Multi-component diffusion laser deposition simulation method based on fluid volume method

Abstract

The invention discloses a multi-component diffusion laser deposition simulation method based on a fluid volume method, and belongs to the technical field of laser deposition. The method comprises the following steps: (1) establishing a grid model; and (2) controlling liquid level tracking by adopting a fluid volumetric method. And (3) setting material thermophysical parameters and the like. (4) tracking and marking a processing area, adding an energy source item, a mass source item and a momentum source item, and setting a component diffusion equation; and (5) setting an initial condition, a boundary condition, a convergence condition, a calculation method, a calculation time length and a calculation step length. And (6) judging a convergence residual error, circularly solving, and entering a next step cycle if the convergence residual error is smaller than a standard. According to the simulation method, the macroscopic morphology and various elements in the laser deposition process can be simulated and predicted, the cost is saved, the test is simplified, and various test process parameters are theoretically guided.

Description

Technical Field

[0001] The invention relates to the technical field of laser deposition, and in particular to a multi-component diffusion laser deposition simulation method based on a fluid volume method. Background Art

[0002] Laser deposition technology is an advanced material surface modification technology. It uses high-energy laser beam radiation to melt the filler material (powder, wire or plate) and the substrate surface together, and then quickly solidifies to form a deposited coating. However, with the continuous application of this technology, it is found that sometimes based on the complex shape and precise size requirements of precision parts, the deposition process will have strict restrictions on the dilution depth and coating height. In addition, the quality of the deposited material is very sensitive to the process parameters. Improper control of the deposition layer may lead to excessive dilution rate of heterogeneous alloys, insufficient content of strengthening phases, and even defects such as layered segregation and intergranular corrosion cracks, resulting in waste generation due to incorrect process parameters.

[0003] The current method to deal with this problem is still to use multiple sets of process parameter tests, which is slow, inefficient, and consumes a lot of processing materials. In addition, there will be test deviations due to operator ability issues, which greatly delays the project progress.

[0004] In addition, the existing technology simulates the cladding process of the same material to obtain the temperature field and flow field distribution of the process, but it is difficult to expand the application of the complex energy transfer process of cladding of different materials. There is also the Euler method to simulate the cladding process of two materials, but this method is difficult to accurately simulate the melt flow, which ultimately leads to a certain discrepancy between the simulated macroscopic morphology of the deposited layer and the actual experiment.

[0005] Therefore, designing a simulation method to predict deposition test results is particularly important for the application of laser deposition technology. Summary of the invention

[0006] The purpose of the present invention is to provide a multi-component diffusion laser deposition simulation method based on the fluid volume method, which can simulate the mixing of the substrate and the powder after melting during the coaxial powder feeding experiment of laser deposition, reduce the experimental cost, simplify the process flow, and accurately display the macroscopic morphology of the dilution area of ​​the laser deposition process and the distribution characteristics of various elements, providing an effective reference for actual production.

[0007] To achieve the above object, the scheme adopted by the present invention is as follows:

[0008] A multi-component diffusion laser deposition simulation method based on a fluid volume method comprises the following steps:

[0009] (1) Using finite element technology to establish a two-dimensional plane model and using meshing software to divide it into multiple groups of grids;

[0010] (2) setting up a multiphase flow calculation method, specifically using fluid volume fraction gradient to track the gas-liquid level;

[0011] (3) Setting the material thermophysical parameters and other physical parameters involved in the melting and solidification process;

[0012] (4) Track and mark the processing area, add energy source terms, mass source terms, momentum source terms, and set component diffusion equations;

[0013] (5) Set initial conditions, boundary conditions, and calculation parameters required for convergence;

[0014] (6) After setting the above parameters, the laser deposition model starts to calculate and judge the convergence residual. If the residual value is less than the standard, it enters the next step cycle; the continuous migration of the laser is simulated, causing the powder and the substrate to undergo melting-mixing-solidification physical phenomena, and finally obtain multiple sets of simulation experimental results.

[0015] In the above step (1), the grid division feature is a two-dimensional grid model with an overall size of 15mm*12.5mm, wherein the gas domain height is 2.5mm and the substrate domain height is 10mm. Taking the working surface (substrate surface to be deposited) as the reference plane, the grid is encrypted in the area from -4mm to +2.5mm in the y direction, using a regular quadrilateral grid with a side length of 0.1mm, and a non-uniform grid is used from -4mm in the y direction downward, with a total number of 6678 grids.

[0016] In the above step (2): the multi-flow calculation method adopts the fluid volume method, which specifically solves the metal phase volume fraction in each grid in turn, and then solves the difference in metal phase volume fractions of adjacent grids to obtain the global volume fraction gradient of the overall model.

[0017] The heat source and mass source terms are added in the defined area of ​​the gas-liquid surface after tracking and move forward continuously as the test proceeds.

[0018] In the above step (3), the thermophysical parameters of each component in the two materials are written into the programming language by means of fitting equations and then loaded into the software, and the thermophysical parameters of the two different materials (stainless steel and cobalt-based alloy) are calculated according to formulas (1)-(2), wherein the thermophysical parameters refer to the density, specific heat, thermal conductivity and viscosity of each material that change with temperature;

[0019] R steel =R Fe M st-Fe +R Co M st-Co +R Cr M st-Cr +R C M st-c(1);

[0020] R S6 =R Fe M S6-Fe +R Co M S6-Co +R Cr M S6-Cr +R C M S6-C (2);

[0021] In formula (1)-(2): R steel is the thermophysical property parameter of stainless steel (i.e. the density, specific heat, thermal conductivity or viscosity of stainless steel changing with temperature), R S6 is the thermophysical property parameter of cobalt-based alloy (i.e., density, specific heat, thermal conductivity or viscosity of cobalt-based alloy changing with temperature), R i is the thermophysical parameter of a single component (element i), M st-i is the mass fraction of a single component (element i) in stainless steel, M S6-i It is the mass fraction of a single component (element i) in a cobalt-based alloy.

[0022] The other physical parameters required to participate in the melting and solidification process include one or more of the material liquidus line, solidus line, latent heat of fusion, component liquidus line slope, solute solvent equilibrium distribution coefficient, component eutectic distribution coefficient and element binary diffusion coefficient.

[0023] In the above step (4), the calculations of the laser energy source term, mass source term and momentum source term are as follows:

[0024]

[0025]

[0026]

[0027] In formula (3): q l is the laser power, η l is the laser absorption efficiency, r b is the effective laser radius, σ z is the thickness of the energy adding region, V s is the scanning speed, and the area in brackets of formula (3) is the continuously moving laser range area.

[0028]

[0029]

[0030] In formula (4)-(5): q mis the total mass of powder added equation, q i Then the addition equation corresponding to each component is, p Add radius to mass, m m is the added mass, η m is the powder utilization rate, σ z Add thickness to the mass, α i It is the mass fraction ratio of the component to the added powder.

[0031] p i =-ρ·g·β·(TT ref ) (6);

[0032] In formula (6): p i Thermal buoyancy equation, ρ is the density of liquid metal, g is the gravitational acceleration, β is the thermal expansion coefficient of the metal mixture, T ref Reference temperature.

[0033] The above energy source term, mass source term and momentum source term are added to the processing area after interface tracking for calculation.

[0034] The lever rule solute diffusion model (microsegregation model) is used to calculate element diffusion. It is assumed that the solute diffuses infinitely in the solid solution. The component diffusion equation is as shown in formula (7);

[0035]

[0036] In formula (7): the diffusion flux of the component is J i , D i is the diffusion coefficient of the ith component, refers to the mass fraction gradient of the ith component; f l is the liquid volume fraction, and ρ is the liquid density.

[0037] In the above step (5), the initial conditions, boundary conditions, and convergence parameters are set as follows:

[0038] The initial conditions are: the global initial temperature is set to 300K; the gas domain is 99.99% air, and the solid region is 99.99% metal phase;

[0039] The boundary conditions are: the upper boundary type of the model is pressure outlet, and the left and right boundary types of the model are wall;

[0040] The convergence parameters are: total number of steps is 53000, single step length is 0.00002 seconds, and single step cycle calculation is 40 times.

[0041] In step (6): the calculated results include the temperature change of the entire region during the laser deposition process, the melt flow in the molten pool, the flow of each component in the metal phase, and the macroscopic morphology of the metal deposition layer.

[0042] The simulated deposition dilution area obtained was compared with the observation results of the sample after metallographic treatment, and the macroscopic morphology and element content of the two were compared to predict the laser deposition dilution area and dilution amount of stainless steel. The simulation of the model was verified by changing the processing parameters.

[0043] In the present invention, the laser deposition substrate is 304LN stainless steel, the deposition powder is Stellite6 cobalt alloy powder, and the experimental process parameters are as follows: laser power is 2000W, scanning speed is 10mm / s, and powder feeding rate is 11.6g / min; the control test parameters are as follows: laser power is 3000W, powder feeding rate is 11.6g / min, and scanning speed is 10mm / s; laser power is 2000W, powder feeding rate is 20g / min, and scanning speed is 7mm / s.

[0044] The multi-component diffusion laser deposition simulation method based on the fluid volume method provided by the present invention has the following advantages:

[0045] 1. By simulating the melting and solidification process of the dilution zone during laser deposition, the content distribution characteristics of each component at different processing parameters and at different times can be obtained, in order to adjust the processing parameters and avoid the deformation of elements and thermal stress caused by uneven element distribution and heat concentration.

[0046] 2. It can accurately obtain the morphology of the deposited layer, including the deposition height and dilution depth, and effectively predict the processing range and forming area of ​​the laser deposition technology.

[0047] 3. The simulation model is small, the grid division is simple, and the calculation speed is fast. The calculation time of a single model using 8 cores is 6 hours. The present invention can reduce processing costs, shorten production cycles, and effectively obtain ideal processing parameters.

[0048] 4. Wide adaptability. The present invention can effectively predict the macroscopic morphology of the dilution zone and the content distribution of each element in the dilution zone after laser deposition processing of various heterogeneous alloys, which plays an important role in guiding production and processing and has great commercial value. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is a flow chart of the simulation method of the present invention;

[0050] Figure 2 Schematic diagram of the grid of the method of the present invention; wherein: 1 is the air domain, 2 is the tracking air interface, 3 is the matrix domain, 4 is the powder and laser addition domain, and 5 is the boundary interface;

[0051] Figure 3This is a comparison diagram between the test and simulation of Example 1 of the present invention with a laser power of 2000W, a powder feeding rate of 11.6g / min, and a scanning speed of 10mm / s;

[0052] Figure 4 This is a comparison chart between the test and simulation of Example 2 of the present invention with a laser power of 3000W, a powder feeding rate of 11.6g / min, and a scanning speed of 10mm / s;

[0053] Figure 5 This is a comparison diagram between the test and simulation of Example 3 of the present invention with a laser power of 2000W, a powder feeding rate of 20g / min, and a scanning speed of 7mm / s;

[0054] Figure 6 The simulated and experimental comparison diagrams of the four element concentrations of the three deposition layers from top to bottom in Example 1 of the present invention are shown; wherein: (a) the element content obtained by EDS element surface scanning of 10 local areas at position A; (b) the element content obtained by EDS element surface scanning of 10 local areas at position B; (c) the element content obtained by EDS element surface scanning of 10 local areas at position C. DETAILED DESCRIPTION

[0055] In order to further understand the present invention, the present invention is described below in conjunction with examples, but the examples are only for further elaboration of the features and advantages of the present invention, rather than for limiting the claims of the present invention.

[0056] Embodiment 1:

[0057] This embodiment provides a multi-component diffuse laser deposition simulation method based on a fluid volume method.

[0058] like Figure 1 and Figure 2 As shown, the present invention is secondary developed based on the fluent module in Ansys software, the computer language used is C language, the main body adopts the VOF two-phase model and the melting and solidification module, and includes the following steps:

[0059] Step 1: If Figure 1 As shown, a simulation model is established according to the substrate size required for the actual laser deposition test. First, the grid drawing module in the Ansys software is used for modeling and grid division. The grid division feature is a two-dimensional grid model with an overall size of 15mm*12.5mm, in which the gas domain height is 2.5mm and the substrate domain height is 10mm. Taking the working surface as the reference plane, the grid is encrypted in the area from -4mm to +2.5mm in the y direction. A regular quadrilateral grid with a side length of 0.1mm is used. A non-uniform grid is used from -4mm in the y direction, and the total number of grids is only 6678. And each edge is named to facilitate setting boundary conditions for each edge later.

[0060] Step 2: Set the multi-flow flow model to VOF, using a two-phase flow form, including air phase and metal phase, and the flow mode is laminar flow. Calculate each unit volume fraction by using the volume of fluid method (VOF), sequentially solve the volume fraction of the metal phase in each grid, and then solve the difference in the volume fraction of the metal phase of the adjacent grids to obtain the global volume fraction gradient of the overall model, so as to calibrate the added area of ​​the source term. Set the phase change melting model and the mushy region coefficient of the model.

[0061] Step 3: Set the thermophysical parameters of each component of the metal phase, including density, specific heat, thermal conductivity, viscosity, and physical parameter constants such as solidus, liquidus, and latent heat of fusion. In order to distinguish the physical properties of the base material and the powder material, the components are superimposed by mass fraction to calculate the various physical parameters of the two materials.

[0062] R steel =R Fe M st-Fe +R Co M st-Co +R Cr M st-Cr +R C M st-C (1);

[0063] R S6 =R Fe M S6-Fe +R Co M S6-Co +R Cr M S6-C r+R C M S6-C (2);

[0064] In formula (1)-(2): R steel is the thermophysical property parameter of stainless steel, R S6 is the thermophysical property parameter of cobalt-based alloy, R i is the thermophysical parameter of a single component (element i), M st-i is the mass fraction of a single component (element i) in stainless steel, M S6-i It is the mass fraction of a single component (element i) in a cobalt-based alloy.

[0065] Set other physical parameters required for the melting and solidification process, including (material liquidus, solidus, latent heat of fusion, component liquidus slope, solute solvent equilibrium distribution coefficient, component eutectic distribution coefficient, and element binary diffusion coefficient).

[0066] Step 4: Using Gaussian distribution of laser energy source term, the formulas of Gaussian distribution of mass source term and momentum source term are:

[0067]

[0068] where q l is the laser power, η l is the laser absorption efficiency, r b is the effective laser radius, σ z is the thickness of the energy-added region, V s is the scanning speed, and the area in the formula brackets is the continuously moving laser range area.

[0069]

[0070]

[0071] where q m is the total mass of powder added equation, q i Then the addition equation corresponding to each component is p Add radius to mass, m m is the added mass, η m is the powder utilization rate, σ z Add thickness to the mass, α i It is the mass fraction ratio of the component to the added powder.

[0072] p i =-ρ·g·β·(TT ref ) (6);

[0073] where p i Thermal buoyancy equation, ρ is the density of liquid metal, g is the gravitational acceleration, β is the thermal expansion coefficient of the metal mixture, T ref Reference temperature. The energy source term, mass source term and momentum source term mentioned above are added to the processing area after interface tracking for calculation. The actual complex processing conditions are simulated by programming to change various processing parameters (laser power, powder feeding rate and scanning speed).

[0074] In addition, the model also involves the addition of surface tension, using the continuous surface force model (CSF) model to convert the surface tension into volume force and add it to the momentum conservation equation.

[0075] The lever rule solute diffusion model (microsegregation model) is used to calculate element diffusion. It is assumed that the solute diffuses infinitely in the solid solution. The component diffusion equation is as shown in formula (7);

[0076]

[0077] In formula (7): the diffusion flux of the component is J i , D i is the diffusion coefficient of the i-th component, refers to the mass fraction gradient of the ith component; fl is the liquid volume fraction, and ρ is the liquid density.

[0078] Step 5: According to the metal phase volume fraction gradient tracking calculation area between each unit, add laser energy source term, powder mass source term on this area, and molten pool momentum source term inside the molten pool. Adding energy and mass to the tracked gas-liquid surface area conforms to the deposition law in the actual processing process, and the fluid volume method can accurately simulate the flow law of the liquid molten pool. Therefore, the model can accurately simulate the macroscopic morphology of the dilution area and the distribution characteristics of each component.

[0079] Step 6: Set boundary conditions and initialize the entire model area, such as Figure 2 As shown. The overall model is initialized into two regions, namely the gas domain and the matrix domain, marked as 1 and 3. The gas content in the gas domain is 99.99% volume fraction of the air phase, and the matrix domain is set to 99.99% volume fraction of the 304LN stainless steel metal phase. During the calculation, the calibrated tracking gas-liquid interface is marked as 2, the laser deposition area is marked as 4, and the multiple boundaries are marked as 5.

[0080] The initialization parameters include:

[0081] Global initial temperature, boundary heat conduction and initial temperature, mass fraction of each component of the substrate metal phase (substrate composition Fe 79.99%, Cr 19.99%, Co 0.01%, C 0.01%).

[0082] The upper boundary is the pressure outlet, and the pressure of the computational domain is kept at one atmosphere. The left and right boundaries are set to wall, the convection heat dissipation is 100W, and the heat transfer efficiency is 0.8.

[0083] Step 7: Select the transient solver SLMPLE algorithm, set the number of single-step cycles to 40, the total number of calculation steps to 53000, the calculation single-step length to 0.00002 seconds, and start the calculation.

[0084] The convergence residual is judged and the solution is looped. If it is less than the standard value, the next step is looped until all steps are calculated. During the calculation process, as the step size increases, the laser deposition area is also refreshed in real time, the processing area keeps moving forward, and a dilution area is generated behind the molten pool.

[0085] After the calculation is completed, various parameters of the deposition layer (temperature field, molten pool flow field, distribution field of various components, deposition layer morphology) are obtained and exported for comparison with actual experiments.

[0086] Step 8: Conduct stainless steel laser deposition test. The processing environment is in the atmospheric environment. The processing parameters are laser power 2000W, powder feeding rate 11.6g / min, and scanning speed 10mm / s. After processing and cooling, use a wire cutting machine to cut into 20mm×15mm×10mm samples. Then use resin to prepare the sample and grind and polish it. The metallographic experiment uses 240 mesh, 400 mesh, 1000 mesh, 2000 mesh, and 2500 mesh sandpaper to grind the sample in turn, and then polish it with diamond polishing agent. Then use aqua regia flow corrosion for 20 seconds after standing for 2.5 hours to obtain a metallographic sample.

[0087] Step 9: Compare the parameters of the simulated dilution area of ​​the sediment layer with the test, and compare the sediment layer height, dilution depth and the content of each component element at each location.

[0088] Step 10: Figure 4-5 As shown, after adjusting the process parameters, the simulation is repeated and the macroscopic morphology of the simulated deposition layer is obtained, and then the laser deposition test with variable process parameters is carried out. The processing parameters are laser power 3000W, powder feeding rate 11.6g / min, and scanning speed 10mm / s. The processing parameters are laser power 2000W, powder feeding rate 20g / min, and scanning speed 7mm / s. The metallographic micrograph finally obtained is compared with the simulated deposition layer for multiple parameters, and it is verified that the present invention has good adaptability to each group of test parameters.

[0089] Step 11: Figure 6 The figure shows the distribution comparison of various elements at three locations from the surface to the bottom layer of the dilution area in the simulation and the experiment of the first embodiment. The content distribution characteristics of each main element are obtained by EDS surface area energy spectrum analysis.

[0090] The material thermophysical parameters used in this simulation method are obtained by summing up the mass fractions of multiple components. Different thermophysical parameters are obtained for metal phases in different regions during the deposition process, which is more in line with the complex physical properties of the molten pool in the actual processing process. The melting lever principle and the convection-diffusion equation are solved at the same time to accurately simulate the mass transfer behavior of each component.

[0091] This simulation method is a continuous solution that complies with the conservation of momentum, energy, mass, and solute convection and diffusion. The additional energy source term takes into account the convection heat dissipation on the molten pool surface, and the momentum source term takes into account the tangential Marangoni flow caused by the temperature gradient and the buoyancy caused by the change in hot solute density.

[0092] To simplify the model, the energy loss due to powder shielding and the mass and energy loss due to metal vapor evaporation are neglected.

[0093] According to one or more embodiments, a multi-component diffusion laser deposition simulation method based on a fluid volume method is disclosed, including a grid model, a laser deposition model, a material thermal property parameter fitting program, and calculated parameters and simulation sample graphs.

[0094] According to one or more embodiments, a laser deposition model program is disclosed, which is stored in a computer. When the program is called and executed, a dynamic simulation of the dilution region of the laser deposition process can be realized. The program can be stored in a mobile device and the Internet (including a USB flash drive, a mobile hard drive, a CD, and a network disk), and a technician can simulate the laser deposition process on other computer devices according to one or more embodiments.

[0095] Professional technicians in this field can use the implementation method disclosed in this article in combination with multiple embodiments to simulate the laser deposition process by changing material parameters or changing processing parameters. Such simulation results should not be considered to exceed the scope of application of the present invention.

[0096] The contents described in this specific embodiment are merely explanations of the present invention, and it should be noted that they are not limitations of the present invention. After reading the entire specification, ordinary technicians in this field may conceive non-creative improvements based on the present invention, but as long as they are within the scope of the claims of the present invention and the corresponding improvements are also considered as the protection scope of the present invention.

Claims

1. A multi-component diffusion laser deposition simulation method based on a fluid volume method, characterized in that: The method is a simulation of depositing a cobalt-based alloy on a stainless steel substrate using laser deposition technology, and specifically includes the following steps: (1) Using finite element technology to establish a two-dimensional plane model and using meshing software to divide it into multiple groups of grids; (2) setting up a multiphase flow calculation method, specifically using fluid volume fraction gradient to track the gas-liquid level; (3) Setting the material thermophysical parameters and other physical parameters involved in the melting and solidification process; (4) Track and mark the processing area, add energy source terms, mass source terms, momentum source terms, and set component diffusion equations; (5) Set initial conditions, boundary conditions, and calculation parameters required for convergence; (6) After setting the above parameters, the laser deposition model starts iterative calculation and determines the convergence residual. If the residual value is less than the standard, it enters the next step cycle; the continuous migration of the laser is simulated, causing the powder and the substrate to undergo melting-mixing-solidification physical phenomena, and finally obtain multiple sets of simulation experimental results.

2. The multi-component diffusion laser deposition simulation method based on the fluid volume method according to claim 1, characterized in that: In step (1), the grid division feature is a two-dimensional grid model with an overall size of 15mm*12.5mm, in which the gas domain height is 2.5mm and the substrate domain height is 10mm; taking the working surface as the reference plane, the grid is encrypted in the area from -4mm to +2.5mm in the y direction, using a regular quadrilateral grid with a side length of 0.1mm, and a non-uniform grid is used from -4mm in the y direction downward.

3. The multi-component diffuse laser deposition simulation method based on the fluid volume method according to claim 1, characterized in that: In step (2): the volume of fluid (VOF) method is used to calculate each unit volume fraction, and the metal phase volume fraction in each grid is obtained sequentially. Then, the difference in metal phase volume fractions of adjacent grids is obtained, and the global volume fraction gradient of the overall model is obtained.

4. The multi-component diffusion laser deposition simulation method based on the fluid volume method according to claim 1, characterized in that: In step (3), the thermophysical property parameters of each component in the two materials are written into a programming language by means of fitting equations and then loaded into the software, and the thermophysical property parameters of the two different materials (stainless steel and cobalt-based alloy) are calculated according to formulas (1)-(2), wherein the thermophysical property parameters refer to the density, specific heat, thermal conductivity and viscosity of each material that change with temperature; R steel =R Fe M st-Fe +R Co M st-Co +R Cr M st-Cr +R C M st-C (1); R s6 =R Fe M S6-Fe +R Co M S6-Co +R Cr M S6-Cr +R C M S6-C (2); In formula (1)-(2): R steel is the thermophysical property parameter of stainless steel (i.e. the density, specific heat, thermal conductivity or viscosity of stainless steel changing with temperature), R s6 is the thermophysical property parameter of cobalt-based alloy (i.e., density, specific heat, thermal conductivity or viscosity of cobalt-based alloy changing with temperature), R i is the thermophysical parameter of a single component (i.e., a certain element i), M st-i is the mass fraction of a single component (element i) in stainless steel, M s6-i It is the mass fraction of a single component (element i) in a cobalt-based alloy.

5. The multi-component diffuse laser deposition simulation method based on the fluid volume method according to claim 1, characterized in that: In step (3), the other physical parameters required for participating in the melting and solidification process include one or more of the material liquidus, solidus, latent heat of fusion, component liquidus slope, solute solvent equilibrium distribution coefficient, component eutectic distribution coefficient and element binary diffusion coefficient.

6. The multi-component diffuse laser deposition simulation method based on the fluid volume method according to claim 1, characterized in that: The calculations of the laser energy source term, mass source term and momentum source term in step (4) are as shown in formulas (3)-(6) respectively; In formula (3): q l is the laser power, η1 is the laser absorption efficiency, r b is the effective laser radius, σ z is the thickness of the energy adding region, V s is the scanning speed, and the area in brackets of formula (3) is the laser range area that is constantly moving; In formula (4)-(5): q m is the total mass of powder added equation, q i Then the addition equation corresponding to each component is p Add radius to mass, m m is the added mass, η m is the powder utilization rate, σ z Add thickness to the mass, α i is the mass fraction ratio of the component to the added powder; p i =-ρ·g·β·(TT ref ) (6); In formula (6): p i Thermal buoyancy equation, ρ is the density of liquid metal, g is the gravitational acceleration, β is the thermal expansion coefficient of the metal mixture, T ref Reference temperature; The energy source term, mass source term and momentum source term mentioned above are added to the processing area after the interface tracking for calculation; The lever rule solute diffusion model is used to calculate element diffusion. It is assumed that the solute diffuses infinitely in the solid solution. The component diffusion equation is as shown in formula (7); In formula (7): the diffusion flux of the component is J i , D i is the diffusion coefficient of the ith component, refers to the mass fraction gradient of the ith component; f l is the liquid volume fraction, and ρ is the liquid density.

7. The multi-component diffusion laser deposition simulation method based on the fluid volume method according to claim 1, characterized in that: In step (5), the initial conditions, boundary conditions, and convergence parameters are set as follows: The initial conditions are: the global initial temperature is set to 300K; the gas domain is 99.99% air, and the solid region is 99.99% metal phase; The boundary conditions are: the upper boundary type of the model is pressure outlet, and the left and right boundary types of the model are wall; The convergence parameters are: total number of steps is 53000, single step length is 0.00002 seconds, and single step cycle calculation is 40 times.

8. The multi-component diffuse laser deposition simulation method based on the fluid volume method according to claim 1, characterized in that: In step (6): the calculated results include the temperature field change in the entire region during the laser deposition process, the melt flow in the molten pool, the flow of each component in the metal phase, and the macroscopic morphology of the metal deposition layer.

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