A numerical and physical coupling simulation based die casting process optimization method

Abstract

The application relates to a mold casting process optimization method based on numerical and physical coupling simulation. The mold casting steel is subjected to multi-physical field coupling numerical simulation in a mold casting process to obtain a solid phase rate change curve from a casting blank surface to a core at a certain height position of the casting blank perpendicular to a horizontal line, a core temperature change curve of a core of the casting blank at the position, and a temperature change curve of the casting blank surface at the position; the temperature change curves are fitted into a temperature-solidification model; the temperature change curve of the casting blank surface is collected and drawn on a mold casting production site, compared with the temperature-solidification model, and optimized; and the simulation of solidification structure and center loose prediction is carried out by taking the corrected temperature-solidification model as a boundary condition. The application is based on numerical simulation results to carry out physical simulation, and the numerical simulation results are taken as criteria to correct the physical simulation. Through coupling analysis of the two simulation methods, the error of the mold casting blank defect prediction method is reduced, and the precision and research efficiency of mold casting blank defect prediction and process improvement are improved.

Description

Technical Field

[0001] This invention belongs to the field of mold casting process optimization, and relates to a mold casting process optimization method based on numerical and physical coupling simulation. Background Technology

[0002] Ingot casting has been used in industrial production for nearly a century. In recent years, continuous casting has been widely adopted due to its high efficiency and continuous process characteristics, and ingot casting now accounts for less than 5% of production. Although continuous casting has significant advantages, it can lead to serious defects such as segregation and cracking in high-alloy steels. Therefore, ingot casting is often used for the production of high-alloy steels. However, inappropriate casting processes can still result in internal defects in the cast billet. Therefore, research on the dynamic solidification behavior of the ingot casting process is of great significance for improving the quality of ingot-cast billets, enhancing the application value of this process in the production of special steels, and reducing production costs.

[0003] Similar to continuous casting, the solidification process in ingot casting is also a black box—an invisible and intangible physical process. Currently, numerical simulation methods are often used to predict solidification defects in ingot casting. However, existing numerical simulation methods are based on the solidification theory of binary alloys, and cannot accurately simulate the solidification behavior of multi-component alloys. Furthermore, current work often only involves adjusting boundary conditions, neglecting the essential factors affecting the solidification process, namely the movement rate of the solid-liquid interface and the temperature change of the liquid core of the billet during solidification, referred to as the "temperature-solidification model." Only by setting boundary conditions based on a high-precision "temperature-solidification model" can the numerical simulation of solidification defects truly reproduce the defect distribution in the actual ingot casting process. At the same time, the simulation methods currently used for the "temperature-solidification model" are relatively limited, lacking verification by other methods, and have certain errors compared to the actual distribution of the physical fields related to the billet. In summary, there is an urgent need for a method that can obtain a high-precision "temperature-solidification model" during the solidification process of ingot casting to predict casting defects, thereby providing a technical reference for the optimization of the ingot casting process. Summary of the Invention

[0004] The purpose of this invention is to provide a method for optimizing the casting process based on numerical and physical coupling simulation. By combining numerical and physical coupling, and using this as a basis, efficient and high-precision numerical simulation prediction of defects in casting billets can be performed. This method overcomes the limitations of previous methods for predicting defects in casting billets, which suffer from large errors and lack of verification, and improves the accuracy and research efficiency of defect prediction and process improvement in casting billets.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for optimizing the die casting process based on numerical and physical coupled simulation includes the following steps:

[0007] 1) For a certain composition and cross-sectional size of the die-cast steel, a multi-physics field coupled numerical simulation is performed on the die-casting process under a certain superheat and cooling regime to obtain the solid fraction change curve from the surface of the die-cast billet to the core, the core temperature change curve of the core of the die-cast billet at a certain height position perpendicular to the horizontal line, and the temperature change curve of the surface of the die-cast billet at that position.

[0008] 2) Fit the core temperature change curve at a certain height position perpendicular to the horizontal line of the molded billet to mathematical model one, and fit the solid fraction change curve from the billet surface to the core at a certain height position perpendicular to the horizontal line of the molded billet to mathematical model two. Mathematical model one and mathematical model two are called "temperature-solidification model".

[0009] 3) At the die casting production site, select the slab produced under the die casting conditions in step 1), and collect the temperature change curve of the slab surface at the same location in step 1) during the die casting process. After collection, plot the curve and compare it with the temperature change curve of the slab surface obtained by numerical simulation in step 1). Optimize the numerical simulation model in step 1) until the temperature change curve of the slab surface obtained by the optimized die casting numerical simulation is completely consistent with the collected temperature change curve of the slab surface. Finally, perform numerical simulation on the die casting process in step 1) again, and fit the mathematical model one and mathematical model two in step 2) again to obtain the "temperature-solidification model".

[0010] 4) Input the “temperature-solidification model” from step 3) into the dendrite growth physical simulation device as a control program to perform physical simulation; obtain the solidification unit from the surface of the billet to the core at the same height position perpendicular to the horizontal line at step 1) of the corresponding molded billet under the mold casting conditions; finally, perform solidification structure experiment characterization on the solidification unit.

[0011] 5) Take the billet from step 3) and cut it at the same height as the corresponding mold billet in step 1) along the longitudinal section. Perform physical and chemical tests on the solidification structure of the section and compare it with the solidification structure of the solidification unit in step 4). Then, make a correction to the "temperature-solidification model" in step 3) until the solidification structure of the solidification unit and the actual billet are completely consistent under the mold casting conditions.

[0012] 6) Select several other sets of casting conditions and repeat steps 1) to 5). Make a second correction to the "temperature-solidification model" in step 5) to ensure that under any casting conditions, the "temperature-solidification model" obtained by step 1) can represent the relevant temperature and solidification physical changes in the actual casting process.

[0013] 7) Using the "temperature-solidification model" after secondary correction in step 6) as the boundary condition, within a reasonable casting condition range, numerical simulation software is used to simulate the solidification structure and central porosity prediction. Finally, based on the simulation results, the process with the highest equiaxed crystal ratio in the core and the lowest central porosity rating is selected as the optimal casting process for this steel grade.

[0014] The casting conditions include one or more of superheating and cooling regimes.

[0015] Compared with the prior art, the beneficial effects of the present invention are:

[0016] 1) Physical simulation is performed based on numerical simulation results, and then the physical simulation is corrected based on the numerical simulation results. Through the coupled analysis of these two simulation methods, the error of the mold casting billet defect prediction method is reduced and the accuracy and research efficiency of mold casting billet defect prediction and process improvement are improved.

[0017] 2) Before conducting the physical simulation, the numerical simulation model of the ingot casting was optimized by taking into account the actual surface temperature changes of the ingot casting. This ensured the accuracy of the numerical simulation, reduced the number of corrections to the subsequent "temperature-solidification" model, and improved the efficiency of simulation prediction.

[0018] 3) After the model for predicting the quality of the molded billet is established, the mold casting process can be directly optimized through simulation alone, without the need for experimental verification, thus saving process optimization costs. Attached Figure Description

[0019] Figure 1 This is a flowchart of the present invention.

[0020] Figure 2 This is a schematic diagram of the experimental apparatus used for physical simulation.

[0021] Figure 2 In the middle: 1-base, 2-heating furnace, 3-crucible, 4-water-cooled copper rod, 5-displacement servo mechanism.

[0022] Figure 3 This is a schematic diagram of the numerical simulation model for die casting.

[0023] Figure 3 In the middle: 6-Insulating cover 7-Feature unit 8-Mold.

[0024] Figure 4 This is a diagram of the solidification structure (1 / 2 dimension) of the ingot under the optimal process.

[0025] Figure 4 In the diagram, the dashed area represents the region with the same area as the solidification unit obtained from the physical simulation.

[0026] Figure 5This is a predicted diagram of the solidification structure of the ingot under the optimal process.

[0027] Figure 6 This is a prediction diagram of shrinkage cavities and porosity of the ingot under the optimal process. Detailed Implementation

[0028] The following uses the process optimization of a certain grade of steel in die casting billet as an example to illustrate the method of die casting process optimization based on numerical and physical coupling simulation. However, it should be noted that the implementation of this invention is not limited to the following embodiments.

[0029] See Figure 1 The method for optimizing the die casting process based on numerical and physical coupled simulation includes the following steps:

[0030] 1) Taking a certain grade of ingot (solute elements include: C, Si, Mn, Ti) produced on-site as an example, the ingot width is 200mm, the height is approximately 600mm, and the mold dimensions are as follows: Figure 3 As shown. The superheat range used for producing the billet using this mold is 20℃~50℃. Considering the material of the mold, the cooling system is divided into weak cooling, medium cooling, and strong cooling. Experimental measurements show that the liquidus temperature of this steel is 1460℃.

[0031] 2) Using a superheat of 20℃ and a mold cooling regime of intercooling as the casting conditions, ANSYS software was used to perform physical field numerical simulation of the casting process. This yielded the solid fraction variation curve from the surface to the core of the cast billet at a certain height perpendicular to the horizontal line, the core temperature variation curve at that location, and the surface temperature variation curve. The core temperature variation curve at this height perpendicular to the horizontal line was then fitted as follows:

[0032]

[0033] In equation (1), T is temperature (K), t is time (s), and A, B, P, m, n, h1, and h2 are fitting coefficients; the solid fraction change curve from the surface of the cast billet to the core at a certain height perpendicular to the horizontal line is fitted as follows:

[0034] V = a + bt + ct 2 +dt 3 +ed 4 (2)

[0035] In equation (2), V is the interface movement distance (mm), t is the time (s), and a, b, c, d and e are fitting coefficients; equations (1) and (2) are called the “temperature-condensation model”.

[0036] 3) At the die casting production site, the steel grade was die-cast using the process described in 2). The temperature change curve of the billet surface at a certain height perpendicular to the horizontal plane during the die casting process was then collected and compared with the numerical simulation results of the curve in 2). The numerical simulation process in 2) was then corrected until the temperature change of the billet surface obtained after numerical simulation of the die casting process completely corresponded to the collected results at the same location. Finally, the "temperature-solidification model" was fitted again, and the fitting parameters are shown in Table 1 and ① of Table 2.

[0037] Table 1. Optimization of Fitting Parameters for Temperature Curves

[0038]

[0039] Table 2. Optimization of fitting parameters for solidification curves

[0040]

[0041] In Tables 1 and 2, L is a coefficient, L = 0.85T m / C 1.5 T m The casting temperature is K; C is the average cooling intensity of the secondary cooling system, W / (m*K).

[0042] 4) Using the "temperature-solidification model" in 3) as boundary conditions, perform a physical simulation of the casting process in 2). The physical simulation apparatus is as follows: Figure 2 As shown. The "temperature-condensation model" from step 3) is input as a control program into the dendrite growth physical simulation device for physical simulation;

[0043] The device includes a heating system, a water cooling system, and a displacement servo mechanism 5. The temperature change curve controls the temperature change in the heating zone, the displacement servo mechanism 5 controls the sample displacement, and the water cooling system is used to simulate heat transfer from the sample and crucible 3 to the outside of the heating furnace 2 during the simulation process, simulating the movement of the solid / liquid interface during casting and controlling the solidification process. The final sample length is half the cross-sectional dimension of the cast billet, representing the solidification unit from the billet surface to the core at the same height perpendicular to the horizontal line at step 3) under specific casting conditions. Solidification unit solidification structure is characterized experimentally. The characterization results are compared with the actual cast billet in step 3), and then the "temperature-solidification model" in step 3) is corrected to ensure that the solidification structure characterization results of the physical simulation are completely consistent with the solidification structure of the actual cast billet. The fitting parameters are shown in Table 1 and Table 2, ②.

[0044] 5) Using 30°C superheat and weak cooling system of the mold and 40°C superheat and strong cooling system of the mold as the other two sets of casting conditions, repeat steps (2) to (4) to make secondary corrections to the “temperature-solidification model”. The fitting parameters are shown in Table 1 and ③ of Table 2.

[0045] 6) Using the modified "temperature-solidification model" from 5) as boundary conditions, Pro-CAST software was used to numerically simulate the solidification structure and shrinkage cavities of the billet at the location in 3) under various in-mold casting conditions. After optimization, the best casting quality was achieved when the superheat was 25℃ and the mold used a medium-cooling system. The actual solidification structure of the billet at this point is as follows: Figure 4 As shown, the numerical simulation prediction of the solidification structure and shrinkage porosity of the cast billet is as follows: Figure 5 , Figure 6 As shown, at this point, the equiaxed grain ratio of the billet core is 46%, and the area of ​​the shrinkage cavity and porosity defect region in the center of the optimized billet accounts for 14% of the total billet area.

Claims

1. A method for optimizing the die casting process based on numerical and physical coupling simulation, characterized in that, Includes the following steps: 1) For a certain composition and cross-sectional size of the die-cast steel, a multi-physics field coupled numerical simulation is performed on the die-casting process under a certain superheat and cooling regime to obtain the solid fraction change curve from the surface of the die-cast billet to the core, the core temperature change curve of the core of the die-cast billet at a certain height position perpendicular to the horizontal line, and the temperature change curve of the surface of the die-cast billet at that position. 2) The core temperature change curve at a certain height position perpendicular to the horizontal line of the molded billet is fitted to mathematical model one, and the solid fraction change curve from the surface of the billet to the core at a certain height position perpendicular to the horizontal line of the molded billet is fitted to mathematical model two. Mathematical model one and mathematical model two are called "temperature-solidification model". 3) At the die casting production site, select the slab produced under the die casting conditions in step 1), and collect the temperature change curve of the slab surface at the same location in step 1) during the die casting process. After collection, plot the curve and compare it with the temperature change curve of the slab surface obtained by numerical simulation in step 1). Optimize the numerical simulation model in step 1) until the temperature change curve of the slab surface obtained by the optimized die casting numerical simulation is completely consistent with the collected temperature change curve of the slab surface. Finally, perform numerical simulation on the die casting process in step 1) again, and fit the mathematical model one and mathematical model two in step 2) again to obtain the "temperature-solidification model". 4) Input the "temperature-solidification model" in step 3) into the dendrite growth physical simulation device as a control program to perform physical simulation; obtain the solidification unit from the surface of the billet to the core at the same height position perpendicular to the horizontal line at step 1) of the corresponding molded billet under the mold casting conditions; finally, perform solidification structure experiment characterization on the solidification unit. 5) Take the billet from step 3) and cut it at the same height as the corresponding mold billet in step 1) along the longitudinal section. Perform physical and chemical tests on the solidification structure of the section and compare it with the solidification structure of the solidification unit in step 4). Then, make a correction to the "temperature-solidification model" in step 3) until the solidification structure of the solidification unit and the actual billet are completely consistent under the mold casting conditions. 6) Select several other sets of casting conditions and repeat steps 1) to 5). Make a second correction to the "temperature-solidification model" in step 5) to ensure that under any casting conditions, the "temperature-solidification model" obtained by step 1) can represent the relevant temperature and solidification physical changes in the actual casting process. 7) Using the "temperature-solidification model" after secondary correction in step 6) as boundary conditions, within a reasonable casting condition range, numerical simulation software is used to simulate the solidification structure and central porosity prediction. Finally, based on the simulation results, the process with the highest equiaxed crystal ratio in the core and the lowest central porosity rating is selected as the optimal casting process for this steel grade.

2. The method for optimizing the die casting process based on numerical and physical coupling simulation according to claim 1, characterized in that, The casting conditions include one or more of superheating and cooling regimes.

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