A prediction method for casting microstructure of nano-phase reinforced aluminum matrix composites

Abstract

A method for predicting the casting microstructure of a nanophase-reinforced aluminum-based composite material belongs to the technical field of composite casting microstructure formation. The present invention solves the problem of poor accuracy of existing numerical simulation methods for nanophase-reinforced aluminum-based composite materials. Based on experimental characterization, the present invention obtains the equivalent solute diffusion coefficient, Si element solute distribution coefficient, size of nanophase clusters and average nucleation undercooling in a sample with a nanophase. Based on the experimental characterization data, a numerical simulation of the casting microstructure of the aluminum-based composite material is performed, which can more accurately predict the characteristics of microstructure formation when a nanophase exists in a composite casting, and provide data support and theoretical guidance for alloy structure regulation and process optimization during the forming process of composite castings. The method of the present invention can be applied to the technical field of composite casting microstructure formation.

Description

Technical Field

[0001] The invention belongs to the technical field of composite material casting microstructure formation, and in particular relates to a method for predicting nano-phase reinforced aluminum-based composite material casting microstructure. Background Art

[0002] In today's green environmental protection, energy conservation and emission reduction environment, the focus is undoubtedly on aerospace, automobile and other fields, so the research and development of lightweight materials is particularly important. Aluminum alloy is known as the "green metal of the 21st century" because of its excellent properties such as low density and good corrosion resistance. Aluminum alloy is the first choice for automobile manufacturing, which means that it is crucial to carry out aluminum alloy research. The mass production of aluminum alloy parts usually adopts the casting method. The main reason is that casting is easy to implement and the production cost is relatively low. However, there are also some outstanding problems in the casting process of aluminum alloy: due to the low cooling rate of the casting process, the microstructure formed is coarse α-Al dendrites and coarse lath-shaped eutectic Si phases coexist. On the one hand, the coarse microstructure reduces the mechanical properties of aluminum alloys. On the other hand, the coarse α-Al grains cause the grain boundaries to widen, thereby increasing the tendency of the alloy to crack hot. These defects reduce the yield of castings.

[0003] According to the Hall-Petch formula, grain refinement is an effective method to improve the microstructure of aluminum alloys, enhance mechanical properties, and increase the yield rate. Cast aluminum alloy parts hope to obtain uniform and fine equiaxed crystal microstructures, because fine equiaxed crystals can ensure the isotropy of mechanical properties, improve toughness and plasticity, and small grains mean small grain boundaries, which reduces the tendency of hot cracking. However, the casting process requires expensive casting equipment to refine the microstructure by increasing the cooling rate, which will increase the production cost of castings.

[0004] Adding nanophases to alloys can also refine the microstructure. At the same time, there are no special requirements for cooling speed and casting equipment, so it has received widespread attention in the industry. At present, adding ceramic nanophases to aluminum alloys during the casting process has been proven to improve the wear resistance, toughness and plasticity of aluminum alloys, while reducing hot cracking casting defects and improving the yield rate. In the field of industrial technology, experimental methods are usually used to explore the effect of nanophase addition on the microstructure and performance of casting products, and to find the optimal amount of nanophase addition. It is blind to explore the quantitative relationship between nanophase addition and microstructure through experimental means. A large number of experiments means a lot of trial and error, which consumes a lot of manpower, material and financial resources, and cannot achieve energy conservation and emission reduction.

[0005] With the development of computer technology, numerical simulation has become an effective means to study the metal solidification process. Numerical simulation can reproduce a series of physical phenomena such as temperature change, solute diffusion, nucleation and growth during the solidification process, which helps to analyze the influence of process parameters on different physical phenomena. It can optimize the best process parameters in a short time and obtain the quantitative relationship between process parameters and microstructure, thus shortening the research and development cycle. However, a large number of numerical simulation studies are currently based on aluminum-based alloys with different components, while there are fewer numerical simulation studies on nano-phase reinforced aluminum-based composites. Moreover, the existing numerical simulations of nano-phase reinforced aluminum-based composites lack the equivalent metal liquid solute diffusion coefficient (D eff ), this parameter cannot be directly measured during the experiment. If a theoretical value is selected, the effect of nanophase on solute diffusion and microstructure refinement cannot be reproduced; there is a lack of solute distribution coefficient that takes into account the effect of nanophase. If theoretical values ​​are selected, the effect of nanophase on solute redistribution cannot be reproduced; the nanophase clusters (average size d p ) attached to the solid-liquid interface caused by the additional Gibbs effect on dendrite growth. Therefore, the accuracy of existing numerical simulations for nanophase-reinforced aluminum-based composites is still poor and cannot well reflect the real physical process. Therefore, it is of great significance to propose a new method for predicting the casting microstructure of nanophase-reinforced aluminum-based composites, which provides necessary theoretical guidance and data reference for the research and development and production of high-performance aluminum-based nanocomposites. Summary of the invention

[0006] The purpose of the present invention is to solve the problem of poor accuracy of existing numerical simulation methods for nano-phase reinforced aluminum-based composite materials, and to propose a prediction method for the casting microstructure of nano-phase reinforced aluminum-based composite materials.

[0007] The technical solution adopted by the present invention to solve the above technical problems is:

[0008] A method for predicting the casting microstructure of a nano-phase reinforced aluminum-based composite material, the method specifically comprising the following steps:

[0009] Step 1: Perform micro-scale meshing on the computational domain of microstructure evolution of the as-cast nano-phase reinforced aluminum-based composite material, and label each cube mesh with (i, j, k), where the value range of i is [1, h], the value range of j is [1, n], and the value range of k is [1, m], and h, n, and m are all integers, wherein i, j, and k are all integers;

[0010] Step 2: Scan the as-cast nano-phase reinforced aluminum matrix composite material with an electron microscope, and calculate the equivalent solute diffusion coefficient D in the nano-phase reinforced aluminum matrix composite material based on the obtained scanning image.eff and the average size of nanophase clusters d p ;

[0011] Step 3: Scan the cast nanophase reinforced aluminum matrix composite material with an electron microscope and calculate the solute distribution coefficient of the nanophase based on the obtained energy spectrum.

[0012] Step 4, measuring the temperature in the cavity of the nano-phase reinforced aluminum-based composite material casting during the cooling process, and obtaining a curve of the temperature change in the cavity of the casting over time, that is, obtaining a cooling curve of the casting;

[0013] Each test time point of the casting cooling curve is recorded as (t ex1 , t ex2 , t ex3 ,…,t exQ ), the temperature corresponding to each test time point is recorded as (T ex1 , T ex2 , T ex3 ,…,T exQ );

[0014] During the casting solidification process, each cube mesh in the computational domain has the same temperature at the same time. Let the temperature of each cube mesh at the initial time be

[0015] And initialize the time tsim = 0, according to the casting cooling curve and the equivalent solute diffusion coefficient D eff , calculate the temperature of the cube grid at time tsim+Δtsim;

[0016] Step 5: Calculate the average nucleation undercooling according to the casting cooling curve, and calculate the nucleation undercooling of each grain according to the average nucleation undercooling, and then randomly distribute each grain to the calculation domain. Each cube grid undergoes nucleation transformation according to the temperature at the moment tsim+Δtsim and the distribution of each grain to obtain a core cube grid, and use each core cube grid as the starting point of each dendrite;

[0017] Step 6: Capture the liquid grid around the core cube grid, and transform the captured liquid grid into a growing grid;

[0018] Step 7: Calculate the weighted average curvature of the solid-liquid interface for the growth grid, calculate the liquid phase composition of the solid-liquid interface based on the weighted average curvature of the solid-liquid interface, and then calculate the solid phase fraction based on the liquid phase composition of the solid-liquid interface;

[0019] If there is a grid whose solid fraction increases to 1, the grid whose solid fraction increases to 1 is changed to a solid state, and step eight is executed;

[0020] If there is no grid whose solid fraction increases to 1, execute step nine;

[0021] Step 8: If there is a liquid grid around the grid newly converted to a solid state in step 7, the liquid grid around the new solid grid is captured, and the state of the captured liquid grid is converted to a growth state;

[0022] If there is no liquid grid around the grid newly converted to solid in step 7, directly execute step 9;

[0023] Step 9: Let tsim=tsim+Δtsim;

[0024] Step 10, calculate the temperature of the cube grid at the time tsim+Δtsim, and determine whether there is still a growing grid in the calculation domain;

[0025] If there are still growing grids in the computational domain, return to step 7;

[0026] Otherwise, the solidification process ends and the step eleven is continued;

[0027] Step 11: Output the average size value of each dendrite in the calculation domain of the nanophase reinforced aluminum matrix composite material.

[0028] Furthermore, in the step 1, the cube meshes obtained by micro-scale meshing include boundary cube meshes and non-boundary cube meshes, and the boundary cube meshes are labeled as (i∈[1,h],j∈[1,2),k∈[1,m]), (i∈[1,2),j∈[1,n],k∈[1,m]), (i∈(2,h],j∈[n-1,n],k∈[1,m]), (i∈[h-1,h],j∈(2,n-1],k∈[1,m]), (i∈(2,h-1],j∈(2,n-1],k∈[m-1,m]), (i∈(2,h-1],j∈(2,n-1],k∈[1,2));

[0029] And each non-boundary cube grid labeled (i,j,k) has 26 neighbor cube grids, that is, the 6 first nearest neighbor cube grids of the non-boundary cube grid labeled (i,j,k) are labeled (i,j,k+1), (i,j,k-1), (i,j+1,k), (i,j-1,k), (i-1,j,k) and (i+1,j,k); the 12 second nearest neighbor cube grids of the non-boundary cube grid labeled (i,j,k) are labeled (i-1,j,k+1), (i+1,j,k+1), (i-1,j,k-1), (i+1,j,k-1), (i,j+1,k+1), ( i,j+1,k-1), (i,j-1,k+1), (i,j-1,k-1), (i-1,j+1,k), (i+1,j+1,k), (i-1,j-1,k) and (i+1,j-1,k); the eight third nearest neighbor cube meshes of the non-boundary cube mesh labeled (i,j,k) are labeled (i-1,j+1,k+1), (i+1,j+1,k+1), (i-1,j-1,k+1), (i+1,j-1,k+1), (i-1,j-1,k+1), (i-1,j+1,k-1), (i+1,j+1,k-1), (i-1,j+1,k-1) and (i+1,j-1,k-1);

[0030] The liquid cube grid satisfies: solid phase fraction f s (i,j,k)=0, state state(i,j,k)=0;

[0031] The growing cube grid satisfies: 0 <f s (i,j,k)<1, state(i,j,k)=1;

[0032] The solid cube grid satisfies: f s (i,j,k)=1, state(i,j,k)=2.

[0033] Furthermore, the specific process of step 2 is as follows:

[0034] Step 2: Scan the as-cast nanophase reinforced aluminum-based composite material using an electron microscope to obtain M scanned images, and measure the thickness of the nanophase adhesion layer on each scanned image, and then calculate the average thickness ∈ of the nanophase adhesion layer of the M scanned images, in μm;

[0035] Step 2: Calculate the equivalent solute diffusion coefficient D according to the average thickness of the nanophase adhesion layer ∈ eff :

[0036]

[0037] Among them, Dlnp is the Brownian diffusion coefficient of the nanophase, in m 2 / s;D l is the diffusion coefficient of the liquid phase solute in the matrix alloy, in m 2 / s; Δx m is the size of the cube grid, in μm;

[0038]

[0039] Among them, r p is the radius of the nanophase, in nm; K B is the Boltzmann constant, in J / K; T l is the liquidus temperature, in °C; μ l is the liquid phase viscosity of aluminum alloy, in kg / (m·s);

[0040] Step 2: randomly select M nanophase clusters in the nanophase aluminum-based composite material, measure the equivalent diameter of each selected nanophase cluster, and then calculate the average value of the equivalent diameters of all selected nanophase clusters, and record the calculated average value as d p .

[0041] Furthermore, the specific process of step three is:

[0042] Randomly select M points inside the α-Al grain of the nano-phase aluminum-based composite material, obtain the Si element value of each point by performing EDS point composition analysis on each point, and then calculate the average Si element value of all the selected points

[0043] Using the average Si element value Calculating the solute partition coefficient for nanophase interactions

[0044]

[0045] Among them, C o is the initial alloy composition of the aluminum alloy, expressed in wt.%.

[0046] Furthermore, the temperature of the cube grid at the time tsim+Δtsim is calculated as follows:

[0047] make Represents the temperature of each cube grid at time tsim and satisfies Then the temperature of each cube grid at the time tsim+Δtsim is for:

[0048]

[0049] Among them, Δtsim is the time step, T exq represents the temperature corresponding to the qth test time point, T ex(q+1) Indicates the temperature corresponding to the q+1th test time point.

[0050] Furthermore, the average nucleation undercooling degree is calculated according to the casting cooling curve, specifically:

[0051] Take the first-order derivative of the casting cooling curve and record the temperature T corresponding to the maximum derivative value max , then the average nucleation undercooling ΔT mean =T l -T max .

[0052] Furthermore, in the step 5, the nucleation undercooling of each grain is calculated according to the average nucleation undercooling, and then the grains are randomly distributed in the calculation domain, and each cube grid undergoes nucleation transformation according to the temperature and the distribution of each grain to obtain a core cube grid; the specific process is:

[0053] Step 51: The supercooling required for heterogeneous nucleation of grains during solidification follows the following Gaussian distribution:

[0054]

[0055] Among them, n max is the maximum nucleation core density, w is the core density, 1≤w≤n max ; ΔT σ is the standard deviation of the Gaussian distribution curve; ΔT mean is the average nucleation undercooling, ΔT nucl-dex Represents the nucleation undercooling corresponding to the grain labeled dwx, 1≤dwx≤n max ×[(Δx m ) 3 ×h×m×n];

[0056] The Gaussian distribution equation is integrated to obtain the nucleation supercooling ΔT corresponding to different grains. nucl-dex ;

[0057] Step 52: After randomly distributing each grain into the computational domain, if at time tsim, there are grains in the cube grid labeled (i, j, k) and satisfy Then the cube grid labeled (i, j, k) undergoes nucleation transformation, and the physical quantity change of the cube grid labeled (i, j, k) is: solid phase fraction f s (i,j,k)=1,C s =C0,C l=0, state state(i,j,k)=2, where C s Solid phase component; C l is the average liquid phase composition in the cube grid, C0 is the initial composition of the aluminum alloy;

[0058] Otherwise, the cube mesh labeled (i, j, k) does not undergo nucleation transformation.

[0059] Furthermore, the weighted average curvature of the solid-liquid interface is calculated for the growth grid, and then the liquid phase composition of the solid-liquid interface is calculated according to the weighted average curvature of the solid-liquid interface, and then the solid phase fraction is calculated according to the liquid phase composition of the solid-liquid interface; the specific process is:

[0060] Step 7.1: For any growth grid, calculate the weighted average curvature k of the solid-liquid interface. wmc :

[0061]

[0062]

[0063] Where ε is the surface energy anisotropy coefficient, Yes x′ The partial derivative with respect to x′ is Yes y′ The partial derivative with respect to y′ is Yes z′ The partial derivative with respect to z′ is

[0064] and f s The first derivatives in the x-, y-, and z-directions, and and The calculation is as follows:

[0065]

[0066] Among them, ψ, φ and θ are the three Euler angles corresponding to the spatial growth direction of the dendrite to which the current growth state grid belongs, -π<ψ<π, 0<φ<π / 2, -π / 4<θ<π / 4;

[0067] Step 72: Calculate the liquid phase composition and solid phase fraction at the solid-liquid interface;

[0068]

[0069] in, is the liquid phase component at the solid-liquid interface, Г is the Gibbs-Thomson coefficient, in °C·m; m l is the liquidus slope, unit is K (wt.%)-1 ;

[0070] The solid fraction increment Δf of the computational grid within a time step Δtsim s :

[0071]

[0072] Among them, k ex is the equilibrium distribution coefficient of the solute Si element, which satisfies the following when the nanophase exists: C l is the average liquid phase composition in the cube grid;

[0073] The solid fraction is:

[0074]

[0075] Furthermore, the calculation method of the average liquid phase composition in the cube grid is:

[0076]

[0077] Among them, ▽·(▽C l ) is C l The second derivative of l is the diffusion coefficient of the liquid phase solute in the matrix alloy, in m 2 / s.

[0078] Furthermore, the average size of each dendrite is calculated as follows:

[0079]

[0080] Where nuclei is the number of dendrites in the simulation calculation, nuclei≤n max ×[(Δx m ) 3 ×h×m×n]; is the average size of dendrites.

[0081] The beneficial effects of the present invention are:

[0082] Based on experimental characterization, the present invention obtains the equivalent solute diffusion coefficient, Si element solute distribution coefficient, size of nanophase clusters and average nucleation undercooling in the sample with nanophase. Based on the experimental characterization data, the numerical simulation of the microstructure of aluminum-based composite casting is performed, which can more accurately predict the characteristics of microstructure formation when nanophase exists in composite castings, solves the problem that the microstructure formation in the casting process of nanophase-reinforced aluminum-based composite materials cannot be accurately predicted, and provides data support and theoretical guidance for alloy structure control and process optimization in the forming process of composite castings.

[0083] The present invention is suitable for numerical prediction of microstructure formation in the casting process of nano-phase reinforced aluminum-based composite materials. The present invention can more accurately predict the microstructure formation in the casting process of aluminum-based composite materials when nano-phases are present, providing theoretical support for optimization of composite material casting forming process. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 is a schematic diagram of a metal casting mold;

[0085] Temperature measurement was performed at position P1; samples were cut from random positions in the casting for microstructure analysis;

[0086] Figure 2 This is the result of the scanning electron microscope (SEM) experimental characterization of the nanophase reinforced Al-7wt%Si composite material under metal mold casting conditions;

[0087] Each picture contains the first precipitated α-Al phase and the added nanophase, where the α-Al phase is dark gray and occupies a larger area, and the nanophase is a white bright spot attached to the α-Al phase. Since the area between the red dotted line and the green dotted line is the nanophase aggregation area, this area is defined as the thickness of the nanophase attachment layer. 10 positions were selected for measurement, and the average value was 1.76μm;

[0088] Figure 3 This is a graph showing the experimental characterization results of scanning electron microscope energy spectrum (EDS) of nanophase reinforced Al-7wt%Si composites under metal mold casting conditions;

[0089] The C, N, Al, Si and Ti element composition values ​​inside the α-Al phase are first precipitated at 10 points. The test points are represented by solid circles, and the α-Al phase is dark gray. Since the Si element is the main added element in the aluminum alloy, the average value of the Si element obtained at the 10 positions is calculated, and the average Si element value is calculated. 1.17wt.%;

[0090] Figure 4 Schematic diagram of the nanophase cluster size at 10 locations in the scanning electron microscope (SEM) experiment of nanophase reinforced Al-7wt%Si composites under metal mold casting conditions;

[0091] The nanophase is bright in color, and the average size of nanophase aggregates is 1.14 μm;

[0092] Figure 5 The cooling curve changes of nano-phase reinforced Al-7wt%Si composite materials during solidification under metal mold casting conditions;

[0093] The black solid line is the experimentally measured cooling curve, and the red solid line is the first-order derivative of the cooling curve. It can be seen that the highest value of the first-order derivative on the red solid line occurs at 5.1s, and the temperature value on the black curve at 5.1s is 621.0℃ (T max ), through T max The average nucleation supercooling value (ΔT mean =T l -T max ) is 2°C;

[0094] FIG6( a ) is an experimental characterization result diagram of the grain structure of the nanophase reinforced Al-7wt%Si composite material under metal mold casting conditions;

[0095] Different colors in the figure represent grains with different orientations;

[0096] FIG6( b ) is a statistical diagram of the grain structure of the nanophase reinforced Al-7wt%Si composite material under metal mold casting conditions;

[0097] The average grain size obtained in the experiment is 130 μm;

[0098] Figure 7(a) is the grain structure diagram obtained by conventional algorithm simulation;

[0099] In the conventional algorithm, D eff It is equivalent to the liquid phase solute diffusion coefficient D of Al-7wt%Si alloy without nanophase. l , D l =6.45×10 -9 m 2 / s; is the solute equilibrium distribution coefficient in the phase diagram (0.13); when there is no nanophase, the nanophase clusters do not affect the liquid phase composition at the solid-liquid interface The calculation of The calculation does not take into account d p The influence of ΔT mean The value (3.2°C) for Al-7wt.%Si alloy in the existing method is adopted;

[0100] FIG. 7( b ) is a grain structure diagram simulated by the method of the present invention;

[0101] The present invention takes into account the influence of the nanophase adhesion layer, so D eff 1.91×10 -11 m 2 / s; the solute distribution coefficient adopts the solute equilibrium distribution coefficient (0.17) obtained by experimental characterization; the nanophase clusters participate in the calculation of the liquid phase composition at the solid-liquid interface, that is, The calculation of d p The influence of experimental measurement d pThe size is 1.14 μm; the conventional algorithm forms a large number of grains, specifically 16, and the average grain size is 62 μm, while the number of grains formed in the present invention is small, specifically 6, and the average grain size is 100 μm;

[0102] FIG7( c ) is a comparison diagram of the average grain size measured by the conventional algorithm, the present invention, and the experiment;

[0103] It can be seen from the figure that the average grain size obtained by simulation of the present invention is closer to that obtained by experiment. DETAILED DESCRIPTION

[0104] Specific implementation method 1: A method for predicting the casting microstructure of a nano-phase reinforced aluminum-based composite material described in this implementation method specifically includes the following steps:

[0105] Step 1: Perform micro-scale meshing on the computational domain of the microstructure evolution of the as-cast nano-phase reinforced aluminum-based composite material, and label each cube mesh with (i, j, k), where the value range of i is [1, h], the value range of j is [1, n], and the value range of k is [1, m], and h, n, and m are all integers (and the values ​​of h, n, and m are all greater than 100), wherein i, j, and k are all integers;

[0106] Step 2: Scan the as-cast nano-phase reinforced aluminum matrix composite material with an electron microscope, and calculate the equivalent solute diffusion coefficient D in the nano-phase reinforced aluminum matrix composite material based on the obtained scanning image (SEM) eff and the average size of nanophase clusters d p ;

[0107] Step 3: Scan the cast nano-phase reinforced aluminum matrix composite material with an electron microscope, and calculate the solute distribution coefficient of the nano-phase based on the obtained energy spectrum (EDS)

[0108] Step 4: Use a NiCr-NiSi thermocouple to measure the temperature in the cavity of the nano-phase reinforced aluminum-based composite casting during the cooling process, and obtain a curve of the temperature change in the casting cavity over time, that is, obtain a casting cooling curve, such as Figure 5 As shown;

[0109] Each test time point of the casting cooling curve is recorded as (t ex1 , t ex2 , t ex3 ,…,t exQ ), the temperature corresponding to each test time point is recorded as (T ex1 , T ex2 , T ex3 , …, T exQ); Due to the limitation of experimental equipment, exQ≤10000;

[0110] During the casting solidification process, each cube mesh in the computational domain has the same temperature at the same time. Let the temperature of each cube mesh at the initial time be

[0111] And initialize the time tsim = 0, according to the casting cooling curve and the equivalent solute diffusion coefficient D eff , calculate the temperature of the cube grid at time tsim+Δtsim;

[0112] Step 5: Calculate the average nucleation undercooling according to the casting cooling curve, and calculate the nucleation undercooling of each grain according to the average nucleation undercooling, and then randomly distribute each grain to the calculation domain. Each cube grid undergoes nucleation transformation according to the temperature at the moment tsim+Δtsim and the distribution of each grain to obtain a core cube grid, and use each core cube grid as the starting point of each dendrite;

[0113] Step 6: Capture the liquid grid around the core cube grid, and transform the captured liquid grid into a growing grid (the captured liquid grid has the same dex value and three Euler angles as the core grid, that is, if the current growing grid is a neighboring grid of a solid grid, then the current growing grid and the solid grid belong to the same dendrite);

[0114] If a liquid grid is a neighbor grid of two solid grids at the same time, it is considered that the dex of the liquid grid is the same as that of any of the two solid grids, that is, the liquid grid can belong to the same dendrite as any of the two solid grids;

[0115] Step 7: Calculate the weighted average curvature of the solid-liquid interface for the growth grid, calculate the liquid phase composition of the solid-liquid interface based on the weighted average curvature of the solid-liquid interface, and then calculate the solid phase fraction based on the liquid phase composition of the solid-liquid interface;

[0116] If there is a grid whose solid fraction increases to 1, the grid whose solid fraction increases to 1 is changed to a solid state, and step eight is executed;

[0117] If there is no grid whose solid fraction increases to 1, execute step nine;

[0118] Step 8: If there is a liquid mesh around the mesh newly converted to a solid state in step 7, the liquid mesh around the new solid mesh is captured, and the state of the captured liquid mesh is converted to a growth state (if the captured current liquid mesh is a neighbor of a new solid mesh, then the current liquid mesh and the new solid mesh have the same dex value and three Euler angles, that is, the current liquid mesh and the new solid mesh belong to the same dendrite);

[0119] If there is no liquid grid around the grid newly converted to solid in step 7, directly execute step 9;

[0120] Step 9: Let tsim=tsim+Δtsim;

[0121] Step 10, calculate the temperature of the cube grid at the time tsim+Δtsim, and determine whether there is still a growing grid in the calculation domain;

[0122] If there are still growing grids in the computational domain, return to step 7;

[0123] Otherwise, the solidification process ends and the step 11 is continued;

[0124] Step 11: Output the average size value of each dendrite in the calculation domain of the nanophase reinforced aluminum matrix composite material.

[0125] The method of the present invention adopts cellular automaton technology to simulate the formation of microstructure of aluminum alloy with nanophase in the casting process. By executing the method of the present invention respectively under different nanophase addition ratios, the quantitative relationship between the nanophase addition ratio and the dendrite size can be obtained, the accuracy of numerical simulation is improved, and the optimal nanophase addition ratio can be obtained.

[0126] Specific implementation method 2: This implementation method is different from specific implementation method 1 in that, in step 1, the cube mesh obtained by micro-scale mesh division includes boundary cube meshes and non-boundary cube meshes, and the boundary cube meshes are labeled as (i∈[1,h],j∈[1,2),k∈[1,m]), (i∈[1,2),j∈[1,n],k∈[1,m]), (i∈(2,h],j∈[n-1,n],k∈[1,m]), (i∈[h-1,h],j∈(2,n-1],k∈[1,m]), (i∈(2,h-1],j∈(2,n-1],k∈[m-1,m]), (i∈(2,h-1],j∈(2,n-1],k∈[1,2));

[0127] That is, the cube meshes with numbers belonging to the above intervals are all boundary meshes;

[0128] And each non-boundary cube grid labeled (i,j,k) has 26 neighbor cube grids, that is, the 6 first nearest neighbor cube grids of the non-boundary cube grid labeled (i,j,k) are labeled (i,j,k+1), (i,j,k-1), (i,j+1,k), (i,j-1,k), (i-1,j,k) and (i+1,j,k); the 12 second nearest neighbor cube grids of the non-boundary cube grid labeled (i,j,k) are labeled (i-1,j,k+1), (i+1,j,k+1), (i-1,j,k-1), (i+1,j,k-1), (i,j+1,k+1), ( i,j+1,k-1), (i,j-1,k+1), (i,j-1,k-1), (i-1,j+1,k), (i+1,j+1,k), (i-1,j-1,k) and (i+1,j-1,k); the eight third nearest neighbor cube meshes of the non-boundary cube mesh labeled (i,j,k) are labeled (i-1,j+1,k+1), (i+1,j+1,k+1), (i-1,j-1,k+1), (i+1,j-1,k+1), (i-1,j-1,k+1), (i-1,j+1,k-1), (i+1,j+1,k-1), (i-1,j+1,k-1) and (i+1,j-1,k-1);

[0129] The liquid cube grid satisfies: solid phase fraction f s (i,j,k)=0, state state(i,j,k)=0;

[0130] The growing cube grid satisfies: 0 <f s (i,j,k)<1, state(i,j,k)=1;

[0131] The solid cube grid satisfies: f s (i,j,k)=1, state(i,j,k)=2.

[0132] The other steps and parameters are the same as those in the first embodiment.

[0133] At the initial moment, all cube meshes obtained by segmentation are liquid meshes.

[0134] Specific implementation method three: Combination Figure 2 and Figure 4 This embodiment is different from the specific embodiment 1 or 2 in that the specific process of step 2 is as follows:

[0135] Step 2: Scan the as-cast nanophase reinforced aluminum matrix composite material using an electron microscope (SEM) to obtain M scanned images, and measure the thickness of the nanophase adhesion layer at the front of the α-Al grain on each scanned image, and then calculate the average thickness ∈ of the nanophase adhesion layer of the M scanned images, in μm;

[0136] Step 2: Calculate the equivalent solute diffusion coefficient D according to the average thickness of the nanophase adhesion layer ∈ eff :

[0137]

[0138] Among them, D lnp is the Brownian diffusion coefficient of the nanophase, in m 2 / s;D l is the diffusion coefficient of the liquid phase solute in the matrix alloy, in m 2 / s; Δx m is the size of the cube grid (i.e., side length), in μm;

[0139]

[0140] Among them, r p is the radius of the nanophase, in nm; K B is the Boltzmann constant, in J / K; T l is the liquidus temperature, in °C; μ l is the liquid phase viscosity of aluminum alloy, in kg / (m·s);

[0141] Step 2: randomly select M nanophase clusters in the nanophase aluminum-based composite material, measure the equivalent diameter of each selected nanophase cluster, and then calculate the average value of the equivalent diameters of all selected nanophase clusters, and record the calculated average value as d p .

[0142] The other steps and parameters are the same as those in the first or second embodiment.

[0143] In order to ensure that the measurement results are statistically significant, the value of M is set to 10 in the present invention. The thickness of the nanophase adhesion layer is defined as the area starting from the nanophase adhesion region and where there is no nanophase distribution or a small amount of nanophase distribution in the α-Al grains.

[0144] Specific implementation method four: Combination Figure 3 This embodiment is different from the first to third embodiments in that the specific process of step three is as follows:

[0145] Randomly select M points inside the α-Al grain of the nano-phase aluminum-based composite material, obtain the Si element value of each point by performing EDS point composition analysis on each point, and then calculate the average Si element value of all the selected points

[0146] Using the average Si element value Calculating the solute partition coefficient for nanophase interactions

[0147]

[0148] Among them, C o is the initial alloy composition of the aluminum alloy, expressed in wt.%.

[0149] The other steps and parameters are the same as those in Specific Embodiments 1 to 3.

[0150] Specific implementation mode 5: This implementation mode is different from any one of the specific implementation modes 1 to 4 in that the temperature of the cube grid at the time tsim+Δtsim is calculated as follows:

[0151] make Represents the temperature of each cube grid at time tsim and satisfies Then the temperature of each cube grid at the time tsim+Δtsim is for:

[0152]

[0153] Among them, Δtsim is the time step, Solidification time is defined as the continuous accumulation of time steps, that is, tsim = ∑Δtsim, T exq represents the temperature corresponding to the qth test time point, T ex(q+1) Indicates the temperature corresponding to the q+1th test time point.

[0154] The other steps and parameters are the same as those in Specific Embodiments 1 to 4.

[0155] The present embodiment is further described below: ex1 ≤tsim≤t ex2 When T exq and T ex(q+12) T ex1 and T ex2 When t ex2 ≤tsim≤t ex3 When T exq and T ex(q+1) T ex2 and T ex3 ; And so on, as the tsim value increases, The value of tsim changes continuously until the tsim value reaches the preset solidification time (Time_end), that is, the calculation ends when tsim = Time_end. When the tsim value increases by one Δtsim, the value calculated at the previous moment is used To update Right now Then restart this moment The calculation of temperature change during solidification is completed as the time steps are accumulated.

[0156] Specific implementation method six: Combination Figure 5 This embodiment is different from the first to fifth embodiments in that the average nucleation undercooling is calculated according to the casting cooling curve, specifically:

[0157] Take the first-order derivative of the casting cooling curve and record the temperature T corresponding to the maximum derivative value max , then the average nucleation undercooling ΔT mean =T l -T max .

[0158] The other steps and parameters are the same as those in Specific Implementation Methods 1 to 5.

[0159] Specific implementation method 7: This implementation method is different from any one of specific implementation methods 1 to 6 in that, in step 5, the nucleation undercooling of each grain is calculated based on the average nucleation undercooling, and then each grain is randomly distributed in the calculation domain, and each cube grid undergoes nucleation transformation according to the temperature and the distribution of each grain to obtain a core cube grid; the specific process is:

[0160] Step 51: The supercooling required for heterogeneous nucleation of grains during solidification follows the following Gaussian distribution:

[0161]

[0162] Among them, n max is the maximum nucleation core density, w is the core density, 1≤w≤n max ; ΔT σ is the standard deviation of the Gaussian distribution curve; ΔT mean is the average nucleation undercooling, ΔT nucl-dex Represents the nucleation undercooling corresponding to the grain labeled dex, 1≤dex≤n max ×[(Δx m ) 3 ×h×m×n],[(Δx m ) 3 ×h×m×n] represents the volume of the computational domain;

[0163] The Gaussian distribution equation is integrated to obtain the nucleation supercooling ΔT corresponding to different grains (cores) mucl-dex ;

[0164] Step 52: After randomly distributing each grain into the computational domain, if at time tsim, there are grains in the cube grid labeled (i, j, k) and satisfy Then the cube grid labeled (i, j, k) undergoes nucleation transformation, and the physical quantity change of the cube grid labeled (i, j, k) is: solid phase fraction f s (i,j,k)=1,C s =C0,C l =0, state state(i,j,k)=2, where C s Solid phase component; C l is the average liquid phase composition in the cube grid, C0 is the initial composition of the aluminum alloy;

[0165] Otherwise, the cube mesh labeled (i, j, k) does not undergo nucleation transformation.

[0166] The other steps and parameters are the same as those in Specific Embodiments 1 to 6.

[0167] In this embodiment, the state of the cube mesh that has undergone nucleation transformation becomes solid, and the cube mesh that has undergone nucleation transformation is used as the core cube mesh.

[0168] Specific embodiment eight: This embodiment differs from any one of specific embodiments one to seven in that the weighted average curvature of the solid-liquid interface is calculated for the growth state grid, and then the liquid phase composition of the solid-liquid interface is calculated based on the weighted average curvature of the solid-liquid interface, and then the solid phase fraction is calculated based on the liquid phase composition of the solid-liquid interface; the specific process is:

[0169] Step 7.1: For any growth grid, calculate the weighted average curvature k of the solid-liquid interface based on the simplified Hoffman-Cahnx vector formula. wmc :

[0170]

[0171] Where ε is the surface energy anisotropy coefficient, Yes x′ The partial derivative with respect to x′ is Yes y′ The partial derivative with respect to y′ is Yes z′ The partial derivative with respect to z′ is

[0172] and fs The first derivatives in the x-, y-, and z-directions, and and The calculation is as follows:

[0173]

[0174] Among them, ψ, φ and θ are the three Euler angles corresponding to the dendrite to which the current growth state grid belongs (that is, if the current growth state grid is a neighboring grid of a solid grid, the current growth state grid and the solid grid belong to the same dendrite) in the spatial growth direction, -π<ψ<π, 0<φ<π / 2, -π / 4<θ<π / 4;

[0175] Step 72: Calculate the liquid phase composition and solid phase fraction at the solid-liquid interface;

[0176]

[0177] in, is the liquid phase component at the solid-liquid interface, Г is the Gibbs-Thomson coefficient, in °C·m; m l is the liquidus slope, unit is K (wt.%) -1 ;

[0178] The solid fraction increment Δf of the computational grid within a time step Δtsim s :

[0179]

[0180] Among them, k ex is the equilibrium distribution coefficient of the solute Si element, which satisfies the following when the nanophase exists: C l is the average liquid phase composition in the cube grid;

[0181] The solid fraction is:

[0182]

[0183] The other steps and parameters are the same as those in Specific Embodiments 1 to 7.

[0184] During the solidification process, as the solidification time tsim accumulates, Δf s Keep accumulating, at a certain moment, When it increases to 1, the grid becomes solid, which means that the grid solidification is completed.

[0185] Specific embodiment 9: This embodiment is different from any one of specific embodiments 1 to 8 in that the calculation method of the average liquid phase composition in the cube grid is:

[0186]

[0187] Among them, ▽·(▽C l ) is C l The second derivative of l is the diffusion coefficient of the liquid phase solute in the matrix alloy, in m 2 / s.

[0188] The other steps and parameters are the same as those in Specific Embodiments 1 to 8.

[0189] At the beginning of the calculation (tsi = 0s), the C l =C o .

[0190] Specific embodiment 10: This embodiment is described in conjunction with FIG. 6 (a) and FIG. 6 (b). This embodiment is different from the specific embodiments 1 to 9 in that the average size value of each dendrite is calculated as follows:

[0191]

[0192] Where nuclei is the number of dendrites in the simulation calculation, nuclei≤n max ×[(Δx m ) 3 ×h×m×n]; is the average size of dendrites.

[0193] The other steps and parameters are the same as those in Specific Embodiments 1 to 9.

[0194] Experimental Section

[0195] TiCN nanophase and Al-7wt%Si alloy were selected as research objects. The cellular automaton model was used to simulate the three-dimensional growth of α-Al dendrites and the growth orientation was randomly selected. The simulation domain size was 200μm×200μm×200μm, and the spatial grid step size Δx m =2μm, so the calculation domain consists of 100×100×100 small cube grids. Thermophysical properties and calculation parameters of Al-7wt%Si alloy are shown in Table 1.

[0196] like Figure 1 As shown, the TiCN / Al-7wt%Si composite material was cast in a rectangular plate-shaped metal mold, and a thermocouple was used to measure the temperature during the solidification process. Metal samples were prepared at random locations in the solidified casting and characterized by SEM and EDS experiments. At the same time, the grain structure was observed and the average grain size was measured.

[0197] Table 1 Numerical simulation thermophysical parameters and calculation parameters of Al-Si alloy

[0198]

[0199]

[0200] The performance comparison between the method of the present invention and the conventional method is shown in FIG7( a ), FIG7 ( b ) and FIG7 ( c ). It can be seen that the method of the present invention can more accurately predict the microstructure formation during the casting process of the nanophase reinforced aluminum-based composite material.

[0201] The above calculation examples of the present invention are only used to explain the calculation model and calculation process of the present invention in detail, and are not intended to limit the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for predicting the casting microstructure of nano-phase reinforced aluminum-based composite materials, characterized in that: The method specifically comprises the following steps: Step 1: Perform micro-scale meshing on the computational domain of the microstructure evolution of the as-cast nano-phase reinforced aluminum-based composite material, and label each cube mesh with (i, j, k), where the value range of i is [1, h], the value range of j is [1, n], and the value range of k is [1, m], where h, n, and m are all integers, and i, j, and k are all integers; Step 2: Scan the as-cast nano-phase reinforced aluminum matrix composite material with an electron microscope, and calculate the equivalent solute diffusion coefficient D in the nano-phase reinforced aluminum matrix composite material based on the obtained scanning image. eff and the average size of nanophase clusters d p ; Step 3: Scan the cast nanophase reinforced aluminum matrix composite material with an electron microscope and calculate the solute distribution coefficient of the nanophase based on the obtained energy spectrum. Step 4, measuring the temperature in the cavity of the nano-phase reinforced aluminum-based composite material casting during the cooling process, and obtaining a curve of the temperature change in the cavity of the casting over time, that is, obtaining a cooling curve of the casting; Each test time point of the casting cooling curve is recorded as (t ex1 , t ex2 , t ex3 , ..., t exQ ), the temperature corresponding to each test time point is recorded as (T ex1 , T ex2 , T ex3 , ..., T exQ ); During the casting solidification process, each cube mesh in the computational domain has the same temperature at the same time. Let the temperature of each cube mesh at the initial time be And initialize the time tsim = 0, according to the casting cooling curve and the equivalent solute diffusion coefficient D eff , calculate the temperature of the cube grid at time tsim+Δtsim; Step 5: Calculate the average nucleation undercooling according to the casting cooling curve, and calculate the nucleation undercooling of each grain according to the average nucleation undercooling, and then randomly distribute each grain to the calculation domain. Each cube grid undergoes nucleation transformation according to the temperature at the moment tsim+Δtsim and the distribution of each grain to obtain a core cube grid, and use each core cube grid as the starting point of each dendrite; Step 6: Capture the liquid grid around the core cube grid, and transform the captured liquid grid into a growing grid; Step 7: Calculate the weighted average curvature of the solid-liquid interface for the growth grid, calculate the liquid phase composition of the solid-liquid interface based on the weighted average curvature of the solid-liquid interface, and then calculate the solid phase fraction based on the liquid phase composition of the solid-liquid interface; If there is a grid whose solid fraction increases to 1, the grid whose solid fraction increases to 1 is changed to a solid state, and step eight is executed; If there is no grid whose solid fraction increases to 1, execute step nine; Step 8: If there is a liquid grid around the grid newly converted to a solid state in step 7, the liquid grid around the new solid grid is captured, and the state of the captured liquid grid is converted to a growth state; If there is no liquid grid around the grid newly converted to solid in step 7, directly execute step 9; Step 9: Let tsim=tsim+Δtsim; Step 10, calculate the temperature of the cube grid at the time tsim+Δtsim, and determine whether there is still a growing grid in the calculation domain; If there are still growing grids in the computational domain, return to step 7; Otherwise, the solidification process ends and the step 11 is continued; Step 11: Output the average size value of each dendrite in the calculation domain of the nanophase reinforced aluminum matrix composite material.

2. The method for predicting the casting microstructure of a nanophase reinforced aluminum-based composite material according to claim 1, characterized in that: In the step 1, the cube meshes obtained by micro-scale meshing include boundary cube meshes and non-boundary cube meshes, and the boundary cube meshes are labeled as (i∈[1,h],j∈[1,2),k∈[1,m]), (i∈[1,2),j∈[1,n],k∈[1,m]), (i∈(2,h],j∈[n-1,n],k∈[1,m]), (i∈[h-1,h],j∈(2,n-1],k∈[1,m]), (i∈(2,h-1],j∈(2,n-1],k∈[m-1,m]), (i∈(2,h-1],j∈(2,n-1],k∈[1,2)); Each non-boundary cube grid labeled (i, j, k) has 26 neighboring cube grids, that is, the 6 first nearest neighbor cube grids of the non-boundary cube grid labeled (i, j, k) are labeled (i, j, k+1), (i, j, k-1), (i, j+1, k), (i, j-1, k), (i-1, j, k) and (i+1, j, k); the 12 second nearest neighbor cube grids of the non-boundary cube grid labeled (i, j, k) are labeled (i-1, j, k+1), (i+1, j, k+1), (i-1, j, k-1), (i+1, j, k-1), (i, j+1, k+1), (i, j+1, k-1), (i, j-1, k+1), (i, j-1, k-1), (i-1, j+1, k), (i+1, j+1, k), (i-1, j-1, k), and (i+1, j-1, k); the 8 third nearest neighbor cube grids of the non-boundary cube grid labeled (i, j, k) are labeled (i-1, j+1, k+1), (i+1, j+1, k+1), (i-1, j-1,k+1), (i+1,j-1,k+1), (i-1,j+1,k-1), (i+1,j+1,k-1), (i-1,j-1,k-1) and (i+1,j-1,k-1); The liquid cube grid satisfies: solid phase fraction fs(i, j, k) = 0, state state(i, j, k) = 0; The growing cube grid satisfies: 0 <f s (i,j,k)<1, state(i,j,k)=1; The solid cube grid satisfies: f s (i, j, k) = 1, state (i, j, k) = 2.

3. The method for predicting the casting microstructure of a nano-phase reinforced aluminum-based composite material according to claim 2, characterized in that: The specific process of step 2 is as follows: Step 2: Scan the as-cast nanophase reinforced aluminum-based composite material using an electron microscope to obtain M scanned images, and measure the thickness of the nanophase adhesion layer on each scanned image, and then calculate the average thickness ∈ of the nanophase adhesion layer of the M scanned images, in μm; Step 2: Calculate the equivalent solute diffusion coefficient D according to the average thickness of the nanophase adhesion layer ∈ eff : Among them, D lnp is the Brownian diffusion coefficient of the nanophase, in m 2 / s;D l is the diffusion coefficient of the liquid phase solute in the matrix alloy, in m 2 / s; Δx m is the size of the cube grid, in μm; Among them, r p is the radius of the nanophase, in nm; K B is the Boltzmann constant, in J / K; T l is the liquidus temperature, in °C; μ l is the liquid phase viscosity of aluminum alloy, in kg / (m·s); Step 2: randomly select M nanophase clusters in the nanophase aluminum-based composite material, measure the equivalent diameter of each selected nanophase cluster, and then calculate the average value of the equivalent diameters of all selected nanophase clusters, and record the calculated average value as d p .

4. The method for predicting the casting microstructure of a nano-phase reinforced aluminum-based composite material according to claim 3, characterized in that: The specific process of step three is: Randomly select M points inside the α-Al grain of the nano-phase aluminum-based composite material, obtain the Si element value of each point by performing EDS point composition analysis on each point, and then calculate the average Si element value of all the selected points Using the average Si element value Calculating the solute partition coefficient for nanophase interactions Wherein, Co is the initial alloy component of the aluminum alloy, and the unit is wt.%.

5. The method for predicting the casting microstructure of a nano-phase reinforced aluminum-based composite material according to claim 4, characterized in that: The temperature of the cube grid at the time tsim+Δtsim is calculated as follows: make Represents the temperature of each cube grid at time tsim and satisfies Then the temperature of each cube grid at the time tsim+Δtsim is for: Among them, Δtsim is the time step, T exq represents the temperature corresponding to the qth test time point, T ex(q+1) Indicates the temperature corresponding to the q+1th test time point.

6. The method for predicting the casting microstructure of a nano-phase reinforced aluminum-based composite material according to claim 5, characterized in that: The average nucleation undercooling degree is calculated according to the casting cooling curve, specifically: Take the first-order derivative of the casting cooling curve and record the temperature T corresponding to the maximum derivative value max , then the average nucleation undercooling ΔT mean =T l -T max .

7. The method for predicting the casting microstructure of a nano-phase reinforced aluminum-based composite material according to claim 6, characterized in that: In the step 5, the nucleation undercooling of each grain is calculated according to the average nucleation undercooling, and then the grains are randomly distributed in the calculation domain. Each cube grid undergoes nucleation transformation according to the temperature and the distribution of each grain to obtain a core cube grid. The specific process is: Step 51: The supercooling required for heterogeneous nucleation of grains during solidification follows the following Gaussian distribution: Among them, n max is the maximum nucleation core density, w is the core density, 1≤w≤n max ; ΔT σ is the standard deviation of the Gaussian distribution curve; ΔT mean is the average nucleation undercooling, ΔT nucl-dex Represents the nucleation undercooling corresponding to the grain labeled dex, 1≤dex≤n max ×[(Δx m ) 3 ×h×m×n]; The Gaussian distribution equation is integrated to obtain the nucleation supercooling ΔT corresponding to different grains. nucl-dex ; Step 52: After randomly distributing each grain into the computational domain, if at time tsim, there are grains in the cube grid labeled (i, j, k) and satisfy Then the cube grid labeled (i, j, k) undergoes nucleation transformation, and the physical quantity change of the cube grid labeled (i, j, k) is: solid phase fraction f s (i, j, k) = 1, C s =C0,C l =0, state state(i, j, k) = 2, where C s Solid phase component; C l is the average liquid phase composition in the cube grid, C0 is the initial composition of the aluminum alloy; Otherwise, the cube mesh labeled (i, j, k) does not undergo nucleation transformation.

8. The method for predicting the casting microstructure of a nano-phase reinforced aluminum-based composite material according to claim 7, characterized in that: The weighted average curvature of the solid-liquid interface is calculated for the growth grid, and then the liquid phase composition of the solid-liquid interface is calculated according to the weighted average curvature of the solid-liquid interface, and then the solid phase fraction is calculated according to the liquid phase composition of the solid-liquid interface; The specific process is: Step 7.1: For any growth grid, calculate the weighted average curvature k of the solid-liquid interface. wmc : Where ε is the surface energy anisotropy coefficient, Yes x′ The partial derivative with respect to x′ is Yes y′ The partial derivative with respect to y′ is Yes z′ The partial derivative with respect to z′ is Q=(n x′ ) 4 +(n y′ ) 4 +(n z′ ) 4 ; and f s The first derivatives in the x-, y-, and z-directions, and and The calculation is as follows: Among them, ψ, φ and θ are the three Euler angles corresponding to the spatial growth direction of the dendrite to which the current growth state grid belongs, -π<ψ<π, 0<φ<π / 2, -π / 4<θ<π / 4; Step 72: Calculate the liquid phase composition and solid phase fraction at the solid-liquid interface; in, is the liquid phase composition at the solid-liquid interface, Γ is the Gibbs-Thomson coefficient, in °C·m; m l is the liquidus slope, unit is K (wt.%) -1 ; The solid fraction increment Δf of the computational grid within a time step Δtsim s : Among them, k ex is the equilibrium distribution coefficient of the solute Si element, which satisfies the following when the nanophase exists: C l is the average liquid phase composition in the cube grid; The solid fraction is:

9. The method for predicting the casting microstructure of a nano-phase reinforced aluminum-based composite material according to claim 8, characterized in that: The calculation method of the average liquid phase composition in the cube grid is: in, It is C l The second derivative of l is the diffusion coefficient of the liquid phase solute in the matrix alloy, in m 2 / s.

10. The method for predicting the casting microstructure of a nano-phase reinforced aluminum-based composite material according to claim 9, characterized in that: The calculation method of the average size value of each dendrite is: Where nuclei is the number of dendrites in the simulation calculation, nuclei≤n max ×[(Δx m ) 3 ×h×m×n]; is the average size of dendrites.

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