CN-121980886-A - Dynamic evolution simulation method and system for microstructure of aluminum alloy Gao Guxiang rheological die casting

CN121980886ACN 121980886 ACN121980886 ACN 121980886ACN-121980886-A

Abstract

The invention relates to a dynamic evolution simulation method and a system for a microstructure of an aluminum alloy Gao Guxiang rheological die casting, belonging to the technical field of aluminum alloy material solidification numerical simulation and calculation material science. The simulation method comprises the steps of (1) establishing a rheological die-casting whole process model, adopting a quantitative phase field model frame to construct a control equation set of a coupling phase field and a solute field, obtaining a macroscopic temperature field by multi-scale parameters, carrying out coupling real-time calling on thermodynamic parameters, adopting a spontaneous nucleation algorithm to simulate secondary alpha 2 -Al phase nucleation in a second-stage rheological die-casting forming process, carrying out numerical solution and visualization, (2) setting boundary conditions, (3) carrying out equation solution, and (4) carrying out data processing. The invention also discloses an analog system. The invention realizes the dynamic simulation of the whole process for the first time, realizes the precise coupling of the thermodynamic multi-physical field from macroscopic to mesoscopic multi-scale, has strong engineering applicability and is suitable for binary alloy.

Inventors

CHEN SONG
Kuang Wangwang
LI DAQUAN
ZHANG FAN
FENG JIAN
ZHANG FAN

Assignees

有研工程技术研究院有限公司

Dates

Publication Date: 20260505
Application Date: 20260409

Claims (10)

1. A simulation method for dynamic evolution of a microstructure of a rheological die casting of an aluminum alloy Gao Guxiang comprises the following steps: Step 1, establishing a rheological die casting whole process model, namely constructing a control equation set of a coupling phase field and a solute field by adopting a quantitative phase field model frame, and realizing accurate prediction of nucleation, growth and solute segregation behaviors of primary alpha 1 -Al and secondary alpha 2 -Al phases; simulating the heat transfer and metal solidification processes of the whole casting and die system by a finite element method to obtain a temperature field and a cooling rate field with space-time variation characteristics; The CALPHAD real-time calculation module is integrated, a thermodynamic database of the target alloy is established by using a CALPHAD method, and corresponding equilibrium distribution coefficients and equilibrium liquid phase concentration parameters are called in real time according to the current local temperature at the time step set by phase field simulation; Simulating secondary alpha 2 -Al phase nucleation in the second-stage rheological die-casting forming process by adopting a spontaneous nucleation algorithm; solving a coupling equation set by adopting a numerical algorithm, performing visual processing on the result, and outputting relevant information of tissue morphology and solute distribution cloud patterns; step 2, setting boundary conditions, namely setting thermodynamic parameters and phase field kinetic parameters of materials; Step 3, solving equations, namely calculating data of a phase field, a solute field and a temperature field, and outputting a calculation data file; and 4, data processing, data extraction and visualization.
2. The method for simulating dynamic evolution of a microstructure of an aluminum alloy Gao Guxiang in rheological die casting according to claim 1, wherein the spontaneous nucleation algorithm comprises: step a. Calculating the local nucleation rate J, Wherein k 1 and k 2 are adjustable parameters, u is the solute supersaturation degree of the solid phase, Wherein C represents a solute concentration-mole fraction, C l eq represents an equilibrium concentration of the liquid phase, k represents an equilibrium solute partitioning coefficient of the solid phase, l represents an equilibrium solute partitioning coefficient of the liquid phase, Representing phase field order parameters; Calculating the probability P of nucleation occurring in a specific time interval and space volume by using a Poisson statistical model, wherein P=1-exp (-J x delta t), and the expected average nucleation event number lambda is in the calculation domain of the phase field simulation for any grid unit (x, y) in the remaining liquid phase region and the region with the time interval delta t ; Step c, randomly generating random numbers between [0,1] at the position where nucleation is possible in the simulation process Once found Then triggering a nucleation event at the location, i.e. letting the location order parameters 1.
3. A method for simulating dynamic evolution of a microstructure of an aluminum alloy Gao Guxiang in rheocasting according to claim 2, wherein at each time step In the method, the spontaneous nucleation module and the phase field evolution module are executed according to the following sequence: Step 1, temperature updating, namely calculating the temperature T (T) corresponding to the current time T according to a preset two-stage cooling rate curve, and inputting the temperature T (T) as the thermodynamic state of the current time step; Step 2, thermodynamic parameter calling, namely calling a CALPHAD thermodynamic database based on the current temperature T (T), and obtaining the equilibrium liquid phase concentration at the temperature through chemical potential equilibrium calculation And solute partitioning coefficient k (T) and passed to a phase field control equation; Step 3, spontaneous nucleation judgment, namely traversing all grid cells in a liquid phase state, and calculating nucleation probability according to the formula for each cell Wherein Is of the formula Generating a definite nucleation rate Uniform random number in interval If (if) Triggering a nucleation event in the grid unit, and generating order parameters Set to 1, characterize the transition of this position to the solid phase; Step 4, solving a phase field equation, namely adopting a finite difference method discrete type to calculate a phase field variable Time derivative of (2) And update Describing migration behavior of a solid-liquid interface; step 5, solving a solute field equation, namely adopting a finite difference method discrete type to calculate the concentration of the solute Time derivative of (2) And update Describe solute redistribution and anti-solute rejection effects; Step 6, outputting the result, and storing the phase field of the current time step according to the preset time interval Solute field Temperature and temperature The field data are used for subsequent tissue morphology analysis and quantitative statistics; After the steps are completed, the simulation enters the next time step, and the simulation is repeatedly executed until the termination condition is reached.
4. The method for simulating dynamic evolution of microstructure of aluminum alloy Gao Guxiang through rheological die casting according to claim 2, wherein the solute field evolution equation is introduced For the anti-solute rejection term, the flux creates a solute flow from the solid phase to the liquid phase in a direction perpendicular to the interface, balancing the solute flow caused by the gradient of chemical potential along the vertically moving interface, thereby eliminating the effects of non-equilibrium diffusion of the solute.
5. The method for simulating dynamic evolution of microstructure of aluminum alloy Gao Guxiang through rheological die casting according to claim 2, wherein obtaining a macroscopic temperature field through multi-scale parameters includes setting injection speed and corresponding temperature technological parameters under initial solid fraction, simulating heat transfer and metal solidification process of whole casting and die system through finite element method, and outputting temperature field with time-space variation characteristic And a cooling rate field The control equation related to the simulation comprises a continuity equation, a momentum equation, an energy equation and a constitutive equation, which are processed by adopting a solver built in software, and the simulation flow is as follows: Step 1, geometric model and grid division, namely constructing the geometric model through three-dimensional modeling software, and checking and repairing after the ProCAST is imported; Step 2, material properties and boundary conditions, namely selecting H13 steel in a software database as a mold material, and setting a casting material by self definition according to thermodynamic software calculation or experimental test results, wherein main parameters comprise density, thermal expansion coefficient, solid phase fraction and non-Newtonian fluid constitutive parameters, wherein rheological behavior of semi-solid slurry is described by using a Power law cut-off model, and parameters are determined by experiments; Step 3, setting solving parameters, namely setting the solving parameters in stages according to the process characteristics of rheological die casting, setting the initial time step of a filling stage to be 10 -4 -10 -3 s and the maximum time step of the filling stage to be 10 -3 s, setting the maximum time step of a solidifying stage to be 0.1 s, activating a free surface model and a gas model, starting a model wall sliding algorithm, and setting a wall surface sliding coefficient to be 0.6-0.9; And 4, after the simulation is completed, obtaining the flowing state, the temperature field evolution and the solid phase fraction distribution of the casting in the whole process of filling and solidifying through a post-processing module, mainly extracting the cooling temperature cooling curve of the target characteristic region along with time, and fitting to obtain corresponding cooling speed data serving as cooling conditions of the phase field simulation rheological die casting solidification process for the subsequent phase field simulation so as to realize the trans-scale modeling from the macroscopic temperature field to the microstructure evolution.
6. The method for simulating dynamic evolution of a rheo-die casting microstructure of an aluminum alloy Gao Guxiang according to claim 1, wherein the setting of thermodynamic parameters and phase field kinetic parameters of the material includes: Step i, setting the grid size, time and space step length of a phase field; step ii, setting initial nucleation conditions; setting phase field, solute field and temperature field boundary conditions including phase Yang Qianyi rate, temperature, solid fraction and cooling rate; And iv, inputting thermodynamic parameters, and performing CALPHAD coupling.
7. The method for simulating dynamic evolution of the microstructure of the aluminum alloy Gao Guxiang through rheological die casting according to claim 6, wherein the method is characterized by solving a phase field, a solute field and a temperature field control equation according to thermodynamic parameters and nucleation conditions to obtain phase field variables, solute distribution and temperature field data which evolve with time, and inputting the phase field, solute and temperature field data files into post-processing software to extract and analyze data of dendrite growth process.
8. The method for simulating dynamic evolution of a rheo-die casting microstructure of an aluminum alloy Gao Guxiang as set forth in claim 1, wherein the aluminum alloy is an Al-Si, al-Cu, or Al-Mg alloy.
9. A simulation system for dynamic evolution of a rheo-die casting microstructure of an aluminum alloy Gao Guxiang, comprising: Step 1, a material parameter input unit inputs physical property parameters and component data of an aluminum alloy to be researched and rheological die casting process parameters; Step 2, a phase field model parameter input unit determines initial parameters used for phase field model calculation; step 3, a thermodynamic parameter coupling unit calls a CALPHAD thermodynamic database in real time to obtain thermodynamic parameters related to temperature; Step 4, a nucleation simulation unit adopts a spontaneous nucleation algorithm to simulate nucleation of a secondary alpha 2 -Al phase; Step 5, an equation solving unit is used for solving a phase field, a solute field and a temperature field control equation according to the determined thermodynamic parameters and nucleation conditions to obtain phase field variables, solute distribution and temperature field data which evolve along with time; and step 6, inputting the phase field, solute and temperature field data files into post-processing software by the data processing unit, and extracting and analyzing data in the dendrite growth process.
10. The simulation system for dynamic evolution of the microstructure of the aluminum alloy Gao Guxiang rheological die casting is characterized in that the die casting process parameters are slurry solid phase fraction and cooling speed, the parameters are obtained through finite element simulation, the thermodynamic parameter coupling unit obtains balance solute distribution coefficients and balance liquid phase concentration parameters by calling a CALPHAD phase diagram database in real time, calculation accuracy of thermodynamic driving force is guaranteed, the nucleation simulation unit adopts a spontaneous nucleation algorithm and comprises the steps of calculating local nucleation rate, calculating nucleation probability by using a Poisson statistical model, introducing nucleation points at positions meeting conditions, and the data processing unit introduces phase field, solute and temperature field data files into post-processing software to generate a visual result of a tissue morphology and a solute distribution cloud diagram.

Description

Dynamic evolution simulation method and system for microstructure of aluminum alloy Gao Guxiang rheological die casting Technical Field The invention relates to a dynamic evolution simulation method and system for microstructure of aluminum alloy Gao Guxiang rheological die casting, in particular to a phase field simulation method and system for predicting dynamic evolution of microstructure in continuous cooling process based on aluminum alloy Gao Guxiang rheological die casting, which are used for predicting and simulating the processes of high solid phase rheological pulping and dynamic evolution of microstructure of die casting of aluminum alloy, and are especially suitable for microstructure evolution simulation of rheological die casting process of aluminum alloy such as Al-Si system, al-Cu system and Al-Mg system. The invention belongs to the technical field of aluminum alloy material solidification numerical simulation and calculation material science. Background The rheologic die casting technology is widely focused as an advanced semi-solid forming technology because the rheologic die casting technology can effectively control the microstructure of castings and improve the quality of the castings. In the rheo-die casting process, the formation and evolution of microstructure has a decisive influence on the final properties of the casting. Different rheological pulping and rheological die casting processes can lead to the change of the solidification path of the metal melt, thereby affecting the microstructure micro segregation defect of the alloy and reducing the mechanical property and the use function of the product. The traditional experiment and characterization means are difficult to effectively capture the dendrite growth and solute evolution dynamic process in the solidification process, for example, a scanning electron microscope is used for analyzing element distribution, only solute distribution rules at the end of solidification can be obtained, solute distribution characteristics of the whole solidification process can not be known, for example, a stage quenching technology can obtain solute distribution characteristics at a certain solid phase rate, a large number of repeated experiments are needed when the solute evolution rules are analyzed, and a synchronous radiation technology can be used for in-situ observation of the dendrite growth process, but is difficult to operate, is easy to influence experimental conditions and environment, and is difficult to quantify the control Jin Zhijing segregation. In recent years, the phase field method has become more and more popular in the field of exploring microstructure evolution and the like. Compared with the traditional numerical simulation method using a sharp interface model, the phase field method is used for carrying out dispersion treatment on the solid-liquid interface, and the dynamic evolution process of a complex interface is not required to be directly tracked. At present, the microstructure simulation of the rheological die casting process at home and abroad mainly has the following problems that firstly, a phase field model is limited by time and space dimensions, the interface thickness and the capillary length in a traditional phase field model are similar, so that the simulation space dimensions are limited, and the microstructure evolution process of an actual casting is difficult to effectively simulate. The "quantitative phase field" model proposed by team Karma, while allowing interface thicknesses much greater than capillary length, has not been widely used for microstructure simulation of the rheodie casting process. In the prior art, thermodynamic parameters (such as solute distribution coefficient and equilibrium liquid phase concentration) mostly adopt constant or simple linear relation, and dynamic change of the thermodynamic parameters in the continuous cooling process is not considered. For example, CN117828950a chinese patent uses zero-noerman condition, while achieving multi-field coupling, does not consider dynamic adjustment of temperature field boundaries with process parameters. Again, the nucleation mechanism simulation is inaccurate, namely the spontaneous nucleation of the secondary alpha 2 -Al phase is a special key link for microstructure formation in the rheological die casting process, but the simulation of the nucleation mechanism in the prior art is mostly dependent on the mode of presetting seeds according to experimental experience, and the accurate description of nucleation dynamics is lacking. In the high solid phase rheo-die casting forming process, the filling and solidifying behaviors of high-viscosity metal melt in a die cavity are key for determining the final structure and performance of the casting. However, the cooling rate of the rheo-die casting forming stage (i.e., the in-cavity solidification stage) is difficult to obtain directly by experimental means due to the complex heat exch