An engineered numerical calculation method for bearing steel heat treatment organization prediction

Abstract

The present application relates to the field of steel heat treatment organization prediction, and particularly relates to an engineering numerical calculation method for bearing steel heat treatment organization prediction, comprising: generating a parameter set based on user input data; introducing the parameter set into a phase field variable, and interpolating the grand potential density of each phase field through a phase field interpolation function to obtain a multi-phase grand potential phase field free energy functional, and constructing a multi-phase grand potential phase field free energy model based on the multi-phase grand potential phase field free energy functional; solving the multi-phase grand potential phase field free energy model to obtain a prediction result for predicting the bearing steel organization, wherein in the solving process: on the one hand, the physical consistency constraint of the phase field variable is introduced, and on the other hand, the diffusion coefficient in the evolution equation of the diffusion potential field is weighted and interpolated through the phase field variable in different phase regions, and the diffusion potential field and the component concentration field are updated according to a preset update order. The present application has clear calculation process and is easy to realize numerically.

Description

Technical Field

[0001] This invention relates to the field of numerical simulation in materials engineering and prediction of microstructure after heat treatment of steel, specifically to an engineering numerical calculation method for predicting the microstructure after heat treatment of bearing steel. Background Technology

[0002] Bearing steel, a key material in engineering applications, ranges in composition from low to high carbon and is primarily used to manufacture rolling bearings and high-reliability transmission components. The performance of this material is closely related to the stability and uniformity of its internal microstructure. During heat treatment processes such as quenching, isothermal transformation, and tempering, the formation and dynamic evolution of multiphase structures (e.g., cementite morphology distribution, phase interface structure, and phase fraction changes) directly determine the core performance indicators of bearing steel, such as fatigue life, wear resistance, and dimensional stability. For medium- and high-carbon bearing steel systems, the precipitation morphology and distribution uniformity of cementite, as well as the synergistic mechanism between different phases, are key factors affecting the final service performance of the material.

[0003] Currently, the control of the microstructure and properties of bearing steel mainly relies on empirical heat treatment process optimization. However, due to the complex coupling effects of multiphase coexistence, interface migration, and diffusion processes, it is difficult to quantitatively predict the microstructure evolution behavior solely through experimental methods. Existing numerical simulation methods are mostly based on single-phase transformation theoretical frameworks or compositional variable modeling, which suffer from problems such as discontinuous interface treatment and poor numerical convergence under multiphase coexistence conditions. In addition, some methods rely too much on theoretical assumptions and lack engineering adaptability to actual heat treatment process parameters (such as temperature field and time parameters), thus limiting their application in bearing steel microstructure prediction. Therefore, there is an urgent need to develop a multiphase microstructure evolution calculation method that combines numerical stability and engineering practicality to accurately predict the phase transformation behavior and microstructure evolution laws during the heat treatment process of bearing steel. Summary of the Invention

[0004] To address the aforementioned technical problems of existing methods, such as reliance on experience, discontinuous handling of multiphase coexistence, poor numerical convergence, and insufficient stability, this invention provides an engineering numerical calculation method for predicting the microstructure of bearing steel after heat treatment. This invention primarily utilizes a multiphase giant potential phase field free energy functional constructed based on a parameter set, introducing phase field variables to describe the multiphase distribution. During the solution process, physical consistency constraints are imposed on the phase field variables, and the diffusion coefficient is weighted and interpolated using these phase field variables. The diffusion potential field and component concentration field are jointly updated according to a preset update order. This allows for numerical prediction of the spatial distribution, morphological evolution, and phase fraction changes of the multiphase microstructure in bearing steel under given heat treatment process conditions, thus providing a directly applicable engineering calculation tool for bearing steel microstructure design and heat treatment process optimization.

[0005] The technical means employed in this invention are as follows:

[0006] An engineering numerical calculation method for predicting the microstructure of bearing steel after heat treatment includes the following steps:

[0007] The system receives the chemical composition of the bearing steel, heat treatment process parameters, and calculation area scale input by the user. Based on the user's input data, it generates a parameter set, which includes a thermodynamic parameter set and a kinetic parameter set. The heat treatment process parameters include the initial temperature, cooling method, and cooling rate.

[0008] The parameter set is introduced into the phase field variables, and the giant potential density of each phase field is interpolated through the phase field interpolation function to obtain the multiphase giant potential phase field free energy functional. Based on the multiphase giant potential phase field free energy functional, a multiphase giant potential phase field free energy model is constructed.

[0009] The multiphase giant potential phase field free energy model is solved to obtain the prediction results for predicting the microstructure of bearing steel. The prediction results include the multiphase microstructure morphology, phase fraction changes and interface migration paths. In the solution process: on the one hand, physical consistency constraints of phase field variables are introduced; on the other hand, the diffusion coefficient in the evolution equation of the diffusion potential field is weighted and interpolated through the phase field variables in different phase regions, and the diffusion potential field and component concentration field are updated according to a preset update order.

[0010] Furthermore, the multiphase giant potential phase field free energy functional is:

[0011]

[0012] in, For multiphase large potential phase field free energy functional, T For temperature, μ Here is the diffusion potential matrix. The phase field vector, V To calculate the volume of the region, k As the first phase, l For the second phase, For huge potential density, For phase field interpolation function, These are interface energy-related parameters, including interface thickness and interface energy. Let be the barrier function. For the phase field variables of the first phase, For the phase field variables of the second phase, This is the gradient operator.

[0013] Furthermore, the physical consistency constraints of the phase field variables include phase field normalization constraints and phase occupancy correction.

[0014] Furthermore, the phase field normalization constraint is:

[0015] At any spatial location and at any calculation time, the sum of the volume fractions of all phases equals 1, and the mathematical expression for the phase field normalization constraint is:

[0016]

[0017] in, For the first Phase in spatial position x Time point t The phase field vector, where N is the total number of phases and Ω is the computational region. t For a point in time, For serial numbers.

[0018] Furthermore, the phase occupancy correction is as follows:

[0019] The phase field variables are normalized and corrected in the single-phase region to make the volume fraction of the main phase 1 and the volume fraction of the other phases 0, so as to eliminate the numerical ambiguity of the interface and ensure mass conservation.

[0020] Furthermore, the evolution equation of the diffusion potential field after weighted interpolation is:

[0021]

[0022] in, A The multiphase thermodynamic response matrix, J For diffusion flux, Component redistribution caused by phase interface migration, Thermodynamic driving term caused by temperature change, For gradient operators, T For temperature, μ For diffusion potential, The phase field vector, t For a point in time.

[0023] Furthermore, during the solution process, the time step is dynamically adjusted based on the phase field evolution rate, the diffusion potential change amplitude, and the interface migration rate to improve the numerical stability and computational efficiency of the multiphase tissue evolution simulation.

[0024] Furthermore, based on the aforementioned multiphase giant potential phase field free energy functional, the following phase field evolution equation is established to update the phase field variables:

[0025]

[0026] in, The phase field vector, For a point in time, For phase field mobility, For multiphase large potential phase field free energy functional, It is a Lagrange factor.

[0027] Furthermore, when the diffusion potential field reaches equilibrium within the computational region or the phase transition interface reaches the boundary of the computational region, the computation process is determined to converge and the iteration is terminated. The criteria for determining that the diffusion potential field has reached equilibrium include: uniform spatial distribution of diffusion potential, zero rate of change of diffusion potential over time, and equal diffusion potential of adjacent phases at the phase interface.

[0028] Furthermore, the method also includes: presetting initial crystal nuclei of the target microstructure in the calculation region, wherein the initial crystal nuclei include austenite, ferrite, cementite, bainite and martensite; initializing the initial crystal nuclei by setting the spatial position, size, number density and crystallographic orientation, in order to simulate the complex interaction behaviors of multiphase microstructures such as competitive growth, synergistic growth, dissolution and coarsening during heat treatment.

[0029] Compared with the prior art, the present invention has the following advantages:

[0030] 1. This invention constructs a multiphase giant potential phase field free energy functional based on a parameter set, introduces phase field variables to describe the multiphase distribution, and realizes quantitative prediction of the microstructure evolution of multi-component multiphase alloys. It can directly couple thermodynamic and kinetic databases, accurately reflect the free energy surface of each phase and the phase transformation driving force, thus providing a reliable theoretical basis for microstructure control under complex heat treatment processes.

[0031] 2. By applying physical consistency constraints to phase field variables during the solution process, this invention ensures that phase field variables always satisfy volume fraction normalization and single-phase region purity, effectively suppresses the appearance of spurious phases caused by numerical errors, guarantees the clarity of interface description and strict satisfaction of matter conservation, and significantly improves the stability of numerical calculation and the reliability of long-term evolution.

[0032] 3. This invention achieves a spatially continuous transition of the diffusion capacity of alloying elements in different phases by weighted interpolation of the diffusion coefficient using phase field variables. It can realistically characterize the migration behavior of interstitial atoms and substitutional atoms at the phase boundary and the solute redistribution, laying a solid foundation for accurately simulating interface migration dynamics, solute dragging effect and segregation phenomenon of multi-component alloys.

[0033] 4. This invention adopts a unified numerical description framework for multiphase microstructure, enabling stable calculation of the microstructure evolution process under multiphase coexistence conditions; it can predict the multiphase microstructure morphology and phase fraction changes of bearing steel under continuous cooling or isothermal heat treatment conditions, exhibiting strong engineering applicability; it can output various engineering-related information such as microstructure morphology, phase fraction, and interface migration paths, facilitating process analysis and comparison; it is applicable to the microstructure prediction of bearing steel and related multiphase steel materials, providing a reliable engineering calculation tool for heat treatment process optimization and microstructure design; the calculation process is clear, easy to implement numerically, and has the potential to be extended to large-scale engineering calculations.

[0034] Based on the above reasons, this invention can be widely applied in fields such as numerical simulation in materials engineering and prediction of microstructure in steel heat treatment. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a flowchart illustrating an engineering numerical calculation method for predicting the microstructure of bearing steel after heat treatment, according to the present invention.

[0037] Figure 2 Numerical simulation of ferrite in this embodiment of the invention α / Austenitic γ The diagram shows the evolution of the phase field over time as the interface passes through and detaches from the cementite grains. Detailed Implementation

[0038] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0039] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0040] like Figure 1 As shown, this invention provides an engineering numerical calculation method for predicting the microstructure of bearing steel after heat treatment, comprising the following steps:

[0041] S1. Receives the chemical composition of the bearing steel, heat treatment process parameters, and calculation area scale input by the user. Based on the user's input data, it generates a parameter set, which includes a thermodynamic parameter set and a kinetic parameter set. The heat treatment process parameters include the initial temperature, cooling method, and cooling rate.

[0042] Based on the chemical composition and initial temperature, corresponding phase equilibrium data, free energy curvature, mobility matrix, etc. are obtained from thermodynamic and kinetic databases to generate thermodynamic parameter sets and kinetic parameter sets. The numerical discrete parameters such as mesh size, interface width parameter and time step are determined according to the scale of the computational domain to form a complete set of phase field calculation parameters, which is used to drive subsequent multiphase field model calculations.

[0043] Specifically, step S1 is used to convert the input conditions available in the engineering into a set of parameters that can be directly called by the multiphase phase field model, thereby establishing the correspondence between engineering technology and numerical calculation.

[0044] At the start of the calculation, the computation region is initialized, including:

[0045] 1) Establish a phase variable field to describe different phase spatial distributions;

[0046] 2) Set the initial temperature field corresponding to the heat treatment process;

[0047] 3) Initialize the component state variables and potential function state variables related to the organizational state;

[0048] 4) Set thermodynamic and kinetic parameters, including interface-related parameters and evolution rate parameters.

[0049] The above initialization steps are used to construct the initial microstructure and environmental conditions corresponding to the actual heat treatment state.

[0050] The engineering input parameter mapping includes automatically generating corresponding thermodynamic and kinetic parameters based on the chemical composition and heat treatment process parameters of bearing steel, and using these parameters as input parameters for the multiphase giant potential phase field model.

[0051] Furthermore, initial nuclei of one or more target microstructures, including but not limited to austenite, ferrite, cementite, bainite, or martensite, are used in the computational region to simulate the competitive growth and interaction processes between multiphase microstructures.

[0052] S2. Introduce the parameter set into the phase field variables, and interpolate the giant potential density of each phase field through the phase field interpolation function to obtain the multiphase giant potential phase field free energy functional. Based on the multiphase giant potential phase field free energy functional, construct the multiphase giant potential phase field free energy model.

[0053] Specifically, step S2 is used to construct a numerical description model suitable for multiphase microstructures of bearing steel, introduce multiple phase variables to characterize the spatial distribution of different phases, and describe the state transition within the multiphase coexistence region through interpolation, thus forming a unified numerical calculation framework for multiphase microstructures.

[0054] Based on the parameter set, a phase field variable set is introduced. , There are 1, 2...N phase field variables. These phase field variables describe the spatial distribution of different phases and satisfy the condition that at any spatial location... .

[0055] Based on this, uniform constraints are imposed on the thermodynamic parameters of each phase to ensure the continuity and numerical stability of the giant potential density of each phase in the phase interface region. A multiphase giant potential phase field free energy functional is constructed:

[0056]

[0057] in, For multiphase large potential phase field free energy functional, T For temperature, μ Here is the diffusion potential matrix. The phase field vector, V To calculate the volume of the region, k As the first phase, l For the second phase, Let i be the giant potential density. For phase field interpolation function, These are interface energy-related parameters, including interface thickness and interface energy. Let be the barrier function. For the phase field variables of the first phase, For the phase field variables of the second phase, This is the gradient operator. The first and second phases do not refer to two specific fixed phases, but rather to any two different phase types. That is, k and l are phase indices, traversing all considered phases.

[0058] This giant potential functional is used to uniformly describe the thermodynamic state under multiphase coexistence conditions and provides numerical driving force for subsequent phase field evolution and diffusion calculations.

[0059] S3. Solve the multiphase giant potential phase field free energy model to obtain the prediction results for predicting the microstructure of bearing steel. The prediction results include multiphase microstructure morphology, phase fraction changes and interface migration paths. In the solution process: on the one hand, physical consistency constraints of phase field variables are introduced; on the other hand, the diffusion coefficient in the evolution equation of the diffusion potential field is weighted and interpolated through the phase field variables in different phase regions. The diffusion potential field and component concentration field are updated according to the preset update order to avoid numerical oscillations in the multiphase interface region.

[0060] Specifically, the physical consistency constraints of phase field variables include phase field normalization constraints and phase occupancy correction, in order to ensure the physical consistency and numerical stability of phase field variables in the multiphase coexistence region.

[0061] In the numerical stability update step of phase field evolution, phase normalization constraints are introduced to the phase field variables to prevent non-physical phases from occupying the multiphase coexistence region or phase field variables from becoming unbalanced.

[0062] The phase field normalization constraint is:

[0063] At any spatial location and at any calculation time, the sum of the volume fractions of all phases equals 1, and its mathematical expression is:

[0064]

[0065] in, For the first The phase field vector at spatial location x and time point t, where N is the total number of phases, Ω is the calculation region, and t is the time point.

[0066] Phase occupancy correction is:

[0067] The truncated or rounded phase field variables are normalized, and corrections are made in the single-phase region to make the volume fraction of the main phase 1 and the volume fraction of the other phases 0, so as to eliminate the numerical ambiguity at the interface and ensure mass conservation.

[0068] Specifically, in the single-phase region far from the interface, only one phase can have a volume fraction of 1, while the remaining phases must strictly have a volume fraction of 0. However, due to numerical errors (truncation error, rounding error), "ghost phases" often appear—that is, in regions that should be pure k-phase, ,and ,in, For the phase field variables of the first phase (k phase), This refers to the phase field variables of the second phase (l phase). Such a small error can introduce incorrect driving forces, causing the interface values ​​to become "fuzzy" and affecting mass conservation. Therefore, a threshold is set for truncation, followed by normalization, and finally correction of the single-phase region.

[0069] Furthermore, in the engineered coupling step of the multiphase diffusion process, the diffusion potential field and component concentration field are jointly updated according to a preset update order to reduce numerical oscillations in the multiphase interface region and improve computational stability. The evolution equation of the weighted interpolated diffusion potential field is:

[0070]

[0071] in, A The multiphase thermodynamic response matrix, J For diffusion flux, Component redistribution caused by phase interface migration, Thermodynamic driving term caused by temperature change, For gradient operators, T For temperature, μ For diffusion potential, The phase field vector, t For a point in time.

[0072] Based on the multiphase giant potential phase field free energy functional, the following phase field evolution equation is established to update the phase field variables:

[0073]

[0074] in, The phase field vector, For a point in time, For phase field mobility, For multiphase large potential phase field free energy functional, It is a Lagrange factor.

[0075] As a preferred embodiment of the present invention, during the solution process, the time step is dynamically adjusted according to the phase field evolution rate, the diffusion potential change amplitude, and the interface migration rate to improve the numerical stability and computational efficiency of multiphase tissue evolution simulation. The calculation process is considered converged and the iteration terminated when the diffusion potential field reaches equilibrium within the computational region or when the phase transition interface reaches the boundary of the computational region. The criteria for determining that the diffusion potential field has reached equilibrium include: uniform spatial distribution of the diffusion potential. The diffusion potential changes at a rate of zero over time. And the diffusion potential of adjacent phases at the phase interface is equal. .

[0076] During the numerical calculation, the calculation time is advanced according to a preset time step, and the following operations are performed sequentially within each time step:

[0077] 1) Update the temperature field according to the continuous cooling or isothermal holding process conditions;

[0078] 2) Calculate the evolution trend of each phase based on the current state and update the phase variable distribution;

[0079] 3) Update the interpolation weights and related parameters for the interface transition area;

[0080] 4) Update the potential function state variables related to organizational evolution;

[0081] 5) Recalculate the component distribution based on the updated state.

[0082] In the adaptive time step control step, the time step is dynamically adjusted based on at least one of the phase field evolution rate, diffusion potential change amplitude, and interface migration rate. By monitoring the phase field change and the diffusion potential change of the components, the time step can automatically adapt to the entire dynamic process from the system being active to the system slowly tending to equilibrium, which can significantly improve computational efficiency while ensuring stability.

[0083] As a preferred embodiment of the present invention, a continuous interpolation function is used in the phase interface region to smoothly transition the diffusion coefficients of different phases, so as to avoid numerical instability caused by abrupt changes in diffusion behavior near the interface.

[0084] In the numerical implementation, the diffusion potential field and component concentration field are jointly updated according to a preset update order to reduce numerical oscillations in the multiphase interface region. A numerical method is used to couple the solution of the phase variable evolution equation and the diffusion control equation, iteratively updating the temperature field, phase variables, and diffusion-related state variables through time steps until a preset calculation termination condition is reached.

[0085] Termination condition determination;

[0086] After each time step, convergence is checked on the calculation results:

[0087] 1) When the evolution of multiphase tissues tends to stabilize;

[0088] 2) The phase interface reaches the boundary of the computational region;

[0089] 3) Then the calculation will terminate;

[0090] 4) If the above conditions are not met,

[0091] Results and Engineering Analysis: The simulation outputs information on multiphase microstructure morphology, phase fraction variations, interface migration paths, and related state variables. It also analyzes the microstructure evolution trends under different heat treatment conditions, providing numerical basis for engineering applications. The simulation results are used to analyze the influence of different heat treatment process parameters on the multiphase microstructure evolution trends of bearing steel, and to assist in the microstructure design and heat treatment process optimization of bearing steel.

[0092] After the calculation is completed, the simulation results are output and analyzed, including:

[0093] 1) Spatial morphological distribution of multiphase structures;

[0094] 2) Changes in phase fraction of each phase;

[0095] 3) Grain size evolution trend;

[0096] 4) Interface migration path and migration rate;

[0097] 5) Local component distribution and gradient information.

[0098] In a preferred embodiment of the present invention, one or more initial nuclei of target structures are preset in the calculation region, including but not limited to austenite, ferrite, cementite, bainite or martensite; the initial nuclei are initialized by setting their spatial position, size, number density and crystallographic orientation, in order to simulate the complex interaction behaviors of multiphase structures such as competitive growth, synergistic growth, dissolution and coarsening during heat treatment.

[0099] Example

[0100] S1. Receives the chemical composition of the bearing steel, heat treatment process parameters, and calculation area scale input by the user. Based on the user's input data, it generates a parameter set, which includes a thermodynamic parameter set and a kinetic parameter set. The heat treatment process parameters include the initial temperature, cooling method, and cooling rate.

[0101] Specifically, refer to Figure 2 This embodiment takes the Fe-C binary bearing steel system as an example, and selects ferrite phase, austenite phase and cementite phase as research objects to illustrate the specific application process of the method of the present invention in the prediction of multiphase microstructure evolution.

[0102] Initially, a pair of cementite grains are placed in the austenite phase region, one close to the α / γ interface and the other far from the α / γ interface. The bottom of the region is a thin layer of ferrite, adjacent to the austenite region, and cementite is distributed in the austenite region.

[0103] Based on the thermodynamic properties of the Fe-C system, the initial composition state and corresponding potential function state variables are set, and interface parameters and evolution rate parameters that match the material properties are configured.

[0104] S2. Introduce the parameter set into the phase field variables, and interpolate the giant potential density of each phase field through the phase field interpolation function to obtain the multiphase giant potential phase field free energy functional. Based on the multiphase giant potential phase field free energy functional, construct the multiphase giant potential phase field free energy model.

[0105] S3. Solve the multiphase giant potential phase field free energy model to obtain the prediction results for predicting the microstructure of bearing steel. The prediction results include the multiphase microstructure morphology, phase fraction changes and interface migration paths. In the solution process: on the one hand, physical consistency constraints of phase field variables are introduced; on the other hand, the diffusion coefficient in the evolution equation of the diffusion potential field is weighted and interpolated through the phase field variables in different phase regions. The diffusion potential field and component concentration field are updated according to the preset update order.

[0106] Spatial characterization of the ferrite, austenite, and cementite phases is performed using phase variables, ensuring that the sum of all phase variables at any location is one.

[0107] In the multiphase coexistence region, an interpolation strategy is used to achieve a continuous transition of state variables between different phases, so as to ensure the numerical stability and computational convergence of the interface region.

[0108] A continuous cooling process is used to reduce the temperature from the high-temperature austenitic region to the target temperature range, and the cooling rate is set according to the actual heat treatment requirements of the bearing steel.

[0109] During the calculation process, time is progressively advanced and the microstructure is updated to achieve mutual transformation and interface migration between the three phases of ferrite, austenite and cementite.

[0110] Through this embodiment, the following simulation results can be obtained: the growth behavior and morphological evolution of cementite; the variation law of phase fraction of each phase with the heat treatment process; the migration characteristics of multiphase interfaces and the interaction behavior between grains.

[0111] The above results can be used to evaluate the effects of different cooling rates, initial microstructures and compositional conditions on the final microstructure of bearing steel, providing reliable engineering numerical support for the optimization of heat treatment processes for bearing steel and related high-carbon steel materials.

[0112] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0113] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An engineering numerical calculation method for predicting the microstructure of bearing steel after heat treatment, characterized in that, Includes the following steps: The system receives the chemical composition of the bearing steel, heat treatment process parameters, and calculation area scale input by the user. Based on the user's input data, it generates a parameter set, which includes a thermodynamic parameter set and a kinetic parameter set. The heat treatment process parameters include the initial temperature, cooling method, and cooling rate. The parameter set is introduced into the phase field variables, and the giant potential density of each phase field is interpolated using the phase field interpolation function to obtain the multiphase giant potential phase field free energy functional. Based on the multiphase giant potential phase field free energy functional, a multiphase giant potential phase field free energy model is constructed. The physical consistency constraints of the phase field variables include phase field normalization constraints and phase occupancy correction. The phase field normalization constraints are as follows: At any spatial location and at any calculation time, the sum of the volume fractions of all phases equals 1, and the mathematical expression for the phase field normalization constraint is: in, For the first Phase in spatial position x Time point t The phase field vector, where N is the total number of phases and Ω is the computational region. t For a point in time, For serial numbers; The multiphase giant potential phase field free energy model is solved to obtain prediction results for bearing steel microstructure. These prediction results include multiphase microstructure morphology, phase fraction changes, and interface migration paths. During the solution process: on the one hand, physical consistency constraints on the phase field variables are introduced; on the other hand, the diffusion coefficients in the evolution equation of the diffusion potential field are weighted and interpolated using the phase field variables in different phase regions. The diffusion potential field and component concentration field are updated according to a preset update order. The multiphase giant potential phase field free energy functional is: in, For multiphase large potential phase field free energy functional, T For temperature, μ Here is the diffusion potential matrix. The phase field vector, V To calculate the volume of the region, k As the first phase, l For the second phase, For huge potential density, For phase field interpolation function, These are interface energy-related parameters, including interface thickness and interface energy. Let be the barrier function. For the phase field variables of the first phase, For the phase field variables of the second phase, This is the gradient operator.

2. The engineering numerical calculation method for predicting the microstructure of bearing steel after heat treatment according to claim 1, characterized in that, The phase occupancy correction is as follows: The phase field variables are normalized and corrected in the single-phase region to make the volume fraction of the main phase 1 and the volume fraction of the other phases 0, so as to eliminate the numerical ambiguity of the interface and ensure mass conservation.

3. The engineering numerical calculation method for predicting the microstructure of bearing steel after heat treatment according to claim 1, characterized in that, The evolution equation of the diffusion potential field after weighted interpolation is: in, A The multiphase thermodynamic response matrix, J For diffusion flux, Component redistribution caused by phase interface migration, Thermodynamic driving term caused by temperature change, For gradient operators, T For temperature, μ For diffusion potential, The phase field vector, t For a point in time.

4. The engineering numerical calculation method for predicting the microstructure of bearing steel after heat treatment according to claim 1, characterized in that, In the solution process, the time step is dynamically adjusted according to the phase field evolution rate, the diffusion potential change amplitude, and the interface migration rate to improve the numerical stability and computational efficiency of multiphase tissue evolution simulation.

5. The engineering numerical calculation method for predicting the microstructure of bearing steel after heat treatment according to claim 1, characterized in that, Based on the aforementioned multiphase giant potential phase field free energy functional, the following phase field evolution equation is established to update the phase field variables: in, The phase field vector, For a point in time, For phase field mobility, For multiphase large potential phase field free energy functional, It is a Lagrange factor.

6. The engineering numerical calculation method for predicting the microstructure of bearing steel after heat treatment according to claim 1, characterized in that, When the diffusion potential field reaches equilibrium within the computational region or the phase transition interface reaches the boundary of the computational region, the computation process is determined to have converged and the iteration is terminated. The criteria for determining that the diffusion potential field has reached equilibrium include: uniform spatial distribution of diffusion potential, zero rate of change of diffusion potential over time, and equal diffusion potential of adjacent phases at the phase interface.

7. The engineering numerical calculation method for predicting the microstructure of bearing steel after heat treatment according to claim 1, characterized in that, The method further includes: presetting initial nuclei of the target microstructure in the calculation region, wherein the initial nuclei include austenite, ferrite, cementite, bainite and martensite; initializing by setting the spatial position, size, number density and crystallographic orientation of the initial nuclei, in order to simulate the complex interaction behavior of competitive growth, synergistic growth, dissolution and coarsening of multiphase microstructures during heat treatment.

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