Three-dimensional model optimization method and system based on perchloric acid particle regional distribution

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a three-dimensional model optimization method and system based on perchloric acid particle regional distribution, and the method comprises the steps: scanning a to-be-modeled sample to obtain a two-dimensional CT image, marking a perchloric acid particle region in the two-dimensional CT image, and carrying out the preprocessing based on the marking information, the method comprises the following steps: obtaining a corresponding two-dimensional slice, overlapping all the two-dimensional slices into a temporary three-dimensional model, dividing the temporary three-dimensional model into a plurality of cubes, counting the volume ratio of perchloric acid particles in each cube, and carrying out filling processing based on the volume ratio to obtain a target three-dimensional model. The method has the advantages that compared with the prior art, the relation between real materials is reflected, complex behaviors and particle interaction of the real materials can be reflected in the subsequent calculation process, calculation precision is improved, the model can be simplified according to actual calculation resources or precision requirements, the model is easy to converge, and calculation is easy and convenient.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional model optimization based on regional distribution of perchloric acid particles, and in particular to a three-dimensional model optimization method and system based on regional distribution of perchloric acid particles. Background Art

[0002] Particle-filled materials are widely used in engineering and manufacturing, significantly impacting the overall performance of the material. Key advantages include increasing material stiffness and strength, reducing production costs, reducing product weight, customizing material properties, improving processability, and enhancing dimensional stability. The addition of particles can significantly enhance the mechanical properties of a material, such as enhancing its impact and wear resistance. However, particle filling can also present disadvantages, such as reducing toughness, increasing specific gravity, affecting surface gloss, and increasing processing difficulty. In certain environments, particle-filled materials may perform inferior to other materials or require additional handling or maintenance. Particle filling has diverse functions, including enhancing strength and stiffness, improving thermal properties, imparting specific functionalities (such as antistatic, thermal insulation, and printability), reducing costs, and improving dimensional stability. For example, inorganic fillers typically have a high hardness, and their addition can significantly increase the surface hardness of plastics. Furthermore, certain fillers can improve the processability of plastics. For example, barium sulfate and glass microspheres can increase the fluidity of resins, thereby improving their processability.

[0003] Establishing an explicit particle-filled material model in simulation software is crucial for accurately simulating the microstructure and behavior of materials. This approach can improve simulation accuracy because it can more accurately capture the geometry, size, and distribution of particles, as well as interactions between particles, such as collisions and sliding. The explicit particle model helps optimize material properties by simulating the effects of particle filling on material strength, stiffness, and thermal properties, as well as imparting specific functions to the material. In addition, it can simulate the behavior of materials under complex conditions, such as the coupling of thermal, fluid, chemical changes, and mechanical effects, providing a theoretical basis for material design and processing. By comparing and verifying with experimental data, model parameters can be further adjusted and optimized to ensure the reliability of simulation results, which is of great significance for research and application in the fields of materials science and engineering.

[0004] In the finite element modeling of particle-filled materials, three-dimensional simulation models of propellants have been established using molecular dynamics or particle-filling algorithms. These three-dimensional models better illustrate the relationship between particle size and mechanical properties than two-dimensional models. However, due to computational complexity and efficiency limitations, the computational units do not match the dimensions of the test objects used for verification, and the microscopic models do not match the material's true state, primarily in the description of particle packing and particle geometry. Currently, to ensure the feasibility of model calculations, these are often based on idealized assumptions, such as simplified particle shapes and types. These assumptions fail to fully reflect the complex behavior of real materials and particle interactions, leading to errors in the calculation results. Summary of the Invention

[0005] Based on this, it is necessary to address the existing three-dimensional model optimization problem based on the regional distribution of perchloric acid particles, and propose a three-dimensional model optimization method and system based on the regional distribution of perchloric acid particles.

[0006] A three-dimensional model optimization method based on the regional distribution of perchloric acid particles, the method comprising:

[0007] The micro-CT scanner is used to scan the sample to be modeled in multiple preset directions to obtain two-dimensional CT images corresponding to each direction;

[0008] Annotating a perchloric acid particle region in the two-dimensional CT image to obtain corresponding annotation information;

[0009] Preprocessing each of the two-dimensional CT images based on the annotation information to obtain corresponding two-dimensional slices;

[0010] Overlapping all the two-dimensional slices into a temporary three-dimensional model, and dividing the temporary three-dimensional model into a plurality of cubes according to preset intervals;

[0011] Count the volume percentage of perchloric acid particles in each cube;

[0012] The cubes with a volume ratio greater than a preset value are filled with perchloric acid particles, and the cubes with a volume ratio less than a preset value are filled with matrix particles to obtain a target three-dimensional model.

[0013] Furthermore, the step of marking the perchloric acid particle region in the two-dimensional CT image to obtain corresponding marking information includes:

[0014] grayscale the two-dimensional CT image to obtain a grayscale image;

[0015] Extracting an area larger than a preset threshold value in the grayscale image and recording the area as a perchloric acid particle area;

[0016] The perchloric acid particle region is marked in the two-dimensional CT image to obtain the marking information.

[0017] Furthermore, the step of marking the perchloric acid particle region in the two-dimensional CT image to obtain corresponding marking information includes:

[0018] Detecting the two-dimensional CT image using a preset edge detection algorithm and extracting multiple closed areas through contour detection;

[0019] Extracting edge features of each closed area;

[0020] determining whether each of the closed areas is the perchloric acid particle area according to the edge features;

[0021] The perchloric acid particle region is marked in the two-dimensional CT image to obtain the marking information.

[0022] Furthermore, the step of scanning the sample to be modeled in multiple preset directions using a micro CT scanning instrument to obtain two-dimensional CT images corresponding to each direction includes:

[0023] The sample to be modeled is placed on a rotating table, and the step length of the rotating table is set;

[0024] The micro CT scanning device is used to scan the sample to be modeled at each step length, thereby obtaining two-dimensional CT images corresponding to each direction.

[0025] Furthermore, the step of preprocessing each of the two-dimensional CT images based on the annotation information to obtain corresponding two-dimensional slices includes:

[0026] Each two-dimensional CT image and the corresponding annotation information are input into the preset image processing software for preprocessing to obtain the corresponding two-dimensional slices.

[0027] Furthermore, the preset image processing software is Avizo.

[0028] Furthermore, the step of dividing the temporary three-dimensional model into a plurality of cubes according to preset intervals includes:

[0029] Obtaining the actual volume of the sample to be modeled;

[0030] Setting a preset interval of the cube according to the actual volume;

[0031] The temporary three-dimensional model is divided into a plurality of cubes according to the preset intervals.

[0032] A three-dimensional model optimization system based on regional distribution of perchloric acid particles, the system comprising:

[0033] A scanning module is used to scan the sample to be modeled in multiple preset directions using a micro-CT scanning instrument to obtain a two-dimensional CT image corresponding to each direction;

[0034] a labeling module, configured to label the perchloric acid particle region in the two-dimensional CT image to obtain corresponding labeling information;

[0035] A preprocessing module, configured to preprocess each of the two-dimensional CT images based on the annotation information to obtain corresponding two-dimensional slices;

[0036] an overlapping module, configured to overlap all the two-dimensional slices into a temporary three-dimensional model, and divide the temporary three-dimensional model into a plurality of cubes according to preset intervals;

[0037] Statistics module, used to count the volume percentage of perchloric acid particles in each cube;

[0038] The filling module is used to fill the cubes whose volume ratio is greater than the preset value with perchloric acid particles, and fill the cubes whose volume ratio is less than the preset value with matrix particles to obtain the target three-dimensional model.

[0039] The beneficial effects of the present invention are as follows: the three-dimensional finite element mesh model that can be theoretically established accurately reflects the relationship between real materials compared with the existing technology, so that in the subsequent calculation process, the complex behavior of real materials and particle interactions can be reflected, the calculation accuracy is improved, and the model can be simplified according to actual computing resources or accuracy requirements. The model is easy to converge and the calculation is simple, and the model size can be arbitrarily selected according to the existing computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] in:

[0042] Figure 1 A diagram illustrating an application environment of a three-dimensional model optimization method based on regional distribution of perchloric acid particles in one embodiment;

[0043] Figure 2 is a flow chart of a method for optimizing a three-dimensional model based on regional distribution of perchloric acid particles in one embodiment;

[0044] Figure 3 FIG1 is a schematic diagram of a processing flow of a two-dimensional CT image in one embodiment;

[0045] Figure 4 FIG1 is a schematic diagram of a processing flow of a temporary three-dimensional model in one embodiment;

[0046] Figure 5 is a three-dimensional schematic diagram of different grid densities in one embodiment;

[0047] Figure 6 1 is a structural block diagram of a device for optimizing a three-dimensional model based on regional distribution of perchloric acid particles in one embodiment;

[0048] Figure 7 FIG. 1 is a structural block diagram of a computer device in one embodiment. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0050] Figure 1 This is a diagram of an application environment optimized based on a three-dimensional model of regional distribution of perchloric acid particles in one embodiment. Figure 1 The three-dimensional model optimization method based on the regional distribution of perchloric acid particles is applied to a three-dimensional model optimization system based on the regional distribution of perchloric acid particles. The three-dimensional model optimization system based on the regional distribution of perchloric acid particles includes a terminal 110 and a server 120. The terminal 110 and the server 120 are connected via a network. The terminal 110 can be a desktop terminal or a mobile terminal. The mobile terminal can be at least one of a mobile phone, a tablet computer, a laptop computer, etc. The server 120 can be implemented as an independent server or a server cluster consisting of multiple servers. The terminal 110 is used to acquire two-dimensional CT images, and the server 120 is used to process and analyze the two-dimensional CT images.

[0051] like Figure 2 As shown, in one embodiment, a three-dimensional model optimization method based on the regional distribution of perchloric acid particles is provided. This method can be applied to both terminals and servers. This embodiment uses the terminal as an example for illustration. The three-dimensional model optimization method based on the regional distribution of perchloric acid particles specifically includes the following steps:

[0052] S1: Scan the sample to be modeled in multiple preset directions using a micro-CT scanner to obtain two-dimensional CT images corresponding to each direction;

[0053] S2: marking the perchloric acid particle region in the two-dimensional CT image to obtain corresponding marking information;

[0054] S3: Preprocessing each of the two-dimensional CT images based on the annotation information to obtain corresponding two-dimensional slices;

[0055] S4: Overlapping all the two-dimensional slices into a temporary three-dimensional model, and dividing the temporary three-dimensional model into a plurality of cubes according to preset intervals;

[0056] S5: Count the volume percentage of perchloric acid particles in each cube;

[0057] S6: Filling the cubes whose volume proportion is greater than the preset value with perchloric acid particles, and filling the cubes whose volume proportion is less than the preset value with matrix particles, to obtain a target three-dimensional model.

[0058] As described in step S1 above, a microCT (Computed Tomography) scanner is used to scan the sample to be modeled in multiple preset directions to obtain two-dimensional CT images corresponding to each direction. It should be noted that scanning in multiple preset directions can be performed by moving the sample to be modeled or by moving the microCT scanner. The preset directions are multiple predetermined directions. In some embodiments, if the sample to be modeled is rotated, the rotation step size can be controlled to achieve scanning in each preset direction. The number of preset directions scanned is not limited, as long as three-dimensional modeling can be achieved. It should be noted that the number of preset directions should not be too small, otherwise the three-dimensional modeling error will be large, nor should it be too large, otherwise the subsequent computational effort will increase and the modeling speed will decrease. Specifically, the number of preset directions is 30-3000, i.e., 30-3000 corresponding two-dimensional CT images are obtained. The sample to be modeled is a sample using perchloric acid particles as a filler, and its matrix can be any other material, which is not limited in this application.

[0059] As described in step S2 above, the perchloric acid particle region in the two-dimensional CT image is annotated to obtain corresponding annotation information. The annotation method is to annotate the closed region therein, specifically, by manual identification or by using other image recognition software to mark the perchloric acid particles therein. Specifically, due to the higher density of perchloric acid particles, the region thereof in the CT image will be lower in brightness than the region containing other filler particles. Furthermore, the edges of perchloric acid particles are irregular, while the edges of other filler particles are more uniform. Therefore, the perchloric acid particle region can also be identified by edge detection. The region is then annotated, for example, by filling with color or other methods, which are not limited in this application.

[0060] As described in step S3 above, each of the two-dimensional CT images is preprocessed based on the annotation information to obtain corresponding two-dimensional slices. The preprocessing may include removing noise, filling pores, segmenting boundaries, and smoothing, so as to obtain corresponding two-dimensional slices. Specifically, the preprocessing may be performed using relevant image processing software. Figure 3 , select any one of the two-dimensional images, select most of the perchloric acid particles in the image through threshold segmentation, then expand outward from a certain point inside the particle to segment the particle boundary; remove all small noise points outside the boundary and fill the pores; finally, segment and smooth the boundary to obtain all the two-dimensional slice information of the perchloric acid particles.

[0061] As described in step S4 above, all the two-dimensional slices are overlapped to form a temporary three-dimensional model, and the temporary three-dimensional model is divided into a plurality of cubes according to a preset interval. All the two-dimensional slices are overlapped to form a temporary three-dimensional model, specifically using a 3D (three-dimensional) container (such as a NumPy array [N-dimensional array] or other three-dimensional data structure) to store the slices. Each slice is stacked together along the Z axis through a calculation function to form a three-dimensional model, and then cut into individual normal cubes, each of which has the same volume. Please refer to Figure 4 If the volume of the perchloric acid particles within each small cube exceeds 50%, the cube is considered perchloric acid; otherwise, it is the matrix. In image recognition software, the small cubes are numbered according to the default numbering rules in Abaqus (a comprehensive finite element software with powerful simulation capabilities), obtaining spatial information about both the perchloric acid particles and the matrix. When the cubes are small enough, the particle geometry, size, and distribution can be accurately captured.

[0062] As described in the above steps S5-S6, the volume percentage of the perchloric acid particles in each cube is counted. That is, in each cube, the volume percentage of the corresponding perchloric acid particles is calculated, and the cubes with a volume percentage greater than the preset value are filled with perchloric acid particles, and the cubes with a volume percentage less than the preset value are filled with matrix particles to obtain the target three-dimensional model. Among them, the preset value is a pre-set value, for example, it can be set to 50%, because the cube can improve the accuracy of the simulation results when simulating complex stress concentration areas and large deformation areas, especially when simulating complex geometric shapes and stress distributions. For particle-filled materials, the three-dimensional finite element model generated based on real microscopic images in the prior art has low accuracy and cumbersome modeling process. In response to this problem, the present invention optimizes its image accuracy, reduces warnings and errors of subsequent mesh models, and improves the convergence of numerical calculations by extracting particles, segmenting particle boundaries, removing independent and small noise points, filling internal pores, segmenting boundaries and smoothing, and binarizing CT scan images in avizo. Secondly, this method uses Python programs to automatically generate and replace the original material node file, without the need to traverse and read the INP (Input File), nor to perform unit and node matching to determine whether the pixel node belongs to the target object, thereby effectively simplifying the modeling process. Each face of the hexahedral unit is a rectangle, which makes it easier to achieve high-quality meshing and more accurate stress analysis in the calculation, so that a three-dimensional finite element mesh model can be established in theory. Compared with the existing technology, it accurately reflects the relationship between real materials, so that in the subsequent calculation process, the complex behavior of real materials and particle interactions can be reflected, the calculation accuracy is improved, and the model can be simplified according to the actual computing resources or accuracy requirements. The model is easy to converge and the calculation is simple, and the model size can be arbitrarily selected according to the existing computing resources.

[0063] In one embodiment, the step S2 of marking the perchloric acid particle region in the two-dimensional CT image to obtain corresponding marking information includes:

[0064] S201: grayscale the two-dimensional CT image to obtain a grayscale image;

[0065] S202: extracting an area larger than a preset threshold value in the grayscale image and recording the area as a perchloric acid particle area;

[0066] S203: Marking the perchloric acid particle region in the two-dimensional CT image to obtain the marking information.

[0067] As described in steps S201-S203 above, grayscaling the 2D CT image is a key step in detecting perchloric acid particle regions. Grayscaling is typically performed by calculating the grayscale value of each pixel, typically using a weighted average of the RGB (Red, Green, Blue) channels. This conversion method emphasizes the importance of each channel in calculating the final grayscale value. Specifically, the formula Gray = 0.2989 × R + 0.5870 × G + 0.1140 × B can be used, where Gray represents the grayscale value. This more accurately reflects the image's brightness information. Once the grayscale image is obtained, the next step is threshold segmentation. This process aims to extract the region containing perchloric acid particles from the grayscale image. By comparing the grayscale value of each pixel with a preset threshold, all pixels above this threshold are marked as foreground (perchloric acid particles), while pixels below this threshold are considered background. This method is not only simple and easy to implement, but also effectively separates the target object in many cases. Finally, in the annotation step, the researchers used the extracted perchloric acid particle region information to clearly annotate the original 2D CT images, generating visual annotation information. This annotation information is an important foundation for subsequent analysis and processing, providing reliable data support for further 3D reconstruction, volume calculation, and particle property analysis.

[0068] In one embodiment, the step S2 of marking the perchloric acid particle region in the two-dimensional CT image to obtain corresponding marking information includes:

[0069] S211: detecting the two-dimensional CT image using a preset edge detection algorithm, and extracting multiple closed areas through contour detection;

[0070] S212: Extracting edge features of each closed area;

[0071] S213: Determine whether each of the closed areas is the perchloric acid particle area according to the edge features;

[0072] S214: Marking the perchloric acid particle region in the two-dimensional CT image to obtain the marking information.

[0073] As described in the above steps S211-S214, Canny edge detection is used because it is very sensitive to edge changes and can better capture the features of irregular edges. After edge detection, contour detection can be used to extract edge areas. For perchloric acid particles, their irregularity can be detected by feature extraction, for example: shape features: such as roundness, aspect ratio, etc., can be used to distinguish objects with regular edges. Edge density: irregular edges often have more edge features, and the edge density of each contour can be calculated as a feature. In some embodiments, a neural network model can be trained with a large amount of feature data, and then the features can be identified by the neural network model to determine whether it is the perchloric acid particle area, and finally the perchloric acid particle area is marked in the two-dimensional CT image to obtain the marking information.

[0074] In a preferred embodiment, brightness detection can be performed in advance, that is, for each pixel in the two-dimensional CT image, the grayscale value is calculated (generally, the weighted average of the RGB channels is calculated), and threshold segmentation is used to extract the areas in the grayscale image that are higher than a preset threshold and recorded as temporary particle areas. Then, edge feature detection is performed to further identify the temporary particle areas, that is, the edge features of each temporary area are extracted; and based on the edge features, it is determined whether each of the closed areas is the perchloric acid particle area.

[0075] In one embodiment, the step S1 of scanning the sample to be modeled in multiple preset directions using a micro-CT scanning instrument to obtain two-dimensional CT images corresponding to each direction includes:

[0076] S101: placing the sample to be modeled on a rotating table, and setting the rotation step of the rotating table;

[0077] S102: Scanning the sample to be modeled at each step length by the micro CT scanning device to obtain two-dimensional CT images corresponding to each direction.

[0078] As described in steps S101-S102 above, the X-ray source emits X-rays that pass through the object being scanned on the sample stage. High-density areas experience significant attenuation. After the sample stage rotates one full revolution, the varying intensities of the radiation falling on the receiver form information about the object's interior. During operation, the sample stage is set to rotate one full revolution at a specific step length. At each step, a set of exposed projection images is scanned. This series of X-ray images constitutes a data set. Using a corresponding reconstruction algorithm, this data is processed to generate cross-sectional information about the specimen. Stacking these cross-sectional layers creates a two-dimensional grayscale image. Sequentially overlaying these two-dimensional images reveals the object's true three-dimensional structure. For example, in propellant, there are only two types of filler particles. Perchloric acid particles are the largest component by volume and have the largest size range, ranging from 50 to 300 μm. Due to the processing technology, their shapes are often close to elliptical, but their edges can still be irregular. Elliptical here refers to ignoring edge features or applying rounding, though a few particles may contain internal pores. Because of its small volume, aluminum powder is filled between the perchloric acid particles. Its size is much smaller than that of the perchloric acid particles, and there is an aluminum powder enrichment area.

[0079] In one embodiment, the step S3 of preprocessing each of the two-dimensional CT images based on the annotation information to obtain corresponding two-dimensional slices includes:

[0080] S301: Input each two-dimensional CT image and corresponding annotation information into a preset image processing software for preprocessing to obtain corresponding two-dimensional slices.

[0081] The pre-processing method mentioned in the above-mentioned step S301 adopts an effective strategy to improve the analysis accuracy of the perchloric acid particle area. Specifically, first, a point is arbitrarily selected as the center in the perchloric acid particle area, and then the area is expanded outward from this point as the starting point to perform regional growth. This method can ensure that the boundary area of ​​the perchloric acid particle is completely extracted, while effectively removing the small noise points outside the boundary, thereby reducing interference. After the initial expansion, the boundaries of the particles are segmented and smoothed to obtain more accurate two-dimensional slice information. The task of smoothing is to reduce the jagged edges of the contour, making subsequent analysis and three-dimensional reconstruction more accurate. It is worth mentioning that the preset image processing software Avizo is a powerful and flexible tool that is widely used in various scientific research and engineering fields, particularly in the fields of materials science, life sciences and geological sciences. Avizo provides a wealth of image processing and analysis tools that can handle a variety of three-dimensional data such as micro-CT, X-ray CT, and MRI (Magnetic Resonance Imaging). Researchers can easily complete image preprocessing, analysis, and visualization by simply entering relevant instructions, which can greatly improve data processing efficiency and help scientists extract valuable information from complex data sets, thereby promoting research progress and the transformation of results.

[0082] In one embodiment, the step S4 of dividing the temporary three-dimensional model into a plurality of cubes according to preset intervals includes:

[0083] S401: Obtaining the actual volume of the sample to be modeled;

[0084] S402: Setting a preset interval of the cube according to the actual volume;

[0085] S403: Divide the temporary three-dimensional model into a plurality of cubes according to the preset intervals.

[0086] As described in steps S401-S403 above, the actual volume of the sample to be modeled is measured or calculated so that cube segmentation can be performed in subsequent steps. It should be noted that this can also be an estimated volume, which is only used to determine the size of the subsequent segmented cube. The preset interval of the cube, that is, the size of the cube, is set according to the actual volume, and then it is divided into multiple cubes according to the preset interval. Hexahedral elements can provide more accurate representation than tetrahedrons, especially when simulating complex geometric shapes and stress distributions. Each face of the hexahedral element is a rectangle, which makes it easier to achieve high-quality meshing and more accurate stress analysis in the calculation. In finite element analysis software, hexahedral elements are widely used in mesh generation technologies that require higher quality to ensure the regularity and quality of the mesh. High-quality hexahedral meshes can improve the accuracy of simulation results, especially when simulating complex stress concentration areas and large deformation areas. However, for complex structures, traditional hexahedral meshes cannot achieve overall division of the model. Based on this consideration, the simplest pixel meshing method is used. The pixel grid (also known as the cube) is a virtual grid system composed of a series of equally spaced horizontal and vertical lines. It divides the screen into countless small squares, each of which is a pixel. The pixel size can be adjusted according to the grid density. The pixel grid helps us align interface elements, ensuring a neat and consistent layout, facilitating force transfer, and significantly reducing computational complexity.

[0087] It should be noted that the final modeling quality is closely related to the image quality and mesh accuracy. In order to make the mesh quality of perchloric acid particles better, the real model is selected with a cube with a side length of 1mm. Perchloric acid particles are filled in the matrix with a very high filling ratio. The size of perchloric acid particles is 50μm-400μm, and the shape of perchloric acid particles is mostly irregular polygons. Here, six mesh densities of 30 cubes / mm, 50 cubes / mm, 60 cubes / mm, 70 cubes / mm, 80 cubes / mm, and 100 cubes / mm in each side length direction are selected for modeling. The results are as follows: Figure 5 As shown in the figure. For perchloric acid particles, when the density is below 60 cubes / mm, the particle model deviates significantly from the actual shape, resulting in low model accuracy and adhesion. Furthermore, in real materials, the filler particles are individual and do not adhere to each other. Particle adhesion can form large, misshapen particles, affecting simulation results. Preliminary analysis of the arrangement density of perchloric acid, the primary filler particle in this type of propellant, indicates that a mesh density of at least 70 cubes / mm is required to avoid particle adhesion and maintain its original shape.

[0088] For micro-granular filler materials, CT scan data is used to extract and extract particles, segment particle boundaries, remove independent and small noise points, fill internal pores, segment boundaries, smooth, and perform binarization. This optimizes image accuracy, reduces warnings and errors in subsequent mesh models, and improves convergence of numerical calculations. A Python program directly generates hexahedral mesh node information for each material component, replacing the node information in the INP file generated by Abaqus. This is simple to use and facilitates practical calculations. For perchloric acid particles ranging in size from tens to hundreds of microns, when the single-sided mesh density is less than 60 cubes / mm, the particle model deviates significantly from the true shape, resulting in low model accuracy. Furthermore, in real materials, filler particles are individual and do not adhere to each other. Particle adhesion can result in bulky, misshapen particles, which can affect simulation results. For the perchloric acid filler particles used in this type of propellant, a mesh density of at least 70 cubes / mm is required to avoid particle adhesion and maintain their original shape.

[0089] Reference Figure 6 The present invention also provides a three-dimensional model optimization system based on the regional distribution of perchloric acid particles, the system comprising:

[0090] The scanning module 10 is used to scan the sample to be modeled in multiple preset directions using a micro-CT scanning instrument to obtain a two-dimensional CT image corresponding to each direction;

[0091] a labeling module 20 for labeling the perchloric acid particle region in the two-dimensional CT image to obtain corresponding labeling information;

[0092] A preprocessing module 30 is configured to preprocess each of the two-dimensional CT images based on the annotation information to obtain corresponding two-dimensional slices;

[0093] An overlapping module 40 is configured to overlap all the two-dimensional slices into a temporary three-dimensional model, and divide the temporary three-dimensional model into a plurality of cubes according to preset intervals;

[0094] A statistics module 50 is used to count the volume percentage of perchloric acid particles in each cube;

[0095] The filling module 60 is used to fill the cubes whose volume proportion is greater than the preset value with perchloric acid particles, and fill the cubes whose volume proportion is less than the preset value with matrix particles, to obtain a target three-dimensional model.

[0096] In one embodiment, the annotation module 20 includes:

[0097] A grayscale processing submodule, configured to perform grayscale processing on the two-dimensional CT image to obtain a grayscale image;

[0098] a marking submodule, for extracting an area in the grayscale image that is larger than a preset threshold and marking the area as a perchloric acid particle area;

[0099] The labeling submodule is used to label the perchloric acid particle area in the two-dimensional CT image to obtain the labeling information.

[0100] In one embodiment, the annotation module 20 includes:

[0101] a closed region extraction submodule, configured to detect the two-dimensional CT image using a preset edge detection algorithm and extract multiple closed regions by contour detection;

[0102] An edge feature extraction submodule, configured to extract edge features of each of the closed areas;

[0103] a perchloric acid particle region determination submodule, configured to determine whether each of the closed regions is the perchloric acid particle region based on the edge features;

[0104] The perchloric acid particle region labeling submodule is used to label the perchloric acid particle region in the two-dimensional CT image to obtain the labeling information.

[0105] In one embodiment, the scanning module 10 includes:

[0106] A placement submodule is used to place the sample to be modeled on a rotating table and set the step length of the rotating table;

[0107] The scanning submodule is used to scan the sample to be modeled at each step length by the micro CT scanning instrument, so as to obtain two-dimensional CT images corresponding to each direction.

[0108] In one embodiment, the pre-processing module 30 includes:

[0109] The annotation information input submodule is used to input each two-dimensional CT image and the corresponding annotation information into the preset image processing software for preprocessing to obtain the corresponding two-dimensional slices.

[0110] In one embodiment, the preset image processing software is Avizo.

[0111] In one embodiment, the overlapping module 40 includes:

[0112] An actual volume acquisition submodule, used to acquire the actual volume of the sample to be modeled;

[0113] A preset interval setting submodule, configured to set a preset interval of the cube according to the actual volume;

[0114] The division submodule is used to divide the temporary three-dimensional model into multiple cubes according to the preset intervals.

[0115] Figure 7 FIG1 shows an internal structure diagram of a computer device in an embodiment. The computer device can be a terminal or a server. Figure 7 As shown, the computer device includes a processor, a memory and a network interface connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor may implement a three-dimensional model optimization method based on the regional distribution of perchloric acid particles. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor may implement a three-dimensional model optimization method based on the regional distribution of perchloric acid particles. It will be understood by those skilled in the art that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0116] In one embodiment, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the following steps:

[0117] The micro-CT scanner is used to scan the sample to be modeled in multiple preset directions to obtain two-dimensional CT images corresponding to each direction;

[0118] Annotating a perchloric acid particle region in the two-dimensional CT image to obtain corresponding annotation information;

[0119] Preprocessing each of the two-dimensional CT images based on the annotation information to obtain corresponding two-dimensional slices;

[0120] Overlapping all the two-dimensional slices into a temporary three-dimensional model, and dividing the temporary three-dimensional model into a plurality of cubes according to preset intervals;

[0121] Count the volume percentage of perchloric acid particles in each cube;

[0122] The cubes with a volume ratio greater than a preset value are filled with perchloric acid particles, and the cubes with a volume ratio less than a preset value are filled with matrix particles to obtain a target three-dimensional model.

[0123] The three-dimensional finite element mesh model that can be established theoretically accurately reflects the relationship between real materials compared to existing technologies, so that in the subsequent calculation process, the complex behavior of real materials and particle interactions can be reflected, which improves the calculation accuracy. The model can also be simplified according to actual computing resources or accuracy requirements. The model is easy to converge and the calculation is simple. The model size can be selected at will according to the existing computing resources.

[0124] In one embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the processor performs the following steps:

[0125] The micro-CT scanner is used to scan the sample to be modeled in multiple preset directions to obtain two-dimensional CT images corresponding to each direction;

[0126] Annotating a perchloric acid particle region in the two-dimensional CT image to obtain corresponding annotation information;

[0127] Preprocessing each of the two-dimensional CT images based on the annotation information to obtain corresponding two-dimensional slices;

[0128] Overlapping all the two-dimensional slices into a temporary three-dimensional model, and dividing the temporary three-dimensional model into a plurality of cubes according to preset intervals;

[0129] Count the volume percentage of perchloric acid particles in each cube;

[0130] The cubes with a volume ratio greater than a preset value are filled with perchloric acid particles, and the cubes with a volume ratio less than a preset value are filled with matrix particles to obtain a target three-dimensional model.

[0131] The three-dimensional finite element mesh model that can be established theoretically accurately reflects the relationship between real materials compared to existing technologies, so that in the subsequent calculation process, the complex behavior of real materials and particle interactions can be reflected, which improves the calculation accuracy. The model can also be simplified according to actual computing resources or accuracy requirements. The model is easy to converge and the calculation is simple. The model size can be selected at will according to the existing computing resources.

[0132] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchl ink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0133] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0134] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A three-dimensional model optimization method based on the regional distribution of perchloric acid particles, characterized in that: The method comprises: The micro-CT scanner is used to scan the sample to be modeled in multiple preset directions to obtain two-dimensional CT images corresponding to each direction; Annotating a perchloric acid particle region in the two-dimensional CT image to obtain corresponding annotation information; Preprocessing each of the two-dimensional CT images based on the annotation information to obtain corresponding two-dimensional slices; Overlapping all the two-dimensional slices into a temporary three-dimensional model, and dividing the temporary three-dimensional model into a plurality of cubes according to preset intervals; Count the volume percentage of perchloric acid particles in each cube; The cubes with a volume ratio greater than a preset value are filled with perchloric acid particles, and the cubes with a volume ratio less than a preset value are filled with matrix particles to obtain a target three-dimensional model.

2. The three-dimensional model optimization method based on the regional distribution of perchloric acid particles according to claim 1, characterized in that: The step of marking the perchloric acid particle area in the two-dimensional CT image to obtain corresponding marking information includes: grayscale the two-dimensional CT image to obtain a grayscale image; Extracting an area larger than a preset threshold value in the grayscale image and recording the area as a perchloric acid particle area; The perchloric acid particle region is marked in the two-dimensional CT image to obtain the marking information.

3. The three-dimensional model optimization method based on the regional distribution of perchloric acid particles according to claim 1, characterized in that: The step of marking the perchloric acid particle area in the two-dimensional CT image to obtain corresponding marking information includes: Detecting the two-dimensional CT image using a preset edge detection algorithm and extracting multiple closed areas through contour detection; Extracting edge features of each closed area; determining whether each of the closed areas is the perchloric acid particle area according to the edge features; The perchloric acid particle region is marked in the two-dimensional CT image to obtain the marking information.

4. The three-dimensional model optimization method based on the regional distribution of perchloric acid particles according to claim 1, characterized in that: The step of scanning the sample to be modeled in multiple preset directions using a micro CT scanning instrument to obtain a two-dimensional CT image corresponding to each direction includes: The sample to be modeled is placed on a rotating table, and the step length of the rotating table is set; The micro CT scanning device is used to scan the sample to be modeled at each step length, thereby obtaining two-dimensional CT images corresponding to each direction.

5. The three-dimensional model optimization method based on the regional distribution of perchloric acid particles according to claim 1, characterized in that: The step of preprocessing each of the two-dimensional CT images based on the annotation information to obtain corresponding two-dimensional slices includes: Each two-dimensional CT image and the corresponding annotation information are input into the preset image processing software for preprocessing to obtain the corresponding two-dimensional slices.

6. The three-dimensional model optimization method based on regional distribution of perchloric acid particles according to claim 5, characterized in that: The preset image processing software is Avizo.

7. The three-dimensional model optimization method based on regional distribution of perchloric acid particles according to claim 1, characterized in that: The step of dividing the temporary three-dimensional model into a plurality of cubes according to preset intervals includes: Obtaining the actual volume of the sample to be modeled; Setting a preset interval of the cube according to the actual volume; The temporary three-dimensional model is divided into a plurality of cubes according to the preset intervals.

8. A three-dimensional model optimization system based on the regional distribution of perchloric acid particles, characterized in that: The system comprises: A scanning module is used to scan the sample to be modeled in multiple preset directions using a micro-CT scanning instrument to obtain a two-dimensional CT image corresponding to each direction; a labeling module, configured to label the perchloric acid particle region in the two-dimensional CT image to obtain corresponding labeling information; A preprocessing module, configured to preprocess each of the two-dimensional CT images based on the annotation information to obtain corresponding two-dimensional slices; an overlapping module, configured to overlap all the two-dimensional slices into a temporary three-dimensional model, and divide the temporary three-dimensional model into a plurality of cubes according to preset intervals; Statistics module, used to count the volume percentage of perchloric acid particles in each cube; The filling module is used to fill the cubes whose volume ratio is greater than the preset value with perchloric acid particles, and fill the cubes whose volume ratio is less than the preset value with matrix particles to obtain the target three-dimensional model.

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