Method and system for acquiring structure and hardness distribution data of wear-resistant steel ball

Abstract

The invention belongs to the technical field of heat treatment process parameter optimization, and particularly relates to a wear-resistant steel ball structure and hardness distribution data obtaining method and system.The method comprises the steps that firstly, material characteristic parameters are obtained through calculation and experiments according to alloy steel components; constructing an axisymmetric three-dimensional steel ball model, integrating material characteristics and heat treatment process parameters, embedding heat transfer, phase change and stress-strain constitutive equations, and compiling to form a multi-physics field coupling finite element model; solving the model to obtain spatial and temporal change data of temperatures, phase change rate constants, transformation volume fractions and hardness of different positions of the steel ball, and fitting through MATLAB to obtain a structure and hardness evolution rule; and finally, outputting optimal wear-resistant steel ball parameters through parameterized scanning and model optimization. The problem that in the prior art, an effective tissue and hardness data obtaining means is lacked in the heat treatment process of the steel balls can be solved.

Description

Technical Field

[0001] This invention belongs to the field of heat treatment process parameter optimization technology, and in particular relates to a method and system for obtaining microstructure and hardness distribution data of wear-resistant steel balls. Background Technology

[0002] Wear-resistant steel balls are an important component in large ball mills used in mining. During equipment operation, they crush and grind ore raw materials, and their overall hardness significantly impacts operating efficiency. Besides altering the material composition, increasing the hardness of steel materials can be achieved through heat treatment processes to further improve their properties. However, the heat treatment process for steel balls is a complex nonlinear problem involving numerous physical fields such as temperature, microstructure, stress, and strain. Traditional research methods struggle to provide in-depth studies of its dynamic evolution.

[0003] Processing plants often rely on experience to roughly determine material composition and heat treatment process parameters. Furthermore, only destructive testing of the heat-treated steel balls can yield information on microstructure and hardness. However, this empirical method has significant limitations, such as long testing cycles, complex operations, and high costs. It also cannot directly obtain microstructure and hardness information, increasing production costs.

[0004] While some finite element models can obtain some data from the heat treatment process, there is no rapid method for acquiring data on microstructure and hardness distribution. Furthermore, existing data exists in isolation and is not correlated with material composition and process parameters, lacking a feedback mechanism, thus limiting its practical application value. Therefore, a faster and more effective method is needed to solve this problem. Summary of the Invention

[0005] The technical problem solved by this invention is to provide a method and system for obtaining microstructure and hardness distribution data of wear-resistant steel balls, so as to solve the problem that the existing technology lacks effective means of obtaining microstructure and hardness data under the heat treatment process of steel balls.

[0006] The basic solution provided by this invention is a method for obtaining microstructure and hardness distribution data of wear-resistant steel balls, comprising:

[0007] S1: Based on the composition of the alloy steel used for wear-resistant steel balls, its material property parameters are obtained through calculation and experimentation;

[0008] S2: Establish an axisymmetric three-dimensional steel ball model based on finite element software, add material property parameters to the axisymmetric three-dimensional steel ball model, and set heat treatment process parameters;

[0009] S3: Add the heat transfer constitutive equation, phase transition constitutive equation and stress-strain constitutive equation to the axisymmetric three-dimensional steel ball model and compile it to obtain a multi-physics coupled finite element model of latent heat of phase transition and phase transition strain.

[0010] S4: Solve the multiphysics coupled finite element model to obtain the continuous change values ​​of temperature over time at different positions of the steel ball, the continuous change values ​​of phase transformation rate constant and transformation volume fraction over time at different positions of the steel ball, and the hardness data at different positions of the steel ball under the preset cooling conditions. Based on MATLAB fitting, obtain the change law of the microstructure and hardness data of the steel ball from the inside to the outside during the heat treatment cooling process.

[0011] S5: The material composition, process parameters, geometric parameters, microstructure, and hardness data of the wear-resistant steel ball are parametrically scanned. The parameters are optimized by using a multi-physics coupled finite element model based on the changes in microstructure and hardness data of the steel ball from the inside to the outside during the heat treatment and cooling process, so as to obtain the optimal wear-resistant steel ball parameters.

[0012] Furthermore, S1 includes:

[0013] S1-1: Based on the composition of the alloy steel used for wear-resistant steel balls, the material property parameters that change with time are calculated using thermodynamic software, including but not limited to density, thermal conductivity, Young's modulus, Poisson's ratio, bulk modulus, specific heat capacity, CCT curve and TTT curve.

[0014] S1-2: The deformation of wear-resistant steel ball specimens caused by phase transformation plasticity at different temperatures was measured by thermal simulation experiments, and the phase transformation parameters during phase transformation were analyzed.

[0015] Furthermore, S2 includes:

[0016] S2-1: Use finite element software to establish an axisymmetric three-dimensional steel ball model, and add the material property parameters and phase transformation parameters of the wear-resistant steel ball to the material properties of the axisymmetric three-dimensional steel ball model;

[0017] S2-2: Set the initial conditions and boundary conditions for the axisymmetric three-dimensional steel ball model respectively.

[0018] Furthermore, S3 includes:

[0019] S3-1: Construct the heat transfer constitutive equation based on Fourier's law of heat conduction, with the following expression:

[0020]

[0021] Where T is temperature and t is time. Let be the partial derivative of temperature with respect to time, and α be the thermal diffusivity. Let Q be the Laplace operator for temperature T, ρ be the material density, and Cp be the constant-pressure hot melt.

[0022] S3-2: Construct the phase transition constitutive equation based on phase transition dynamics, the expression of which is:

[0023] X = 1 - exp(-Kt) n )

[0024] Where X is the phase change volume fraction, K is the phase change rate constant, n is the Avrami exponent, and t is time;

[0025] S3-3: Based on the elastoplastic constitutive model, von Mises yield criterion, and flow law, the stress-strain constitutive equation is constructed for isotropic materials, expressed as follows:

[0026]

[0027] Where, σ ij Let f(σ) represent the stress tensor. ij S represents the yield function. ii S jj S ij Denotes the deviatoric stress tensor, σ y The yield stress;

[0028] S3-4: Using the temperature parameter T as the connection point, the heat transfer constitutive equation, phase transition constitutive equation, and stress-strain constitutive equation are compiled and discretized to generate a system of thermo-mechanical coupled linear algebraic equations, characterizing the multi-physics coupled finite element model. The expression is as follows:

[0029]

[0030] Where T(x,t) is the temperature field function, x is the spatial coordinate, and t is time; The phase change volume fraction. Let σ represent the stress field function, and σ represent the stress. Indicates material properties as a function of phase change volume fraction Changes;

[0031] S3-5: Based on the dimensions of the axisymmetric three-dimensional steel ball model, the mesh is divided and the number of meshes is determined. An adaptive mesh refinement algorithm that adjusts according to the number of meshes is introduced into the multiphysics coupled finite element model to obtain a multiphysics coupled finite element model that automatically adjusts the mesh density according to the axisymmetric three-dimensional steel ball model. A time step is added for transient calculation.

[0032] Furthermore, S4 includes:

[0033] S4-1: Based on preset cooling conditions and different positions of the steel ball, the continuous change value of temperature T(x,y,z,t) at the target position (x,y,z) with time t is output through a multi-physics coupled finite element model. The Fourier heat conduction law is applied inside the steel ball, and a custom heat convection htc(T) is used at the boundary.

[0034] S4-2: Based on preset cooling conditions and different positions of the steel ball, the phase transition rate constant K(x,y,z,t) and the phase transition volume fraction Z(x,y,z,t) at the target position (x,y,z) are continuously output as a function of time t through a multi-physics coupled finite element model.

[0035] S4-3: Based on preset cooling conditions and different positions of the steel ball, the hardness data HV(x,y,z,t) at the target position (x,y,z) is output through a multiphysics coupled finite element model and hybrid rules. The expression is:

[0036]

[0037] Among them, X i (x,y,z,t) represents the volume fraction of the phase transformation structure, H i The corresponding hardness is given by n, where n is the number of phase transformation structures.

[0038] S4-4: Based on the data obtained in S4-1 to S4-3 above, the changes in microstructure and hardness of the steel ball from the inside to the outside during the heat treatment cooling process were obtained using a MATLAB fitting program, generating a fitting equation with the following expression:

[0039]

[0040] in, T represents x The relative volume fraction of tissue i at temperature, X i (x,y,z,t,T x ) represents T x The volume fraction of tissue i at time t at (x,y,z) coordinates under temperature, T x Indicates the set temperature; X i (x,y,z,t,T0) represents the volume fraction of tissue i at time t at coordinates (x,y,z) at temperature T0, and T0 represents the ambient set temperature; T represents x The relative hardness value of tissue i at temperature, H i (x,y,z,t,T x ) represents T x The hardness value of tissue i at time t at (x,y,z) coordinates under temperature, H i(x,y,z,t,T0) represents the hardness value of tissue i at time t at coordinates (x,y,z) at temperature T0.

[0041] Furthermore, S5 includes:

[0042] S5-1: Parametric scan input variables include the material composition, process parameters, and geometric parameters of the axisymmetric three-dimensional steel ball model of the wear-resistant steel ball;

[0043] S5-2: Input the input variables of S5-1 into the multiphysics coupled finite element model, and use the MATLAB-based fitting program to obtain the changes in the microstructure and hardness of the steel ball from the inside to the outside during the heat treatment cooling process, and output the predicted microstructure and hardness data.

[0044] S5-3: Based on the predicted microstructure and hardness data, adjust the parameters of the input variables until the optimal wear-resistant steel ball parameters are obtained.

[0045] A system for acquiring microstructure and hardness distribution data of wear-resistant steel balls, applied to the aforementioned method for acquiring such data, includes a material property parameter processing module, a model construction and parameter configuration module, a multiphysics coupling model compilation module, a simulation solution and data processing module, and a process optimization and parameter scanning module, wherein:

[0046] The material property parameter processing module is used to obtain the material property parameters of the alloy steel used for wear-resistant steel balls through calculation and experimentation based on the composition of the alloy steel.

[0047] The model building and parameter configuration module is used to build an axisymmetric three-dimensional steel ball model based on finite element software, add material property parameters to the axisymmetric three-dimensional steel ball model, and set heat treatment process parameters.

[0048] The multiphysics coupling model compilation module is used to add the heat transfer constitutive equation, phase change constitutive equation and stress-strain constitutive equation to the axisymmetric three-dimensional steel ball model and compile it to obtain a multiphysics coupling finite element model of phase change latent heat-phase change strain.

[0049] The simulation and data processing module is used to solve the multi-physics coupled finite element model, and obtain the continuous change values ​​of temperature over time at different positions of the steel ball, the continuous change values ​​of phase transformation rate constant and transformation volume fraction over time at different positions of the steel ball, and the hardness data at different positions of the steel ball under the preset cooling conditions. Based on MATLAB fitting, the change law of the microstructure and hardness data of the steel ball from the inside to the outside during the heat treatment cooling process is obtained.

[0050] The process optimization and parameter scanning module is used to perform parameterized scanning of the material composition, process parameters, geometric parameters and microstructure, and hardness data of the axisymmetric three-dimensional steel ball model. Through a multi-physics coupled finite element model, the parameters are optimized according to the changes in microstructure and hardness data of the steel ball from the inside to the outside during the heat treatment and cooling process, so as to obtain the optimal wear-resistant steel ball parameters.

[0051] The principle and advantages of this invention are as follows: First, based on the alloy steel composition of the wear-resistant steel ball, material characteristic parameters are obtained through calculation and experimentation; then, an axisymmetric three-dimensional steel ball model is constructed using finite element software, integrating material characteristics and heat treatment process parameters, and the constitutive equations of heat transfer, phase transformation, and stress-strain are compiled into a multi-physics coupled finite element model; by solving the model, data such as temperature, phase transformation parameters, and hardness are obtained, and the microstructure and hardness variation laws are fitted using MATLAB; finally, by parametric scanning combined with model optimization, the optimal quenching heat treatment process is obtained, realizing multi-physics collaborative simulation and process optimization.

[0052] The advantages are as follows: By using a multi-physics coupled finite element model for precise simulation, continuous change data of temperature, phase transformation and hardness at different locations of the steel ball can be obtained, and the evolution law of microstructure and hardness from the inside to the outside during the heat treatment cooling process can be clearly grasped; through parametric scanning and process optimization, the uniformity of steel ball microstructure and hardness compliance rate can be improved in a targeted manner, effectively shortening the process development cycle and reducing the test cost, and providing a more efficient and high-quality quenching heat treatment process for wear-resistant steel balls, thereby enhancing product performance and market competitiveness. Attached Figure Description

[0053] Figure 1 This is a flowchart of an embodiment of the present invention;

[0054] Figure 2 This is a TTT curve of the alloy steel used for wear-resistant steel balls in an embodiment of the present invention;

[0055] Figure 3 This is a CCT curve of the alloy steel used for wear-resistant steel balls in an embodiment of the present invention;

[0056] Figure 4 This is a phase transformation plasticity curve of alloy steel used for wear-resistant steel balls in an embodiment of the present invention;

[0057] Figure 5 This is an axisymmetric three-dimensional steel ball model diagram of the wear-resistant steel ball in an embodiment of the present invention;

[0058] Figure 6 This is a temperature gradient cloud map of the steel ball model under flowing air in an embodiment of the present invention;

[0059] Figure 7 This is a cooling curve diagram of the steel ball model under circulating air in an embodiment of the present invention;

[0060] Figure 8 This is a microstructure gradient cloud map of the steel ball model under circulating air in an embodiment of the present invention;

[0061] Figure 9 This is a hardness gradient cloud map of the steel ball model under flowing air in an embodiment of the present invention.

[0062] Figure 10 This is a microstructure-hardness fitting curve of the steel ball model under flowing air in an embodiment of the present invention;

[0063] Figure 11 This is a temperature gradient cloud map of the steel ball model under flowing cooling oil in an embodiment of the present invention;

[0064] Figure 12 This is a cooling curve diagram of the steel ball model under flowing cooling oil in an embodiment of the present invention;

[0065] Figure 13 This is a microstructure gradient cloud map of the steel ball model under flowing cooling oil in an embodiment of the present invention;

[0066] Figure 14 This is a hardness gradient cloud map of the steel ball model under flowing cooling oil in an embodiment of the present invention;

[0067] Figure 15 This is a microstructure-hardness fitting curve of the steel ball model under flowing cooling oil in an embodiment of the present invention;

[0068] Figure 16 This is a temperature gradient cloud map of the steel ball model under flowing coolant in an embodiment of the present invention;

[0069] Figure 17 This is a cooling curve diagram of the steel ball model under flowing coolant in an embodiment of the present invention;

[0070] Figure 18 This is a microstructure gradient cloud map of the steel ball model under flowing coolant in an embodiment of the present invention;

[0071] Figure 19 This is a hardness gradient cloud map of the steel ball model under flowing coolant in an embodiment of the present invention;

[0072] Figure 20 This is a microstructure-hardness fitting curve of the steel ball model under flowing coolant in an embodiment of the present invention;

[0073] Figure 21 This is a microstructure-hardness fitting curve of the steel ball model after process optimization in an embodiment of the present invention. Detailed Implementation

[0074] The following detailed description illustrates the specific implementation method:

[0075] The basic implementation examples are as follows: Figure 1 As shown: A method for obtaining microstructure and hardness distribution data of wear-resistant steel balls, including:

[0076] S1: Based on the composition of the alloy steel used for wear-resistant steel balls, its material property parameters are obtained through calculation and experimentation; wherein, S1 includes:

[0077] S1-1: Based on the composition of the alloy steel used for wear-resistant steel balls, the material property parameters that change with time are calculated using thermodynamic software, including but not limited to density, thermal conductivity, Young's modulus, Poisson's ratio, bulk modulus, specific heat capacity, CCT curve and TTT curve.

[0078] S1-2: The deformation of wear-resistant steel ball specimens caused by phase transformation plasticity at different temperatures was measured by thermal simulation experiments, and the phase transformation parameters during phase transformation were analyzed.

[0079] In this embodiment, the chemical composition of the alloy steel used for the wear-resistant steel balls is shown in Table 1 below:

[0080] Table 1. Chemical composition (wt%) of alloy steel for wear-resistant steel balls

[0081] element C Si Mn Cr Fe content 0.35-0.50 1.56-1.65 1.85-2.0 0.50-0.75 Bal.

[0082] Input the component range values ​​in Table 1 above into the General Steel module in Jmatpro software, then enter the Dynamic module to generate thermophysical parameters, and then return to the main page to enter the TTT / CCT Diagrams module to generate TTT and CCT curves.

[0083] The thermophysical parameters include density, thermal conductivity, Young's modulus, Poisson's ratio, bulk modulus, and specific heat capacity; while the TTT curve is shown in the figure. Figure 2 As shown, the CCT curve is as follows: Figure 3 As shown, the TTT curve and CCT curve contain information on 10 phase transformations, specifically: austenite → pearlite; austenite → ferrite; austenite → bainite; austenite → martensite; pearlite → ferrite; pearlite → bainite; pearlite → martensite; ferrite → bainite; ferrite → martensite; bainite → martensite.

[0084] Then, through thermal simulation experiments, we obtained the following results: Figure 4 The various phase transition temperature points and phase transition parameters are shown, where the horizontal axis represents temperature and the vertical axis represents the specimen size change measured by the dilatometer. From this, the thermal expansion characteristics and phase transition parameters of the alloy steel used for wear-resistant steel balls can be analyzed.

[0085] S2: Establish an axisymmetric three-dimensional steel sphere model based on finite element software, add material property parameters to the axisymmetric three-dimensional steel sphere model, and set heat treatment process parameters; wherein, S2 includes:

[0086] S2-1: Use finite element software to establish an axisymmetric three-dimensional steel ball model, and add the material property parameters and phase transformation parameters of the wear-resistant steel ball to the material properties of the axisymmetric three-dimensional steel ball model;

[0087] S2-2: Set the initial conditions and boundary conditions for the axisymmetric three-dimensional steel ball model respectively.

[0088] In this embodiment, the material property parameters obtained in step S1 are input into the finite element software to establish a material library, an axisymmetric geometric sphere with a diameter of 150 mm is created, a free tetrahedral mesh is drawn, and a boundary transition layer is set, resulting in 33242 domain elements, as shown below. Figure 5 As shown; then the initial conditions, boundary conditions and process parameters are set respectively, and the calculation step size is set to range(0,5,3000); range(3000,10,6000); In this embodiment, the initial conditions are specifically set as follows: the initial ambient temperature is 20℃; the initial temperature of the steel ball is 840℃; the boundary condition distribution is set as the heat transfer condition equation: htc(a), htc(o), htc(w); the tempering temperature is set to 200℃ and the time is set to 1200s.

[0089] S3: The heat transfer constitutive equation, phase transition constitutive equation, and stress-strain constitutive equation are added to the axisymmetric three-dimensional steel sphere model and compiled to obtain a multiphysics coupled finite element model of latent heat of phase transition and phase transition strain; wherein, S3 includes:

[0090] S3-1: Construct the heat transfer constitutive equation based on Fourier's law of heat conduction, with the following expression:

[0091]

[0092] Where T is temperature and t is time. α is the partial derivative of temperature with respect to time, used to describe the rate of change of temperature over time, reflecting the dynamic changes of temperature in the time dimension; α is the thermal diffusivity, which reflects the material's ability to propagate temperature changes and is related to the material's thermal conductivity, density, and specific heat capacity. is the Laplace operator for temperature T, used to describe the distribution of temperature in three spatial dimensions, reflecting the rate of change of the temperature gradient; Q is the heat source, ρ is the material density, and Cp is constant-pressure hot melting.

[0093] S3-2: Construct the phase transition constitutive equation based on phase transition dynamics, the expression of which is:

[0094] X = 1 - exp(-Kt)n )

[0095] Where X is the phase change volume fraction, K is the phase change rate constant, n is the Avrami exponent, and t is time;

[0096] S3-3: Based on the elastoplastic constitutive model, von Mises yield criterion, and flow law, the stress-strain constitutive equation is constructed for isotropic materials, expressed as follows:

[0097]

[0098] Where, σ ij Let f(σ) represent the stress tensor. ij f(σ) represents the yield function, used to determine whether a material has entered the yield state. ij When f(σ) = 0, the material reaches the yield state and begins to undergo plastic deformation. ij When ) < 0, the material is in an elastic state and only undergoes elastic deformation; S ii S jj S ij σ represents the component of the deviatoric stress tensor in the direction corresponding to the subscript. y The yield stress;

[0099] S3-4: Using the temperature parameter T as the connection point, the heat transfer constitutive equation, phase transition constitutive equation, and stress-strain constitutive equation are compiled and discretized to generate a system of thermo-mechanical coupled linear algebraic equations, characterizing the multi-physics coupled finite element model. The expression is as follows:

[0100]

[0101] Where T(x,t) is the temperature field function, x is the spatial coordinate, and t is time, representing the temperature distribution at different spatial locations x and different times t; The phase change volume fraction. This indicates that no phase transition occurred. This indicates that the phase transition is complete; Let σ represent the stress field function, and σ represent the stress. Indicates material properties as a function of phase change volume fraction The parameters mentioned above are coupled through temperature T; heat conduction affects the temperature field T(x,t), and temperature changes drive phase transitions. Evolution, phase transition, changes material properties and thus affects the stress field Stress and temperature also have an inverse effect on heat transfer and phase transition, together forming a multi-physics coupled finite element model;

[0102] S3-5: Based on the dimensions of the axisymmetric 3D steel sphere model, the mesh is generated and the number of meshes is determined. An adaptive mesh refinement algorithm that adjusts based on the number of meshes is introduced into the multiphysics coupled finite element model, resulting in a multiphysics coupled finite element model with automatically adjusted mesh density based on the axisymmetric 3D steel sphere model. A time step is then added for transient calculations. The mesh generation process is as shown in the implementation scheme of S2 above, i.e. Figure 5 The introduced adaptive mesh refinement algorithm aims to improve the computational accuracy and efficiency of multiphysics coupled finite element models. Specifically, it first calculates the error index of each mesh element, taking the temperature gradient as an example:

[0103]

[0104] Where, η i This represents the error index of the i-th cell. This represents the temperature gradient of the unit.

[0105] Then set the error threshold η th The expression is:

[0106]

[0107] Where η0 is the basic error threshold and d is the radius of the steel ball;

[0108] Therefore, mesh elements with error indices greater than the error threshold are refined, while mesh elements with smaller error indices are coarsened. This process yields a multiphysics coupled finite element model that can automatically adjust the mesh density according to the model's geometry. Finally, a time step is added.

[0109] S4: Solve the multiphysics coupled finite element model to obtain the continuous temperature variation over time, the continuous phase transformation rate constant and transformation volume fraction over time, and the hardness data at different locations of the steel ball under preset cooling conditions. Based on MATLAB fitting, obtain the variation law of microstructure and hardness data of the large-diameter steel ball from the inside to the outside during the heat treatment cooling process. S4 includes:

[0110] S4-1: Based on preset cooling conditions and different positions of the steel ball, the continuous change value of temperature T(x,y,z,t) at the target position (x,y,z) with time t is output through a multi-physics coupled finite element model. The Fourier heat conduction law is applied inside the steel ball, and a custom heat convection htc(T) is used at the boundary.

[0111] S4-2: Based on preset cooling conditions and different positions of the steel ball, the phase transition rate constant K(x,y,z,t) and the phase transition volume fraction X(x,y,z,t) at the target position (x,y,z) are continuously output as a function of time t through a multi-physics coupled finite element model.

[0112] S4-3: Based on preset cooling conditions and different positions of the steel ball, the hardness data HV(x,y,z,t) at the target position (x,y,z) is output through a multiphysics coupled finite element model and hybrid rules. The expression is:

[0113]

[0114] Among them, X i (x,y,z,t) represents the volume fraction of the phase transformation structure, H i The corresponding hardness is given by n, where n is the number of phase transformation structures.

[0115] S4-4: Based on the data obtained in S4-1 to S4-3 above, the changes in microstructure and hardness of the steel ball from the inside to the outside during the heat treatment cooling process were obtained using a MATLAB fitting program, generating a fitting equation with the following expression:

[0116]

[0117] in, T represents x The relative volume fraction of tissue i at temperature, X i (x,y,z,t,T x ) represents T x The volume fraction of tissue i at time t at (x,y,z) coordinates under temperature, T x Indicates the set temperature; X i (x,y,z,t,T0) represents the volume fraction of tissue i at time t at coordinates (x,y,z) at temperature T0, and T0 represents the ambient set temperature; T represents x The relative hardness value of tissue i at temperature, H i (x,y,z,t,T x ) represents T x The hardness value of tissue i at time t at (x,y,z) coordinates under temperature, H u (x,y,z,t,T0) represents the hardness value of tissue i at time t at coordinates (x,y,z) at temperature T0.

[0118] The preset cooling conditions include circulating air, circulating cooling oil, and circulating coolant. The cooling oil is quenching oil, which has a slower cooling rate; the coolant is quenching fluid, which has a faster cooling rate. Both are commercially available models. Based on the content range of each component of the alloy steel in S1, dynamic data on the temperature, microstructure, and hardness of the wear-resistant steel ball at different locations over time under the three cooling conditions were calculated. The results obtained are as follows: Figures 6 to 20 As shown, where:

[0119] Figure 6 This is a temperature gradient cloud map under circulating air. Figure 7 This is a cooling curve diagram under circulating air. Figure 11 This is a temperature gradient cloud map under circulating cooling oil. Figure 12 This is a cooling curve diagram under circulating cooling oil. Figure 16 This is a temperature gradient cloud map under flowing coolant. Figure 17 The cooling curves under flowing coolant conditions show the temperature distribution and cooling process of the steel ball's surface, middle, and core under flowing air, flowing coolant, and flowing coolant conditions. Different cooling rates are clearly observed during the quenching stage, which will greatly affect the evolution and distribution of the microstructure, and thus affect the hardness and distribution.

[0120] Figure 8 This is a tissue gradient cloud map under circulating air. Figure 9 This is a hardness gradient cloud map under circulating air. Figure 13 This is a microstructure gradient cloud map under circulating cooling oil. Figure 14 This is a hardness gradient cloud map of circulating cooling oil. Figure 18 This is a tissue gradient cloud map under flowing coolant. Figure 19 This is a hardness gradient cloud map under flowing coolant, from which the distribution and content of microstructure types under flowing air, flowing cooling oil, and flowing coolant, as well as the hardness distribution and magnitude after the process, can be obtained; specifically: the microstructure types of steel balls after heat treatment are mainly retained austenite, pearlite, bainite, and martensite, with... Figure 8 For example, blue represents retained austenite, green represents pearlite, red represents bainite, and cyan represents martensite. Comparing conditions under flowing air, flowing cooling oil, and flowing coolant, the retained austenite content is lowest in all cases, gradually increasing with process switching. The pearlite content is around 30%, gradually decreasing with process switching. The bainite content also gradually decreases with process switching, from 22% to around 10%. The martensite content increases significantly with process switching, from 2% to around 70%. Hardness values ​​are closely related to the content and type of microstructure. Figure 9 , Figure 14 and Figure 19It can be seen that the difference in hardness between the inside and outside is the smallest and the distribution is the most uniform under the air-flowing condition among the three processes. However, the maximum value is the lowest among the three processes (maximum hardness value of air-flowing is 236.3 HV, maximum hardness value of cooling oil-flowing is 700 HV, and maximum hardness value of coolant-flowing is 725 HV). The hardness values ​​under cooling oil-flowing and coolant-flowing conditions are relatively close, but... Figure 19 It can be seen that under the conditions of circulating coolant, there is a buffer zone with a maximum hardness of about 3 mm on the surface of the steel ball, and Figure 14 This area does not exist under conditions of circulating cooling oil.

[0121] Figure 10 This is a graph showing the fit between the microstructure and hardness under circulating air. Figure 15 This is a microstructure-hardness fitting curve under circulating cooling oil. Figure 20 The microstructure-hardness fitting curves under flowing coolant conditions reveal the direct relationship between microstructure and hardness under different cooling conditions: For quenched steel, the microstructure factors most significantly affecting hardness are bainite and martensite. Under flowing air cooling conditions, pearlite has the highest content while martensite has the lowest, resulting in the lowest hardness value and the smallest difference between internal and external hardness. Under flowing oil and coolant cooling conditions, martensite has the highest content, resulting in the highest hardness value. However, under flowing oil cooling conditions, the pearlite content is higher than the bainite content, leading to a slightly lower hardness value and a larger difference between internal and external hardness. Under flowing coolant conditions, bainite content is higher than pearlite and its distribution is more uniform, resulting in the highest hardness value and a relatively smaller difference between internal and external hardness.

[0122] S5: Parametrically scan the material composition, process parameters, geometric parameters, microstructure, and hardness data of the wear-resistant steel ball's axisymmetric three-dimensional model. Optimize the parameters using a multiphysics coupled finite element model based on the changes in microstructure and hardness data from the inside out during the heat treatment and cooling process, obtaining the optimal wear-resistant steel ball parameters and the optimal heat treatment process. S5 includes:

[0123] S5-1: Parametric scan input variables include the material composition, process parameters, and geometric parameters of the axisymmetric three-dimensional steel ball model of the wear-resistant steel ball;

[0124] S5-2: Input the input variables of S5-1 into the multiphysics coupled finite element model, and use the MATLAB-based fitting program to obtain the changes in the microstructure and hardness of the steel ball from the inside to the outside during the heat treatment cooling process, and output the predicted microstructure and hardness data.

[0125] S5-3: Based on the predicted microstructure and hardness data, adjust the parameters of the input variables until the optimal wear-resistant steel ball parameters are obtained.

[0126] The parametric scan input variables are: material composition [mC, mSi, mMn, nCr], and process parameters [T0, T]. x [,htc(T)], model geometric parameters d;

[0127] The output variable is: tissue content [f] MA (x,t),f MP (x,t),f MB (x,t),f MM (x,t)], hardness [f] HV [x,t];

[0128] In this embodiment, the initial parameters are:

[0129] Material composition: mC = 0.39, mSi = 1.59, mMn = 1.95, mCr = 0.5, the remainder is Fe;

[0130] Process parameters: T0 = 20℃, T x =840℃, htc(T)=htc(a / o / w);

[0131] Model geometric parameters: d = 75 mm;

[0132] f MM (x=75, t=20)≈0.7 and f HV (x=70-75, t=20)≈700 is the optimal value for microstructure / hardness. The process selection flow is: parametric scanning → multiphysics coupled finite element model → data fitting → prediction of optimal microstructure / hardness values. Specifically, a thermo-mechanical coupled linear algebraic equation system was obtained by fitting the microstructure and hardness data of the steel ball from the inside out during the heat treatment cooling process using MATLAB. The optimized process parameters are as follows:

[0133] Material composition: mC = 0.6, mSi = 1.72, mMn = 1.9, mCr = 0.75, the remainder is Fe;

[0134] Process parameters: T0 = 20℃, T x =840℃, htc(T)=htc(a / o / w);

[0135] Model geometric parameters: d = 75 mm;

[0136] In predicting the evolution of organizations, f MM (x=75, t=20)=0.75,f HV (x = 70 - 75, t = 20) = 710;

[0137] Ultimately, we obtained Figure 21The microstructure-hardness fitting curves of the optimized process, as shown in the experimental characterization, indicate that the hardness value has been further improved (maximum hardness value is 730 HV), while the difference between internal and external hardness is smaller (approximately 330 HV), and a thicker buffer zone for the maximum hardness value is present (approximately 5 mm thick). Furthermore, the measured data and predicted data are largely consistent, demonstrating the significant practicality of the method for obtaining microstructure and hardness distribution data of wear-resistant steel balls according to this invention.

[0138] In another embodiment of this invention, a system for acquiring microstructure and hardness distribution data of wear-resistant steel balls is also included, applied to the aforementioned method for acquiring microstructure and hardness distribution data of wear-resistant steel balls. This system includes a material property parameter processing module, a model construction and parameter configuration module, a multiphysics coupling model compilation module, a simulation solution and data processing module, and a process optimization and parameter scanning module, wherein:

[0139] The material property parameter processing module is used to obtain the material property parameters of the alloy steel used for wear-resistant steel balls through calculation and experimentation based on the composition of the alloy steel.

[0140] The model building and parameter configuration module is used to build an axisymmetric three-dimensional steel ball model based on finite element software, add material property parameters to the axisymmetric three-dimensional steel ball model, and set heat treatment process parameters.

[0141] The multiphysics coupling model compilation module is used to add the heat transfer constitutive equation, phase change constitutive equation and stress-strain constitutive equation to the axisymmetric three-dimensional steel ball model and compile it to obtain a multiphysics coupling finite element model of phase change latent heat-phase change strain.

[0142] The simulation and data processing module is used to solve the multi-physics coupled finite element model, and obtain the continuous change values ​​of temperature over time at different positions of the steel ball, the continuous change values ​​of phase transformation rate constant and transformation volume fraction over time at different positions of the steel ball, and the hardness data at different positions of the steel ball under the preset cooling conditions. Based on MATLAB fitting, the change law of the microstructure and hardness data of the steel ball from the inside to the outside during the heat treatment cooling process is obtained.

[0143] The process optimization and parameter scanning module is used to perform parameterized scanning of the material composition, process parameters, geometric parameters and microstructure, and hardness data of the axisymmetric three-dimensional steel ball model. Through a multi-physics coupled finite element model, the parameters are optimized according to the changes in microstructure and hardness data of the steel ball from the inside to the outside during the heat treatment and cooling process, so as to obtain the optimal wear-resistant steel ball parameters.

[0144] The above are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A method for obtaining microstructure and hardness distribution data of wear-resistant steel balls, characterized in that: include: S1: Based on the composition of the alloy steel used for wear-resistant steel balls, its material property parameters are obtained through calculation and experimentation; S2: Establish an axisymmetric three-dimensional steel ball model based on finite element software, add material property parameters to the axisymmetric three-dimensional steel ball model, and set heat treatment process parameters; S3: Add the heat transfer constitutive equation, phase transition constitutive equation and stress-strain constitutive equation to the axisymmetric three-dimensional steel ball model and compile it to obtain a multi-physics coupled finite element model of latent heat of phase transition and phase transition strain. S4: Solve the multiphysics coupled finite element model to obtain the continuous change values ​​of temperature over time at different positions of the steel ball, the continuous change values ​​of phase transformation rate constant and transformation volume fraction over time at different positions of the steel ball, and the hardness data at different positions of the steel ball under the preset cooling conditions. Based on MATLAB fitting, obtain the change law of the microstructure and hardness data of the steel ball from the inside to the outside during the heat treatment cooling process. S5: The material composition, process parameters, geometric parameters, microstructure, and hardness data of the wear-resistant steel ball are parametrically scanned. The parameters are optimized by using a multi-physics coupled finite element model based on the changes in microstructure and hardness data of the steel ball from the inside to the outside during the heat treatment and cooling process, so as to obtain the optimal wear-resistant steel ball parameters.

2. The method for obtaining microstructure and hardness distribution data of wear-resistant steel balls according to claim 1, characterized in that: S1 includes: S1-1: Based on the composition of the alloy steel used for wear-resistant steel balls, the material property parameters that change with time are calculated using thermodynamic software, including but not limited to density, thermal conductivity, Young's modulus, Poisson's ratio, bulk modulus, specific heat capacity, CCT curve and TTT curve. S1-2: The deformation of wear-resistant steel ball specimens caused by phase transformation plasticity at different temperatures was measured by thermal simulation experiments, and the phase transformation parameters during phase transformation were analyzed.

3. The method for obtaining microstructure and hardness distribution data of wear-resistant steel balls according to claim 1, characterized in that: S2 includes: S2-1: Use finite element software to establish an axisymmetric three-dimensional steel ball model, and add the material property parameters and phase transformation parameters of the wear-resistant steel ball to the material properties of the axisymmetric three-dimensional steel ball model; S2-2: Set the initial conditions and boundary conditions for the axisymmetric three-dimensional steel ball model respectively.

4. The method for obtaining microstructure and hardness distribution data of wear-resistant steel balls according to claim 1, characterized in that: S3 includes: S3-1: Construct the heat transfer constitutive equation based on Fourier's law of heat conduction, with the following expression: Where T is temperature and t is time. Let be the partial derivative of temperature with respect to time, and α be the thermal diffusivity. Let Q be the Laplace operator for temperature T, ρ be the material density, and Cp be the constant-pressure hot melt. S3-2: Construct the phase transition constitutive equation based on phase transition dynamics, the expression of which is: X=1-exp(-Kt n ) Where X is the phase change volume fraction, K is the phase change rate constant, n is the Avrami exponent, and t is time; S3-3: Based on the elastoplastic constitutive model, von Mises yield criterion, and flow law, the stress-strain constitutive equation is constructed for isotropic materials, expressed as follows: Where, σ ij Let f(σ) represent the stress tensor. ij S represents the yield function. ii S jj S ij Denotes the deviatoric stress tensor, σ y The yield stress; S3-4: Using the temperature parameter T as the connection point, the heat transfer constitutive equation, phase transition constitutive equation, and stress-strain constitutive equation are compiled and discretized to generate a system of thermo-mechanical coupled linear algebraic equations, characterizing the multi-physics coupled finite element model. The expression is as follows: Where T(x,t) is the temperature field function, x is the spatial coordinate, and t is time; The phase change volume fraction. Let σ represent the stress field function, and σ represent the stress. Indicates material properties as a function of phase change volume fraction Changes; S3-5: Based on the dimensions of the axisymmetric three-dimensional steel ball model, the mesh is divided and the number of meshes is determined. An adaptive mesh refinement algorithm that adjusts according to the number of meshes is introduced into the multiphysics coupled finite element model to obtain a multiphysics coupled finite element model that automatically adjusts the mesh density according to the axisymmetric three-dimensional steel ball model. A time step is added for transient calculation.

5. The method for obtaining microstructure and hardness distribution data of wear-resistant steel balls according to claim 4, characterized in that: S4 includes: S4-1: Based on preset cooling conditions and different positions of the steel ball, the continuous change value of temperature T(x,y,z,t) at the target position (x,y,z) with time t is output through a multi-physics coupled finite element model. The Fourier heat conduction law is applied inside the steel ball, and a custom heat convection htc(T) is used at the boundary. S4-2: Based on preset cooling conditions and different positions of the steel ball, the phase transition rate constant K(x,y,z,t) and the phase transition volume fraction X(x,y,z,t) at the target position (x,y,z) are continuously output as a function of time t through a multi-physics coupled finite element model. S4-3: Based on preset cooling conditions and different positions of the steel ball, the hardness data HV(x,t,z,t) at the target position (x,y,z) is output through a multiphysics coupled finite element model and hybrid rules. The expression is: Among them, X i (x,y,z,t) represents the volume fraction of the phase transformation structure, H i The corresponding hardness is given by n, where n is the number of phase transformation structures. S4-4: Based on the data obtained in S4-1 to S4-3 above, the changes in microstructure and hardness of the steel ball from the inside to the outside during the heat treatment cooling process were obtained using a MATLAB fitting program, generating a fitting equation with the following expression: Where, ΔX i,Tx T represents x The relative volume fraction of tissue i at temperature, X i (x,y,z,t,T x ) represents T x The volume fraction of tissue i at time t at (x,y,z) coordinates under temperature, T x Indicates the set temperature; X i (x,y,z,t,T0) represents the volume fraction of tissue i at time t at coordinates (x,y,z) at temperature T0, where T0 represents the ambient set temperature; ΔH i,Tx T represents x The relative hardness value of tissue i at temperature, H i (x,y,z,t,T x ) represents T x The hardness value of tissue i at time t at (x,y,z) coordinates under temperature, H u (x,y,z,t,T0) represents the hardness value of tissue i at time t at coordinates (x,y,z) at temperature T0.

6. The method for obtaining microstructure and hardness distribution data of a wear-resistant steel ball according to claim 5, characterized in that: S5 includes: S5-1: Parametric scan input variables include the material composition, process parameters, and geometric parameters of the axisymmetric three-dimensional steel ball model of the wear-resistant steel ball; S5-2: Input the input variables of S5-1 into the multiphysics coupled finite element model, and use the MATLAB-based fitting program to obtain the changes in the microstructure and hardness of the steel ball from the inside to the outside during the heat treatment cooling process, and output the predicted microstructure and hardness data. S5-3: Based on the predicted microstructure and hardness data, adjust the parameters of the input variables until the optimal wear-resistant steel ball parameters are obtained.

7. A system for acquiring microstructure and hardness distribution data of wear-resistant steel balls, applied to the method for acquiring microstructure and hardness distribution data of wear-resistant steel balls as described in any one of claims 1-6, characterized in that: It includes modules for material property parameter processing, model building and parameter configuration, multiphysics coupling model compilation, simulation solving and data processing, and process optimization and parameter scanning. The material property parameter processing module is used to obtain the material property parameters of the alloy steel used for wear-resistant steel balls through calculation and experimentation based on the composition of the alloy steel. The model building and parameter configuration module is used to build an axisymmetric three-dimensional steel ball model based on finite element software, add material property parameters to the axisymmetric three-dimensional steel ball model, and set heat treatment process parameters. The multiphysics coupling model compilation module is used to add the heat transfer constitutive equation, phase change constitutive equation and stress-strain constitutive equation to the axisymmetric three-dimensional steel ball model and compile it to obtain a multiphysics coupling finite element model of phase change latent heat-phase change strain. The simulation and data processing module is used to solve the multi-physics coupled finite element model, and obtain the continuous change values ​​of temperature over time at different positions of the steel ball, the continuous change values ​​of phase transformation rate constant and transformation volume fraction over time at different positions of the steel ball, and the hardness data at different positions of the steel ball under the preset cooling conditions. Based on MATLAB fitting, the change law of the microstructure and hardness data of the steel ball from the inside to the outside during the heat treatment cooling process is obtained. The process optimization and parameter scanning module is used to perform parameterized scanning of the material composition, process parameters, geometric parameters and microstructure, and hardness data of the axisymmetric three-dimensional steel ball model. Through a multi-physics coupled finite element model, the parameters are optimized according to the changes in microstructure and hardness data of the steel ball from the inside to the outside during the heat treatment and cooling process, so as to obtain the optimal wear-resistant steel ball parameters.

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