White corundum anti-pulverization performance detection method

Abstract

The application provides a detection method for the anti-pulverization performance of white corundum, and belongs to the technical field of material performance detection. The method comprises seven logically progressive steps, i.e. sample standardization preparation, multi-mode working condition simulation loading, multi-layer screening detection after pulverization, multi-dimensional pulverization characteristic extraction, stress pulverization mathematical model construction based on an exponential-power law mixed model, and comprehensive evaluation based on a sensitivity weighting method. The exponential-power law mixed model accurately depicts the exponential growth characteristics of the pulverization rate near the critical stress, and the sensitivity weighting method makes the determination of the weight coefficient have a clear physical meaning and objectivity, thereby avoiding subjective deviation caused by artificial weighting. Compared with the prior art, the application can more scientifically, comprehensively and objectively evaluate the anti-pulverization performance of white corundum, and provides a reliable basis for quality control and application selection of the white corundum.

Description

Technical Field

[0001] This invention belongs to the field of material performance testing and comprehensive evaluation technology, and in particular relates to a method for testing the anti-powdering performance of white fused alumina. Background Technology

[0002] White fused alumina is a corundum material produced by high-temperature smelting of industrial alumina powder. Due to its excellent properties such as high hardness, high wear resistance, and good chemical stability, it is widely used in abrasives, refractory materials, and precision casting. In practical applications, white fused alumina particles need to withstand the combined effects of various complex stresses, including mechanical extrusion, thermal shock, and vibration friction. If its anti-pulverization properties are insufficient, the particles are prone to breakage and the generation of fine dust, which not only reduces the material's efficiency and service life but also causes environmental pollution and production safety hazards.

[0003] Existing methods only simulate single stresses, failing to reflect the complex working conditions of white fused alumina under multiple stresses simultaneously in practical applications, leading to discrepancies between test results and actual application performance. The test results are discretized; existing methods treat each test as an isolated event, failing to perform correlation analysis and mathematical modeling for test results under different stress conditions. This makes it impossible to establish a quantitative relationship between stress conditions and the degree of pulverization, hindering its use for performance prediction and quality control. Furthermore, the evaluation indicators are coarse; existing methods typically only compare the mass change of a fixed particle size before and after treatment, or simply calculate the percentage of powder generated, failing to finely characterize the changes in the entire particle size distribution and losing a significant amount of valuable information. Summary of the Invention

[0004] This invention addresses the technical problems existing in the testing of white fused alumina's anti-chalking properties by proposing a reasonable, simple, and theoretically sound method for testing white fused alumina's anti-chalking properties.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: a method for testing the anti-chalking performance of white fused alumina, comprising the following steps:

[0006] S1. The white fused alumina sample to be tested is sieved, and the main particle size range corresponding to the target application scenario is selected as the test particle size. The particles in this range are used as test samples. The samples are placed in a drying oven and dried to constant weight. After cooling, several samples of equal mass are weighed.

[0007] S2. The samples prepared in S1 were divided into three experimental groups and one blank control group, and different types of stress loading treatments were applied to each group.

[0008] S3. After processing in S2, each group of samples is placed on a multi-layer sieve device for sieving to form the original particle size distribution data of each group of samples.

[0009] S4. Based on the raw particle size distribution data obtained in S3, extract the median particle size offset. Particle size distribution span ratio Weighted micron powder index Particle size distribution similarity Four dimensions were used to extract feature parameters that characterize the degree of powdering;

[0010] S5. Collect data from at least K (K≥10) repeated experiments to construct a dataset. Each data sample contains stress input variables and four pulverization characteristic output variables. Based on this dataset, establish a mathematical model between stress input variables and pulverization characteristic outputs.

[0011] S6. Set the preset standard test conditions , Substituting into the mathematical model of S5, calculate the predicted values ​​of each pulverization characteristic of the sample under the standard conditions. ;

[0012] S7. Perform range normalization on the predicted values ​​of each pulverization characteristic;

[0013] S8. Weighted fusion of the four normalized features yields the comprehensive evaluation index. The comprehensive evaluation index The calculation formula is:

[0014] ,

[0015] in, , , , Here are the weighting coefficients, and the formula for calculating these weighting coefficients is:

[0016] ;

[0017] ;

[0018] ;

[0019] ;

[0020] in, For pressure sensitivity, For temperature sensitivity, To assess overall sensitivity, For standard test temperature, For standard test pressure, These are the weighting coefficients. The intensity coefficient of the exponential term. For logarithmic strength coefficients, The power law exponent of pressure. The temperature power law exponent. This is the critical pressure value. This is the critical temperature value. This is a temperature reference value. , , , The solution is obtained through nonlinear regression fitting. The feature parameter number includes , , , ;

[0021] S9, Comprehensive Evaluation Index The anti-chalking performance level of the white fused alumina under test is determined by comparing it with a preset grade threshold.

[0022] Preferably, the pressure range of the single mechanical stress group is 5MPa-50MPa, and the holding time is 10s-300s; the heating temperature range of the single thermal stress group is 600℃-1200℃, the holding time is 10min-120min, and a rapid cooling treatment is performed after holding.

[0023] Preferably, the S2 multi-layer screening device is equipped with m layers of screens with different mesh sizes (m≥5) installed from top to bottom, with the mesh size increasing sequentially. The bottom layer is the base plate, and the screening time is set. After screening, weigh and record the mass of the remaining particles on each screen and the mass of the powder in the chassis.

[0024] Preferably, the median particle size offset of S4 The calculation formula is

[0025] ,

[0026] in, This represents the median particle size offset. This represents the median grain size of the original white corundum. The median particle size of the pulverized white fused alumina is given by the particle size distribution span ratio. The calculation formula is:

[0027] ;

[0028] ;

[0029] in, This represents the ratio of particle size distribution span. This refers to the particle size corresponding to a cumulative mass percentage of 10%. This refers to the particle size corresponding to a cumulative mass percentage of 90%. The particle size corresponding to a cumulative mass percentage of 50%. The span of the processed particle size distribution. The weighted micron index represents the particle size distribution range before processing. The calculation formula is:

[0030] ;

[0031] ;

[0032] in, For the weighted micron powder index, The number of screen layers. For the first The quality of the micro powder collected on the sieve mesh For the first Weighting coefficients for micronized powders This represents the initial total mass of the sample. For the first The similarity of the particle size distribution to the mesh size of the sieve. The calculation formula is:

[0033] ;

[0034] ;

[0035] in, For particle size distribution similarity, The number of screen layers. The first in the distribution after pulverization The mass percentage of particle size, This represents the original grain size distribution of white fused alumina when it is not subjected to stress. For the control group sample at the 1st Particle size quality, The total mass of the control group sample, the S4 weighted micron index In this process, smaller particle sizes are assigned a larger weighting coefficient, which is inversely proportional to the sieve aperture.

[0036] Preferably, the mathematical model for calculating the relationship between the stress input variable and the pulverization characteristic output in step S5 is as follows:

[0037] ;

[0038] in, Characterized by powdering. The eigenvalues ​​represent the pulverization characteristics of white fused alumina under stress-free conditions. The intensity coefficient of the exponential term. For logarithmic strength coefficients, For mechanical pressure, This is the critical pressure value. The temperature power law exponent. This is the critical temperature value. This is a temperature reference value. This is the thermal stress temperature. Number the feature parameters. For mechanical pressure Thermal stress temperature is Powdering characteristics at that time.

[0039] Preferably, the feature parameter number includes , , , The , The experiment was pre-calibrated by conducting pulverization experiments under different stress levels, plotting the pulverization rate as a function of stress, and taking the stress value corresponding to the point where the slope of the curve changed abruptly as the critical stress.

[0040] The preferred calculation formula for the range normalization process in step S7 is as follows:

[0041] ;

[0042] in, Normalized eigenvalues For the first The predicted value of each feature, For the first The minimum value of each feature. For the first The maximum value of each feature, The feature parameter number includes , , , .

[0043] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0044] 1. By setting up single stress groups and coupled stress groups, the influence of different stress types and their interactions on pulverization was systematically studied, which is closer to the actual working conditions.

[0045] 2. By extracting multidimensional features, the pulverization behavior is characterized from four mathematical dimensions, preserving fine information about the granularity distribution and improving the accuracy of the evaluation.

[0046] 3. By constructing a hybrid mathematical model of exponential-power law, a quantitative relationship between stress conditions and pulverization degree was established, realizing the improvement from discrete experimental points to continuous function law, and the model has clear physical meaning and extrapolation ability.

[0047] 4. By using the sensitivity weighting method, the determination of the weight coefficients is entirely based on the mathematical characteristics of the model, avoiding the subjective bias of human weighting and making the evaluation results more objective and reliable.

[0048] 5. By using weighted fusion, the four feature parameters are combined into one evaluation index, which simplifies the evaluation results and facilitates practical application. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a schematic diagram of a method for testing the anti-powdering properties of white fused alumina. Detailed Implementation

[0051] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described below in conjunction with the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0052] Numerous specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways than those described herein, and therefore the invention is not limited to the specific embodiments disclosed in the following specification.

[0053] Examples, such as Figure 1 A method for testing the anti-pulverization performance of white fused alumina is proposed. In practical applications, white fused alumina particles need to withstand the combined effects of various complex stresses such as mechanical extrusion, thermal shock, and vibration friction. If its anti-pulverization performance is insufficient, the particles are prone to breakage and the generation of fine dust, which will not only reduce the material's efficiency and service life, but also cause environmental pollution and production safety hazards.

[0054] Existing methods only simulate single stresses, failing to reflect the complex working conditions of white fused alumina under multiple stresses simultaneously in practical applications, leading to discrepancies between test results and actual application performance. Existing methods also exhibit discretized test results, treating each test as an isolated event without correlation analysis or mathematical modeling under different stress conditions. This makes it impossible to establish a quantitative relationship between stress conditions and the degree of pulverization, hindering its use for performance prediction and quality control. Furthermore, existing methods use coarse evaluation metrics, typically comparing only the mass change of a fixed particle size before and after treatment or simply calculating the percentage of powder generated, failing to finely characterize the changes in the entire particle size distribution and losing a significant amount of valuable information.

[0055] To eliminate uneven particle size distribution and moisture content differences in the original samples and ensure that subsequent tests are conducted under the same standard, this method involves sieving the white fused alumina samples to be tested. The main particle size range corresponding to the target application scenario is selected as the test particle size, and particles within this range are used as test samples. The samples are dried in a drying oven to constant weight, cooled, and then several equal-mass samples are weighed. This standardized preparation eliminates the influence of batch-to-batch differences on the test results, ensures the comparability of subsequent stress loading and feature extraction, and improves the repeatability of the test results.

[0056] To address the limitations of existing methods that rely on a single stress condition for simulation and to accurately reflect the pulverization behavior of white fused alumina under complex stress environments, the samples prepared in S1 were divided into three experimental groups and a blank control group. Different types of stress loading were applied to each group. The pressure range for the single mechanical stress group was 5 MPa–50 MPa, with a holding time of 10–300 s. The heating temperature range for the single thermal stress group was 600℃–1200℃, with a holding time of 10–120 min, followed by rapid cooling. By using a single stress group as a benchmark and coupled stress groups to simulate complex conditions, the effects of different stress types and their interactions on pulverization behavior were systematically studied.

[0057] To achieve precise characterization of the particle size distribution after pulverization and provide a data foundation for multidimensional feature extraction, the samples after S2 processing were placed on a multi-layer sieving device for sieving. This device consisted of m layers of sieves with varying mesh sizes (m≥5) arranged sequentially from top to bottom, with the mesh size increasing progressively, and a bottom plate as the base. A sieving time t was set. After sieving, the mass of the remaining particles on each sieve layer and the mass of the powder in the bottom plate were weighed and recorded, forming the raw particle size distribution data for each sample group. Through fine sieving, the complete particle size distribution, from coarse particles to extremely fine dust, was captured, achieving a particle size resolution more than twice that of the industry standard, providing a high-quality data foundation for subsequent multidimensional feature extraction.

[0058] To overcome the problem of coarsening of evaluation indicators in existing methods, this scheme comprehensively characterizes pulverization behavior from multiple dimensions. It employs the following four feature extraction methods, based on the raw particle size distribution data obtained from S3, to extract features from the median particle size offset. Particle size distribution span ratio Weighted Micron Index Particle size distribution similarity Four dimensions are used to extract feature parameters that characterize the degree of pulverization; the median particle size offset in S4 The calculation formula is:

[0059] ,

[0060] in, This represents the median particle size offset. This represents the median grain size of the original white corundum. The median particle size of the pulverized white fused alumina is given by the particle size distribution span ratio. The calculation formula is:

[0061] ;

[0062] ;

[0063] in, This represents the ratio of particle size distribution span. This refers to the particle size corresponding to a cumulative mass percentage of 10%. This refers to the particle size corresponding to a cumulative mass percentage of 90%. The particle size corresponding to a cumulative mass percentage of 50%. The span of the processed particle size distribution. The weighted micron index represents the particle size distribution range before processing. The calculation formula is:

[0064] ;

[0065] ;

[0066] in, For the weighted micron powder index, The number of screen layers. For the first The quality of the micro powder collected on the sieve mesh For the first Weighting coefficients for micronized powders This represents the initial total mass of the sample. For the first The similarity of the particle size distribution to the mesh size of the sieve. The calculation formula is:

[0067] ;

[0068] ;

[0069] in, For particle size distribution similarity, The number of screen layers. The first in the distribution after pulverization The mass percentage of particle size, This represents the original grain size distribution of white fused alumina when it is not subjected to stress. For the control group sample at the 1st Particle size quality, The total mass of the control group sample, the S4 weighted micron index In this system, smaller particle sizes are assigned a larger weighting coefficient, which is inversely proportional to the sieve aperture. Median particle size It is the center point on the cumulative particle size distribution curve, representing the upper limit of the size of 50% of the particles, compared to the average particle size or the mode particle size. It exhibits better stability, repeatability, and anti-interference capabilities, and can most robustly reflect the overall translational trend of particle size distribution. In white fused alumina applications, the median particle size directly affects key performance indicators such as grinding efficiency, bulk density, and flowability. Changes in particle size distribution span can be directly correlated with the degree of degradation in material performance, thus possessing clear engineering value. The aim is to quantify the uniformity of the pulverization process by comparing the relative width of the particle size distribution before and after pulverization, distinguishing different pulverization mechanisms, and compensating for the shortcomings of relying solely on median particle size shift. The inability to determine changes in distribution morphology is a drawback. Weighted micron powder index. It specifically captures the most harmful fine dust during the pulverization process and uses a weighted mechanism to achieve precise and differentiated evaluation of the hazard level of microparticles with different particle sizes. The weighting coefficients are set based on clear physical principles: the smaller the dust particle size, the larger its specific surface area, the higher its chemical activity, and the stronger its airborne properties. Its harm to grinding efficiency, environmental pollution, and operator health risks increases exponentially, thus it is assigned a higher weight. Particle size distribution similarity. Introducing global morphological similarity as a fourth dimension compensates for the inability of local indicators to identify complex morphological distortions. Based on cosine similarity calculation, it comprehensively considers the mass proportion of all particle levels, focusing on the distribution shape rather than absolute position or scale, making it an indicator that can judge the degree of pulverization from the overall contour level. Together with the first three, it constitutes a comprehensive three-dimensional characterization system, ensuring that pulverization can be accurately identified and quantified regardless of whether it manifests as uniform grinding, surface peeling, overall fragmentation, or complex distortion, thereby achieving a complete mathematical description and scientific evaluation of pulverization behavior.

[0070] To transform discrete experimental data into continuous mathematical relationships and establish a quantitative relationship between stress conditions and pulverization degree, data from at least K (K≥10) repeated experiments were collected to construct a dataset. Each data sample contains a stress input variable and four pulverization characteristic output variables. Based on this dataset, a mathematical model between stress input and pulverization characteristic outputs was established. The calculation formula for the mathematical model between the stress input variable and the pulverization characteristic outputs is as follows:

[0071] ,

[0072] in, Characterized by powdering. The eigenvalues ​​represent the pulverization characteristics of white fused alumina under stress-free conditions. The intensity coefficient of the exponential term. For logarithmic strength coefficients, For mechanical pressure, This is the critical pressure value. The temperature power law exponent. This is the critical temperature value. This is a temperature reference value. This is the thermal stress temperature. Number the feature parameters. For mechanical pressure Thermal stress temperature is Powdering characteristics at that time. The eigenvalues ​​are used to represent the pulverization characteristics of white fused alumina under stress-free conditions, ensuring that the model has a clear physical starting point. By utilizing the characteristics of the logarithmic function, which grows rapidly in the early stage and slows down in the later stage, the elastic deformation and damage accumulation process in the low-stress zone are accurately characterized. The reference value ensures dimensionlessness and numerical stability, while the power-law exponent reflects the linear, superlinear, or sublinear amplification effect of temperature on pulverization. The exponential term, through the combination of power law and exponential function, captures the power-law exponent of crack instability propagation and thermal shock damage when the material is close to the critical pressure or temperature.

[0073] To avoid subjective weighting bias and achieve objectivity and physical interpretability of the evaluation results, the four normalized features are weighted and fused to obtain a comprehensive evaluation index. The comprehensive evaluation index The calculation formula is:

[0074] ,

[0075] in, , , , Here are the weighting coefficients, and the formula for calculating these weighting coefficients is:

[0076] ;

[0077] ;

[0078] ;

[0079] ;

[0080] in, For pressure sensitivity, For temperature sensitivity, To assess overall sensitivity, For standard test temperature, For standard test pressure, These are the weighting coefficients. The intensity coefficient of the exponential term. For logarithmic strength coefficients, The power law exponent of pressure. The temperature power law exponent. This is the critical pressure value. This is the critical temperature value. This is a temperature reference value. , , , The solution is obtained through nonlinear regression fitting. The feature parameter number includes , , , This invention employs a sensitivity-based weighted fusion method to enhance the objectivity and physical interpretability of the evaluation results. (Pressure sensitivity) With temperature sensitivity Derived from the partial derivatives of the exponential-power-law hybrid model with respect to pressure and temperature, it quantifies the first... The intensity of the response of each pulverization characteristic to stress changes under standard test conditions. Overall sensitivity. The sensitivity of pressure and temperature is Euclideanically synthesized to reflect the overall response intensity of the feature to stress changes. A higher sensitivity means it is more acutely aware of material performance changes under stress and should be given higher weight in the comprehensive evaluation. Weighting coefficients are normalized to convert the comprehensive sensitivity into weights, ensuring the sum of the weights of the four features is 1, and the weight allocation is entirely determined by the material's physical response characteristics. Standard testing conditions provide a unified benchmark for sensitivity calculation, ensuring the comparability of test results from different batches and times. Each weighting coefficient of the comprehensive evaluation index carries a clear physical meaning, representing the sensitivity of the feature to stress changes. Higher sensitivity indicates that the feature is better at capturing material performance degradation and is therefore more important in the evaluation. The comprehensive evaluation index Q is compared with a preset grade threshold to determine the anti-powdering performance grade of the tested white fused alumina, avoiding subjective bias from manual weighting.

[0081] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments for application in other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for testing the anti-chalking performance of white fused alumina, characterized in that, Includes the following steps: S1. The white fused alumina sample to be tested is sieved, and the main particle size range corresponding to the target application scenario is selected as the test particle size. The particles in this range are used as test samples. The samples are placed in a drying oven and dried to constant weight. After cooling, several samples of equal mass are weighed. S2. The samples prepared in S1 were divided into three experimental groups and one blank control group, and different types of stress loading treatments were applied to each group. S3. After processing in S2, each group of samples is placed on a multi-layer sieve device for sieving to form the original particle size distribution data of each group of samples. S4. Based on the raw particle size distribution data obtained in S3, extract the median particle size offset. Particle size distribution span ratio Weighted micron powder index Particle size distribution similarity Four dimensions were used to extract feature parameters that characterize the degree of powdering; S5. Collect data from at least K (K≥10) repeated experiments to construct a dataset. Each data sample contains stress input variables and four pulverization characteristic output variables. Based on this dataset, establish a mathematical model between stress input variables and pulverization characteristic outputs. S6. Set the preset standard test conditions. , Substituting into the mathematical model of S5, calculate the predicted values ​​of each pulverization characteristic of the sample under the standard conditions. ; S7. Perform range normalization on the predicted values ​​of each pulverization characteristic; S8. Weighted fusion of the four normalized features yields the comprehensive evaluation index. The comprehensive evaluation index The calculation formula is: ， in, , , , Here are the weighting coefficients, and the formula for calculating these weighting coefficients is: ； ； ； ； in, For pressure sensitivity, For temperature sensitivity, To assess overall sensitivity, For standard test temperature, For standard test pressure, These are the weighting coefficients. The intensity coefficient of the exponential term. For logarithmic strength coefficients, The power law exponent of pressure. The temperature power law exponent. This is the critical pressure value. This is the critical temperature value. This is a temperature reference value. , , , The solution is obtained through nonlinear regression fitting. The feature parameter number includes , , , ; S9, Comprehensive Evaluation Index The anti-chalking performance level of the white fused alumina under test is determined by comparing it with a preset grade threshold.

2. The method for testing the anti-chalking performance of white fused alumina according to claim 1, characterized in that, The three experimental groups in S2 are: a single mechanical stress group, in which the sample is placed in a pressure testing device and a preset mechanical pressure is applied. Holding time Simulates the extrusion condition of white fused alumina in a cold state; a single thermal stress group places the sample in a high-temperature device and heats it to a predetermined temperature at a preset heating rate. Insulation time Then, rapid cooling is performed to simulate the working conditions of white fused alumina under thermal shock conditions. The coupled stress group first applies a single thermal stress to the sample in the manner of the second group, and then applies a single mechanical stress in the manner of the first group after cooling, to simulate the actual working conditions of white corundum under thermo-mechanical coupling.

3. The method for testing the anti-chalking performance of white fused alumina according to claim 2, characterized in that, The pressure range of the single mechanical stress group is 5MPa-50MPa, and the holding time is 10s-300s; the heating temperature range of the single thermal stress group is 600℃-1200℃, the holding time is 10min-120min, and a rapid cooling treatment is performed after holding.

4. The method for testing the anti-chalking performance of white fused alumina according to claim 1, characterized in that, The S2 multi-layer screening equipment consists of m layers of screens with different mesh sizes (m≥5) installed from top to bottom, with the mesh size increasing sequentially. The bottom layer is the base plate, and the screening time is set. After screening, weigh and record the mass of the remaining particles on each screen and the mass of the powder in the chassis.

5. The method for testing the anti-chalking performance of white fused alumina according to claim 1, characterized in that, The median particle size offset of S4 The calculation formula is: ， in, This represents the median particle size offset. This represents the median grain size of the original white corundum. The median particle size of the pulverized white fused alumina is given by the particle size distribution span ratio. The calculation formula is: ； ； in, This represents the ratio of particle size distribution span. This refers to the particle size corresponding to a cumulative mass percentage of 10%. This refers to the particle size corresponding to a cumulative mass percentage of 90%. The particle size corresponding to a cumulative mass percentage of 50%. The span of the processed particle size distribution. The weighted micron index represents the particle size distribution range before processing. The calculation formula is: ； ； in, For the weighted micron powder index, The number of screen layers. For the first The quality of the micro powder collected on the sieve mesh For the first Weighting coefficients for micronized powders This represents the initial total mass of the sample. For the first The similarity of the particle size distribution to the mesh size of the sieve. The calculation formula is: ； ； in, For particle size distribution similarity, The number of screen layers. The first in the distribution after pulverization The mass percentage of particle size, This represents the original grain size distribution of white fused alumina when it is not subjected to stress. For the control group sample at the 1st Particle size quality, The total mass of the control group sample, the S4 weighted micron index In this process, smaller particle sizes are assigned a larger weighting coefficient, which is inversely proportional to the sieve aperture.

6. The method for testing the anti-chalking performance of white fused alumina according to claim 1, characterized in that, The mathematical model for calculating the relationship between the stress input variable and the pulverization characteristic output in S5 is as follows: ， in, Characterized by powdering. The eigenvalues ​​represent the pulverization characteristics of white fused alumina under stress-free conditions. The intensity coefficient of the exponential term. For logarithmic strength coefficients, For mechanical pressure, This is the critical pressure value. The temperature power law exponent. This is the critical temperature value. This is a temperature reference value. This is the thermal stress temperature. Number the feature parameters. For mechanical pressure Thermal stress temperature is The powdering characteristics at that time.

7. The method for testing the anti-chalking performance of white fused alumina according to claim 1, characterized in that, The feature parameter number includes , , , The , The experiment was pre-calibrated by conducting pulverization experiments under different stress levels, plotting the pulverization rate as a function of stress, and taking the stress value corresponding to the point where the slope of the curve changed abruptly as the critical stress.

8. The method for testing the anti-chalking performance of white fused alumina according to claim 1, characterized in that, The calculation formula for the range normalization process in step S7 is as follows: ， in, Normalized eigenvalues For the first The predicted value of each feature, For the first The minimum value of each feature. For the first The maximum value of each feature, The feature parameter number includes , , , .

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