Process optimization method for improving surface cracking of large steel ingot based on finite element model

Abstract

The invention discloses a process optimization method for improving surface cracking of a large steel ingot based on a finite element model, and belongs to the technical field of computer aided design optimization casting processes. The method comprises the following steps: S1, acquiring at least one group of thermophysical parameters of a steel ingot material according to chemical components of the steel ingot material; s2, establishing a casting three-dimensional model of the steel ingot; s3, grid division and parameter setting are conducted on the casting three-dimensional model, wherein parameters comprise at least one set of thermophysical parameters obtained in the step S1; s4, performing simulation calculation of a temperature field and a stress field in the casting process on the casting three-dimensional model to obtain surface stress when the solidification of the steel ingot is finished; and S5, the structure of the cast three-dimensional model of the steel ingot is optimized, the step S2 to the step S5 are repeated till the optimization termination condition is met, and the stress of the edge of the steel ingot is reduced. By means of the method, the edge surface stress generated when steel ingot solidification is finished can be reduced, and longitudinal cracks on the edge surface of the steel ingot are reduced.

Description

Technical Field

[0001] The invention relates to the technical field of computer-aided design optimization casting process, and in particular to a process optimization method for improving surface cracking of large steel ingots based on a finite element model. Background Art

[0002] Large steel ingots are widely used in nuclear power, metallurgy, petrochemical and other fields, such as nuclear reactor pressure vessels, motor rotors of large power stations, support rollers in large mechanical equipment, etc. The quality of steel ingots will directly affect the quality of the final forgings. Typical defects of ordinary die-cast steel ingots include shrinkage, segregation, non-metallic inclusions and surface cracks. Surface cracks in steel ingots are generally believed to be caused by thermal stress, and can be divided into transverse cracks and longitudinal cracks according to the direction of the cracks. Among them, the longitudinal cracks are mainly due to the fact that the edge of the steel ingot is first separated from the mold wall by the chilling effect of the steel ingot mold, while other parts still remain in contact with the mold wall. Therefore, the chilling layer of the edge is thinner than the chilling layer of other contact surfaces. The thicker chilling layer produces tensile stress on the thinner area, resulting in stress concentration on the edge and cracks.

[0003] The use of numerical simulation technology to study the filling and solidification process of steel ingots can guide the production of large steel ingots. Casting simulation software and simulation technology have played an important role in the research of steel ingots. However, the current numerical simulation research on surface cracks of large steel ingots mainly focuses on the stress field analysis of the steel ingot mold, lacks analytical research on the steel ingot, and is even more difficult to optimize the process through casting simulation analysis to reduce the longitudinal cracks on the surface of the steel ingot. Summary of the invention

[0004] In view of the above analysis, the present invention aims to provide a process optimization method for improving surface cracking of large steel ingots based on a finite element model, so as to solve the problem that longitudinal cracks are easily generated in large steel ingots during casting.

[0005] An embodiment of the present invention provides a process optimization method for improving surface cracking of large steel ingots based on a finite element model, comprising: step S1, obtaining at least one set of thermophysical property parameters of the steel ingot material according to the chemical composition of the steel ingot material; step S2, establishing a three-dimensional casting model of the steel ingot; step S3, meshing and parameter setting of the three-dimensional casting model, wherein the parameters include at least one set of thermophysical property parameters obtained in step S1; step S4, simulating and calculating the temperature field and stress field of the casting process of the three-dimensional casting model according to at least one set of thermophysical property parameters obtained in step S1, and obtaining the surface stress at the end of solidification of the steel ingot; step S5, optimizing the structure of the three-dimensional casting model of the steel ingot, and repeating steps S2 to S5 until the optimization termination condition is reached, so that the surface stress of the edge of the steel ingot is reduced.

[0006] Furthermore, if multiple sets of thermophysical property parameters are obtained in step S1, then in step S4, the casting three-dimensional model is simulated and calculated based on the multiple sets of thermophysical property parameters to obtain multiple corresponding complete solidification times of the steel ingots; a set of thermophysical property parameters is screened based on each complete solidification time; based on the screened thermophysical property parameters, the casting three-dimensional model is simulated and calculated based on the temperature field and stress field of the casting process to obtain the surface stress at the end of solidification of the steel ingot.

[0007] Furthermore, when screening the thermophysical property parameters, the complete solidification times and the actual demoulding time of the steel ingot are compared to screen out a set of thermophysical property parameters that are closest to the actual demoulding time.

[0008] Furthermore, in step S2, the established casting three-dimensional model includes: a steel ingot, a heat-insulating cap, a steel ingot mold, a chassis, refractory bricks, and a covering agent.

[0009] Further, in step S5, optimizing the structure of the casting three-dimensional model of the steel ingot includes: optimizing the prism shape of the steel ingot, and / or optimizing the thickness of the steel ingot mold.

[0010] Further, in step S5, optimizing the edge shape of the steel ingot includes: increasing the diameter of the fillet of the edge of the steel ingot.

[0011] Furthermore, in step S5, when optimizing the edge shape of the steel ingot, the optimization termination condition includes: the maximum principal stress value on the edge surface of the steel ingot after optimization is reduced by less than or equal to 5%.

[0012] Further, when optimizing the thickness of the steel ingot mold, the optimization termination condition also includes: compared with the original thickness of the steel ingot mold, the increased thickness of the steel ingot mold is greater than or equal to 100 mm.

[0013] Furthermore, in step S3, the parameters to be set include: thermophysical parameters of the materials of each component, the initial temperature of each component, and the interface heat transfer coefficient between the components.

[0014] Furthermore, the initial temperature of the steel ingot is set to be 60-80° C. higher than the liquidus temperature.

[0015] Compared with the prior art, the method of the present invention can achieve at least one of the following beneficial effects:

[0016] 1. The present invention simulates and calculates the temperature field and stress field of the large steel ingot casting process to obtain the stress distribution during the solidification process of the large steel ingot, so that the casting process of the steel ingot can be continuously optimized according to the surface stress of the steel ingot to reduce the surface stress of the steel ingot and alleviate the longitudinal cracks on the surface of the edge of the steel ingot, which can not only save production costs and shorten the cleaning time, but also reduce the environmental pollution caused by the crack cleaning process and the impact on the health of workers.

[0017] 2. When obtaining multiple sets of spare data of thermophysical property parameters, the embodiment of the present invention obtains different complete solidification times of the steel ingot by simulating and calculating the temperature field of the casting three-dimensional model with different thermophysical property parameters, and then selects a set of most suitable thermophysical property parameters according to the complete solidification time for subsequent temperature field and stress field calculations, which can improve the accuracy of the simulation results of the steel ingot stress field and provide reliable reference data for subsequent process optimization.

[0018] 3. The present invention can reduce the surface stress of large steel ingots by optimizing the steel ingot edge shape and / or the thickness of the steel ingot mold, thereby alleviating the longitudinal cracking of the steel ingot edges.

[0019] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the embodiments of the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0021] Figure 1 The figure is a schematic flow chart of a process optimization method for improving surface cracking of large steel ingots according to an embodiment of the present invention.

[0022] Figure 2 Graph showing the interface heat transfer coefficient between the steel ingot and the steel ingot mold according to Example 1 of the present invention.

[0023] Figure 3 The figure is a schematic diagram of a process for determining the thermophysical property parameters of a steel ingot material according to an embodiment of the present invention.

[0024] Figure 4 This is a temperature variation curve diagram of the sample in Example 1 of the present invention during the temperature increase and decrease heat treatment process.

[0025] Figure 5 This is the endothermic and exothermic curve of the sample of Example 1 of the present invention.

[0026] Figure 6 This is an exothermic peak curve diagram of the cooling process of Example 1 of the present invention.

[0027] Figure 7 This is a solid phase ratio curve diagram of Example 1 of the present invention.

[0028] Figure 8It is a partial schematic diagram of the three-dimensional model of the large steel ingot of Example 1 of the present invention; wherein (a) is the initial three-dimensional model, (b) and (c) are three-dimensional models after different edge shape optimizations, respectively.

[0029] Fig. 9 Schematic diagram of stress analysis results of a large steel ingot according to Example 1 of the present invention; wherein (a) is an initial three-dimensional model, and (b) and (c) are three-dimensional models after different edge-type optimizations, respectively.

[0030] Fig.10 It is a partial schematic diagram of the three-dimensional model of a large steel ingot according to Example 1 of the present invention; wherein (a) is the initial three-dimensional model, and (b) and (c) are three-dimensional models for optimizing different steel ingot mold thicknesses, respectively.

[0031] Fig.11 Schematic diagram of stress analysis results of a large steel ingot according to Example 1 of the present invention; wherein (a) is a three-dimensional model of the original steel ingot mold thickness, and (b) and (c) are three-dimensional models for optimizing different steel ingot mold thicknesses, respectively. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of the present invention clearer, exemplary embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. For the sake of clarity and conciseness, not all features of the actual implementation are described in the specification.

[0033] It is also necessary to explain here that, in order to avoid obscuring the present invention due to unnecessary details, only the device structure and / or processing steps closely related to the scheme of the present invention are shown in the accompanying drawings, while other details that are not very relevant to the present invention are omitted.

[0034] The embodiment of the present invention provides a process optimization method for improving surface cracking of large steel ingots based on a finite element model, such as Figure 1 As shown, it includes:

[0035] Step S1, obtaining at least one set of thermophysical property parameters of the steel ingot material according to the chemical composition of the steel ingot material;

[0036] Step S2, establishing a three-dimensional casting model of the steel ingot;

[0037] Step S3, meshing and parameter setting of the casting three-dimensional model, wherein the parameters include at least one set of thermophysical property parameters obtained in step S1;

[0038] Step S4, performing simulation calculations on the temperature field and stress field of the casting process on the three-dimensional casting model according to at least one set of thermophysical property parameters obtained in step S1, and obtaining the surface stress of the steel ingot at the end of solidification;

[0039] Step S5, optimizing the structure of the casting three-dimensional model of the steel ingot, and repeating steps S2 to S5 until the optimization termination condition is reached, so that the surface stress of the edge of the steel ingot is reduced and the optimal steel ingot casting process is obtained.

[0040] In this embodiment of the present invention, the temperature field and stress field of the large steel ingot casting process are simulated and calculated to obtain the stress distribution during the solidification process of the large steel ingot, so that the casting process of the steel ingot can be continuously optimized according to the surface stress of the steel ingot, so as to reduce the surface stress of the steel ingot and alleviate the longitudinal cracks on the surface of the edge of the steel ingot.

[0041] When there are longitudinal cracks on the surface of a large steel ingot, the surface cracks must be removed before the ingot is forged, and the cleaning of the longitudinal cracks on the surface of the steel ingot requires a complicated process: deeper cracks must be removed in large pieces by flame cutting, and shallower cracks are removed by oxygen blowing and melting. During the entire cleaning process, if the surface temperature of the steel ingot drops, it needs to be reheated and cleaned again. After all the cleaning is completed, the temperature of the steel ingot is no longer in the forging range, and it needs to be reheated again before forging. This process not only consumes a lot of energy and labor costs, but also generates a lot of gas and noise pollution, and the harm to the human body and the environment is immeasurable. Therefore, the process optimization method of the present invention is used to improve or eliminate cracks, which can not only save production costs, shorten cleaning time, and have considerable economic benefits, but also reduce environmental pollution and the impact on the health of workers, and has good social benefits.

[0042] Among them, the thermophysical property parameters include the parameters required for temperature field calculation: solid phase ratio curve, thermal conductivity, density and thermal enthalpy; the thermophysical property parameters also include the parameters required for stress field calculation: thermal expansion coefficient, Young's modulus, Poisson's ratio, yield strength and plastic modulus.

[0043] In some embodiments, Figure 1 As shown, if a set of thermophysical parameters is obtained in step S1, then in step S4, the temperature field and stress field of the casting three-dimensional model are simulated and calculated directly according to the thermophysical parameters obtained in step S1 to obtain the surface stress distribution data when the ingot solidifies.

[0044] In some embodiments, Figure 1 As shown, if multiple groups of thermophysical property parameters are obtained in step S1, step S4 includes:

[0045] According to multiple groups of thermophysical property parameters, the casting process temperature field simulation calculation is performed on the casting three-dimensional model to obtain multiple corresponding ingot complete solidification times;

[0046] According to each complete solidification time, a set of thermophysical property parameters are screened;

[0047] According to the selected thermophysical parameters, the temperature field and stress field of the casting three-dimensional model are simulated and calculated to obtain the surface stress of the ingot at the end of solidification.

[0048] When obtaining backup data of multiple groups of thermophysical property parameters, the embodiments of the present invention obtain different complete solidification times of the steel ingot by simulating and calculating the temperature field of the casting three-dimensional model with different thermophysical property parameters, and then screen out a group of most suitable thermophysical property parameters according to the complete solidification time to perform subsequent superposition calculation of the temperature field and stress field, which can improve the accuracy of the simulation calculation results of the steel ingot stress field and provide reliable reference data for subsequent process optimization.

[0049] It should be noted that the above-selected set of most suitable thermophysical property parameters are used in the simulation calculation of the temperature field and stress field of the original casting three-dimensional model and the optimized casting three-dimensional model.

[0050] Furthermore, when screening the thermophysical property parameters, the calculated complete solidification times and the actual demolding time of the ingot are compared, and a set of thermophysical property parameters closest to the actual demolding time is screened out as the most reasonable set of thermophysical property parameters, and subsequent temperature field and stress field simulation calculations are carried out to ensure the accuracy and reliability of the simulation calculation results.

[0051] In some embodiments, in step S2, a three-dimensional modeling software is used to establish a casting three-dimensional model of a large steel ingot, and the established casting three-dimensional model includes a steel ingot, an insulation cap, an ingot mold, a chassis, refractory bricks, and a covering agent.

[0052] In some embodiments, in steps S3 to S5, the casting process is simulated using ProCAST software. In step S3, the established three-dimensional model is imported into ProCAST software and meshed. When meshing, the mesh can be divided according to tetrahedral meshes to obtain a tetrahedral mesh model.

[0053] In step S3, when setting parameters for the grid model, the set parameters include the thermophysical property parameters of the materials of each component, the initial temperature, and the interface heat transfer coefficients between the components.

[0054] The thermal properties of other components can be the thermal properties of the materials in the software database.

[0055] The interface heat transfer coefficient between the ingot and the ingot mold can be obtained by using the data in the software database, for example, Figure 2 The interface heat transfer coefficient curve between the steel ingot and the steel ingot mold is shown. The interface heat transfer coefficient between other components can be set as a constant based on experience. For example, the interface heat transfer coefficient between refractory bricks, covering agents and other components can be set to 200W / m2 / K.

[0056] The initial temperature includes the initial temperature of all components, which can be set according to on-site production experience. In some embodiments, the initial temperature of the ingot (i.e., the pouring temperature) is set to increase 60 to 80°C on the basis of the liquidus temperature. Since the ingot mold needs to be hot-air baked before pouring, the initial temperature of the ingot mold is predicted to be 30 to 50°C.

[0057] In addition, the set parameters also include calculation operation parameters and storage parameters. The calculation operation parameters include calculation stop conditions (for example, the temperature at which the calculation stops), calculation time step, etc. The storage parameters refer to the frequency of saving the calculation results.

[0058] In step S5, optimizing the casting three-dimensional model structure of the steel ingot includes: optimizing the rib shape of the steel ingot, and / or optimizing the thickness of the steel ingot mold. The present invention can reduce the surface stress of the steel ingot rib at the end of solidification by optimizing and adjusting the rib shape of the steel ingot or the thickness of the steel ingot mold, thereby reducing the longitudinal cracking of the surface of the steel ingot rib.

[0059] In addition, optimizing the ingot mold thickness on the basis of optimizing the ingot edge shape can significantly reduce the surface stress of the ingot edge, thereby greatly reducing the longitudinal cracking of the ingot edge.

[0060] In some embodiments, when optimizing the edge shape of the steel ingot, the size of the edge of the steel ingot may be optimized, for example, by increasing the diameter of the fillet of the edge of the steel ingot.

[0061] In some embodiments, when optimizing the prism shape of the steel ingot in step S5, the optimization termination condition includes: the maximum principal stress value on the surface of the prism of the steel ingot after optimization is reduced by less than or equal to 5%, that is, the ratio of the difference between the maximum principal stress value after optimization and before the optimization to the maximum principal stress value before the optimization is less than or equal to 5%. As the diameter of the fillet of the prism increases, the heat dissipation surface area of ​​the steel ingot decreases, which is not conducive to heat dissipation. In addition, if the fillet diameter of the prism is infinitely increased, the surface of the steel ingot is close to the circumferential surface. From the actual production experience, the cracking tendency of the cylindrical billet is more serious. This embodiment limits the reduction of the maximum principal stress value to avoid the reduction of the heat dissipation effect and the increase of the cracking tendency due to the excessive diameter of the fillet of the prism.

[0062] In some embodiments, when optimizing the thickness of the steel ingot mold, the thickness of the steel ingot mold is gradually increased to continuously reduce the surface stress of the steel ingot edge.

[0063] In some embodiments, when optimizing the thickness of the ingot mold, the optimization termination condition also includes: compared with the original thickness, the increased thickness of the ingot mold is greater than or equal to 100 mm. After the molten steel is poured into the ingot mold, the surface of the ingot solidifies rapidly to form a shell. As the shell shrinks, an air gap is formed between the ingot and the ingot mold wall. At this time, the cooling effect of the ingot mold is reduced, and the cooling effect brought by increasing the thickness of the ingot mold will not be obvious; at the same time, increasing the thickness will also increase the production cost of the ingot mold. Therefore, this embodiment avoids the cooling effect of the ingot mold on the ingot and the excessively high production cost by limiting the increased size of the ingot mold.

[0064] When using casting simulation software to simulate the casting of large steel ingots, the thermophysical parameters of the steel ingot material are used. When using ProCAST software for simulation calculations, you can use its own thermodynamic calculation module to calculate the thermophysical parameters. The thermodynamic calculation module includes three models: Lever, Scheil and Back Diffusion models. Among them, the Lever and Scheil models correspond to two different microsegregation models respectively. The Lever model applies the lever law, that is, the solute can diffuse well in the solid phase; the Scheil model assumes that the solute has no diffusion in the solid phase, and both models assume that the solute is fully diffused in the liquid phase; the Back Diffusion model assumes that the solute can partially diffuse in the solid phase, which is a model between Lever and Scheil. The main difference between these three models is the shape of the solid phase ratio curve at the end of solidification and the different solidus temperatures.

[0065] For the same ingot material, different thermodynamic calculation models are used for calculation, and different thermophysical parameters are obtained. Using these thermophysical parameters for casting simulation will result in different simulation results. It can be seen that the rationality and reliability of thermophysical parameters will directly affect the accuracy of casting simulation results. However, there is currently no detailed report on the applicability of the three thermodynamic calculation models and the method of obtaining thermophysical parameters. The thermophysical parameters used in existing reports usually only randomly select one of the models for calculation or set according to experience, resulting in the rationality and accuracy of the simulation calculation results cannot be guaranteed.

[0066] In order to improve the accuracy and reliability of the thermal physical property parameters, in some embodiments of the present invention, Figure 3 As shown, step S1 includes:

[0067] Step S11, using a variety of thermodynamic calculation models in the ProCAST software to calculate the thermophysical property parameters of the steel ingot material according to the chemical composition of the steel ingot material, and obtaining multiple groups of thermophysical property parameters, each group of thermophysical property parameters including a solid-liquid phase line calculation value;

[0068] Step S12, measuring the sample of the steel ingot to obtain the solidus-liquidus phase measured value;

[0069] Step S13, comparing and analyzing multiple sets of solid-liquid phase line calculated values ​​and solid-liquid phase line measured values ​​obtained by using multiple thermodynamic calculation models, and determining whether the multiple sets of solid-liquid phase line calculated values ​​meet preset requirements;

[0070] Step S14, when the solid-liquid phase line calculated value meets the preset requirements, all the thermophysical property parameters corresponding to the solid-liquid phase line calculated value are selected as backup data;

[0071] Step S15, when multiple groups of solid-liquid phase line calculation values ​​do not meet the preset requirements, remove at least one element whose content meets the preset threshold in the chemical composition, and recalculate the thermophysical property parameters until the solid-liquid phase line calculation values ​​meet the preset requirements.

[0072] The present invention obtains at least one set of thermophysical parameters of ingot materials for backup through a comprehensive method combining simulation calculation and experimental measurement, thereby improving the rationality and reliability of the thermophysical parameters; in some cases, multiple thermophysical parameters are screened in combination with the simulation calculation results of the subsequent temperature field, further improving the rationality and reliability of the thermophysical parameters, and helping to improve the accuracy of the casting process simulation results, thereby enabling an efficient and accurate process optimization process to be carried out. The method of the present invention is used to determine the thermophysical parameters, which can reduce the cost of trial and error, and improve the efficiency of research and development and product quality.

[0073] In some embodiments, the preset requirement may be that the difference between the solid-liquid phase line calculated value and the solid-liquid phase line measured value does not exceed 20°C, that is, the difference between the solid phase line calculated value and the solid phase line measured value does not exceed 20°C, and the difference between the liquidus line calculated value and the liquidus line measured value also does not exceed 20°C.

[0074] In step S15, when multiple groups of solid-liquid phase line calculation values ​​do not meet the preset requirements, at least one element whose content is less than a predetermined threshold in the chemical composition is removed, and the thermophysical property parameters are recalculated to obtain thermophysical property parameters that meet the requirements. The predetermined threshold is 1wt.%, that is, at least one element whose mass percentage is less than 1wt.% is removed.

[0075] In some embodiments of the present invention, Figure 3 As shown, in step S12, measuring the sample of the steel ingot to obtain the solid-liquid phase line measured value includes:

[0076] preparing samples of steel ingots;

[0077] Perform differential thermal analysis on the sample to obtain the heat absorption and release curve of the sample;

[0078] According to the endothermic and exothermic curves, the actual measured values ​​of the solid-liquid phase line are determined.

[0079] The embodiment of the present invention can obtain a true and reliable solidus-liquidus phase line measured value by performing a differential thermal analysis test on a sample of a steel ingot, and the test method is simple.

[0080] In some embodiments, when preparing the sample of the steel ingot, the sample is heated to 1200-1250°C and kept warm for 15-30 hours to homogenize the sample and ensure the uniformity of the sample. If the holding temperature is too low or the time is too short, it will be detrimental to the uniform diffusion of the alloy elements and the elimination of the precipitated phase in the cast state of the steel ingot material; if the holding temperature is too high or the time is too long, high-temperature ferrite will be formed in the sample or over-burning will occur, affecting the accuracy of the test results.

[0081] Furthermore, after the sample is homogenized, the surface of the sample is polished to avoid contamination. The sample can be processed into φ2*2mm to facilitate subsequent differential thermal analysis testing.

[0082] In some embodiments, when a differential thermal analysis test is performed on a sample, the sample is heated to a first critical temperature at a constant heating rate, then cooled to a second critical temperature at a first cooling rate, and then cooled to room temperature at a second cooling rate; wherein the first cooling rate is determined based on the actual cooling rate of the ingot material, and the second cooling rate is greater than the first cooling rate.

[0083] It should be noted that the first critical temperature and the second critical temperature respectively refer to the starting temperature and the ending temperature of cooling using the first cooling rate, wherein the first critical temperature can be set to increase 50°C to 100°C based on the liquidus calculated value, and the second critical temperature can be set to decrease 50°C to 100°C based on the solidus calculated value.

[0084] This embodiment uses the solid-liquid phase line calculated value as a reference, and expands the temperature range of the solid-liquid phase line by increasing and decreasing 50-100°C at both ends to cover the calculation error of the solid-liquid phase line calculated value, thereby ensuring that the sample solidifies at a slower first cooling rate between the actual solid phase line temperature and the liquid phase line temperature during the test, thereby improving the accuracy of the measured value. At the same time, it avoids the temperature range being too wide, which may lead to reduced test efficiency and equipment loss.

[0085] The liquidus calculated value and the solidus calculated value used may be selected based on experience as a more reasonable set of the multiple sets of solidus-liquidus calculated values ​​obtained in step S11.

[0086] In some embodiments, the heating rate may be any value in the range of 10 to 20 k / s. The second cooling rate may be any value in the range of 10 to 20 k / s.

[0087] The embodiment of the present invention adopts a segmented cooling heat treatment method so that the sample melt in the first cooling stage is as close as possible to the actual cooling process during ingot casting, so as to improve the authenticity and reliability of the test results; at the same time, rapid cooling in the second cooling stage can shorten the test time, improve test efficiency, and avoid equipment loss.

[0088] In some embodiments, Figure 3 As shown, according to the endothermic and exothermic curves, the solid-liquid phase line measured value of the steel ingot is determined, including:

[0089] Intercept the exothermic peak curve contained in the cooling process of the endothermic and exothermic curves;

[0090] Integrate the exothermic peak curve to obtain the solid phase ratio curve of the sample;

[0091] According to the solid phase ratio curve, the actual measured value of the solid-liquid phase line is determined.

[0092] The solid-liquid phase line is determined by integrating the exothermic peak curve. Compared with the traditional method of measuring the solid-liquid phase line (for example, reading the solid-liquid phase line by drawing a tangent at the peak), the solid-liquid phase line obtained by the method of the embodiment of the present invention is more accurate and has a smaller deviation.

[0093] Specifically, since it is necessary to focus on the solidification process of the steel ingot material, the partial curve of the exothermic peak contained in the cooling process of the sample's endothermic and exothermic curve is intercepted, and the baseline of the exothermic peak curve is reset to zero to ensure the accuracy of the integral calculation. By integrating the exothermic peak curve, the solid phase rate curve of the sample that changes with temperature can be obtained. The solid phase line and liquid phase line of the steel ingot material can be directly read in the solid phase rate curve, that is, the solid-liquid phase line measured value is obtained.

[0094] It should be noted that the embodiments of the present invention use the measured solid phase ratio curve to determine whether the solid-liquid phase line calculated by the thermodynamic calculation model is accurate. In the simulation calculation process of the temperature field, a complete set of thermophysical property parameters calculated by the thermodynamic calculation model is used to ensure the mutual matching between the various parameters.

[0095] In step S12, when using ProCAST software to calculate the thermophysical parameters of the steel ingot material, in the material parameter calculation module, select the iron-based alloy, input the chemical composition of the steel ingot material, select three thermodynamic calculation models in turn for calculation, and obtain three different sets of solid-liquid phase line calculation values.

[0096] When the Back Diffusion model is selected for calculation, the average cooling rate is set to 0.01-1 K / s. The average cooling rate set within this range has little effect on the calculation results of the thermophysical parameters. Preferably, the average cooling rate is set to 0.01 K / s.

[0097] The method of the present invention can be used to better use ProCAST software to reasonably select thermophysical property parameters, improve the accuracy of simulation calculation results, and provide a reliable basis for process optimization.

[0098] The process optimization method of the present invention is further illustrated by using specific examples below.

[0099] Example 1

[0100] The process optimization simulation calculation is carried out on the steel ingot of 83t-45Cr5NiMoV material.

[0101] Step S1, determining the thermophysical property parameters of the steel ingot material.

[0102] (1) Calculate the thermal physical parameters of the steel ingot material:

[0103] In the material parameter calculation module, select the iron-based alloy and enter the chemical composition of the steel ingot material 45Cr5NiMoV. The chemical composition range of 45Cr5NiMoV is shown in Table 1, and the remaining components are iron and unavoidable impurities. In the calculation module, the chemical composition of the steel ingot material is set to the middle value of the content range of each component to calculate the thermophysical parameters; for elements such as P and S that only set the upper limit of the content range, set the element content to the upper limit of the element content range.

[0104] The Scheil, Lever, and Back Diffusion models (specifying the average cooling rate to be 0.01 K / s) were used in turn to calculate, and three sets of solid-liquid phase line calculated values ​​were obtained, which were 1169-1468°C, 1391-1467°C, and 1384-1468°C, respectively.

[0105] Table 1 Chemical composition of 45Cr5NiMoV material

[0106]

[0107] (2) Sample preparation: The sample was heated to 1250°C and kept at this temperature for 20 h before being water-cooled to homogenize the sample. The sample was then processed into small samples of φ2*2 mm and the surface was polished to avoid contamination.

[0108] (3) Differential thermal analysis test: Figure 4 The heat treatment process shown is to heat and cool the sample. Specifically, the sample is heated to 1550°C at a heating rate of 10°C / min, then cooled to 1300°C at a first cooling rate of 1°C / min, and then cooled to room temperature at a second cooling rate of 20°C / min.

[0109] Among them, according to experience, the solidus of steel is generally around 1400℃, of which 1169-1468℃ is obviously unreasonable, so the solidus and liquidus calculated value 1391-1467℃ calculated by the Lever model is selected to determine the first key temperature and the second key temperature, of which the first key temperature is 1467℃+83℃=1550℃, and the second key temperature is 1391℃-91℃=1300℃. According to experience, the actual average cooling rate of large steel ingots in this temperature range is about 1℃ / min.

[0110] The endothermic and exothermic curves obtained in the final test are as follows: Figure 5 shown.

[0111] (4) Determine the measured value of the solid-liquid phase line: Figure 5 The exothermic peak curve included in the cooling process of the endothermic and exothermic curve shown in the figure is cut out, and its baseline is reset to zero, and the following is obtained: Figure 6 The exothermic peak curve shown; Figure 6 By integrating the exothermic peak curve shown in the figure, we can get the solid phase ratio curve of the steel ingot material changing with temperature, as shown in Figure 7 As shown; from the solid phase ratio curve, it can be directly read that the liquidus measured value is 1472℃, and the solidus measured value is 1378℃.

[0112] (5) Compare the three sets of solid-liquid phase line calculated values ​​with the solid-liquid phase line measured values, and select 1391-1467℃ and 1384-1468℃ with differences less than 20℃ as backup data.

[0113] Step S2, establishing a three-dimensional model of the casting process of a large steel ingot:

[0114] The casting 3D model includes: insulation cap, ingot mold, chassis, ingot, refractory bricks, covering agent, etc.

[0115] Step S3, meshing the 3D model and setting parameters:

[0116] The above casting three-dimensional model was imported into ProCAST software for meshing to obtain a tetrahedral mesh model.

[0117] The parameters of the above grid model are set. The main parameters include: (a) the thermal properties of the materials of each component, where the thermal properties of the steel ingot material are the thermal properties of the backup data obtained in step S1; (b) the initial temperature: the initial temperature of the steel ingot is 1540°C, and the initial temperature of the steel ingot mold is 50°C; (c) the interface heat transfer coefficient between the components: the interface heat transfer coefficient between the steel ingot and the steel ingot mold is as follows: Figure 2 As shown; the interface heat transfer coefficient between refractory bricks, covering agents and other components is set to 200W / m 2 / K; (d) Calculation operation parameters and storage parameters: calculation stop time is 1380℃, time step is 20s; storage frequency is 180.

[0118] Step S4, simulate the temperature field of the grid model after setting the parameters. The thermophysical parameters of the steel ingot use the above two sets of spare data, and the obtained complete solidification times are 23.58h and 22.95h respectively. Compared with the demolding time of 22h in the actual production of steel ingots, the thermophysical parameters corresponding to the complete solidification time of 22.95h (wherein the calculated value of the solidus is 1384°C and the calculated value of the liquidus is 1468°C) are selected as the most reasonable parameters for subsequent simulation calculations.

[0119] Based on the above parameter settings, the simulation calculation of stress field is added to analyze the maximum principal stress on the edge surface of the ingot at the end of solidification, such as Fig. 9 As shown in (a).

[0120] Step S5, optimizing the casting three-dimensional model:

[0121] Firstly, based on the original casting 3D model, the edge shape of the ingot was optimized, and the stress field simulation calculation was performed again to analyze the maximum principal stress on the edge surface of the ingot at the end of solidification.

[0122] Figure 8 (a) shows a partial schematic diagram of the three-dimensional model of the original prismatic steel ingot. Figure 8 (b) and (c) respectively show the structural schematic diagrams of the casting three-dimensional model after the first and second edge shape optimizations based on the original three-dimensional model, wherein the diameter of the edge fillet of the original steel ingot is 48 mm, and after the first edge shape optimization, the diameter of the edge fillet increases to 1.4 times of the original edge shape, and after the second edge shape optimization, the diameter of the edge fillet increases to 2.4 times of the original edge shape.

[0123] The corresponding stress field simulation results are as follows: Fig. 9 As shown in (a), (b), and (c). The maximum principal stress on the surface of the ingot edge at the end of solidification was analyzed. Compared with the basic scheme of the original three-dimensional model, the maximum principal stress value after the second edge shape optimization was significantly reduced, and the reduction rate was about 5% compared with the maximum stress value after the first edge shape optimization. Therefore, the three-dimensional model with edge shape optimization was selected as the basic scheme for superposition optimization.

[0124] Secondly, based on the second prism optimization, the thickness of the ingot mold is optimized and the stress field simulation calculation is performed again. Fig.10(a), (b), and (c) show the structural schematic diagrams of the original steel ingot mold, the first increase in the thickness of the steel ingot mold, and the second increase in the thickness of the steel ingot mold, respectively. The thickness of the original steel ingot mold 20 is 350 mm, the increase in the thickness of the steel ingot mold for the first time is 50 mm, and the increase in the thickness of the steel ingot mold for the second time is 100 mm. Figure 8 The edge shape after the second edge shape optimization shown in (c).

[0125] The corresponding stress field simulation results are as follows: Fig.11 As shown in (a), (b), and (c). The maximum principal stress on the edge surface of the steel ingot at the end of solidification was analyzed. Compared with the basic scheme of the original steel ingot mold thickness, the maximum stress value on the edge surface of the steel ingot was significantly reduced after the second increase in the thickness of the steel ingot mold. Therefore, the second edge shape optimization and the second increase in the thickness of the steel ingot mold were selected as the best process optimization scheme for 83t-45Cr5NiMoV steel ingot.

[0126] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A process optimization method for improving surface cracking of large steel ingots based on a finite element model, characterized in that: include: Step S1, obtaining at least one set of thermophysical property parameters of the steel ingot material according to the chemical composition of the steel ingot material; Step S2, establishing a three-dimensional casting model of the steel ingot; Step S3, meshing and parameter setting of the casting three-dimensional model, wherein the parameters include the at least one set of thermophysical property parameters obtained in step S1; Step S4, performing simulation calculations on the temperature field and stress field of the casting process on the three-dimensional casting model according to at least one set of thermophysical property parameters obtained in step S1, and obtaining the surface stress of the steel ingot at the end of solidification; Step S5, optimizing the structure of the casting three-dimensional model of the steel ingot, and repeating steps S2 to S5 until the optimization termination condition is reached, so that the surface stress of the edge of the steel ingot is reduced.

2. The method according to claim 1, characterized in that If multiple sets of thermophysical property parameters are obtained in step S1, then in step S4, According to the multiple groups of thermophysical property parameters, a simulation calculation of the temperature field of the casting process is performed on the casting three-dimensional model to obtain multiple corresponding complete solidification times of the steel ingots; According to each of the complete solidification times, a group of the thermophysical property parameters are selected; According to the screened thermophysical property parameters, the temperature field and stress field of the casting process are simulated and calculated for the casting three-dimensional model to obtain the surface stress of the steel ingot at the end of solidification.

3. The method according to claim 2, characterized in that When screening the thermophysical property parameters, the complete solidification time is compared with the actual demoulding time of the steel ingot, and a group of the thermophysical property parameters closest to the actual demoulding time is screened out.

4. The method according to claim 1, characterized in that: In step S2, the established casting three-dimensional model includes: a steel ingot, a heat-insulating cap, a steel ingot mold, a chassis, refractory bricks, and a covering agent.

5. The method according to claim 4, characterized in that In the step S5, optimizing the structure of the casting three-dimensional model of the steel ingot includes: The prism shape of the steel ingot is optimized, and / or the thickness of the steel ingot mold is optimized.

6. The method according to claim 5, characterized in that In the step S5, optimizing the edge shape of the steel ingot includes: increasing the diameter of the fillet of the edge of the steel ingot.

7. The method according to claim 6, characterized in that In the step S5, when optimizing the prism shape of the steel ingot, the optimization termination condition includes: After optimization, the maximum principal stress value on the surface of the edge of the steel ingot is reduced by less than or equal to 5%.

8. The method according to claim 5, characterized in that When optimizing the thickness of the steel ingot mold, the optimization termination condition also includes: Compared with the original thickness of the steel ingot mold, the increased thickness of the steel ingot mold reaches 100 mm.

9. The method according to claim 4, characterized in that In step S3, the parameters to be set include: thermophysical parameters of the materials of each component, the initial temperature of each component, and the interface heat transfer coefficient between the components.

10. The method according to claim 9, characterized in that The initial temperature of the steel ingot is set to be 60-80° C. higher than the liquidus temperature.

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