Modeling method of laser-material interaction process in additive manufacturing

Abstract

The present invention provides a modeling method for the laser-material interaction process in additive manufacturing. The method defines the computational domain space, performs meshing, and completes model initialization. The method calculates the defocus amount and the laser spot radius on the substrate surface. The laser beam is discretized into several sub-beams, and initial energy is assigned to each sub-beam. An index traverses each sub-beam, assigning the sub-beam's energy to the solid particles and fluid surface area grids along the beam path. Based on the energy and reflection state of the sub-beams, the melting state of the solid particles and fluid is determined, and the surface morphology of the molten pool is tracked. All computational domain information is stored as the boundary condition for the next time step, and the above steps are repeated until the modeling is complete. Accurately simulating the energy input of the laser to the complex and dynamic molten pool during the additive manufacturing process helps improve the accuracy of quality prediction, evaluation, and optimization of metal additive manufacturing parts.

Description

Technical Field

[0001] The present invention relates to the field of additive manufacturing, and in particular to a modeling method for the interaction process between laser and material in additive manufacturing. Background Art

[0002] Additive manufacturing (AM) is a key manufacturing method relative to subtractive and isocratic manufacturing, and metal AM is a key sub-category of AM technology. The primary technical approach for metal AM involves melting and solidifying powder or wire, then stacking it layer by layer to create a pre-defined three-dimensional model. This unique approach offers significant advantages for manufacturing parts with complex structures and operating conditions. In recent years, due to its unique advantages, metal AM has been widely adopted in fields such as aerospace, biomedicine, and automotive manufacturing.

[0003] Despite the current demands of metal additive manufacturing (AM) applications, the quality of parts produced by this technology still has many shortcomings, making it extremely urgent to improve the quality of AM parts. In recent years, simulation methods have been widely used to predict, evaluate, and optimize the quality of AM parts. This method is considered one of the most economical and effective methods for improving the quality of AM parts.

[0004] Currently, simulation models for metal additive manufacturing primarily include continuum models, computational fluid dynamics (CFD) models, and computational fluid dynamics-discrete element method (CFD-DEM) models. These models share a single "independent variable"—heat input—with all other dependent variables. Therefore, the accuracy of the overall model is crucial for modeling the interaction between the laser and the material.

[0005] Currently, the main heat source models for metal additive manufacturing include the Gaussian heat source model, the double ellipse heat source model, and ray tracing methods. The Gaussian and double ellipse heat source models are fitted heat source models with high computational efficiency, but lack a physical basis and authenticity. Existing ray tracing methods can simulate the process of laser reflection heating between substrates, but cannot be used with solid particles in discrete element methods and cannot simulate the energy density loss caused by the deviation of the processing plane from the laser focal plane.

[0006] While millimeter-scale melt pool simulations are achieved by setting various boundary conditions, the discrete element method employed only captures the initial powder state and fails to capture the powder state during the process. Furthermore, the multi-reflection model only reflects reflections between fluids. This type of model cannot investigate the impact of powder motion on the AM process and is particularly challenging for powder-feed AM processes like laser cladding. Summary of the Invention

[0007] The present invention proposes a modeling method for the laser-material interaction process in additive manufacturing to solve the technical problem of inaccurate energy transmission modeling in existing additive manufacturing models.

[0008] To solve the above technical problems, the present invention provides a modeling method for the laser-material interaction process in additive manufacturing, comprising the following steps:

[0009] Step S1: Delimiting a computational domain space based on the additive manufacturing processing range, meshing the domain space, and completing model initialization;

[0010] Step S2: Obtain the initial laser emission position, laser focus position, and substrate position, and calculate the defocus amount and the laser spot radius on the substrate surface;

[0011] Step S3: Discretizing the laser beam into a plurality of sub-beams, obtaining the position and direction information of each sub-beam, and allocating initial energy to each sub-beam according to the laser energy distribution type, defocus amount, and distance of the sub-beam relative to the laser focus;

[0012] Step S4: Indexing and traversing each of the sub-beams, and imparting the energy of the sub-beam to the solid particles and the fluid surface area grid on the beam path according to the real-time position coordinate information; the sub-beam is continuously reflected between the solid particles or the fluid surface area grid or between the two;

[0013] Step S5: judging the melting state of solid particles and fluid based on the energy and reflection state of the sub-beam, and tracking the surface morphology of the molten pool;

[0014] Step S6: Store all computational domain information as the boundary conditions for the next time step, and repeat steps S2 to S5 until the modeling is completed.

[0015] Preferably, in step S2, the laser spot radius on the substrate surface is R b The calculation method is:

[0016] ;

[0017] ;

[0018] Where, R f represents the focal plane laser spot radius; L Indicates the amount of defocus; L r represents the Rayleigh length; M 2 represents the laser quality factor; λ Indicates the laser wavelength.

[0019] Preferably, step S3 includes the following steps: discretizing the laser beam into a plurality of sub-beams, setting a laser energy distribution type based on the laser type; distributing the energy of all the sub-beams based on the laser energy distribution type, summing the energy, and calibrating based on the following formula; if the result does not meet the requirements, multiplying the energy of all the sub-beams by a corresponding adjustment factor until the result meets the requirements:

[0020] ;

[0021] Where, represents the total laser energy; n represents the number of sub-beams; Represents the energy of the sub-beam.

[0022] Preferably, the fluid surface area grid in step S4 is determined by a grid fluid fraction.

[0023] Preferably, step S4 includes:

[0024] Step S41: selecting a sub-beam, and traversing all solid particles and surface meshes based on the beam path of the sub-beam to determine the solid particles or surface meshes passed by the sub-beam;

[0025] Step S42: according to the determination result of step S41, the energy of the sub-beam is transferred to the corresponding solid particles or surface grids according to the absorption rate;

[0026] Step S43: Calculating the exit point and exit vector of the sub-beam after it encounters a solid particle or a surface grid, and updating the remaining energy of the sub-beam;

[0027] Step S44: repeating steps S41 to S43, and performing a loop traversal for all sub-beams until all the sub-beams do not pass through any solid particles or surface meshes.

[0028] Preferably, in step S41, the solid particles passed by the sub-beam are determined by the following method: traversing all solid particles, and based on the distance from the coordinate point of the solid particle to the sub-beam, if the distance is less than the radius of the solid particle, traversing the grid between the exit point of the sub-beam and the solid particle to check whether there is a surface grid or a grid filled with fluid, if so, the sub-beam has not passed through the solid particle, otherwise it has passed through.

[0029] Preferably, in step S41, the surface mesh passed by the sub-beam is determined by the following method: all surface meshes are traversed, and according to the surface mesh coordinates, the surface mesh fluid fraction and the interface normal vector within the mesh, the interface equation is reconstructed using the piecewise linear method PLIC, and then the intersection coordinates of the vector of the sub-beam and the interface equation are calculated; if the intersection coordinates are within the surface mesh and there are no solid particles between the exit point of the sub-beam and the intersection coordinates, then the sub-beam passes through the surface mesh; otherwise, the sub-beam does not pass through the surface mesh.

[0030] Preferably, when there are several surface grids that meet the determination condition, the surface grid closest to the laser emission point is selected as the surface grid for capturing the sub-beam.

[0031] Preferably, step S5 includes:

[0032] Step S51: After energy is imparted to each of the sub-beams in step S4, the temperature rise and fall of each solid particle and all the fluids are calculated to determine whether the temperature of the solid particle reaches the solidus of the material. If so, the solid particle is converted into a fluid, and whether the fluid is melted is determined based on the fluid temperature;

[0033] Step S52: Set the time step, solve the Navier-Stokes NS equations and the energy equation to obtain the state information of the molten pool, and use the fluid volume VOF method to track the surface morphology of the molten pool.

[0034] Preferably, in step S1, when initializing the model, the laser cladding process powder adopts a powder feeding method, and the solid particle powder is sprayed from four directions to the focus of the laser on the intended deposition plane; the position information of the solid particle powder is calculated according to gravity and the drag force of the shielding gas; in step S4, when the energy of the sub-beam is assigned to the solid particles and the fluid surface area grid on the beam path, the ratio of the laser energy density on the horizontal plane where the solid particle powder is located and the laser energy density on the intended deposition plane is calculated to obtain an attenuation factor, and the original energy of the sub-beam is multiplied by the attenuation factor and the absorption rate before being assigned to the solid particle powder. The beneficial effects of the present invention include at least: the present invention realizes the laser reflection simulation between the solid particles and the fluid grid, solves the problem of inaccurate energy transfer modeling in the additive manufacturing CFD-DEM fully coupled model; takes into account the influence of the laser defocus amount, which helps to improve the accuracy of the multi-layer additive manufacturing simulation; regards the powder as a movable solid particle before melting, breaks through the limitation of the traditional CFD-DEM model that the powder cannot move, and provides model support for the study of powder movement in the additive manufacturing process. It can accurately simulate the energy input of the laser to the complex and dynamic molten pool during the additive manufacturing process, which helps to improve the accuracy of quality prediction, evaluation and optimization of metal additive manufacturing parts. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Schematic diagram of a method flow in an embodiment of the present invention;

[0036] Figure 2 This is a diagram of laser discretization and laser energy distribution according to an embodiment of the present invention;

[0037] Figure 3 This is a calculation diagram of the exit point and exit vector of the laser after reflection according to an embodiment of the present invention;

[0038] Figure 4 This is a test diagram of laser reflection energy according to an embodiment of the present invention;

[0039] Figure 5 Graphs showing simulation results of solid particles, fluid, and solid particles converted into fluid after laser application according to an embodiment of the present invention;

[0040] Figure 6 This is a simulation diagram of powder in laser cladding according to an embodiment of the present invention;

[0041] Figure 7 Schematic diagram of powder defocusing in laser cladding according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following is a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0043] Example 1

[0044] like Figure 1 As shown, an embodiment of the present invention provides a modeling method for the laser-material interaction process in additive manufacturing, comprising the following steps:

[0045] Step S1: Delineate the computational domain space based on the additive manufacturing processing range, mesh the domain space, and complete model initialization.

[0046] The model is initialized by combining material thermophysical properties, process parameters, scanning strategy, and powder distribution. Powder distribution is simulated using the discrete element method based on the additive manufacturing process type, with the powder distribution pattern determined by the additive manufacturing process type. For example, in laser cladding, the powder is fed by a powder nozzle, spraying the powder toward the laser focus. Alternatively, in laser cladding, the powder is spread evenly across the substrate surface using a scraper.

[0047] Specifically, a substrate model is established in a rectangular computational domain, and material parameters and initial conditions are assigned. The computational domain is then discretized using a hexahedral grid. The height of the computational domain above the substrate should be no less than 1 mm. The substrate is initialized as a fluid.

[0048] In this embodiment, a discrete element model of the powder bed is established based on the size and shape of the powder particles and the thickness of the powder bed in the experiment; in the discrete element model, the thickness of the powder bed is controlled by the distance between the substrate and the bottom of the scraper, and the size of the powder particles is controlled by the particle size distribution of the experimental powder; the thickness of the powder bed is 40μm, and the powder size distribution is D10=17.3μm, D50=34.1μm, D90=55.2μm, where the particle size parameters represent the particle sizes of 10%, 50%, and 90% within the measured size values, and the powder particles are initialized as solid particles.

[0049] Step S2: Obtain the initial laser emission position, laser focus position and substrate position, and calculate the defocus amount and the laser spot radius on the substrate surface.

[0050] The defocus value is the distance between the laser focal plane and the plane. The defocus value has a sign. A positive sign or "+" indicates that the laser focal plane is above the intended deposition plane, and a negative sign or "-" indicates that the laser focal plane is below the intended deposition plane. The laser spot radius of the intended deposition plane is R b The focal plane laser spot radius Rf and defocus L calculate:

[0051] ;

[0052] ;

[0053] Where, R f represents the focal plane laser spot radius; L Indicates the amount of defocus; L r represents the Rayleigh length; M 2 represents the laser quality factor; λ Indicates the laser wavelength.

[0054] Step S3: Discrete the laser beam into several sub-beams, obtain the position and direction information of each sub-beam, and assign initial energy to each sub-beam according to the laser energy distribution type, defocus amount and the distance of the sub-beam relative to the laser focus.

[0055] Specifically, the distance between the laser focal plane and the intended deposition plane is calculated to determine the defocus amount and the laser spot radius on the intended deposition plane. Based on the spot radius, the laser is discretized into several sub-beams. In this embodiment, only the melt path of the first layer of printing is simulated, so the laser spot radius on the intended deposition plane is equal to the laser spot radius on the laser focal plane, specifically 40 μm. Figure 2 As shown, the laser is evenly discretized into 200*200 sub-beams.

[0056] In this embodiment, the laser energy distribution type can be Gaussian distribution, flat-bottom distribution, or elliptical distribution, which is specifically selected according to the laser type. The present invention does not limit this, and the following description uses Gaussian distribution.

[0057] In this embodiment, the laser energy distribution conforms to the Gaussian distribution, and the distribution is:

[0058] ;

[0059] Where A is the laser absorptivity, P is the laser power, and r is the distance from the sub-beam to the laser center. In this embodiment, A is 0.3 and P is 290W. The energy of all sub-beams is distributed and summed to see if they meet the requirements. If not, the energy of all sub-beams is multiplied by the corresponding adjustment factor until they meet the requirements:

[0060] ;

[0061] Where, represents the total laser energy; n represents the number of sub-beams; Represents the energy of the sub-beam.

[0062] For example, in this embodiment, the initial laser emission position should be above the calculation domain, and there should be no fluid or solid particles above the laser emission position.

[0063] In this embodiment, the fluid is divided into solid fluid and liquid fluid, which are controlled by a computational fluid dynamics (CFD) model. The solid particles are rigid bodies and will not deform, and are controlled by a discrete element model.

[0064] Step S4: Index through each sub-beam, and impart the energy of the sub-beam to the solid particles and fluid surface area grid on the beam path according to the real-time position coordinate information; the sub-beam is continuously reflected between the solid particles or the fluid surface area grid or between the two.

[0065] Specifically, the method includes the following steps:

[0066] Step S41: First, find the fluid surface grid according to the grid fluid score, obtain the surface grid coordinate information, surface grid fluid score information and grid interface normal vector information, obtain the coordinate information and diameter information of all solid particles, and store the above information in an array.

[0067] Step S42: Select a sub-beam, traverse all solid particles, and determine whether the sub-beam passes through the solid particle based on the distance from the solid particle coordinate point to the sub-beam. If the sub-beam passes through the solid particle, traverse the grid between the sub-beam exit point and the solid particle to check whether any grid is a surface grid or a grid filled with fluid. If so, the sub-beam does not pass through the solid particle, otherwise it passes through.

[0068] Step S43: If the sub-beam does not pass through the solid particles, all surface grids are traversed, and the interface equation is obtained by reconstructing the interface using the piecewise linear method (PLIC) according to the surface grid coordinates, the surface grid fluid fraction, and the interface normal vector within the grid. The coordinates of the intersection of the sub-beam vector and the interface equation are then calculated. If the intersection coordinates are within the grid, the sub-beam passes through the grid.

[0069] In this embodiment, the piecewise linear method PLIC interface reconstruction method is used to calculate the reflection point and reflection vector of the laser sub-beam, thereby improving the accuracy of the laser reflection model.

[0070] Step S44: According to steps S42 and S43, the energy of the sub-beam is transferred to the corresponding solid particles or surface grids according to the specific absorption rate. In this embodiment, the absorption rate is calculated by the Fresnel equation. Then, the exit point and exit vector of the sub-beam after encountering the solid particles or surface grid are calculated, and the remaining energy of the sub-beam is updated. The exit vector of the sub-beam after reflection is calculated as follows: Figure 3 shown.

[0071] Step S45: Repeat steps S42 to S44 until the sub-beam does not pass through any solid particles or surface grids, or until the remaining energy of the sub-beam is low, i.e., less than a set threshold. In this embodiment, the repetition of steps S42 to S44 may be stopped when the energy of the sub-beam is less than 10% of the initial energy of the sub-beam.

[0072]

[0073] Where, Represents a sub-beam i reflection n The energy after the second sub-beam i The initial energy.

[0074] In this embodiment, Figure 4As shown in the figure, in order to save calculation time, the energy remaining after the laser reflection is tested, and it is determined that in this material, the remaining energy after the laser is reflected twice can be ignored.

[0075] Step S46: Use the OpenMP loop parallel algorithm to traverse all sub-beams and repeat steps S42 to S45.

[0076] In this embodiment, the criterion for determining whether the sub-beam passes through the solid particle in step S42 and step S43 is that the distance from the center point of the solid particle to the sub-beam vector is less than the radius of the solid particle; if the sub-beam passes through the solid particle, the coordinates of the intersection of the sub-beam and the surface of the solid particle are calculated, and then the direction vector of the sub-beam after reflection is obtained according to the law of reflection of light; the reflection direction vector of the sub-beam after passing through the surface grid is also calculated according to the law of reflection of light.

[0077] Since the method for determining whether a sub-beam passes through a surface grid may result in the presence of multiple surface grids that meet the conditions, in an embodiment of the present invention, the surface grid closest to the laser emission point is selected from the multiple surface grids that meet the conditions as the surface grid that captures the sub-beam.

[0078] It should be noted that the order of judging whether the sub-beam passes through the surface grid or solid particles in steps S42 to S43 can be swapped. It is possible to first judge the solid particles and then check whether there is a surface grid or a grid filled with fluid blocking it, and then judge whether it passes through the fluid. It is also possible to first judge the surface grid and then check whether there is a solid particle blocking it, and then judge whether it passes through the solid particles.

[0079] Step S5: judging the melting state of solid particles and fluid based on the energy and reflection state of the sub-beam, and tracking the surface morphology of the molten pool;

[0080] Specifically, after all beamlets are allocated their energy, the temperature rise and fall of each solid particle and all fluids is calculated to determine whether the solid particle temperature has reached the material's solidus. If so, the solid particle is converted into a fluid, and its melting is determined based on the fluid temperature. A time step is then set, and the Navier-Stokes equations and energy equations are solved to obtain information about the melt pool's state. The volume of fluid (VOF) method is then used to track the melt pool's surface morphology.

[0081] Step S6: Store all computational domain information as the boundary conditions for the next time step, and repeat steps S2 to S5 until the modeling is completed.

[0082] Specifically, if Figure 5As shown in the figure, taking the selective laser melting process as an example, the substrate and powder particles are heated normally. The substrate temperature melts after reaching the solidus, and the new morphology is calculated by the fluid control equation and the VOF equation. Before the powder temperature reaches the solidus, the solid particle morphology is maintained and controlled by the particle motion control equation. After reaching the solidus, it is converted into a fluid with fluid characteristics and is controlled by the NS equation and the energy equation.

[0083] Example 2

[0084] This embodiment is based on the steps of Example 1, and improves and supplements the powder distribution part.

[0085] The difference is that the powder is no longer laid by a scraper but by a nozzle; the powder spraying angle will change with the change of the cladding layer height; the energy allocated to the powder by the sub-beam is related to the height of the powder.

[0086] The application of high-fidelity modeling methods for the laser-material interaction process in additive manufacturing to the simulation of laser cladding process includes:

[0087] like Figure 6 As shown, powder is sprayed from four directions to the focus of the laser on the intended deposition plane. Several characteristic parameters of the powder laid by the nozzle are determined based on the data in the experiment: powder spraying speed, powder spraying amount, powder size, and powder spraying angle.

[0088] The position information of the powder is calculated based on gravity and the drag force of the shielding gas. For example, the relationship between the shielding gas and the powder is a one-way coupling. In particular, the one-way coupling refers to the shielding gas transferring momentum to the powder.

[0089] like Figure 7 As shown in the figure, when allocating energy to the powder, the height of the powder is taken into consideration. The distance from the powder to the laser focal plane is calculated to obtain the spot radius of the laser on the horizontal plane where the powder is located. The ratio of the laser energy density on this plane to the laser energy density on the intended deposition plane is then calculated to obtain the attenuation factor. The original energy of the sub-beam is multiplied by the attenuation factor and the absorptivity before being allocated to the powder.

[0090] By adopting the solution of this embodiment, the influence of powder position on the laser-material interaction is calculated more accurately, which is beneficial for simulating additive manufacturing processes with complex powder distribution and high melt channel height.

[0091] The technical features of the above embodiments may be combined in any manner. To simplify the description, not all possible combinations of the technical features in the above embodiments are described. Only preferred embodiments of the present invention are presented. While the description is relatively specific and detailed, it should not be construed as limiting the scope of the present invention. As long as there are no contradictions in the combination of these technical features, they should be considered to be within the scope of this specification.

[0092] It should be noted that those skilled in the art may make various modifications and improvements without departing from the scope of the present invention, and these modifications and improvements fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be based on the appended claims.

Claims

1. A modeling method for the laser-material interaction process in additive manufacturing, characterized by: The following steps are involved: Step S1: Delimiting a computational domain space based on the additive manufacturing processing range, meshing the domain space, and completing model initialization; Step S2: Obtain the initial laser emission position, laser focus position, and substrate position, and calculate the defocus amount and the laser spot radius on the substrate surface; Step S3: Discretizing the laser beam into a plurality of sub-beams, obtaining the position and direction information of each sub-beam, and allocating initial energy to each sub-beam according to the laser energy distribution type, defocus amount, and distance of the sub-beam relative to the laser focus; Step S4: Indexing and traversing each of the sub-beams, and imparting the energy of the sub-beam to the solid particles and the fluid surface area grid on the beam path according to the real-time position coordinate information; the sub-beam is continuously reflected between the solid particles or the fluid surface area grid or between the two; Step S5: judging the melting state of solid particles and fluid based on the energy and reflection state of the sub-beam, and tracking the surface morphology of the molten pool; Step S6: Store all computational domain information as the boundary conditions for the next time step, and repeat steps S2 to S5 until the modeling is completed.

2. The method for modeling the laser-material interaction process in additive manufacturing according to claim 1, characterized in that: In step S2, the laser spot radius on the substrate surface is R b The calculation method is: ； ； Where, R f represents the focal plane laser spot radius; L Indicates the amount of defocus; L r represents the Rayleigh length; M 2 represents the laser quality factor; λ Indicates the laser wavelength.

3. The modeling method for the laser-material interaction process in additive manufacturing according to claim 1, characterized in that: Step S3 includes the following steps: discretizing the laser beam into a plurality of sub-beams, setting the laser energy distribution type based on the laser type; distributing the energy of all the sub-beams based on the laser energy distribution type, summing the energy, and calibrating based on the following formula; if the energy does not meet the requirements, multiplying the energy of all the sub-beams by the corresponding adjustment factor until the energy meets the requirements: ； Where, represents the total laser energy; n represents the number of sub-beams; Represents the energy of the sub-beam.

4. The method for modeling the laser-material interaction process in additive manufacturing according to claim 1, characterized in that: In step S4, the fluid surface area grid is determined by the grid fluid fraction.

5. The method for modeling the laser-material interaction process in additive manufacturing according to claim 1, characterized in that: Step S4 includes: Step S41: selecting a sub-beam, and traversing all solid particles and surface meshes based on the beam path of the sub-beam to determine the solid particles or surface meshes passed by the sub-beam; Step S42: according to the determination result of step S41, the energy of the sub-beam is transferred to the corresponding solid particles or surface grids according to the absorption rate; Step S43: Calculating the exit point and exit vector of the sub-beam after it encounters a solid particle or a surface grid, and updating the remaining energy of the sub-beam; Step S44: repeating steps S41 to S43, and performing a loop traversal for all sub-beams until all the sub-beams do not pass through any solid particles or surface meshes.

6. The method for modeling the laser-material interaction process in additive manufacturing according to claim 5, characterized in that: In step S41, the solid particles passed by the sub-beam are determined by the following method: all solid particles are traversed, and based on the distance from the coordinate point of the solid particle to the sub-beam, if the distance is less than the radius of the solid particle, the grid between the exit point of the sub-beam and the solid particle is traversed for verification to determine whether there is a surface grid or a grid filled with fluid. If so, the sub-beam has not passed through the solid particle, otherwise it has passed through.

7. The method for modeling the laser-material interaction process in additive manufacturing according to claim 5, characterized in that: In step S41, the surface mesh passed by the sub-beam is determined by the following method: all surface meshes are traversed, and according to the surface mesh coordinates, the surface mesh fluid fraction and the interface normal vector within the mesh, the interface equation is reconstructed using the piecewise linear method PLIC, and then the intersection coordinates of the vector of the sub-beam and the interface equation are calculated. If the intersection coordinates are within the surface mesh and there are no solid particles between the exit point of the sub-beam and the intersection coordinates, then the sub-beam passes through the surface mesh; otherwise, the sub-beam does not pass through the surface mesh.

8. The method for modeling the laser-material interaction process in additive manufacturing according to claim 7, characterized in that: When there are several surface grids that meet the determination condition, the surface grid closest to the laser emission point is selected as the surface grid for capturing the sub-beam.

9. The method for modeling the laser-material interaction process in additive manufacturing according to claim 1, characterized in that: Step S5 includes: Step S51: After energy is imparted to each of the sub-beams in step S4, the temperature rise and fall of each solid particle and all the fluids are calculated to determine whether the temperature of the solid particle reaches the solidus of the material. If so, the solid particle is converted into a fluid, and whether the fluid is melted is determined based on the fluid temperature; Step S52: Set the time step, solve the Navier-Stokes NS equations and the energy equation to obtain the state information of the molten pool, and use the fluid volume VOF method to track the surface morphology of the molten pool.

10. The modeling method for the laser-material interaction process in additive manufacturing according to claim 1, characterized in that: In step S1, when initializing the model, the laser cladding process adopts a powder feeding method, and the solid particle powder is sprayed from four directions to the focus of the laser on the intended deposition plane; the position information of the solid particle powder is calculated according to gravity and the drag force of the shielding gas; in step S4, when the energy of the sub-beam is assigned to the solid particles and the fluid surface area grid on the beam path, the ratio of the laser energy density on the horizontal plane where the solid particle powder is located and the laser energy density on the intended deposition plane is calculated to obtain the attenuation factor, and the original energy of the sub-beam is multiplied by the attenuation factor and the absorption rate before being assigned to the solid particle powder.

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