Foaming material porosity evaluation method and system

Abstract

The invention relates to the technical field of porosity evaluation, in particular to a foam material porosity evaluation method and system, which comprises the following steps: scanning a foam material at multiple angles through CT scanning equipment to obtain a gray image, then carrying out artifact and noise elimination on the gray image subjected to CT scanning, and then segmenting the gray image by using a threshold segmentation method to obtain a segmented image; then, the porosity in the gray level image is solved, the gray level image is converted into a black and white image with obvious contrast, a three-dimensional mesoscopic finite element model is established, a slice is selected from the three-dimensional mesoscopic finite element model, the porosity of the slice is calculated, and the porosity is compared with the porosity calculated through the gray level image. According to the method, through cooperation of the steps, the optimal gray threshold values of the matrix material and the air can be distinguished, the porosity of the gray image is solved, the three-dimensional microscopic analysis model of the foaming material is reconstructed, the porosity is compared with the porosity calculated through the gray image on the basis of the reconstructed model, and the porosity calculation precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of porosity evaluation, and particularly to a method and system for evaluating the porosity of foamed materials. Background Art

[0002] The porosity of foamed materials is one of their important physical properties. It not only affects the basic properties such as the density and strength of the materials, but also has a significant impact on aspects such as the thermal conductivity, acoustic performance, permeability, and chemical reaction activity of the materials. Due to their characteristics such as light weight, good heat insulation and sound insulation effects, and strong energy absorption and buffering capabilities, foamed materials have been widely used in many fields such as construction, packaging, and automobile manufacturing. Therefore, accurately evaluating the porosity of foamed materials is crucial for optimizing their design and application.

[0003] The existing porosity evaluation methods cannot compare and verify the measured porosity while calculating the porosity of foamed materials from grayscale images, which reduces the measurement accuracy of the porosity. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for evaluating the porosity of foamed materials, which have the advantages of being able to obtain the best threshold for distinguishing the gray levels of matrix materials and air from the grayscale images obtained by CT scanning, calculating the porosity of the grayscale images, being able to directly generate a finite element analysis model of the specimen from the scanned images based on the mapping grid idea, realizing the reconstruction of the three-dimensional mesoscopic analysis model of the foamed material, and comparing with the porosity calculated from the grayscale images based on the reconstructed model, thereby improving the calculation accuracy of the porosity, and solving the problems raised in the background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A method for evaluating the porosity of foamed materials, comprising the following steps:

[0006] S1. Scan the foamed material from multiple angles by a CT scanning device, collect the projection data of the X-ray attenuation after passing through the object, and then obtain the X-ray attenuation value information of different points in the object through mathematical processing, and layer by layer convert it into a grayscale image for observation.

[0007] S2. Eliminate artifacts and noise in the grayscale image obtained by CT scanning to optimize the image quality and enhance the signal-to-noise ratio of the image;

[0008] S3. Use the threshold segmentation method to segment the grayscale image to obtain the number of pixel points with a gray value of 0 and the number of pixel points with a gray value of 1;

[0009] Specifically, S3 includes the following steps:

[0010] S3.1. In the CT image, the gray value of the air part is 0, and the gray value of the specimen part is 0 to 255. The gray value of each pixel corresponds to the material density of the specimen at that location.

[0011] S3.2, performing threshold segmentation on the CT image, when the gray value is 0, it indicates that the pixel point is a pore structure of the foam material, and when the gray value is not 0, it indicates that the pixel point is a matrix structure of the foam material;

[0012] S3.3, based on the principle of threshold segmentation method, the image is binarized, that is, the gray value of the input image is divided into 0 or 1 according to different thresholds;

[0013] S4, counting the number of pixels with a gray value of 0 and the total number of pixels in the gray image, and calculating the porosity in the gray image;

[0014] S5, converting the grayscale image into a black-and-white image with obvious contrast, and establishing a three-dimensional microscopic finite element model;

[0015] S6. Select a slice on the 3D microscopic finite element model, calculate the porosity of the slice, and compare it with the porosity calculated by the grayscale image.

[0016] Preferably, in S1, by taking a series of two-dimensional projection pictures of rock samples from multiple angles and stacking the two-dimensional projections, three-dimensional CT data of the rock samples can be obtained, thereby achieving fine three-dimensional visualization of the rock pore structure. Moreover, since different components in the rock have different densities and X-ray attenuation characteristics, each component has a different grayscale value in the CT image. The higher the density of the component in the rock, the higher the grayscale value of the image and the brighter it appears.

[0017] Preferably, in S2, any one of non-local mean filtering, Gaussian filtering and median filtering can be used to optimize the image quality and enhance the image signal-to-noise ratio.

[0018] Preferably, the non-local mean filtering algorithm has a good filtering and denoising effect on images. The algorithm is based on the non-local similarity characteristics of images. When processing images, it mainly uses the similarity of neighborhoods of different pixel points and groups pixels with large similarity into one phase, thereby achieving the purpose of reducing noise while maintaining image features as much as possible.

[0019] Preferably, the formula in S3.3 is as follows:

[0020]

[0021] Among them, g(i,j) is the output result, f(i,j) is the grayscale value of the input image at the coordinate (i,j), and T is the threshold.

[0022] Preferably, in the formula of S3.3, the threshold T is taken as 1, that is, the pixel points with a gray value of 0 are classified as 0, and the pixel points with a gray value not equal to 0, that is, the gray value ranging from 1 to 255, are classified as 1. That is, the pixel points with a gray value classified as 0 are the pore structure, and the pixel points with a gray value classified as 1 are the matrix structure, thus realizing the accurate segmentation of the pore structure and the matrix structure of the foamed material.

[0023] Preferably, in S4, the formula for solving the porosity in the grayscale image is as follows:

[0024] N 总 =N 0 +N 1

[0025]

[0026] where N 总 is the total number of pixel points, N 0 is the number of pixel points with a gray value of 0, N 1 is the number of pixel points with a gray value of 1, and P is the porosity.

[0027] Preferably, in S5, the steps for establishing a three-dimensional meso-scale finite element model are as follows:

[0028] S5.1. Establish a new point cloud matrix according to the attributes of each point in the image as pores or matrix.

[0029] S5.2. Take the first point in the matrix, generate a cube element and the corresponding 8 nodes centered on it. If this point is determined to be the matrix during the binarization process, determine the attribute of this unit as the cell wall, otherwise determine it as a pore.

[0030] S5.3. Advance row by row, column by column, and layer by layer in this way, and a regular, orderly, and uniformly divided finite element model is generated in the entire point cloud matrix.

[0031] Preferably, in S6, randomly intercept a slice on the three-dimensional meso-scale finite element model, count the number of matrix units and air units on the slice, and calculate the porosity P of the slice by dividing the number of air units by the sum of the number of matrix units and air units. Compare the calculated porosity P of the slice with the porosity calculated from the grayscale image, and at the same time calculate the error between the two.

[0032] A foamed material porosity evaluation system includes a CT scanning module, which is used to collect the projection data of the X-ray attenuated after passing through the object, and then obtain the X-ray attenuation value information of different points in the object through mathematical processing, and layer by layer convert it into a grayscale image for observation;

[0033] An image processing module for removing artifacts and noise from grayscale images of CT scans to optimize picture quality and enhance the signal-to-noise ratio of the images;

[0034] A threshold segmentation module for segmenting grayscale images to obtain the number of pixel points with a grayscale value of 0 and the number of pixel points with a grayscale value of 1;

[0035] A calculation module for counting the number of pixel points with a grayscale value of 0 and the total number of pixel points in the grayscale image to calculate the porosity of the grayscale image;

[0036] A model reconstruction module for converting the grayscale image into a black-and-white image with obvious contrast and establishing a three-dimensional mesoscopic finite element model;

[0037] A comparison and verification module for selecting a slice on the three-dimensional mesoscopic finite element model, calculating the porosity of the slice, and comparing it with the porosity calculated from the grayscale image.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0039] First, through threshold segmentation-based binary processing of CT images, the present invention classifies pixel points with a grayscale value of 0 as 0, and pixel points with a grayscale value not equal to 0 (i.e., the grayscale value ranges from 1 to 255) as 1. By calculating the ratio of the number of pixel points with a grayscale value of 0 in the specimen part of the CT image to the total number of pixel points with grayscale values of 0 and 1, the porosity of the grayscale image of the foamed material can be obtained.

[0040] Second, the present invention can distinguish the optimal threshold of the grayscale between the matrix material and air through the grayscale image obtained by CT scanning. Based on the mapping grid idea, a finite element analysis model of the specimen is directly generated from the scanned image, realizing the reconstruction of the three-dimensional mesoscopic analysis model of the foamed material. By comparing the reconstructed model with the porosity calculated from the grayscale image, the calculation accuracy of the porosity is improved. Description of the Drawings

[0041] Figure 1 is a flowchart of the present invention;

[0042] Figure 2 is a system block diagram of the present invention. Detailed Embodiments

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0044] Please refer to Figures 1 to 2 , the present invention provides a technical solution: a method for evaluating the porosity of a foaming material, comprising the following steps:

[0045] S1. Scan the foaming material from multiple angles by a CT scanning device, collect the projection data after the X-ray attenuates through the object, and then obtain the X-ray attenuation value information of different points in the object through mathematical processing, and layer by layer convert it into a grayscale image for observation.

[0046] In CT scanning, the core technology of converting projection data into the internal attenuation coefficient (grayscale image) of the object through mathematical methods is the inverse process of the Radon transform. The specific implementation usually adopts the filtered back-projection algorithm (Filtered BackProjection, FBP), and in modern technology, iterative reconstruction algorithms (such as algebraic reconstruction technology, statistical iterative methods) may also be used. Mathematical tools that may also be used include the Fourier slice theorem: the one-dimensional Fourier transform of the projection is equal to a slice of the two-dimensional Fourier transform of the object, providing a theoretical basis for FBP; numerical integration and interpolation: dealing with discrete projection data and the conversion from polar coordinates to rectangular coordinates.

[0047] The processing flow is as follows:

[0048] Data acquisition: Rotate and scan through the X-ray source and detector to collect the projection data p(θ,t) at different angles θ1, θ2,..., θN;

[0049] Preprocessing: Correct the uneven detector response, X-ray hardening effect, and noise (such as median filtering);

[0050] Filtered back-projection: Apply a Ramp filter (implemented in the frequency domain or spatial domain) to each angle projection to eliminate blurring;

[0051] Back-projection superposition to generate a cross-sectional image;

[0052] Image generation: Map the attenuation coefficient μ(x,y) to a grayscale value (such as 0 for black and high attenuation for white).

[0053] In S1, by taking a series of two-dimensional projection pictures of the rock sample from multiple angles and stacking the two-dimensional projections, the three-dimensional CT data of the rock sample can be obtained, realizing the fine three-dimensional visualization of the pore structure of the rock. And because different components in the rock have different densities and X-ray attenuation characteristics, each component has different grayscale values in the CT image, and the higher the density of the component in the rock, the higher the grayscale value of the image and the brighter it appears.

[0054] The test was conducted on a μCT225kVFCB high-precision micro-CT test system jointly developed by Taiyuan University of Technology and the Institute of Applied Electronics of the China Academy of Engineering Physics. The X-ray imaging system in the test system is mainly composed of high-precision work turntable and fixture, micro-focus X-ray machine, digital flat panel detector, horizontal movement mechanism, machine base and acquisition and analysis system, etc., so as to realize three-dimensional CT scanning and analysis of metal and non-metal materials.

[0055] S2, removing artifacts and noise from the grayscale image of the CT scan to optimize the image quality and enhance the image signal-to-noise ratio;

[0056] In S2, any one of non-local mean filtering, Gaussian filtering and median filtering can be used to optimize the image quality and enhance the image signal-to-noise ratio;

[0057] The non-local mean filtering algorithm has a good filtering and denoising effect on images. The algorithm is based on the non-local similarity characteristics of images. When processing images, it mainly focuses on the similarity of neighborhoods of different pixel points, and groups pixels with large similarity into one phase, thereby achieving the purpose of reducing noise while maintaining image features as much as possible.

[0058] S3, using the threshold segmentation method to segment the grayscale image, and obtain the number of pixels with a grayscale value of 0 and the number of pixels with a grayscale value of 1;

[0059] The specific steps in S3 include:

[0060] S3.1. In the CT image, the gray value of the air part is 0, and the gray value of the specimen part is 0 to 255. The gray value of each pixel corresponds to the material density of the specimen at that location.

[0061] S3.2, performing threshold segmentation on the CT image, when the gray value is 0, it indicates that the pixel point is a pore structure of the foam material, and when the gray value is not 0, it indicates that the pixel point is a matrix structure of the foam material;

[0062] S3.3, based on the principle of threshold segmentation method, the image is binarized, that is, the gray value of the input image is divided into 0 or 1 according to different thresholds;

[0063] The formula in S3.3 is as follows:

[0064]

[0065] Among them, g(i,j) is the output result, f(i,j) is the gray value of the input image at the coordinate (i,j), and T is the threshold;

[0066] In the formula of S3.3, the threshold T is taken as 1, that is, the pixel points with a gray value of 0 are classified as 0, and the pixel points with a gray value not equal to 0, that is, the pixel points with a gray value ranging from 1 to 255, are classified as 1. That is, the pixel points with a gray value classified as 0 are the pore structure, and the pixel points with a gray value classified as 1 are the matrix structure, thus realizing the accurate segmentation of the pore structure and the matrix structure of the foamed material.

[0067] S4. Count the number of pixel points with a gray value of 0 and the total number of pixel points in the gray image, and calculate the porosity of the gray image;

[0068] In S4, the formula for calculating the porosity of the gray image is as follows:

[0069] N 总 = N 0 + N 1

[0070]

[0071] where N 总 is the total number of pixel points, N 0 is the number of pixel points with a gray value of 0, N 1 is the number of pixel points with a gray value of 1, and P is the porosity.

[0072] By performing binary processing on the CT image based on threshold segmentation, the pixel points with a gray value of 0 are classified as 0, and the pixel points with a gray value not equal to 0 (i.e., the gray value ranges from 1 to 255) are classified as 1. By calculating the ratio of the number of pixel points with a gray value of 0 in the specimen part of the CT image to the total number of pixel points with gray values of 0 and 1, the porosity of the gray image of the foamed material can be obtained.

[0073] S5. Convert the gray image into a black-and-white image with obvious contrast and establish a three-dimensional meso-scale finite element model;

[0074] In S5, the steps for establishing a three-dimensional meso-scale finite element model are as follows:

[0075] S5.1. Establish a new point cloud matrix according to the attributes of each point in the image as pores or matrix;

[0076] S5.2. Take the first point in the matrix, generate a cube element and the corresponding 8 nodes centered on it. If this point is determined to be a matrix during the binary processing, the attribute of this unit is determined to be the cell wall, otherwise it is determined to be a pore;

[0077] S5.3. Advance row by row, column by column, and layer by layer in this way, and a regular, ordered, and uniformly divided finite element model is generated in the entire point cloud matrix.

[0078] S6. Select a slice on the three-dimensional meso-scale finite element model, calculate the porosity of this slice, and compare it with the porosity calculated from the grayscale image.

[0079] In S6, randomly intercept a slice on the three-dimensional meso-scale finite element model, count the number of matrix elements and air elements on the slice, and calculate the porosity P of the slice. The porosity P can be calculated by dividing the number of air elements by the sum of the number of matrix elements and air elements. Compare the calculated porosity P of the slice with the porosity calculated from the grayscale image, and at the same time calculate the error between the two.

[0080] The grayscale image obtained by CT scanning can distinguish the best threshold of the grayscale of the matrix material and air. Based on the mapping grid idea, a finite element analysis model of the specimen is directly generated from the scanned image, realizing the reconstruction of the three-dimensional meso-scale analysis model of the foaming material. Compare with the porosity calculated from the grayscale image based on the reconstructed model, improving the calculation accuracy of the porosity.

[0081] A porosity evaluation system for foaming materials, including a CT scanning module, which is used to collect the projection data after the attenuation of X-rays passing through the object, and then process it by mathematical methods to obtain the X-ray attenuation value information of different points in the object, and layer by layer convert it into a grayscale image for observation;

[0082] An image processing module, which is used to eliminate artifacts and noise in the grayscale image obtained by CT scanning to optimize the image quality and enhance the signal-to-noise ratio of the image;

[0083] A threshold segmentation module, which is used to segment the grayscale image to obtain the number of pixel points with a grayscale value of 0 and the number of pixel points with a grayscale value of 1;

[0084] A calculation module, which is used to count the number of pixel points with a grayscale value of 0 and the total number of pixel points in the grayscale image, and calculate the porosity in the grayscale image;

[0085] A model reconstruction module, which is used to convert the grayscale image into a black-and-white image with obvious contrast and establish a three-dimensional meso-scale finite element model;

[0086] A comparison and verification module, which selects a slice on the three-dimensional meso-scale finite element model, calculates the porosity of this slice, and compares it with the porosity calculated from the grayscale image.

[0087] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the porosity of a foaming material, characterized in that: The following steps are involved: S1. Scan the foam material at multiple angles using a CT scanner to collect projection data after X-rays pass through the object and attenuate. Then, use mathematical methods to obtain X-ray attenuation value information at different points in the object and convert it into grayscale images layer by layer for observation. S2, removing artifacts and noise from the grayscale image of the CT scan to optimize the image quality and enhance the image signal-to-noise ratio; S3, using the threshold segmentation method to segment the grayscale image, and obtain the number of pixels with a grayscale value of 0 and the number of pixels with a grayscale value of 1; The specific steps in S3 include: S3.

1. In the CT image, the gray value of the air part is 0, and the gray value of the specimen part is 0 to 255. The gray value of each pixel corresponds to the material density of the specimen at that location. S3.2, performing threshold segmentation on the CT image, when the gray value is 0, it indicates that the pixel point is a pore structure of the foam material, and when the gray value is not 0, it indicates that the pixel point is a matrix structure of the foam material; S3.3, based on the principle of threshold segmentation method, the image is binarized, that is, the gray value of the input image is divided into 0 or 1 according to different thresholds; S4, counting the number of pixels with a gray value of 0 and the total number of pixels in the gray image, and calculating the porosity in the gray image; S5, converting the grayscale image into a black-and-white image with obvious contrast, and establishing a three-dimensional microscopic finite element model; S6. Select a slice on the 3D microscopic finite element model, calculate the porosity of the slice, and compare it with the porosity calculated by the grayscale image.

2. A method for evaluating the porosity of a foamed material according to claim 1, characterized in that: In S1, by taking a series of two-dimensional projection images of rock samples from multiple angles and stacking the two-dimensional projections, three-dimensional CT data of the rock samples can be obtained, achieving detailed three-dimensional visualization of the rock pore structure. In addition, since different components in the rock have different densities and X-ray attenuation characteristics, each component has a different grayscale value in the CT image. The higher the density of the component in the rock, the higher the grayscale value of the image and the brighter it appears.

3. A method for evaluating the porosity of a foamed material according to claim 1, characterized in that: In S2, any one of non-local mean filtering, Gaussian filtering and median filtering can be used to optimize the image quality and enhance the image signal-to-noise ratio.

4. A method for evaluating the porosity of a foamed material according to claim 3, characterized in that: The non-local mean filtering algorithm is based on the non-local similarity characteristics of images. When processing images, it focuses on the similarity of neighborhoods of different pixels and groups pixels with large similarity into one phase.

5. A method for evaluating the porosity of a foamed material according to claim 1, characterized in that: The formula in S3.3 is as follows: Among them, g(i,j) is the output result, f(i,j) is the grayscale value of the input image at the coordinate (i,j), and T is the threshold.

6. A method for evaluating the porosity of a foamed material according to claim 5, characterized in that: In the formula of S3.3, the threshold T is taken as 1, that is, the pixel points with a gray value of 0 are divided into 0, and the gray value is not 0, that is, the pixel points with a gray value range of 1 to 255 are divided into 1. That is, the pixel points with a gray value of 0 are pore structures, and the pixel points with a gray value of 1 are matrix structures.

7. A method for evaluating the porosity of a foamed material according to claim 6, characterized in that: In S4, the porosity formula in the grayscale image is solved as follows: A 总 =N0+N1 Where N 总 is the total number of pixels, N0 is the number of pixels with a gray value of 0, N1 is the number of pixels with a gray value of 1, and P is the porosity.

8. A method for evaluating the porosity of a foamed material according to claim 6, characterized in that: In S5, the steps to establish a three-dimensional microscopic finite element model are as follows: S5.1, establishing a new point cloud matrix according to the attributes of each point in the image as pores or matrix; S5.2, take the first point in the matrix, and generate a cube unit and corresponding 8 nodes with it as the center. If the point is determined to be a matrix in the binarization process, the attribute of this unit is determined to be a cell wall, otherwise it is determined to be a pore; S5.

3. By proceeding row by row, column by column, and layer by layer in this way, a regularly ordered, evenly divided finite element model is generated in the entire point cloud matrix.

9. A method for evaluating the porosity of a foamed material according to claim 8, characterized in that: In S6, a slice is randomly cut from the three-dimensional microscopic finite element model, and the number of matrix units and the number of air units on the slice are counted. The porosity P of the slice can be calculated by dividing the number of air units by the sum of the number of matrix units and the number of air units. The calculated porosity P of the slice is compared with the porosity calculated by the grayscale image, and the error between the two is calculated.

10. A foam material porosity evaluation system, characterized in that: It includes a CT scanning module, which is used to collect projection data after the X-rays pass through the object and attenuate, and then use mathematical methods to process the X-ray attenuation value information at different points in the object, and convert it into grayscale images layer by layer for observation; Image processing module, used to remove artifacts and noise from grayscale images of CT scans to optimize image quality and enhance image signal-to-noise ratio; The threshold segmentation module is used to segment the grayscale image and obtain the number of pixels with a grayscale value of 0 and the number of pixels with a grayscale value of 1; A calculation module is used to count the number of pixels with a gray value of 0 and the total number of pixels in the gray image to calculate the porosity in the gray image; Model reconstruction module, used to convert grayscale images into black-and-white images with obvious contrast and establish three-dimensional microscopic finite element models; The comparison and verification module selects a slice on the 3D microscopic finite element model, calculates the porosity of the slice, and compares it with the porosity calculated from the grayscale image.

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