Two-dimensional image pore analysis method

Abstract

The invention discloses a two-dimensional image pore analysis method which comprises the following steps: selecting a solid porous material as a to-be-detected material, cutting the solid porous material into a cuboid-shaped sample, pretreating the sample, and sealing the sample in rubber resin; carrying out primary grinding and final grinding treatment on the sample sealed in the rubber resin; the sample is subjected to shooting pretreatment, and an image of the sample is shot; converting the image into a grey-scale map to obtain an average pore grey-scale threshold value of the sample; based on the gray-scale map and the average gray-scale pore gray-scale threshold value, obtaining the two-dimensional plane porosity, the pore size distribution proportion and the pore size distribution detail drawing of the image; repeating the previous step to obtain the two-dimensional plane porosity and the pore size distribution proportion of all the images of the sample, and calculating the two-dimensional plane average porosity and the average pore size distribution proportion of the sample. According to the invention, the porosity and pore size distribution of the material to be measured are determined through a physical means and an image analysis technology, and measurement and analysis of any solid porous material can be realized while the measurement efficiency, precision and environmental protection are ensured.

Description

Technical Field

[0001] The invention relates to the technical field of material analysis, and in particular to a two-dimensional image pore analysis method. Background Art

[0002] The macroscopic properties of solid materials, such as mechanical properties, frost resistance, permeability, and durability, are closely related to the microscopic pore structure, content, and pore size distribution of the material itself.

[0003] At present, there are three conventional methods for determining the porosity of materials. The first method is the gas adsorption method, which is to apply a certain relative pressure (nitrogen partial pressure / the saturated vapor pressure of nitrogen at liquid nitrogen temperature) to liquid nitrogen at liquid nitrogen temperature (77K) to promote nitrogen adsorption in the material. In particular, when the relative pressure is ≥0.4, nitrogen will condense in the pores. At this time, the pore volume and pore size distribution can be calculated by the BJH (Barrot-Joyner-Halenda) principle. However, the accuracy of the measurement results of this method is usually affected by the adsorption properties of the material surface. It is only applicable to the pore measurement of rigid materials. It has strict requirements on the measurement temperature and cannot test mesopores and macropores above 500nm, resulting in low measurement accuracy, high measurement cost and long measurement time. The second method is the immersion volume method, which is to immerse the material in water, measure the weight of the material before and after complete water absorption, calculate the volume of water absorbed (pore volume) and the volume of the sample to be tested, and compare the volumes of the two to quickly obtain the porosity of the sample to be tested. However, at present, this method has obvious limitations. It is only applicable to materials that are easily wetted, insoluble in water, and do not react chemically with water. In addition, the porosity obtained by this method is affected by whether the material fully absorbs water, which will lead to low accuracy of the measurement results. The third method is the mercury injection method, which uses pressure to press mercury into the pores of the evacuated material, and obtains the pore volume and size data of the material based on the pressure and volume of the injected mercury, thereby calculating the pore volume and pore size distribution. Although this method can provide highly accurate test results in most cases, it is also accompanied by several significant limitations during its implementation: first, there is a risk of poisoning of workers due to exposure to mercury; second, if the discarded samples are not handled properly, it may cause serious environmental pollution problems; third, due to the high mercury pressure required for the test, this method is mainly suitable for analyzing materials with strong rigidity; fourth, the overall test cost is relatively high; fifth, the range of pore sizes that can be measured is subject to certain restrictions.

[0004] Therefore, there is an urgent need for a measurement and analysis method that is low-cost, easy to operate, less affected by uncontrollable external factors, and has high measurement accuracy for the porosity of materials. Summary of the invention

[0005] The purpose of the present invention is to provide a two-dimensional image pore analysis method to solve the problems existing in the above-mentioned prior art. Through physical processing means and image analysis technology, the pore structure, content and pore size distribution of the material to be tested can be determined. While ensuring the measurement efficiency, accuracy, greenness and environmental protection, it can also measure and analyze the pores of any solid porous material.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0007] A two-dimensional image pore analysis method comprises the following steps:

[0008] S1. Select any solid porous material as the material to be tested and cut it into a rectangular sample, then pretreat the sample and seal the sample in rubber resin;

[0009] S2, performing initial grinding and final grinding on the sample sealed in the rubber resin;

[0010] S3, performing pre-shooting processing on the sample, and then shooting an image of the sample;

[0011] S4, converting the image into a grayscale image, and obtaining an average pore grayscale threshold of the sample based on the grayscale image;

[0012] S5. Based on the grayscale image and the average grayscale pore grayscale threshold, the two-dimensional plane porosity, pore size distribution ratio and pore distribution detailed image of the image are obtained;

[0013] S6. Repeat S5 to obtain the two-dimensional plane porosity and pore size distribution ratio of all images of the sample, take the average of the two-dimensional plane porosity and pore size distribution ratio of all images, and obtain the two-dimensional plane average porosity and average pore size distribution ratio of the sample.

[0014] Furthermore, the S1 includes: S11, selecting any solid porous material as the material to be tested, cutting the material to be tested into sheet materials, and then cutting the sheet materials into rectangular samples; S12, pre-treating the sample: using sandpaper to grind the observation surface of the sample until there are no cutting marks visible on the observation surface, and then drying the sample; S13, placing the observation surface of the sample face down into a rubber mold, pouring a transparent epoxy resin with a mass ratio of curing agent to resin of 1:3 into the rubber mold, and after the transparent epoxy resin is cooled and solidified, removing the rubber mold and taking out the sample.

[0015] Further, in S11, a grinding wheel cutter is used to cut the material to be tested into a sheet material, preferably, the thickness of the sheet material is 10±1 cm; a low-speed precision cutter is used to cut the sheet material into a rectangular sample, preferably, the length and width of the sample are 2±0.5 cm, and the thickness is 1±0.5 cm;

[0016] In the S12, the mesh number of the sandpaper is 320 meshes, and metallographic polishing liquid is added to the observation surface several times during the polishing, and the observation surface is cooled and polished successively until there are no visible cutting marks on the observation surface, and each polishing time is 30 seconds, and then an optical microscope is used to check whether the observation surface is over-polished to produce cracks or damage; the sample is dried in a vacuum drying oven, and the drying temperature is set to 60° C. for 48 hours;

[0017] In S13, the inner diameter of the rubber mold is 30 mm, and during the casting process, the distance between the observation surface and the bottom of the rubber mold is kept less than 2 mm.

[0018] Further, the S2 includes: S21, performing initial grinding treatment on the sample sealed in the rubber resin: using low-grit sandpaper to grind until the observation surface of the sample is exposed; then using high-grit sandpaper to re-grind and flatten the observation surface; S22, performing final grinding treatment on the sample sealed in the rubber resin: using four groups of polishing pastes of 5μm, 3.5μm, 1.0μm, and 0.5μm in sequence to polish the sample step by step; S23, confirming that there are no visible grinding marks on the observation surface of the sample before drying.

[0019] Further, in S21, the mesh number of the low-grit sandpaper is 320 mesh, and metallographic polishing liquid is added multiple times during polishing to polish the polishing surface successively, and each polishing time is 10 to 20 seconds; the high-grit sandpaper is 600 mesh and 1200 mesh, and the observation surface is gradually polished and smoothed using 600 mesh and 1200 mesh sandpaper, and each polishing time is within 30 seconds;

[0020] In S22, each group of polishing pastes is polished for 1 minute in each side length direction perpendicular to the sample observation surface during the polishing process;

[0021] In S23, an optical microscope is used to visually inspect the surface to find that there are no visible grinding marks; a vacuum drying oven is used to dry the sample, and the drying temperature is set to 60° C. for 48 hours.

[0022] Furthermore, the S3 includes: S31, fixing the sample on the sample stage so that the observation surface of the sample and the sample stage form a passage through the conductive carbon tape, and spraying gold on the observation surface. If a portable device is used as a shooting instrument, only gold spraying is required; S32, using a scanning electron microscope to capture an image of the sample, with no less than 30 shooting areas for each sample.

[0023] Furthermore, the parameters of the scanning electron microscope are set as follows: acceleration voltage: 15KV, magnification: 1000 times, working distance: 12.7mm, and shooting area size: 207μm×138μm.

[0024] Furthermore, the S4 includes: S41, inputting the captured images into the image preprocessing system in sequence, the image preprocessing system automatically reads the images and converts the images with color and light and shadow differences into grayscale images; S42, assigning each pixel of different color in the grayscale image to the corresponding grayscale value, and evenly dividing the grayscale of 0 to 255 into 128 grayscale intervals, obtaining the number of pixels in each grayscale interval, and drawing a grayscale value-pixel function relationship curve; S43, accumulating and summing the pixels in the grayscale image, and obtaining the cumulative pixel ratio in each grayscale interval, generating a grayscale value-cumulative pixel ratio function relationship curve, and outputting the intersection value of the tangents of the two end curves at the first turning point in the grayscale value-cumulative pixel ratio function relationship curve as the pore grayscale threshold; S44, taking the average of the pore grayscale thresholds of the grayscale images converted from images of different shooting areas of the same sample to obtain the average pore grayscale threshold of the sample.

[0025] Further, the S5 includes: S51, inputting the grayscale image and the average pore grayscale threshold into the image post-processing system at the same time, the image post-processing system identifies the area of ​​the grayscale image, the grayscale value of the pixel points and the total number of pixels, and normalizes the grayscale image; S52, the image post-processing system obtains the grayscale value of each pixel point according to the grayscale value of the pixel points through binarization processing, converts the pixel points in the grayscale image whose grayscale values ​​are greater than the average pore grayscale threshold value into pure white pixel points, and converts the pixel points whose grayscale values ​​are less than the average pore grayscale threshold value into pure black pixel points; S53, repeating S52 to generate a detailed pore distribution map; S 54. Cumulatively sum the converted pure black pixels in the pore distribution detail map to obtain a first cumulative value, calculate the ratio of the first cumulative value to the total number of pixels in the grayscale image, and obtain the percentage of holes; S56. Perform size recognition on the pore distribution detail map, cumulatively sum the pure black pixels in each interval after size recognition to obtain a second cumulative value in each interval, calculate the ratio of the second cumulative value in each interval to the first cumulative value, and obtain the pore size distribution ratio; S57. Obtain the two-dimensional plane porosity of the image corresponding to the current grayscale image by pore percentage * 100 / area of ​​the grayscale image.

[0026] Furthermore, in S56, the interval range of size recognition includes pore size <20nm, 20-50nm, 50-200nm, >200nm or pore size <0.01μm, 0.01-0.05μm, 0.05-0.1μm, 0.1-5μm, >5μm.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1. The present invention is simple and quick to operate, and can significantly reduce the cost of current conventional porosity and pore size distribution measurement methods, thereby promoting the in-depth application and popularization of scientific research on material microstructure characteristics in a wider range of fields.

[0029] 2. The present invention utilizes image analysis technology to effectively identify the full-size pore size of the material, and can also generate an intuitive pore distribution detail map, providing a theoretical basis and reference for in-depth exploration of the macroscopic performance evolution of the material, performance degradation mechanism, and strengthening mechanism analysis.

[0030] 3. The present invention utilizes the pore-fixing technology of rubber resin, which not only effectively ensures the integrity of the pore structure of the material during the initial grinding and final grinding stages, avoids the damage caused by human factors, but also significantly improves the measurement accuracy of the pore content and pore size distribution ratio.

[0031] 4. The present invention can significantly improve the analysis efficiency of material porosity and pore size distribution, so that the analysis process can be completed quickly within 3 to 8 minutes.

[0032] 5. The present invention is a physical technology, which is not affected by external factors such as temperature, humidity, pressure, adsorption, etc. during the measurement process, and no toxic or harmful reagents are used during the measurement process. It is applicable to rock, concrete, wood or any other solid porous material.

[0033] 6. The method of the present invention can process high-resolution images taken by any device, not limited to images taken by electron microscopes, and can extract the porosity of any pore size and pore size range. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The figure is a flow chart of the method of the present invention.

[0035] Figure 2 This is the analysis code diagram of the image pre-processing system of the present invention.

[0036] Figure 3 This is the analysis code diagram of the image post-processing system of the present invention.

[0037] Figure 4 This is a sample diagram of a concrete sample taken in an embodiment of the present invention.

[0038] Figure 5 This is an image taken of a concrete sample in an embodiment of the present invention.

[0039] Figure 6 Detailed diagram of pore distribution of concrete sample in an embodiment of the present invention.

[0040] Figure 7 It is a function relationship curve diagram of gray value-pixel point of concrete sample in the embodiment of the present invention.

[0041] Figure 8 It is a functional relationship curve diagram of the gray value-cumulative pixel ratio of the concrete sample in the embodiment of the present invention.

[0042] Fig. 9 2D plane porosity map of the image of the concrete sample in the embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore they cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance.

[0045] like Figure 1 As shown, the present invention provides a two-dimensional image pore analysis method, comprising the following steps:

[0046] S1. Select any solid porous material as the material to be tested and cut it into a rectangular sample; then pretreat the sample and seal the sample in rubber resin;

[0047] S2, performing initial grinding and final grinding on the sample sealed in the rubber resin;

[0048] S3, performing pre-shooting processing on the sample, and then shooting an image of the sample;

[0049] S4, converting the image into a grayscale image, and obtaining an average pore grayscale threshold of the sample based on the grayscale image;

[0050] S5. Based on the grayscale image and the average grayscale pore grayscale threshold, the two-dimensional plane porosity, pore size distribution ratio and pore distribution detailed image of the image are obtained;

[0051] S6. Repeat S5 to obtain the two-dimensional plane porosity and pore size distribution ratio of all images of the sample, take the average of the two-dimensional plane porosity and pore size distribution ratio of all images, and obtain the two-dimensional plane average porosity and average pore size distribution ratio of the sample.

[0052] The present invention is a technology that uses physical methods to measure the two-dimensional plane porosity and pore size ratio of materials. It is applicable to any solid porous material, including concrete, rock, soil, wood, tile, metal, etc. It has strong applicability, simple operation and unified process. The present invention can effectively reduce the measurement cost of porosity and pore size ratio, improve the range of pore size measurement, and improve analysis efficiency.

[0053] Compared with the gas adsorption method, the present invention is applicable to the full-size pore size analysis of any solid porous material at room temperature and pressure, and has the advantages of high measurement accuracy and short analysis time. Compared with the water immersion volume method, the present invention does not need to consider the functional characteristics of the material itself, and is not affected by external factors such as human factors and accidental factors during the measurement and analysis process, making the measurement results more reliable. Compared with the mercury injection method, the present invention uses image analysis technology, and the whole process from sample preparation to porosity results is green and pollution-free, and can also reduce measurement costs and broaden the measurement range of pore size.

[0054] In some embodiments of the present invention, S1 includes: S11, selecting any solid porous material as the material to be tested, using a grinding wheel cutter to cut the material to be tested into a sheet material with a thickness of 10 cm, and then using a low-speed precision cutter to cut the sheet material into a rectangular sample with a length, width and thickness of 2 cm; S12, pre-treating the sample: using 320-mesh sandpaper to grind the observation surface of the sample, the observation surface is any cut surface cut by the low-speed precision cutter, because the cutting machine used for the initial cutting into sheets is a grinding wheel cutter, which has a large disturbance and is not suitable as an observation surface, and when cutting the sheet material to take out a rectangular sample, a low-speed precision cutter is used for cutting during processing and cutting, which has a small disturbance on the sample and is suitable as an observation surface; during grinding, it is necessary to add metallographic polishing liquid to the grinding surface in a timely manner, cool the grinding surface, and grind it step by step until there are no cutting marks visible to the naked eye on the observation surface, in this It is necessary to further inspect it with the help of an optical microscope to ensure that the observation surface has no cracks or other signs of damage due to excessive polishing. If there are cutting marks, cracks or other damage, it is necessary to re-polish or select new materials. Each polishing time lasts 30 seconds, and after each polishing, an ultrasonic cleaner is required to thoroughly remove the dust on the surface. The qualified samples are then dried in a vacuum drying oven. The drying temperature is set to 60°C and the time is 48 hours. S13. Place the observation surface of the sample face down into a rubber mold with an inner diameter of 30 mm, ensuring that the distance between the observation surface and the bottom of the rubber mold is less than 2 mm, and then pour a transparent epoxy resin with a mass ratio of curing agent to resin of 1:3 into the rubber mold. After the transparent epoxy resin is cooled and solidified, the rubber mold is removed to take out the sample. In order to fully fill the resin into the pores of the sample, a vacuum cold mounting instrument is required to perform vacuum resin pouring on the sample.

[0055] In some embodiments of the present invention, S2 includes: S21, performing preliminary grinding on the sample sealed in the rubber resin: using 320 mesh sandpaper to grind until the observation surface of the sample is slightly exposed, where it is necessary to observe through an optical microscope, adding metallographic polishing liquid multiple times during grinding to grind the grinding surface successively, and each grinding time is 10 to 20 seconds; then using 600 mesh and 1200 mesh sandpaper to gradually re-grind and smooth the observation surface, and each grinding time is within 30 seconds. After each grinding, an ultrasonic cleaner is used to clean the dust on the surface; S22, performing final grinding on the sample sealed in the rubber resin: using 5μ The sample was polished step by step with four groups of polishing pastes of 1.0μm, 3.5μm, 1.0μm and 0.5μm. Each group of polishing paste was polished for 1min in each side length direction perpendicular to the sample observation surface during the polishing process to ensure that there were no residual scratches. At the same time, in order to effectively reduce the temperature and improve the polishing efficiency, metallographic polishing liquid was used as the lubricating and cooling medium, and the sample was thoroughly cleaned with an ultrasonic cleaner before each change of the polishing direction to ensure the polishing effect and no polishing paste residue in the pores. S23. After confirming that there were no visible polishing marks on the observation surface of the sample, a vacuum drying oven was used for drying. The drying temperature was set to 60℃ and the time was 48h. Similarly, the observation surface of the sample was placed under an optical microscope to confirm whether there were obvious polishing marks. If there were, it was re-polished or new materials were selected for processing.

[0056] Preferably, in S1 and S2, in order to avoid hydration reaction between the sample and water, the cleaning liquid in the ultrasonic cleaning instrument is anhydrous ethanol.

[0057] In some embodiments of the present invention, the S3 includes: S31, the sample is fixed on the sample stage by bonding with conductive carbon tape, so that the observation surface of the sample and the sample stage form a path through the conductive carbon tape to increase the conductivity of the sample. In addition, before shooting, it is necessary to use a sputtering instrument to spray gold on the observation surface. If a portable device such as a mobile phone is used as a shooting instrument, only gold spraying is required; S32, the image of the sample is captured using a scanning electron microscope (SEM), and the shooting area of ​​each sample is not less than 30, preferably 30 to 50, so as to perform statistical analysis on its porosity later. The brightness and contrast settings of the same sample must be kept consistent when shooting. The parameters of the scanning electron microscope are set as follows: acceleration voltage: 15KV, magnification: 1000 times, working distance: 12.7mm, shooting area size: 207μm×138μm.

[0058] In some embodiments of the present invention, the S4 includes: S41, inputting the captured images into the image pre-processing system in sequence, the image pre-processing system automatically reads the images and converts the images with color and light and shadow differences into a grayscale image with a grayscale distribution of 0 to 255 (0 is black and 255 is white); S42, assigning each pixel of different colors in the grayscale image to the corresponding grayscale value, and evenly dividing the grayscale of 0 to 255 into 128 grayscale intervals, obtaining the number of pixels in each grayscale interval, and drawing a grayscale value-pixel function relationship curve; S 43. Cumulatively sum the pixels in the grayscale image and obtain the cumulative pixel ratio in each grayscale interval, the cumulative pixel ratio = (cumulative number of pixels in the grayscale interval * 100) / total number of pixels (%), generate a grayscale value - cumulative pixel ratio function curve, and output the intersection value of the tangents of the two end curves at the first turning point in the grayscale value - cumulative pixel ratio function curve as the pore grayscale threshold; S44. Take the average of the pore grayscale thresholds of the grayscale images converted from images of different shooting areas of the same sample to obtain the average pore grayscale threshold of the sample.

[0059] Since the rubber resin filled in the sample pores is displayed as black (grayscale is 0) in the grayscale image, the first peak in the grayscale value-pixel function relationship curve is the pore peak, so the image pre-processing system can automatically identify the intersection value of the tangents of the two end curves at the first turning point in the grayscale value-cumulative pixel ratio function relationship curve as the pore grayscale threshold. Compared with the threshold value obtained by conventional image processing technology, the threshold value determined by the present invention has a higher correlation with the image and is more accurate. The S4 is implemented based on the image pre-processing system, and the analysis code of the image pre-processing system is as follows: Figure 2 shown.

[0060] In some embodiments of the present invention, the S5 includes: S51, inputting the grayscale image and the average pore grayscale threshold into the image post-processing system at the same time, the image post-processing system identifies the area of ​​the grayscale image, the grayscale value of the pixel points and the total number of pixels, and normalizes the grayscale image, that is, adjusting the brightness, contrast and saturation of the input grayscale image to preset fixed values, thereby eliminating the influence of external factors on the analysis results; S52, the image post-processing system obtains the grayscale value of each pixel point according to the grayscale value of the pixel point through binarization processing, converts the pixel points in the grayscale image whose grayscale values ​​are greater than the average pore grayscale threshold value into pure white pixels (grayscale = 255), and converts the pixel points whose grayscale values ​​are less than the average pore grayscale threshold value into pure black pixels. (grayscale = 0); S53, repeat S52 to eliminate the recognition error and generate a pore distribution detail map; S54, accumulate and sum the converted pure black pixels in the pore distribution detail map to obtain a first cumulative value, calculate the ratio of the first cumulative value to the total number of pixels in the grayscale image, and obtain the percentage of holes; S56, perform size recognition on the pore distribution detail map, accumulate and sum the pure black pixels in each interval after size recognition to obtain a second cumulative value in each interval, calculate the ratio of the second cumulative value in each interval to the first cumulative value, and obtain the pore size distribution ratio; S57, obtain the two-dimensional plane porosity of the image corresponding to the current grayscale image through the percentage of holes * 100 / area of ​​the grayscale image. When the pore distribution details are used for size identification, the interval range, i.e., the pore size interval division, can be set according to actual needs. In the field of cement-based materials, there are two conventional division intervals. The first is pore size <20nm, 20-50nm, 50-200nm, >200nm, and the second is pore size <0.01μm, 0.01-0.05μm, 0.05-0.1μm, 0.1-5μm, >5μm. The S5 is implemented based on the image post-processing system. The analysis codes of the image post-processing system are as follows: Figure 3 shown.

[0061] Finally, repeat S5 to obtain the two-dimensional plane porosity and pore size distribution ratio of all images of the sample, and take the average value of the two-dimensional plane porosity and pore size distribution ratio of all images. Specifically, draw a scatter plot and take the average value to obtain the two-dimensional plane average porosity and average pore size distribution ratio of the sample.

[0062] Example

[0063] In this embodiment, concrete specimens are selected as test materials. Four groups of different concrete specimens are designed according to whether fibers are added and different preloads are applied. The proportions are shown in Table 1, which are: C30 ordinary concrete without fibers (numbered F0P0λ0); C30 concrete without fibers and preloaded with 15 MPa (numbered F15P0λ0); C30 ordinary fiber concrete with a fiber volume content of 0.15% and a slenderness ratio of 3.13 (numbered F0P0.15λ313); C30 fiber concrete with a fiber volume content of 0.15% and a slenderness ratio of 3.13 and preloaded with 15 MPa (numbered F15P0.15λ313). After pouring the above specimens for 24 hours, the molds were removed and standard curing was performed for 28 days; after curing for 28 days, the above specimens were sampled and prepared according to S1 of the present invention, processed in S2, and imaged by SEM in S3, and finally the images obtained were analyzed and processed in sequence by S4 to S6 to obtain the plane porosity and average pore size distribution of each concrete specimen, and the test results are shown in Table 2. In addition, the concrete specimens were tested by mercury injection, and the test results are shown in Table 3 to evaluate the accuracy of the porosity results measured by the present invention.

[0064] Taking the fiber concrete (numbered F15P0.15λ313) with 15MPa preload C30 and fiber volume content of 0.15% and slenderness ratio of 313 as an example, the sample of the concrete sample is shown in the figure below: Figure 4 As shown in Figure 2, the images of the concrete samples are as follows: Figure 5 The pore distribution details of the concrete sample are shown in Figure 6 As shown in the figure, the gray value-pixel function curve of the concrete sample is as follows: Figure 7 As shown in the figure, the function curve of the gray value-cumulative pixel ratio of the concrete sample is as follows: Figure 8 As shown, the average porosity of the two-dimensional plane of the image of the concrete sample is Fig. 9 As shown. Figure 8 In the gray value-cumulative pixel ratio function curve, the tangents of the two end curves at the first turning point are y=3.9193x-423.44 and y=0.121x-9.0887 respectively. The intersection value of the two tangents is 109, that is, the pore gray threshold of the current concrete sample (F15P0.15λ313) is 109.

[0065] Table 1. Concrete mix ratio

[0066]

[0067] Table 2. Test results of the method of the present invention

[0068]

[0069]

[0070] Table 3. Mercury intrusion test results

[0071]

[0072] Tables 2 and 3 show the test results of the same specimen by the present invention and the mercury injection method. By comparison, it is found that the porosity values ​​of the two are similar, but the results obtained by the present invention are smaller than those obtained by the mercury injection method. This is because the porosity measured by the present invention is only the plane porosity, not the three-dimensional porosity, that is, for the same sample, the mercury injection method measures all pores of the sample in 360 degrees, including some non-penetrating pores; while the present invention only measures the plane 360-degree pores of the sample, and the non-penetrating pores on the back and side of the sample cannot be measured. The results of the present invention are in line with the actual situation, so the results are more reliable.

[0073] Finally, it should be noted that the above embodiments are only preferred embodiments of the present invention to illustrate the technical solutions of the present invention, rather than limiting them, and certainly not limiting the patent scope of the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention. In other words, any changes or modifications made to the main design concept and spirit of the present invention that have no substantive significance, and the technical problems they solve are still consistent with the present invention, should be included in the protection scope of the present invention. In addition, the direct or indirect application of the technical solutions of the present invention in other related technical fields is also included in the patent protection scope of the present invention.

Claims

1. A two-dimensional image pore analysis method, characterized in that: The following steps are involved: S1. Select any solid porous material as the material to be tested and cut it into a rectangular sample, then pretreat the sample, and then seal the sample in rubber resin; S2, performing initial grinding and final grinding on the sample sealed in the rubber resin; S3, performing pre-shooting processing on the sample, and then shooting an image of the sample; S4, converting the image into a grayscale image, and obtaining an average pore grayscale threshold of the sample based on the grayscale image; S5. Based on the grayscale image and the average grayscale pore grayscale threshold, the two-dimensional plane porosity, pore size distribution ratio and pore distribution detailed image of the image are obtained; S6. Repeat S5 to obtain the two-dimensional plane porosity and pore size distribution ratio of all images of the sample, take the average of the two-dimensional plane porosity and pore size distribution ratio of all images, and obtain the two-dimensional plane average porosity and average pore size distribution ratio of the sample.

2. A two-dimensional image pore analysis method according to claim 1, characterized in that: The S1 comprises: S11, selecting any solid porous material as the material to be tested, cutting the material to be tested into sheet materials, and then cutting the sheet materials into rectangular samples; S12, pre-treating the sample: using sandpaper to grind the observation surface of the sample until there are no cutting marks visible on the observation surface, and then drying the sample; S13, placing the observation surface of the sample facing downward into a rubber mold, pouring a transparent epoxy resin with a curing agent and resin mass ratio of 1:3 into the rubber mold, and after the transparent epoxy resin is cooled and solidified, removing the rubber mold and taking out the sample.

3. A two-dimensional image pore analysis method according to claim 2, characterized in that: In S11, a grinding wheel cutter is used to cut the material to be tested into a sheet material, preferably, the thickness of the sheet material is 10±1 cm; a low-speed precision cutter is used to cut the sheet material into a rectangular sample, preferably, the length and width of the sample are 2±0.5 cm, and the thickness is 1±0.5 cm; In the S12, the mesh number of the sandpaper is 320 meshes, and metallographic polishing liquid is added to the observation surface several times during the polishing, and the observation surface is cooled and polished successively until there are no visible cutting marks on the observation surface, and each polishing time is 30 seconds, and then an optical microscope is used to check whether the observation surface is over-polished to produce cracks or damage; the sample is dried in a vacuum drying oven, and the drying temperature is set to 60° C. for 48 hours; In S13, the inner diameter of the rubber mold is 30 mm, and during the casting process, the distance between the observation surface and the bottom of the rubber mold is kept less than 2 mm.

4. A two-dimensional image pore analysis method according to claim 1, characterized in that: The S2 includes: S21, performing initial grinding treatment on the sample sealed in the rubber resin: using low-grit sandpaper to grind until the observation surface of the sample is exposed; then using high-grit sandpaper to re-grind and level the observation surface; S22, performing final grinding treatment on the sample sealed in the rubber resin: using four groups of polishing pastes of 5.0μm, 3.5μm, 1.0μm, and 0.5μm in sequence to gradually polish the sample; S23, confirming that there are no visible grinding marks on the observation surface of the sample before drying.

5. A two-dimensional image pore analysis method according to claim 4, characterized in that: In the S21, the mesh number of the low-grit sandpaper is 320 mesh, and metallographic polishing liquid is added multiple times during the polishing to polish the polishing surface successively, and each polishing time is 10 to 20 seconds; the high-grit sandpaper is 600 mesh and 1200 mesh, and the observation surface is gradually polished and smoothed using 600 mesh and 1200 mesh sandpaper, and each polishing time is within 30 seconds; In S22, each group of polishing pastes is polished for 1 minute in each side length direction perpendicular to the sample observation surface during the polishing process; In S23, an optical microscope is used to visually inspect the surface to find that there are no visible grinding marks; a vacuum drying oven is used to dry the sample, and the drying temperature is set to 60° C. for 48 hours.

6. A two-dimensional image pore analysis method according to claim 1, characterized in that: The S3 includes: S31, fixing the sample on the sample stage so that the observation surface of the sample and the sample stage form a passage through the conductive carbon tape, and spraying gold on the observation surface. If a portable device is used as a shooting instrument, only gold spraying is required; S32, using a scanning electron microscope to capture an image of the sample, with no less than 30 shooting areas for each sample.

7. A two-dimensional image pore analysis method according to claim 6, characterized in that: The parameters of the scanning electron microscope were set as follows: acceleration voltage: 15 KV, magnification: 1000 times, working distance: 12.7 mm, and shooting area size: 207 μm×138 μm.

8. A two-dimensional image pore analysis method according to claim 1, characterized in that: The S4 includes: S41, inputting the captured images into the image pre-processing system in sequence, the image pre-processing system automatically reads the images and converts the images with color and light and shadow differences into grayscale images; S42, assigning each pixel of different color in the grayscale image to the corresponding grayscale value, and evenly dividing the grayscale of 0 to 255 into 128 grayscale intervals, obtaining the number of pixels in each grayscale interval, and drawing a grayscale value-pixel function relationship curve; S43, accumulating and summing the pixels in the grayscale image, and obtaining the cumulative pixel ratio in each grayscale interval, generating a grayscale value-cumulative pixel ratio function relationship curve, and outputting the intersection value of the tangents of the two end curves at the first turning point in the grayscale value-cumulative pixel ratio function relationship curve as the pore grayscale threshold; S44, taking the average of the pore grayscale thresholds of the grayscale images converted from images of different shooting areas of the same sample to obtain the average pore grayscale threshold of the sample.

9. A two-dimensional image pore analysis method according to claim 1, characterized in that: The S5 comprises: S51, inputting the grayscale image and the average pore grayscale threshold into the image post-processing system at the same time, the image post-processing system identifies the area of ​​the grayscale image, the grayscale value of the pixel points and the total number of pixels, and normalizes the grayscale image; S52, the image post-processing system obtains the grayscale value of each pixel point according to the grayscale value of the pixel points through binarization processing, converts the pixel points in the grayscale image whose grayscale value is greater than the average pore grayscale threshold into pure white pixel points, and converts the pixel points whose grayscale value is less than the average pore grayscale threshold into pure black pixel points; S53, repeating S52 to generate a detailed pore distribution map; S54 , cumulatively sum the converted pure black pixels in the pore distribution detail map to obtain a first cumulative value, calculate the ratio of the first cumulative value to the total number of pixels in the grayscale image, and obtain the percentage of holes; S56, perform size recognition on the pore distribution detail map, cumulatively sum the pure black pixels in each interval after size recognition to obtain a second cumulative value in each interval, calculate the ratio of the second cumulative value in each interval to the first cumulative value, and obtain the pore size distribution ratio; S57, obtain the two-dimensional plane porosity of the image corresponding to the current grayscale image through the percentage of holes * 100 / area of ​​the grayscale image.

10. A two-dimensional image pore analysis method according to claim 9, characterized in that: In the S56, the interval range of size recognition includes pore size <20nm, 20-50nm, 50-200nm, >200nm or pore size <0.01μm, 0.01-0.05μm, 0.05-0.1μm, 0.1-5μm, >5μm.

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