Nodular cast iron wear resistance evaluation, regulation and control method based on electronic work function

Abstract

The invention discloses a spheroidal graphite cast iron wear resistance evaluation, regulation and control method based on an electronic work function, and relates to the technical field of surface engineering and performance evaluation.The method comprises the steps that electronic work function data and material characteristic data of the surface of spheroidal graphite cast iron are collected; extracting features of the electronic work function data to obtain electronic work function features; based on the electronic work function characteristics, establishing a mathematical model to analyze the correlation between the electronic work function data and the material characteristic data; and 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 the nodular cast iron. The characterization technology, the complex data analysis method and the multivariate statistical model are combined at the same time, the relation between the electronic structure and the macroscopic performance of the material can be deeply understood, and a comprehensive evaluation model is established to predict the wear resistance of the nodular cast iron; the method guides the design of materials and the optimization of the production process.

Description

Technical Field

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

[0002] As an important engineering material, ductile iron is widely used in machinery, automobiles, pipelines and other fields due to its excellent mechanical properties and good casting properties. In recent years, with the continuous advancement 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 microstructure analysis. Although these methods are intuitive, they are often time-consuming and labor-intensive, and it is difficult to reveal the intrinsic connection between the microstructure and macroscopic properties of the material. With the development of materials science, researchers have gradually realized that the electronic structure of the material surface has an important influence on its performance. As an important parameter to characterize the electronic structure of the material surface, the electron work function is closely related to the physical and chemical properties of the material. However, research that directly correlates the electron work function with the wear resistance of ductile iron is still rare, and there is still huge room for exploration in this field.

[0003] The existing evaluation methods for the wear resistance of ductile iron have the following shortcomings: First, although the traditional wear test method can directly reflect the wear resistance of the material, it is difficult to provide an explanation of the microscopic mechanism, and the test process is time-consuming and costly; second, although the method based on microstructure analysis can reveal the microstructural characteristics of the material, it is difficult to accurately predict its macroscopic wear resistance; third, the existing evaluation methods often ignore the influence of the surface electronic structure of the material on the wear resistance, which limits the comprehensive understanding and precise regulation of the material properties. In addition, there is a lack of a systematic evaluation method that can comprehensively consider the material composition, microstructure and surface electronic properties, which makes the optimization of the wear resistance of ductile iron challenging. Therefore, it is urgent 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 that it is difficult to accurately predict and effectively regulate the wear resistance of ductile iron from a microscopic level.

[0006] In order 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 electron work function, which comprises:

[0008] Collect the electron work function data and material property data of ductile iron surface;

[0009] Extracting the characteristics of the electron work function data to obtain electron work function characteristics;

[0010] Based on the electron work function characteristics, a mathematical model is established to analyze the correlation between the electron work function data and the material characteristic data;

[0011] The relationship between the electron work function characteristics and the wear resistance is analyzed, and a comprehensive evaluation model is established to evaluate the wear resistance of ductile iron.

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

[0013] The electron work function characteristics include an average electron work function value, an electron work function standard deviation, a local distribution, and a spatial distribution characteristic.

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

[0015] ;

[0016] in, is the spatial weight function, is the two-dimensional electron work function distribution, is the gradient operator, is the spatial variation characteristic;

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

[0018] ;

[0019] Where A is the amplitude, which controls the maximum value of the weight, ( , ) are the center point coordinates, usually chosen as the center of the region of interest, is the standard deviation, which controls the width of the Gaussian distribution;

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

[0021] ;

[0022] in, yes right The partial derivative of The rate of change in direction, yes right The partial derivative of represents the rate of change in the y direction, and are unit vectors in the x and y directions.

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

[0024] Determining the correlation coefficient between the electron work function characteristics and the material characteristic data, and generating a correlation coefficient matrix;

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

[0026] Residual analysis was performed to test whether the correlation hypothesis was established;

[0027] Whether the correlation analysis between the electron work function data and the material characteristic data is completed is determined according to the goodness of fit of the mathematical model.

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

[0029] The calculation of the coefficient of determination is shown below:

[0030] ;

[0031] in, is the observed value, is the model prediction value, is the mean of the observed values, m is the sample size, and R² is the coefficient of determination.

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

[0033] Preliminarily capturing the linear relationship between the material property and the wear resistance to obtain a first correlation;

[0034] Performing a nonlinear transformation on the first correlation to obtain a second correlation;

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

[0036] 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;

[0037] Wear resistance is determined based on the output of the comprehensive evaluation model.

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

[0039] ;

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

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

[0042] ;

[0043] Among them, f( ) = ln(1 + ), used to perform logarithmic transformation on some parameters; k is the number of parameters that do not require nonlinear transformation;

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

[0045] ;

[0046] in, is the interaction coefficient, indicating and The strength of the interaction between two parameters, is the pth input parameter, is the qth input parameter;

[0047] The output of the comprehensive evaluation model is expressed as follows:

[0048] ;

[0049] in, is the sth common factor, is its influence coefficient, t is the number of factors selected, For the comprehensive rating.

[0050] In a second aspect, an embodiment of the present invention provides a ductile iron wear resistance evaluation and control system based on electron work function, which includes:

[0051] A collection module, used to collect electron work function data and material property data of the ductile iron surface;

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

[0053] An analysis module, for establishing a mathematical model to analyze the correlation between the electron work function data and the material property data based on the electron work function characteristics; and analyzing the relationship between the electron work function characteristics and wear resistance;

[0054] Evaluation module, 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, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, any step of the above-mentioned method for evaluating and controlling the wear resistance of ductile iron based on electron work function is implemented.

[0056] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: 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 electron work function is implemented.

[0057] The beneficial effect of the present invention is to provide a scientific basis for optimizing the performance of ductile iron by systematically studying the characteristics of the electronic work function of ductile iron and its relationship with material composition, microstructure and wear resistance. At the same time, combined with characterization technology, complex data analysis methods and multivariate statistical models, not only can we deeply understand the relationship between the electronic structure and macroscopic performance of the material, 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, help develop ductile iron materials with better performance, and promote technological innovation and industrial application in related fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0059] Figure 1 This is a flow chart of the evaluation and control method of the wear resistance of ductile iron based on electron work function. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0062] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0063] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional 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 directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and 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 direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0065] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0066] Example 1

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

[0068] First, a series of ductile iron samples with different elemental compositions and microstructures are prepared. These samples should cover the expected composition range and microstructural variations. Each sample is subjected to a detailed chemical composition analysis, and the microstructure is fully characterized using optical microscopy, scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS). Focus is placed on the size, morphology, and distribution density of graphite nodules, as well as the composition of the matrix phase (such as the proportion of ferrite and pearlite). At the same time, the phase composition and crystal structure of the sample are determined using X-ray diffraction (XRD) technology.

[0069] Scanning Kelvin probe microscopy (SKPM) is used to measure the electron work function of the prepared sample surface at the nanoscale. SKPM is a non-contact atomic force microscopy technique that can simultaneously obtain the morphology of the sample surface and the distribution of the electron work function. The following points should be noted during measurement:

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

[0071] During the measurement process, the ambient temperature, humidity and air pressure need to be controlled to reduce the impact of external factors.

[0072] Multiple areas and repeated measurements were performed on each sample to ensure the reliability and representativeness of the data.

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

[0074] Furthermore, the obtained electron work function data were statistically analyzed and image processed. The average electron work function value, standard deviation, and local electron work function distribution of different phase regions (such as graphite spheres and matrix) of each sample were calculated. The SKPM images were processed using image analysis software to extract the spatial distribution characteristics of the electron work function, such as gradient and uniformity. These data were compared and correlated with the previously obtained chemical composition and microstructure data.

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

[0076] ;

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

[0078] The local electron work function distribution of different phase regions (such as graphite spheres and matrix) is: is the graphite sphere area, The base area.

[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 expressed by simple statistics, the present invention uses gradient and spatial integral 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] in, is the spatial weight function, is the two-dimensional electron work function distribution, is the gradient operator, is the spatial variation feature.

[0082] in, is a two-dimensional Gaussian function, specifically, as shown in the following formula:

[0083] ;

[0084] Where A is the amplitude, which controls the maximum value of the weight, ( , ) are the center point coordinates, usually chosen as the center of the region of interest, is the standard deviation, controlling the width of the Gaussian distribution.

[0085] Among them, the electron work function gradient As shown below:

[0086] ;

[0087] in, yes right The partial derivative of The rate of change in direction, yes right The partial derivative of represents the rate of change in the y direction, and are unit vectors in the x and y directions.

[0088] If necessary, and Multiplying them together will give us a vector field, , ) is set as the interface center between the graphite ball and the substrate, focusing on analyzing the changes in the electron work function near the interface, and then adjusting To control the size of the region of interest, if there are multiple regions of interest in the sample, use the weighted sum of multiple Gaussian functions as ,Finally, the whole sample surface is integrated 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 the material characteristics. In this embodiment, weighted standardized differences are used to compare the actual measured values ​​with the expected values, 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 compositions and microstructural features considered; is the weight of the i-th feature; is the average electron work function of the i-th feature-related region; and are the expected electron work function mean and standard deviation of the ith feature, respectively; is the correlation coefficient between the electron work function and the chemical composition and microstructural characteristics.

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

[0093] The above obtained , , By adding them together, we can get the comprehensive electron work function analysis index, which reflects the electron emission characteristics, surface properties and microstructural characteristics of the material. During the production process, these indicators can be used as quality control standards. For example, if the mean or distribution characteristics of the electron work function of a 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 characteristics, namely the average electron work function value, standard deviation, local electron work function distribution in different phase regions, spatial distribution characteristics (such as gradient, uniformity), chemical composition data (weight percentage of each element, such as C, Si, Mn, P, S, etc.), microstructural characteristics (such as graphite nodule number density, average size, roundness, and volume fraction of ferrite and pearlite, etc.), correlation analysis was performed.

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

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

[0097] Spearman rank correlation coefficient: used to evaluate monotonic correlation, suitable for nonlinear relationships, and the value range is also [-1, 1].

[0098] Get a correlation coefficient matrix, which contains:

[0099] The correlation coefficient between the electron work function and each chemical component element;

[0100] Correlation coefficient between electron work function and each microstructural feature;

[0101] The mutual correlation coefficients among the variables.

[0102] It can be expressed as follows:

[0103] ;

[0104] in, is the correlation coefficient between the ith variable and the jth variable. 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 correlation analysis, we selected variables with strong correlation for multiple regression analysis. Through regression analysis, we obtained the following data:

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

[0107] The coefficient of determination R² is used to assess the goodness of fit of the model;

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

[0109] The residual analysis results are used to test whether the model assumptions are valid.

[0110] Specifically, the multiple linear regression model is used for multiple regression analysis, and the determination coefficient R² is as follows:

[0111] ;

[0112] in, is the observed value, is the model prediction value, is the mean 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 below:

[0114] ;

[0115] in, yes The standard error of .

[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 fit goodness, 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, according to the steps of the above correlation analysis, the relationship between the electron work function characteristics and wear resistance, as well as the influence of other material properties, was analyzed to establish a comprehensive evaluation model to predict the wear resistance of ductile iron. First, the correlation strength between the key parameters such as electron work function characteristics, chemical composition, and microstructure and wear resistance was determined through correlation analysis. Then, the quantitative relationship between these parameters and the volume loss rate was established using multivariate regression analysis, and the weight coefficient of each parameter was obtained. Finally, significant interaction effects were identified using variance analysis.

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

[0119] First, the linear relationship between material properties and wear resistance is captured based on the multivariate linear regression model, which is expressed as follows:

[0120] ;

[0121] in, 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] In order to capture nonlinear relationships, some important parameters are transformed nonlinearly:

[0123] ;

[0124] Among them, f( ) = ln(1 + ), which is used to perform logarithmic transformation on some parameters. k is the number of parameters that do not require nonlinear 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 as follows:

[0126] ;

[0127] in, is the interaction coefficient, indicating and The strength of the interaction between two parameters, is the pth input parameter, is the qth input parameter.

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

[0129] ;

[0130] in, is the sth common factor, is its influence coefficient, t is the number of factors selected, For the comprehensive rating.

[0131] The model output is the predicted volume loss rate (indicating the quality assurance of the wear resistance of graphite cast iron). When 0 ≤ VLR <0.1, it indicates excellent wear resistance; 0.1 ≤ VLR < 0.3 indicates good wear resistance, 0.3 ≤ VLR < 0.6 indicates average wear resistance, and VLR ≥ 0.6 indicates poor wear resistance. The specific determination is based on actual conditions.

[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 researchers and engineers with a powerful tool to continuously improve the wear resistance of ductile iron.

[0133] Furthermore, this embodiment also provides a ductile iron wear resistance evaluation and control system based on electron work function, comprising:

[0134] A collection module, used to collect electron work function data and material property data of the ductile iron surface;

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

[0136] An analysis module, for establishing a mathematical model to analyze the correlation between the electron work function data and the material property data based on the electron work function characteristics; and analyzing the relationship between the electron work function characteristics and wear resistance;

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

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

[0139] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the 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 the 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, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

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

[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 fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0142] Example 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 electron 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] In order to verify the analytical method for the relationship between the electron work function and wear resistance of ductile iron proposed in this 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 samples were prepared using standard spheroidizing and inoculation processes, and heat treatment was performed after casting to obtain the target microstructure.

[0145] A comprehensive chemical composition analysis was performed on each sample, and the main element content was determined using an optical reading spectrometer. Subsequently, the microstructure was characterized using high-resolution optical microscopy and field emission scanning electron microscopy (FE-SEM). The size distribution, roundness, and number density of graphite spheres were measured using image analysis software. The volume fraction of ferrite and pearlite in the matrix was determined by the point counting method. In addition, the phase composition and crystal structure of the samples were analyzed using X-ray diffraction (XRD) technology.

[0146] Next, a scanning Kelvin probe microscope (SKPM) was used to measure the electron work function of the sample surface at the nanoscale. 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 areas were selected for each sample for scanning, and each area was measured three times 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, the probe amplitude was set to 100nm, and the resonant frequency was approximately 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. The local electron work function distribution analysis was performed for the graphite sphere and the matrix area respectively. Furthermore, 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, in order to focus on analyzing the changes in the electron work function near the interface between the graphite sphere and the matrix.

[0148] In order to establish the correlation between the characteristics of the electron work function and the material properties, all samples were subjected to standard wear tests. A pin-on-disc wear tester was used, the grinding 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, with electron work function characteristics, chemical composition, and microstructural parameters as input variables and volume loss rate as output variable. The weight coefficients of each parameter were determined by multivariate regression analysis, and the prediction performance of the model was evaluated using cross-validation method.

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

[0151]

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

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

[0154] The data show that the average electron work function value of the samples is significantly correlated with 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, 4.68eV and 4.75eV, respectively. This may be due to the solid solution strengthening effect of carbon atoms in the iron matrix, which increases the energy required for electrons to escape from the surface of the material. At the same time, the silicon content and the electron work function show a negative correlation trend, 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%), and the corresponding electron work functions are lower (4.45eV and 4.46eV).

[0155] Effect of microstructure on electron work function:

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

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

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

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

[0160] The data show that there is a clear 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) show excellent wear resistance, with volume loss rates of 0.07mm³ / Nm and 0.075mm³ / 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 wear resistance. Samples with smaller standard deviations generally have better wear resistance, which indicates that a uniform distribution of electron work functions helps to form consistent surface properties and reduce local wear.

[0161] In summary, this invention provides an innovative and high-precision means for evaluating and predicting the performance of ductile iron by introducing electron work function analysis, especially the quantitative method of its spatial distribution characteristics. This not only deepens the understanding of the relationship between the microstructure and macroscopic properties of materials, but also opens up new ways for quality control and performance optimization of ductile iron. Future research can further explore the relationship between 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 rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in 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 electron work function, characterized in that: include, Collect the electron work function data and material property data of ductile iron surface; Extracting the characteristics of the electron work function data to obtain electron work function characteristics; Based on the electron work function characteristics, a mathematical model is established to analyze the correlation between the electron work function data and the material characteristic data; The relationship between the electron work function characteristics and the wear resistance is analyzed, and a comprehensive evaluation model is established to evaluate the wear resistance of ductile iron.

2. The method for evaluating and controlling the wear resistance of ductile iron based on the electron work function according to claim 1, characterized in that: The material property data includes chemical composition data and microstructure characterization data; The electron work function characteristics include an average electron work function value, an electron work function standard deviation, a local distribution, and a spatial distribution characteristic.

3. The method for evaluating and controlling the wear resistance of ductile iron based on electron work function according to claim 2, characterized in that: The spatial distribution characteristics use two basic mathematical tools, gradient and spatial integration, to capture the spatial variation characteristics of the electron work function data, and are expressed by the following formula: ； in, is the spatial weight function, is the two-dimensional electron work function distribution, is the gradient operator, is the spatial variation characteristic; The spatial weight function is specifically shown in the following formula: ； Where A is the amplitude, which controls the maximum value of the weight, ( , ) are the center point coordinates, usually chosen as the center of the region of interest, is the standard deviation, which controls the width of the Gaussian distribution; The two-dimensional electron work function distribution is expressed by the following formula: ； in, yes right The partial derivative of The rate of change in direction, yes right The partial derivative of , which represents the rate of change in the y direction, and are unit vectors in the x and y directions respectively.

4. The method for evaluating and controlling the wear resistance of ductile iron based on electron work function according to claim 3, characterized in that: A mathematical model is established to analyze the correlation between the electron work function data and the material property data: Determining the correlation coefficient between the electron work function characteristics and the material characteristic data, and generating a correlation coefficient matrix; Performing a multiple regression analysis based on the correlation coefficient matrix; Residual analysis was performed to test whether the correlation hypothesis was established; Whether the correlation analysis between the electron work function data and the material characteristic data is completed is determined according to the goodness of fit of the mathematical model.

5. The method for evaluating and controlling the wear resistance of ductile iron based on the electron work function according to claim 4, characterized in that: The goodness of fit is judged based on the coefficient of determination; The calculation of the coefficient of determination is shown below: ； in, is the observed value, is the model prediction value, is the mean of the observed values, m is the sample size, and R² is the coefficient of determination.

6. The method for evaluating and controlling the wear resistance of ductile iron based on electron work function according to claim 5, characterized in that: The comprehensive evaluation model is established to evaluate the wear resistance of ductile iron, as follows: Preliminarily capturing the linear relationship between the material property and the wear resistance to obtain a first correlation; Performing a nonlinear transformation on the first correlation to obtain a second correlation; Performing an interaction effect analysis on the second correlation to obtain a 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; Wear resistance is determined based on the output of the comprehensive evaluation model.

7. The method for evaluating and controlling the wear resistance of ductile iron based on electron work function according to claim 6, characterized in that: The calculation of the first correlation is expressed by the following formula: ； in, is the i-th input parameter, is the corresponding regression coefficient, n is the total number of parameters; The calculation of the second correlation is expressed by the following formula: ； Among them, f( ) = ln(1 + ), used to perform logarithmic transformation on some parameters; k is the number of parameters that do not require nonlinear transformation; The calculation of the third correlation is expressed by the following formula: ； in, is the interaction coefficient, indicating and The strength of the interaction between two parameters, is the pth input parameter, is the qth input parameter; The output of the comprehensive evaluation model is expressed as follows: ； in, is the sth common factor, is its influence coefficient, t is the number of factors selected, For the comprehensive rating.

8. A ductile iron wear resistance evaluation and control system based on electron work function, based on the ductile iron wear resistance evaluation and control method based on electron work function according to any one of claims 1 to 7, characterized in that: include, A collection module, used to collect electron work function data and material property data of the ductile iron surface; A feature extraction module, used to extract the features of the electron work function data to obtain electron work function features; An analysis module, for establishing a mathematical model to analyze the correlation between the electron work function data and the material property data based on the electron work function characteristics; and analyzing the relationship between the electron work function characteristics and wear resistance; Evaluation module, used to establish a comprehensive evaluation model to evaluate the wear resistance of ductile iron.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for evaluating and regulating the wear resistance of ductile iron based on electron work function according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for evaluating and regulating the wear resistance of ductile iron based on electron work function according to any one of claims 1 to 7 are implemented.

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