Method and system for detecting hardness of wear-resistant material

Abstract

The invention relates to the technical field of hardness detection, in particular to a wear-resistant material hardness detection method and system. The method comprises the following steps: in a loading and pressure maintaining stage, irradiating an indentation area by using a multi-angle discrete spectrum light source and synchronously acquiring an image sequence; identifying a microstructure heterogeneous area based on the reflectivity difference of the image to obtain a material microstructure heterogeneous degree parameter; reconstructing the three-dimensional surface topography of the indentation area, calculating the spatial deviation between the three-dimensional surface topography and the ideal indenter model so as to determine an asymmetric distortion parameter, and obtaining a corrected indentation projection area considering the influence of the heaving or sinking of the material; a hardness value is calculated by using a hardness calculation model, a weight coefficient of the model is adjusted according to a heterogeneity parameter, and input comprises correction of an indentation projection area and an asymmetric distortion parameter. According to the scheme provided by the invention, the accuracy and representativeness of hardness detection can be improved by comprehensively quantifying the three-dimensional shape distortion and the microscopic heterogeneity.

Description

Technical Field

[0001] This invention relates to the field of hardness testing technology. More specifically, this invention relates to a method and system for testing the hardness of wear-resistant materials. Background Technology

[0002] Hardness is a key mechanical indicator for evaluating the wear resistance of wear-resistant materials. Currently, the hardness testing technology used in industry is the indentation hardness method, such as Vickers and Brinell hardness tests. The principle is to use an indenter of a specific geometry to press into the material surface under a preset load, measure the indentation size after unloading, and then calculate the hardness value based on the ratio of the load to the projected area of ​​the indentation. This method is simple to operate and technically mature, and has become a standardized means of evaluating material properties. However, this method is based on ideal assumptions, namely that the tested material is homogeneous and isotropic. This is often difficult to satisfy for modern high-performance wear-resistant materials with complex microstructures, thus affecting the accuracy and representativeness of the test results.

[0003] When the indentation hardness test is applied to modern high-performance wear-resistant materials, such as metal matrix composites, high-entropy alloys, and surface coatings, its limitations become increasingly apparent. The microstructure of these materials often exhibits significant heterogeneity, such as a mixed distribution of hard and soft phases or elemental segregation. Therefore, the results of single-point indentation tests are greatly affected by the location of the indenter, exhibiting strong randomness and making it difficult to accurately reflect the overall average hardness of the material, resulting in unrepresentative evaluation results.

[0004] Furthermore, during the indentation process, the material around the indentation undergoes plastic deformation, producing "bulges" or "depressions." This causes a significant deviation between the projected area of ​​the indentation measured by two-dimensional optical imaging and the actual contact projected area, introducing substantial measurement errors. Moreover, when residual stress exists within the material or the microstructure exhibits anisotropy, the morphology of the indentation will show asymmetric distortion, further exacerbating the complexity and uncertainty of the measurement. Therefore, existing technologies cannot effectively address the influence of complex factors such as the inhomogeneity of the material's microstructure and the distortion of the three-dimensional morphology of the indentation, making it difficult to accurately represent the hardness of complex wear-resistant materials. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for testing the hardness of wear-resistant materials, in order to solve the problem that it is difficult to accurately represent the hardness of complex wear-resistant materials in the prior art; to this end, this invention provides solutions in the following two aspects.

[0006] In a first aspect, the present invention provides a method for testing the hardness of wear-resistant materials, comprising the following steps: During the loading and holding stage of applying load to the surface of the wear-resistant material, a multi-angle discrete spectral light source is used to irradiate the indentation area formed by the indenter and the material, and a set of image sequences of the indentation area are acquired simultaneously. Based on the reflectance differences of the discrete spectral images in the image sequence, the heterogeneous regions of the microstructure are identified and quantified to obtain the material structure heterogeneity parameters. Using the image frames acquired under the multi-angle discrete spectral light source irradiation, the three-dimensional surface morphology of the indentation area is reconstructed, and the spatial deviation between the three-dimensional surface morphology and the ideal indenter geometric model is calculated to determine the asymmetric distortion parameters representing the material bulges or depressions. Based on the three-dimensional surface morphology of the indentation area, the corrected indentation projection area considering the influence of material bulges or depressions is calculated. The hardness value of the wear-resistant material is calculated using a hardness calculation model, wherein the weight coefficients of the hardness calculation model are adjusted according to the material structure heterogeneity parameters, and the inputs of the hardness calculation model include the corrected indentation projection area and the asymmetric distortion parameters.

[0007] Preferably, the step of illuminating the indentation area formed by the indenter and the material using a multi-angle discrete spectral light source and simultaneously acquiring a series of images of the indentation area includes: using three LED light sources containing wavelengths of 405nm, 532nm, and 650nm to illuminate the indentation area from three angles of 30 degrees, 45 degrees, and 60 degrees relative to the normal of the material surface, respectively; and using a camera to acquire the image sequence throughout the entire process from the start of loading to the end of holding pressure.

[0008] Preferably, the step of obtaining the material tissue heterogeneity parameter includes: selecting a frame image acquired under illumination with a specific wavelength light source from the pressure holding stage of the image sequence; processing the image to segment the microstructure heterogeneous region and the matrix; calculating the ratio of the area of ​​the microstructure heterogeneous region to the total area of ​​the indentation analysis region, and using it as the material tissue heterogeneity parameter.

[0009] Preferably, the step of processing the image includes: performing grayscale processing on the image, and then performing binarization segmentation using the Otsu method.

[0010] Preferably, the step of calculating the asymmetric distortion parameter includes: using photometric stereo method, selecting three frames of images acquired under illumination from at least three different angle light sources during the pressure holding stage of the image sequence, and reconstructing the three-dimensional morphology of the indentation area; registering the three-dimensional morphology with an ideal indenter geometric model having the same indentation depth, and calculating the height difference between the two at multiple corresponding sampling points; and using the root mean square value of all height differences as the asymmetric distortion parameter.

[0011] Preferably, the number N of the corresponding sampling points is greater than 1000.

[0012] Preferably, the step of calculating the projected area of ​​the corrected indentation includes: fitting the original material surface as a reference plane using the region far from the influence of the indentation in the reconstructed three-dimensional surface morphology; determining the closed contour line where the three-dimensional surface morphology intersects the reference plane; and taking the area of ​​the two-dimensional projection region enclosed by the closed contour line as the projected area of ​​the corrected indentation.

[0013] Preferably, the camera acquires data at a rate of not less than 50 frames per second.

[0014] Preferably, the heterogeneous region of the microstructure is a region formed by phase transition or elemental segregation.

[0015] In a second aspect, a hardness testing system for wear-resistant materials includes: The processor; the memory storing computer instructions for testing the hardness of wear-resistant materials, which, when executed by the processor, cause the system to perform the aforementioned method for testing the hardness of wear-resistant materials.

[0016] The beneficial effects of this invention are as follows: By reconstructing the three-dimensional surface morphology of the indentation area in situ during the indentation process, this invention can calculate the corrected indentation projection area that takes into account the influence of material "bulges" or "depressions," and further quantify the asymmetric distortion caused by residual stress or microstructure anisotropy. This makes the basic data for hardness calculation closer to the physically real contact morphology, reducing errors caused by two-dimensional measurements. Moreover, by utilizing the reflectance difference identified by discrete spectral images, the material microstructure heterogeneity parameter is identified and calculated, objectively quantifying the degree of inhomogeneity of the microstructure within the indentation area. This ensures that hardness evaluation no longer ignores the intrinsic properties of the material, making the resulting hardness values ​​more statistically significant and representative overall. In other words, by establishing a comprehensive calculation model that combines the three key factors of corrected indentation area, asymmetric distortion, and microstructure heterogeneity, this invention can reflect the influence of complex factors on hardness measurement, solving the problem of inaccurate and poor representativeness of measurement results caused by ignoring the intrinsic inhomogeneity of the material and three-dimensional morphology distortion, thereby achieving a more reliable representation of the hardness of high-performance wear-resistant materials. Attached Figure Description

[0017] Figure 1 The flowchart illustrating the steps of the wear-resistant material hardness testing method in this embodiment is shown in the schematic diagram. Figure 2 The schematic diagram illustrates the structural block diagram of the wear-resistant material hardness testing system in this embodiment. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0019] like Figure 1As shown in this embodiment, a method for testing the hardness of wear-resistant materials includes the following steps: Step S1: During the loading and pressure holding stage of applying load to the surface of the wear-resistant material, a multi-angle discrete spectral light source is used to irradiate the indentation area formed by the indenter and the material, and a set of image sequences of the indentation area are acquired simultaneously.

[0020] Specifically, the wear-resistant material to be tested (e.g., a cobalt-based composite material containing tungsten carbide particles) is placed on the sample stage of the Vickers hardness tester. A ring-shaped light source array is integrated above the indenter. This array comprises four groups of LED light sources symmetrically distributed at different tilt angles (e.g., 15 degrees, 30 degrees, 45 degrees, and 60 degrees). Each group of light sources contains three independent discrete spectral channels: red (630 nm), green (525 nm), and blue (470 nm). During the application of a load (e.g., 10 kgf), a high-speed CCD camera and the light source controller are synchronously triggered via a data acquisition card. From the application of the load to the end of the pressure holding period, the camera continuously acquires images at a rate of 100 frames per second. Simultaneously, the light source controller cyclically illuminates LEDs of different angles and colors in a preset sequence to ensure that each frame corresponds to a specific lighting condition, ultimately obtaining a sequence of images containing the material's dynamic response and multispectral information.

[0021] In an optional embodiment, a multi-angle discrete spectral light source is used to illuminate the indentation area formed by the indenter and the material, and a series of images of the indentation area are acquired simultaneously. This includes: using three LED light sources containing wavelengths of 405nm, 532nm, and 650nm to illuminate the indentation area from three angles of 30 degrees, 45 degrees, and 60 degrees relative to the normal of the material surface, respectively; and using a CCD camera with a resolution of 1920×1080 pixels to acquire the image sequence at a acquisition rate of not less than 50 frames per second throughout the entire process from the start of loading to the end of holding pressure.

[0022] When performing indentation tests on duplex steel, a multispectral, multi-angle lighting design was employed. This combination of three light sources with different wavelengths and angles ensured comprehensive identification of the material's surface morphology and microstructure. For example, 405nm violet light is more sensitive to fine surface scratches and slip bands, 532nm green light can clearly distinguish between ferrite and martensite phases, and 650nm red light better reflects the material's surface roughness. This multispectral, multi-angle lighting design provides rich data input for subsequent three-dimensional morphology reconstruction and microstructure heterogeneity analysis.

[0023] During indentation loading and holding, high temporal resolution image sequences can completely record the plastic deformation during indentation formation and the creep behavior that may occur during the holding stage. For example, in a test with a total duration of 20 seconds, the camera worked continuously at a rate of 50 frames per second, acquiring a total of 1000 frames. This not only recorded the plastic deformation process at the moment of indentation formation but also completely captured the creep or phase transformation behavior of the material over time during the holding stage. The complete spatiotemporal dataset allows the analysis of material mechanical behavior to go beyond the indentation morphology and trace its entire evolution.

[0024] Step S2: Based on the reflectance differences of discrete spectral images in the image sequence, identify and quantify the heterogeneous regions of the microstructure to obtain the heterogeneity parameters of the material structure.

[0025] Specifically, from the acquired image sequence, three frames were selected, each captured at the same pressure holding time and illuminated by the red, green, and blue spectral channels under vertical illumination. Since the material components have different reflectivities for specific wavelengths of light (e.g., carbide particles have high reflectivity for green light, while the cobalt matrix has high reflectivity for red light), the contrast between the two phases can be significantly enhanced by calculating the difference images of different color channels (e.g., normalized green channel intensity minus normalized red channel intensity). The processed difference images are then binarized, identifying the regions representing tungsten carbide particles as white and the matrix regions as black. Finally, the proportion of the total area occupied by white pixels within the indentation-affected region is calculated, and this proportion is used as a parameter for the heterogeneity of the material microstructure.

[0026] In an optional embodiment, the step of obtaining the material microstructure heterogeneity parameter includes: selecting a frame image acquired under illumination with a specific wavelength light source from the holding pressure stage of the image sequence; processing the image to segment the microstructure heterogeneous region and the matrix; calculating the ratio of the area of ​​the microstructure heterogeneous region to the total area of ​​the indentation analysis region, and using this ratio as the material microstructure heterogeneity parameter. The microstructure heterogeneous region is a region formed by phase transition or elemental segregation.

[0027] In this embodiment, a 532nm wavelength light source is selected as the specific wavelength light source. At a wavelength of 532nm, the reinforcing phase particles and the aluminum matrix typically exhibit the greatest difference in optical reflectivity, which is beneficial for subsequent image segmentation.

[0028] In this embodiment, the image processing steps include: after grayscale processing of the image, binarization segmentation is performed using the Otsu method. After converting the selected 1920×1080 pixel color image into an 8-bit grayscale image, the Otsu method automatically calculates an optimal grayscale threshold for image binarization. For example, when the optimal grayscale threshold is 158, all pixels with grayscale values ​​higher than 158 are identified as high-reflectivity second-phase particles, while those lower than 158 are identified as the matrix. Through this processing, a complex grayscale image is transformed into an image with only black and white colors, clearly indicating different material component regions, thereby achieving automatic identification and segmentation of microstructures.

[0029] After segmentation, the area of ​​the heterogeneous region of the microstructure is obtained by calculating the total number of pixels marked as second-phase particles in the image. The ratio of this ratio to the total area of ​​the pre-defined indentation analysis region is used as a parameter for the heterogeneity of the material microstructure. In a specific case, the total area of ​​the second-phase particles obtained after segmentation... The pixel size is 125,000 pixels, and the defined indentation area is a square area with a side length of 800 pixels. The total area of ​​the indentation analysis area is... With a resolution of 640,000 pixels, the heterogeneity parameter of the material structure can be calculated. Approximately 0.195.

[0030] Step S3: Using the image frames acquired under the illumination of the multi-angle discrete spectral light source, reconstruct the three-dimensional surface morphology of the indentation area, calculate the spatial deviation between the three-dimensional surface morphology and the ideal indenter geometric model, and determine the asymmetric distortion parameters representing the material bulge or depression; based on the three-dimensional surface morphology of the indentation area, calculate the corrected indentation projection area that takes into account the influence of the material bulge or depression.

[0031] Specifically, four images acquired during the pressure holding stage, illuminated by light sources from four different angles, were selected. Based on the principle of photometric stereochemistry, the normal vector of the material surface corresponding to the pixel was calculated according to the brightness variation of the same pixel in these four images and the known directions of the four light sources. This calculation was repeated for all pixels within the indentation area to obtain the surface normal vector field of the entire area. By integrating the normal vector field, a three-dimensional height map with sub-micron precision of the indentation and surrounding area was reconstructed. A standard ideal Vickers indenter pyramid model defined by mathematical formula was best-fitted and aligned with the reconstructed three-dimensional morphology. The height difference between the two at corresponding points in space was calculated to form a deviation distribution matrix. By calculating the skewness coefficient or first-order moment center offset of the deviation distribution matrix, the asymmetric distortion parameters that quantify the degree of deviation of the indentation geometry from the central symmetry were obtained.

[0032] In the obtained 3D surface topography data, the original surface reference plane of the material is determined by fitting an unaffected flat area around the indentation. A search is then performed along the contour of the 3D topography to find all points whose height values ​​are equal to the height of the reference plane; the closed contour line formed by these points represents the true contact boundary between the material and the indenter. The 2D projected area enclosed by the closed contour line is calculated. This 2D projected area reflects the increased contact range due to material bulges or decreased contact range due to material depressions; this is the corrected indentation projected area.

[0033] In an optional embodiment, the step of calculating the asymmetric distortion parameter includes: using photometric stereo method, selecting three frames of images acquired under illumination from at least three different angle light sources during the pressure holding stage of the image sequence, and reconstructing the three-dimensional morphology of the indentation area; registering the three-dimensional morphology with an ideal indenter geometric model having the same indentation depth, and calculating the height difference between the two at multiple corresponding sampling points; and using the root mean square value of all height differences as the asymmetric distortion parameter.

[0034] The specific formula for calculating the asymmetric distortion parameter is as follows: , in, For asymmetric distortion parameters, To reconstruct the three-dimensional topography The height value of each sampling point. This represents the height value of the corresponding point on the ideal indenter geometric model.

[0035] Specifically, when testing a rolled titanium alloy sheet, the material exhibits anisotropy, causing inconsistent material bulges around the indentation in different directions. To measure this irregular morphology, three frames were extracted from the image sequence acquired during the holding pressure stage, illuminated by light sources at angles of 30 degrees, 45 degrees, and 60 degrees respectively.

[0036] By employing the photometric stereo method, utilizing the brightness differences of the same pixel in the three aforementioned image frames, and combining this with the known directions of three light sources, the normal direction of the material surface corresponding to that pixel can be calculated. Those skilled in the art should know that the photometric stereo method is prior art and will not be elaborated upon here.

[0037] Perform the above calculations on all pixels within the indentation area to obtain the complete surface normal vector field. Then, through numerical integration, the three-dimensional surface morphology of the indentation and surrounding area with sub-micron precision can be reconstructed.

[0038] A standard, ideal 3D digital model of the indenter (e.g., a Perkovich indenter model) with the same depth as the maximum indentation depth measured experimentally is generated. An algorithm is used to optimally register this ideal model with the reconstructed actual 3D topography. After registration, a large number of sampling points are uniformly selected within the indentation area, and the difference between the actual topography height and the ideal model height at each sampling point is calculated. Since the material bulge along the rolling direction is smaller, while the bulge perpendicular to the rolling direction is larger, the height difference reflects the asymmetry of the indentation. In this embodiment, the number of sampling points exceeds 5000.

[0039] Finally, the root mean square value of all these height differences is calculated, and this value is used as the final asymmetric distortion parameter. For example, an asymmetric distortion parameter value of 60 nanometers quantitatively represents the degree of indentation shape deviation caused by material anisotropy.

[0040] In an optional embodiment, the step of calculating the corrected indentation projection area includes: fitting the original material surface as a reference plane using a region in the reconstructed three-dimensional surface topography that is far from the influence of the indentation; determining a closed contour line where the three-dimensional surface topography intersects the reference plane; and using the area of ​​the two-dimensional projection region enclosed by the closed contour line as the corrected indentation projection area.

[0041] For an austenitic stainless steel with a high work hardening index, the material around the indenter will bulge significantly during indentation loading, forming piles. In this case, the indentation projection area calculated based on the indentation depth function will ignore this bulging material that actually contacts the indenter, leading to an underestimation of the hardness value.

[0042] To address the aforementioned issues, firstly, from the 3D surface topography data of the indentation reconstructed using techniques such as photometric stereochemistry, regions far from the indentation center and unaffected by plastic deformation are selected. For example, regions more than five times the radius of the indentation center can be chosen, as the topographic data here can represent the original surface state of the material before the indentation was formed. Then, using the data points from the selected region, a mathematical plane is fitted using the least squares method; this plane is defined as the reference plane representing the original material surface.

[0043] In three-dimensional space, the calculated reconstructed indentation morphology intersects with the reference plane established in the previous step. Connecting all these intersection points forms a closed profile. This profile represents the true contact boundary between the indenter and the material (including all raised portions).

[0044] Finally, the area enclosed by this closed contour line on the two-dimensional plane is calculated. This area is the corrected indentation projection area, taking into account the effects of material bulges or depressions. For example, for a case where the area calculated by the depth function is 150 square micrometers, after considering the bulge effect, the corrected true contact area may reach 175 square micrometers. The hardness value calculated using this corrected area better reflects the true properties of the material.

[0045] Step S4: Calculate the hardness value of the wear-resistant material using a hardness calculation model, wherein the weighting coefficients of the hardness calculation model are adjusted according to the material microstructure heterogeneity parameter, and the inputs of the hardness calculation model include the corrected indentation projection area and the asymmetric distortion parameter.

[0046] Specifically, the formula for calculating hardness in the hardness calculation model is as follows: , in This is the hardness value. For the applied load, The corrected indentation projection area is calculated in step S3. The asymmetric distortion parameters calculated in step S3, The material microstructure heterogeneity parameter is calculated in step S2. and These are empirical constants obtained through extensive experimental calibration of standard samples.

[0047] In the above calculation formula, the material microstructure heterogeneity parameter Used to dynamically adjust and correct the projected area of ​​the indentation. Asymmetric distortion parameters Weights in the calculation. For example, when measuring a large... When the value is 0, it indicates that the microstructure of the material is highly heterogeneous. In this case, the model will correspondingly increase the weight of the asymmetric distortion parameter, because the heterogeneous structure easily leads to irregular material flow, resulting in more significant shape distortion.

[0048] Known load Correcting the projected area of ​​the indentation Asymmetric distortion parameters Substitute the measured values ​​into the material's heterogeneity parameters. In the model with adjusted weights, the hardness value of the wear-resistant material is finally calculated, which can comprehensively reflect various complex factors of the material.

[0049] This invention also provides a system for testing the hardness of wear-resistant materials. For example... Figure 2As shown, the system includes a processor and a memory, the memory storing computer program instructions, which, when executed by the processor, implement the wear-resistant material hardness testing method described above according to the present invention.

[0050] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and therefore will not be described in detail here.

[0051] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented by computer-readable / executable instructions stored or otherwise maintained on such a computer-readable medium.

[0052] In the description of this specification, "multiple" means at least two, such as two, three or more, etc., unless otherwise expressly and specifically defined.

[0053] While various embodiments of the invention have been shown and described in this specification, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention.

Claims

1. A method for testing the hardness of wear-resistant materials, characterized in that, Includes the following steps: During the loading and pressure holding stage of applying load to the surface of wear-resistant material, a multi-angle discrete spectral light source is used to irradiate the indentation area formed by the indenter and the material, and a set of image sequences of the indentation area are acquired simultaneously. Based on the reflectance differences of discrete spectral images in the image sequence, the heterogeneous regions of microstructure are identified and quantified to obtain the heterogeneity parameters of the material structure. Using image frames acquired under the illumination of the multi-angle discrete spectral light source, the three-dimensional surface morphology of the indentation area is reconstructed, the spatial deviation between the three-dimensional surface morphology and the ideal indenter geometric model is calculated, and the asymmetric distortion parameters representing the material bulge or depression are determined; based on the three-dimensional surface morphology of the indentation area, the corrected indentation projection area considering the influence of material bulge or depression is calculated. The hardness value of the wear-resistant material is calculated using a hardness calculation model, wherein the weighting coefficients of the hardness calculation model are adjusted according to the heterogeneity parameter of the material structure, and the inputs of the hardness calculation model include the corrected indentation projection area and the asymmetric distortion parameter.

2. The method for testing the hardness of wear-resistant materials according to claim 1, characterized in that, The method involves irradiating the indentation area formed by the indenter and the material using a multi-angle discrete spectral light source, and simultaneously acquiring a series of images of the indentation area, including: Three LED light sources with wavelengths of 405nm, 532nm and 650nm were used to illuminate the indentation area from three angles of 30 degrees, 45 degrees and 60 degrees relative to the normal of the material surface, respectively. The image sequence was acquired using a camera throughout the entire process from the start of loading to the end of pressure holding.

3. The method for testing the hardness of wear-resistant materials according to claim 1, characterized in that, The step of obtaining the heterogeneity parameters of the material structure includes: From the pressure holding phase of the image sequence, select an image frame acquired under illumination by a light source of a specific wavelength; The image is processed to segment the heterogeneous regions of the microstructure from the matrix; The ratio of the area of ​​the heterogeneous microstructure region to the total area of ​​the indentation analysis region is calculated and used as the heterogeneity parameter of the material structure.

4. The method for testing the hardness of wear-resistant materials according to claim 3, characterized in that, The steps for processing the image include: performing grayscale processing on the image, and then performing binarization segmentation using the Otsu method.

5. The method for testing the hardness of wear-resistant materials according to claim 1, characterized in that, The steps for calculating the asymmetric distortion parameters include: Using the photometric stereo method, three frames of images acquired under illumination from at least three different angles during the pressure holding stage of the image sequence are selected to reconstruct the three-dimensional morphology of the indentation area. The three-dimensional topography is registered with the geometric model of an ideal indenter with the same indentation depth, and the height difference between the two at multiple corresponding sampling points is calculated. The root mean square value of all height differences is used as the asymmetric distortion parameter.

6. The method for testing the hardness of wear-resistant materials according to claim 5, characterized in that, The number of corresponding sampling points N is greater than 1000.

7. The method for testing the hardness of wear-resistant materials according to claim 1, characterized in that, The step of calculating the projected area of ​​the corrected indentation includes: The original material surface is fitted as a reference plane using the region far from the indentation effect in the reconstructed 3D surface morphology; Determine the closed contour line where the three-dimensional surface topography intersects the reference plane; The area of ​​the two-dimensional projection region enclosed by the closed contour line is taken as the projected area of ​​the corrected indentation.

8. The method for testing the hardness of wear-resistant materials according to claim 2, characterized in that, The camera acquires data at a rate of no less than 50 frames per second.

9. A method for testing the hardness of wear-resistant materials according to any one of claims 1-8, characterized in that, The heterogeneous regions of the microstructure are regions formed by phase transitions or elemental segregation.

10. A hardness testing system for wear-resistant materials, characterized in that, include: processor; A memory storing computer instructions for testing the hardness of wear-resistant materials, which, when executed by the processor, cause the system to perform the wear-resistant material hardness testing method according to claim 1.

Images