Method, device and computer readable medium for measuring porosity of porous composite materials

Abstract

The present invention provides a method for measuring the porosity of a porous composite material, comprising: obtaining a corresponding grayscale distribution map based on a CT image of the porous composite material; determining an investigation interval [x 0 ,x 10 ] Provide a series of grayscale threshold gradient values ​​x n , and calculate the porosity z of the composite material n ; Let (X n ,Y n )=(logx n ,z n ) and press X n Scatter points (X 0 ,Y 0 ) to (X u ,Y u ) are sorted, and the first m scattered points are subjected to univariate linear regression to solve the linear regression correlation coefficient R m 2 ; Extract a k value that meets the preset conditions; for the scattered points (X 0 ,Y 0 ) to (X u ,Y u ) to obtain the first linear equation; perform linear regression on the k+1th and k+2th scattered points to obtain the second linear equation; solve the intersection point (X k′ ,Y k′ ), and then according to the relationship X k′ =logx k′ Solving for x k′ ; Based on x k′ Segment CT images and calculate the porosity of composite materials.

Description

Technical Field

[0001] The invention relates to the field of material detection, and in particular to a method, a device and a computer-readable medium for measuring the porosity of a porous composite material. Background Art

[0002] X-ray computed tomography (CT) technology has the characteristics of being able to non-destructively detect the internal structure of an object and reconstruct high-density resolution images. CT technology can be applied to the study of porous media structures. By extracting micropores or even nanopores from CT images, porosity and pore structure analysis can be performed.

[0003] Related technologies use threshold segmentation to process CT images and obtain the porosity of the material. The threshold segmentation method sets a grayscale threshold and regards pixels below the threshold in the CT image as pores. The ratio of all pixels below the threshold to the total pixels is the porosity of the sample.

[0004] In the related art, the grayscale threshold selection is to find the more significant pore features in the composite material by repeatedly modulating and filtering the initial scan image, and then adjust the position of the pore grayscale threshold in the grayscale distribution map of the entire specimen so that the significant pore features identified manually can be automatically identified by the analysis software and determined as a reasonable pore feature grayscale threshold. Summary of the invention

[0005] The inventors found that the method of manually determining the grayscale threshold value has high requirements on the CT scanning imaging quality of the specimen and the filtering and analysis skills of the scanned image. Even experienced operators need a long time to select the characteristic defects as the reference benchmark for the pore threshold adjustment, and spend even longer time to select the appropriate pore characteristic grayscale threshold value so that the scanning analysis software can identify the outline of the benchmark defect as much as possible and accurately. Since this method of determining the pore characteristic grayscale threshold value is greatly affected by human subjective factors, the detected porosity and characteristic distribution also vary from person to person, the repeatability is poor, and the efficiency is not high.

[0006] The inventors further discovered that the scanning ray attenuation caused by pores and its corresponding grayscale value change are caused by the change in the total volume of pores on the scanning line. The logarithm of its grayscale value and the statistical porosity form a highly fitted linear relationship. When the selected grayscale threshold exceeds the pore content, part of the resin will be mistakenly considered as pores and counted, causing the statistical porosity to rise sharply, and the slope of its logarithmic curve with the grayscale threshold will also increase significantly. Therefore, the grayscale threshold corresponding to the inflection point where the logarithmic curve of the porosity and grayscale threshold deviates from the linear relationship is the objectively accurate grayscale threshold of the pore characteristic. Based on the above discovery, the present disclosure provides a standardized method for measuring the porosity of porous composite materials, which reduces the influence of human subjective factors on the detection results and improves the repeatability and accuracy of the detection.

[0007] In some aspects, the present disclosure provides a method for measuring the porosity of a void-containing composite material, comprising:

[0008] (1) obtaining a CT image of the porous composite material to obtain a corresponding grayscale distribution map, wherein the abscissa of the grayscale distribution map is the grayscale value x, and the ordinate is the pixel quantity y corresponding to the grayscale value;

[0009] (2) Determine the investigation interval [x0,x 10 ],x0<x 10 , including the following operations:

[0010] Determine the peak distribution mode of the grayscale distribution graph;

[0011] When the grayscale distribution diagram is unimodal, define x 10 is the gray value corresponding to the top of the peak, and x0 is the gray value corresponding to the bottom of the peak;

[0012] When the grayscale distribution is determined to be bimodal, x0 and x 10 are the gray values ​​corresponding to the vertices of the two peaks;

[0013] (3) Provide a series of grayscale threshold gradient values ​​x n , and based on each x n Segment CT images and calculate the porosity z of the composite material n ;

[0014] in, n and u are integers, the value of n ranges from 0 to u, and u ≥ 5;

[0015] (4) Order (X n ,Y n )=(logx n ,z n ), get (X n ,Y n ) and press X nFrom small to large, scatter points (X0, Y0) to (X u ,Y u ) are sorted, and the first m scattered points are subjected to univariate linear regression to solve the linear regression correlation coefficient R m 2 , where m is a positive integer, the value of m ranges from 3 to v, v is a positive integer, 3<v≤u+1;

[0016] (5) Based on the correlation coefficient R obtained in the previous step m 2 , extract a k value that meets the following conditions;

[0017] When m = k, R m 2 ≥R0;

[0018] When m=k+1, R m 2 <R0;

[0019] Where k is a positive integer, R o = 0.9 to 0.999;

[0020] (6) For the scattered points (X0, Y0) to (X u , Y u ) to perform univariate linear regression on the first k scattered points in (X0, Y0) to obtain the first linear equation; u , Y u ) performs a univariate linear regression on the k+1th and k+2th scattered points in the equation to obtain the second linear equation; solve the intersection point (X k′ , Y k′ ), then according to the relationship X k′ = log x k′ Solving for x k′ , x k′ That is, the standard grayscale threshold of the porous composite material;

[0021] (7) Based on the standard grayscale threshold x k′ Segment CT images and calculate the porosity of composite materials.

[0022] In some embodiments, the value of v satisfies the following conditions: when m=v+1, R v+1 2 <R0.

[0023] In some embodiments, the pixel quantity refers to the volume or number of pixels.

[0024] In some embodiments, u=8-12, for example, u=10.

[0025] In some embodiments, k≤u, for example, k≤u-1.

[0026] In some embodiments, wherein R0=0.99.

[0027] In some embodiments, the method further comprises the step of acquiring a CT image of the porous composite material using a CT device.

[0028] In some embodiments, calculating the porosity zn of the composite material includes the following operation: identifying pixels in the CT image that are smaller than a grayscale threshold as pores.

[0029] In some embodiments, the void-containing composite material is a resin-based composite material.

[0030] In some embodiments, the void-containing composite material is a carbon fiber reinforced resin-based composite material.

[0031] In some embodiments, the void-containing composite material is a carbon fiber fabric-reinforced resin-based composite material.

[0032] In some aspects, the present disclosure provides a composite material porosity measurement device, comprising:

[0033] Memory; and

[0034] A processor coupled to the memory, wherein the processor is configured to execute any one of the above methods for measuring the porosity of a composite material based on instructions stored in the memory device.

[0035] In some aspects, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above methods for measuring the porosity of a composite material.

[0036] Terminology

[0037] A "porous composite material" refers to a composite material (part) having pores (eg, open or closed pores) inside. The composite material may be a fiber-reinforced resin-based composite material.

[0038] The basic principle of "CT equipment" is based on the attenuation and absorption characteristics of radiation in the object being tested. The absorption ability of a substance to radiation is related to the properties of the substance. Therefore, by using the attenuation law and distribution of X-rays or gamma rays with a certain energy and intensity emitted by radioactive nuclides or other radiation sources in the object being tested, it is possible to obtain detailed information inside the object through the detector display, and finally display it in the form of an image using computer information processing and image reconstruction technology.

[0039] Beneficial Effects

[0040] The present disclosure provides a standardized method for determining the grayscale threshold of pore characteristics, which reduces the influence of human subjective judgment and can avoid the steps of determining the characteristic pore reference and adjusting the grayscale threshold to fit the characteristic pore contour, thereby greatly improving the efficiency of determining the grayscale threshold of pore characteristics.

[0041] The porosity detection results obtained by the disclosed method are highly efficient and have good repeatability. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a CT scan image of a porous composite material;

[0043] Figure 2 It is a grayscale distribution diagram with a bimodal distribution;

[0044] Figure 3 It is a grayscale distribution diagram with a single peak distribution;

[0045] Figure 4 For the first four scattered points (X n , Y n ) and the fitted linear equation;

[0046] Figure 5 For the first five scattered points (X n , Y n ) and the fitted linear equation;

[0047] Figure 6 The first four scattered points and the first linear equation of the fit, the fifth and sixth scattered points and the second linear equation of the fit are shown;

[0048] Figure 7 A flow chart showing a method for measuring porosity of a porous composite material according to one embodiment;

[0049] Figure 8 Some schematic diagrams of porosity measurement devices for porous composite materials are shown;

[0050] Fig. 9 Some schematic diagrams of porosity measurement devices for porous composite materials are shown. DETAILED DESCRIPTION

[0051] The embodiments of the present invention will be described in detail below in conjunction with the examples, but those skilled in the art will appreciate that the following examples are only used to illustrate the present invention and should not be considered to limit the scope of the present invention. If no specific conditions are specified in the examples, they are carried out according to normal conditions or the conditions recommended by the manufacturer. If the manufacturer is not specified for the reagents or instruments used, they are all conventional products that can be obtained commercially.

[0052] The basic principle of CT detection of pores in composite materials is as follows: first, obtain the grayscale value distribution map of the signal image of the scanning ray passing through the object to be detected, and then determine the grayscale threshold corresponding to the pore characteristics based on the grayscale value distribution characteristics of the signal image. The scanning signal processing software treats the voxel area below the grayscale threshold as pores, and the pore distribution characteristic information in the composite material can be obtained.

[0053] A T800 grade carbon fiber laminate composite material sample is provided, the size of which is 30 mm (length) × 6 mm (width) × 6 mm (height), and the internal structure of which is [0 / 45 / 0 / -45] 4s laminate sequence. The porosity of the sample is tested using the method of the present invention. The steps are as follows:

[0054] 1. Use a computer tomography device (GE's Vtome m300CT device) to scan the composite material sample to obtain a gray value distribution diagram corresponding to the scanning ray attenuation, such as Figure 1 shown.

[0055] 2. Determine the investigation interval [x0, x 10 ],x0<x 10 , including the following operations:

[0056] Determine the peak distribution mode of the grayscale distribution graph;

[0057] When the grayscale distribution diagram is judged to be unimodal (e.g. Figure 2 The unimodal grayscale distribution diagram is shown, where the horizontal axis is the normalized CT scan grayscale value and the vertical axis is the voxel volume). Then, x is defined as 10 is the gray value corresponding to the top of the peak, and x0 is the gray value corresponding to the bottom of the peak;

[0058] When the grayscale distribution diagram is judged to be bimodal (e.g. Figure 2 The bimodal grayscale distribution diagram is shown, where the horizontal axis is the normalized CT scan grayscale value and the vertical axis is the voxel volume). Then, x0 and x 10 are the grayscale values ​​corresponding to the vertices of the two peaks.

[0059] For the composite material in this example, the grayscale distribution is bimodal.

[0060] 3. Provide 11 grayscale threshold gradient values ​​x n , and based on each x n Segment CT images and calculate the porosity z of the composite material n , and calculate x n The logarithm of log x n ;in, n and u are integers, and the value of n ranges from 0 to 10. The results are shown in the following table:

[0061]

[0062] 4. Let X n = log x n , Y n =z n , get multiple scattered points (X n , Y n ), the value of n traverses from 0 to 10, and press X n From small to large, scatter points (X0, Y0) to (X 10 , Y 10 ) are sorted, and the first m scattered points are subjected to univariate linear regression to solve the correlation coefficient R m 2 , where m is a positive integer, and the value of m ranges from 3 to 7. The results are shown in the following table. When m = 4, R m 2 =0.9934, when m=7, R m 2 Down to 0.713.

[0063] m 3 4 5 6 7 <![CDATA[R m 2 ]]> 0.9985 0.9934 0.9748 0.8539 0.713

[0064] Figure 4 For the first four scattered points (X n , Y n ) and the fitted linear equation.

[0065] Figure 5 For the first five scattered points (X n , Y n ) and the fitted linear equation.

[0066] 5. Based on the correlation coefficient R obtained in the previous step m 2 , extract a k value that meets the following conditions;

[0067] When m = k, R m 2 ≥R0;

[0068] When m=k+1, R m 2 <R0;

[0069] Wherein k is a positive integer, R0=0.99.

[0070] For the composite material in this example, it can be seen from the above table that when m=4, R4 2 Satisfy ≥0.99, R5 2 Satisfies <0.99. Therefore, k=4.

[0071] When R 2When ≥R0, it is considered that the gray threshold points on the curve are all gray levels of the reaction porosity; when R 2 When <R0, it is considered that the larger gray threshold point contains the influence information of non-porosity.

[0072] 6. For scattered points (X0, Y0) to (X 10 , Y 10 ) to perform univariate linear regression on the first four scattered points in (X0, Y0) to obtain the first linear equation; 10 , Y 1o ) to perform a univariate linear regression on the 5th and 6th scattered points in the equation to obtain the second linear equation; solve the intersection point (X k′ , Y k′ ,), then according to the relationship X k′ = log x k′ Solving for x k′ , x k′ That is, the standard grayscale threshold of the porous composite material; Figure 6 The first linear equation, the second linear equation, and their intersection are shown.

[0073] (7) Based on the standard grayscale threshold x k The CT images were segmented and the porosity of the composites was calculated.

[0074] For the scattered points (log x0, z0), (log x1, z1)...(log x u , z u ) to perform a linear regression on the first four scattered points (let X = log x n , Y = z n ), and obtain the first linear equation: Y=6.0237X-25.936;

[0075] For the scattered points (log x0, z0), (log x1, z1)...(log x 10 , z 10 ) and the 5th and 6th scattered points in the univariate linear regression (let X = log x n , Y = z n ), and obtain the second linear equation: Y=31.933X-139.72;

[0076] Figure 6 The first linear equation and the second linear equation are shown. The intersection point of the first linear equation and the second linear equation is solved as (log x k , z k ), where z k =0.517848, log x k=4.3906702, further solve to obtain the standard gray threshold x k =24585.

[0077] Set the grayscale threshold x on the CT image analysis software k =24585, the calculated porosity is 0.55%, which is the porosity calculated by the method of the present invention.

[0078] From the above process, it can be known that the present invention plots the logarithmic relationship curve between porosity and threshold under different grayscale thresholds, finds the inflection point where the slope of the curve rises significantly, and uses the grayscale threshold corresponding to the inflection point as the accurate threshold of the pore feature. The basic principle of this method is that the scanning ray attenuation caused by pores and its corresponding grayscale value change are caused by the change in the total pore volume on the scanning line. The grayscale value logarithm and the statistical porosity form a linear relationship with a high degree of fit. When the selected grayscale threshold exceeds the pore content, part of the resin will be mistakenly regarded as pore statistics, causing the statistical porosity to rise sharply, and the slope of the logarithmic curve with the grayscale threshold will also rise significantly. Therefore, the grayscale threshold corresponding to the inflection point where the logarithmic curve of porosity and grayscale threshold deviates from the linear relationship is the objectively accurate grayscale threshold of the pore feature. This method can significantly reduce the dependence of pore detection accuracy on the quality of CT scanning images, avoid the dependence of the operations of filtering the initial scan image to find characteristic pores as a benchmark and adjusting the grayscale threshold to fit the characteristic pore contour on human experience, and ensure the objectivity and high repeatability of porosity calculation in terms of efficiency and accuracy.

[0079] Figure 7 A flow chart of porosity measurement of porous composite materials is shown in FIG. Figure 7 As shown, a method for measuring the porosity of a porous composite material comprises:

[0080] S10 obtaining a corresponding grayscale distribution map based on the CT image of the porous composite material, wherein the abscissa of the grayscale distribution map is the grayscale value x, and the ordinate is the pixel quantity y corresponding to the grayscale value;

[0081] S20 determines the investigation interval [x0, x 10 ],x0<x 10 , including the following operations:

[0082] Determine the peak distribution mode of the grayscale distribution graph;

[0083] When the grayscale distribution diagram is unimodal, define x 10 is the gray value corresponding to the top of the peak, and x0 is the gray value corresponding to the bottom of the peak;

[0084] When the grayscale distribution is determined to be bimodal, x0 and x 10 are the gray values ​​corresponding to the vertices of the two peaks;

[0085] S30 provides a series of grayscale threshold gradient values ​​x n , and based on each x n Segment CT images and calculate the porosity z of the composite material n ;

[0086] in, n and u are integers, the value of n ranges from 0 to u, and u ≥ 5;

[0087] S40 Order (X n , Y n )=(logx n , z n ), provide (X n , Y n ) and press X n From small to large, scatter points (X0, Y0) to (X u , Y u ) are sorted, and the first m scattered points are subjected to univariate linear regression to solve the linear regression correlation coefficient R m 2 , where m is a positive integer, the value of m ranges from 3 to v, v is a positive integer, 3<v≤u+1;

[0088] S50 is based on the correlation coefficient R obtained in the previous step m 2 , extract a k value that meets the following conditions;

[0089] When m = k, R m 2 ≥R0;

[0090] When m=k+1, R m 2 <R0;

[0091] Where k is a positive integer, R0 = 0.9 to 0.999;

[0092] S60 scatters points (X0, Y0) to (X u , Y u ) to perform univariate linear regression on the first k scattered points in (X0, Y0) to obtain the first linear equation; u , Y u ) performs a univariate linear regression on the k+1th and k+2th scattered points in the equation to obtain the second linear equation; solve the intersection point (X k′ , Y k′ ), then according to the relationship X k′ = log x k′ Solving for x k′ , x k′That is, the standard grayscale threshold of the porous composite material;

[0093] S70 is based on the standard grayscale threshold x k′ Segment CT images and calculate the porosity of composite materials.

[0094] Figure 8 Schematic diagrams of porosity measurement devices for some porous composite materials are shown.

[0095] like Figure 8 As shown, the porosity measuring device 7 of the porous composite material of this embodiment includes: a memory 71 and a processor 72 coupled to the memory 71, and the processor 72 is configured to execute the porosity measuring method of the porous composite material in any embodiment of the present disclosure based on the instructions stored in the memory 71.

[0096] The memory 71 may include, for example, a system memory, a fixed non-volatile storage medium, etc. The system memory may store, for example, an operating system, an application program, a boot loader, a database, and other programs.

[0097] Fig. 9 Some schematic diagrams of porosity measurement devices for porous composite materials are shown.

[0098] like Fig. 9 As shown, the porosity measurement device 8 of the porous composite material of this embodiment includes: a memory 810 and a processor 820 coupled to the memory 810, and the processor 820 is configured to perform the porosity measurement of the porous composite material in any of the aforementioned embodiments based on the instructions stored in the memory 810.

[0099] The memory 810 may include, for example, a system memory, a fixed non-volatile storage medium, etc. The system memory may store, for example, an operating system, an application program, a boot loader, and other programs.

[0100] The porosity measuring device 8 for porous composite materials may also include an input / output interface 830, a network interface 840, a storage interface 850, etc. These interfaces 830, 840, 850, the memory 810, and the processor 820 may be connected, for example, via a bus 860. The input / output interface 830 provides a connection interface for input / output devices such as a display, a mouse, a keyboard, and a touch screen. The network interface 840 provides a connection interface for various networked devices. The storage interface 850 provides a connection interface for external storage devices such as SD cards and USB flash drives.

[0101] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable non-transient storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] So far, the porosity measurement method of the pore-containing composite material, the porosity measurement device of the pore-containing composite material and the computer-readable storage medium according to the present disclosure have been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Based on the above description, those skilled in the art can fully understand how to implement the technical solution disclosed herein.

[0103] The method and system of the present disclosure may be implemented in many ways. For example, the method and system of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above, unless otherwise specifically stated. In addition, in some embodiments, the present disclosure may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present disclosure. Therefore, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

[0104] Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A method for measuring the porosity of a porous composite material, comprising: (1) obtaining a CT image of the porous composite material to obtain a corresponding grayscale distribution map, wherein the abscissa of the grayscale distribution map is the grayscale value x, and the ordinate is the pixel quantity y corresponding to the grayscale value; (2) Determine the investigation interval [x0,x 10 ],x0<x 10 , including the following operations: Determine the peak distribution mode of the grayscale distribution graph; When the grayscale distribution diagram is unimodal, define x 10 is the gray value corresponding to the top of the peak, and x0 is the gray value corresponding to the bottom of the peak; When the grayscale distribution is determined to be bimodal, x0 and x 10 are the gray values ​​corresponding to the vertices of the two peaks; (3) Provide a series of grayscale threshold gradient values ​​x n , and based on each x n Segment CT images and calculate the porosity z of the composite material n ; in, n and u are integers, the value of n ranges from 0 to u, and u ≥ 5; (4) Order (X n ,Y n )=(logx n ,z n ), get (X n ,Y n ) and press X n From small to large, scatter points (X0, Y0) to (X u ,Y u ) are sorted, and the first m scattered points are subjected to univariate linear regression to solve the linear regression correlation coefficient R m 2 , where m is a positive integer, the value of m ranges from 3 to v, v is a positive integer, 3<v≤u+1; (5) Based on the correlation coefficient R obtained in the previous step m 2 , extract a k value that meets the following conditions; When m = k, R m 2 ≥R0; When m=k+1, R m 2 <R0; Where k is a positive integer, R0 = 0.9 to 0.999; (6) For the scattered points (X0, Y0) to (X u ,Y u ) to perform univariate linear regression on the first k scattered points in (X0, Y0) to obtain the first linear equation; u ,Y u ) performs a univariate linear regression on the k+1th and k+2th scattered points in the equation to obtain the second linear equation; solve the intersection point (X k′ ,Y k′ ), and then according to the relationship X k′ =logx k′ Solving for x k′ , x k′ That is, the standard grayscale threshold of the porous composite material; (7) Based on the standard grayscale threshold x k′ Segment CT images and calculate the porosity of composite materials.

2. The method according to claim 1, wherein: u＝8～12。 3. The method according to claim 1, wherein: k≤u.

4. The method according to claim 1, wherein: R0＝0.99。 5 . The method according to claim 1 , further comprising the step of acquiring a CT image of the porous composite material using a CT device.

6. The method according to claim 1, wherein: Calculate the porosity z of the composite material n The method includes the following operations: pixels in the CT image that are smaller than a grayscale threshold are identified as pores. The method according to claim 1 , wherein the pore-containing composite material is a resin-based composite material.

8. The method according to claim 1, wherein the pore-containing composite material is a carbon fiber reinforced resin-based composite material.

9. A composite material porosity measuring device, comprising: Memory; and A processor coupled to the memory, wherein the processor is configured to execute the method for measuring the porosity of a composite material according to any one of claims 1 to 8 based on instructions stored in the memory device.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method for measuring the porosity of a composite material according to any one of claims 1 to 8 is implemented.

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