Method, device and storage medium for predicting mold life in continuous casting process

Abstract

The invention discloses a method, device and storage medium for predicting the life of a crystallizer in a continuous casting process, comprising: constructing a three-dimensional finite element model of the crystallizer and a three-dimensional finite element model of a cast billet; performing back calculation on the crystallizer to be predicted to obtain the heat flux density of the crystallizer and determine the heat flux density of the cast billet; performing thermomechanical coupling analysis on the three-dimensional finite element model of the crystallizer using the heat flux density of the crystallizer as a boundary condition of the crystallizer to obtain the temperature field of the crystallizer and the stress and strain field of the crystallizer; performing thermomechanical coupling analysis on the three-dimensional finite element model of the cast billet based on the boundary condition of the cast billet to obtain the temperature field of the cast billet and the stress and strain field of the cast billet; based on the crystallizer temperature field data, the stress and strain field of the crystallizer, the temperature field of the cast billet, the stress and strain field of the cast billet, the three-dimensional finite element model of the crystallizer and the three-dimensional finite element model of the cast billet, using a friction and wear model to simulate the friction and wear between the crystallizer and the cast billet; and determining the life of the crystallizer to be predicted based on the friction and wear results.

Description

Technical Field

[0001] The present invention relates to the technical field of continuous casting, and in particular to a method, device and storage medium for predicting the life of a crystallizer in a continuous casting process. Background Art

[0002] As the core equipment in the continuous casting process, the structure, material and performance of the crystallizer are directly related to the efficiency of the continuous casting process and the quality of the continuous casting billet. In the continuous casting production process, due to the reciprocating vibration of the crystallizer and the billet drawing movement, there is a relative movement between the crystallizer and the billet. According to the friction theory, there is friction between the crystallizer and the billet. Under the action of friction, the surface material of the crystallizer is continuously lost during the relative movement, resulting in wear. This seriously affects the service life of the crystallizer, the quality and output of the billet. Therefore, studying the friction and wear mechanism and the friction and wear behavior between the crystallizer and the billet is of great significance for extending the service life of the copper plate and improving the quality of the billet.

[0003] At present, the life of the crystallizer is usually calculated by an empirical formula. However, this method of predicting the life of the crystallizer requires the accumulation of a large amount of production data in advance, resulting in low prediction efficiency of the crystallizer life. At the same time, due to the complexity and variability of the actual production process, the empirical formula may not be able to fully and accurately reflect all influencing factors, resulting in low prediction accuracy of the crystallizer life. Summary of the invention

[0004] The present invention provides a method, device and storage medium for predicting the life of a crystallizer in a continuous casting process, which are mainly capable of improving the prediction accuracy and prediction efficiency of the life of the crystallizer.

[0005] According to a first aspect of the present invention, a method for predicting mold life in a continuous casting process is provided, comprising:

[0006] Acquire the crystallizer property data of the crystallizer to be predicted, and acquire the physical property parameters of the casting steel type and the casting property data of the casting in the continuous casting process where the crystallizer to be predicted is located;

[0007] Based on the crystallizer property data and the physical property parameters, a three-dimensional finite element model of the crystallizer corresponding to the crystallizer to be predicted is constructed, based on the ingot property data, a three-dimensional finite element model of the ingot is constructed, and the crystallizer to be predicted is back-calculated to obtain the crystallizer heat flux density of the crystallizer to be predicted, and the ingot heat flux density of the ingot is determined;

[0008] The heat flux density of the crystallizer is used as a crystallizer boundary condition, and based on the crystallizer boundary condition, a thermal-mechanical coupling analysis is performed on a three-dimensional finite element model of the crystallizer in the continuous casting process to obtain crystallizer temperature field data and crystallizer stress-strain field data of the crystallizer to be predicted;

[0009] The heat flux density of the casting billet is used as a casting billet boundary condition, and based on the casting billet boundary condition, a thermal-mechanical coupling analysis is performed on a three-dimensional finite element model of the casting billet in the continuous casting process to obtain casting billet temperature field data and casting billet stress-strain field data of the casting billet;

[0010] Based on the crystallizer temperature field data, the crystallizer stress-strain field data, the ingot temperature field data, the ingot stress-strain field data, the crystallizer three-dimensional finite element model, and the ingot three-dimensional finite element model, a preset friction and wear model is used to simulate the friction and wear between the crystallizer to be predicted and the ingot to obtain a friction and wear result;

[0011] Based on the friction and wear results, the life of the crystallizer to be predicted is determined.

[0012] According to a second aspect of the present invention, there is provided a device for predicting the life of a mold in a continuous casting process, comprising:

[0013] An acquisition unit, used to acquire the crystallizer property data of the crystallizer to be predicted, and to acquire the physical property parameters of the casting steel type and the casting property data of the casting in the continuous casting process where the crystallizer to be predicted is located;

[0014] A construction unit is used to construct a three-dimensional finite element model of a crystallizer corresponding to the crystallizer to be predicted based on the crystallizer property data and the physical property parameters, construct a three-dimensional finite element model of the cast billet based on the cast billet property data, and perform back calculation on the crystallizer to be predicted to obtain the crystallizer heat flux density of the crystallizer to be predicted, and determine the cast billet heat flux density of the cast billet;

[0015] a first coupling analysis unit, configured to use the heat flux density of the crystallizer as a crystallizer boundary condition, and perform a thermal-mechanical coupling analysis on a three-dimensional finite element model of the crystallizer in the continuous casting process based on the crystallizer boundary condition, so as to obtain crystallizer temperature field data and crystallizer stress-strain field data of the crystallizer to be predicted;

[0016] a second coupling analysis unit, configured to use the heat flux density of the casting billet as a casting billet boundary condition, and perform a thermal-mechanical coupling analysis on a three-dimensional finite element model of the casting billet in the continuous casting process based on the casting billet boundary condition, so as to obtain casting billet temperature field data and casting billet stress-strain field data of the casting billet;

[0017] A friction unit is used to input the crystallizer temperature field data, the crystallizer stress-strain field data, the billet temperature field data, and the billet stress-strain field data into a preset friction and wear model to simulate the friction and wear between the crystallizer to be predicted and the billet, and obtain a friction and wear result;

[0018] A determination unit is used to determine the life of the crystallizer to be predicted based on the friction and wear result.

[0019] According to a third aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above method for predicting the life of a crystallizer in a continuous casting process.

[0020] According to a fourth aspect of the present invention, there is provided a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method for predicting the life of a crystallizer in a continuous casting process when executing the program.

[0021] According to a method, device and storage medium for predicting the life of a crystallizer in a continuous casting process provided by the present invention, compared with the current method of calculating the life of a crystallizer using an empirical formula, the present invention obtains the crystallizer property data of the crystallizer to be predicted, as well as the physical property parameters of the casting steel type and the casting property data of the ingot in the continuous casting process where the crystallizer to be predicted is located; and based on the crystallizer property data and the physical property parameters, a three-dimensional finite element model of the crystallizer corresponding to the crystallizer to be predicted is constructed, and based on the ingot property data, a three-dimensional finite element model of the ingot is constructed, and the crystallizer to be predicted is back-calculated to obtain the crystallizer heat flux density of the crystallizer to be predicted, and the casting heat flux density of the ingot is determined; at the same time, the crystallizer heat flux density is used as the crystallizer boundary condition, and based on the crystallizer edge The three-dimensional finite element model of the crystallizer in the continuous casting process is subjected to a thermomechanical coupling analysis based on the boundary conditions to obtain the crystallizer temperature field data and the crystallizer stress-strain field data of the crystallizer to be predicted; the heat flux density of the cast billet is used as the cast billet boundary condition, and based on the cast billet boundary condition, the three-dimensional finite element model of the cast billet in the continuous casting process is subjected to a thermomechanical coupling analysis to obtain the cast billet temperature field data and the cast billet stress-strain field data of the cast billet; then, based on the crystallizer temperature field data, the crystallizer stress-strain field data, the cast billet temperature field data, the cast billet stress-strain field data, the three-dimensional finite element model of the crystallizer, and the three-dimensional finite element model of the cast billet, a preset friction and wear model is used to simulate the friction and wear between the crystallizer to be predicted and the cast billet to obtain the friction and wear result; finally, based on the friction and wear result, the life of the crystallizer to be predicted is determined. Therefore, by establishing three-dimensional finite element models of the crystallizer and the billet respectively, and back-calculating the three-dimensional finite element model of the crystallizer and the billet respectively, the back-calculated heat flux density is used as the boundary condition of the billet and the crystallizer, and transient strong coupling is adopted to perform thermal-mechanical coupling analysis on the billet and the crystallizer respectively, and the crystallizer temperature field data, crystallizer stress-strain field data, billet temperature field data, and billet stress-strain field data are obtained. Finally, based on the above data, the friction and wear model is used to realize the friction and wear simulation between the crystallizer and the billet, and the crystallizer life is determined according to the friction and wear results. The friction and wear simulation can simulate the structure in more detail. The friction and wear process between the crystal mold and the ingot takes into account more influencing factors and boundary conditions, so as to obtain more accurate life prediction results. At the same time, there is no need to obtain a large amount of production data, which improves the efficiency of life prediction. That is, the friction and wear behavior between the crystal mold and the ingot is simulated by comprehensively considering multiple factors such as stress, strain, temperature field, etc., so as to determine the wear result. There is no need to actually build an experimental device and perform experimental operations, which greatly saves cost and time. At the same time, in the process of friction and wear simulation, multiple factors such as stress, strain, temperature field, etc. are comprehensively considered, which can improve the simulation accuracy and thus improve the accuracy of crystallizer life prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0023] Figure 1 A flow chart of a method for predicting mold life in a continuous casting process provided by an embodiment of the present invention is shown;

[0024] Figure 2 Another flow chart of a method for predicting mold life in a continuous casting process provided by an embodiment of the present invention is shown;

[0025] Figure 3 A schematic structural diagram of a device for predicting the life of a mold in a continuous casting process provided by an embodiment of the present invention is shown;

[0026] Figure 4 A schematic structural diagram of another device for predicting the life of a crystallizer in a continuous casting process provided by an embodiment of the present invention is shown;

[0027] Figure 5 A schematic diagram of the physical structure of a computer device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0028] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0029] At present, the method of calculating the life of the crystallizer by using an empirical formula results in low prediction accuracy and low prediction efficiency of the crystallizer life.

[0030] In order to solve the above problems, the embodiment of the present invention provides a method for predicting the life of a mold in a continuous casting process, such as Figure 1 As shown, the method includes:

[0031] 101. Acquire crystallizer property data of the crystallizer to be predicted, and acquire physical property parameters of the type of casting steel and ingot property data of the ingot in the continuous casting process where the crystallizer to be predicted is located.

[0032] Among them, the crystallizer to be predicted is the core equipment in the continuous casting process, and there is relative movement between the crystallizer to be predicted and the billet during the continuous casting process; the crystallizer attribute data includes the structural parameters, thermophysical parameters, process parameters, etc. of the crystallizer to be predicted, the structural parameters include the effective height, width and narrow surface length, taper, wall thickness, structural dimension data, shape, material type, etc. of the crystallizer to be predicted, the thermophysical parameters include the density, specific heat capacity, thermal conductivity, etc. of the crystallizer to be predicted, the process parameters include the water removal volume and cooling water inlet and outlet temperatures of the crystallizer to be predicted, thermocouple temperature, cooling water flow rate, the material of each component (such as copper plate and cooling water channel), three-dimensional dimension data, layout of cooling water channel, taper of copper plate, shape and position of immersion nozzle, etc.; the physical property parameters include the physical property parameters required for thermal analysis of casting steel and the physical property parameters required for structural analysis, the physical property parameters required for thermal analysis include density, specific heat, thermal conductivity, etc., the physical property parameters required for structural analysis include Poisson's ratio, elastic modulus, yield strength, etc.; the ingot property data include the geometric size data of the ingot (such as the overall size of the ingot: including length, width, height (or diameter, for round ingots), etc., the size of key parts: the detailed size of some special structures or key parts (such as gates, risers, cooling channels, etc.) that may exist on the ingot), material property data (such as ingot material type: such as steel, aluminum, copper, etc., physical and thermal properties: including material density, thermal conductivity, specific heat capacity, melting point, thermal expansion coefficient, etc.), process condition data (casting temperature: the initial temperature of the ingot during casting, cooling conditions: including cooling rate, type of cooling medium (such as water, air, etc.) and temperature, layout and parameters of cooling equipment (such as cooling water channels, cooling fans, etc.), boundary conditions: such as contact thermal resistance between the ingot and the mold, convection heat transfer coefficient between the ingot surface and the external environment, etc.), meshing parameters (such as mesh type and mesh density, etc.), etc.

[0033] For the embodiment of the present invention, if the friction process between the crystallizer to be predicted and the ingot is to be simulated, it is first necessary to construct a three-dimensional finite element model between the crystallizer to be predicted and the ingot respectively. Based on this, it is necessary to obtain the crystallizer property data of the crystallizer to be predicted in the actual continuous casting scenario. Since the physical properties of the casting steel type are crucial for simulating the flow, heat transfer and solidification process of the molten steel in the crystallizer, it is also necessary to obtain the physical properties of the casting steel type, and construct a three-dimensional finite element model of the crystallizer to be predicted based on the crystallizer property data and physical properties. Similarly, by obtaining the ingot property data of the ingot in the actual continuous casting scenario, a three-dimensional finite element model of the ingot that runs relative to the crystallizer to be predicted is constructed. Then, according to the three-dimensional finite element model of the crystallizer and the three-dimensional finite element model of the billet, the gradual friction and wear behavior of the crystallizer and the billet to be predicted is simulated. By constructing a three-dimensional finite element model of the crystallizer and the billet, the contact interface between the inner wall of the crystallizer and the billet can be accurately simulated. This interface is the key area where friction movement occurs. Its shape, size and surface characteristics have a direct impact on the friction behavior. Through the model, the stress distribution, temperature change and material deformation of the contact interface can be analyzed in detail. At the same time, the three-dimensional finite element model can simulate the movement process of the billet in the crystallizer, including sliding, rolling or mixed friction modes. By setting appropriate friction coefficients and boundary conditions, the magnitude, direction and distribution of friction forces under different working conditions can be calculated and analyzed, as well as how these forces affect the solidification quality, surface quality and shape accuracy of the billet, etc., so that the friction and wear behavior between the crystallizer and the billet can be accurately simulated, thereby improving the prediction accuracy of the crystallizer life.

[0034] 102. Based on the crystallizer attribute data and physical property parameters, a three-dimensional finite element model of the crystallizer to be predicted is constructed. Based on the ingot attribute data, a three-dimensional finite element model of the ingot is constructed. The crystallizer to be predicted is back-calculated to obtain the crystallizer heat flux density of the crystallizer to be predicted, and the ingot heat flux density of the ingot is determined.

[0035] Among them, the heat flux density of the crystallizer refers to the heat flux density that the crystallizer is subjected to during the crystal growth or continuous casting process; the heat flux density of the ingot reflects the heat exchange between the ingot and the surrounding environment (such as cooling water, protective slag, etc.), which has a direct impact on the solidification process, microstructure and final product quality of the ingot.

[0036] For the embodiments of the present invention, since the magnitude of the heat flux density determines the distribution and change of the temperature field inside the crystallizer, it is necessary to obtain the heat flux density of the crystallizer to be predicted. Specifically, the initial conditions and boundary conditions can be first set in the three-dimensional finite element model of the crystallizer. The initial conditions may include the initial temperature distribution of the ingot, and the boundary conditions include the temperature and flow rate of the cooling water, and the contact thermal resistance between the ingot and the crystallizer, etc. Then, the three-dimensional finite element model of the crystallizer is solved using numerical methods (such as the finite difference method, the finite element method, etc.) to simulate the solidification process of the ingot in the crystallizer. During this process, the model will calculate the temperature field, stress field and heat flux density distribution inside the ingot. Finally, by comparing the simulation results with the experimental data (such as the outlet temperature of the ingot), the model is calibrated and adjusted, and the calibrated model is used to inversely calculate the heat flux density of the crystallizer, that is, by finding the ingot temperature distribution that matches the experimental data in the model, and calculating the heat flux density borne by the inner wall of the crystallizer under the distribution. Similarly, based on the established three-dimensional finite element analysis model of the ingot and crystallizer, the physical models required for the simulation are determined, including heat transfer models (such as heat conduction, convection and radiation), solidification models (such as isothermal solidification, latent heat release, etc.) and possible phase change models. At the same time, the initial conditions and boundary conditions are determined: the initial temperature distribution of the ingot, the temperature and flow rate of the crystallizer cooling water, and the contact thermal resistance between the ingot and the crystallizer are set, and then numerical simulation is performed: that is, finite element analysis or other numerical methods are used to solve the heat transfer and solidification equations, simulate the solidification process of the ingot in the crystallizer, and finally calculate the temperature field inside the ingot, the position of the solidification front, and the heat flux density distribution that changes with time. Since the heat flux density of the crystallizer plays a vital role in the thermal-mechanical coupling analysis, it affects the temperature field distribution and solidification process of the crystallizer. Therefore, before predicting the temperature field data and stress-strain field data of the crystallizer, it is necessary to determine the heat flux density of the crystallizer, so as to ensure the accuracy of the determination of the temperature field data and stress-strain field data of the crystallizer. At the same time, the heat flux density of the crystallizer and the heat flux density of the ingot affect the life of the crystallizer. Therefore, considering the heat flux density of the crystallizer and the heat flux density of the ingot in the process of predicting the life of the crystallizer can improve the prediction accuracy of the life of the crystallizer.

[0037] The heat flux density of the crystallizer is used as the boundary condition of the crystallizer, and based on the boundary condition of the crystallizer, a thermal-mechanical coupling analysis is performed on the three-dimensional finite element model of the crystallizer in the continuous casting process to obtain the crystallizer temperature field data and the crystallizer stress-strain field data of the crystallizer to be predicted.

[0038] Among them, the crystallizer temperature field data refers to the data set composed of the temperature values ​​of each point inside the crystallizer to be predicted at different times or spatial positions, and the crystallizer stress-strain field data refers to the stress and strain distribution data generated by the crystallizer to be predicted during the continuous casting process due to factors such as temperature changes and mechanical loads, which respectively reflect the thermal distribution and mechanical behavior inside the crystallizer to be predicted.

[0039] For the embodiment of the present invention, the heat flux density of the crystallizer is applied as a boundary condition to the interface where the crystallizer to be predicted contacts the ingot, and the convective heat transfer coefficient and cooling water temperature between the outer wall of the crystallizer and the cooling water, as well as the insulation or heat dissipation conditions of the bottom and sides of the crystallizer are set. At the same time, the three-dimensional model of the crystallizer is meshed using finite element analysis software. The density and type of the mesh (such as tetrahedron, hexahedron, etc.) should be determined according to the analysis accuracy and computing resources. At the contact interface between the crystallizer to be predicted and the ingot and in the area with a large temperature gradient, a denser mesh should be used to improve the calculation accuracy. Then, a suitable solver (such as an implicit solver or an explicit solver) and solution parameters (such as time step, convergence criterion, etc.) are selected. For thermal-mechanical coupling analysis, it is necessary to solve the heat conduction equation and the mechanical equation at the same time. The finite element analysis software is run to perform thermal-mechanical coupling analysis on the three-dimensional finite element model of the crystallizer. During the analysis, the software will automatically calculate the temperature field distribution and stress-strain field distribution inside the crystallizer, thereby obtaining the crystallizer temperature field data and crystallizer stress-strain field data of the crystallizer to be predicted.

[0040] The heat flux density of the ingot is used as the boundary condition of the ingot, and based on the boundary condition of the ingot, the thermal-mechanical coupling analysis is performed on the three-dimensional finite element model of the ingot in the continuous casting process to obtain the ingot temperature field data and the ingot stress-strain field data.

[0041] Among them, the ingot temperature field data refers to the temperature distribution information of each point inside the ingot that changes with time during the solidification process; the ingot stress and strain field data refers to the stress and strain distribution information generated by factors such as temperature change, phase change, and mechanical constraint during the solidification process of the ingot, which respectively reflect the thermal state and mechanical behavior of the ingot during the solidification process.

[0042] For the embodiment of the present invention, a three-dimensional geometric model is established according to the actual size and shape of the billet, and is imported into the finite element analysis software, and the thermophysical properties (such as thermal conductivity, specific heat capacity, density, latent heat of solidification, etc.) and mechanical properties (such as elastic modulus, Poisson's ratio, thermal expansion coefficient, yield strength, etc.) of the billet material are defined. At the same time, the heat flux density boundary condition is set, that is, the billet heat flux density is applied as a boundary condition to the interface between the billet and the crystallizer to be predicted, as well as other heat exchange areas that may exist on the surface of the billet (such as a water spray cooling section), and the initial temperature distribution of the billet is set. For the contact area between the billet and other parts of the continuous casting machine (such as guide rollers), it is necessary to define the corresponding contact thermal resistance and heat exchange conditions, and then the finite element analysis software is used to perform the three-dimensional finite element model of the billet. Meshing is performed. In areas with large temperature gradients, stress concentrations, or significant changes in material properties (such as solidification fronts, hot spots, etc.), denser meshes should be used to improve calculation accuracy, and solvers and solution parameters should be set, that is, appropriate solvers (such as implicit solvers or explicit solvers) and solution parameters (such as time step, convergence criterion, etc.) should be selected. For thermal-mechanical coupling analysis, the heat conduction equation and the mechanical equation need to be solved simultaneously to ensure that the coupling relationship between the two is correctly handled. Then, a thermal-mechanical coupling analysis is performed on the three-dimensional finite element model of the ingot, that is, the finite element analysis software is run to perform a thermal-mechanical coupling analysis. During the analysis, the software will automatically calculate the temperature field distribution and stress-strain field distribution inside the ingot, thereby obtaining the ingot temperature field data and ingot stress-strain field data of the ingot. Therefore, by respectively determining the temperature field data and stress-strain field data of the crystallizer and the billet to be predicted, since the temperature field data can evaluate the thermal stability and cooling capacity of the crystallizer, this is helpful to discover potential thermal fatigue and thermal stress problems, and the stress-strain field data can be used to evaluate the fatigue life of the crystallizer, that is, the temperature field data and stress-strain field data of the crystallizer and the billet have a vital influence on the life of the crystallizer. Therefore, in the process of predicting the life of the crystallizer, the crystallizer temperature field data, the crystallizer stress-strain field data, the billet temperature field data, and the billet stress-strain field data are comprehensively considered, thereby improving the prediction accuracy of the crystallizer life.

[0043] Based on the crystallizer temperature field data, crystallizer stress-strain field data, ingot temperature field data, ingot stress-strain field data, crystallizer three-dimensional finite element model, ingot three-dimensional finite element model, the friction and wear between the predicted crystallizer and the ingot is simulated using a preset friction and wear model to obtain the friction and wear results.

[0044] For the embodiment of the present invention, the crystallizer unit, the billet shell unit, the crystallizer temperature field data, the crystallizer stress-strain field data, the billet temperature field data, the billet stress-strain field data, the crystallizer three-dimensional finite element model, and the billet three-dimensional finite element model are imported into the finite element software, such as ABAQUS (Advanced Simulation for Engineering and Sciences, an engineering simulation software) software, and the UMESHMOTION subroutine is written with the help of the ABAQUS user subroutine interface to control the wear direction of the crystallizer three-dimensional finite element model grid and the billet three-dimensional finite element model grid, and the ALE (arbitrary Lagrangian-Eulerian, arbitrary Lagrangian-Eulerian) adaptive grid technology (Arbitrary Lagrangian-Eulerian) is combined to realize the control of unit shape distortion during large deformation or material loss. At the same time, the movement of the billet shell is controlled by the user subroutine DISP (display, a subroutine) to realize the back and forth movement of the billet shell, and finally the wear finite element model is made close to the actual situation by adopting reasonable boundary conditions. Among them, the finite element calculation formula of the ARCHARD wear model is:

[0045]

[0046] Among them, dh is the wear depth of the crystallizer to be predicted, K is the Archard wear coefficient; H is the hardness of the crystallizer and the ingot material, v is the relative slip speed between the crystallizer to be predicted and the ingot, mm / s; dt is the relative sliding time between the crystallizer to be predicted and the ingot, s; P is the contact stress, MPa. It should be noted that when the wear simulation is performed in ABAQUS finite element software, the wear behavior is usually described by moving the contact node, and the movement of the contact node is calculated by the ARCHARD wear formula. In order to control the wear direction of the node, it is necessary to manually define the wear direction during the wear process of the copper plate coating of the crystallizer. The wear direction is defined in three cases, and the corresponding nodes are internal nodes, boundary nodes, and corner nodes. In the first case, when the node is an internal node, the wear direction is the average normal direction of the four adjacent grids. In the second case, when the node is a boundary node, the wear direction of the node is defined as the line connecting the boundary node and its lower adjacent node. In the third case, when the node is a corner node, since the node belongs to both the wide and narrow sides of the crystallizer, the wear direction of the crystallizer is defined as the 45° direction along the copper plate coating of the crystallizer. In order to enable the UMESHMONTION subroutine to better identify the nodes, it is necessary to extract the surface node number of the copper plate coating of the crystallizer and the second layer node number of the boundary in the preprocessing. However, the node numbers extracted by the ABAQUS preprocessing are sorted according to the size of the numbers, and the adjacent node numbers corresponding to the boundary nodes cannot be obtained. This paper needs to use the PYTHON program to obtain the node numbers of the boundary nodes by subtracting the coordinates of the nodes. Since there are too many extracted node numbers, the node numbers are put into an external file. The external file array is divided into two columns. The first column of the array is the node number, and the second column is set according to the nodes at different positions. The boundary node is set to 0, the corner node is set to -1, and the internal node is the number of the adjacent node. When the UMESHMONTION subroutine is read in, the UMESHMONTION subroutine can identify the node numbers in three different cases and wear according to the direction set in the UMESHMONTION subroutine. Finally, the area of ​​adaptive mesh adjustment is set through the step module in ABAQUS, and the constraint method of adaptive mesh adjustment is set to User-defined, so that the UMESHMOTION subroutine can refresh the mesh and control the mesh wear direction. ALE (Arbitrary Lagrangian-Eulerian) adaptive mesh technology is implemented. When the contact node moves too much, mesh distortion and even negative volume will occur, which will affect the finite element results. Therefore, it is necessary to redraw the mesh for these units. The mesh refresh process is implemented using ALE (Arbitrary Lagrangian-Eulerian) adaptive mesh technology in ABAQUS.ALE grid technology combines the characteristics of pure Lagrangian and Euler algorithms, which can make the grid flow independently from the material, and ensure the quality of the grid during the analysis process without changing the original topology of the grid. In the UMESHMONTION subroutine, the incremental step, the number of grid sweeps, and the initial assignment flag are set to determine whether to refresh the grid. In the ABAQUS main program, the number of grid refreshes is set to 4 times to ensure the quality of the grid during the calculation process and avoid grid deformation, grid penetration, and grid overlap. In each incremental step, the grid is refreshed 5 times in total. When the refresh rate is consistent when the number of refreshes is 1, 2, 3, and 4, it indicates that the grid refresh is successful. If the grid is not refreshed, there may be grid quality problems. In this way, the quality of the grid can be monitored and guaranteed during the calculation. In order to study the friction and wear behavior of the coating on the copper plate of the crystallizer under sinusoidal vibration, its sinusoidal displacement is defined in the DISP subroutine, and its motion formula is shown as follows:.

[0047]

[0048] Among them, U(1) is the displacement of the mold to be predicted, mm; h is the vibration stroke (twice the amplitude), mm; f is the vibration frequency, min-1; TIME(1) is the current time step, s, and SIN is the motion function. Furthermore, the initial conditions and boundary conditions of the three-dimensional finite element model are set as follows:

[0049] 1) The crystallizer back plate adopts a completely fixed boundary condition, that is, δ x =δ y =δ z =0, where δ x is the displacement constraint in the x direction of the symmetry plane of the billet perpendicular to the X-axis direction, δ y is the displacement constraint in the y direction of the symmetry plane of the billet perpendicular to the Y axis, δ z It is the displacement constraint in the z direction of the symmetry plane of the billet perpendicular to the Z axis.

[0050] 2) The displacement boundary condition of the symmetry plane between the mold and the ingot is determined according to the position of the symmetry plane. The displacement constraint in the x direction of the symmetry plane perpendicular to the x-axis direction of the ingot is set to 0, that is, δ x =0; the displacement constraint in the y direction of the symmetry plane of the casting perpendicular to the Y axis is set to 0, that is, δ y =0.

[0051] 3) The surface of the ingot along the Z direction is the displacement boundary condition, which is controlled by the displacement subroutine DISP.

[0052] 4) Establish the contact surface between the crystallizer and the shell of the ingot, and set parameters such as the friction coefficient.

[0053] The embodiment of the present invention obtains a more accurate heat flux density by back calculation, and calculates the temperature field distribution and stress and strain of the crystallizer and the billet on this basis. By extracting the deformed crystallizer and paper shell unit for friction and wear simulation calculation, the wear of the crystallizer under different conditions is analyzed. By using the design method of the present invention, by comparing the wear of the crystallizer under different conditions, the friction and wear calculation of the slab crystallizer has a more accurate and practical quantitative evaluation standard, thereby improving the prediction accuracy of the crystallizer life, and has important reference significance for improving the slab crystallizer process and increasing the service life of the slab crystallizer. In addition, the embodiment of the present invention simulates the friction and wear behavior between the crystallizer and the billet by comprehensively considering multiple factors such as stress, strain, temperature field, etc., so as to determine the wear result, without actually building an experimental device and performing experimental operations, thereby greatly saving cost and time, that is, improving the prediction efficiency of the crystallizer life, and at the same time, in the process of friction and wear simulation, multiple factors such as stress, strain, temperature field, etc. are comprehensively considered, which can improve the simulation accuracy, thereby improving the prediction accuracy of the crystallizer life.

[0054] 106. Based on the friction and wear results, determine the life of the crystallizer to be predicted.

[0055] For the embodiments of the present invention, in the process of simulating the friction and wear behavior between the crystallizer and the ingot, the amount of oversteel, taper and wear, lubrication condition, protective slag performance, cooling water quality, etc. of the crystallizer to be predicted are obtained in real time. Through a comprehensive analysis of the above factors, the life of the crystallizer can be predicted more accurately.

[0056] According to a method for predicting the life of a crystallizer in a continuous casting process provided by the present invention, compared with the current method of artificially estimating the life of a crystallizer based on the amount of steel overload, the present invention establishes three-dimensional finite element models of a crystallizer and a billet respectively, and back-calculates the three-dimensional finite element model of the crystallizer and the three-dimensional finite element model of the billet respectively, uses the back-calculated heat flux density as the boundary condition of the billet and the crystallizer, adopts transient strong coupling, and performs thermal-mechanical coupling analysis on the billet and the crystallizer respectively to obtain crystallizer temperature field data, crystallizer stress-strain field data, billet temperature field data, and billet stress-strain field data. Finally, based on the above data, a friction and wear model is used to realize friction and wear simulation between the crystallizer and the billet, and the life of the crystallizer is determined according to the friction and wear results, thereby simulating the friction and wear behavior between the crystallizer and the billet by comprehensively considering multiple factors such as stress, strain, and temperature field, so as to determine the wear result, without actually building an experimental device and performing experimental operations, thereby greatly saving cost and time. At the same time, in the process of friction and wear simulation, multiple factors such as stress, strain, and temperature field are comprehensively considered, which can improve the simulation accuracy and thus improve the prediction accuracy of the crystallizer life.

[0057] Further, in order to better illustrate the above process of classifying data, as a refinement and extension of the above embodiment, the embodiment of the present invention provides another method for predicting the life of a mold in a continuous casting process, such as Figure 2 As shown, the method includes:

[0058] 201. Acquire crystallizer property data of the crystallizer to be predicted, and acquire physical property parameters of the type of casting steel and ingot property data of the ingot in the continuous casting process where the crystallizer to be predicted is located.

[0059] For the embodiment of the present invention, by measuring the three-dimensional structural dimension data of the crystallizer to be predicted and the ingot on site, the data that cannot be measured can be found in the database, so that the crystallizer property data of the crystallizer to be predicted can be obtained, and the ingot property data of the ingot in the continuous casting process where the crystallizer to be predicted is obtained. Further, in order to construct a three-dimensional finite element model of the crystallizer, it is also necessary to obtain the physical property parameters of the casting steel in the continuous casting process where the crystallizer to be predicted is located. Based on this, step 201 specifically includes: using the preset performance simulation software to determine the thermal physical property parameters required for thermal analysis of the casting steel, wherein the thermal physical property parameters include at least one of the density, specific heat, and thermal conductivity of the casting steel; using the preset performance simulation software to determine the structural physical property parameters required for structural analysis of the casting steel, wherein the structural physical property parameters include at least one of Poisson's ratio, elastic modulus, and yield strength.

[0060] Specifically, the preset performance simulation software can be JMatPro software (a metal material performance simulation software), and the physical properties required for thermal analysis of steel grades are calculated using JMatPro software, including density ρ, specific heat c, thermal conductivity k, etc., and the physical properties required for structural analysis of steel grades are calculated using JMatPro software, including Poisson's ratio v, elastic modulus E, yield strength σ s wait.

[0061] 202. Based on the crystallizer attribute data and physical property parameters, a three-dimensional finite element model of the crystallizer to be predicted is constructed. Based on the ingot attribute data, a three-dimensional finite element model of the ingot is constructed. The crystallizer to be predicted is back-calculated to obtain the crystallizer heat flux density of the crystallizer to be predicted, and the ingot heat flux density of the ingot is back-calculated to obtain the ingot heat flux density of the ingot.

[0062] The heat flux density of the crystallizer includes the heat flux density of the hot surface of the crystallizer and the heat flux density of the cold surface of the crystallizer.

[0063] For the embodiment of the present invention, a 1 / 4 three-dimensional finite element model of the coating layer of the crystallizer can be constructed. The model adopts a hexahedral mesh, and the coating layer and cooling water meshes are refined. Specifically, the three-dimensional finite element model of the crystallizer can be constructed according to the crystallizer property data, physical property parameters and other data, using finite element model construction software such as ANSYS, SolidWorks Simulation, Abaqus, etc. Similarly, a 1 / 4 three-dimensional finite element model of the ingot is established. The model adopts a hexahedral mesh, and the coating layer and cooling water meshes are refined. Specifically, the three-dimensional finite element model of the ingot can be constructed according to the ingot property data and other data, using finite element model construction software such as ANSYS, SolidWorks Simulation, Abaqus, etc. Furthermore, in order to accurately predict the life of the crystallizer, it is also necessary to determine the heat flux density of the crystallizer. Based on this, step 202 specifically includes: determining the crystallizer height direction coordinate z with the meniscus position as the origin, the first crystallization back-calculation coefficient A and the second crystallization back-calculation coefficient B of the back-calculation process, the cold surface temperature T of the predicted crystallizer and the cooling water temperature T of the cooling water used in the continuous casting process w , the thermal conductivity of the cooling water λ w , the equivalent diameter d of the cooling water seam w , cooling water density ρ w , cooling water flow rate u w , Viscosity of cooling water μ w , specific heat of cooling water c w Calculate the heat flux density q of the hot surface of the crystallizer based on the crystallizer height direction coordinate z, the first crystallization back-calculation coefficient A, and the second crystallization back-calculation coefficient B. r ; Based on the thermal conductivity λ w , equivalent diameter d w , density ρ w , flow rate u w , viscosity μ w Specific heat c w , calculate the convection heat transfer coefficient h between the water tank containing the cooling water and the cooling water w Based on the cold surface temperature T, cooling water temperature T w , Convective heat transfer coefficient h w , calculate the heat flux density q of the cold surface of the crystallizer l .

[0064] Specifically, the heat flux density of the hot surface of the crystallizer is calculated by the following formula:

[0065]

[0066] Among them, q ris the heat flux density of the hot surface of the crystallizer, A is the undetermined coefficient of the first crystallization back calculation, B is the undetermined coefficient of the second crystallization back calculation, z is the crystallizer height direction coordinate of the predicted crystallizer with the meniscus position as the origin, and m is the height unit. Further, the heat flux density of the cold surface of the crystallizer is calculated by the following formula:

[0067]

[0068] q l =h w (TT w )

[0069] Among them, q l is the heat flux density of the cold surface of the crystallizer, h w is the convection heat transfer coefficient between the water tank containing the cooling water and the cooling water, T is the cold surface temperature of the crystallizer, and T w is the cooling water temperature, w is the thermal conductivity of cooling water, d w is the equivalent diameter of the cooling water gap, ρ w is the density of cooling water, u w is the cooling water flow rate, μ w is the viscosity of cooling water, c w is the specific heat of cooling water.

[0070] Furthermore, in order to predict the life of the crystallizer, it is also necessary to determine the heat flux density of the ingot. Based on this, step 202 also includes: determining the first ingot back-calculation coefficient C and the second ingot back-calculation coefficient D of the ingot; based on the first ingot back-calculation coefficient C and the second ingot back-calculation coefficient D, and the predicted crystallizer height direction coordinate z with the meniscus position as the origin, calculate the heat flux density q of the ingot.

[0071] Specifically, the heat flux density of the hot surface of the casting billet is obtained according to the heat flux density obtained in the back calculation process of the crystallizer, so the calculation formula of the heat flux density of the hot surface of the casting billet is as follows:

[0072]

[0073] Among them, q is the heat flux density of the ingot, C is the back-calculation coefficient to be determined for the first ingot, D is the back-calculation coefficient to be determined for the second ingot, and z is the predicted crystallizer height coordinate with the meniscus position as the origin.

[0074] The heat flux density of the crystallizer is used as the boundary condition of the crystallizer, and based on the boundary condition of the crystallizer, a thermal-mechanical coupling analysis is performed on the three-dimensional finite element model of the crystallizer in the continuous casting process to obtain the crystallizer temperature field data and the crystallizer stress-strain field data of the crystallizer to be predicted.

[0075] For the embodiment of the present invention, the crystallizer temperature field data and the crystallizer stress-strain field data are related to the prediction of the crystallizer life. Therefore, in order to accurately predict the crystallizer life, it is first necessary to determine the crystallizer temperature field data and the crystallizer stress-strain field data. Based on this, step 203 specifically includes: determining the crystallization thermal property data, crystallization mechanical property data, crystallization thermal boundary conditions and crystallization mechanical boundary conditions of the crystallizer to be predicted, and dividing the three-dimensional finite element model of the crystallizer into a first preset number of crystallization units; setting the solution parameters of a preset solver, wherein the solution parameters include at least one of the solution time, the number of iterations, and the convergence condition; based on the solution parameters, the crystallization thermal property data, the crystallization mechanical property data, the crystallization thermal boundary conditions, the crystallization mechanical boundary conditions, and the crystallizer boundary conditions, using the preset solver to perform thermal-mechanical coupling analysis on each of the crystallization units to obtain the crystallizer temperature field data and the crystallizer stress-strain field data of the crystallizer to be predicted.

[0076] Among them, the crystallization thermal property data include the thermal conductivity and specific heat capacity of the crystallizer to be predicted; the crystallization mechanical property data include the thermal expansion coefficient, elastic modulus, Poisson's ratio, etc. of the crystallizer to be predicted; the crystallization thermal boundary conditions refer to the temperature, flow rate and cooling efficiency of the cooling water, heat flux, temperature boundary conditions, external environmental conditions, etc.; the crystallization mechanical boundary conditions refer to the bottom of the crystallizer or certain fixed points set as fixed constraints, thermal stress, etc.; the preset number is set according to actual needs.

[0077] Specifically, a 1 / 4 three-dimensional finite element model of the crystallizer coating was established. The model used a hexahedral grid, refined the coating and cooling water grids, and determined the three-dimensional heat transfer control equation as follows:

[0078]

[0079] Where T is temperature, k effThe thermal conductivity of the crystallizer, x, y and z are the coordinate axes of the Cartesian coordinate system, and then the thermomechanical coupling steady-state model of the crystallizer is established through the established 1 / 4 three-dimensional finite element model of the crystallizer and the obtained back-calculated heat flux density results, and the temperature field data and stress-strain data of the crystallizer are calculated using the bilinear isotropic hardening model (BISO). Specifically, the three-dimensional finite element model of the crystallizer is imported into the finite element analysis software for meshing. The density and type of the mesh (such as tetrahedron, hexahedron) need to be determined according to the analysis accuracy and computing resources. In areas with large temperature gradients or stress concentrations, finer meshes should be used to improve analysis accuracy. And define the physical and mechanical properties of the crystallizer material, such as thermal conductivity, specific heat capacity, thermal expansion coefficient, elastic modulus, Poisson's ratio, etc. At the same time, set boundary conditions, such as crystallization thermal boundary conditions and crystallization mechanical boundary conditions. Crystallization thermal boundary conditions, such as cooling water channel: set the temperature, flow rate and cooling efficiency of cooling water. These parameters directly affect the temperature distribution of the crystallizer copper plate. The contact surface between the ingot and the crystallizer: set the heat flux or temperature boundary conditions according to the casting temperature of the ingot and the heat release during the solidification process. This usually involves the problem of heat conduction and latent heat release between the ingot and the crystallizer. External environment: consider the influence of air convection and radiation on the outer wall of the crystallizer, and set the corresponding boundary conditions. Crystallization mechanical boundary conditions, such as constraint conditions: usually set the bottom of the crystallizer or some fixed points as fixed constraints to prevent the influence of rigid body displacement on the analysis results. Thermal stress: thermal expansion differences caused by temperature gradients will generate thermal stress. In thermal-mechanical coupling analysis, it is necessary to consider the influence of thermal stress and mechanical stress on the deformation and stress of the crystallizer at the same time. Then, set the solver parameters in the finite element analysis software, such as the solution time step, convergence criterion, number of iterations, etc. These parameters can improve the stability and accuracy of the solution, select the appropriate solution method, such as direct coupling method or indirect coupling method, based on the solution parameters, crystallization thermal property data, crystallization mechanical property data, crystallization thermal boundary conditions, crystallization mechanical boundary conditions, and crystallizer boundary conditions, run the finite element analysis software, and perform thermal-mechanical coupling solution. During the solution process, the software will gradually calculate the temperature field data and stress-strain field data in the crystallizer according to the set boundary conditions and initial conditions. The thermal-mechanical coupling analysis takes into account the mutual influence of the crystallizer temperature field and stress-strain field at the same time, and can more accurately simulate the thermal stress and mechanical stress state of the crystallizer during the actual working process. This two-way coupling effect makes the analysis results of the temperature field and stress-strain closer to the actual situation, and improves the accuracy and reliability of the analysis.

[0080] The heat flux density of the ingot is used as the boundary condition of the ingot, and based on the boundary condition of the ingot, a thermal-mechanical coupling analysis is performed on the three-dimensional finite element model of the ingot in the continuous casting process to obtain the ingot temperature field data and the ingot stress-strain field data.

[0081] For the embodiment of the present invention, the temperature field data of the ingot and the stress and strain field data of the ingot are related to the prediction of the life of the crystallizer. Therefore, in order to accurately predict the life of the crystallizer, it is necessary to first determine the temperature field data of the ingot and the stress and strain field data of the ingot. Based on this, step 204 specifically includes: determining the three-dimensional heat transfer control equation of the ingot; determining the thermoelastic-plastic constitutive equation of the ingot; determining the plastic constitutive equation of the plastic deformation stage; determining the initial conditions and boundary conditions of the ingot of the three-dimensional finite element model of the ingot; based on the initial conditions and boundary conditions of the ingot, coupling the three-dimensional heat transfer control equation and the thermoelastic-plastic constitutive equation to obtain the ingot. A mathematical model for thermal-mechanical coupling analysis of a billet; meshing the three-dimensional finite element model of the billet to obtain a second preset number of billet units and billet nodes, and determining the billet thermal property data, billet mechanical property data, heat exchange conditions between the billet and the surrounding environment, billet constraints, and mechanical loads of the billet; based on the billet units and billet nodes, the billet thermal property data, billet mechanical property data, heat exchange conditions between the billet and the surrounding environment, billet constraints, mechanical loads, and billet boundary conditions, a preset solver is used to solve the mathematical model for thermal-mechanical coupling analysis of the billet to obtain billet temperature field data and billet stress-strain field data of the billet.

[0082] Specifically, the three-dimensional heat transfer control equation of the casting is determined as follows:

[0083]

[0084] Where T is the casting temperature, ρ is the casting density, c eff is the specific heat capacity of the ingot, k eff is the thermal conductivity of the billet, L is the latent heat of solidification of the billet, f s is the solid phase ratio of the ingot, t is the continuous casting time, x, y and z are the Cartesian coordinates and the solidification heat transfer differential equation.

[0085] The elastic constitutive equation that determines the elastic deformation stage is as follows:

[0086]

[0087] Where T is the heat transfer temperature of the casting, d t {ε ij} is the increment of the total strain tensor of the billet during the elastic deformation stage, is the increment of elastic strain tensor of the billet during elastic deformation stage, is the increment of the thermal strain tensor in the elastic deformation stage, δ ij is the Kronecker symbol, α is the linear thermal expansion coefficient, dT represents the temperature increment, d t {σ ij} is the increment of the total stress tensor in the elastic deformation stage, σij is the total stress tensor in the elastic deformation stage, ε kl represents the strain tensor of the billet in the elastic deformation stage, v represents the Poisson's ratio of the billet, E represents the elastic modulus of the billet, δ lj represents the first Kronecker symbol, δ kl represents the second Kronecker symbol, δ ik represents the third Kronecker symbol, δ jl represents the fourth Kronecker symbol, δ jk represents the fifth Kronecker symbol, δ il represents the sixth Kronecker symbol, Represents the increment of the shrinkage strain tensor of the casting during the elastic deformation stage.

[0088] The plastic constitutive equation that determines the plastic deformation stage is as follows:

[0089]

[0090] Among them, d s {ε ij} is the increment of the total strain tensor in the plastic deformation stage, is the increment of the elastic strain tensor in the plastic deformation stage, is the increment of the plastic strain tensor in the plastic deformation stage, is the increment of thermal strain tensor in the plastic deformation stage, δ ij , δ kl are the Kronecker symbol, α is the linear thermal expansion coefficient, dT represents the temperature increment, and d s {σ ij} is the increment of the total stress tensor in the plastic deformation stage, is the elastic stiffness matrix, Q represents the plastic potential function, σ kl represents the elastic stress tensor in the plastic deformation stage, F represents the yield function, A represents the plastic modulus, represents the increment of the elastic strain tensor during the plastic deformation stage, Represents the increment of the plastic strain tensor during the plastic deformation stage.

[0091] Furthermore, the initial condition and boundary condition of the ingot are adiabatic boundary condition and displacement boundary condition respectively, the symmetry plane of the ingot is set as adiabatic boundary condition, and the displacement boundary condition of the symmetry plane of the ingot is determined according to the position of the symmetry plane, such as the displacement constraint in the x-direction of the symmetry plane of the ingot perpendicular to the X-axis direction is set to 0, that is; the displacement constraint in the y-direction of the symmetry plane of the ingot perpendicular to the Y-axis direction is set to 0, and the three-dimensional heat transfer control equation and the thermo-elastoplastic constitutive equation (the elastic constitutive equation in the elastic deformation stage, the plastic constitutive equation in the plastic deformation stage) are coupled to form a set of thermomechanical coupling equations, that is, a mathematical model for thermomechanical coupling analysis of the ingot, which describes the overall behavior of the ingot under the action of thermomechanical coupling. At the same time, the three-dimensional finite element model of the ingot is meshed to obtain a second preset number (set according to actual needs) of ingot units and ingot nodes, and the ingot thermal property data, ingot mechanical property data, heat exchange conditions between the ingot and the surrounding environment, ingot constraint conditions, mechanical load and other data of the ingot are determined, wherein the ingot thermal property data refers to the thermal conductivity, specific heat capacity, density, etc. of the ingot; the ingot mechanical property data refers to the elastic modulus, bulk ratio, thermal expansion coefficient of the ingot; the heat exchange condition refers to the convection heat transfer coefficient, radiation heat transfer coefficient, etc. between the ingot and the surrounding environment; the ingot constraint conditions refer to fixed constraints and sliding constraints, etc.; the mechanical load is the static pressure of the molten steel, the billet drawing force, etc. Then, based on the above data, the preset solver in the finite element analysis software is used to solve the thermomechanical coupling equations to obtain the ingot temperature field data and ingot stress and strain field data of the ingot. The embodiment of the present invention solves the temperature field data and the stress-strain field data of the ingot by means of thermomechanical coupling analysis. Since the thermomechanical coupling analysis simultaneously considers the heat conduction and mechanical behavior of the ingot during the solidification and cooling process, it can more comprehensively reflect the temperature, stress and strain state inside the ingot. At the same time, by coupling and solving the three-dimensional heat transfer control equation and the thermo-elasto-plastic constitutive equation, it can more accurately predict the temperature field and stress-strain field distribution of the ingot under complex conditions, avoiding the errors that may be caused by a single analysis.

[0092] 205. Determine the friction direction between the crystallizer to be predicted and the ingot, the movement amount of the contact node, the sinusoidal displacement of the crystallizer to be predicted, the wear depth, and the displacement boundary conditions between the crystallizer to be predicted and the ingot.

[0093] Based on the crystallizer temperature field data, crystallizer stress-strain field data, ingot temperature field data, ingot stress-strain field data, friction direction, contact node movement, sinusoidal displacement, wear depth, displacement boundary conditions, crystallizer three-dimensional finite element model, ingot three-dimensional finite element model, a preset friction and wear model is used to simulate the friction and wear between the crystallizer and the ingot to be predicted, and obtain the friction and wear results.

[0094] Among them, the surface of the billet along the Z direction is the displacement boundary condition. Specifically, in order to accurately simulate the friction and wear between the crystallizer and the billet, it is also necessary to obtain the friction direction between the crystallizer and the billet to be predicted, the movement of the contact node, the sinusoidal displacement of the crystallizer to be predicted, the wear depth, and the displacement boundary conditions between the crystallizer and the billet to be predicted. Then, the crystallizer temperature field data, the crystallizer stress strain field data, the billet temperature field data, the billet stress strain field data, the friction direction, the movement of the contact node, the sinusoidal displacement, the wear depth, the displacement boundary conditions, the crystallizer three-dimensional finite element model, and the billet three-dimensional finite element model are input into the preset friction and wear model to simulate the friction and wear between the crystallizer and the billet to be predicted. With the help of the ABAQUS user subroutine interface, the UMESHMONTION subroutine is written to control the wear direction of the grid and the ALE adaptive grid technology (Arbitrary Lagrangian-Eulerian) is combined to realize the control of the unit shape distortion during large deformation or material loss. At the same time, the movement of the billet shell is controlled by the user subroutine DISP to realize the back and forth movement of the billet shell. Finally, by adopting reasonable boundary condition settings, the wear finite element model is made close to the actual situation. Through friction and wear simulation, the friction and wear conditions of the crystallizer can be obtained, and finally the life of the crystallizer can be predicted based on the friction and wear conditions.

[0095] 207. Based on the friction and wear results, determine the life of the crystallizer to be predicted.

[0096] Specifically, in the process of simulating the friction and wear between the crystallizer and the ingot, the wear of the inner wall of the crystallizer is monitored in real time by installing sensors and other equipment. These sensors can measure parameters such as temperature, pressure, and vibration. According to the monitoring results, the wear rate of the crystallizer is calculated. The faster the wear rate, the shorter the service life of the crystallizer. The embodiment of the present invention simulates the friction and wear behavior between the crystallizer and the ingot by comprehensively considering multiple factors such as stress, strain, and temperature field, so as to determine the wear result. There is no need to actually build an experimental device and perform experimental operations, thereby greatly saving costs and time. At the same time, in the process of friction and wear simulation, multiple factors such as stress, strain, and temperature field are comprehensively considered, which can improve the simulation accuracy and thus improve the prediction accuracy of the crystallizer life.

[0097] According to another method for predicting the life of a crystallizer in a continuous casting process provided by the present invention, compared with the current method of artificially estimating the life of a crystallizer based on the amount of steel overload, the present invention establishes three-dimensional finite element models of a crystallizer and a billet respectively, and back-calculates the three-dimensional finite element model of the crystallizer and the three-dimensional finite element model of the billet respectively, uses the back-calculated heat flux density as the boundary condition of the billet and the crystallizer, adopts transient strong coupling, and performs thermal-mechanical coupling analysis on the billet and the crystallizer respectively to obtain crystallizer temperature field data, crystallizer stress-strain field data, billet temperature field data, and billet stress-strain field data. Finally, based on the above data, a friction and wear model is used to realize friction and wear simulation between the crystallizer and the billet, and the life of the crystallizer is determined according to the friction and wear results, thereby simulating the friction and wear behavior between the crystallizer and the billet by comprehensively considering multiple factors such as stress, strain, and temperature field, so as to determine the wear result, without actually building an experimental device and performing experimental operations, thereby greatly saving cost and time. At the same time, in the process of friction and wear simulation, multiple factors such as stress, strain, and temperature field are comprehensively considered, which can improve the simulation accuracy and thus improve the prediction accuracy of the crystallizer life.

[0098] Further, as Figure 1 In a specific implementation, an embodiment of the present invention provides a device for predicting the life of a mold in a continuous casting process, such as Figure 3 As shown, the device includes: an acquisition unit 31 , a construction unit 32 , a first coupling analysis unit 33 , a second coupling analysis unit 34 , a friction unit 35 , and a determination unit 36 ​​.

[0099] The acquisition unit 31 can be used to acquire the crystallizer property data of the crystallizer to be predicted, and to acquire the physical property parameters of the casting steel type and the casting property data of the casting in the continuous casting process where the crystallizer to be predicted is located.

[0100] The construction unit 32 can be used to construct a three-dimensional finite element model of the crystallizer corresponding to the crystallizer to be predicted based on the crystallizer property data and the physical property parameters, construct a three-dimensional finite element model of the crystallizer based on the ingot property data, and back-calculate the crystallizer to be predicted to obtain the crystallizer heat flux density of the crystallizer to be predicted.

[0101] The first coupling analysis unit 33 can be used to use the heat flux density of the crystallizer as the boundary condition of the crystallizer, and based on the boundary condition of the crystallizer, perform thermal-mechanical coupling analysis on the three-dimensional finite element model of the crystallizer in the continuous casting process to obtain the crystallizer temperature field data and crystallizer stress-strain field data of the crystallizer to be predicted.

[0102] The second coupling analysis unit 34 can be used to use the heat flux density of the ingot as the boundary condition of the ingot, and based on the ingot boundary condition, perform thermal-mechanical coupling analysis on the three-dimensional finite element model of the ingot in the continuous casting process to obtain the ingot temperature field data and ingot stress-strain field data of the ingot.

[0103] The friction unit 35 can be used to input the crystallizer temperature field data, crystallizer stress-strain field data, ingot temperature field data, and ingot stress-strain field data into a preset friction and wear model to simulate the friction and wear between the crystallizer to be predicted and the ingot, and obtain a friction and wear result.

[0104] The determination unit 36 ​​may be configured to determine the life of the crystallizer to be predicted based on the friction and wear result.

[0105] In a specific application scenario, in order to obtain the physical properties of the casting steel type in the continuous casting process where the crystallizer is to be predicted, such as Figure 4 As shown, the device further comprises an analysis unit 37 .

[0106] The analysis unit 37 can be used to determine the thermophysical property parameters required for thermal analysis of the casting steel using preset performance simulation software, wherein the thermophysical property parameters include at least one of the density, specific heat, and thermal conductivity of the casting steel; and to determine the structural property parameters required for structural analysis of the casting steel using the preset performance simulation software, wherein the structural property parameters include at least one of Poisson's ratio, elastic modulus, and yield strength.

[0107] In a specific application scenario, in order to determine the heat flux density of the crystallizer, the construction unit 32 includes a first determination module 321 and a calculation module 322 .

[0108] The first determination module 321 can be used to determine the crystallizer height direction coordinate z of the predicted crystallizer with the meniscus position as the origin, the first crystallization back-calculation coefficient A and the second crystallization back-calculation coefficient B of the back-calculation process, the predicted crystallizer cold surface temperature T and the cooling water temperature T of the cooling water used in the continuous casting process. w , the thermal conductivity of the cooling water λ w , the equivalent diameter d of the cooling water seam w , cooling water density ρ w , cooling water flow rate u w , Viscosity of cooling water μ w , specific heat of cooling water c w .

[0109] The calculation module 322 can be used to calculate the heat flux density q of the hot surface of the crystallizer based on the crystallizer height direction coordinate z, the first crystallization back-calculation coefficient A, and the second crystallization back-calculation coefficient B. r .

[0110] The calculation module 322 can also be used to calculate the thermal conductivity λ w , equivalent diameter d w , density ρ w , flow rate u w , viscosity μ w Specific heat c w , calculate the convection heat transfer coefficient h between the water tank containing the cooling water and the cooling water w .

[0111] The calculation module 322 can also be used to calculate the temperature of the cooling surface T and the cooling water temperature T w , Convective heat transfer coefficient h w , calculate the heat flux density q of the cold surface of the crystallizer l .

[0112] In a specific application scenario, for the heat flux density of the ingot, the first determination module 321 can also be used to determine the first ingot back-calculation coefficient C and the second ingot back-calculation coefficient D of the ingot.

[0113] The calculation module 322 can also be used to calculate the heat flux density q of the ingot based on the first ingot back-calculated coefficient C, the second ingot back-calculated coefficient D, and the predicted crystallizer height direction coordinate z with the meniscus position as the origin.

[0114] In a specific application scenario, in order to perform thermal-mechanical coupling analysis on the three-dimensional finite element model of the crystallizer, the first coupling analysis unit 33 includes a second determination module 331 , a setting module 332 , and a coupling analysis module 333 .

[0115] The second determination module 331 can be used to determine the crystallization thermal property data, crystallization mechanical property data, crystallization thermal boundary conditions and crystallization mechanical boundary conditions of the crystallizer to be predicted, and divide the three-dimensional finite element model of the crystallizer into a first preset number of crystallization units.

[0116] The setting module 332 may be used to set the solution parameters of the preset solver, wherein the solution parameters include at least one of the solution time, the number of iterations, and the convergence condition.

[0117] The coupling analysis module 333 can be used to perform thermal-mechanical coupling analysis on each of the crystallization units based on the solution parameters, crystallization thermal property data, crystallization mechanical property data, crystallization thermal boundary conditions, crystallization mechanical boundary conditions, and crystallizer boundary conditions using the preset solver to obtain the crystallizer temperature field data and crystallizer stress-strain field data of the crystallizer to be predicted.

[0118] In a specific application scenario, in order to perform thermal-mechanical coupling analysis on the three-dimensional finite element model of the ingot, the second coupling analysis unit 34 includes a third determination module 341 , a coupling module 342 , and a solution module 343 .

[0119] The third determination module 341 can be used to determine the three-dimensional heat transfer control equation of the ingot.

[0120] The third determination module 341 may also be used to determine the elastic constitutive equation.

[0121] The third determination module 341 can also be used to determine the plastic constitutive equation.

[0122] The third determination module 341 may also be used to determine the initial conditions and boundary conditions of the three-dimensional finite element model of the cast billet.

[0123] The coupling module 342 can be used to couple the three-dimensional heat transfer control equation and the thermal elastic-plastic constitutive equation based on the initial conditions and boundary conditions of the casting billet to obtain a mathematical model for thermal-mechanical coupling analysis of the casting billet.

[0124] The third determination module 341 can also be used to mesh the three-dimensional finite element model of the ingot to obtain a second preset number of ingot units and ingot nodes, and determine the ingot thermal property data, ingot mechanical property data, heat exchange conditions between the ingot and the surrounding environment, ingot constraint conditions, and mechanical load of the ingot.

[0125] The solution module 343 can be used to solve the mathematical model of the thermal-mechanical coupling analysis of the billet based on the billet units and billet nodes, billet thermal property data, billet mechanical property data, heat exchange conditions between the billet and the surrounding environment, billet constraints, mechanical loads, and billet boundary conditions using a preset solver to obtain the billet temperature field data and billet stress-strain field data of the billet.

[0126] In a specific application scenario, in order to perform friction and wear simulation, the friction unit 35 includes a fourth determination module 351 and a friction and wear simulation module 352 .

[0127] The fourth determination module 351 can be used to determine the friction direction between the predicted crystallizer and the cast billet, the movement of the contact node, the sinusoidal displacement of the predicted crystallizer, the wear depth, and the displacement boundary conditions between the predicted crystallizer and the cast billet.

[0128] The friction and wear simulation module 352 can be used to simulate the friction and wear between the predicted crystallizer and the billet based on the crystallizer temperature field data, crystallizer stress and strain field data, billet temperature field data, billet stress and strain field data, friction direction, movement of contact nodes, sinusoidal displacement, wear depth, displacement boundary conditions, crystallizer three-dimensional finite element model, and billet three-dimensional finite element model, and obtain friction and wear results by using the preset friction and wear model.

[0129] It should be noted that for other corresponding descriptions of the functional modules involved in the continuous casting process mold life prediction device provided by the embodiment of the present invention, reference can be made to Figure 1 The corresponding description of the method shown will not be repeated here.

[0130] Based on the above Figure 1 The method shown, accordingly, the embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the following steps are implemented: obtaining crystallizer property data of the crystallizer to be predicted, and obtaining the physical property parameters of the casting steel type and the ingot property data of the ingot in the continuous casting process where the crystallizer to be predicted is located; based on the crystallizer property data and the physical property parameters, constructing a three-dimensional finite element model of the crystallizer corresponding to the crystallizer to be predicted, based on the ingot property data, constructing a three-dimensional finite element model of the ingot of the ingot, and back-calculating the crystallizer to be predicted to obtain the crystallizer heat flux density of the crystallizer to be predicted, and determining the ingot heat flux density of the ingot; using the crystallizer heat flux density as the crystallizer boundary condition, and based on the crystallizer boundary condition The three-dimensional finite element model of the crystallizer in the continuous casting process is subjected to a thermomechanical coupling analysis based on the conditions to obtain the crystallizer temperature field data and the crystallizer stress-strain field data of the crystallizer to be predicted; the heat flux density of the cast billet is used as the cast billet boundary condition, and based on the cast billet boundary condition, the three-dimensional finite element model of the cast billet in the continuous casting process is subjected to a thermomechanical coupling analysis to obtain the cast billet temperature field data and the cast billet stress-strain field data of the cast billet; based on the crystallizer temperature field data, the crystallizer stress-strain field data, the cast billet temperature field data, the cast billet stress-strain field data, the three-dimensional finite element model of the crystallizer, and the three-dimensional finite element model of the cast billet, a preset friction and wear model is used to simulate the friction and wear between the crystallizer to be predicted and the cast billet to obtain a friction and wear result; based on the friction and wear result, the life of the crystallizer to be predicted is determined.

[0131] Based on the above Figure 1The method shown and Figure 3 The embodiment of the device shown in the figure, the embodiment of the present invention also provides a physical structure diagram of a computer device, such as Figure 5 As shown, the computer device includes: a processor 41, a memory 42, and a computer program stored in the memory 42 and executable on the processor, wherein the memory 42 and the processor 41 are both arranged on a bus 43; and when the processor 41 executes the program, the following steps are implemented: obtaining crystallizer property data of the crystallizer to be predicted, and obtaining physical property parameters of the type of casting steel and the ingot property data of the ingot in the continuous casting process where the crystallizer to be predicted is located; constructing a three-dimensional finite element model of the crystallizer corresponding to the crystallizer to be predicted based on the crystallizer property data and the physical property parameters, constructing a three-dimensional finite element model of the ingot based on the ingot property data, and performing back calculation on the crystallizer to be predicted to obtain the crystallizer heat flux density of the crystallizer to be predicted, and determining the ingot heat flux density of the ingot; using the crystallizer heat flux density as the result The invention relates to a crystallizer boundary condition, and based on the crystallizer boundary condition, a thermal-mechanical coupling analysis is performed on the three-dimensional finite element model of the crystallizer in the continuous casting process to obtain the crystallizer temperature field data and the crystallizer stress-strain field data of the crystallizer to be predicted; the heat flux density of the cast billet is used as the cast billet boundary condition, and based on the cast billet boundary condition, a thermal-mechanical coupling analysis is performed on the three-dimensional finite element model of the cast billet in the continuous casting process to obtain the cast billet temperature field data and the cast billet stress-strain field data of the cast billet; based on the crystallizer temperature field data, the crystallizer stress-strain field data, the cast billet temperature field data, the cast billet stress-strain field data, the crystallizer three-dimensional finite element model, and the cast billet three-dimensional finite element model, a preset friction and wear model is used to simulate the friction and wear between the crystallizer to be predicted and the cast billet to obtain the friction and wear result; based on the friction and wear result, the life of the crystallizer to be predicted is determined.

[0132] Through the technical scheme of the present invention, the present invention obtains the crystallizer property data of the crystallizer to be predicted, and obtains the physical property parameters of the casting steel type and the casting property data of the ingot in the continuous casting process where the crystallizer to be predicted is located; and based on the crystallizer property data and the physical property parameters, constructs a three-dimensional finite element model of the crystallizer corresponding to the crystallizer to be predicted, and based on the ingot property data, constructs a three-dimensional finite element model of the ingot, and back-calculates the crystallizer to be predicted to obtain the crystallizer heat flux density of the crystallizer to be predicted, and determines the casting heat flux density of the ingot; at the same time, the crystallizer heat flux density is used as the crystallizer boundary condition, and the three-dimensional finite element model of the crystallizer in the continuous casting process is back-calculated based on the crystallizer boundary condition. A thermomechanical coupling analysis is performed to obtain the crystallizer temperature field data and the crystallizer stress-strain field data of the crystallizer to be predicted; and the heat flux density of the cast billet is used as the cast billet boundary condition, and based on the cast billet boundary condition, a thermomechanical coupling analysis is performed on the three-dimensional finite element model of the cast billet in the continuous casting process to obtain the cast billet temperature field data and the cast billet stress-strain field data of the cast billet; then, based on the crystallizer temperature field data, the crystallizer stress-strain field data, the cast billet temperature field data, the cast billet stress-strain field data, the crystallizer three-dimensional finite element model, and the cast billet three-dimensional finite element model, a preset friction and wear model is used to simulate the friction and wear between the crystallizer to be predicted and the cast billet to obtain a friction and wear result; finally, based on the friction and wear result, the life of the crystallizer to be predicted is determined. Therefore, by establishing three-dimensional finite element models of the crystallizer and the billet respectively, and back-calculating the three-dimensional finite element model of the crystallizer and the billet respectively, the back-calculated heat flux density is used as the boundary condition of the billet and the crystallizer, and transient strong coupling is adopted to perform thermal-mechanical coupling analysis on the billet and the crystallizer respectively, and the crystallizer temperature field data, crystallizer stress-strain field data, billet temperature field data, and billet stress-strain field data are obtained. Finally, based on the above data, the friction and wear model is used to realize the friction and wear simulation between the crystallizer and the billet, and the crystallizer life is determined according to the friction and wear results. Therefore, the friction and wear behavior between the crystallizer and the billet is simulated by comprehensively considering multiple factors such as stress, strain, and temperature field, so as to determine the wear result, without actually building an experimental device and performing experimental operations, thereby greatly saving cost and time. At the same time, in the process of friction and wear simulation, multiple factors such as stress, strain, and temperature field are comprehensively considered, which can improve the simulation accuracy and thus improve the prediction accuracy of the crystallizer life.

[0133] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0134] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting the life of a continuous casting mold, characterized in that: include: Acquire the crystallizer property data of the crystallizer to be predicted, and acquire the physical property parameters of the casting steel type and the casting property data of the casting in the continuous casting process where the crystallizer to be predicted is located; Based on the crystallizer property data and the physical property parameters, a three-dimensional finite element model of the crystallizer corresponding to the crystallizer to be predicted is constructed, based on the ingot property data, a three-dimensional finite element model of the ingot is constructed, and the crystallizer to be predicted is back-calculated to obtain the crystallizer heat flux density of the crystallizer to be predicted, and the ingot heat flux density of the ingot is back-calculated to obtain the ingot heat flux density of the ingot; The heat flux density of the crystallizer is used as a crystallizer boundary condition, and based on the crystallizer boundary condition, a thermal-mechanical coupling analysis is performed on a three-dimensional finite element model of the crystallizer in the continuous casting process to obtain crystallizer temperature field data and crystallizer stress-strain field data of the crystallizer to be predicted; The heat flux density of the casting billet is used as a casting billet boundary condition, and based on the casting billet boundary condition, a thermal-mechanical coupling analysis is performed on a three-dimensional finite element model of the casting billet in the continuous casting process to obtain casting billet temperature field data and casting billet stress-strain field data of the casting billet; Based on the crystallizer temperature field data, the crystallizer stress-strain field data, the ingot temperature field data, the ingot stress-strain field data, the crystallizer three-dimensional finite element model, and the ingot three-dimensional finite element model, a preset friction and wear model is used to simulate the friction and wear between the crystallizer to be predicted and the ingot to obtain a friction and wear result; Based on the friction and wear results, the life of the crystallizer to be predicted is determined.

2. The method according to claim 1, characterized in that Obtaining the physical property parameters of the type of casting steel in the continuous casting process where the crystallizer to be predicted is located, including: Determining the thermophysical property parameters required for thermal analysis of the casting steel by using preset performance simulation software, wherein the thermophysical property parameters include at least one of density, specific heat, and thermal conductivity of the casting steel; The preset performance simulation software is used to determine the structural physical property parameters required for structural analysis of the cast steel grade, wherein the structural physical property parameters include at least one of Poisson's ratio, elastic modulus, and yield strength.

3. The method according to claim 1, characterized in that The heat flux density of the crystallizer includes the heat flux density of the hot surface of the crystallizer and the heat flux density of the cold surface of the crystallizer; The back-calculating the crystallizer to be predicted to obtain the crystallizer heat flux density of the crystallizer to be predicted includes: Determine the crystallizer height direction coordinate z with the meniscus position as the origin, the first crystallization back-calculation coefficient A and the second crystallization back-calculation coefficient B of the back-calculation process, the cold surface temperature T of the predicted crystallizer and the cooling water temperature of the cooling water used in the continuous casting process , the thermal conductivity of the cooling water , the equivalent diameter of the cooling water seam , cooling water density , cooling water flow rate , Viscosity of cooling water Specific heat of cooling water ; Based on the crystallizer height direction coordinate , the first crystal back-calculation coefficient A, the second crystal back-calculation coefficient B, calculate the heat flux density of the hot surface of the crystallizer ,in, ; Based on the thermal conductivity , equivalent diameter ,density , flow rate , Viscosity Specific heat , calculate the convection heat transfer coefficient between the water tank containing the cooling water and the cooling water ,in, ; Based on the cold surface temperature T, cooling water temperature , Convective heat transfer coefficient , calculate the heat flux density of the cold surface of the crystallizer ,in, .

4. The method according to claim 1, characterized in that The back-calculating of the ingot to obtain the ingot heat flux density of the ingot comprises: Determine the first casting blank back-calculation coefficient C and the second casting blank back-calculation coefficient D of the casting blank; Based on the first ingot back-calculated undetermined coefficient C, the second ingot back-calculated undetermined coefficient D, and the predicted crystallizer height direction coordinate z with the meniscus position as the origin, the ingot heat flux density q is calculated, wherein: .

5. The method according to claim 1, characterized in that The method of performing a thermal-mechanical coupling analysis on the three-dimensional finite element model of the crystallizer in the continuous casting process based on the crystallizer boundary conditions to obtain crystallizer temperature field data and crystallizer stress-strain field data of the crystallizer to be predicted includes: Determining crystallization thermal property data, crystallization mechanical property data, crystallization thermal boundary conditions and crystallization mechanical boundary conditions of the crystallizer to be predicted, and dividing the three-dimensional finite element model of the crystallizer into a first preset number of crystallization units; Setting solution parameters of a preset solver, wherein the solution parameters include at least one of solution time, number of iterations, and convergence condition; Based on the solution parameters, crystallization thermal property data, crystallization mechanical property data, crystallization thermal boundary conditions, crystallization mechanical boundary conditions, and crystallizer boundary conditions, the preset solver is used to perform thermal-mechanical coupling analysis on each of the crystallization units to obtain the crystallizer temperature field data and crystallizer stress-strain field data of the crystallizer to be predicted.

6. The method according to claim 1, characterized in that The step of performing a thermal-mechanical coupling analysis on a three-dimensional finite element model of a cast billet in a continuous casting process based on the cast billet boundary conditions to obtain the cast billet temperature field data and the cast billet stress-strain field data of the cast billet includes: Determine the three-dimensional heat transfer control equation of the ingot, wherein the three-dimensional heat transfer control equation is , T is the casting temperature, is the density of the ingot, is the specific heat capacity of the ingot, is the thermal conductivity of the billet, L is the latent heat of solidification of the billet, is the solid phase ratio of the ingot, t is the continuous casting time, x, y and z are the solidification heat transfer differential equations in the Cartesian coordinate system; Determine the thermoelastic-plastic constitutive equation of the ingot, wherein the thermoelastic-plastic constitutive equation includes the elastic constitutive equation in the elastic deformation stage and the plastic constitutive equation in the plastic deformation stage, and the elastic constitutive equation is , T is the heat transfer temperature of the casting, is the increment of the total strain tensor of the billet during the plastic deformation stage, is the increment of elastic strain tensor of the billet during elastic deformation stage, is the increment of the thermal strain tensor in the elastic deformation stage, is the linear thermal expansion coefficient, dT represents the temperature increment, is the increment of the total stress tensor in the elastic deformation stage, is the total stress tensor in the elastic deformation stage, represents the strain tensor of the ingot during the elastic deformation stage, represents the Poisson's ratio of the ingot, E represents the elastic modulus of the ingot, represents the first Kronecker symbol, represents the second Kronecker symbol, represents the third Kronecker symbol, represents the fourth Kronecker symbol, represents the fifth Kronecker symbol, represents the sixth Kronecker symbol, It represents the increment of the shrinkage strain tensor of the ingot during the elastic deformation stage; Determine the plastic constitutive equation in the plastic deformation stage. The plastic constitutive equation is , is the increment of the total strain tensor in the plastic deformation stage, is the increment of the elastic strain tensor in the plastic deformation stage, is the increment of the plastic strain tensor in the plastic deformation stage, is the increment of thermal strain tensor in the plastic deformation stage, , are the Kronecker symbol, is the linear thermal expansion coefficient, dT represents the temperature increment, is the increment of the total stress tensor in the plastic deformation stage, is the elastic stiffness matrix, Q represents the plastic potential function, represents the elastic stress tensor in the plastic deformation stage, F represents the yield function, A represents the plastic modulus, represents the increment of the elastic strain tensor during the plastic deformation stage, It represents the increment of plastic strain tensor in the plastic deformation stage; Determining the initial conditions and boundary conditions of the three-dimensional finite element model of the casting blank; Based on the initial conditions and boundary conditions of the casting billet, the three-dimensional heat transfer control equation and the thermo-elastic-plastic constitutive equation are coupled to obtain a mathematical model for thermo-mechanical coupling analysis of the casting billet; Meshing the three-dimensional finite element model of the ingot to obtain a second preset number of ingot units and ingot nodes, and determining ingot thermal property data, ingot mechanical property data, heat exchange conditions between the ingot and the surrounding environment, ingot constraint conditions, and mechanical load of the ingot; Based on the billet units and billet nodes, billet thermal property data, billet mechanical property data, heat exchange conditions between the billet and the surrounding environment, billet constraint conditions, mechanical loads, and billet boundary conditions, the mathematical model of the billet thermal-mechanical coupling analysis is solved using a preset solver to obtain the billet temperature field data and billet stress-strain field data of the billet.

7. The method according to claim 1, characterized in that Based on the crystallizer temperature field data, the crystallizer stress-strain field data, the billet temperature field data, the billet stress-strain field data, the crystallizer three-dimensional finite element model, and the billet three-dimensional finite element model, a preset friction and wear model is used to simulate the friction and wear between the to-be-predicted crystallizer and the billet to obtain a friction and wear result, including: Determine the friction direction between the predicted crystallizer and the cast billet, the movement amount of the contact node, the sinusoidal displacement of the predicted crystallizer, the wear depth, and the displacement boundary conditions between the predicted crystallizer and the cast billet; Based on the crystallizer temperature field data, crystallizer stress-strain field data, billet temperature field data, billet stress-strain field data, friction direction, contact node movement, sinusoidal displacement, wear depth, displacement boundary conditions, crystallizer three-dimensional finite element model, and billet three-dimensional finite element model, the preset friction and wear model is used to simulate the friction and wear between the predicted crystallizer and the billet to obtain the friction and wear results.

8. A device for predicting the life of a continuous casting mold, characterized in that: include: An acquisition unit, used to acquire the crystallizer property data of the crystallizer to be predicted, and to acquire the physical property parameters of the casting steel type and the casting property data of the casting in the continuous casting process where the crystallizer to be predicted is located; A construction unit is used to construct a three-dimensional finite element model of a crystallizer corresponding to the crystallizer to be predicted based on the crystallizer property data and the physical property parameters, to construct a three-dimensional finite element model of the cast billet based on the cast billet property data, and to back-calculate the crystallizer to be predicted to obtain the crystallizer heat flux density of the crystallizer to be predicted, and to back-calculate the cast billet to obtain the cast billet heat flux density of the cast billet; a first coupling analysis unit, configured to use the heat flux density of the crystallizer as a crystallizer boundary condition, and perform a thermal-mechanical coupling analysis on a three-dimensional finite element model of the crystallizer in the continuous casting process based on the crystallizer boundary condition, so as to obtain crystallizer temperature field data and crystallizer stress-strain field data of the crystallizer to be predicted; a second coupling analysis unit, configured to use the heat flux density of the casting billet as a casting billet boundary condition, and perform a thermal-mechanical coupling analysis on a three-dimensional finite element model of the casting billet in the continuous casting process based on the casting billet boundary condition, so as to obtain casting billet temperature field data and casting billet stress-strain field data of the casting billet; A friction unit is used to input the crystallizer temperature field data, the crystallizer stress-strain field data, the billet temperature field data, and the billet stress-strain field data into a preset friction and wear model to simulate the friction and wear between the crystallizer to be predicted and the billet, and obtain a friction and wear result; A determination unit is used to determine the life of the crystallizer to be predicted based on the friction and wear result.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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