A method for assessing the scratch resistance of a material

Abstract

The application discloses a method for evaluating the scratch resistance of materials, and belongs to the technical field of material performance detection. The method comprises the following steps: under a preset friction condition simulating a daily use scene, a hard abrasive particle-containing abrasive is used to apply a friction action to a surface of a to-be-tested base material to form a scratch area; a gray-scale image containing the scratch area is obtained by using an optical imaging system; and based on the image, a quantitative characterization parameter for characterizing the scratch resistance of the to-be-tested base material is calculated. Through real abrasive particle scratch simulation, automatic imaging and a quantitative algorithm associated with perception, the application realizes objective and repeatable quantitative evaluation of the scratch resistance of hard materials such as glass and metal, effectively overcomes the problem that traditional hardness testing or manual microscopic observation is disconnected with actual user experience, and is suitable for material research and development and quality control of consumer electronic products.

Description

Technical Field

[0001] This invention relates to the field of material performance testing technology, and in particular to a method for evaluating the scratch resistance of materials. Background Technology

[0002] In recent years, to overcome the problems of low efficiency, large human error, and disconnect from actual use scenarios in traditional hardness testing when evaluating the scratch resistance of electronic glass, the industry has attempted to develop dedicated testing devices. For example, Chinese invention patent CN110455654B discloses a testing device and method for the scratch resistance of electronic glass surfaces. This device uses multiple sample mounting holes on a horizontal moving guide plate and a friction rod with weights mounted on a fixed guide rod to allow a servo motor-driven friction scribing head to simultaneously reciprocate and scribble multiple samples. The scratch width is then manually measured using a microscope to evaluate scratch resistance. While this approach improves test throughput and reduces angle control errors, it still has significant limitations: First, the scratching method relies on rigid tips such as tungsten carbide and pencils for directional scratching, which cannot simulate the random abrasive wear behavior caused by amorphous hard particles such as dust in the daily environment; second, the result evaluation depends on operators manually measuring the scratch width under a microscope, which is not only inefficient but also easily affected by subjective judgment, making it difficult to achieve objective quantitative analysis; third, this method does not establish quantitative indicators directly related to user visual perception, and cannot effectively reflect the visibility of scratches or the degree of appearance degradation.

[0003] Meanwhile, existing standardized testing methods, such as ASTM D7027 and ASTM D1044, mostly use diamond scratching needles or standard grinding wheels, and the evaluation parameters, such as critical load and haze change, lack a direct correlation with the surface scratches that are of most concern to consumers of consumer electronics. Although some studies have attempted to introduce image analysis technology, these are mostly aimed at flexible thin film materials, and the algorithms used do not consider the characteristics of hard substrates such as glass and metal, which exhibit high-gloss scratches due to enhanced light scattering after scratching. Nor have they constructed a composite quantitative model that can simultaneously reflect the scratch area and brightness.

[0004] Therefore, there is an urgent need for a method that can realistically simulate everyday abrasive scratch scenarios, is applicable to hard electronic structural materials, and is based on optical imaging to achieve objective, automated, and highly visually perceptual quantitative evaluation of scratch resistance performance, in order to make up for the shortcomings of existing technologies in terms of realism, objectivity, and engineering applicability. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for testing and evaluating scratch resistance. This method can highly simulate real-world usage scenarios and objectively and accurately quantify the test results, thereby effectively evaluating the scratch resistance performance of substrates such as glass and metal.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: This invention provides a method for evaluating the scratch resistance of materials, comprising the following steps: Scraping step: Under preset friction conditions simulating daily use scenarios, an abrasive containing hard abrasive grains is used to apply friction to the surface of the substrate to be tested, so as to form a scratched area on the surface of the substrate to be tested; wherein, the friction conditions include the normal load applied to the abrasive, the friction stroke, the reciprocating frequency, and the type of abrasive; Imaging steps: Use an optical imaging system to acquire a grayscale image of the surface of the substrate under test, including the scratched area; Analysis steps: Based on the grayscale image, calculate the quantitative characterization parameters of the scratched area according to a preset algorithm. The quantitative characterization parameters are used to characterize the scratch resistance of the substrate under test. The calculation of the quantitative characterization parameters is based on pixels with grayscale values ​​not lower than the background grayscale baseline value, and is obtained by normalizing the number of pixels at each grayscale level and their relative brightness weight.

[0007] In the method for evaluating the scratch resistance of materials of the present invention, the substrate to be tested includes: glass, chemically strengthened glass, coated glass, aluminum alloy, stainless steel or magnesium alloy structural parts used for the casing or display cover of consumer electronic products.

[0008] In the method for evaluating the scratch resistance of materials according to the present invention, the abrasive is sandpaper with quartz particles attached to its surface.

[0009] In the method for evaluating the scratch resistance of materials according to the present invention, the sandpaper has a grit size of 120 mesh.

[0010] In the method for evaluating the scratch resistance of a material according to the present invention, the preset friction conditions include: applying a constant normal load by means of weights, and performing multiple cycles of friction on the surface of the substrate to be tested at a set reciprocating speed and a fixed stroke.

[0011] In the method for evaluating the scratch resistance of materials according to the present invention, the constant normal load is 1.5 kgf.

[0012] In the method for evaluating the scratch resistance of materials according to the present invention, the optical imaging system is an automatic optical inspection system equipped with a high-resolution industrial camera and a controllable illumination source.

[0013] In the method for evaluating the scratch resistance of materials according to the present invention, the process of calculating quantitative characterization parameters in the analytical step includes the following sub-steps: (1) The grayscale image is preprocessed, that is, pixels with grayscale values ​​less than the background grayscale baseline value x0 are removed, and the effective pixel set is retained; (2) In the set of effective pixels, for each gray level x, calculate the corresponding weighted pixel count. The calculation formula is: ,in, This represents the number of pixels with gray level x. (3) Calculate the quantitative characterization parameter n, and the calculation formula is as follows: The quantitative characterization parameter n serves as the basis for evaluating the scratch resistance of the substrate under test. The larger the value, the larger the overall brightness and area of ​​the scratched area, and the weaker the scratch resistance.

[0014] In the method for evaluating the scratch resistance of materials according to the present invention, the method for determining the background grayscale baseline value x0 includes: acquiring grayscale images of the same batch of substrates under the same imaging conditions in an unscratched state, statistically analyzing their grayscale value distribution, and using a preset upper limit threshold of the distribution as the background grayscale baseline value.

[0015] The method of the present invention for evaluating the scratch resistance of materials further includes: The scratching, imaging, and analysis steps are repeatedly performed at multiple different locations on the surface of the substrate to be tested to obtain multiple evaluation parameters. Based on the multiple evaluation parameters, the final scratch resistance evaluation result is determined by statistical aggregation. The statistical aggregation method includes at least one of arithmetic mean, median, maximum value, weighted average, or mean after removing outliers.

[0016] The present invention has the following beneficial effects: This invention constructs a scratch resistance assessment method that closely resembles real-world user experience by scratching the test substrate under simulated everyday usage conditions and combining optical imaging with a grayscale-weighted quantitative analysis algorithm. This method not only considers the area of ​​the scratch but also introduces grayscale values ​​as a brightness weight, enabling the assessment results to more accurately reflect the visual salience of the scratch. This overcomes the problem of traditional indirect parameters such as hardness indicators or haze changes being disconnected from user perception, significantly improving the objectivity, repeatability, and practical guidance value of material scratch resistance evaluation. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a schematic diagram illustrating the steps of a method for evaluating the scratch resistance of materials according to an embodiment of the present invention.

[0018] Figure 2 This is a grayscale image of ordinary double-strength glass after scratching, provided in an embodiment of the present invention.

[0019] Figure 3 The image provided in this embodiment of the invention is a grayscale image of ordinary microcrystalline glass after scratching.

[0020] Figure 4 This is a grayscale image of a microcrystalline glass with a hardened film coated on its surface after being scratched, as provided in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] The embodiments of the present invention will now be described in further detail with reference to the accompanying drawings. It should be understood that the embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the scope of the invention.

[0023] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for testing and evaluating scratch resistance. This method can highly simulate real-world usage scenarios and objectively and accurately quantify the test results, thereby effectively evaluating the scratch resistance performance of substrates such as glass and metal.

[0024] like Figure 1 As shown, this embodiment of the invention provides a method for evaluating the scratch resistance of a material, the method comprising the following steps: S1, Scraping Step: Under preset friction conditions simulating daily use scenarios, an abrasive containing hard abrasive particles is used to apply friction to the surface of the substrate to be tested, so as to form a scratched area on the surface of the substrate to be tested; wherein, the friction conditions include the normal load applied to the abrasive, the friction stroke, the reciprocating frequency and the abrasive type, and the normal load and the abrasive type are determined according to the typical use environment of consumer electronics products.

[0025] S2, Imaging step: Use an optical imaging system to acquire a grayscale image of the surface of the substrate under test, including the scratched area.

[0026] S3, Analysis steps: Based on the grayscale image, calculate the quantitative characterization parameters of the scratch area according to a preset algorithm. The quantitative characterization parameters are used to characterize the scratch resistance of the substrate under test. The calculation of the quantitative characterization parameters is based on pixels with grayscale values ​​not lower than the background grayscale baseline value, and is obtained by normalizing the number of pixels at each grayscale level and their relative brightness weight.

[0027] The substrates to be tested in this invention include: glass, chemically strengthened glass, coated glass, aluminum alloy, stainless steel, or magnesium alloy structural components used in the casings or display covers of consumer electronics products. The method of this invention for evaluating the scratch resistance of materials can directly serve the selection of surface materials and process verification for products with high appearance requirements, such as smartphones, smartwatches, and automotive central control screens. It avoids the insufficient applicability caused by existing testing standards primarily targeting plastic materials, effectively supporting the appearance reliability design of high-end electronic products.

[0028] Scratches on glass and other substrates are caused by dust and other particles in the environment, with sizes on the μm level. In some embodiments of this invention, the abrasive is sandpaper with quartz particles attached to its surface, and the grit size of the sandpaper is 120 mesh. Using quartz sandpaper as the abrasive, because its main component is silicon dioxide and its Mohs hardness is approximately 7, highly simulates the scratching effect of common hard contaminants such as dust and grit in the daily environment. Compared to common friction materials such as diamond abrasives, it more realistically reproduces the random abrasive wear behavior encountered by users in pockets, backpacks, or vehicle environments, significantly improving the real-world representativeness of the test results. The selection of 120-mesh quartz sandpaper balances scratching efficiency with controllable damage morphology, effectively inducing visible scratches for quantitative analysis while avoiding atypical damage due to excessively large particles, ensuring good consistency of test conditions across different batches, and is suitable for industrial quality control scenarios. Besides quartz sandpaper, other abrasives with similar particle size and hardness can also be used.

[0029] In some embodiments of this invention, a testing device similar to that in Chinese invention patent CN110455654B is used to scratch the substrate under test. A friction rod with weights is mounted on a fixed guide rod, and abrasive materials such as sandpaper fixed to the other end of the friction rod are driven by a servo motor to reciprocate and rub the substrate under test. The preset friction conditions include: applying a constant normal load with weights, and performing multiple cycles of friction on the surface of the substrate under test at a set reciprocating speed and a fixed stroke. The constant normal load is 1.5 kgf. Through mechanical modeling and actual measurements, it was found that a constant normal load of 1.5 kgf can highly simulate the forces experienced by the substrate under test in everyday life scenarios. For example, a mobile phone falling freely, the maximum force of a hand pressing the screen during gameplay, and the frictional force experienced by a mobile phone placed in a pocket while walking. In some embodiments of this invention, the reciprocating frequency is set to 40 times / minute to simulate the speed at which the mobile phone screen and the substrate under test rub against a table during user use, and the speed at which a hand slides during gameplay. The friction stroke is set according to the abrasive and product size. In some embodiments of the present invention, the substrate to be tested is glass with a size of 154 mm. 70mm, abrasive 20mm 20mm sandpaper has a friction stroke of 40mm.

[0030] By applying a constant normal load with weights and incorporating preset reciprocating speeds and strokes, the friction process is standardized and reproducible. This eliminates load fluctuations and trajectory deviations caused by manual operation, making test data comparable across different laboratories or production lines, and providing a reliable basis for material development and incoming material inspection. Setting the constant normal load to 1.5 kgf, derived from mechanical modeling and statistical measurements based on everyday user carrying scenarios, effectively stimulates micro-scratch responses on material surfaces while avoiding atypical failures due to overload. This results in test results that are both sensitive and stable, making it particularly suitable for evaluating subtle differences in scratch resistance in substrates such as high-end glass covers.

[0031] In some embodiments of the present invention, the optical imaging system is an automatic optical inspection system, equipped with a high-resolution industrial camera and a controllable illumination source. The light source is monochromatic white light, and the pixel accuracy of the grayscale image is ≤0.015mm. The automatic optical inspection system uses monochromatic white light, eliminating color interference. This allows for significant optical contrast between the scratched area and the normal area of ​​the substrate under test, clearly presenting the scratch in the grayscale image, which improves the accuracy of subsequent image analysis. Traditional cameras, on the other hand, use color optical imaging, resulting in complex image coordinates. This is only suitable for subjective human judgment and cannot provide quantitative indicators to support performance evaluation for mass production.

[0032] In some embodiments of the present invention, the process of calculating quantitative characterization parameters in the analysis step includes the following sub-steps: (1) The grayscale image is preprocessed, that is, pixels with grayscale values ​​less than the background grayscale baseline value x0 are removed, and the effective pixel set is retained.

[0033] (2) In the set of effective pixels, for each gray level x, calculate the corresponding weighted pixel count. The calculation formula is: ,in, This represents the number of pixels with a gray level of x.

[0034] (3) Calculate the quantitative characterization parameter n, and the calculation formula is as follows: The quantitative characterization parameter n serves as the basis for evaluating the scratch resistance of the substrate under test. The larger the value, the larger the overall brightness and area of ​​the scratched area, and the weaker the scratch resistance.

[0035] In this embodiment of the invention, by introducing The weighted pixel count calculation model, which normalizes the effective grayscale range, yields a quantitative characterization parameter n that integrates both the scratch coverage area and visual brightness information. Compared to simple counting or average grayscale, this model better reflects the human eye's sensitivity to bright, large-area scratches, making the evaluation results highly correlated with the user's subjective experience and significantly improving the method's perceptual effectiveness.

[0036] It is understood that the above process of calculating the quantitative characterization parameters is only a specific embodiment of the present invention. The core idea of ​​the algorithm is: for each gray level x, the number of pixels According to its relative brightness Weighting is applied, and then a normalized average is performed on the effective grayscale range to construct a composite index that integrates scratch area and scratch brightness. The core lies in the weighting of area and brightness. Based on this, by adjusting the weighting function, normalization strategy, and introducing spatial or texture information, a series of variations with similar technical effects can be derived. For example, the method described above for calculating quantitative characterization parameters uses linear weighting. In addition to the above, the following weighting functions can also be used: (1) Power function weighting: .

[0037] If α>1, such as 1.2~2.0: it can enhance the contribution of highlighting scratches, and is more suitable for scenarios that emphasize glaring scratches; If α < 1, such as 0.5, the influence of extreme bright spots can be weakened, and noise immunity can be improved.

[0038] (2) Logarithmic weighting: Logarithmic weighting can simulate the visual response of the human eye.

[0039] In the above method for calculating quantitative characterization parameters, the denominator is the number of gray levels, 255- This belongs to grayscale interval length normalization. In addition, the total image area normalization method can also be used, with the calculation formula as follows: .in, This method, using the total number of pixels, is suitable for assessing the overall degree of appearance degradation. Alternatively, without normalization, the weighted total can be directly output as a quantitative characterization parameter. .

[0040] In this embodiment of the invention, the method for determining the background grayscale baseline value x0 includes: acquiring grayscale images of the same batch of substrates under the same imaging conditions in an unscratched state, statistically analyzing their grayscale value distribution, and using a preset upper limit threshold of the distribution as the background grayscale baseline value. The preset upper limit threshold is selected from one of the following: the maximum grayscale value of the grayscale distribution; the 95th percentile of the grayscale distribution; the 99th percentile of the grayscale distribution; or the maximum grayscale value minus a preset safety margin.

[0041] By determining the background grayscale baseline value based on the upper limit threshold of the grayscale distribution of the unscratched sample, the scratched area and the background are effectively separated. This effectively suppresses the interference of surface texture, imaging noise or minor defects, ensuring that only the bright signals truly generated by scratching are included in the analysis, thus improving the signal-to-noise ratio and accuracy of quantitative evaluation.

[0042] In this embodiment of the invention, the method for evaluating the scratch resistance of a material further includes: The scratching, imaging, and analysis steps are repeatedly performed at multiple different locations on the surface of the substrate to be tested to obtain multiple evaluation parameters. Based on the multiple evaluation parameters, the final scratch resistance evaluation result is determined by statistical aggregation. The statistical aggregation method includes at least one of arithmetic mean, median, maximum value, weighted average, or mean after removing outliers.

[0043] The scratching, imaging, and analysis steps are repeatedly performed at multiple different locations, and the final scratch resistance assessment result is determined by statistical aggregation based on the multiple evaluation parameters. This effectively reduces random errors caused by local material inhomogeneity, surface micro-defects, or minor operational deviations, significantly improves the statistical robustness and engineering applicability of the test results, and meets the stringent requirements of industrial-grade quality control for data reliability.

[0044] The method for evaluating the scratch resistance of materials provided by this invention allows for convenient comparative analysis of the scratch resistance of glass from different suppliers, glass with different chemical strengthening processes, or glass with different coating schemes. This provides reliable and quantifiable data support for product material selection and process improvement. It is understood that for glass of different materials, the same scratching procedure and friction conditions must be performed to ensure the comparability of the calculated quantitative characterization parameters.

[0045] In one specific embodiment of the present invention, the scratch resistance of glass cover plates made of ordinary double-strength glass, microcrystalline glass, and microcrystalline glass with a surface hardening film is evaluated. Samples of each type of glass, measuring 154mm in size, are cut from various glass samples. 70mm pieces were used to obtain glass cover plates of uniform size. The specific procedure for assessing scratch resistance is as follows: (1) Sample preparation and cleaning. Visually inspect the surface of the glass cover sample to be tested to ensure it is initially free of scratches, abrasions, bubbles, cracks, detachment, or other visible abnormalities. Then, use a lint-free cloth dampened with analytical grade alcohol to wipe the surface five times with uniform pressure to remove surface oil and contaminants. Subsequently, use an ionizer to blow on the surface for 2 minutes to remove static electricity and fine dust particles, ensuring the cleanliness of the initial test surface. This step is crucial for ensuring the accuracy of the test results; any initial surface contamination or defects may lead to deviations in the grayscale analysis results.

[0046] (2) Simulated scratching. A reciprocating friction testing machine was used to perform the scratching operation. The prepared glass cover sample was securely fixed on the working platform of the testing machine. A 20mm diameter... 20mm, 120-grit quartz sandpaper was used to scratch the glass cover plate. The weight of the weight was set to 1.5kg, the test stroke to be 40mm, and the reciprocating frequency to be 40 times / minute, with 10 back-and-forth strokes per stroke. The testing machine was started, and the friction rod drove the quartz sandpaper to reciprocate and rub the coated surface of the glass cover plate. During the friction process, the abrasive grains on the quartz sandpaper slid on the glass surface with controlled pressure and speed, simulating the abrasive wear caused by hard particles such as sand and dust on the product surface in daily use.

[0047] To ensure comprehensive testing and statistical reliability, four different areas were uniformly selected from the same glass cover sample, and the above scratching operation was repeated for each area, forming four independent scratch test areas. Each time a test area was changed, a completely new sheet of quartz sandpaper was used to avoid inconsistent testing conditions between different areas due to abrasive wear or clogging. Throughout the entire rubbing process, it was ensured that the rubbing rod remained still to guarantee the consistency of the scratch direction and the uniformity of the scratched area.

[0048] (3) Grayscale image acquisition The scratched glass cover sample was placed on the stage of an automated optical inspection device equipped with a monochromatic white light source. The selection and configuration of the light source should ensure uniform illumination of the test area and sufficient grayscale contrast from the scratches. The focal length and illumination parameters of the automated optical inspection device were adjusted to clearly capture the microscopic scratches on the glass surface. Subsequently, each scratched area, approximately 20 mm x 40 mm in size, was automatically scanned and photographed, generating high-fidelity 8-bit or higher grayscale digital images, which were then transmitted to a connected computer for further analysis.

[0049] (4) Image analysis and quantization calculation Run the accompanying image analysis software on the computer and process the acquired grayscale image according to the analysis steps in S3 above to calculate the quantitative characterization parameter n. The background baseline value x0 = 51. Calculate the quantitative characterization parameter n for each of the four test locations on the same glass cover sample. Finally, take the arithmetic mean of these four quantitative characterization parameters as the final scratch resistance evaluation result for the glass cover sample.

[0050] Figure 2 This is a grayscale image of ordinary double-strength glass after scratching, provided in an embodiment of the present invention. Figure 3 The image provided in this embodiment of the invention is a grayscale image of ordinary microcrystalline glass after scratching. Figure 4 This is a grayscale image of a microcrystalline glass with a hardened film coated on its surface after being scratched, as provided in an embodiment of the present invention. Figures 2-4 Each area includes four scratching zones. The typical number of pixels corresponding to each grayscale level in each area of ​​ordinary dual-strength glass. and weighted pixel count As shown in Table 1, the quantitative characterization parameter n for regions 1-4 are 6.65, 3.94, 7.95, and 5.66, respectively, with an arithmetic mean of 6.05. The table also shows the number of pixels corresponding to typical gray levels in each region of ordinary microcrystalline glass. and weighted pixel count As shown in Table 2, the quantitative characterization parameter n for regions 1-4 are 7.48, 5.86, 9.96, and 7.42, respectively, with an arithmetic mean of 7.68. The table also shows the number of pixels corresponding to typical gray levels in each region of the microcrystalline glass with a hardened film on its surface. and weighted pixel count As shown in Table 3, the quantitative characterization parameter n for regions 1-4 are 1.21, 0.27, 0.51, and 0.90, respectively, with an arithmetic mean of 0.72. It should be noted that, due to space limitations, only data for some typical gray levels are listed in Tables 1-3. The final quantitative characterization parameter n is calculated based on complete data for all effective gray levels (x≥x0). Scratches appear as brighter areas with larger gray values ​​in the grayscale image. From... Figures 2-4 look, Figure 4 The fewest scratches, Figure 2 Secondly, Figure 3 The highest. Therefore, from a visual perspective, the order of scratch resistance from strongest to weakest is: microcrystalline glass with a hardened coating on the surface > ordinary double-strength glass > ordinary microcrystalline glass. The results obtained from the final quantitative characterization parameter n are also consistent.

[0051] Table 1. Number of pixels corresponding to typical gray levels of ordinary dual-strength glass and weighted pixel count

[0052]

[0053] Table 2. Number of pixels corresponding to typical gray levels of microcrystalline glass and weighted pixel count

[0054]

[0055] Table 3. Number of pixels corresponding to typical gray levels of microcrystalline glass with a hardened coating on the surface. and weighted pixel count

[0056]

[0057] This invention presents a method for assessing scratch resistance that closely resembles real-world user experience. This method involves scratching the substrate under simulated everyday usage conditions and combining optical imaging with a grayscale-weighted quantitative analysis algorithm. The method considers not only the area of ​​the scratch but also introduces grayscale values ​​as a brightness weight, enabling the assessment results to more accurately reflect the visual salience of the scratch. This overcomes the problem of traditional indirect parameters such as hardness indicators or haze changes being disconnected from user perception, significantly improving the objectivity, repeatability, and practical guidance value of material scratch resistance evaluation.

[0058] The above are merely specific embodiments of the present invention and should not be construed as limiting the scope of the present invention. Equivalent variations made by those skilled in the art based on this invention, as well as changes well-known to those skilled in the art, should still fall within the scope of the present invention.

Claims

1. A method for evaluating the scratch resistance of a material, characterized in that, Includes the following steps: Scraping step: Under preset friction conditions simulating daily use scenarios, an abrasive containing hard abrasive grains is used to apply friction to the surface of the substrate to be tested, so as to form a scratched area on the surface of the substrate to be tested; wherein, the friction conditions include the normal load applied to the abrasive, the friction stroke, the reciprocating frequency, and the type of abrasive; Imaging steps: Use an optical imaging system to acquire a grayscale image of the surface of the substrate under test, including the scratched area; Analysis steps: Based on the grayscale image, calculate the quantitative characterization parameters of the scratched area according to a preset algorithm. The quantitative characterization parameters are used to characterize the scratch resistance of the substrate under test. The calculation of the quantitative characterization parameters is based on pixels with grayscale values ​​not lower than the background grayscale baseline value, and is obtained by normalizing the number of pixels at each grayscale level and their relative brightness weight.

2. The method for evaluating the scratch resistance of a material according to claim 1, characterized in that, The substrate to be tested includes: glass, chemically strengthened glass, coated glass, aluminum alloy, stainless steel or magnesium alloy structural components used for the casing or display cover of consumer electronics products.

3. The method for evaluating the scratch resistance of a material according to claim 1, characterized in that, The abrasive is sandpaper with quartz particles attached to its surface.

4. The method for evaluating the scratch resistance of a material according to claim 3, characterized in that, The sandpaper has a grit size of 120 mesh.

5. The method for evaluating the scratch resistance of a material according to claim 1, characterized in that, The preset friction conditions include: applying a constant normal load by weights and performing multiple cycles of friction on the surface of the substrate to be tested at a set reciprocating speed and a fixed stroke.

6. The method for evaluating the scratch resistance of a material according to claim 5, characterized in that, The constant normal load is 1.5 kgf.

7. The method for evaluating the scratch resistance of a material according to claim 1, characterized in that, The optical imaging system is an automatic optical inspection system, equipped with a high-resolution industrial camera and a controllable illumination source.

8. The method for evaluating the scratch resistance of a material according to claim 1, characterized in that, The process of calculating the quantitative characterization parameters in the analytical steps includes the following sub-steps: (1) The grayscale image is preprocessed, that is, pixels with grayscale values ​​less than the background grayscale baseline value x0 are removed, and the effective pixel set is retained; (2) In the set of effective pixels, for each gray level x, calculate the corresponding weighted pixel count. The calculation formula is: ,in, This represents the number of pixels with gray level x. (3) Calculate the quantitative characterization parameter n, and the calculation formula is as follows: The quantitative characterization parameter n serves as the basis for evaluating the scratch resistance of the substrate under test. The larger the value, the larger the overall brightness and area of ​​the scratched area, and the weaker the scratch resistance.

9. The method for evaluating the scratch resistance of a material according to claim 8, characterized in that, The method for determining the background grayscale baseline value x0 includes: acquiring grayscale images of the same batch of substrates under the same imaging conditions in an unscratched state, statistically analyzing their grayscale value distribution, and using a preset upper limit threshold of the distribution as the background grayscale baseline value.

10. The method for evaluating the scratch resistance of a material according to claim 1, characterized in that, Also includes: The scratching, imaging, and analysis steps are repeatedly performed at multiple different locations on the surface of the substrate to be tested to obtain multiple evaluation parameters. Based on the multiple evaluation parameters, the final scratch resistance evaluation result is determined by statistical aggregation. The statistical aggregation method includes at least one of arithmetic mean, median, maximum value, weighted average, or mean after removing outliers.

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