Multi-component sample three-dimensional modeling method and system based on micro-CT scanning

Abstract

The invention provides a multi-component sample three-dimensional modeling method and system based on micro-CT scanning, and the method comprises the steps: collecting a two-dimensional CT image set, carrying out the graying, setting different threshold values, carrying out the binarization, obtaining a target binarization image set of each component, carrying out the overlapping, obtaining a sub-three-dimensional model of each component, and carrying out the three-dimensional modeling of each component. And inputting each sub-three-dimensional model into a preset three-dimensional container to obtain a target three-dimensional model corresponding to the to-be-modeled sample. The method has the beneficial effects that the internal structure of the to-be-modeled sample is obtained in a multi-angle and high-precision manner, so that the accuracy and reliability of data are greatly improved, the distribution relationship of each component in a three-dimensional space is finely displayed, a rich data basis is provided for subsequent material performance analysis, and the method is suitable for popularization and application. And researchers can deeply analyze the mechanical properties, thermal properties and the like of the multi-component material.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-component sample three-dimensional modeling based on micro-CT scanning, and in particular to a multi-component sample three-dimensional modeling method and system based on micro-CT scanning. Background Art

[0002] Granular-filled materials are widely used in many engineering fields, such as civil engineering, materials science, and chemical engineering. The properties and behavior of these materials are influenced by the arrangement, shape, size, and interactions of their internal particles. Therefore, a deep understanding of the microstructure of granular-filled materials and their behavior under varying conditions is crucial for optimizing their design and improving their properties. Simulation, as an important research tool, plays a key role in this process.

[0003] Currently, the simulation calculations of granular filler materials are usually carried out using numerical methods such as the finite element method (FEM) and the discrete element method (DEM). However, in reality, granular filler materials are usually composed of multiple types of filler materials, and the characteristics and interactions of these materials may significantly affect the overall performance. In existing simulation methods, in order to reduce the amount of calculation and improve the quality of the mesh, researchers often ignore the influence of minor components and focus on the simulation of major particles. Although this simplification improves the computational efficiency to a certain extent, it also leads to a decrease in the accuracy of the simulation results, which cannot fully reflect the behavior of the actual material. Summary of the Invention

[0004] Based on this, it is necessary to address the existing three-dimensional modeling problem of multi-component samples based on micro-CT scanning, and propose a three-dimensional modeling method and system for multi-component samples based on micro-CT scanning.

[0005] A method for three-dimensional modeling of a multi-component sample based on micro-CT scanning, the method comprising:

[0006] Scan the modeling sample in multiple preset directions using a micro-CT scanning instrument to obtain a corresponding two-dimensional CT image set;

[0007] performing grayscale processing on the two-dimensional CT images in the two-dimensional CT image set to obtain a grayscale image set;

[0008] Obtaining the physical properties of each component of the sample to be modeled, and setting different thresholds for the grayscale image set according to the physical properties of each component to generate a binary image set corresponding to each component;

[0009] Labeling corresponding component regions in each of the binary image sets to obtain a corresponding target binary image set;

[0010] Overlap each target binary image set to obtain a sub-3D model of each component;

[0011] Each sub-3D model is input into a preset 3D container to obtain the target 3D model corresponding to the sample to be modeled.

[0012] Furthermore, after the step of inputting each sub-three-dimensional model into a preset three-dimensional container to obtain a target three-dimensional model corresponding to the sample to be modeled, the method further includes:

[0013] Dividing the target three-dimensional model into a plurality of cubes according to preset intervals;

[0014] Count the volume proportions of each component in each cube;

[0015] Fill the entire cube with the component with the largest volume share to obtain the target three-dimensional model.

[0016] Furthermore, before the step of labeling the corresponding component regions in each of the binary image sets to obtain the corresponding target binary image set, the method further includes:

[0017] Detecting the target binary image in the target binary image set by a preset edge detection algorithm, and extracting multiple closed areas by contour detection;

[0018] Extracting edge features of each closed area;

[0019] determining whether each of the closed areas is a component area of ​​a corresponding component according to the edge features;

[0020] Convert the area that is not a component area into another color of the binary image.

[0021] Furthermore, the step of inputting each sub-three-dimensional model into a preset three-dimensional container to obtain a target three-dimensional model corresponding to the sample to be modeled includes:

[0022] The material properties and boundary conditions corresponding to each sub-3D model are obtained and input into a preset 3D container to obtain the target 3D model corresponding to the sample to be modeled.

[0023] Furthermore, after the step of obtaining the material properties and boundary conditions corresponding to each sub-three-dimensional model and inputting them into a preset three-dimensional container to obtain a target three-dimensional model corresponding to the sample to be modeled, the method further includes:

[0024] Performing a simulation experiment on the target three-dimensional model to obtain a prediction result, and performing an experiment corresponding to the simulation experiment on the sample to be modeled to obtain an actual result;

[0025] The predicted result and the actual result are compared, and the validity of the target three-dimensional model is evaluated based on the comparison result.

[0026] Furthermore, after the step of overlapping each target binary image set to obtain a sub-3D model of each component, the method further includes:

[0027] The corresponding pore threshold is set according to the physical properties of the pores to generate a binary pore image set corresponding to the pores;

[0028] Annotating corresponding pore regions in the binary pore image set to obtain a corresponding target binary pore image set;

[0029] Overlap the target binary pore image set to obtain the sub-3D pore model corresponding to the pores;

[0030] Inputting the sub-three-dimensional pore model into the target three-dimensional model to obtain a temporary three-dimensional model;

[0031] The empty parts in the temporary three-dimensional model are supplemented with a matrix, thereby obtaining an accurate three-dimensional model corresponding to the sample to be modeled.

[0032] Furthermore, the step of scanning the sample to be modeled in multiple preset directions using a micro-CT scanning instrument to obtain a corresponding two-dimensional CT image set includes:

[0033] The sample to be modeled is placed on a rotating table, and the step length of the rotating table is set;

[0034] The sample to be modeled at each step length is scanned by the micro CT scanning instrument, thereby obtaining two-dimensional CT images corresponding to various directions, thereby obtaining a two-dimensional CT image set.

[0035] A multi-component sample three-dimensional modeling system based on micro-CT scanning, the system comprising:

[0036] A scanning module, used to scan the sample to be modeled in multiple preset directions using a micro-CT scanning instrument to obtain a corresponding two-dimensional CT image set;

[0037] a grayscale processing module, configured to perform grayscale processing on the two-dimensional CT images in the two-dimensional CT image set to obtain a grayscale image set;

[0038] an acquisition module, configured to acquire the physical properties of each component of the sample to be modeled, and set different thresholds for the grayscale image set according to the physical properties of each component, so as to generate a binary image set corresponding to each component;

[0039] A labeling module, configured to label the corresponding component regions in each of the binary image sets to obtain a corresponding target binary image set;

[0040] An overlapping module is used to overlap each target binary image set to obtain a sub-3D model of each component;

[0041] The input module is used to input each sub-3D model into a preset 3D container to obtain a target 3D model corresponding to the sample to be modeled.

[0042] The present invention has the following beneficial effects: By acquiring a set of two-dimensional CT images, grayscaling them, and binarizing them using different thresholds, a target set of binarized images of each component is obtained. These images are then overlapped to obtain a sub-3D model of each component. Each sub-3D model is then input into a preset 3D container to obtain the target 3D model corresponding to the sample to be modeled. This method enables multi-angle, high-precision acquisition of the internal structure of the sample to be modeled, significantly improving the accuracy and reliability of the data. It also provides a detailed display of the distribution relationships of the components in 3D space, providing a rich data foundation for subsequent material performance analysis, enabling researchers to more deeply analyze the mechanical and thermal properties of multi-component materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] 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.

[0044] in:

[0045] Figure 1 A diagram illustrating an application environment of a method for three-dimensional modeling of multi-component samples based on micro-CT scanning in one embodiment;

[0046] Figure 2 A flowchart of a method for three-dimensional modeling of a multi-component sample based on micro-CT scanning in one embodiment;

[0047] Figure 3 A schematic diagram of a process for three-dimensional modeling of a multi-component sample based on micro-CT scanning in one embodiment;

[0048] Figure 4 A block diagram of a device for three-dimensional modeling of multi-component samples based on micro-CT scanning in one embodiment;

[0049] Figure 5 FIG. 1 is a structural block diagram of a computer device in one embodiment. DETAILED DESCRIPTION

[0050] 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.

[0051] Figure 1 This is a diagram of the application environment for three-dimensional modeling of multi-component samples based on micro-CT scanning in one embodiment. Figure 1 The multi-component sample three-dimensional modeling method based on microCT scanning is applied to a multi-component sample three-dimensional modeling system based on microCT scanning. The multi-component sample three-dimensional modeling system based on microCT scanning 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 a two-dimensional CT image set, and the server 120 is used to analyze the two-dimensional CT image set.

[0052] like Figure 2 As shown, in one embodiment, a method for three-dimensional modeling of a multi-component sample based on micro-CT scanning is provided. This method can be applied to both a terminal and a server. This embodiment uses the terminal as an example for illustration. The method for three-dimensional modeling of a multi-component sample based on micro-CT scanning specifically includes the following steps:

[0053] The present invention provides a method for three-dimensional modeling of a multi-component sample based on micro-CT scanning, the method comprising:

[0054] S1: Scan the sample to be modeled in multiple preset directions using a micro-CT scanner to obtain a corresponding two-dimensional CT image set;

[0055] S2: performing grayscale processing on the two-dimensional CT images in the two-dimensional CT image set to obtain a grayscale image set;

[0056] S3: obtaining the physical properties of each component of the sample to be modeled, and setting different thresholds for the grayscale image set according to the physical properties of each component to generate a binary image set corresponding to each component;

[0057] S4: marking the corresponding component regions in each of the binary image sets to obtain a corresponding target binary image set;

[0058] S5: overlapping each target binary image set to obtain a sub-3D model of each component;

[0059] S6: Input each sub-3D model into a preset 3D container to obtain a target 3D model corresponding to the sample to be modeled.

[0060] As described in step S1 above, a microCT scanner (Computed Tomography) scans the sample to be modeled in multiple preset directions to obtain a corresponding set of two-dimensional CT images. The first step of the method is to scan the sample to be modeled in multiple directions using the microCT scanner to obtain two-dimensional CT images in multiple preset directions, thereby forming a set of two-dimensional CT images. MicroCT technology has the characteristics of high resolution and high contrast, which can clearly capture the internal microstructure of the sample, providing rich raw data for subsequent modeling.

[0061] As described in step S2 above, the obtained two-dimensional CT image set is grayscaled to obtain a grayscale image set more suitable for subsequent analysis. The purpose of grayscale processing is to reduce the complexity of the image, making subsequent processing more efficient. By adjusting the grayscale level of the image, the contrast between different components can be enhanced, providing support for subsequent image segmentation.

[0062] As described in step S3 above, the physical properties of each component of the sample to be modeled are obtained, and different thresholds are set for the grayscale image set according to the physical properties of each component to generate a binary image set corresponding to each component. According to these physical properties, appropriate thresholds can be set for different components. Specifically, since each component has different energy absorption of X-rays, it has different brightness, and therefore has different grayscale values ​​in the grayscale image. Therefore, different thresholds are used to set grayscale thresholds, which can be set specifically according to the grayscale value of each component in the grayscale image. In addition, the grayscale value of each component in the grayscale image can be obtained in advance through experiments, thereby achieving accurate segmentation of the grayscale image. The output of this step is a binary image set corresponding to each component, in which different components are clearly separated to facilitate subsequent analysis. Among them, binarization is a common technique in image processing. It converts an image into an image with only two grayscale values ​​(usually 0 and 255, representing black and white, respectively) (i.e., a binary image). The physical properties of each component are specifically the amount of energy absorbed by X-rays. Based on the principle of CT scanning, materials of different densities have different X-ray absorption capabilities. The denser the structure, the stronger the X-ray absorption capacity, the higher the grayscale value, and the "brighter" it appears in the reconstructed image. Based on this characteristic, the microscopic components of the propellant can be easily distinguished from the grayscale image. This application uses aluminum particles and perchloric acid particles as examples. Aluminum particles have the highest density and the strongest X-ray absorption capacity. The structure with the highest grayscale value (brightest color) in the image is the aluminum particle; the density of perchloric acid particles is lower than that of aluminum particles, so the structure with a lower grayscale value than aluminum particles is the perchloric acid particle; followed by the HTPB (Hydroxyl-terminated polybutadiene) matrix. Furthermore, since air absorbs almost no X-rays and its grayscale value is essentially zero (black), the black areas in the image represent pores, or initial defects. Therefore, by setting multiple thresholds based on brightness, we can extract binary image sets representing different components.

[0063] As described in step S4 above, the corresponding component regions in each of the binary image sets are annotated to obtain a corresponding target binary image set. The annotations are used to better identify the spatial distribution of each component. The resulting target binary image set clearly displays the precise location of each component, providing the necessary information for constructing a three-dimensional model. The annotation method is not limited, and for example, annotation can be performed by coloring or other methods, such as circling.

[0064] As described in step S5 above, each target binary image set is overlapped to obtain a sub-3D model of each component. Figure 3By overlapping, the component three-dimensional model corresponding to the target binary image can be generated, which reflects the arrangement and mutual relationship of various components in the three-dimensional space, and can help understand and reconstruct the entire three-dimensional model.

[0065] As described in step S6 above, each sub-three-dimensional model is input into a preset three-dimensional container to obtain a target three-dimensional model corresponding to the sample to be modeled, and each sub-three-dimensional model is input into a preset three-dimensional container to be combined into a target three-dimensional model corresponding to the sample to be modeled. The transition from sub-three-dimensional model to overall three-dimensional model construction is completed, and the microstructure of the particle filling material can be vividly presented in the computer. Using micro-CT scanning, the internal structure of the sample to be modeled can be obtained in a multi-angle and high-precision manner, thereby greatly improving the accuracy and reliability of the data, and meticulously displaying the distribution relationship of each component in three-dimensional space, providing a rich data basis for subsequent material performance analysis, enabling researchers to more deeply analyze the mechanical properties, thermal properties, etc. of multi-component materials.

[0066] In one embodiment, after step S6 of inputting each sub-three-dimensional model into a preset three-dimensional container to obtain a target three-dimensional model corresponding to the sample to be modeled, the method further includes:

[0067] S701: Divide the target three-dimensional model into a plurality of cubes according to preset intervals;

[0068] S702: Count the volume proportions of each component in each cube;

[0069] S703: Fill the entire cube with the component with the largest volume share to obtain the target three-dimensional model.

[0070] As described in above-mentioned steps S701-S703, the target three-dimensional model is cut into individual normal bodies, and the volume of each cube is the same. The component with the largest volume ratio in each small cube is determined to be the component. In image recognition software, the small cube is numbered according to the default numbering rule in Abaqus (a finite element software with powerful comprehensive simulation computing ability), and the spatial information of each component is obtained. When the cube is small enough, the geometric shape, size and distribution of the particles can be accurately captured. The present invention is directed to this problem, by extracting particle selection, particle boundary segmentation, removing independent and small noise points, filling internal pores, boundary segmentation and smoothing, binarization processing to CT scan images in avizo (Ai Weizhuo), optimizing its image accuracy, reducing the warnings and errors of subsequent grid models, and improving the convergence of numerical calculations. Secondly, this method realizes the automatic generation and replacement of original material node files by Python program, without traversing and reading INP (Input File, input file), without also performing unit and node matching, judging whether pixel nodes belong to target objects, thereby effectively simplifying the modeling process. Each face of the hexahedral element is a rectangle, which makes it easier to achieve high-quality meshing and more accurate stress analysis in calculations. This allows a theoretically achievable three-dimensional finite element mesh model to be established. Compared with existing technologies, it accurately reflects the relationship between real materials, allowing the complex behavior of real materials and particle interactions to be reflected in subsequent calculations, thereby improving calculation accuracy. The model can also be simplified based on actual computing resources or accuracy requirements. The model is easy to converge, the calculation is simple, and the model size can be arbitrarily selected based on existing computing resources.

[0071] In one embodiment, before the step S4 of labeling the corresponding component regions in each of the binary image sets to obtain the corresponding target binary image set, the method further includes:

[0072] S301: detecting a target binary image in the target binary image set by using a preset edge detection algorithm, and extracting a plurality of closed areas by using contour detection;

[0073] S302: Extracting edge features of each closed area;

[0074] S303: Determine whether each of the closed areas is a component area of ​​a corresponding component according to the edge features;

[0075] S304: Converting the area that is not a component area into another color of the binary image.

[0076] As described in steps S301-S304 above, Canny edge detection is used because it is highly sensitive to edge variations and can effectively capture the characteristics of irregular edges. Different components have different edge characteristics. Therefore, the captured edge features can be used to determine the component type of the closed region. After edge detection, contour detection can be used to extract the edge region. For the identified component, its irregularity can be detected through feature extraction. For example, shape features such as roundness and aspect ratio 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 using a large amount of feature data. The neural network model then identifies features to determine whether the corresponding closed region is the component region of the corresponding component. The component region is the image region of the component in the binary image. This allows the error in light detection to be extracted and converted to another color. Since the binary image only has two colors, converting to another color actually converts the color to the opposite color, i.e., from white to black or from black to white, depending on the actual binarization process.

[0077] In one embodiment, the step S6 of inputting each sub-three-dimensional model into a preset three-dimensional container to obtain a target three-dimensional model corresponding to the sample to be modeled includes:

[0078] S601: Obtain material properties and boundary conditions corresponding to each sub-three-dimensional model, and input them into a preset three-dimensional container to obtain a target three-dimensional model corresponding to the sample to be modeled.

[0079] As described in step S601 above, these material properties may include, but are not limited to, physical properties such as density, elastic modulus, shear modulus, and thermal conductivity. These properties provide the necessary foundation for simulation and analysis, allowing subsequent performance analysis to be more realistic. For example, understanding the material's elastic modulus can help researchers predict the model's deformation behavior under external forces. Obtaining the boundary conditions corresponding to each sub-3D model is also crucial. Boundary conditions define the constraints and forces a model will experience under specific environmental or external conditions. Boundary conditions can include fixed ends, free ends, or conditions where a certain force or temperature is applied. This information is crucial for simulating the material's performance in real-world applications and helps further verify the accuracy of the model. After collecting this information, it is input into a pre-set 3D container. The 3D container is a virtual simulation environment designed to connect multiple sub-models into a complete whole. Within this framework, the sub-3D models, their material properties, and boundary conditions are integrated to form a target 3D model of the sample to be modeled. This effectively supports subsequent material performance analysis and application, such as enabling more accurate mechanical and thermal analysis, thereby promoting technological advancements in various engineering fields.

[0080] In one embodiment, after obtaining the material properties and boundary conditions corresponding to each sub-three-dimensional model and inputting them into a preset three-dimensional container to obtain the target three-dimensional model corresponding to the sample to be modeled, step S6 further includes:

[0081] S711: performing a simulation experiment on the target three-dimensional model to obtain a prediction result, and performing an experiment corresponding to the simulation experiment on the sample to be modeled to obtain an actual result;

[0082] S712: Compare the predicted result and the actual result, and evaluate the validity of the target three-dimensional model according to the comparison result.

[0083] As described in steps S711-S712 above, the simulation process involves simulating the target 3D model using tools such as computer-aided engineering (CAE) to predict the material's behavior under various mechanical, thermal, or fluid dynamics conditions. By setting appropriate loading and boundary conditions, simulation can provide quantitative analysis of material properties, including key metrics such as stress, strain, and deformation. Actual physical experiments corresponding to the results of simulation experiments (e.g., mechanical, thermal, or fluid dynamics) are performed on the modeled sample. The sample is placed in an environment similar to the simulation conditions, a known load or heat is applied, and the actual response is obtained using sensors or other measuring devices. This experimental data provides a realistic reference for subsequent comparative analysis. The predicted results must be compared in detail with the actual results. This process not only clearly reveals the model's predictive capabilities but also helps identify potential biases and sources of error. The comparison can include differences in stress and strain distribution between the simulated and actual results, as well as performance data such as attenuation behavior and durability. Based on the comparison results, the validity of the target 3D model is evaluated. If the predicted results match the actual results within a reasonable range, the constructed target 3D model is accurate and reliable and can be used for further design optimization and performance prediction. Conversely, if there is a significant deviation, the model needs to be adjusted and improved, which may involve re-evaluating material properties, boundary conditions, or simulation parameters to improve model accuracy.

[0084] In one embodiment, after the step S5 of overlapping each target binary image set to obtain a sub-3D model of each component, the method further includes:

[0085] S621: setting a corresponding pore threshold according to the physical properties of the pores to generate a binary pore image set corresponding to the pores;

[0086] S622: Labeling corresponding pore regions in the binary pore image set to obtain a corresponding target binary pore image set;

[0087] S623: Overlapping the target binary pore image set to obtain a sub-3D pore model corresponding to the pores;

[0088] S624: Inputting the sub-three-dimensional pore model into the target three-dimensional model to obtain a temporary three-dimensional model;

[0089] S625: Supplementing the remaining parts of the temporary three-dimensional model with a matrix, thereby obtaining an accurate three-dimensional model corresponding to the sample to be modeled.

[0090] As described in steps S621-S625 above, pore detection is achieved. In some embodiments, after measuring other components, the remaining components can be considered pores. However, in actual experiments, it has been found that some components are mistakenly identified as pores. Therefore, pore detection can be performed. Specifically, a corresponding pore threshold is set based on the physical properties of the pores to generate a binary pore image set corresponding to the pores. Pores generally play a critical role in granular materials, affecting the mechanical properties and permeability of the material. The corresponding pore regions in the generated binary pore image set are accurately labeled to obtain a target binary pore image set. This ensures that the spatial position and shape of each pore are clearly identified, providing a data foundation for pore morphological analysis and further enhancing the model's realism. The target binary pore image set is overlapped to obtain a sub-3D pore model corresponding to the pores. This sub-3D pore model will provide important information for the final 3D material model, helping researchers understand the microstructure of granular materials and assess its impact on macroscopic properties. The matrix is ​​used to supplement the empty parts in the temporary three-dimensional model, and finally an accurate three-dimensional model corresponding to the sample to be modeled is obtained. By supplementing the matrix material in the model, the relationship between the pores and the matrix can be reflected, so that the final model can more realistically reproduce the structure and performance characteristics of the actual sample. This enhances the comprehensiveness and accuracy of the target three-dimensional model. Modeling the pore structure of multi-component granular materials can provide a more realistic and detailed perspective for the analysis of mechanical properties, permeability and other characteristics of the material. It not only improves the performance of the material model in numerical calculations, but also promotes the scientific nature of material performance prediction, providing more accurate data support for various engineering applications.

[0091] 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 a corresponding two-dimensional CT image set includes:

[0092] S101: placing the sample to be modeled on a rotating table, and setting the rotation step of the rotating table;

[0093] 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, thereby obtaining a two-dimensional CT image set.

[0094] 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 of the radiation. After the sample stage rotates one full revolution, the radiation of varying intensities falls on the receiver, forming 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 forms 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, propellant contains only two types of filler particles. Perchloric acid particles are the largest component by volume and have the widest size range, ranging from 50 to 300 μm. Due to the processing technology, their shapes are mostly elliptical, with a few particles containing internal pores. Aluminum powder, due to its small size, is packed between the perchloric acid particles, resulting in aluminum-rich areas.

[0095] In one embodiment, after the step S4 of labeling the corresponding component regions in each of the binary image sets to obtain the corresponding target binary image set, the method further includes:

[0096] S501: Inputting each binary image in the binary image set and the corresponding annotation information into a preset image processing software for preprocessing to obtain a corresponding two-dimensional slice;

[0097] S502: Overlap each two-dimensional slice to obtain a three-dimensional sub-model of each component.

[0098] 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 process a variety of three-dimensional data such as micro-CT, X-ray CT, MRI (Magnetic Resonance Imagin, 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.

[0099] Reference Figure 4 The present invention provides a multi-component sample three-dimensional modeling system based on micro-CT scanning, the system comprising:

[0100] 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 corresponding two-dimensional CT image set;

[0101] A grayscale processing module 20 is configured to perform grayscale processing on the two-dimensional CT images in the two-dimensional CT image set to obtain a grayscale image set;

[0102] An acquisition module 30 is used to acquire the physical properties of each component of the sample to be modeled, and set different thresholds for the grayscale image set according to the physical properties of each component to generate a binary image set corresponding to each component;

[0103] A labeling module 40 is used to label the corresponding component areas in each of the binary image sets to obtain a corresponding target binary image set;

[0104] An overlapping module 50 is used to overlap each target binary image set to obtain a sub-3D model of each component;

[0105] The input module 60 is used to input each sub-3D model into a preset 3D container to obtain a target 3D model corresponding to the sample to be modeled.

[0106] In one embodiment, the multi-component sample three-dimensional modeling system based on micro-CT scanning further includes:

[0107] A cube division module, configured to divide the target three-dimensional model into a plurality of cubes according to preset intervals;

[0108] Volume proportion statistics module, used to count the volume proportion of each component in each cube;

[0109] The cube filling module is used to fill the entire cube with the component with the largest volume share to obtain the target three-dimensional model.

[0110] In one embodiment, the multi-component sample three-dimensional modeling system based on micro-CT scanning further includes:

[0111] a target binary image detection module, configured to detect the target binary image in the target binary image set using a preset edge detection algorithm, and extract multiple closed areas through contour detection;

[0112] An edge feature extraction module, configured to extract edge features of each closed area;

[0113] a component region determination module, configured to determine whether each of the closed regions is a component region of a corresponding component according to the edge features;

[0114] The color conversion module is used to convert the area that is not a component area into another color of the binary image.

[0115] In one embodiment, the input module 60 includes:

[0116] The material property acquisition submodule is used to obtain the material properties and boundary conditions corresponding to each sub-3D model and input them into a preset 3D container to obtain the target 3D model corresponding to the sample to be modeled.

[0117] In one embodiment, the multi-component sample three-dimensional modeling system based on micro-CT scanning further includes:

[0118] A simulation experiment module is used to perform a simulation experiment on the target three-dimensional model to obtain a prediction result, and to perform an experiment corresponding to the simulation experiment on the sample to be modeled to obtain an actual result;

[0119] The result comparison module is used to compare the predicted result with the actual result and evaluate the validity of the target three-dimensional model according to the comparison result.

[0120] In one embodiment, the multi-component sample three-dimensional modeling system based on micro-CT scanning further includes:

[0121] A binary pore image set generation module is used to set a corresponding pore threshold according to the physical properties of the pores to generate a binary pore image set corresponding to the pores;

[0122] a pore region labeling module, configured to label corresponding pore regions in the binary pore image set to obtain a corresponding target binary pore image set;

[0123] A target binary pore image set overlapping module is used to overlap the target binary pore image set to obtain a sub-three-dimensional pore model corresponding to the pores;

[0124] A sub-3D pore model input module is used to input the sub-3D pore model into the target 3D model to obtain a temporary 3D model;

[0125] The matrix supplement module is used to supplement the empty parts in the temporary three-dimensional model with the matrix, so as to obtain an accurate three-dimensional model corresponding to the sample to be modeled.

[0126] In one embodiment, the scanning module 10 includes:

[0127] A step size setting submodule is used to place the sample to be modeled on a rotating table and set the step size of the rotating table;

[0128] The scanning submodule is used to scan the sample to be modeled at each step length by the micro CT scanning instrument, thereby obtaining two-dimensional CT images corresponding to each direction, thereby obtaining a two-dimensional CT image set.

[0129] Figure 5 FIG1 shows an internal structure diagram of a computer device in an embodiment. The computer device can be a terminal or a server. Figure 5 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 can implement three-dimensional modeling of multi-component samples based on micro-CT scanning. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can implement three-dimensional modeling of multi-component samples based on micro-CT scanning. It will be understood by those skilled in the art that Figure 5 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.

[0130] 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:

[0131] Scan the modeling sample in multiple preset directions using a micro-CT scanning instrument to obtain a corresponding two-dimensional CT image set;

[0132] performing grayscale processing on the two-dimensional CT images in the two-dimensional CT image set to obtain a grayscale image set;

[0133] Obtaining the physical properties of each component of the sample to be modeled, and setting different thresholds for the grayscale image set according to the physical properties of each component to generate a binary image set corresponding to each component;

[0134] Labeling corresponding component regions in each of the binary image sets to obtain a corresponding target binary image set;

[0135] Overlap each target binary image set to obtain a sub-3D model of each component;

[0136] Each sub-3D model is input into a preset 3D container to obtain the target 3D model corresponding to the sample to be modeled.

[0137] It is possible to obtain the internal structure of the sample to be modeled in a multi-angle and high-precision manner, thereby greatly improving the accuracy and reliability of the data, and finely displaying the distribution relationship of each component in three-dimensional space, providing a rich data basis for subsequent material performance analysis, enabling researchers to more deeply analyze the mechanical properties, thermal properties, etc. of multi-component materials.

[0138] 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:

[0139] Scan the modeling sample in multiple preset directions using a micro-CT scanning instrument to obtain a corresponding two-dimensional CT image set;

[0140] performing grayscale processing on the two-dimensional CT images in the two-dimensional CT image set to obtain a grayscale image set;

[0141] Obtaining the physical properties of each component of the sample to be modeled, and setting different thresholds for the grayscale image set according to the physical properties of each component to generate a binary image set corresponding to each component;

[0142] Labeling corresponding component regions in each of the binary image sets to obtain a corresponding target binary image set;

[0143] Overlap each target binary image set to obtain a sub-3D model of each component;

[0144] Each sub-3D model is input into a preset 3D container to obtain the target 3D model corresponding to the sample to be modeled.

[0145] It is possible to obtain the internal structure of the sample to be modeled in a multi-angle and high-precision manner, thereby greatly improving the accuracy and reliability of the data, and finely displaying the distribution relationship of each component in three-dimensional space, providing a rich data basis for subsequent material performance analysis, enabling researchers to more deeply analyze the mechanical properties, thermal properties, etc. of multi-component materials.

[0146] 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).

[0147] The technical features of the above embodiments can be combined arbitrarily. In order 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.

[0148] 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 modeling method for multi-component samples based on micro-CT scanning, characterized in that: The method comprises: Scan the modeling sample in multiple preset directions using a micro-CT scanning instrument to obtain a corresponding two-dimensional CT image set; performing grayscale processing on the two-dimensional CT images in the two-dimensional CT image set to obtain a grayscale image set; Obtaining the physical properties of each component of the sample to be modeled, and setting different thresholds for the grayscale image set according to the physical properties of each component to generate a binary image set corresponding to each component; Labeling corresponding component regions in each of the binary image sets to obtain a corresponding target binary image set; Overlap each target binary image set to obtain a sub-3D model of each component; Each sub-3D model is input into a preset 3D container to obtain the target 3D model corresponding to the sample to be modeled.

2. The multi-component sample three-dimensional modeling method based on micro-CT scanning according to claim 1, characterized in that: After the step of inputting each sub-three-dimensional model into a preset three-dimensional container to obtain a target three-dimensional model corresponding to the sample to be modeled, the method further includes: Dividing the target three-dimensional model into a plurality of cubes according to preset intervals; Count the volume proportions of each component in each cube; Fill the entire cube with the component with the largest volume share to obtain the target three-dimensional model.

3. The multi-component sample three-dimensional modeling method based on micro-CT scanning according to claim 1, characterized in that: Before the step of labeling the corresponding component regions in each of the binary image sets to obtain the corresponding target binary image set, the method further includes: Detecting the target binary image in the target binary image set by a preset edge detection algorithm, and extracting multiple closed areas by contour detection; Extracting edge features of each closed area; determining whether each of the closed areas is a component area of ​​a corresponding component according to the edge features; Convert the area that is not a component area into another color of the binary image.

4. The multi-component sample three-dimensional modeling method based on micro-CT scanning according to claim 1, characterized in that: The step of inputting each sub-three-dimensional model into a preset three-dimensional container to obtain a target three-dimensional model corresponding to the sample to be modeled includes: The material properties and boundary conditions corresponding to each sub-3D model are obtained and input into a preset 3D container to obtain the target 3D model corresponding to the sample to be modeled.

5. The multi-component sample three-dimensional modeling method based on micro-CT scanning according to claim 1, characterized in that: After the step of obtaining the material properties and boundary conditions corresponding to each sub-three-dimensional model and inputting them into a preset three-dimensional container to obtain a target three-dimensional model corresponding to the sample to be modeled, the method further includes: Performing a simulation experiment on the target three-dimensional model to obtain a prediction result, and performing an experiment corresponding to the simulation experiment on the sample to be modeled to obtain an actual result; The predicted result and the actual result are compared, and the validity of the target three-dimensional model is evaluated based on the comparison result.

6. The multi-component sample three-dimensional modeling method based on micro-CT scanning according to claim 1, characterized in that: After the step of overlapping each target binary image set to obtain a sub-3D model of each component, the method further includes: The corresponding pore threshold is set according to the physical properties of the pores to generate a binary pore image set corresponding to the pores; Annotating corresponding pore regions in the binary pore image set to obtain a corresponding target binary pore image set; Overlap the target binary pore image set to obtain the sub-3D pore model corresponding to the pores; Inputting the sub-three-dimensional pore model into the target three-dimensional model to obtain a temporary three-dimensional model; The empty parts in the temporary three-dimensional model are supplemented with a matrix, thereby obtaining an accurate three-dimensional model corresponding to the sample to be modeled.

7. The multi-component sample three-dimensional modeling method based on micro-CT scanning 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 corresponding two-dimensional CT image set includes: The sample to be modeled is placed on a rotating table, and the step length of the rotating table is set; The sample to be modeled at each step length is scanned by the micro CT scanning instrument, thereby obtaining two-dimensional CT images corresponding to various directions, thereby obtaining a two-dimensional CT image set.

8. A multi-component sample three-dimensional modeling system based on micro-CT scanning, characterized in that: The system comprises: A scanning module, used to scan the sample to be modeled in multiple preset directions using a micro-CT scanning instrument to obtain a corresponding two-dimensional CT image set; a grayscale processing module, configured to perform grayscale processing on the two-dimensional CT images in the two-dimensional CT image set to obtain a grayscale image set; an acquisition module, configured to acquire the physical properties of each component of the sample to be modeled, and set different thresholds for the grayscale image set according to the physical properties of each component, so as to generate a binary image set corresponding to each component; A labeling module, configured to label the corresponding component regions in each of the binary image sets to obtain a corresponding target binary image set; An overlapping module is used to overlap each target binary image set to obtain a sub-3D model of each component; The input module is used to input each sub-3D model into a preset 3D container to obtain a target 3D model corresponding to the sample to be modeled.

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