Identification and in-situ quantitative statistical distribution characterization of microcracks on the surface of metal materials

Abstract

The present invention discloses a method for identifying and characterizing the in-situ quantitative statistical distribution of microcracks on the surface of metal materials. The method comprises surface preparation of the metal material, determination of grayscale thresholds for characteristic structures such as microcracks and inclusions, setting of energy spectrum analysis parameters, fully automatic acquisition of characteristic structure images over a large scale using a scanning electron microscope, feature extraction and energy spectrum analysis, screening and identification of microcracks, and in-situ quantitative statistical distribution characterization of the number, area fraction, and length of the microcracks. Based on the characteristic that microcracks on the metal surface can be embedded in abrasive and polishing material components during metallographic sample preparation, the present invention extracts backscattered electron images of metallographic surface features, combines energy spectrum analysis results with morphological quantitative parameters for data mining and screening, and simultaneously achieves rapid location, identification, and quantitative statistics of surface microcracks while analyzing inclusions. The method is accurate and efficient, and the resolution of microcrack detection is greatly improved, down to the micron level.

Description

Technical Field

[0001] The present invention relates to the technical field of metal material microstructure analysis, and in particular to a method for identifying microcracks on the surface of a metal material and characterizing their in-situ quantitative statistical distribution. Background Art

[0002] During the smelting, solidification, and forging processes of metal materials, metallurgical defects such as segregation, inclusions, porosity, and delamination inevitably occur within and on the material's surface. Thermal stress can also cause cracks in weak areas of the material, significantly impacting its service life and performance. Therefore, identifying and characterizing surface and internal defects and cracks is essential. For example, surface cracking in finished bearing steel wire is a major concern, and controlling crack size to less than 50 μm has become a bottleneck in improving bearing steel quality. Materials with high alloying element content, such as aluminum, magnesium, titanium, high-temperature alloys, and advanced high-strength steels, offer significant advantages such as high specific strength and specific stiffness and are widely used in aerospace applications. Solution-aging is the primary heat treatment technique for regulating the mechanical properties of these alloys, so the base material often contains high levels of alloying elements to ensure sufficient precipitation. During the welding thermal cycle, solute segregation is very likely to occur during the solidification cooling phase, and partial melting of the base material grain boundaries (where a low-melting-point eutectic phase exists) is also common. Under this premise, welded joints are very prone to thermal cracks (including solidification cracks and liquefaction cracks) under the action of thermal stress. The generation of thermal cracks destroys the structural integrity and restricts the promotion and application of new materials in the aerospace field. At present, the main methods for characterizing material cracks are metallographic methods and some non-destructive testing methods. The metallographic method is affected by scratches, inclusions and tissue boundaries on the material surface, and the recognition rate of cracks is low. Some microcracks can only be displayed after being corroded by metallographic etching liquid. If quantitative characterization of cracks is required, it is necessary to combine manual measurement. Therefore, the characterization process is relatively complicated and the characterization efficiency is low. At the same time, the quantitative parameters obtained are relatively few. In addition, the metallographic method is limited by the number of observation fields, and can usually only characterize cracks in a single or several local fields of view. Therefore, it cannot represent the location information and distribution of cracks on the material surface over a larger range. Currently, some nondestructive testing methods, such as eddy current, ultrasonic, X-ray CT, and magnetic particle testing, can characterize internal defects, inclusions, and cracks in materials. However, these methods only respond to large cracks and inclusions within the material and are unable to identify microcracks and defects smaller than 50 μm. Therefore, it is necessary to develop methods for identifying and statistically characterizing metal microcracks to meet the needs of metal material quality control and performance improvement. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for identifying and characterizing the in-situ quantitative statistical distribution of microcracks on the surface of metal materials. The method adopts a combination of automatic image acquisition by a scanning electron microscope, characteristic structure energy spectrum component analysis and target screening to identify microcracks on the surface of metal materials and perform in-situ quantitative statistical distribution characterization of the number, area fraction and length of microcracks, thereby solving the problems of low recognition reliability, poor quantitativeness, low resolution and insufficient statistical representativeness in traditional methods for identifying and characterizing microcracks in metal materials.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A method for identifying and characterizing in-situ quantitative statistical distribution of microcracks on the surface of a metal material, the method comprising the following steps:

[0006] S1, a block sample of a certain volume is cut from the metal material whose microcracks need to be characterized, and the surface to be tested is inlaid, ground and polished to obtain the sample to be tested;

[0007] S2, for the sample to be tested, collect scanning electron microscope images of microcracks and inclusion characteristic structures, determine the grayscale threshold, perform binarization processing, and extract characteristic particles;

[0008] S3, setting the energy spectrometer analysis parameters, collecting and analyzing the energy spectrum of the extracted characteristic particle centers;

[0009] S4, performs fully automatic SEM image acquisition, feature recognition, and energy spectrum analysis of surface feature structures of materials within a large scale range: after setting the voltage, beam current, brightness, contrast, and feature recognition grayscale threshold, the coordinates of the four vertices (X1, Y1, Z1), (X2, Y2, Z2), (X3, Y3, Z3), and (X4, Y4, Z4) of the large-scale rectangular scanning area are set, where Z1 to Z4 are obtained after real-time focusing at the set coordinates; based on the feature detection software equipped with the SEM, the number of fields of view to be acquired is calculated, and the steps of image acquisition, binary segmentation and extraction of feature tissue structures, positioning and morphological parameter measurement, feature tissue structure energy spectrum analysis, and composition recording are performed for each field of view, and a data report is generated;

[0010] S5, screening, identification and in-situ quantitative statistical distribution characterization of microcracks on the surface of materials within a large size range: data screening is performed on the data report containing characteristic structural morphological parameters and component information, and the aspect ratio, distribution angle, constituent elements and content of the microcracks on the surface of the material are used to determine whether they are microcracks existing in the sample itself, and the position, number, length and area fraction of the microcracks in all fields of view within a large size range are statistically analyzed.

[0011] Furthermore, in step S1, the surface to be tested is subjected to inlaying, grinding and polishing to obtain a sample to be tested, which specifically includes:

[0012] Use SiC sandpaper of different grit sizes to gradually grind the surface of the sample. Before polishing, confirm whether the direction of the wear marks is single and uniform. Otherwise, re-polishing is required.

[0013] The polished surface is subjected to rough polishing and fine polishing treatment using a suspension of diamonds of different particle sizes or polishing paste and a matching polishing cloth to obtain a bright mirror surface and a polished sample to be tested.

[0014] Furthermore, in step S2, for the sample to be tested, a scanning electron microscope image of the characteristic structure of microcracks and inclusions is collected, and a grayscale threshold is determined, and a binarization process is performed to extract characteristic particles, which specifically includes:

[0015] Load the polished sample to be tested into the SEM sample chamber, select the appropriate voltage, and search for suitable inclusions or foreign particles at 1000-3000x magnification. After clear focus, adjust the brightness and contrast and collect the backscattered electron image, so that the grayscale value of the matrix image is 25000-30000, and the grayscale value of inclusions, second phases or foreign particles is around 5000-15000. Keep the brightness and contrast parameters of the instrument unchanged, and search for areas with scratches and foreign particles on the edge of the sample. Set the appropriate magnification and image resolution to ensure that particles with a size of 1μm can be detected.

[0016] After clear focus, collect the backscattered electron image of the current field of view, set the grayscale threshold within 15000-25000, and perform binarization processing through the image processing module to check whether all characteristic particles larger than 1μm in the field of view are extracted and segmented. If all characteristic particles larger than 1μm are effectively segmented and extracted, then the grayscale threshold setting is reasonable.

[0017] Furthermore, in step S3, setting the energy spectrometer analysis parameters specifically includes:

[0018] Set appropriate electron microscope beam current and acquisition time. When acquiring energy spectrum signals, the detector dead time should be less than 50%.

[0019] The energy spectrum signal acquisition time of a single particle must ensure that the energy signal counts per second are greater than 10,000.

[0020] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the method for identifying and characterizing the in-situ quantitative statistical distribution of microcracks on the surface of metal materials provided by the present invention, (1) based on the characteristics that microcracks on the metal surface will be embedded in the components of abrasives and polishing materials during the metallographic sample preparation process, by extracting the backscattered electron image of the metallographic surface features, combining the energy spectrum analysis results and morphological quantitative parameters for data mining and screening, while analyzing the inclusions, the surface microcracks are quickly located, identified and quantitatively counted, and the method is accurate and efficient; (2) by combining the scanning electron microscope with the energy spectrum analyzer, the characteristic structure backscattered electron image within a large range of the material is automatically collected, the feature is located, the component is analyzed, the crack is identified and counted, and the position information and distribution of the surface cracks of the material within a large range are obtained, and the limitation of the metallographic method that only a single or several local fields of view can be characterized. The crack data analyzed is more comprehensive and more statistically representative; (3) based on the backscattered electron image of the scanning electron microscope for microcracks to be identified and segmented, the detection resolution of microcracks is greatly improved compared with the metallographic method and conventional non-destructive testing methods, which can be as low as the micron level. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0022] Figure 1 Flowchart of the method for identifying microcracks on the surface of metal materials and characterizing their in-situ quantitative statistical distribution according to the present invention;

[0023] Figure 2 This is an electronic image of strip-shaped particles;

[0024] Figure 3 Schematic diagram of energy spectrum analysis results of strip particles;

[0025] Figure 4 This is a statistical distribution diagram of crack length of sample No. 1 in Example 1 of the present invention;

[0026] Figure 5 This is a statistical distribution diagram of crack length of sample No. 2 in Example 2 of the present invention;

[0027] Figure 6 This is a schematic diagram of the crack morphology of sample No. 1 after metallographic corrosion of the present invention. DETAILED DESCRIPTION

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0029] The purpose of the present invention is to provide a method for identifying and characterizing the in-situ quantitative statistical distribution of microcracks on the surface of metal materials. The method adopts a combination of automatic image acquisition by a scanning electron microscope, characteristic structure energy spectrum component analysis and target screening to identify microcracks on the surface of metal materials and perform in-situ quantitative statistical distribution characterization of the number, area fraction and length of microcracks, thereby solving the problems of low recognition reliability, poor quantitativeness, low resolution and insufficient statistical representativeness in traditional methods for identifying and characterizing microcracks in metal materials.

[0030] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] like Figure 1 As shown, the method for identifying and characterizing the in-situ quantitative statistical distribution of microcracks on the surface of a metal material provided by the present invention comprises the following steps:

[0032] S1, a block sample of a certain volume is cut from the metal material whose microcracks need to be characterized, and the surface to be tested is inlaid, ground and polished to obtain the sample to be tested;

[0033] S2, for the sample to be tested, collect scanning electron microscope images of microcracks and inclusion characteristic structures, determine the grayscale threshold, perform binarization processing, and extract characteristic particles;

[0034] S3, setting the energy spectrometer analysis parameters, collecting and analyzing the energy spectrum of the extracted characteristic particle centers;

[0035] S4, performs fully automatic SEM image acquisition, feature recognition, and energy spectrum analysis of surface feature structures of materials within a large scale range: after setting the voltage, beam current, brightness, contrast, and feature recognition grayscale threshold, the coordinates of the four vertices (X1, Y1, Z1), (X2, Y2, Z2), (X3, Y3, Z3), and (X4, Y4, Z4) of the large-scale rectangular scanning area are set, where Z1 to Z4 are obtained after real-time focusing at the set coordinates; based on the feature detection software equipped with the SEM, the number of fields of view to be acquired is calculated, and the steps of image acquisition, binary segmentation and extraction of feature tissue structures, positioning and morphological parameter measurement, feature tissue structure energy spectrum analysis, and composition recording are performed for each field of view, and a data report is generated;

[0036] S5, screening, identification and in-situ quantitative statistical distribution characterization of microcracks on the surface of materials within a large size range: data screening is performed on the data report obtained containing characteristic structural morphological parameters and component information. The microcracks on the surface of the material introduce fine particles of abrasives such as sandpaper and some components of the polishing suspension during the sample grinding and polishing process. Therefore, the microcracks will appear as long strips of particles in the scanning electron backscattered image. Their distribution orientation is inconsistent with the direction of the wear scar, and a large amount of Fe and C components will be detected in the components. Therefore, it is possible to judge whether the microcracks exist in the sample itself based on their aspect ratio, distribution angle, constituent elements and content, and to perform statistics on the position, number, length and area fraction of the microcracks in all fields of view within a large size range.

[0037] Wherein, in the step S1, a block sample of a certain volume is cut from the metal material whose microcracks need to be characterized, and the surface to be tested is inlaid, ground and polished to obtain the sample to be tested, which specifically includes:

[0038] Take a block sample with an area of ​​less than 30*30mm and a height of less than 15mm from the area where cracks need to be characterized on the metal material to be tested. Samples with a height of less than 10mm can be embedded in resin and then surface ground and polished.

[0039] After the sample is cut, the surface to be tested is ground with 180 mesh, 600 mesh, 1000 mesh and 1500 mesh SiC sandpaper. For softer materials such as copper-based and aluminum-based materials, sandpaper with a mesh size of greater than 2000 can be used for the final fine grinding. When changing each sandpaper with a different mesh size, the large sand grains of the previous sandpaper remaining on the sample surface must be cleaned. At the same time, the newly replaced sandpaper can be used with other samples for a short time to remove the large particles and other impurities remaining on the surface of the new sandpaper. Then, the sample to be tested is ground in a direction perpendicular to the scratch direction of the previous sandpaper.

[0040] Polishing of metallographic specimens removes wear marks on the grinding surface of the specimens. Before polishing, it is necessary to carefully confirm whether the direction of the wear marks is single and uniform, otherwise re-polishing is required; polishing is carried out in two steps. The first step is rough polishing, using a 6μm diamond suspension, and the second step is fine polishing, using a diamond suspension with a particle size of about 1μm. For metal materials with lower hardness such as aluminum-based and copper-based materials, while matching the polishing suspension with a smaller particle size, a larger amount of lubricant is added to avoid the generation of larger wear marks; for harder metals, a hairless polishing cloth is used, and for softer materials, a long-haired polishing cloth is used.

[0041] In step S2, for the sample to be tested, a scanning electron microscope image of the characteristic structure of microcracks and inclusions is collected, and a grayscale threshold is determined, and a binarization process is performed to extract characteristic particles, which specifically includes:

[0042] Load the polished sample to be tested into the SEM sample chamber, select the appropriate voltage, and search for suitable inclusions or foreign particles at 1000-3000x magnification. After clear focus, adjust the brightness and contrast and collect the backscattered electron image, so that the grayscale value of the matrix image is 25000-30000, and the grayscale value of inclusions, second phases or foreign particles is around 5000-15000. Keep the brightness and contrast parameters of the instrument unchanged, and search for areas with scratches and foreign particles on the edge of the sample. Set the appropriate magnification and image resolution to ensure that particles with a size of 1μm can be detected.

[0043] After clear focus, collect the backscattered electron image of the current field of view, set the grayscale threshold within 15000-25000, and perform binarization processing through the image processing module to check whether all characteristic particles larger than 1μm in the field of view are extracted and segmented. If all characteristic particles larger than 1μm are effectively segmented and extracted, then the grayscale threshold setting is reasonable.

[0044] In step S3, the energy spectrometer analysis parameters are set, specifically including: in order to improve the analysis efficiency, setting a suitable electron microscope beam current and acquisition time; when acquiring energy spectrum signals, the dead time of the detector should be less than 50%; at the same time, in order to effectively identify some light elements with atomic numbers less than or equal to 20, it is necessary to appropriately extend the energy spectrum signal acquisition time of a single particle to ensure that the energy signal counts per second are greater than 10,000.

[0045] In the specific implementation process, taking the metallographic sample of bearing steel as an example, the following steps are specifically included:

[0046] 1) Metallographic sample preparation of bearing steel:

[0047] Two bearing steel samples from different heats were taken. The samples underwent smelting, forging, softening annealing, and heat treatment processes. The reference composition of the samples is shown in Table 1. Block samples with an area of ​​15*15mm and a height of 12mm were cut from the areas where cracks needed to be characterized in the bearing steel samples of the two heats. The test surfaces of the cut samples were ground with 180 mesh, 600 mesh, 1000 mesh, and 1500 mesh SiC sandpaper. Each time a different mesh size of sandpaper was changed, the large sand grains from the previous sandpaper remaining on the sample surface were cleaned. The newly replaced sandpaper can be used for a short grinding time with other samples to remove the large particles and other impurities remaining on the surface of the new sandpaper. The sample to be tested was then ground in the direction perpendicular to the wear mark of the previous sandpaper. After grinding with 1500 mesh sandpaper, carefully confirm that the wear mark direction is single and uniform before further polishing. Polishing is carried out in two steps, using a hairless polishing cloth. The first step is rough polishing, using a 6μm diamond suspension or polishing paste. The second step is fine polishing, using a suspension or polishing paste with a particle size of about 1μm, until a bright mirror surface is produced.

[0048] Table 1 Main chemical composition of bearing steel samples (wt%)

[0049]

[0050] 2) SEM image acquisition and grayscale threshold determination of characteristic structures such as microcracks and inclusions:

[0051] Load the polished No. 1 sample to be tested into the scanning electron microscope sample chamber, select an accelerating voltage of 20 kV, and search for suitable inclusions, second phases, or foreign impurity particles at 3000 times. After clear focus, adjust the brightness and contrast so that the grayscale value of the matrix image is 26000 and the grayscale value of the inclusions, second phases, or foreign impurities is around 6000. At this time, the brightness value is 88.6% and the contrast is 36.0%. Fix the brightness and contrast parameters of the instrument unchanged, set the image resolution to 2048*2048, and calculate based on the field of view size that to achieve reliable detection of 1 micron particles, it is more appropriate to set the magnification to 600 times. At this time, the instrument's minimum particle resolution is 0.45 μm. Look for areas with more scratches and foreign matter on the edge of the sample. After clear focus, collect the backscattered electron image of the current field of view, set the initial threshold to 22000, perform binarization processing in the image processing module, check whether all characteristic particles larger than 1μm in the field of view are extracted and segmented, and change the threshold appropriately to avoid excessive extraction or omission of particles in the field of view. Finally, the particle recognition threshold is set to 21000, at which time some wear marks and strip-shaped particles can also be effectively identified.

[0052] 3) Setting the energy spectrometer analysis parameters, collecting and analyzing the energy spectrum of the center of the extracted strip-shaped characteristic particles, when the electron microscope beam current is 13μA, the acquisition time per pixel is 0.5S, the detector dead time is 40%, and the energy signal counts per second is 11000. Light elements such as C, Si, and Al can also be effectively detected. The morphology of the strip-shaped particles is as follows: Figure 2 , the results of the central energy spectrum analysis are as follows Figure 3 shown.

[0053] 4) Fully automated SEM backscattered electron image acquisition, feature recognition, and energy spectrum analysis of large-scale feature structures. With the voltage, beam current, brightness, contrast, and grayscale threshold for feature recognition set, the coordinates of the four vertices of a large rectangular scanning area were set. Due to the uneven edges of the sample after the polishing process, and relatively severe external wear marks and impurity contamination, the image acquisition and feature analysis were performed in a rectangular area in the center of the sample, avoiding the 1-2 mm area around the edge of the sample. The scanning area was 10 mm x 10 mm, and the coordinates of the four vertices of the rectangular area were set to (6.00, -5.00, 9.246), (-4.00, -5.00, 9.255), (-4.00, 5.00, 9.259), and (6.00, 5.00, 9.250). Z1 to Z4 were recorded after real-time focusing. The scanning electron microscope's feature detection software automatically calculates the number of fields to be captured, totaling 690. It then automatically performs steps for each field, including image acquisition, binary segmentation and extraction of characteristic structures, location and morphological parameter measurement, energy spectrum analysis of characteristic structures, and composition recording, ultimately generating a data report. Table 2 shows the data report for some particles measured in the first heat sample.

[0054] Table 2 Partial analysis results of particles in a large range measured by scanning electron microscopy combined with energy spectrum analysis

[0055]

[0056]

[0057] 5) Screening, identification and in-situ quantitative statistical distribution characterization of microcracks on the surface of materials within a large size range:

[0058] The obtained data report containing characteristic structural morphological parameters and component information was screened. The microcracks on the material surface introduced fine particles of sandpaper and some components of polishing suspension during the sample grinding and polishing process. Therefore, in the particle detection, its shape is long and narrow, and its aspect ratio is basically greater than 3. The distribution direction of its characteristic objects is inconsistent with the direction of the wear scar. The measurement found that the direction of the wear scar is relatively regular, basically distributed in the range of 30.0-36.0 degrees, while the direction of the cracks is relatively scattered, but many longer cracks are basically distributed around 120 degrees, which is closely related to the internal stress and grain boundary distribution of the material. At the same time, a large amount of Fe and C components will be detected in the cracks with an aspect ratio of long cracks. Therefore, it can be judged whether it is a microcrack existing in the sample itself based on its aspect ratio, distribution angle, constituent elements and content, and the number, length and area fraction of microcracks in all fields of view within a large size range are statistically analyzed. For the bearing steel samples studied, the microcrack identification conditions are set as follows: the aspect ratio is greater than 3, the distribution direction is less than 30 degrees or greater than 37 degrees, and the C content is greater than 5% and the composition does not contain Al. For example, the features No. 37, 545, 2460, 2797 and 5279 in Table 2 can be identified as cracks. According to the crack identification conditions, the data in the feature data report of the two furnace samples (No. 1 and No. 2) are screened, and the statistical results of the number, area fraction and length of all cracks in the measurement area of ​​the two samples are obtained (Table 3). The number of cracks in different crack length intervals is as follows Figure 4 and Figure 5 shown.

[0059] It can be seen that sample No. 1 has relatively more surface cracks. In order to compare the reliability of the method, the current area was used to reveal the micro cracks by metallographic etching. The polished sample No. 1 was metallographically etched with saturated picric acid aqueous solution + sodium 12-alkylbenzene sulfonate + hydrochloric acid (0.05%) for 10 minutes to etch out the grain structure and cracks. The crack morphology observed under the metallographic microscope is as follows: Figure 6 As shown, it can be seen that sample No. 1 does have many microcracks with sizes mainly between 5 and 20 microns, while sample No. 2 has almost no visible cracks, which is consistent with the statistical data of this method.

[0060] Table 3 Statistical results of the number, area fraction and length of all cracks in the measurement area of ​​the two samples

[0061]

[0062] In summary, the present invention provides a method for identifying and characterizing the in-situ quantitative statistical distribution of microcracks on the surface of metal materials, including surface preparation of metal materials, determination of grayscale thresholds of characteristic structures such as microcracks and inclusions, setting of energy spectrometer analysis parameters, fully automatic acquisition of characteristic structure images within a large size range by scanning electron microscope, feature extraction and energy spectrum analysis, screening and identification of microcracks, and in-situ quantitative statistical distribution characterization of quantity, area fraction and length, etc.; the present invention is based on the characteristic that microcracks on the metal surface will be embedded in abrasive and polishing material components during the metallographic sample preparation process. By extracting backscattered electron images of metallographic surface features, combining energy spectrum analysis results and morphological quantitative parameters for data mining and screening, the method achieves rapid positioning, identification and quantitative statistics of surface microcracks while analyzing inclusions, and the method is accurate and efficient; at the same time, it also overcomes the limitation of the metallographic method that can only characterize cracks in a single or several local fields of view. The analyzed crack data is more comprehensive and more statistically representative. At the same time, the detection resolution of microcracks has been greatly improved, which can be as low as the micron level.

[0063] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for identifying and characterizing in-situ quantitative statistical distribution of microcracks on the surface of metal materials, characterized in that: The following steps are involved: S1, a block sample of a certain volume is cut from the metal material whose microcracks need to be characterized, and the surface to be tested is inlaid, ground and polished to obtain the sample to be tested; S2, for the sample to be tested, collect scanning electron microscope images of microcracks and inclusion characteristic structures, determine the grayscale threshold, perform binarization processing, and extract characteristic particles; Specifically include: Load the polished sample to be tested into the SEM sample chamber, select the appropriate voltage, and search for suitable inclusions or foreign particles at 1000-3000x magnification. After clear focus, adjust the brightness and contrast and collect the backscattered electron image, so that the grayscale value of the matrix image is 25000-30000, and the grayscale value of inclusions, second phases or foreign particles is around 5000-15000. Keep the brightness and contrast parameters of the instrument unchanged, and search for areas with scratches and foreign particles on the edge of the sample. Set the appropriate magnification and image resolution to ensure that particles with a size of 1μm can be detected. After clear focus, collect the backscattered electron image of the current field of view, set the grayscale threshold within 15000-25000, and perform binarization processing through the image processing module to check whether all characteristic particles larger than 1μm in the field of view are extracted and segmented. If all characteristic particles larger than 1μm are effectively segmented and extracted, then the grayscale threshold setting is reasonable; S3, setting the energy spectrometer analysis parameters, and performing energy spectrum acquisition and analysis on the extracted characteristic particle centers; wherein setting the energy spectrometer analysis parameters specifically includes: Set appropriate electron microscope beam current and acquisition time. When acquiring energy spectrum signals, the detector dead time should be less than 50%. The energy spectrum signal acquisition time of a single particle must ensure that the energy signal counts per second are greater than 10,000; S4, performs fully automatic SEM image acquisition, feature recognition, and energy spectrum analysis of surface feature structures of materials within a large scale range: After setting the voltage, beam current, brightness, contrast, and feature recognition grayscale threshold, the coordinates of the four vertices (X1, Y1, Z1), (X2, Y2, Z2), (X3, Y3, Z3), and (X4, Y4, Z4) of the large-scale rectangular scanning area are set, where Z1 to Z4 are obtained after real-time focusing at the set coordinates; based on the feature detection software equipped with the SEM, the number of fields of view to be acquired is calculated, and the steps of image acquisition, binary segmentation and extraction of feature tissue structures, positioning and morphological parameter measurement, energy spectrum analysis of feature tissue structures, and composition recording are performed for each field of view, and a data report is generated; S5, screening, identification and in-situ quantitative statistical distribution characterization of microcracks on the surface of materials within a large size range: data screening is performed on the data report containing characteristic structural morphological parameters and component information, and the aspect ratio, distribution angle, constituent elements and content of the microcracks on the surface of the material are used to determine whether they are microcracks existing in the sample itself, and the position, number, length and area fraction of the microcracks in all fields of view within a large size range are statistically analyzed.

2. The method for identifying and characterizing in-situ quantitative statistical distribution of microcracks on the surface of metal materials according to claim 1, characterized in that: In step S1, the surface to be tested is inlaid, ground and polished to obtain a sample to be tested, which specifically includes: Use SiC sandpaper of different grit sizes to gradually grind the surface of the sample. Before polishing, confirm whether the direction of the wear marks is single and uniform. Otherwise, re-polishing is required. The polished surface is subjected to rough polishing and fine polishing treatment using a suspension of diamonds of different particle sizes or polishing paste and a matching polishing cloth to obtain a bright mirror surface and a polished sample to be tested.

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