High-temperature diffusion homogenization process optimization method suitable for austenitic stainless steel large casting and forging

Abstract

The invention relates to the field of metal material homogenization processes, in particular to a high-temperature diffusion homogenization process optimization method suitable for large austenitic stainless steel castings and forgings, and provides a set of efficient and reliable process parameter optimization method by integrating a ferrite dissolution model and a cylinder heating temperature field prediction model. A program is developed accordingly, a high-precision ferrite dissolution model is constructed based on experimental data, a large ingot casting temperature field is simulated by combining a finite volume method, technological parameters including diffusion temperature, time and a temperature rise path are optimized, and critical conditions of homogenization treatment are determined by dynamically calculating the ferrite dissolution amount. Compared with the prior art, the method effectively solves the problem of temperature difference influence in the process of eliminating the ferritic structure of the large casting and forging, improves the homogenization efficiency and quality of the material, and is suitable for homogenization treatment of austenitic stainless steel and other high-temperature alloys.

Description

Technical Field

[0001] The invention relates to the field of metal material homogenization technology, and in particular to a high-temperature diffusion homogenization technology optimization method suitable for large-scale austenitic stainless steel castings and forgings. Background Art

[0002] Austenitic stainless steel is widely used in nuclear energy, chemical industry and marine engineering due to its excellent corrosion resistance and mechanical properties. However, in the actual manufacturing process, due to the segregation of elements during solidification and uneven cooling during hot working, harmful phases rich in ferrite are easily generated. This structure not only reduces the corrosion resistance of the material, but may also lead to the formation of processing cracks, seriously affecting product quality. At present, the industry mainly uses high-temperature diffusion annealing technology to eliminate ferrite, and has constructed a model for the diffusion of segregated elements [Liu Xingang. Theoretical and experimental research on high-temperature diffusion of large forgings [D]. Yanshan University, 2004.].

[0003] However, in the field of large castings and forgings, the model of ferrite dissolution is still imperfect. The existing diffusion model can only describe the element distribution and it is difficult to fully predict the ferrite dissolution process. In addition, the significant internal temperature difference of large castings and forgings during the heating process has an important impact on ferrite dissolution. Therefore, it is necessary to propose a prediction model for the temperature field of castings and forgings. Currently, the methods for calculating temperature fields mainly include analytical method [Meyghani BA Comparison of Different Finite Element Methods in the Thermal Analysis of Friction Stir Welding (FSW) [J]. Metals, 2017, 7 (10): 450.], finite difference method, finite element method [Khalesidoost S, Faiz J, Mazaheri T, et al. An overview of thermal modelling techniques for permanent magnet machines [J]. IET science, measurement & technology, 2022, 16 (4): 219-241.] and finite volume method [Zeng K, Pal D, Stucker BA review of thermal analysis methods in Laser Sintering and Selective Laser Melting [C]. / / Solid Freeform Fabrication Symposium proceedings, August 2012: University Of Texas At Austin, 2012: 796-814.]. Among them, the energy conservation idea of ​​the finite volume method is more suitable for dealing with transmission problems, and can handle more complex conditions while ensuring accuracy.

[0004] At present, high-temperature diffusion models and heating temperature field simulations have not been effectively integrated, and there is a lack of a comprehensive homogenization process parameter optimization method. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a high-temperature diffusion homogenization process optimization method suitable for large austenitic stainless steel castings and forgings, which effectively solves the problem of temperature difference influence in the process of eliminating ferrite structure of large castings and forgings, improves the homogenization efficiency and quality of the material, and is suitable for homogenization treatment of austenitic stainless steel and other high-temperature alloys.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] The present invention provides a high-temperature diffusion homogenization process optimization method applicable to large-scale austenitic stainless steel castings and forgings, comprising the following steps:

[0008] S1: Based on Fourier heat conduction equation and finite volume method, the temperature field control equation of cylindrical castings and forgings is established;

[0009] S2: Based on the established control equation, an adiabatic condition is set for the cylindrical axis boundary. At the same time, for the heated surface, according to the actual situation of radiation and convection heat transfer in the actual heating environment, a third type of mixed boundary condition containing radiation and convection heat transfer is set;

[0010] S3: Use arithmetic progression to generate non-uniform grids to divide the calculation area;

[0011] S4: Introduce the ferrite dissolution kinetic model, combine the simulated temperature field information, dynamically calculate the ferrite dissolution amount, and associate the temperature field with the ferrite dissolution situation;

[0012] S5: Adopt interpolation algorithm to adjust the thermal conductivity and specific heat capacity of materials according to the real-time temperature changes, so that the entire process model is more in line with the actual material characteristics;

[0013] S6: Optimize the time step in the process model according to the CFL stability condition;

[0014] S7: Based on the optimized process model, the temperature evolution of the core area and the change of ferrite content are monitored in real time. According to the monitoring data, the heating parameters are dynamically adjusted to improve the uniformity of the austenitic stainless steel material structure.

[0015] Furthermore, S1 specifically includes the following steps:

[0016] According to the actual working conditions of cylindrical castings and forgings, they are simplified into two-dimensional axisymmetric models to reduce the calculation dimension;

[0017] Select the adapted Fourier heat conduction equation and transform it into the cylindrical coordinate system;

[0018] The calculation area of ​​the model is meshed, and the heat conduction equation is integrated on each mesh unit using the finite volume method to discretize the equation;

[0019] Determine the third type of mixed boundary conditions for the adiabatic and heated surfaces of the cylinder axis, as well as the initial temperature distribution;

[0020] The discrete equations are organized into a system of equations that are easy to solve, and the temperature field control equations of cylindrical castings and forgings are established;

[0021] Further, in S1, the control equation is as follows:

[0022]

[0023] Where: k is the thermal conductivity; the dimensions of the grid region (i,j) are Δr and Δz, and its center temperature is T i,j ; r 1 and r 2 are the distances from the horizontal boundary of the grid to the axis; the four adjacent temperatures of the control volume are on the left: T i-1,j , right side: T i+1,j , upper side: T i,j+1 , bottom side: T i,j-1 .

[0024] Furthermore, S2 specifically includes the following steps:

[0025] Based on the established control equations of S1, for the cylindrical axis boundary, according to the physical meaning of the adiabatic condition, its heat flux is set to zero;

[0026] For the heated surface, relevant data of the actual heating environment are collected. According to the material of the heating furnace and different environmental conditions, the appropriate emissivity is selected, and the convective heat transfer coefficient is determined by Newton's law of cooling. Then, combined with the Stefan-Boltzmann law, the radiation heat transfer and convection heat transfer are comprehensively considered to construct a mathematical expression for the third type of mixed boundary condition including radiation terms and convection terms.

[0027] Furthermore, the mathematical expression of the third type of mixed boundary condition is:

[0028]

[0029] Where h is the convective heat transfer coefficient; ε is the emissivity, which is taken as 0.8; σ is the Stefan-Boltzmann constant; T s is the surface temperature; T ∞ is the ambient temperature;

[0030] The convective heat transfer coefficient is expressed as Where V is the velocity of the furnace gas (m / s) and L is the size of the heat exchange area (m).

[0031] Furthermore, S3 specifically includes the following steps:

[0032] Determine the size of castings and forgings, measure key dimensional parameters such as radius and height, and estimate the temperature gradients that may occur in different areas;

[0033] Select appropriate arithmetic progression tolerances based on the size of castings and forgings and expected temperature gradients;

[0034] Taking the axis of the cylindrical casting as the starting point, the radial and axial positions of each grid node are determined by an arithmetic progression according to the selected tolerance. In the area where the temperature gradient changes greatly, the grid spacing is made smaller, while in the area where the temperature gradient is relatively gentle, the grid spacing is appropriately increased.

[0035] Furthermore, S4 specifically includes the following steps:

[0036] The activation energy E in the ferrite dissolution kinetic model was experimentally determined for austenitic stainless steels of different compositions;

[0037] Introduce the established ferrite dissolution kinetic model into the current process system;

[0038] Extract temperature data at each location and time from the simulated temperature field information;

[0039] According to the ferrite dissolution kinetics model, the extracted temperature data is substituted into it, and the ferrite dissolution amount at different times and in different areas is dynamically calculated according to the established formula and algorithm of the model;

[0040] During the calculation process, the corresponding relationship between the temperature of each point in the temperature field and the amount of ferrite dissolved at the corresponding position is established to closely link the temperature field with the ferrite dissolution.

[0041] Further, in S4, the ferrite dissolution kinetic model is:

[0042]

[0043] Among them, F 0 is the initial ferrite content, t is the holding time (h), τ(T) is the characteristic time (h) corresponding to temperature T, reflecting the dissolution rate of ferrite, and the formula is used. Among them, τ 0 is the pre-factor and E is the activation energy.

[0044] Furthermore, S5 specifically includes the following steps:

[0045] Determine the data range of material thermal conductivity and specific heat capacity changing with temperature, and collect corresponding thermal conductivity and specific heat capacity experimental data at different temperatures;

[0046] Construct a series of "thermal physical parameter-temperature" data pairs, input the collected data pairs into the linear interpolation algorithm formula, construct a straight line equation between adjacent data points, and estimate the thermal conductivity and specific heat capacity function values ​​corresponding to any temperature between the data points;

[0047] When the process model is running, the current simulated temperature value is obtained in real time, substituted into the linear interpolation equation, and the corresponding thermal conductivity and specific heat capacity values ​​at the temperature are calculated;

[0048] The calculation results are updated to the entire process model, allowing the model to quickly and effectively dynamically adjust the material thermal properties according to real-time temperature changes during the simulation process.

[0049] Furthermore, S6 specifically includes the following steps:

[0050] Obtain the mathematical principles and physical meanings of the CFL stability conditions, and clarify the correlation formulas between them and factors such as the time step, space step, and heat conduction characteristics in the process model;

[0051] Extract the currently used spatial step data and related heat conduction parameters such as the thermal diffusivity of the material from the constructed process model;

[0052] Substitute the obtained spatial step and heat conduction parameters into the formula of CFL stability condition, and obtain the range of time step value under the premise of ensuring the stability of calculation process through mathematical calculation;

[0053] Within the range of the time step value, comprehensively consider factors such as calculation efficiency, simulation accuracy and actual process requirements, and determine the most appropriate time step value through multiple trial calculations or optimization algorithms;

[0054] Apply the determined time step to the process model.

[0055] Furthermore, S7 specifically includes the following steps:

[0056] In the optimized process model, multiple monitoring points are precisely set for the core area;

[0057] Using the model calculation function, the real-time temperature data of each monitoring point is continuously collected, and the ferrite content at each point at the corresponding time is calculated based on the ferrite dissolution kinetics model;

[0058] The collected temperature and ferrite content data are summarized and analyzed, compared with the pre-set uniformity standards to determine whether they are met. Based on the analysis results, the temperature parameters are dynamically adjusted with the goal of improving organizational uniformity.

[0059] Working principle of the present invention:

[0060] The temperature field model established by the finite volume method in the present invention simplifies large cylindrical castings and forgings into axisymmetric meridian planes, thereby significantly reducing the computational complexity. At the same time, the non-uniform grid division strategy combined with the dynamic thermophysical property parameter input ensures the accuracy and practical applicability of the model.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] 1. Significantly improve the uniformity of austenitic stainless steel material structure, reduce performance differences caused by uneven structure, and enhance the overall quality and reliability of the product.

[0063] Second, by optimizing the time step to ensure uniform heat transfer, while ensuring the stability and efficiency of the simulation calculation, the accuracy of the temperature field simulation is effectively improved, thereby improving the accuracy of the ferrite dissolution process simulation.

[0064] 3. Dynamically adjust the heating parameters based on model monitoring data to achieve precise control of the heating process of austenitic stainless steel castings and forgings, greatly reduce the ferrite content and improve material properties.

[0065] Fourth, it provides a complete and practical industrial-grade process optimization method that is easy to operate and can be applied without complex professional knowledge, thus lowering the technical threshold and facilitating promotion.

[0066] 5. It is not only suitable for large castings and forgings of austenitic stainless steel, but can also be expanded to large castings and forgings of other similar materials by adjusting parameters. It has wide applicability and provides strong support for related production in different industrial fields.

[0067] 6. This method provides a solid technical foundation for the development of related applications, solves the problem of complex operation and difficulty in using traditional simulation software, and brings great convenience to industrial production.

[0068] 7. It can support real-time query of temperature and ferrite content changes in any area of ​​castings and forgings, provide detailed data support for process optimization, and help operators make accurate decisions in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is the axisymmetric characteristic of the meridian plane.

[0070] Figure 2 It is the meridian plane equidistant grid division.

[0071] Figure 3 is the overall temperature field.

[0072] Figure 4 It is the core temperature evolution curve and ferrite dissolution curve.

[0073] Figure 5 is the temperature curve in the radius / height direction through the center of mass.

[0074] Figure 6 This is the visualization interface in the application example. DETAILED DESCRIPTION

[0075] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. Component models, material names, connection structures, control methods, algorithms and other features not clearly described in this technical solution are all considered to be common technical features disclosed in the prior art.

[0076] Example 1

[0077] This embodiment provides a high temperature diffusion homogenization process optimization method applicable to large austenitic stainless steel castings and forgings, comprising the following steps:

[0078] S1: Based on Fourier heat conduction equation and finite volume method, the temperature field control equation of cylindrical castings and forgings is established;

[0079] S1 specifically includes the following steps:

[0080] According to the actual working conditions of cylindrical castings and forgings, they are simplified into a two-dimensional axisymmetric model to reduce the calculation dimension. Figure 1 ;

[0081] Select the adapted Fourier heat conduction equation and transform it into the cylindrical coordinate system;

[0082] The calculation area of ​​the model is meshed, and the heat conduction equation is integrated on each mesh unit using the finite volume method to discretize the equation;

[0083] Determine the third type of mixed boundary conditions for the adiabatic and heated surfaces of the cylinder axis, as well as the initial temperature distribution;

[0084] The discrete equations are organized into a system of equations that are easy to solve, and the temperature field control equations of cylindrical castings and forgings are established;

[0085] S2: Based on the established control equation, an adiabatic condition is set for the cylindrical axis boundary. At the same time, for the heated surface, according to the actual situation of radiation and convection heat transfer in the actual heating environment, a third type of mixed boundary condition containing radiation and convection heat transfer is set;

[0086] S2 specifically includes the following steps:

[0087] Based on the established control equations of S1, for the cylindrical axis boundary, according to the physical meaning of the adiabatic condition, its heat flux is set to zero;

[0088] For the heated surface, relevant data of the actual heating environment are collected. According to the material of the heating furnace and different environmental conditions, the appropriate emissivity is selected, and the convective heat transfer coefficient is determined by Newton's law of cooling. Then, combined with the Stefan-Boltzmann law, the radiation heat transfer and convection heat transfer are comprehensively considered to construct a mathematical expression for the third type of mixed boundary condition including radiation terms and convection terms.

[0089] S3: Use arithmetic progression to generate non-uniform grids to divide the calculation area;

[0090] S3 specifically includes the following steps:

[0091] Determine the size of castings and forgings, measure key dimensional parameters such as radius and height, and estimate the temperature gradients that may occur in different areas;

[0092] Select appropriate arithmetic progression tolerances based on the size of castings and forgings and expected temperature gradients;

[0093] Taking the axis of the cylindrical casting as the starting point, the radial and axial positions of each grid node are determined by arithmetic progression according to the selected tolerance. In the area where the temperature gradient changes greatly, the grid spacing is made smaller, while in the area where the temperature gradient is relatively gentle, the grid spacing is appropriately increased. Figure 2 .

[0094] S4: Introduce the ferrite dissolution kinetic model, combine the simulated temperature field information, dynamically calculate the ferrite dissolution amount, and associate the temperature field with the ferrite dissolution situation;

[0095] S4 specifically includes the following steps:

[0096] The activation energy E in the ferrite dissolution kinetic model was experimentally determined for austenitic stainless steels of different compositions;

[0097] Introduce the established ferrite dissolution kinetic model into the current process system;

[0098] Extract temperature data at each location and time from the simulated temperature field information;

[0099] According to the ferrite dissolution kinetics model, the extracted temperature data is substituted into it, and the ferrite dissolution amount at different times and in different areas is dynamically calculated according to the established formula and algorithm of the model;

[0100] During the calculation process, the corresponding relationship between the temperature of each point in the temperature field and the amount of ferrite dissolved at the corresponding position is established to closely link the temperature field with the ferrite dissolution.

[0101] S5: Adopt interpolation algorithm to adjust the thermal conductivity and specific heat capacity of materials according to the real-time temperature changes, so that the entire process model is more in line with the actual material characteristics;

[0102] S5 specifically includes the following steps:

[0103] Determine the data range of material thermal conductivity and specific heat capacity changing with temperature, and collect corresponding thermal conductivity and specific heat capacity experimental data at different temperatures;

[0104] Construct a series of "thermal physical parameter-temperature" data pairs, input the collected data pairs into the linear interpolation algorithm formula, construct a straight line equation between adjacent data points, and estimate the thermal conductivity and specific heat capacity function values ​​corresponding to any temperature between the data points;

[0105] When the process model is running, the current simulated temperature value is obtained in real time, substituted into the linear interpolation equation, and the corresponding thermal conductivity and specific heat capacity values ​​at the temperature are calculated;

[0106] The calculation results are updated to the entire process model, allowing the model to quickly and effectively dynamically adjust the material thermal properties according to real-time temperature changes during the simulation process.

[0107] S6: Optimize the time step in the process model according to the CFL stability condition (Courant-Friedrichs-Lévy condition);

[0108] S6 specifically includes the following steps:

[0109] Obtain the mathematical principles and physical meanings of the CFL stability conditions, and clarify the correlation formulas between them and factors such as the time step, space step, and heat conduction characteristics in the process model;

[0110] Extract the currently used spatial step data and related heat conduction parameters such as the thermal diffusivity of the material from the constructed process model;

[0111] Substitute the obtained spatial step and heat conduction parameters into the formula of CFL stability condition, and obtain the range of time step value under the premise of ensuring the stability of calculation process through mathematical calculation;

[0112] Within the range of the time step value, comprehensively consider factors such as calculation efficiency, simulation accuracy and actual process requirements, and determine the most appropriate time step value through multiple trial calculations or optimization algorithms;

[0113] Apply the determined time step to the process model.

[0114] S7: Based on the optimized process model, the temperature evolution of the core area and the change of ferrite content are monitored in real time. According to the monitoring data, the heating parameters are dynamically adjusted to improve the uniformity of the austenitic stainless steel material structure.

[0115] S7 specifically includes the following steps:

[0116] In the optimized process model, multiple monitoring points are precisely set for the core area;

[0117] Using the model calculation function, the real-time temperature data of each monitoring point is continuously collected, and the ferrite content at each point at the corresponding time is calculated based on the ferrite dissolution kinetics model;

[0118] The collected temperature and ferrite content data are summarized and analyzed, compared with the pre-set uniformity standards to determine whether they are met. Based on the analysis results, the temperature parameters are dynamically adjusted with the goal of improving organizational uniformity.

[0119] When implementing this embodiment, the following steps and factors need to be considered:

[0120] When treating austenitic stainless steel, a specific process is used to optimize its performance. First, austenitic stainless steel is subjected to high-temperature diffusion treatment, and the temperature is kept at 1200°C to 1230°C for 10 to 50 hours. This process is designed to allow the ferrite to fully dissolve in the austenite matrix, or to leave only a small amount in a spheroidized form, thereby laying a good foundation for subsequent forging. In order to accurately control the ferrite content, we use the method of statistical ferrite area fraction to accurately characterize the ferrite content in austenitic stainless steel. In specific operations, the metallographic image after electrolytic corrosion with 10% hot oxalic acid solution is carefully analyzed using microscopic analysis. With the help of professional microscope equipment, the area ratio of ferrite in the metallographic image is observed, and the ferrite area fraction is obtained, providing quantitative data support for the entire process to ensure the accuracy and effectiveness of the process.

[0121] The diffusion temperature and time are optimized and determined by the following formula:

[0122]

[0123] Among them, F 0 is the initial ferrite content, t is the holding time (h), τ(T) is the characteristic time corresponding to temperature T (h).

[0124] The characteristic time (under which the ferrite content is sufficiently low after treatment and spheroidization must have occurred) is determined in the form of:

[0125]

[0126] Among them, τ 0 is the pre-factor and E is the activation energy.

[0127] By constructing a temperature field simulation, the heating path of the diffusion treatment is deeply optimized. The core goal is to ensure that the temperature difference between the core and surface areas of the ingot is always less than 10°C, thereby effectively avoiding the problem of uneven organization caused by excessive temperature difference. After the high-temperature diffusion treatment is completed, the ferrite content of austenitic stainless steel can be accurately controlled to less than 3% of the initial content, or a small amount of ferrite can be evenly distributed in a spheroidized state. During the simulation process, the temperature prediction model uses the finite volume method to discretize and solve the Fourier heat conduction equation to obtain accurate temperature distribution data. At the same time, the actual heating environment is fully considered, the boundary conditions are set to include radiation and convection heat transfer, the heat flux density is accurately calculated, and the real heating scene is restored to the greatest extent. In addition, the process parameters are comprehensively optimized, covering the selection of multi-stage heating temperature ranges, accurately setting the target ferrite content peak and its existence area according to the needs of different stages, and strictly controlling the temperature difference inside the ingot, providing a full range of guarantees for improving the overall performance of austenitic stainless steel.

[0128] In the method of the present invention, the optimization of process parameters is a key link in improving the performance of austenitic stainless steel. By selecting a multi-stage heating temperature range, according to the characteristics of austenitic stainless steel in different processing stages, a suitable temperature range is set in the heating and insulation processes, so that ferrite can be more reasonably dissolved in the austenite matrix during high-temperature diffusion treatment. Set the target ferrite content peak and existence area, clarify the desired ferrite content standard, and the location where a small amount of ferrite should exist, so as to ensure the stability and uniformity of the material microstructure. To control the temperature difference inside the ingot, on the one hand, by constructing a temperature field simulation to optimize the diffusion treatment heating path, ensure that the temperature difference between the core and the surface area is less than 10°C, and on the other hand, continuously monitor and control the entire process to reduce the thermal stress and organizational differences caused by the temperature difference, and comprehensively improve the overall performance of austenitic stainless steel.

[0129] Establishment of temperature field control equation: Based on Fourier heat conduction equation and combined with the energy conservation principle of finite volume method, the temperature field in the meridian plane of the cylinder is discretized to form the control equation as follows:

[0130]

[0131] where k is the thermal conductivity; the dimensions of the grid region (i,j) are Δr and Δz, and its center temperature is T i,j ; r 1 and r 2 are the distances from the horizontal boundary of the grid to the axis; the four adjacent temperatures of the control volume are on the left: T i-1,j , right side: T i+1,j , upper side: T i,j+1 , bottom side: T i,j-1 .

[0132] Setting of boundary conditions: setting adiabatic conditions for the meridian boundary corresponding to the cylindrical axis, setting the third type of mixed boundary conditions for the heated surface, and considering the influence of radiation and convection heat transfer at the same time, simulating the heat flux distribution characteristics in the actual industrial heating environment. The expression is:

[0133]

[0134] Where h is the convective heat transfer coefficient; ε is the emissivity, which is taken as 0.8; σ is the Stefan-Boltzmann constant; T s is the surface temperature; T ∞ is the ambient temperature. The convective heat transfer coefficient is calculated using the formula Where V is the velocity of the furnace gas (m / s) and L is the size of the heat exchange area (m).

[0135] Non-uniform grid division: In view of the large temperature difference from the axis to the surface during the heating process of the cylinder, a non-uniform grid division strategy generated by an arithmetic sequence is adopted to make the representative volume of each grid as equal as possible, thereby improving the calculation accuracy and reducing the calculation cost.

[0136] Combination of ferrite dissolution laws: Based on the temperature field simulation, combined with the kinetic model of ferrite dissolution, the relationship between ferrite content and temperature and holding time is described:

[0137]

[0138] Among them, F 0 is the initial ferrite content, t is the holding time (h), τ(T) is the characteristic time (h) corresponding to temperature T, reflecting the dissolution rate of ferrite, and the formula is used. Among them, τ 0 is the pre-exponential factor, and E is the activation energy. Combined with the results of temperature field simulation, the amount of ferrite dissolved is dynamically calculated to ensure that ferrite is completely eliminated or converted into a stable spheroidized structure during the heating process.

[0139] Dynamic thermophysical parameters: The thermal conductivity and specific heat capacity of the material are dynamically adjusted through the interpolation algorithm to adapt to the impact of temperature changes on the thermophysical parameters, thereby enhancing the applicability of the model and the accuracy of the calculation results.

[0140] Time step optimization: Based on the CFL stability condition, the time step is optimized to achieve a balance between stability and accuracy, thereby improving the efficiency and reliability of the simulation.

[0141] Integrated simulation of temperature field and organizational evolution: Integrate the temperature field simulation results with the ferrite dissolution law to provide a prediction method for organizational homogenization. By real-time monitoring of the core area temperature evolution and ferrite content changes, dynamically adjust the heating parameters (such as temperature, time, etc.) to achieve precise control of the homogenization effect. The ferrite content is updated in the form of:

[0142]

[0143] Where F(t i ) is time t i Ferrite content at t (%); Δt = t i+1 -t i is the time step (h), and the time step is equally spaced.

[0144] Temperature field query and visualization function: This embodiment is also used for corresponding program design and provides a user-friendly visualization interface. Figure 6 , supports querying and analyzing the temperature and ferrite content changes in any area. By inputting the coordinates of the target point or area, the temperature distribution and ferrite dissolution state are output in real time, providing data support for process optimization.

[0145] Application Example 1

[0146] This application example is applicable to the homogenization treatment of austenitic stainless steel to eliminate ferrite. The key is to reduce the local enrichment of ferrite forming / stabilizing elements through element diffusion at high temperature, aiming to reveal the law of ferrite dissolution, and identify the differences in homogenization process in different areas by combining the temperature distribution of the treated part during the heating process, so as to provide data support for the homogenization treatment of castings and forgings.

[0147] Set the example parameters as shown in the following table:

[0148]

[0149] The parameters were input into the heat transfer simulation and ferrite content calculator developed in the steps of this example.

[0150] The simulation results are as follows:

[0151] Query the temperature of any area

[0152]

[0153] There is also an overall temperature field, such as Figure 3 The core temperature evolution curve and ferrite dissolution curve are as follows: Figure 4 The temperature curve in the radius / height direction through the center of mass is as follows: Figure 5According to the results, it is believed that under this heating condition, the temperature difference inside the casting and forging has almost disappeared, and the extended heating time required for microstructure homogenization can be calculated according to the ferrite dissolution model.

[0154] The above description of the embodiments is to facilitate the understanding and use of the invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the present invention should be within the scope of protection of the present invention.

Claims

1. A high temperature diffusion homogenization process optimization method suitable for large austenitic stainless steel castings and forgings, characterized in that: The following steps are involved: S1: Based on Fourier heat conduction equation and finite volume method, the temperature field control equation of cylindrical castings and forgings is established; S2: Based on the established control equation, an adiabatic condition is set for the cylindrical axis boundary. At the same time, for the heated surface, according to the actual situation of radiation and convection heat transfer in the actual heating environment, a third type of mixed boundary condition containing radiation and convection heat transfer is set; S3: Use arithmetic progression to generate non-uniform grids to divide the calculation area; S4: Introduce the ferrite dissolution kinetic model, combine the simulated temperature field information, dynamically calculate the ferrite dissolution amount, and associate the temperature field with the ferrite dissolution situation; S5: Adopt interpolation algorithm to adjust the thermal conductivity and specific heat capacity of materials according to the real-time temperature changes, so that the entire process model is more in line with the actual material characteristics; S6: Optimize the time step in the process model according to the CFL stability condition; S7: Based on the optimized process model, the temperature evolution of the core area and the change of ferrite content are monitored in real time. According to the monitoring data, the heating parameters are dynamically adjusted to improve the uniformity of the austenitic stainless steel material structure.

2. The high temperature diffusion homogenization process optimization method for austenitic stainless steel large castings and forgings according to claim 1, characterized in that: S1 specifically includes the following steps: According to the actual working conditions of cylindrical castings and forgings, they are simplified into two-dimensional axisymmetric models to reduce the calculation dimension; Select the adapted Fourier heat conduction equation and transform it into the cylindrical coordinate system; The calculation area of ​​the model is meshed, and the heat conduction equation is integrated on each mesh unit using the finite volume method to discretize the equation; Determine the third type of mixed boundary conditions for the adiabatic and heated surfaces of the cylinder axis, as well as the initial temperature distribution; The discrete equations are organized into a system of equations that are easy to solve, and the temperature field control equations of cylindrical castings and forgings are established; In S1, the control equation is as follows: Where: k is the thermal conductivity; the dimensions of the grid region (i,j) are Δr and Δz, and its center temperature is T i,j ; r1 and r2 are the distances from the grid horizontal boundary to the axis; the four adjacent temperatures of the control volume are on the left: T i-1,j , right side: T i+1,j , upper side: T i,j+1 , bottom side: T i,j-1 .

3. The high temperature diffusion homogenization process optimization method for large austenitic stainless steel castings and forgings according to claim 1, characterized in that: S2 specifically includes the following steps: Based on the established control equations of S1, for the cylindrical axis boundary, according to the physical meaning of the adiabatic condition, its heat flux is set to zero; For the heated surface, relevant data of the actual heating environment are collected. According to the material of the heating furnace and different environmental conditions, the appropriate emissivity is selected, and the convective heat transfer coefficient is determined by Newton's law of cooling. Then, combined with the Stefan-Boltzmann law, the radiation heat transfer and convection heat transfer are comprehensively considered to construct a mathematical expression for the third type of mixed boundary condition including radiation terms and convection terms.

4. The high temperature diffusion homogenization process optimization method for austenitic stainless steel large castings and forgings according to claim 3, characterized in that: The mathematical expression of the third type of mixed boundary condition is: Where h is the convective heat transfer coefficient; ε is the emissivity, which is taken as 0.8; σ is the Stefan-Boltzmann constant; T s is the surface temperature; T∞ is the ambient temperature; The convective heat transfer coefficient is expressed as Where V is the velocity of the furnace gas (m / s) and L is the size of the heat exchange area (m).

5. The high temperature diffusion homogenization process optimization method for large austenitic stainless steel castings and forgings according to claim 1, characterized in that: S3 specifically includes the following steps: Determine the size of castings and forgings, measure key dimensional parameters such as radius and height, and estimate the temperature gradients that may occur in different areas; Select appropriate arithmetic progression tolerances based on the size of castings and forgings and expected temperature gradients; Taking the axis of the cylindrical casting as the starting point, the radial and axial positions of each grid node are determined by an arithmetic progression according to the selected tolerance. In the area where the temperature gradient changes greatly, the grid spacing is made smaller, while in the area where the temperature gradient is relatively gentle, the grid spacing is appropriately increased.

6. The high temperature diffusion homogenization process optimization method for austenitic stainless steel large castings and forgings according to claim 1, characterized in that: S4 specifically includes the following steps: The activation energy E in the ferrite dissolution kinetic model was experimentally determined for austenitic stainless steels of different compositions; Introduce the established ferrite dissolution kinetic model into the current process system; Extract temperature data at each location and time from the simulated temperature field information; According to the ferrite dissolution kinetics model, the extracted temperature data is substituted into it, and the ferrite dissolution amount at different times and in different areas is dynamically calculated according to the established formula and algorithm of the model; During the calculation process, the corresponding relationship between the temperature of each point in the temperature field and the amount of ferrite dissolved at the corresponding position is established to closely link the temperature field with the ferrite dissolution.

7. The high temperature diffusion homogenization process optimization method for austenitic stainless steel large castings and forgings according to claim 6, characterized in that: In S4, the ferrite dissolution kinetic model is: Where F0 is the initial ferrite content, t is the holding time (h), τ(T) is the characteristic time (h) corresponding to temperature T, reflecting the dissolution rate of ferrite, and is expressed by the formula: Where τ0 is the pre-exponential factor and E is the activation energy.

8. The high temperature diffusion homogenization process optimization method for austenitic stainless steel large castings and forgings according to claim 1, characterized in that: S5 specifically includes the following steps: Determine the data range of material thermal conductivity and specific heat capacity changing with temperature, and collect corresponding thermal conductivity and specific heat capacity experimental data at different temperatures; Construct a series of "thermal physical parameter-temperature" data pairs, input the collected data pairs into the linear interpolation algorithm formula, construct a straight line equation between adjacent data points, and estimate the thermal conductivity and specific heat capacity function values ​​corresponding to any temperature between the data points; When the process model is running, the current simulated temperature value is obtained in real time, substituted into the linear interpolation equation, and the corresponding thermal conductivity and specific heat capacity values ​​at the temperature are calculated; The calculation results are updated to the entire process model, allowing the model to quickly and effectively dynamically adjust the material thermal properties according to real-time temperature changes during the simulation process.

9. The high temperature diffusion homogenization process optimization method for austenitic stainless steel large castings and forgings according to claim 1, characterized in that: S6 specifically includes the following steps: Obtain the mathematical principles and physical meanings of the CFL stability conditions, and clarify the correlation formulas between them and factors such as the time step, space step, and heat conduction characteristics in the process model; Extract the currently used spatial step data and related heat conduction parameters such as the thermal diffusivity of the material from the constructed process model; Substitute the obtained spatial step and heat conduction parameters into the formula of CFL stability condition, and obtain the range of time step value under the premise of ensuring the stability of calculation process through mathematical calculation; Within the range of the time step value, the most appropriate time step value is determined by comprehensively considering factors such as calculation efficiency, simulation accuracy, and actual process requirements through multiple trial calculations or by using an optimization algorithm; Apply the determined time step to the process model.

10. The high temperature diffusion homogenization process optimization method applicable to large austenitic stainless steel castings and forgings according to claim 1, characterized in that: S7 specifically includes the following steps: In the optimized process model, multiple monitoring points are precisely set for the core area; Using the model calculation function, the real-time temperature data of each monitoring point is continuously collected, and the ferrite content at each point at the corresponding time is calculated based on the ferrite dissolution kinetics model; The collected temperature and ferrite content data are summarized and analyzed, compared with the pre-set uniformity standards to determine whether they are met. Based on the analysis results, the temperature-related parameters are dynamically adjusted with the goal of improving organizational uniformity.

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