trial-and-error cost, and improve the R & D efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. The drawings in the following description are only exemplary, and for those of ordinary skill in the art, without creative efforts, other implementation drawings can be obtained according to the provided drawings.

[0048] Figure 1 is a data processing flow chart;

[0049] Figure 2 is a particle damage calculation flow chart;

[0050] Figure 3 Schematic diagram for determining particle damage type;

[0051] Figure 4 This is a flowchart for evaluating particle distribution uniformity;

[0052] Figure 5 Here is a schematic diagram for determining the uniformity of particle distribution: (a) is the initial data, (b) is the calculation of the distance between particles, and (c) is the evaluation of the uniformity of distribution.

[0053] Figure 6 These are 10 vol.% TiB2 / AlX-CT data: (a) is the three-dimensional structure of the particles, and (b) is a slice along the XZ direction.

[0054] Figure 7 The data is 10 vol.% TiB2 / 7085AlOM: (a) is the original image, and (b) is the extracted data.

[0055] Figure 8 The data are 17 vol.% SiC / 2024Al X-CT data: (a) Three-dimensional structure of particles, (b) XZ direction slices;

[0056] Figure 9 The data is 17 vol.% SiC / 2024AlOM: (a) is the original image, and (b) is the extracted data. Detailed Implementation

[0057] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the specific embodiments, structures, features, and effects according to the present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "an embodiment" or "an embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0058] The purpose of this invention is to provide a multi-scale quantitative analysis method for particle damage and distribution uniformity in particle-reinforced composite materials. By processing 3D CT data and 2D SEM / OM images, it accurately statistically analyzes particle fracture and debonding damage and quantifies particle distribution uniformity. Based on an ellipsoidal or elliptical geometric model, this method introduces a threshold factor to set a discrimination criterion, automatically classifies and statistically analyzes fracture and debonding damage, and simultaneously assesses distribution uniformity based on interparticle interaction distances, achieving multi-scale fully automated quantitative analysis of the material's microstructure. This invention can accurately evaluate the mechanical properties of composite materials, provide data support for optimizing manufacturing processes, significantly reduce experimental trial-and-error costs, and improve R&D efficiency. The specific scheme is as follows:

[0059] This invention provides a method for evaluating the particle quality of particle-reinforced composite materials, including a method for analyzing particle damage and distribution uniformity, the specific process of which is shown in the appendix. Figure 1 As shown, four different calculation methods are provided based on data type and calculation requirements, each including the following steps:

[0060] Step 1): Obtain three-dimensional X-ray computed tomography (CT) data, or two-dimensional scanning electron microscope (SEM) image data, or two-dimensional optical microscope (OM) image data of the particle-reinforced composite material;

[0061] Commercial image analysis software such as Avizo and ImageJ were used to preprocess the above two-dimensional or three-dimensional image data. This included: first, denoising, contrast enhancement, and thresholding segmentation were performed on the original images; then, all enhancement particles were separated from the images, and the new coordinates ((xc,yc,zc)), semi-axis length along the principal direction, and volume and other morphometric parameters of each particle were extracted; at the same time, automatic thresholding segmentation was used to extract damaged areas (such as pores or cracks) in the images, and their center coordinates were recorded.

[0062] Step 2): Construct a geometric model of the reinforcing phase particles based on the aforementioned morphometric parameters, and introduce a threshold factor k to quantitatively assess the particle damage state and / or particle distribution uniformity of the particle-reinforced composite material. Specifically, when the multidimensional image data is 3D X-ray computed tomography (CT) data, the geometric model is an ellipsoidal model; when the multidimensional image data is 2D scanning electron microscope (SEM) image data or 2D optical microscope (OM) image data, the geometric model is an elliptical model. The threshold factor k is 1.0-1.5 (for certain specific materials, a value exceeding this range can be determined manually).

[0063] It should be noted that this method approximates particles using a geometric model, introduces a threshold factor to set discrimination criteria, automatically classifies and statistically analyzes fracture and debonding damage, and assesses distribution uniformity based on interparticle interaction distances, thus achieving multi-scale, fully automated quantitative analysis of the material's microstructure. This invention can accurately evaluate the mechanical properties of composite materials, provide data support for optimizing preparation processes, significantly reduce experimental trial-and-error costs, and improve R&D efficiency.

[0064] CT data can provide detailed three-dimensional information on the internal microstructure of materials, laying the foundation for accurate identification of particles and damage. Damage statistics and uniform distribution at the two-dimensional scale, using scanning electron microscopy (SEM) or optical microscopy (OM) images as the primary input source, provide two-dimensional information on the internal microstructure of materials.

[0065] In some implementations, the assessment of particle damage status includes the following steps:

[0066] Calculate the Euclidean distance dij between the center of the damage and the center of the reinforcement phase particles;

[0067] Calculate the distance sj between the intersection point of the line connecting the center of the damage and the center of the reinforcement phase particles on the surface of the geometric model and the center of the reinforcement phase particles;

[0068] If dij < k × sj, then it is determined that the reinforcement phase particle is related to the damage;

[0069] According to the number of particles related to the same damage, determine the damage type; the specific process of this step is as Figure 2 and 3 shown, including:

[0070] If the number of particles related to the damage is 0, it is determined as matrix damage; if the number of particles related to the damage is 1, it is determined as particle debonding damage; if the number of particles related to the damage is greater than or equal to 2, it is determined as particle fracture damage;

[0071] Furthermore, calculate the total volume or total area of the particles corresponding to the particle debonding damage and the particle fracture damage, and calculate the percentages they respectively account for the total volume or total area of all the reinforcement phase particles, to obtain the debonding ratio and fracture ratio of the particle reinforced composite material.

[0072] In some embodiments, the evaluation of the particle distribution uniformity is as Figure 4 and 5 shown, and specifically includes the following steps:

[0073] Traverse all the reinforcement phase particles and calculate the distance d between the centers of particle i and particle j ij ;

[0074] Respectively calculate the distances si and sj between the centers of particle i and particle j and the surface of the geometric model in the direction of the line connecting the centers of particle i and particle j;

[0075] If dij < k × (si + sj), then it is determined that there is an agglomeration tendency between particle i and particle j;

[0076] Statistically calculate the total volume or total area of all the particles determined to have an agglomeration tendency, and calculate the percentage it accounts for the total volume or total area of all the reinforcement phase particles, to obtain the ratio of the agglomerated particles in the particle reinforced composite material.

[0077] Among them, the specific geometric equation of the ellipsoid model is:

[0078]

[0079] Among them, (x, y, z) are the three-dimensional space coordinates of any point on the surface of the ellipsoid;

[0080] (xc,yc,zc): are the coordinates of the center of the ellipsoid;

[0081] a is the length of the semi-axis of the ellipsoid along the x-axis;

[0082] b is the length of the semi-axis of the ellipsoid in the y-axis direction;

[0083] c is the length of the semi-axis of the ellipsoid in the z-axis direction;

[0084] The specific geometric equations of the elliptical model are as follows:

[0085]

[0086] Where (x,y) are the two-dimensional plane coordinates of any point on the ellipse;

[0087] (xc, yc): are the coordinates of the center of the ellipse;

[0088] a is the length of the semi-axis of the ellipsoid along the x-axis;

[0089] b is the length of the semi-axis of the ellipsoid in the y-axis direction.

[0090] Compared with the prior art, the present invention has the following significant advantages:

[0091] (1) Multi-scale analysis capability: This invention supports particle damage and particle distribution uniformity analysis at both three-dimensional and two-dimensional scales, and is applicable to CT data and two-dimensional image data, covering a variety of application scenarios from stereoscopic to planar images.

[0092] (2) High precision and automation: Through mathematical modeling and optimization algorithms, fully automated quantitative analysis of particle fracture, debonding and distribution uniformity is realized, reducing manual intervention and improving calculation accuracy and efficiency.

[0093] (3) Highly comprehensive: It integrates particle fracture, debonding and distribution uniformity analysis functions, which can comprehensively evaluate the microstructure characteristics of particle-reinforced composite materials and provide a scientific basis for material design and performance optimization.

[0094] (4) Flexibility and versatility: The method is applicable to different types of particle-reinforced composite materials, the input data format is simple, it is easy to combine with existing commercial image processing software, and it has a wide range of application prospects.

[0095] The present invention will be further described below with reference to specific embodiments.

[0096] Example 1

[0097] This embodiment provides a method for assessing particle damage in a 10 vol.% TiB2 / Al composite material, including the following steps:

[0098] Step 1): Obtain three-dimensional X-ray computed tomography (X-CT) data of the TiB2 / Al composite material, such as... Figure 6 As shown, the image includes sliced ​​images and extracted particle models, with a resolution of 0.65 μm;

[0099] The segmented particle model was analyzed using the commercial software AVIZO to extract the geometric information of the particles, including morphometric parameters such as the semi-axis length of the principal direction and the volume, and the data was saved as a CSV file.

[0100] Step 2): Construct an ellipsoidal model of the reinforcing phase particles based on the above morphometric parameters, and perform calculations by setting a threshold factor (k = 1.2).

[0101] The results showed that the debonding rate of the material was 2.28% and the fracture rate was 0.25%, indicating that the damage mode was mainly debonding, reflecting the relatively weak interfacial bonding strength.

[0102] Example 2

[0103] This embodiment provides a 10 vol.% TiB 2 / The particle damage assessment method for 7085Al composite materials includes the following steps:

[0104] Step 1): Obtain TiB 2 / OM images of 7085Al composite materials, such as Figure 7 As shown, the image includes the original image and the extraction results (particles are blue and damage is red). The center coordinates of the damage, as well as the position and geometric information of the particles, were obtained using AVIZO software, and the data was saved as a CSV file.

[0105] Step 2): Construct an elliptical model of the reinforcing phase particles based on the above morphometric parameters, and perform calculations by setting a threshold factor (k = 1.05).

[0106] Calculations showed that the material had a debonding rate of 15.78% and a fracture rate of 15.72%, indicating significant internal damage. Furthermore, the proportions of debonding and fracture were close.

[0107] Example 3

[0108] This embodiment provides a method for evaluating the particle uniformity of a 17 vol.% SiC / 2024Al composite material, including the following steps:

[0109] Step 1): Obtain three-dimensional X-ray computed tomography (X-CT) data of the SiC / 2024Al composite material, such as... Figure 8As shown, the sliced ​​image and the extracted particle model are presented. AVIZO software was used to perform Label Analysis on the segmented particles to obtain location information and geometric parameters, including particle center coordinates, principal axis semi-axis length, and volume, which were then saved as a standardized CSV file.

[0110] Step 2): Construct an ellipsoidal model of the reinforcing phase particles based on the above morphometric parameters, and set a threshold factor (k = 1.001) to perform homogeneity analysis.

[0111] The results showed that about 28.9% of the particles in the material had a tendency to agglomerate, indicating that the particle distribution was somewhat uneven, which provided an important reference for subsequent material optimization.

[0112] Example 4

[0113] This embodiment provides a method for evaluating the particle uniformity of a 17 vol.% SiC / 2024Al composite material, including the following steps:

[0114] Step 1): Obtain the OM image of the 17 vol.% SiC / 2024Al composite material, such as... Figure 9 As shown, the original image and the extracted particle distribution results are presented.

[0115] Step 2): Based on the aforementioned morphometric parameters, an elliptical model of the reinforcing phase particles was constructed, and a threshold factor (k = 1.001) was set for two-dimensional homogeneity analysis. The results show that approximately 37% of the particles in this material exhibit a tendency to aggregate. Compared to the three-dimensional analysis, the two-dimensional analysis shows greater data fluctuations due to the randomness of the sampling location, reflecting the potential impact of the sampling method on homogeneity assessment.

[0116] Through the above embodiments, the method of the present invention has successfully achieved quantitative analysis of particle damage and distribution uniformity in particle-reinforced composite materials at both three-dimensional and two-dimensional scales. The method is computationally efficient and yields accurate and reliable results, providing crucial support for the design, performance optimization, and quality control of composite materials.

[0117] It will be readily understood by those skilled in the art that, without conflict, the advantageous technical features of the above-mentioned methods can be freely combined and superimposed.

[0118] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention. The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the protection scope of the present invention.

Claims

1. A method for evaluating the particle quality of a particle-reinforced composite material, characterized in that, It includes the following steps: Step 1): Obtain the multi-dimensional image data of the particulate reinforced composite to be evaluated. Based on the multi-dimensional image data, extract the morphometric parameters of the reinforcing phase particles and the central position coordinates of the damage in the material; Step 2): Construct a geometric model of the reinforcing phase particles according to the morphometric parameters, and introduce a threshold factor k to evaluate the particle quality of the particulate reinforced composite; Among them, the evaluation of the particle quality of the particulate reinforced composite includes the evaluation of the particle damage state and / or the particle distribution uniformity.

2. The method for evaluating the particle quality of the particle-reinforced composite material according to claim 1, characterized in that, In the said Step 1): The multi-dimensional image data is one of three-dimensional X-ray computed tomography (CT) data, two-dimensional scanning electron microscope (SEM) image data, and two-dimensional optical microscope (OM) image data.

3. The method for evaluating the particle quality of the particle-reinforced composite material according to claim 1, characterized in that, In the said Step 1): The morphometric parameters include the central coordinates of the reinforcing phase particles, the scale parameters in the directions of each main axis, and the area or volume.

4. The method for evaluating the particle quality of the particle-reinforced composite material according to claim 2, characterized in that, When the multi-dimensional image data is three-dimensional X-ray computed tomography (CT) data, the geometric model is an ellipsoid model; The specific geometric equation of the ellipsoid model: When the multi-dimensional image data is two-dimensional scanning electron microscope (SEM) image data or two-dimensional optical microscope (OM) image data, the geometric model is an ellipse model; The specific geometric equation of the ellipse model:

5. The method for evaluating the particle quality of the particle-reinforced composite material according to claim 1, characterized in that, The evaluation of the particle damage state includes the following steps: Calculate the Euclidean distance dij between the damage center and the center of the reinforcing phase particle; Calculate the distance sj between the intersection point of the line connecting the damage center and the center of the reinforcing phase particle on the surface of the geometric model and the center of the reinforcing phase particle; If dij < k×sj, it is determined that the reinforcing phase particle is related to the damage; Determine the damage type according to the number of particles related to the same damage.

6. The method for evaluating the particle quality of the particle-reinforced composite material according to claim 5, characterized in that, The determination of the damage type according to the number of particles related to the same damage includes: If the number of particles related to the damage is 0, it is determined as matrix damage; [[ID= ​ 7. The method for evaluating the particle quality of the particle-reinforced composite material according to claim 6, characterized in that, ​ ​ 8. The method for evaluating the particle quality of the particle-reinforced composite material according to claim 1, characterized in that, ​ Iterate through all reinforcing phase particles and calculate the distance d between the centers of particle i and particle j. ij ; ​ ​ 9. The method for evaluating the particle quality of the particle-reinforced composite material according to claim 8, characterized in that, ​ ​ 10. The evaluation method according to any one of claims 1-9, characterized in that, ​

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