A method for evaluating and regulating the wear resistance of ductile iron based on the electron work function

Abstract

The present invention discloses a method for evaluating and regulating the wear resistance of ductile iron based on the electronic work function, which relates to the technical fields of surface engineering and performance evaluation. The method includes collecting the electronic work function data and material characteristic data on the surface of ductile iron; extracting the characteristics of the electronic work function data to obtain the electronic work function characteristics; establishing a mathematical model based on the electronic work function characteristics to analyze the correlation between the electronic work function data and the material characteristic data; analyzing the relationship between the electronic work function characteristics and the wear resistance, and establishing a comprehensive evaluation model to evaluate the wear resistance of ductile iron. The present invention combines characterization techniques, complex data analysis methods and multivariate statistical models at the same time. It can not only deeply understand the connection between the electronic structure and macroscopic properties of materials, but also establish a comprehensive evaluation model to predict the wear resistance of ductile iron; this method guides the design of materials and the optimization of the production process.

Description

Technical Field

[0001] The present invention relates to the technical fields of surface engineering and performance evaluation, and in particular, to a method for evaluating and regulating the wear resistance of ductile iron based on the electron work function. Background Art

[0002] As an important engineering material, ductile iron has excellent mechanical properties and good casting properties, which makes it widely used in the fields of machinery, automobiles, pipelines, etc. In recent years, with the continuous progress of industrial technology, the requirements for the wear resistance of ductile iron have been increasing. Traditional wear resistance evaluation methods mainly rely on macroscopic mechanical tests and microstructural analysis. Although these methods are intuitive, they are often time-consuming and laborious, and it is difficult to reveal the internal relationship between the microscopic structure and macroscopic properties of materials. With the development of materials science, researchers have gradually realized that the electronic structure of the material surface has an important impact on its properties. As an important parameter characterizing the electronic structure of the material surface, the electron work function is closely related to the physical and chemical properties of the material. However, the research directly relating the electron work function to the wear resistance of ductile iron is still rare, and there is still a huge exploration space in this field.

[0003] The existing methods for evaluating the wear resistance of ductile iron have the following deficiencies: First, although the traditional wear test methods can directly reflect the wear resistance of materials, it is difficult to provide an explanation of the microscopic mechanism, and the test process is time-consuming and costly; Second, the methods based on microstructural analysis can reveal the microscopic structural characteristics of materials, but it is difficult to accurately predict their macroscopic wear resistance; Third, the existing evaluation methods often ignore the influence of the electronic structure of the material surface on the wear resistance, which limits the comprehensive understanding and precise regulation of material properties. In addition, the lack of a systematic evaluation method that can comprehensively consider material composition, microstructure, and surface electronic characteristics makes it challenging to optimize the wear resistance of ductile iron. Therefore, there is an urgent need to develop a new evaluation method that can reveal the essence of the wear resistance of ductile iron from the microscopic electronic structure level. Summary of the Invention

[0004] In view of the problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is the problem of being difficult to accurately predict and effectively regulate the wear resistance of ductile iron from the microscopic level.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides a method for evaluating and regulating the wear resistance of ductile iron based on the electron work function, which includes,

[0008] Collecting the electron work function data and material characteristic data on the surface of ductile iron;

[0009] Extract the features of the electron work function data to obtain electron work function features;

[0010] Based on the electron work function features, establish a mathematical model to analyze the correlation between the electron work function data and the material property data;

[0011] Analyze the relationship between the electron work function features and the wear resistance performance, and establish a comprehensive evaluation model to evaluate the wear resistance performance of ductile iron.

[0012] As a preferred solution of the method for evaluating and regulating the wear resistance performance of ductile iron based on the electron work function according to the present invention, wherein: the material property data includes chemical composition data and microstructure characterization data;

[0013] The electron work function features include the average electron work function value, the standard deviation of the electron work function, the local distribution, and the spatial distribution features.

[0014] As a preferred solution of the method for evaluating and regulating the wear resistance performance of ductile iron based on the electron work function according to the present invention, wherein: the spatial distribution features use two basic mathematical tools, gradient and spatial integration, to capture the spatial variation features of the electron work function data, and are represented by the following formula:

[0015] ;

[0016] wherein, is the spatial weight function, is the two-dimensional electron work function distribution, is the gradient operator, is the spatial variation feature quantity;

[0017] The spatial weight function is specifically shown in the following formula:

[0018] ;

[0019] wherein, A is the amplitude, controlling the maximum value of the weight, ( , ) is the center point coordinate, usually selected as the center of the region of interest, is the standard deviation, controlling the width of the Gaussian distribution;

[0020] The two-dimensional electron work function distribution is represented by the following formula:

[0021] ;

[0022] wherein, is the partial derivative of with respect to The rate of change in the is the partial derivative with respect to which represents the rate of change in the y direction, and are the unit vectors in the x and y directions.

[0023] As a preferred embodiment of the method for evaluating and regulating the wear resistance of ductile iron based on the electronic work function according to the present invention, wherein: a mathematical model is established to analyze the correlation between the electronic work function data and the material property data:

[0024] Determine the correlation coefficient between the electronic work function characteristics and the material property data to generate a correlation coefficient matrix;

[0025] Perform multiple regression analysis based on the correlation coefficient matrix;

[0026] Perform residual analysis to test whether the correlation hypothesis holds;

[0027] Judge whether the correlation analysis of the electronic work function data and the material property data is completed according to the goodness of fit of the mathematical model.

[0028] As a preferred embodiment of the method for evaluating and regulating the wear resistance of ductile iron based on the electronic work function according to the present invention, wherein: the goodness of fit is judged according to the coefficient of determination;

[0029] The calculation of the coefficient of determination is shown in the following formula:

[0030] ;

[0031] Wherein, is the observed value, is the model predicted value, is the average value of the observed values, m is the number of samples, and R² is the coefficient of determination.

[0032] As a preferred embodiment of the method for evaluating and regulating the wear resistance of ductile iron based on the electronic work function according to the present invention, wherein: establishing a comprehensive evaluation model to evaluate the wear resistance of ductile iron includes the following steps:

[0033] Preliminarily capture the linear relationship between the material properties and the wear resistance to obtain the first correlation;

[0034] Perform a non-linear transformation on the first correlation to obtain the second correlation;

[0035] Perform an interaction effect analysis on the second correlation to obtain the third correlation;

[0036] Perform factor analysis on the material properties and the wear resistance performance, and integrate the third correlation to obtain the output of the comprehensive evaluation model;

[0037] Determine the wear resistance performance according to the output of the comprehensive evaluation model.

[0038] As a preferred solution of the method for evaluating and regulating the wear resistance performance of ductile iron based on the electronic work function according to the present invention, wherein: the calculation of the first correlation is expressed by the following formula:

[0039] ;

[0040] wherein, is the i-th input parameter, is the corresponding regression coefficient, and n is the total number of parameters;

[0041] The calculation of the second correlation is expressed by the following formula:

[0042] ;

[0043] wherein, f( ) = ln(1 + ), which is used to perform logarithmic transformation on some parameters; k is the number of parameters that do not require non-linear transformation;

[0044] The calculation of the third correlation is expressed by the following formula:

[0045] ;

[0046] wherein, is the interaction term coefficient, indicating and the intensity of the interaction between the two parameters, is the p-th input parameter, is the q-th input parameter;

[0047] The output of the comprehensive evaluation model is expressed by the following formula:

[0048] ;

[0049] wherein, is the s-th common factor, is its influence coefficient, t is the number of selected factors, is the comprehensive score.

[0050] In a second aspect, an embodiment of the present invention provides a system for evaluating and regulating the wear resistance performance of ductile iron based on the electronic work function, which includes:

[0051] The acquisition module is used to acquire the electron work function data and material property data on the surface of ductile iron;

[0052] The feature extraction module is used to extract the features of the electron work function data to obtain electron work function features;

[0053] The analysis module is used to establish a mathematical model based on the electron work function features to analyze the correlation between the electron work function data and the material property data; analyze the relationship between the electron work function features and the wear resistance;

[0054] The evaluation module is used to establish a comprehensive evaluation model to evaluate the wear resistance of ductile iron.

[0055] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the processor executes the computer program, any step of the above-mentioned method for evaluating and regulating the wear resistance of ductile iron based on the electron work function is implemented.

[0056] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by a processor, any step of the above-mentioned method for evaluating and regulating the wear resistance of ductile iron based on the electron work function is implemented.

[0057] The beneficial effects of the present invention are as follows: By systematically studying the electron work function characteristics of ductile iron and their relationships with material composition, microstructure, and wear resistance, a scientific basis is provided for optimizing the performance of ductile iron. At the same time, combining characterization techniques, complex data analysis methods, and multivariate statistical models can not only deeply understand the connection between the electronic structure and macroscopic properties of materials, but also establish a comprehensive evaluation model to predict the wear resistance of ductile iron; this method can guide material design and production process optimization, contribute to the development of ductile iron materials with more excellent performance, and promote technological innovation and industrial applications in related fields. Description of the Drawings

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. Among them:

[0059] Figure 1 It is a flowchart of the method for evaluating and regulating the wear resistance of ductile iron based on the electron work function. Detailed Embodiments

[0060] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0061] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0062] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other with other embodiments.

[0063] The present invention is described in detail in conjunction with schematic diagrams. When elaborating on the embodiments of the present invention, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally out of the general proportion, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions of length, width, and depth should be included.

[0064] At the same time, in the description of the present invention, it should be noted that the orientation or positional relationships indicated by terms such as "upper, lower, inner, and outer" are based on the orientation or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, the terms "first, second, or third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0065] Unless otherwise clearly defined and limited in the present invention, the terms "installed, connected, and coupled" should be understood in a broad sense. For example, they can be fixedly connected, detachably connected, or integrally connected; they can also be mechanically connected, electrically connected, or directly connected, or indirectly connected through an intermediate medium, or the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0066] Embodiment 1

[0067] Refer to Figure 1, which is the first embodiment of the present invention. This embodiment provides a method for evaluating and regulating the wear resistance of ductile iron based on the electron work function, including:

[0068] First, prepare a series of ductile iron samples with different elemental compositions and microstructures. These samples should cover the expected composition range and microstructure changes. Conduct a detailed chemical composition analysis on each sample, and comprehensively characterize the microstructure using means such as optical microscopy, scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS). Focus on characteristics such as the size, morphology, distribution density of graphite balls, and the composition of the matrix phase (such as the proportion of ferrite and pearlite). At the same time, use X-ray diffraction (XRD) technology to determine the phase composition and crystal structure of the samples.

[0069] Use a scanning Kelvin probe microscope (SKPM) to measure the electron work function at the nanoscale on the surface of the prepared samples. SKPM is a non-contact atomic force microscopy technique that can simultaneously obtain the topography and electron work function distribution information of the sample surface. The following points need to be noted during the measurement:

[0070] The surface of the sample needs to be finely polished to ensure surface flatness and cleanliness.

[0071] During the measurement process, it is necessary to control the environmental temperature, humidity, and air pressure to reduce the influence of external factors.

[0072] Conduct multiple measurements in multiple regions and multiple times for each sample to ensure the reliability and representativeness of the data.

[0073] Record detailed information such as probe parameters, scanning range, and resolution during the measurement process for subsequent analysis and reproduction.

[0074] Furthermore, conduct statistical analysis and image processing on the obtained electron work function data. Calculate the average electron work function value, standard deviation of each sample, and the local electron work function distribution in different phase regions (such as graphite balls, matrix). Use image analysis software to process the SKPM images and extract the spatial distribution characteristics of the electron work function, such as gradient, uniformity, etc. Compare and correlate these data with the previously obtained chemical composition and microstructure data.

[0075] Preferably, calculate the average electron work function value and standard deviation of each sample. By dividing the standard deviation by the average value, the coefficient of variation is obtained , to compare the degree of dispersion in different regions, as shown in the following formula:

[0076] ;

[0077] Among them, and are the average electron work function and the standard deviation of the graphite sphere region, respectively; and are the average electron work function and the standard deviation of the matrix region, respectively.

[0078] The local electron work function distributions in different phase regions (such as graphite spheres and matrix) are as follows: is the graphite sphere region, is the matrix region.

[0079] In the process of extracting the spatial distribution characteristics of the electron work function, since the spatial distribution characteristics of the electron work function are difficult to be represented by simple statistics, the present invention uses gradients and spatial integrals to capture the spatial variation characteristics of the electron work function, and uses a spatial weight function to assign different weights to different regions, which can be expressed by the following formula:

[0080] ;

[0081] where is the spatial weight function, is the two-dimensional electron work function distribution, is the gradient operator, is the spatial variation characteristic quantity.

[0082] where is a two-dimensional Gaussian function. Specifically, it is shown by the following formula:

[0083] ;

[0084] where A is the amplitude, which controls the maximum value of the weight, ( , ) is the center point coordinate, usually selected as the center of the region of interest, is the standard deviation, which controls the width of the Gaussian distribution.

[0085] where the electron work function gradient is shown by the following formula:

[0086] ;

[0087] where is the partial derivative of with respect to , representing the rate of change in the direction, is the partial derivative of with respect to , representing the rate of change in the y direction,

[0088] It should be noted that when And Multiplying them will result in a vector field. Set ([[]] , ) as the interface center of the graphite sphere and the matrix, focus on analyzing the change of electron work function near the interface, and then control the size of the concerned area by adjusting [[[]] . If there are multiple regions of interest in the sample, use the weighted sum of multiple Gaussian functions as [[[]] . Finally, integrate the entire sample surface to obtain a scalar value.

[0089] To further compare and correlate these data with the previously obtained chemical composition and microstructure data, it is necessary to quantify the relationship between the electron work function and material characteristics. In this embodiment, the weighted standardized difference is used to compare the actual measured value with the expected value, and the correlation coefficient is used to represent the overall correlation degree as shown in the following formula:

[0090] ;

[0091] where n is the number of chemical composition and microstructure characteristics considered; is the weight of the i-th characteristic; is the average electron work function of the region related to the i-th characteristic; And are the mean and standard deviation of the expected electron work function of the i-th characteristic respectively; is the correlation coefficient between the electron work function and the chemical composition and microstructure characteristics.

[0092] It should be noted that the above formula calculates the weighted standardized difference between the actually measured electron work function and the electron work function expected based on the chemical composition and microstructure, and uses the correlation coefficient [[[]] to quantify the overall correlation degree between the electron work function and the chemical composition and microstructure characteristics.

[0093] Adding the [[[]] , , obtained above, the comprehensive electron work function analysis index can be obtained. This index reflects the electron emission characteristics, surface properties and microstructure characteristics of the material. During the production process, these indexes can be used as quality control standards. For example, if the mean or distribution characteristics of the electron work function of a certain batch of products deviate significantly from the expected range, it indicates that there is a problem in the production process.

[0094] Furthermore, based on the obtained features, namely the average electron work function value, standard deviation, local electron work function distribution in different phase regions, spatial distribution features (such as gradient, uniformity), chemical composition data (weight percentages of various elements, such as C, Si, Mn, P, S, etc.), and microstructural features (such as the number density, average size, roundness of graphite spheres, and volume fractions of ferrite and pearlite, etc.); correlation analysis is carried out.

[0095] More specifically, statistical methods are used to quantify the correlations between the electron work function and various variables, mainly using two correlation coefficients:

[0096] Pearson correlation coefficient: used to evaluate linear correlations, with a value range of [-1, 1].

[0097] Spearman rank correlation coefficient: used to evaluate monotonic correlations, applicable to non-linear relationships, and also with a value range of [-1, 1].

[0098] A correlation coefficient matrix is obtained, which contains:

[0099] The correlation coefficients between the electron work function and each chemical composition element;

[0100] The correlation coefficients between the electron work function and each microstructural feature;

[0101] The mutual correlation coefficients between various variables.

[0102] It can be expressed by the following formula:

[0103] ;

[0104] where, is the correlation coefficient between the i-th variable and the j-th variable, and the elements on the diagonal are all 1 because the correlation coefficient of each variable with itself is 1.

[0105] Based on the results of the correlation analysis, variables with stronger correlations are selected for multiple regression analysis. Through regression analysis, the following data are obtained:

[0106] The coefficients (β values) of the regression equation;

[0107] Coefficient of determination R², used to evaluate the goodness of fit of the model;

[0108] The p-value of each independent variable, used to judge its statistical significance;

[0109] The results of residual analysis, used to test whether the model assumptions hold.

[0110] Specifically, when using a multiple linear regression model for multiple regression analysis, the coefficient of determination R² is shown as follows:

[0111] ;

[0112] wherein, is the observed value, is the model predicted value, is the average value of the observed values, and m is the number of samples.

[0113] For each independent variable xi, calculate its statistic t and the corresponding p-value as shown in the following formula:

[0114] ;

[0115] wherein, is 's standard error.

[0116] The mathematical model composed of the above parts describes the relationship between the electron work function and material properties. By estimating model parameters, evaluating model goodness of fit, testing statistical significance, and performing residual analysis, a comprehensive model can be obtained to understand and predict the relationship between the electron work function and material properties.

[0117] Furthermore, following the steps of the above correlation analysis, analyze the relationship between the electron work function characteristics and wear resistance, as well as the influence of other material properties, so as to establish a comprehensive evaluation model to predict the wear resistance of ductile iron. First, through correlation analysis, the association strength between key parameters such as electron work function characteristics, chemical composition, and microstructure and wear resistance is determined. Then, using multiple regression analysis, a quantitative relationship between these parameters and the volume loss rate is established, and the weight coefficients of each parameter are obtained. Finally, analysis of variance is used to identify significant interaction effects.

[0118] For example, in a feasible embodiment, the model for estimating the wear resistance of ductile iron is constructed as follows:

[0119] First, based on a multiple linear regression model to capture the linear relationship between material properties and wear resistance, which is represented by the following formula:

[0120] ;

[0121] wherein, is the i-th input parameter (such as electron work function characteristics, chemical composition, microstructure, etc.), is the corresponding regression coefficient, and n is the total number of parameters.

[0122] To capture non-linear relationships, non-linear transformations are performed on some important parameters:

[0123] ;

[0124] wherein, f( ) = ln(1 + ), which is used for logarithmic transformation of some parameters. k is the number of parameters that do not require non - linear transformation.

[0125] Next, consider the interaction effects between important parameters, that is, allow the model to capture the mutual influence between parameters and improve the prediction accuracy, which is expressed by the following formula:

[0126] ;

[0127] Among them, is the interaction term coefficient, representing and the intensity of the interaction between two parameters, is the p - th input parameter, is the q - th input parameter.

[0128] Finally, integrate the results of factor analysis to capture potential common factors:

[0129] ;

[0130] Among them, is the s - th common factor, is its influence coefficient, t is the number of selected factors, is the comprehensive score.

[0131] What the model outputs is the predicted volume loss rate (representing the quality guarantee of the wear resistance of ductile iron). When 0 ≤ VLR < 0.1, it represents excellent wear resistance; when 0.1 ≤ VLR < 0.3, it is good wear resistance; when 0.3 ≤ VLR < 0.6, it is general wear resistance; when VLR ≥ 0.6, it is poor wear resistance. The specific determination is made according to the actual situation.

[0132] This model can not only accurately predict the wear resistance of ductile iron, but also guide the direction of material optimization through sensitivity analysis, providing a powerful tool for researchers and engineers to continuously improve the wear resistance of ductile iron.

[0133] Furthermore, this embodiment also provides a ductile iron wear resistance evaluation and regulation system based on the electronic work function, including:

[0134] A collection module, which is used to collect the electronic work function data and material characteristic data on the surface of ductile iron;

[0135] A feature extraction module, which is used to extract the features of the electronic work function data to obtain electronic work function features;

[0136] An analysis module, configured to establish a mathematical model based on the electronic work function characteristics to analyze the correlation between the electronic work function data and the material property data; and analyze the relationship between the electronic work function characteristics and the wear resistance.

[0137] An evaluation module, configured to establish a comprehensive evaluation model to evaluate the wear resistance of ductile iron.

[0138] This embodiment also provides a computer device, applicable to the situation of the method for evaluating and regulating the wear resistance of ductile iron based on the electronic work function, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for evaluating and regulating the wear resistance of ductile iron based on the electronic work function as proposed in the above embodiment.

[0139] This computer device may be a terminal, and this computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of this computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of this computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of this computer device may be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0140] This embodiment also provides a storage medium, on which a computer program is stored, and when this program is executed by a processor, it implements the method for evaluating and regulating the wear resistance of ductile iron based on the electronic work function as proposed in the above embodiment.

[0141] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0142] Embodiment 2

[0143] This is the second embodiment of the present invention. This embodiment provides a method for evaluating and regulating the wear resistance of ductile iron based on the electronic work function. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0144] To verify the analysis method for the relationship between the electron work function and wear resistance of ductile iron proposed in the present invention, a series of detailed experimental studies were carried out. First, 7 ductile iron samples with different compositions and microstructures were prepared, covering the common composition range of ductile iron. The sample preparation adopted standard spheroidizing and inoculation treatment processes, and heat treatment was carried out after casting to obtain the target microstructure.

[0145] Comprehensive chemical composition analysis was carried out on each sample, and a direct-reading spectrometer was used to determine the main element contents. Subsequently, the microstructure was characterized using a high-resolution optical microscope and a field emission scanning electron microscope (FE-SEM). Image analysis software was used to measure the size distribution, roundness, and number density of graphite nodules. The volume fractions of ferrite and pearlite in the matrix were determined by the point counting method. In addition, X-ray diffraction (XRD) technology was used to analyze the phase composition and crystal structure of the samples.

[0146] Next, a scanning Kelvin probe microscope (SKPM) was used to measure the electron work function at the nanoscale on the sample surface. To ensure the measurement accuracy, the sample surface was finely polished to a mirror finish and ultrasonically cleaned in a dust-free environment. The SKPM measurement was carried out in a constant temperature and humidity chamber, with the temperature controlled at 23±0.5°C and the relative humidity maintained at 50±2%. Five different regions were selected for scanning on each sample, and each region was measured 3 times repeatedly to ensure the reliability and representativeness of the data. The scanning range was set to 10μm×10μm, the resolution was 256×256 pixels, and the scanning rate was 1Hz. A platinum-iridium alloy coated probe was used, and the probe amplitude was set to 100nm, with a resonance frequency of about 75kHz.

[0147] After obtaining the original SKPM data, professional image analysis software was used for processing and statistical analysis. The average electron work function value and standard deviation of each sample were calculated, and the coefficient of variation was calculated. Local electron work function distribution analysis was carried out for the graphite nodule and matrix regions respectively. Further, the spatial distribution characteristics of the electron work function were extracted, where the parameter A of the Gaussian function was set to 1 and σ was set to 2μm to focus on analyzing the change of the electron work function near the interface between the graphite nodule and the matrix.

[0148] To establish the correlation between the electron work function characteristics and material properties, a standard wear test was carried out on all samples. A pin-on-disc wear testing machine was used, the counter material was GCr15 bearing steel, the load was 50N, the sliding speed was 0.5m / s, and the test time was 2 hours. The mass loss before and after the test was measured by a precision balance, and the volume loss rate (VLR) was calculated in combination with the sample density.

[0149] Finally, a comprehensive evaluation model was constructed, taking the electron work function characteristics, chemical composition, and microstructural parameters as input variables, and the volume loss rate as the output variable. The weight coefficients of each parameter were determined through multiple regression analysis, and the prediction performance of the model was evaluated using the cross-validation method.

[0150] Based on the above experimental process, the following data results were obtained:

[0151] Sample Number Carbon Content (%) Silicon Content (%) Manganese Content (%) Phosphorus Content (%) Sulfur Content (%) Number of Graphite Spheres Graphite Sphere Size Spheroidization Rate (%) Ferrite Content (%) Pearlite Content (%) Flat Function Value Standard Deviation Spatial Eigenvalue Volume Loss Rate BGI-1 3.62 2.48 0.35 0.025 0.008 180 25 92 65 35 4.52 0.18 0.85 0.09 BGI-2 3.75 2.15 0.42 0.028 0.010 210 22 88 55 45 4.68 0.22 0.78 0.14 BGI-3 3.58 2.62 0.38 0.022 0.007 195 28 95 75 25 4.45 0.15 0.92 0.07 BGI-4 3.70 2.30 0.40 0.026 0.009 200 24 90 60 40 4.60 0.20 0.82 0.11 BGI-5 3.65 2.55 0.36 0.024 0.008 185 26 93 70 30 4.48 0.17 0.88 0.08 BGI-6 3.80 2.05 0.45 0.030 0.012 220 20 85 50 50 4.75 0.25 0.72 0.18 BGI-7 3.60 2.58 0.37 0.023 0.007 190 27 94 72 28 4.46 0.16 0.90 0.075

[0152] Through the analysis of the above experimental data, the following conclusions were drawn:

[0153] Relationship between electron work function and material composition and microstructure:

[0154] The data shows that there is an obvious correlation between the average electron work function value of the samples and their chemical composition and microstructural characteristics. Samples with higher carbon content (such as BGI-2 and BGI-6) exhibit higher average electron work function values, which are 4.68 eV and 4.75 eV respectively. This may be due to the solid solution strengthening effect of carbon atoms in the iron matrix, increasing the energy required for electrons to escape from the material surface. At the same time, the silicon content shows a negative correlation with the electron work function, which is consistent with the role of silicon as a ferrite stabilizing element. For example, BGI-3 and BGI-7 have higher silicon contents (2.62% and 2.58%), corresponding to lower electron work functions (4.45 eV and 4.46 eV).

[0155] Effect of microstructure on electron work function:

[0156] The number density, size, and spheroidization rate of graphite nodules have a significant impact on the distribution of electron work function. Sample BGI-3 has the highest spheroidization rate (95%) and larger graphite nodule size (28 μm), and at the same time shows the lowest average electron work function (4.45 eV) and the smallest standard deviation (0.15 eV). This indicates that a high-quality graphite nodule morphology helps to reduce the electron work function on the material surface and make its distribution more uniform. In contrast, BGI-6 has the lowest spheroidization rate (85%), corresponding to the highest electron work function (4.75 eV) and the largest standard deviation (0.25 eV), indicating that irregular graphite morphology will lead to significant fluctuations in the local electron work function.

[0157] Association between spatial distribution characteristics and material properties:

[0158] It can be seen from the calculated spatial feature value (SF) that the SF value is positively correlated with the overall performance of the material. The SF value of BGI-3 is the highest (0.92), corresponding to the lowest volume loss rate (0.07 mm³ / Nm); while the SF value of BGI-6 is the lowest (0.72), and its volume loss rate is the highest (0.18 mm³ / Nm). This result verifies the effectiveness of the spatial distribution feature analysis method proposed by the present invention, which can capture the subtle differences in the electron work function distribution and establish a connection with the macroscopic properties of the material.

[0159] Relationship between electron work function characteristics and wear resistance:

[0160] The data shows that there is an obvious positive correlation between the average electron work function value and the volume loss rate. Samples with lower electron work functions (such as BGI-3 and BGI-7) exhibit excellent wear resistance, with volume loss rates of 0.07 mm³ / Nm and 0.075 mm³ / Nm respectively. This may be because a lower electron work function is conducive to the formation of a stable surface oxide film, thereby improving the wear resistance of the material. In addition, the standard deviation of the electron work function is also negatively correlated with the wear resistance. Samples with a smaller standard deviation usually have better wear resistance, indicating that a uniform electron work function distribution helps to form consistent surface properties and reduce local wear.

[0161] In summary, by introducing electron work function analysis, especially the quantification method of its spatial distribution characteristics, the present invention provides an innovative and high-precision means for the performance evaluation and prediction of ductile iron. This not only deepens the understanding of the relationship between the microstructure and macroscopic properties of materials, but also opens up new ways for the quality control and performance optimization of ductile iron. Future research can further explore the relationship between the electron work function and other properties (such as fatigue strength, toughness, etc.), as well as the possibility of extending this method to other metal material systems.

[0162] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for evaluating and regulating the wear resistance of ductile iron based on the electron work function, characterized in that: including, collecting the electron work function data and material property data on the surface of ductile iron; extracting the features of the electron work function data to obtain electron work function features; establishing a mathematical model based on the electron work function features to analyze the correlation between the electron work function data and the material property data; determining the correlation coefficient between the electron work function features and the material property data to generate a correlation coefficient matrix; performing multiple regression analysis based on the correlation coefficient matrix; performing residual analysis to test whether the correlation hypothesis holds; judging whether the correlation analysis of the electron work function data and the material property data is completed according to the goodness of fit of the mathematical model; analyzing the relationship between the electron work function features and the wear resistance, and establishing a comprehensive evaluation model to evaluate the wear resistance of ductile iron; The establishment of the comprehensive evaluation model for evaluating the wear resistance of ductile iron is as follows: initially capturing the linear relationship between the material properties and the wear resistance to obtain the first correlation; performing a non-linear transformation on the first correlation to obtain the second correlation; performing an interaction effect analysis on the second correlation to obtain the third correlation; performing factor analysis on the material properties and the wear resistance, and integrating the third correlation to obtain the output of the comprehensive evaluation model; determining the wear resistance according to the output of the comprehensive evaluation model.

2. The method for evaluating and regulating the wear resistance of ductile iron based on electron work function according to claim 1, wherein: The material property data includes chemical composition data and microstructure characterization data; The electron work function features include the average electron work function value, the standard deviation of the electron work function, the local distribution, and the spatial distribution features.

3. The method for evaluating and regulating the wear resistance of ductile iron based on the electronic work function according to claim 2, characterized in that: The spatial distribution features use two basic mathematical tools, gradient and spatial integration, to capture the spatial variation features of the electron work function data, and are represented by the following formula: ； wherein, is a spatial weight function, is a two-dimensional electron work function distribution, is a gradient operator, is a spatially varying characteristic quantity; The spatial weight function is specifically shown by the following formula: ； where A is the amplitude, controlling the maximum value of the weight, ( , ) are the center coordinates, selected as the center of the region of interest, is the standard deviation, controlling the width of the Gaussian distribution; The two-dimensional electron work function distribution is represented by the following formula: ； Among them, is the partial derivative of indicating the rate of change in the direction, is the partial derivative of indicating the rate of change in the y direction, and are the unit vectors in the x and y directions respectively.

4. The method for evaluating and regulating the wear resistance of ductile iron based on the electron work function according to claim 3, wherein: The goodness of fit is judged according to the coefficient of determination; The calculation of the coefficient of determination is shown by the following formula: ； Among them, is the observed value, is the model predicted value, is the average value of the observed values, m is the number of samples, and R² is the coefficient of determination.

5. The method for evaluating and regulating the wear resistance of ductile iron based on the electron work function according to claim 4, characterized in that: The calculation of the first correlation is represented by the following formula: ； Among them, is the i-th input parameter, is the corresponding regression coefficient, and n is the total number of parameters; The calculation of the second correlation is represented by the following formula: ； where f( ) = ln(1 + ), which is used to perform logarithmic transformation on some parameters; k is the number of parameters that do not require non-linear transformation; The calculation of the third correlation is represented by the following formula: ； Among them, is the interaction term coefficient, indicating and the strength of the interaction between the two parameters, is the p-th input parameter, is the q-th input parameter; The output of the comprehensive evaluation model is represented by the following formula: ； Among them, is the s-th common factor, is its influence coefficient, t is the number of selected factors, is the comprehensive score.

6. A system for evaluating and regulating the wear resistance of ductile iron based on the electronic work function, based on the method for evaluating and regulating the wear resistance of ductile iron based on the electronic work function according to any one of claims 1 to 5, characterized in that: including, a collection module for collecting the electron work function data and material property data on the surface of ductile iron; a feature extraction module for extracting the features of the electron work function data to obtain electron work function features; an analysis module for establishing a mathematical model based on the electron work function features to analyze the correlation between the electron work function data and the material property data; analyzing the relationship between the electron work function features and the wear resistance; an evaluation module for establishing a comprehensive evaluation model to evaluate the wear resistance of ductile iron.

7. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for evaluating and regulating the wear resistance of ductile iron based on the electron work function according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for evaluating and regulating the wear resistance of ductile iron based on the electron work function according to any one of claims 1 to 5.

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