Method for measuring porosity of porous materials based on image analysis
By using molten metals with different atomic numbers to wet porous materials and performing image analysis, the complexity and accuracy issues of porous material porosity measurement have been solved, achieving efficient and accurate porosity measurement.
Patent Information
- Application Number
- CN202611422884.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-09-14
- Publication Date
- 2026-11-06
AI Technical Summary
Existing technologies for measuring the porosity of porous materials are complex to operate, have long testing cycles, and lack sufficient accuracy and applicability, making it difficult to achieve efficient and accurate measurements.
The porous material is impregnated with molten metal and a metal or alloy with a different atomic number to form a filled sample. Backscattered electron images are acquired by scanning electron microscopy, and the open and closed pore regions are extracted based on the grayscale distribution. The area is then counted to measure the porosity.
This method enables efficient and accurate measurement of porosity in porous materials, avoiding damage to the pore structure and improving measurement accuracy and speed.
Smart Images
Figure CN123045947A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of material structure measurement technology, and in particular to a method for measuring the porosity of porous materials based on image analysis. Background Technology
[0002] With the development of materials technology, porous materials are gradually being widely used in aerospace, new energy, electronic packaging and other fields. Since the performance of porous materials is related to their pore structure, accurate measurement of the porosity of porous materials is of great significance for material performance evaluation and process optimization.
[0003] In related technologies, methods for measuring the porosity of porous materials mainly include density methods, gas adsorption methods, and mercury intrusion porosimetry. The density method calculates porosity by measuring the apparent density and framework density of the material; its accuracy depends on accurately obtaining the true density of the framework material. The gas adsorption method utilizes the adsorption characteristics of gas molecules on the material surface to characterize the pore structure, suitable for measuring micropores and mesopores, but the testing process is relatively complex and time-consuming. The mercury intrusion porosimetry applies pressure to allow mercury to enter the material pores and characterizes the pore structure based on the relationship between pressure and the amount of mercury introduced; however, this method requires high testing conditions and carries the risk of mercury contamination. Therefore, these technologies suffer from operational complexity, long testing cycles, and insufficient accuracy and applicability in measuring the porosity of complex porous materials, making it difficult to achieve efficient and accurate measurement of the porosity of porous materials. Summary of the Invention
[0004] Therefore, it is necessary to provide a method for measuring the porosity of porous materials based on image analysis, wherein the porous material is a metal-based porous material, and the method includes:
[0005] The porous material is wetted with molten metal until it reaches a fully filled state to obtain a filled sample; the molten metal has a different atomic number from the porous material, and the melting point of the molten metal is lower than that of the porous material.
[0006] After the molten metal in the filling sample solidifies, multiple cross-sections of the filling sample in the depth direction are acquired using a scanning electron microscope to obtain backscattered electron images corresponding to each cross-section.
[0007] Based on the grayscale distribution of the backscattered electron images, closed-aperture regions and open-aperture regions are extracted from each of the backscattered electron images;
[0008] The areas of the open and closed regions in each of the backscattered electron images are statistically analyzed to obtain the open porosity, closed porosity, and total porosity of the porous material.
[0009] In one embodiment, the backscattered electron image corresponding to the cross section includes multiple backscattered electron images corresponding to the cross section; the areas of open and closed regions in each backscattered electron image are statistically analyzed to obtain the open porosity, closed porosity, and total porosity of the porous material, including: statistically analyzing the areas of open and closed regions in each backscattered electron image corresponding to the same cross section to obtain the proportion of open area and closed area in each backscattered electron image; obtaining the cross-sectional open porosity, cross-sectional closed porosity, and total porosity based on the proportion of open area and closed area in each backscattered electron image corresponding to the same cross section; and obtaining the open porosity, closed porosity, and total porosity of the porous material based on the cross-sectional open porosity, cross-sectional closed porosity, and total porosity of each cross section in the depth direction of the filled sample.
[0010] In one embodiment, the step of acquiring images of multiple cross-sections of the filled sample in the depth direction using a scanning electron microscope to obtain backscattered electron images corresponding to each cross-section includes: determining multiple sampling regions in each cross-section; acquiring images of each sampling region in the cross-section using a scanning electron microscope to obtain backscattered electron images corresponding to each sampling region in the cross-section.
[0011] In one embodiment, each of the backscattered electron images contains at least a predetermined number of pore structures.
[0012] In one embodiment, based on the grayscale distribution of the backscattered electron image, closed-aperture regions and open-aperture regions are extracted from each of the backscattered electron images, including: determining a first grayscale interval with the first grayscale threshold as the upper limit based on a preset first grayscale threshold; determining a second grayscale threshold in the backscattered electron image based on the grayscale distribution of the backscattered electron image; the second grayscale threshold being greater than the first grayscale threshold; when the atomic number of the molten metal is less than the atomic number of the porous material, determining a second grayscale interval with the grayscale value between the first grayscale threshold and the second grayscale threshold; when the atomic number of the molten metal is greater than the atomic number of the porous material, determining a second grayscale interval with the second grayscale threshold as the lower limit; and extracting closed-aperture regions and open-aperture regions from each of the backscattered electron images based on the first grayscale interval and the second grayscale interval.
[0013] In one embodiment, the method further includes: statistically analyzing the positions and areas of open and closed regions in each of the backscattered electron images to obtain pore distribution information of the porous material.
[0014] In one embodiment, the difference in atomic number between the molten metal and the porous material is not less than a preset threshold.
[0015] In one embodiment, after the molten metal in the filling sample solidifies, the step of acquiring images of multiple cross-sections of the filling sample in the depth direction using a scanning electron microscope includes: after the molten metal in the filling sample solidifies, performing multiple material removal processes on the filling sample in the depth direction to form cross-sections of the filling sample corresponding to different depths; and acquiring images of the cross-sections of the filling sample corresponding to different depths using a scanning electron microscope to obtain backscattered electron images corresponding to each cross-section.
[0016] In one embodiment, the material removal process includes at least one of chemical mechanical polishing, plasma etching, surface etching, and sandpaper abrasion.
[0017] In one embodiment, the porous material is formed of at least one metal selected from aluminum, nickel, copper, titanium, and silver.
[0018] The aforementioned image analysis-based method for measuring the porosity of porous materials utilizes molten metal with a lower melting point and a different atomic number than the porous material to wet the metal-based porous material. This allows the molten metal to fully fill the open pores in the porous material without damaging its pore structure, forming a filled sample. Furthermore, it enables the unfilled closed-pore areas, the open-pore areas filled by the molten metal, and the corresponding skeletal areas of the porous material to exhibit significant grayscale differences under backscattered electron imaging (BSE). This allows for accurate extraction of open and closed-pore areas from the BSE image based on its grayscale distribution. By statistically analyzing the areas of open and closed-pore areas in multiple cross-sections along the depth direction of the filled sample, the overall open-pore porosity, closed-pore porosity, and total porosity of the porous material can be obtained, achieving efficient and accurate measurement of the porosity of porous materials. Attached Figure Description
[0019] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0020] Figure 1 This is an application environment diagram of an image analysis-based porous material porosity measurement method in one embodiment.
[0021] Figure 2 This is a schematic diagram of the process for calculating the porosity of a porous material in one embodiment;
[0022] Figure 3 This is a schematic diagram of the process for obtaining a backscattered electron image in one embodiment;
[0023] Figure 4 This is a flowchart illustrating the process of extracting open and closed aperture regions in one embodiment;
[0024] Figure 5 This is a flowchart illustrating a method for measuring the porosity of porous materials based on image analysis in another embodiment.
[0025] Figure 6 This is a secondary electron image of a cross-section of a porous copper sample in one embodiment;
[0026] Figure 7 This is a backscattered electron image of a sample cross section in one embodiment;
[0027] Figure 8 This is a schematic diagram of the result of grayscale thresholding of a backscattered electron image in one embodiment. Detailed Implementation
[0028] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0029] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various objects, but these objects are not limited by these terms. These terms are only used to distinguish the first object from the second object. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.
[0030] In one embodiment, such as Figure 1 As shown, an image analysis-based method for measuring the porosity of porous materials is provided, wherein the porous material is a metal-based porous material. The method includes the following steps:
[0031] Step S101: Use molten metal to wet the porous material until the porous material is fully filled to obtain a filled sample.
[0032] The porous material is a metal-based porous material, which can be formed from one or more metals. In an exemplary embodiment, the porous material is formed from at least one metal selected from aluminum, nickel, copper, titanium, and silver. Exemplarily, the porous material may include, but is not limited to, porous aluminum, porous nickel, porous copper, porous titanium, and porous silver. The molten metal may be a metal or alloy with a different atomic number than the porous material and a melting point lower than that of the porous material. Optionally, the molten metal may include, but is not limited to, tin, copper, aluminum, indium, silver, lead, and their alloys. Exemplarily, when the molten metal or porous material is formed from a single metallic element, its corresponding atomic number is the atomic number of that metallic element; when the molten metal or porous material is an alloy, its corresponding atomic number is the effective atomic number of the alloy material.
[0033] The molten metal exhibits good wettability with the porous material. For example, in this step, the porous material can be immersed in the molten metal, allowing the molten metal to fully fill the pores in the porous material through capillary wetting, thus achieving a fully filled state and obtaining a filled sample. The pores in the porous material refer to the porous structure that communicates with the external environment. A fully filled state can mean that all the pores in the porous material are filled by the molten metal. For example, the porous material can be completely immersed in the molten metal and maintained for a preset time to achieve a fully filled state.
[0034] Step S102: After the molten metal in the filling sample solidifies, a scanning electron microscope is used to acquire images of multiple cross-sections of the filling sample in the depth direction to obtain backscattered electron images corresponding to each cross-section.
[0035] The process involves cooling the filled sample to solidify the molten metal. The sample is then surface-cleaned, and multiple cross-sections are sequentially prepared along its depth. Scanning electron microscopy (SEM) is used to acquire images of these cross-sections at different depths, obtaining backscattered electron images corresponding to each cross-section. For example, at least one backscattered electron image can be acquired for each cross-section, or multiple backscattered electron images can be acquired for the same cross-section. For example, one or more backscattered electron images can be acquired for the same region within the same cross-section, or one or more backscattered electron images can be acquired for multiple regions within the same cross-section. For example, the surface cleaning of the filled sample can include, but is not limited to, one or more processes such as laser cleaning and ultrasonic cleaning, and the cross-section preparation process can include, but is not limited to, one or more processes such as chemical mechanical polishing, plasma etching, surface etching, and sandpaper polishing.
[0036] Step S103: Based on the grayscale distribution of the backscattered electron image, extract the closed-aperture region and the open-aperture region from each backscattered electron image respectively.
[0037] Since the molten metal enters only the open pores of the porous material under capillary wetting, but not the closed pores (i.e., the pore structure in the porous material that is not connected to the external environment), the cross section of the filled sample can include the skeleton region corresponding to the porous material skeleton, the open pore region corresponding to the open pore structure in the porous material filled by the molten metal, and the closed pore region corresponding to the closed pore structure in the porous material that is not filled by the molten metal.
[0038] Backscattered electron imaging (BSO) can be used to characterize compositional differences between different regions of a material. Under the same or comparable imaging conditions, regions with higher effective atomic numbers typically exhibit stronger backscattered electron signals and show higher grayscale values in BSO images; regions with lower effective atomic numbers typically show lower grayscale values; and regions not filled with material typically show the lowest grayscale values in BSO images, with a significant difference from the grayscale values of the material regions. Therefore, based on the grayscale differences of each pixel in the BSO image, closed-pore regions (not filled with material), open-pore regions (corresponding to molten metal), and framework regions (corresponding to porous materials) in the image can be identified, distinguished, or characterized. It is understood that the grayscale values of closed-pore regions in BSO images are lower than those of open-pore and framework regions. Specifically, when the atomic number of the molten metal is lower than that of the porous material, the grayscale value of the open-pore region is lower than that of the framework region; when the atomic number of the molten metal is greater than that of the porous material, the grayscale value of the open-pore region is greater than that of the framework region.
[0039] For example, grayscale analysis can be performed on the backscattered electron image to obtain its grayscale distribution. Based on one or more factors such as the grayscale value distribution, the grayscale relationship between adjacent pixels, the connectivity of image regions with different grayscale values, and morphological features, closed-aperture and open-aperture regions in the backscattered electron image can be determined. For example, based on the grayscale value distribution of the backscattered electron image, a first grayscale threshold capable of distinguishing closed-aperture regions from material regions (including open-aperture regions and skeleton regions) and a second grayscale threshold capable of distinguishing open-aperture regions from skeleton regions can be determined. The backscattered electron image can then be segmented based on these two grayscale thresholds to identify closed-aperture regions with grayscale values less than the first grayscale threshold. Furthermore, based on the atomic number relationship between the molten metal and the porous material, the relationship between the grayscale value of the open-aperture region and the second grayscale threshold can be determined, and the open-aperture regions can be extracted from the image accordingly. For example, the closed-aperture region and the open-aperture region can also be extracted from the backscattered electron image based on the gray value relationship between the closed-aperture region, the open-aperture region, and the skeleton region, and in combination with the regional connectivity relationship or morphological features between pixels with the same or similar gray values.
[0040] Step S104: Statistically analyze the areas of open and closed regions in each backscattered electron image to obtain the open porosity, closed porosity, and total porosity of the porous material.
[0041] For each backscattered electron image, the number of pixels in the open and closed regions can be counted to obtain the area of the open and closed regions in each backscattered electron image. Then, based on the area ratio of the open regions in the image, the regional open porosity of the corresponding sampling area can be obtained; based on the area ratio of the closed regions in the image, the regional closed porosity of the corresponding sampling area can be obtained; and based on the area ratio of the total area of the open and closed regions in the image, the regional total porosity of the corresponding sampling area can be obtained. For example, the average values of the regional open porosity, regional closed porosity, and regional total porosity corresponding to each backscattered electron image can be calculated to obtain the overall open porosity, closed porosity, and total porosity of the porous material.
[0042] In the aforementioned image analysis-based method for measuring the porosity of porous materials, by using molten metal with a lower melting point and a different atomic number than the porous material to wet the metal-based porous material, the molten metal can fully fill the open pores in the porous material to form a filled sample without damaging the pore structure. Furthermore, the unfilled closed pore areas, the open pore areas filled by the molten metal, and the skeletal areas corresponding to the porous material in the cross-section of the filled sample show significant grayscale differences under backscattered electron imaging. This allows for accurate extraction of open and closed pore areas from the backscattered electron image based on the grayscale distribution. By statistically analyzing the areas of open and closed pore areas in the backscattered electron images of multiple cross-sections along the depth direction of the filled sample, the overall open porosity, closed porosity, and total porosity of the porous material can be obtained, achieving efficient and accurate measurement of the porosity of porous materials.
[0043] In one exemplary embodiment, such as Figure 2 As shown, the backscattered electron image corresponding to the cross section includes multiple backscattered electron images corresponding to the cross section; by statistically analyzing the areas of open and closed regions in each backscattered electron image, the open porosity, closed porosity, and total porosity of the porous material can be obtained, which may include:
[0044] Step S201: Statistically analyze the areas of open and closed regions in each backscattered electron image corresponding to the same cross section to obtain the proportion of open and closed areas in each backscattered electron image.
[0045] Each cross section in the filled sample may correspond to multiple backscattered electron images. These multiple backscattered electron images may include multiple backscattered electron images corresponding to the same region in the same cross section, or they may include multiple backscattered electron images corresponding to different regions in the same cross section.
[0046] In this step, for each cross-section, the number of pixels in the open and closed aperture regions of one or more corresponding backscattered electron images can be counted to obtain the area of the open and closed aperture regions in each backscattered electron image. Then, the ratio of the area of the open aperture region to the area of the image can be used to obtain the proportion of the open aperture area in the backscattered electron image, and the ratio of the area of the closed aperture region to the area of the image can be used to obtain the proportion of the closed aperture area in the backscattered electron image.
[0047] Step S202: Based on the proportion of open area and the proportion of closed area corresponding to each backscattered electron image of the same cross section, obtain the cross section open porosity, cross section closed porosity and cross section total porosity.
[0048] After obtaining the percentage of open and closed areas corresponding to each backscattered electron image for the same cross-section, the cross-sectional open porosity, closed porosity, and total porosity can be further calculated. For example, the cross-sectional open porosity can be obtained from the average percentage of open areas corresponding to multiple backscattered electron images, and the cross-sectional closed porosity can be obtained from the average percentage of closed areas corresponding to multiple backscattered electron images. Then, the total porosity of the cross-section can be obtained by summing the cross-sectional open porosity and closed porosity.
[0049] Step S203: Based on the open porosity, closed porosity, and total porosity of the porous material corresponding to each cross section in the depth direction of the filled sample, the open porosity, closed porosity, and total porosity of the cross section are obtained.
[0050] Specifically, after obtaining the open porosity, closed porosity, and total porosity of multiple cross-sections at different depths of the filled sample, the open porosity of the porous material can be obtained from the average value of the open porosity of the multiple cross-sections, the closed porosity of the porous material can be obtained from the average value of the closed porosity of the multiple cross-sections, and the total porosity of the porous material can be obtained from the average value of the total porosity of the multiple cross-sections.
[0051] In this embodiment, multiple backscattered electron images are acquired at each cross section. Based on the proportion of open and closed pore areas in the multiple backscattered electron images of the same cross section, the open porosity, closed porosity, and total porosity of the cross section are calculated. Then, by combining the open porosity, closed porosity, and total porosity of multiple cross sections, the overall open porosity, closed porosity, and total porosity of the porous material are calculated. This reduces the impact of imaging errors and uneven distribution of pore structure in the depth direction on porosity measurement, which is beneficial to obtaining more accurate porosity measurement results.
[0052] In one exemplary embodiment, such as Figure 3 As shown, scanning electron microscopy is used to acquire images of multiple cross-sections of a filled sample along the depth direction, obtaining backscattered electron images corresponding to each cross-section. This can include:
[0053] Step S301: Determine multiple sampling areas in each cross section.
[0054] For each cross section of the filled sample, multiple sampling regions can be defined within each cross section. Each sampling region can correspond to an imaging field of view for backscattered electron imaging using a scanning electron microscope. For example, the number of sampling regions corresponding to each cross section can be no less than a preset threshold, such as no less than five. For example, for each cross section of the filled sample, a corresponding number of non-overlapping sampling regions can be randomly selected from the cross section based on the same number of sampling regions, or a corresponding number of sampling regions can be selected based on preset positions. For example, at least one central sampling region can be determined based on the center position of the cross section, and multiple peripheral sampling regions can be determined based on multiple peripheral positions uniformly distributed along the periphery of the cross section.
[0055] Step S302: Use a scanning electron microscope to acquire images of each sampling area in the cross section to obtain backscattered electron images corresponding to each sampling area in the cross section.
[0056] For each sampling region in the cross section, an image of that region can be acquired using a scanning electron microscope to obtain a backscattered electron image corresponding to that region. For example, at least three image acquisitions can be performed on each sampling region in the same cross section to obtain multiple backscattered electron images corresponding to each sampling region.
[0057] In this embodiment, by determining multiple sampling areas in each cross section and acquiring images of each sampling area separately, the influence of imaging errors and uneven distribution of pore structure on porosity measurement can be reduced, which is beneficial to obtaining more accurate porosity measurement results.
[0058] In one exemplary embodiment, each backscattered electron image contains at least a predetermined number of pore structures.
[0059] In this process, when acquiring images of a cross-section of a filled sample using a scanning electron microscope, backscattered electron images can be acquired for each sampling region within the cross-section at a preset magnification, ensuring that each backscattered electron image corresponds to an imaging field of view of a preset size. This magnification ensures that the imaging field of view contains at least a preset number of pore structures, thus ensuring that each acquired backscattered electron image contains at least a preset number of pore structures. For example, each backscattered electron image may contain at least 30 uniformly distributed pore structures.
[0060] In this embodiment, by ensuring that each backscattered electron image contains at least a preset number of pore structures, the situation where the imaging field of view is too small, making it difficult for the image to accurately reflect the pore structure of the cross section, can be avoided, which is beneficial to improving the accuracy of porosity measurement results.
[0061] In one exemplary embodiment, such as Figure 4 As shown, based on the grayscale distribution of the backscattered electron image, the closed-aperture region and the open-aperture region can be extracted from each backscattered electron image, which may include:
[0062] Step S401: Determine the first grayscale range with the first grayscale threshold as the upper limit based on the preset first grayscale threshold.
[0063] Backscattered electron imaging can be used to characterize the compositional differences between different regions or phases in a material. Under the same or comparable imaging conditions, regions with higher effective atomic numbers usually have stronger backscattered electron signals and can exhibit higher gray values in backscattered electron imaging; regions with lower effective atomic numbers usually exhibit lower gray values; and regions not filled by material usually exhibit the lowest gray values in backscattered electron imaging and show significant differences from the gray values of material regions.
[0064] Since the closed-pore structures in the filled sample are not filled by molten metal, the closed-pore regions in its cross-section can correspond to the lowest grayscale values in the backscattered electron image. In this step, a first grayscale threshold can be obtained based on the upper limit of the grayscale value of the closed-pore region in the image under the imaging conditions of the backscattered electron image, and a first grayscale interval with the upper limit of the first grayscale threshold can be determined. This first grayscale interval can be used to extract the closed-pore region from the backscattered electron image. For example, the lower limit of the first grayscale interval can be 0.
[0065] Step S402: Determine a second grayscale threshold in the backscattered electron image based on the grayscale distribution of the backscattered electron image; the second grayscale threshold is greater than the first grayscale threshold.
[0066] Specifically, grayscale analysis can be performed on the backscattered electron image to obtain its grayscale distribution. Then, a second grayscale threshold can be determined based on the grayscale distribution of pixels in the backscattered electron image whose grayscale values are greater than a first grayscale threshold. For example, grayscale values in the image greater than the first grayscale threshold can be clustered to obtain two grayscale value categories, and the second grayscale threshold is obtained based on the boundary between these two categories. Then, a second grayscale interval corresponding to the pore region can be determined based on the second grayscale threshold and the atomic number relationship between the molten metal and the porous material. When the atomic number of the molten metal is less than the atomic number of the porous material, the process proceeds to step S403; when the atomic number of the molten metal is greater than the atomic number of the porous material, the process proceeds to step S404.
[0067] Step S403: When the atomic number of the molten metal is less than the atomic number of the porous material, determine the second grayscale range between the first grayscale threshold and the second grayscale threshold.
[0068] When the atomic number of the molten metal is less than that of the porous material, the gray value of the molten metal in the backscattered electron image is lower than that of the porous material. In this case, the gray value range between the first gray value threshold and the second gray value threshold can be determined as the second gray value range corresponding to the open area.
[0069] Step S404: When the atomic number of the molten metal is greater than the atomic number of the porous material, a second grayscale range with the second grayscale threshold as the lower limit is determined.
[0070] Specifically, when the atomic number of the molten metal is greater than that of the porous material, the grayscale value of the molten metal in the backscattered electron image is higher than that of the porous material. In this case, the grayscale value range that is not less than the second grayscale threshold can be defined as the second grayscale range corresponding to the open area. For example, taking an 8-bit grayscale image as an example, the upper limit of the second grayscale range can be 255.
[0071] Step S405: Extract the closed-aperture region and the open-aperture region from each backscattered electron image according to the first grayscale range and the second grayscale range.
[0072] Specifically, after determining the first grayscale range and the second grayscale range, the backscattered electron image can be segmented according to the upper and lower limits of the first grayscale range to extract the closed-aperture region from the image, and the backscattered electron image can be segmented according to the upper and lower limits of the second grayscale range to extract the open-aperture region from the image.
[0073] In this embodiment, a first grayscale range is determined based on a preset first grayscale threshold, and a second grayscale range is determined based on the grayscale distribution of the backscattered electron image and the atomic number relationship between the molten metal and the porous material. Then, based on the first grayscale range and the second grayscale range, closed-pore regions and open-pore regions are extracted from each backscattered electron image, which enables efficient and accurate extraction of different types of porous regions.
[0074] In an exemplary embodiment, the method may further include: statistically analyzing the positions and areas of open and closed regions in each backscattered electron image to obtain pore distribution information of the porous material.
[0075] After extracting the closed-pore and open-pore regions from each backscattered electron image, the positions and areas of these regions can be statistically analyzed to obtain pore structure information such as location, morphological features, and pore size. This information can then be combined from multiple backscattered electron images to obtain the overall pore distribution information of the porous material. For example, the pore distribution information may include, but is not limited to, the location distribution information, pore size distribution information, and morphological feature information of various pore structures. The location distribution information may include the distribution characteristics of open-pore and closed-pore structures in the same cross-section, as well as their distribution characteristics at different depths within the porous material. The pore size distribution information may include the size distribution characteristics of open-pore and closed-pore structures.
[0076] In this embodiment, by statistically analyzing the location and area of the open and closed pore regions to obtain pore distribution information of the porous material, more pore structure characteristics of the porous material can be obtained, providing more auxiliary information for the performance analysis of the porous material.
[0077] In one exemplary embodiment, the difference in atomic number between the molten metal and the porous material is not less than a preset threshold.
[0078] Specifically, the molten metal used to fill the porous material can be determined based on its atomic number and melting point. For example, an atomic number range centered on the atomic number of the porous material can be determined based on a preset threshold, and a metal element or alloy with a melting point lower than that of the porous material can be selected from outside this atomic number range as the corresponding molten metal. For example, the preset threshold can be 20. For example, taking porous copper as an example, molten tin can be used to fill it.
[0079] In this embodiment, by selecting molten metal with an atomic number difference of not less than a preset threshold from that of the porous material, the contrast between the filled open area and the framework area of the porous material can be stabilized under backscattered electron imaging. This helps to avoid threshold misjudgment caused by insufficient contrast and improves the accuracy of the porosity measurement results of the porous material.
[0080] In an exemplary embodiment, after the molten metal in the filling sample solidifies, images of multiple cross-sections of the filling sample in the depth direction are acquired using a scanning electron microscope. This may include: after the molten metal in the filling sample solidifies, performing multiple material removal processes on the filling sample in the depth direction to form cross-sections of the filling sample corresponding to different depths; and acquiring images of the cross-sections of the filling sample corresponding to different depths using a scanning electron microscope to obtain backscattered electron images corresponding to each cross-section.
[0081] In this process, after the molten metal in the filling sample solidifies, the filling sample can be subjected to multiple material removal processes along its depth direction. Each material removal process can form a cross-section of the filling sample corresponding to a specific depth. In an exemplary embodiment, the material removal process may include at least one of chemical mechanical polishing, plasma etching, surface etching, and sandpaper abrasion.
[0082] In this process, after each material removal process, a scanning electron microscope can be used to acquire an image of the cross-section formed by the material removal process on the filled sample to obtain the backscattered electron image corresponding to the cross-section. Then, by repeating the material removal process and image acquisition multiple times, backscattered electron images corresponding to multiple cross-sections at different depths can be obtained.
[0083] In one exemplary embodiment, such as Figure 5 As shown, an image analysis-based method for measuring the porosity of porous materials is provided, which may include the following steps:
[0084] Step S501: Immerse the metal-based porous material into molten metal, allowing the molten metal to fully fill the open-pore structure in the porous material under capillary wetting. After cooling and solidification, a filled sample is obtained.
[0085] The porous material can be formed from at least one metal selected from aluminum, nickel, copper, titanium, and silver. The molten metal has a different atomic number than the porous material, and the melting point of the molten metal is lower than that of the porous material. For example, the difference in atomic number between the molten metal and the porous material is not less than a preset threshold.
[0086] Step S502 involves subjecting the filled sample to static cooling and material removal to expose the cross-section of its porous structure.
[0087] During the settling period, the filled sample should be protected from external contamination. The settling time can range from 1 second to 2 hours. The filled sample can be cooled to room temperature using methods such as air cooling, water cooling, or general air cooling. Then, the surface of the filled sample can be cleaned using one or more methods, such as laser cleaning or ultrasonic cleaning. Subsequently, at least one material removal method, including but not limited to chemical mechanical polishing, plasma etching, surface etching, and sandpaper abrasion, can be used to remove material from the filled sample in the depth direction to expose the porous cross-section.
[0088] Step S503: Use a scanning electron microscope to acquire an image of the cross section to obtain a backscattered electron image of the cross section.
[0089] This process involves defining multiple sampling regions within a cross-section and acquiring images of each region using a scanning electron microscope to obtain backscattered electron images corresponding to those regions. Each backscattered electron image contains at least a predetermined number (e.g., 30) of pore structures. For example, the number of sampling regions corresponding to each cross-section can be no less than a predetermined threshold, such as no less than 5. For example, at least three image acquisitions can be performed on each sampling region within the same cross-section to obtain multiple backscattered electron images corresponding to each region.
[0090] Step S504: Based on the grayscale distribution of the backscattered electron image, extract the closed-aperture region and the open-aperture region from the backscattered electron image respectively.
[0091] Specifically, a first grayscale range with an upper limit of the first grayscale threshold can be determined based on a preset first grayscale threshold. A second grayscale threshold in the backscattered electron image can be determined based on the grayscale distribution of the backscattered electron image, wherein the second grayscale threshold is greater than the first grayscale threshold. When the atomic number of the molten metal is less than the atomic number of the porous material, a second grayscale range between the first and second grayscale thresholds can be determined. When the atomic number of the molten metal is greater than the atomic number of the porous material, a second grayscale range with a lower limit of the second grayscale threshold can be determined. Then, closed-aperture and open-aperture regions can be extracted from the backscattered electron image based on the first and second grayscale ranges, respectively.
[0092] Step S505: Statistically analyze the areas of open and closed regions in each backscattered electron image to obtain the cross-sectional open porosity, cross-sectional closed porosity, and cross-sectional total porosity.
[0093] Specifically, the areas of open and closed regions in each backscattered electron image corresponding to the same cross section can be statistically analyzed to obtain the proportion of open and closed areas in each backscattered electron image. Then, based on the proportion of open and closed areas in each backscattered electron image corresponding to the same cross section, the cross section open porosity, cross section closed porosity, and cross section total porosity can be obtained.
[0094] Step S506: Continue material removal along the depth direction, repeat steps S502 to S505 above to obtain the porosity of each cross section corresponding to multiple depths, and take the average value of the porosity of each cross section to obtain the overall porosity of the porous material.
[0095] Specifically, by repeating steps S502 to S505, multiple material removal processes, image acquisitions, and porosity statistics are performed on the filled sample along its depth direction to obtain the cross-sectional open porosity, closed porosity, and total porosity corresponding to different depth sections of the filled sample. Then, by averaging the open porosity, closed porosity, and total porosity of multiple cross-sections, the overall open porosity, closed porosity, and total porosity of the porous material can be obtained. Furthermore, the pore distribution information of the porous material can be obtained by statistically analyzing the positions and areas of open and closed regions in each backscattered electron image.
[0096] The following example, using the measurement of porosity in porous copper, illustrates the image analysis-based porosity measurement method for porous materials in this embodiment.
[0097] First, the porous copper sample was completely immersed in molten tin for 3 seconds, then removed and cooled to room temperature in air. Next, the surface of the filled sample was cleaned and progressively polished with sandpaper until the porous copper framework was exposed. Then, scanning electron microscopy (SEM) was used to acquire images of the polished cross-section. High-resolution backscattered electron images were acquired at 300x magnification at the four corners and center of the filled sample, with each region captured three times. The acquired raw images were converted to 8-bit grayscale images, and based on the grayscale differences of different phases in the SEM backscattered electron images, the images were segmented into multiple regions by setting a dual-level grayscale threshold. The cross-sectional area was divided into porous regions (closed-pore regions), copper framework regions, and tin-filled regions (open-pore regions), and each was assigned a different grayscale value for ternary characterization. Low grayscale regions correspond to porous regions, medium grayscale regions correspond to copper framework regions, and high grayscale regions correspond to tin-filled regions. Subsequently, the area ratio of different pore types was calculated to obtain the open porosity, closed porosity, and total porosity of the cross-section. Then, the filled sample was ground three times along the depth direction, and the above steps were repeated to obtain the open porosity, closed porosity, and total porosity of the cross-section at different depths. Finally, the open porosity, closed porosity, and total porosity of the obtained cross-sections at different depths were statistically analyzed and their average values were calculated to obtain the overall open porosity, closed porosity, and total porosity of the porous copper sample. The porosity measurement results of the porous copper sample are shown in Table 1.
[0098] Table 1
[0099]
[0100] Please refer to the following: Figures 6 to 8 , Figure 6 Secondary electron image of a cross-section of a porous copper sample without molten tin filling. Figure 7Backscattered electron image of the cross-section of a porous copper sample after filling it with molten tin. Figure 8 To Figure 7 The diagram shows the result of grayscale thresholding segmentation of the backscattered electron image. For example, Figure 6 As shown, for porous copper samples without molten tin, secondary electron images of their cross-sections can reveal the pore structure, but it is difficult to measure the porosity at the same depth for complex pore structures, and it is also difficult to distinguish between open and closed pore structures. Figure 7 As shown, for a porous copper sample filled with molten tin, the backscattered electron image of its cross-section contains image regions corresponding to three different gray levels. Therefore, based on the brightness relationship between the closed-hole region, tin, and copper in the backscattered electron imaging, a dual-level gray-level threshold can be set according to the gray-level distribution of the backscattered electron image to segment the backscattered electron image into multiple regions. The cross-sectional region is divided into a closed-hole region, a copper skeleton region, and a tin-filled region (open-hole region), and each region is assigned a different gray-level value for ternary characterization to obtain the image shown below. Figure 8 The grayscale threshold segmentation result is shown. This allows for the accurate extraction of open and closed pore regions within a cross-section at the same depth, enabling efficient and accurate determination of the open porosity, closed porosity, and total porosity of porous materials.
[0101] The method in this embodiment can achieve at least the following beneficial effects:
[0102] 1. The molten metal has good wettability with the metal-based porous material matrix and can completely penetrate the open pores, which can fully fill the open pore areas in the porous material and effectively distinguish between the open and closed pore areas in the porous material; moreover, the contrast between the filler and the matrix is stable under backscattered electron imaging, which can avoid threshold misjudgment caused by insufficient contrast and is conducive to improving the accuracy of porosity measurement of metal-based porous materials.
[0103] 2. By grinding and repeatedly measuring layer by layer along the depth direction of the porous material, the porosity of multiple depth layers of the material can be obtained. Then, by combining the porosity of multiple depth layers and taking the average value, the overall porosity can be obtained. This can overcome the limitation of only measuring planar porosity in related technologies, and obtain measurement results that can reflect the overall pore structure characteristics of the material. Furthermore, the porosity uniformity of the material along the depth direction can be measured based on the porosity of multiple depth layers.
[0104] 3. By utilizing the physical property that molten metal enters the open-pore structure of porous materials only under capillary wetting, and that the closed-pore structure in porous materials remains empty and not connected to the external environment, gray-scale three-zone separation is performed on the backscattered electron image of the cross section to extract the open-pore and closed-pore regions and to statistically analyze the open-pore porosity and closed-pore porosity. This can provide richer structural auxiliary information for application scenarios that rely on the pore connectivity of porous materials.
[0105] 4. The method in this embodiment is simple to operate, highly efficient, does not require the use of polluting reagents such as mercury, and has a wide range of applications. It can be used for pore structure characterization and quality control of various metal-based porous materials such as aluminum foam, porous copper, and porous nickel.
[0106] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.
[0107] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.
[0108] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.
Claims
1. A method for measuring the porosity of porous materials based on image analysis, characterized in that, The porous material is a metal-based porous material, and the method includes: The porous material is wetted with molten metal until it reaches a fully filled state to obtain a filled sample; the molten metal has a different atomic number from the porous material, and the melting point of the molten metal is lower than that of the porous material. After the molten metal in the filling sample solidifies, multiple cross-sections of the filling sample in the depth direction are acquired using a scanning electron microscope to obtain backscattered electron images corresponding to each cross-section. Based on the grayscale distribution of the backscattered electron images, closed-aperture regions and open-aperture regions are extracted from each of the backscattered electron images; The areas of the open and closed regions in each of the backscattered electron images are statistically analyzed to obtain the open porosity, closed porosity, and total porosity of the porous material.
2. The method according to claim 1, characterized in that, The backscattered electron image corresponding to the cross section includes multiple backscattered electron images corresponding to the cross section; the areas of open-pore and closed-pore regions in each backscattered electron image are statistically analyzed to obtain the open-pore porosity, closed-pore porosity, and total porosity of the porous material, including: The areas of the open and closed regions in each backscattered electron image corresponding to the same cross section are statistically analyzed to obtain the proportion of open area and the proportion of closed area in each backscattered electron image. Based on the proportion of open area and the proportion of closed area corresponding to each backscattered electron image of the same cross section, the cross section open porosity, cross section closed porosity and cross section total porosity are obtained; The open porosity, closed porosity, and total porosity of the porous material are obtained based on the open porosity, closed porosity, and total porosity of each cross section in the depth direction of the filled sample.
3. The method according to claim 2, characterized in that, The step of acquiring images of multiple cross-sections of the filled sample along the depth direction using a scanning electron microscope to obtain backscattered electron images corresponding to each cross-section includes: Multiple sampling regions are determined in each of the aforementioned cross sections; Images of each sampling region in the cross section are acquired using a scanning electron microscope to obtain backscattered electron images corresponding to each sampling region in the cross section.
4. The method according to claim 1, characterized in that, Each of the backscattered electron images contains at least a predetermined number of pore structures.
5. The method according to claim 1, characterized in that, Based on the grayscale distribution of the backscattered electron images, closed-aperture regions and open-aperture regions are extracted from each of the backscattered electron images, including: Based on a preset first grayscale threshold, a first grayscale range is determined with the first grayscale threshold as its upper limit. Based on the grayscale distribution of the backscattered electron image, a second grayscale threshold is determined in the backscattered electron image; the second grayscale threshold is greater than the first grayscale threshold. When the atomic number of the molten metal is less than the atomic number of the porous material, a second grayscale range is determined where the grayscale value is between the first grayscale threshold and the second grayscale threshold. When the atomic number of the molten metal is greater than the atomic number of the porous material, a second grayscale range with the second grayscale threshold as the lower limit is determined; Based on the first grayscale range and the second grayscale range, closed-aperture regions and open-aperture regions are extracted from each of the backscattered electron images.
6. The method according to claim 1, characterized in that, The method further includes: The positions and areas of the open and closed regions in each of the backscattered electron images are statistically analyzed to obtain the pore distribution information of the porous material.
7. The method according to claim 1, characterized in that, The difference in atomic number between the molten metal and the porous material is not less than a preset threshold.
8. The method according to claim 1, characterized in that, After the molten metal in the filling sample solidifies, images of multiple cross-sections of the filling sample in the depth direction are acquired using a scanning electron microscope, including: After the molten metal in the filling sample solidifies, the filling sample is subjected to multiple material removal processes in the depth direction of the filling sample to form cross sections of the filling sample corresponding to different depths. The backscattered electron images of each cross section at different depths of the filled sample were acquired using a scanning electron microscope.
9. The method according to claim 8, characterized in that, The material removal process includes at least one of the following: chemical mechanical polishing, plasma etching, surface etching, and sandpaper grinding.
10. The method according to any one of claims 1 to 9, characterized in that, The porous material is formed from at least one metal selected from aluminum, nickel, copper, titanium, and silver.