Wheel-rail wear evolution prediction method and device

Abstract

The application provides a wheel-rail wear evolution prediction method and device, and belongs to the technical field of railway wheel-rail material performance evaluation. The wheel-rail wear evolution prediction method comprises the following steps: selecting a wheel-rail material and processing the wheel-rail material into a main sample for a wear test; performing the wear test and obtaining a test result, wherein the test result comprises surface hardness and wear of the wear test sample measured at different cycle times; fitting model parameters according to the test result and establishing a wheel-rail material wear behavior prediction model; and predicting wheel-rail wear through the wheel-rail material wear behavior prediction model. The application significantly improves the precision and physical interpretability of wear prediction, effectively supports the accurate formulation of maintenance decisions, and avoids resource waste caused by excessive maintenance or safety hazards caused by maintenance delay.

Description

Technical Field

[0001] This invention relates to the field of railway wheel and rail material performance evaluation technology, and in particular to a method and apparatus for predicting wheel and rail wear evolution. Background Technology

[0002] Wear is a common failure mode of wheel-rail materials in actual service. Wear not only changes the geometry of the wheel and rail but also affects the initiation and propagation of fatigue cracks through the redistribution of contact stress, making it a key regulating mechanism for the failure evolution of wheel-rail systems. Therefore, quantitative prediction of wear behavior is a prerequisite for structural life assessment, maintenance optimization, and safe operation control. Furthermore, from an economic perspective, wheel-rail wear directly leads to expensive component replacements and manual maintenance. Therefore, maintenance decisions must be planned in advance. Without wear prediction, over-repair and wasted costs may occur, or delayed maintenance may lead to accidents.

[0003] Currently, the most commonly used wear prediction model was proposed by the British scientist Archard, and its formula is as follows:

[0004]

[0005] In the above formula, V is the total wear amount, k is the wear coefficient (empirical parameter), s is the total slip, p is the normal load, and H is the initial hardness of the softer material in the friction pair. This prediction model is simple and intuitive, and is widely used in engineering.

[0006] However, the above model's description of the intrinsic properties of materials is too simplistic, only considering the initial hardness of softer materials. In actual wheel-rail wear processes, the hardness of the wheel-rail contact surface changes, and the microstructure dynamically evolves along with wear; all of these factors can affect the amount of wheel-rail wear. Furthermore, the hardening processes of different wheel-rail materials are not entirely the same, and the hardening mechanisms of different wheel-rail materials are also correlated with the stress and slip conditions. Therefore, considering only the initial hardness makes it difficult to effectively predict the wear behavior of wheel-rail materials.

[0007] In view of this, based on years of experience in production and design in this and related fields, the inventor has designed a wheel-rail wear evolution prediction method and device through repeated experiments, in order to solve the problems existing in the prior art. Summary of the Invention

[0008] The purpose of this invention is to provide a method and apparatus for predicting wheel and rail wear evolution, which can accurately predict wheel and rail wear of different wheel and rail materials.

[0009] To achieve the above objectives, this invention proposes a method for predicting wheel-rail wear evolution, wherein the method includes:

[0010] Select wheel and rail materials and process the wheel and rail materials into the main specimens for the grinding test;

[0011] A grinding test was conducted and the test results were obtained, including the surface hardness and wear amount of the grinding sample measured at different cycles.

[0012] Based on the experimental results, fit the model parameters and establish a prediction model for wheel-rail material wear behavior;

[0013] Wheel and rail wear is predicted using the wheel and rail material wear behavior prediction model.

[0014] This invention also proposes a wheel-rail wear evolution prediction device, comprising:

[0015] The data acquisition module is used to acquire the surface hardness data and wear data of the wheel-rail grinding samples measured at different cycles in the wheel-rail grinding experiment.

[0016] The parameter fitting module is used to fit at least one of the following parameters based on the experimental data acquired by the data acquisition module: wear coefficient. The sensitivity coefficient of wear to hardness (a) and the hardness saturation value. Hardening saturation rate parameter β, plastic strain scaling factor Stress level sensitivity coefficient m;

[0017] The model building module is used to establish a prediction model for wheel-rail material wear behavior, as described above.

[0018] The prediction module, based on the aforementioned wheel-rail material wear behavior prediction model, outputs the wear rate variation curve with the number of cycles and / or the total wear amount variation curve with the number of cycles, according to the input normal load F, slip, contact stress, and friction coefficient μ.

[0019] Compared with the prior art, the present invention has the following features and advantages:

[0020] The wheel-rail wear evolution prediction method and device proposed in this invention introduces the surface equivalent cyclic hardness H(N), a state variable that evolves with the number of cycles, and establishes an intrinsic physical mechanism of "load-plastic-hardening-wear" driven by cumulative equivalent plastic strain. This mechanism can accurately capture and predict the nonlinear evolution characteristics of wheel-rail wear from the initial rapid wear to the later slow and stable wear, significantly improving the accuracy of wear prediction. At the same time, model parameters with clear physical meaning can be calibrated through a small number of standardized double-disc grinding experiments, making the model usable for laboratory evaluation and extrapolation to various complex actual working conditions for quantitative prediction. This provides a scientific and reliable theoretical basis for wheel-rail system life assessment, maintenance optimization, and new material selection, effectively supporting the accurate formulation of maintenance decisions and avoiding resource waste or safety hazards caused by over-maintenance or maintenance delays. Attached Figure Description

[0021] Figure 1 This is a graph showing the change in surface hardness of the rail grinding sample as a function of the number of cycles.

[0022] Figure 2 This is a graph showing the change in wear rate of the rail grinding sample of the present invention with the number of cycles.

[0023] Figure 3 The graph shows the total wear amount of the rail grinding sample of the present invention as a function of the number of cycles.

[0024] Figure 4 This is a schematic diagram of the wheel-rail wear evolution prediction method of the present invention. Detailed Implementation

[0025] The details of the present invention can be more clearly understood by referring to the accompanying drawings and the description of specific embodiments. However, the specific embodiments of the present invention described herein are for illustrative purposes only and should not be construed as limiting the invention in any way. Under the teachings of this invention, those skilled in the art can conceive of any possible modifications based on the invention, and these should all be considered to fall within the scope of the invention.

[0026] This invention proposes a method for predicting wheel-rail wear evolution, wherein the method includes:

[0027] Select wheel and rail materials and process them into the main specimens for the grinding test;

[0028] A grinding test was conducted and the test results were obtained. The test results included the surface hardness and wear amount of the grinding samples measured at different cycles.

[0029] Based on the experimental results, fit the model parameters and establish a prediction model for the wear behavior of wheel-rail materials;

[0030] Wheel and rail wear is predicted using a wheel and rail material wear behavior prediction model.

[0031] The wheel-rail wear evolution prediction method proposed in this invention, such as Figure 4 As shown, by selecting wheel and rail materials and preparing main grinding samples, a double-disc grinding test was conducted to obtain surface hardness and wear data under different cycles. Then, model parameters were fitted and a wheel and rail material wear behavior prediction model including the surface equivalent cyclic hardness that varies with the number of cycles was established. This enables quantitative prediction of the wear evolution behavior of wheel and rail materials under different working conditions, which can provide guidance for on-site lubrication, grinding and other maintenance procedures. It also helps to select wheel and rail materials that are more suitable for specific working conditions to extend the service life of wheel and rail components based on a small number of experiments. Moreover, the model parameters all have clear physical meanings and can be obtained through experimental calibration, which has good engineering feasibility.

[0032] This invention also proposes a wheel-rail wear evolution prediction device, comprising:

[0033] The data acquisition module is used to acquire the surface hardness data and wear data of the wheel-rail grinding samples measured at different cycles in the wheel-rail grinding experiment.

[0034] The parameter fitting module is used to fit at least one of the following parameters based on the experimental data obtained by the data acquisition module: wear coefficient. The sensitivity coefficient of wear to hardness (a) and the hardness saturation value. Hardening saturation rate parameter β, plastic strain scaling factor And the stress level sensitivity coefficient m;

[0035] The model building module is used to establish the aforementioned wheel-rail material wear behavior prediction model;

[0036] The prediction module, based on the wheel-rail material wear behavior prediction model, outputs the wear rate variation curve and / or the total wear amount variation curve with the number of cycles based on the input normal load F, slip, contact stress, and friction coefficient μ.

[0037] The wheel-rail wear evolution prediction device proposed in this invention collects surface hardness and wear data under different cycles of the wear experiment through a data acquisition module, and calibrates the wear coefficient through a parameter fitting module. Sensitivity coefficient α, hardness saturation value The model is constructed using a model building module to establish a prediction model for the wear behavior of wheel and rail materials. The prediction module outputs the wear rate variation curve and / or the total wear variation curve with the number of cycles based on the input normal load, slip, contact stress and friction coefficient. This enables quantitative prediction of the wear evolution behavior of wheel and rail materials under different working conditions, providing guidance for on-site lubrication, grinding and other maintenance procedures. Based on a small number of experiments, it can also help select wheel and rail materials that are more suitable for specific working conditions, thereby extending the service life of wheel and rail components.

[0038] In an optional embodiment of the present invention, the calculation formula for the wheel-rail material wear behavior prediction model is as follows:

[0039]

[0040] In the formula, Wear rate is the mass of surface layer removed per contact cycle. The normal load refers to the vertical load force in the double-disc grinding test. This refers to the sliding distance within a single contact, which is significant in a dual-disc grinding test. = Circumference of the main specimen × slip. Its meaning is the wear coefficient, fitted based on two sets of dual-disc wear experiments. 'a' is the sensitivity coefficient of wear to hardness, also fitted based on two sets of dual-disc wear experiments. H(N) is the surface equivalent cyclic hardness, a nonlinear, saturable function; it is a mapping result of the material state and a variable that varies with the number of contact cycles N. Specifically, since hardness continuously evolves during the wear process, this prediction model extends hardness from a constant value to a state variable that varies with the number of cycles. By coupling the normal load, sliding distance, and surface equivalent cyclic hardness, the wear rate is calculated. After obtaining the surface equivalent cyclic hardness H(N), the wear rate as a function of the number of cycles can be plotted based on this formula. Integrating this curve yields the total wear amount as a function of the number of cycles. In the initial stage of establishing the prediction model, the wear coefficient... Since the wear sensitivity coefficient α to hardness is an unknown quantity, it needs to be fitted and calibrated based on the wear values ​​measured in two sets of dual-disc wear experiments at characteristic cycle counts to finally establish a complete prediction model. By establishing a quantitative relationship between the wear rate and the surface equivalent cyclic hardness that changes with the number of contact cycles, the material work hardening evolution process is introduced into wear prediction. This can reflect the influence of hardness changes on the wear rate under different cycle counts. Through experimental fitting of the wear coefficient and the wear sensitivity coefficient to hardness, a quantitative prediction of the wear rate and total wear amount of wheel-rail materials as a function of cycle counts is achieved, providing a model basis with clear physical meaning for assessing wheel-rail wear evolution behavior.

[0041] In one alternative embodiment of this implementation, a dual-disc grinding test is conducted, with the key test parameters being: contact stress, slip, medium, and termination speed.

[0042] In an optional example, the actual vertical load is calculated using Hertzian contact theory formulas. The maximum normal contact stress in the wheel-rail contact area can be expressed as:

[0043]

[0044] in, p0 is the vertical load applied to the specimen, a and b are the major and minor semi-axes of the contact ellipse, respectively.

[0045] Preferably, the contact stress p0 needs to take into account the type of train corresponding to high-speed railways. It is agreed that the wheel-rail contact stress corresponding to a 17t axle load EMU is 1200MPa.

[0046] In one optional example, the larger the slip s, the greater the surface tangential force, and the faster the crack initiation and propagation, but the wear rate also increases sharply. The double-disc grinding test method of the present invention selects two groups of slip: 0.5% and 2%.

[0047] In an alternative example, since grease or liquid media typically exhibit an oil wedge effect, they can accelerate crack propagation. This dual-disc grinding test method does not use any other media and continues with a dry grinding state.

[0048] In one optional example, 50,000 revolutions was selected as the termination point. During the test, the rotation was stopped at 5,000, 10,000, 20,000, 30,000, 40,000, and 50,000 revolutions respectively, and the surface hardness and wear of the main sample and the auxiliary sample were measured.

[0049] In an optional embodiment of the present invention, H(N) is represented as:

[0050] ,

[0051] In the formula, H(N) is the surface equivalent cyclic hardness; This represents the initial hardness value of the material. This represents the hardness value of the material after saturation. The hardening saturation rate parameter, The equivalent plastic strain is the product.

[0052] Specifically, based on the hardness values ​​obtained from the dual-disc grinding test at characteristic cycle counts, the hardness evolution law is fitted according to the above formula, and the hardening saturation rate parameter is obtained through fitting. At the same time, determine the initial hardness value of the material. and the hardness value after saturation A cumulative equivalent plastic strain-driven surface equivalent cyclic hardness evolution model was established, expressing material hardness as a state variable that increases nonlinearly with each cycle until saturation. This model can quantitatively reflect the dynamic evolution of hardness in the wheel-rail contact surface caused by work hardening. It enables accurate characterization of material hardening laws and provides a hardness input that reflects the true state changes of the material for wear prediction.

[0053] In one alternative embodiment of this implementation, the accumulated equivalent plastic strain The calculation formula is:

[0054]

[0055] In the formula, The product of the equivalent plastic strain and the material shear modulus are given. is the plastic strain scaling factor per unit cycle, where m represents the stress level sensitivity. The yield strength of the material. Let be the coefficient of friction at the wheel-rail interface. This is the normal load.

[0056] Specifically, Its physical meaning is that under wheel-rail rolling contact conditions, the near-surface material of the rail surface undergoes periodic shear stress loading and unloading under repeated normal and tangential loads. When the contact stress exceeds the material's yield strength, irreversible plastic deformation occurs locally with each wheel-rail contact cycle. It is used to characterize the degree of plastic accumulation in the material during rolling contact and is an important state variable describing work hardening, wear evolution, and rolling contact fatigue damage.

[0057] In an alternative example, assuming the load, coefficient of friction, and contact conditions remain constant, the plastic strain produced in each cycle can be considered constant, and the cumulative plastic strain can be expressed as:

[0058]

[0059] Preferably, under the condition of yielding, the plastic strain caused by a single wheel-rail contact cycle can be expressed by an empirical cyclic plasticity relationship as follows:

[0060]

[0061] Where G is the material shear modulus. is the plastic strain scaling factor per unit cycle, where m represents the stress level sensitivity. and m are parameters reflecting the cyclic plasticity of the material, which can be obtained by fitting through a standard cyclic loading-unloading test (controlled equivalent stress).

[0062] Preferably, the Von Mises equivalent stress criterion is used to convert shear stress into equivalent stress. The shear yield stress is expressed as:

[0063]

[0064] in, The yield strength of the material.

[0065] Preferably, considering friction and creep, the maximum shear stress in the contact area can be approximated as:

[0066]

[0067] in, For the maximum shear stress, Let be the coefficient of friction at the wheel-rail interface. This represents the normal contact stress.

[0068] Preferably, when At that time, it is assumed that the cycle does not produce plastic strain, that is... This expression reflects that plastic strain is driven only by the super-yield shear stress portion and exhibits a nonlinear growth characteristic.

[0069] In an optional example, the plastic strain scaling factor per unit cycle is obtained by fitting a standard cyclic loading-unloading test. The stress level sensitivity coefficient m is used to obtain the plastic strain scale factor and stress level sensitivity coefficient per unit cycle, reflecting the cyclic plasticity characteristics of the material, through standard cyclic loading and unloading tests. The intrinsic property parameters of the material are introduced into the calculation process of cumulative equivalent plastic strain, enabling the quantitative characterization of accumulated plastic strain to be based on the material's true mechanical response. This enhances the correlation between model parameters and the material's cyclic plasticity behavior, providing reliable intrinsic property inputs for accurate prediction of the evolution of the surface equivalent cyclic hardness.

[0070] In one optional embodiment of this implementation, a curve showing the change in hardness with the number of cycles is plotted based on the experimental results, and the hardness saturation value is obtained by fitting the curve showing the change in hardness with the number of cycles. And the hardening saturation rate parameter β.

[0071] Specifically, after the double-disc grinding test, the surface hardness data obtained from different cycles were analyzed and processed. A curve showing the change in hardness with the number of cycles was plotted with the number of cycles as the abscissa and surface hardness as the ordinate. The curve showed that the hardness increased with the increase of the number of cycles and gradually tended to stabilize. The hardness scatter points on the curve were fitted to obtain the hardness saturation value of the material. The fitting results, along with the hardening saturation rate parameter β, show that the hardness of the rail surface reaches saturation after approximately 10,000 cycles. This provides a crucial intrinsic parameter for calculating the equivalent cyclic hardness of the surface layer, enabling wear prediction to reflect the true change in material hardness as it nonlinearly increases with the number of cycles until saturation.

[0072] In one alternative embodiment of this implementation, a scatter plot of total wear amount versus cycle number is plotted based on the test results, and a wear coefficient is fitted based on the scatter plot. The sensitivity coefficient α of wear to hardness was determined. By plotting a scatter plot of total wear amount with the number of cycles and fitting the wear coefficient and the sensitivity coefficient of wear to hardness, the key parameters of the wear rate model were experimentally calibrated. This enabled the wheel-rail material wear behavior prediction model to establish a complete quantitative calculation relationship based on measured wear data, thereby achieving the prediction output of wear rate and total wear amount under different cycle numbers.

[0073] In an optional embodiment of the present invention, when the wheel and rail material is rail steel, CL60 wheel steel or ER8 wheel steel is selected as the auxiliary sample; when the wheel and rail material is wheel steel, U71Mn steel or U75V steel is selected as the auxiliary sample. By matching the material of the auxiliary sample with the material of the main sample, the material pairing relationship of the grinding test is made consistent with the actual wheel and rail service condition, ensuring that the double-disc grinding test can reflect the interaction characteristics of wheel and rail materials under real working conditions, and providing an experimental data basis with engineering practical correspondence for the establishment of the prediction model.

[0074] In an optional embodiment of the present invention, the key test parameters for the dual-disc grinding test are contact stress and slip, and at least two sets of dual-disc grinding tests with different key test parameters are conducted. By conducting at least two sets of dual-disc grinding tests with contact stress and slip as key test parameters, wear test data under different working conditions are obtained, providing multi-condition data support for the fitting and calibration of correlation coefficients in the wear prediction model, enabling the established wear prediction model to be applicable to the prediction of wheel-rail wear evolution behavior under different combinations of contact stress and slip.

[0075] In one optional example of this implementation, two sets of repeated tests are required under the same set of key test parameters. This allows for the acquisition of mutually verifiable independent experimental data, ensuring the reliability of the test results and providing a repeatable experimental data basis for fitting and calibrating the wear prediction model parameters.

[0076] Example

[0077] The specific implementation process of the present invention will now be described in detail with reference to the embodiments:

[0078] The present invention will be described in detail with reference to specific embodiments. However, it should not be construed as limiting the scope of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0079] (1) Determine the research subjects

[0080] Clearly define the type of wheel and rail material to be evaluated. When rail steel is used as the main specimen, CL60 wheel steel or ER8 wheel steel should be used as the auxiliary specimen; when wheel steel is used as the main specimen, U71Mn steel or U75V steel should be used as the auxiliary specimen.

[0081] Before the double-disc grinding experiment, it is necessary to characterize the fundamental intrinsic properties of the research object, including: material shear modulus G, yield strength, etc. With initial hardness The plastic strain scaling factor per unit cycle was obtained by fitting a standard cyclic loading-unloading test (controlled equivalent stress). With stress level sensitivity coefficient m.

[0082] To ensure the reliability of the test results, two sets of repeated tests need to be conducted under the same set of grinding parameters.

[0083] (2) Selection of parameters for the double-disc grinding tester

[0084] The key test parameters for conducting double-disc grinding tests are contact stress and slip. In order to simulate the correlation coefficient in the model, at least two sets of experiments with different parameters need to be set up, such as high stress (1200MPa) + small slip (0.5%) and small stress (1000MPa) + large slip (2%).

[0085] During the experiment, the rotation was stopped at 5000, 10,000, 20,000, 30,000, 40,000 and 50,000 respectively, and the surface hardness and wear of the sample were measured. At the same time, the change of torque was monitored during the experiment, and the friction coefficient can be obtained through subsequent data analysis.

[0086] (3) Establish a prediction model for wheel-rail material wear behavior

[0087] After the experiment, the two sets of experimental data were analyzed and processed, such as... Figure 1 As shown (red dots represent measured values, blue lines represent fitted curves), the analysis begins with... Figure 1 The hardness curve of the material versus the number of cycles was fitted with hardness scatter points according to the formula for equivalent cyclic hardness of the surface layer to obtain the hardness saturation value. The hardening saturation rate parameter β is obtained by fitting the cycle number corresponding to the hardness saturation value.

[0088] After obtaining H(N) as described by the formula for the equivalent cyclic hardness of the surface layer, the wear rate as a function of the number of cycles can be obtained, such as... Figure 2As shown, for Figure 2 The integral represents the curve of total wear as a function of the number of cycles, and then at this point... The relationship between 'a' and 'a' is still unknown and needs to be fitted using measured data.

[0089] For the scatter plot of the total wear amount measured in the experiment as a function of the number of cycles, such as Figure 3 As shown (red dots represent measured values, blue line represents the fitted curve), the fitted curve... Together with a, we finally obtain a wear model based on the wear rate formula.

[0090] The detailed explanations of the above embodiments are intended only to explain the present invention so as to facilitate a better understanding of the present invention. However, these descriptions should not be construed as limiting the present invention for any reason. In particular, the various features described in different embodiments can be arbitrarily combined with each other to form other embodiments. Unless there is an explicit description to the contrary, these features should be understood to be applicable to any embodiment, and not limited to the described embodiments.

Claims

1. A method for predicting wheel-rail wear evolution, characterized in that, The wheel-rail wear evolution prediction method includes: Select wheel and rail materials and process the wheel and rail materials into the main specimens for the grinding test; A grinding test was conducted and the test results were obtained, including the surface hardness and wear amount of the grinding sample measured at different cycles. Based on the experimental results, fit the model parameters and establish a prediction model for wheel-rail material wear behavior; Wheel and rail wear is predicted using the wheel and rail material wear behavior prediction model.

2. The wheel-rail wear evolution prediction method as described in claim 1, characterized in that, The calculation formula for the wheel-rail material wear behavior prediction model is as follows: In the formula, For wear rate, For normal load, The sliding distance within a single contact. denoted as the wear coefficient, α as the wear sensitivity coefficient to hardness, and H(N) as the surface equivalent cyclic hardness.

3. The wheel-rail wear evolution prediction method as described in claim 1, characterized in that, H(N) is represented as: ， In the formula, This represents the initial hardness value of the material. This represents the hardness value of the material after saturation. The hardening saturation rate parameter, To accumulate equivalent plastic strain.

4. The wheel-rail wear evolution prediction method as described in claim 3, characterized in that, Cumulative equivalent plastic strain The calculation formula is: In the formula, The product of the equivalent plastic strain and the material shear modulus are given. is the plastic strain scaling factor per unit cycle, where m represents the stress level sensitivity. The yield strength of the material. Let be the coefficient of friction at the wheel-rail interface. This is the normal load.

5. The wheel-rail wear evolution prediction method as described in claim 4, characterized in that, The plastic strain scaling factor per unit cycle was obtained by fitting a standard cyclic loading-unloading test. With stress level sensitivity coefficient m.

6. The wheel-rail wear evolution prediction method as described in claim 3, characterized in that, Based on the test results, a curve showing the change in hardness with the number of cycles was plotted, and the hardness saturation value was obtained by fitting the curve showing the change in hardness with the number of cycles. And the hardening saturation rate parameter β.

7. The wheel-rail wear evolution prediction method as described in claim 2, characterized in that, Based on the test results, a scatter plot of total wear amount versus cycle number was plotted, and a wear coefficient was fitted based on the scatter plot. And the sensitivity coefficient α of wear to hardness.

8. The wheel-rail wear evolution prediction method as described in claim 1, characterized in that, When the wheel and rail material is rail steel, CL60 or ER8 wheel steel is selected as a supplementary sample; when the wheel and rail material is wheel steel, U71Mn or U75V steel is selected as a supplementary sample.

9. The wheel-rail wear evolution prediction method as described in claim 1, characterized in that, The key test parameters for the grinding test are contact stress and slip, and at least two sets of grinding tests with different key test parameters are conducted.

10. The wheel-rail wear evolution prediction method as described in claim 9, characterized in that, Two sets of repeated tests are required for the same set of key test parameters.

11. A wheel-rail wear evolution prediction device, characterized in that, include: The data acquisition module is used to acquire the surface hardness data and wear data of the wheel-rail grinding test specimens measured at different cycles. The parameter fitting module is used to fit at least one of the following parameters based on the experimental data acquired by the data acquisition module: wear coefficient. The sensitivity coefficient of wear to hardness (a) and the hardness saturation value. Hardening saturation rate parameter β, plastic strain scaling factor Stress level sensitivity coefficient m; The model building module is used to establish a wheel-rail material wear behavior prediction model as described in any one of claims 1-10; The prediction module, based on the aforementioned wheel-rail material wear behavior prediction model, outputs the wear rate variation curve with the number of cycles and / or the total wear amount variation curve with the number of cycles, according to the input normal load F, slip, contact stress, and friction coefficient μ.

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