Electron beam selective melting temperature field process parameter optimization method based on fluent

Abstract

The invention provides an electron beam selective melting temperature field process parameter optimization method based on Fluent, and belongs to the field of additive manufacturing, and the method comprises the following steps: establishing a three-dimensional geometric model of a metal part sample block, carrying out grid division on a geometric file of the three-dimensional model, calculating to obtain physical property parameters of a printing material alloy, importing the grid file and the physical property parameters into the Fluent, and obtaining a printing material alloy. According to a transient heat conduction model of Fluent, a self-defined UDF program is carried out at the same time to simulate dynamically changing heat source power and spatial distribution thereof, dynamically update the position and power distribution of a heat source, simulate the heating process of an actual electron beam, and output analogue simulation data. And analyzing analogue simulation data to obtain temperature distribution conditions of different areas, and optimizing simulation parameters of the next step. The complex temperature field in the electron beam selective melting process can be accurately simulated, and the defects in the aspects of accurately controlling the dynamic state of a heat source and optimizing process parameters are overcome through combination of self-adaptive grid technology generation and dynamic heat source modeling.

Description

Technical Field

[0001] The present invention belongs to the field of additive manufacturing, and particularly relates to a method for optimizing process parameters of the temperature field of electron beam selective melting based on fluent. Background Art

[0002] Electron beam selective melting, also known as electron beam 3D printing technology (SEBM), as an efficient metal forming technology, plays a crucial role in the fields of aerospace, energy, medical, etc. Compared with traditional subtractive manufacturing processes, SEBM can quickly and efficiently manufacture metal parts with complex structures, especially showing significant advantages in the manufacturing of large titanium alloy components. However, due to the complex thermophysical phenomena and microstructural evolution involved in the electron beam additive manufacturing process, it is easily affected by problems such as heat accumulation and residual stress during the forming process, which can lead to deformation, cracks or internal defects of the parts. Therefore, it is crucial to reasonably optimize and control the electron beam additive manufacturing process.

[0003] The change of the temperature field has a crucial impact on the quality, performance and process stability of the parts. It not only directly affects the density, microstructure and mechanical properties of the parts, but also plays an important role in aspects such as residual stress and thermal deformation. In electron beam additive manufacturing, the melting and cooling rates of materials are affected by parameters such as electron beam power, scanning speed, and powder supply. The distribution of the temperature field directly determines the shape of the molten pool and the grain growth during the solidification process, which has an important impact on the microstructure of the final part. At the same time, in electron beam additive manufacturing, the materials are continuously heated and cooled during the layer-by-layer stacking process, resulting in rapid changes in the temperature field. This change will cause the materials in different regions to expand and contract, thereby causing the generation of residual stress. It not only affects the microstructure and mechanical properties of the materials, but also has an impact on the residual stress, shape accuracy and final quality of the parts. By reasonable process parameter adjustment, temperature control and thermal management measures, the problems caused by the temperature field can be effectively reduced, and the reliability and performance of the additive manufacturing parts can be improved.

[0004] In the prior art, Flow3D is usually used for numerical simulation of the temperature field of electron beam additive manufacturing to achieve the purpose of optimizing process parameters. However, this method has the problem of insufficient accuracy when simulating the changes of complex temperature fields, which affects the subsequent optimization effect of the printing process parameters and limits the application of electron beam selective melting technology in the field of high-precision metal additive manufacturing. Summary of the Invention

[0005] In order to solve the problem of insufficient accuracy in the temperature field simulation during the electron beam additive manufacturing process, the present invention provides a method for optimizing process parameters of the temperature field of electron beam selective melting based on fluent.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An optimization method for process parameters of the temperature field of electron beam selective melting based on Fluent, comprising the following steps:

[0008] Construct a three-dimensional geometric model of the metal component sample block and output the corresponding geometric file;

[0009] Perform mesh division on the geometric file to obtain a mesh file; obtain the composition of the printing material alloy and calculate the material physical property parameters;

[0010] Import the mesh file and the material physical property parameters into Fluent, import the UDF program for simulating the dynamic heat source into Fluent for simulation, dynamically update the position and power distribution of the heat source, simulate the heating process of the actual electron beam, and output the simulation data;

[0011] Analyze the simulation data to obtain the temperature distribution in different regions, and optimize the next-step simulation process parameters according to the temperature distribution.

[0012] Preferably, for the UDF program for simulating the dynamic heat source, a Gaussian heat source UDF program is written according to the heat source power distribution, and the Gaussian distribution formula is used, specifically:

[0013]

[0014] where Q is the total heat source power, x 0 、y 0 is the heat source center position, and r 0 is the heat source characteristic radius.

[0015] Preferably, during the processing of the UDF program, the heat source scans along the x-axis direction, and the position and power of the heat source are dynamically updated according to the time step to simulate the actual electron beam melting process and the change of the temperature field.

[0016] Preferably, the mesh division of the geometric file specifically includes the following steps:

[0017] Import the geometric file into the mesh division software, perform structured division on the heat source surface, the heat source surface includes the solid, fluid interface and the heat source area, use hexahedron meshes to adapt to the solid domain, and encrypt the meshes in the heat source action area and the solid-fluid interface area;

[0018] Based on the change of the temperature field, dynamically adjust the mesh density through the adaptive mesh generation technology. The adaptive generation technology sets the heat source temperature gradient threshold, identifies the areas that need to be encrypted, and dynamically adjusts the mesh according to the change of the temperature field after each time step;

[0019] After the mesh generation is completed, a mesh file is obtained.

[0020] Preferably, during the Fluent simulation process, it also includes setting boundary conditions and time step sizes. Specifically, setting the boundary conditions is to select the heat source surface. The heat source surface is used as the heat flux boundary condition, and the UDF program is applied to the heat source surface to transfer heat source data. The time step size is set according to the movement and power change of the heat source.

[0021] Preferably, obtaining the composition of the printed material alloy and calculating the material physical property parameters specifically means inputting the composition of the material alloy into the material performance simulation software Jmatpro for calculation to obtain the material physical property parameters. Among them, the material physical property parameters specifically include viscosity, thermal conductivity, density, heat of fusion, solidus temperature, and liquidus temperature.

[0022] Preferably, the output simulation data specifically includes temperature field data, heat source movement trajectory, and power change data.

[0023] Preferably, analyzing the simulation data to obtain the temperature distribution in different regions specifically means importing the simulation data into the visualization software Tecplot to analyze and calculate the temperature distribution in different regions, display the contour map of the temperature field and the temperature change curve, and optimize the next simulation parameters according to the contour map of the temperature field and the temperature change curve.

[0024] A method for optimizing process parameters of the electron beam selective melting temperature field based on Fluent provided by the present invention has the following beneficial effects:

[0025] The present invention performs mesh generation on the geometric file of the three-dimensional geometric model of the metal component sample block, dynamically adjusts the mesh density around the heat source, and improves the calculation accuracy of the key area. By setting the UDF program that simulates the dynamic heat source and importing it into Fluent, the position and power distribution of the heat source are dynamically updated, the heating process of the actual electron beam is simulated, the real-time dynamic control of the heat source power, shape, and trajectory is realized, the temperature field evolution in the actual process is accurately simulated, which is beneficial to optimizing the finished product quality. Through the analysis of the simulation data, the temperature distribution in different regions is obtained, the heat transfer effect is evaluated, and the parameter settings are feedback-adjusted to optimize the printing process. Reduce the problems caused by the temperature field change and improve the reliability of the additive manufacturing parts. Description of the Drawings

[0026] In order to more clearly illustrate the embodiments of the present invention and its design scheme, the accompanying drawings required for this embodiment will be briefly introduced below. The accompanying drawings in the following description are only partial embodiments of the present invention. For those of ordinary skill in the art, other accompanying drawings can be obtained based on these drawings without creative efforts.

[0027] Figure 1 It is the technical roadmap of an optimization method for process parameters of the temperature field in electron beam selective melting based on Fluent according to an embodiment of the present invention.

[0028] Figure 2 It is the contour map of the surface temperature field distribution of the sample block during the printing process of H13 steel according to an embodiment of the present invention.

[0029] Figure 3 It is the contour map of the sectional temperature field distribution of the sample block during the printing process of H13 steel according to an embodiment of the present invention.

[0030] Figure 4 It is the temperature change curve corresponding to a certain time point during the printing process of H13 steel according to an embodiment of the present invention.

[0031] Figure 5 It shows the distribution of the temperature of H13 steel printing along the y-axis on the surface according to an embodiment of the present invention. Detailed implementation manners

[0032] In order to enable those skilled in the art to better understand the technical solution of the present invention and be able to implement it, the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the protection scope of the present invention.

[0033] Embodiment

[0034] The present invention provides an optimization method for process parameters of the temperature field in electron beam selective melting based on Fluent, for the additive manufacturing simulation of H13 steel (the substrate is 45 steel), as Figure 1 shown, specifically including the following steps:

[0035] Step 1: Three-dimensional model establishment. According to the printing size (100mm x 100mm x 10mm) of the target sample block, three-dimensional geometric modeling is carried out using software such as UG and Rhino. The model includes two parts: the substrate (45 steel) and the printing material (H13 steel). During the modeling process, the substrate area is set as 45 steel material, and the sample block area is set as H13 steel. After the modeling is completed, the geometric model is exported as a geometric file in a format recognizable by Ansys (such as STEP or IGES). The geometric file serves as the basis for mesh generation and simulation calculation.

[0036] Step 2: Mesh generation. The exported geometric file is imported into ANSYS Meshing software for mesh generation.

[0037] The hexahedral mesh structure is used, and the mesh is refined in the heat source action area and the solid-fluid interface area. The specific steps are as follows:

[0038] (1) Select the mesh generation method: According to the characteristics of the computational domain, use the structured mesh generation method to meet the computational requirements of the solid region.

[0039] (2) Mesh refinement: Refine the mesh in the heat source region and its surrounding areas to improve the computational accuracy and ensure that the temperature gradient of the heat source can be accurately captured.

[0040] (3) Export the mesh file: After completing the mesh generation, check the mesh quality to ensure there are no severe mesh distortions, and finally export the mesh file to prepare for importing into Fluent for simulation.

[0041] Adaptive mesh generation. Enable the adaptive mesh function. By real-time monitoring the changes in the temperature field, dynamically adjust the mesh density. In the moving path and action area of the heat source, the mesh will be automatically refined to ensure sufficient mesh resolution in the high-temperature regions and areas with large temperature gradients. This helps improve the computational accuracy and optimize the use of computational resources. It specifically includes the following steps:

[0042] Set the temperature field change threshold: Determine which regions need to be meshed more densely by setting the change threshold of the temperature field;

[0043] Dynamic mesh adjustment: Automatically update the mesh density after each time step according to the changes in the temperature field, especially in the areas around the heat source;

[0044] Mesh accuracy control: In the scanning path of the heat source and the action area of the heat source, make the temperature distribution of the heat source more accurate by refining the mesh.

[0045] Step 3: Import into Fluent for settings. Import the meshed file into the Fluent software and perform the settings before simulation in the Fluent software. Specifically, the following settings are included:

[0046] Set the model type: Select the transient model as the movement process of the electron beam changes with time; Set the gravity model: Turn on the gravity model to consider the possible influence of gravity on the temperature field and the shape of the molten pool; Activate the energy equation: Activate the energy equation for heat transfer calculation; Enable the melting and solidification model: Select the melting and solidification model in the simulation and set appropriate parameters (such as the mush coefficient of the mushy zone is 100000); Select the turbulence model: Since the flow during the printing process can generally be assumed to be laminar, select the laminar model for simulation.

[0047] Step 4: Set the materials and calculate the physical property parameters of the set materials. Use the Jmatpro software to input the chemical compositions of H13 steel and 45 steel and calculate the physical property parameters of the materials. The calculated physical property parameters include: viscosity, thermal conductivity, density, heat of fusion, solidus temperature, and liquidus temperature.

[0048] Save the calculated results in a CSV file for easy import into the Fluent software.

[0049] Step 5: Write the Gaussian heat source UDF program.

[0050] According to the heat source power distribution, use Visual Studio to write the Gaussian heat source UDF program to simulate the power distribution of the electron beam. The written UDF program will dynamically update the heat source position and power according to the time step. The specific Gaussian distribution formula is:

[0051]

[0052] where Q is the total power of the heat source, x 0 , y 0 is the center position of the heat source, and r 0 is the characteristic radius of the heat source.

[0053] Use Visual Studio to write the UDF program. Assume the heat source power is 1000W and the heat source scans along the x-axis. The program dynamically updates the position and power of the heat source according to the time step to simulate the actual electron beam melting process. By controlling the dynamic behavior of the heat source, the heating effect of the electron beam can be accurately reproduced in the simulation, thus effectively simulating the change of the temperature field.

[0054] Step 6: Set the boundary conditions. In the Boundary Conditions settings of Fluent, name the heat source area "source" and set this area as the heat flux boundary condition. Then, apply the written UDF program to this area to ensure that the power of the heat source can be transferred to Fluent for calculation. The specific settings are as follows: Set the heat source as the heat flux boundary condition: In the boundary condition settings, select the heat source surface "source" and set it as the heat flux boundary condition. Apply the UDF program: Apply the heat source UDF program to the heat source area to ensure that the power distribution of the heat source can be transferred to Fluent.

[0055] Step 7: Set the time step of the simulation calculation. According to the moving speed (3m / s) and scanning path of the heat source, reasonably set the simulation time step. The time step should be small enough to ensure that the change of the temperature field can be reflected in time and the calculation stability can be maintained. During the simulation process, dynamically adjust the time step according to the movement and power change of the heat source. Initial setting of the time step: According to the moving speed of the heat source and the mesh division situation, select a suitable initial time step. During the simulation process, judge whether it is necessary to adjust the time step by monitoring the change of the temperature field and the heat source power.

[0056] Step 8: Import and Compile the UDF Program to Simulate the Dynamic Heat Source. Import the written UDF program into Fluent for compilation and check whether the program is successfully compiled. Ensure that the position and power distribution of the heat source can be dynamically updated in Fluent to simulate the heating process of the actual electron beam. In Fluent, select Define>User-Defined>Funct ions>Compi le, import and compile the program. Ensure that the UDF program runs correctly, and the dynamic changes of the heat source should be correctly reflected in the simulation.

[0057] Step 9: Simulation Calculation and Result Export. Start the simulation calculation and ensure the stability of the simulation process. Calculate until the simulation results converge. After the calculation is completed, export the simulation results, including temperature field data, heat source movement trajectory, and power change data.

[0058] Step 10: Data Visualization and Result Analysis. Use data visualization software such as Tecplot to analyze the simulation results. By observing the temperature field changes during the simulation process, obtain the contour map of the temperature distribution and the temperature change curve graph. As Figure 2 and Figure 3 shown, they are respectively the contour map of the surface temperature field distribution and the sectional temperature field distribution of the sample block during the H13 steel printing process. Through the temperature change curve and the temperature contour map, the temperature changes during the printing process can be clarified. As Figure 4 shown, it is the temperature change curve corresponding to a certain moment during the H13 steel printing process. Determine the parameter settings in the case of ultra-high temperature. Excessive temperature changes will cause the printed sample block to over-melt or not melt. In the traditional method of controlling temperature, it can only be determined by the operator during the printing process, while through simulation, the temperature changes under different parameters can be accurately controlled to guide the simulation. Analyze the temperature distribution in different regions, evaluate the heat transfer effect between H13 steel and 45 steel, and optimize the printing process.

[0059] Step 11: According to the temperature field changes during the simulation process, adjust the process parameters, including adjusting the rotation speed, arc energy, and rod feeding rate. Feed the parameter adjustment back into the UDF program for the next simulation parameter optimization.

[0060] The present invention has the following remarkable advantages: (1) The present invention uses a custom heat source method based on UDF, replacing the traditional surface heat source method. It precisely defines the power, shape, and movement trajectory of the heat source, realizes real-time dynamic control of the heat source distribution, can more comprehensively simulate the evolution law of the temperature field in the actual processing process, and significantly improves the flexibility and accuracy of the simulation. It is applicable not only to the case where the substrate and the printing material are the same, but also to heat transfer between substrates and printing samples of different materials. (2) Traditional process parameters (such as temperature, energy density, laser power, scanning speed, etc.) often rely on the derivation of empirical formulas or empirical data, but the selection of these parameters often fails to fully consider the complex interactions of factors such as material properties, heat conduction, and thermal stress. Therefore, this experience-based formulation method is likely to lead to inaccurate parameter settings under different process conditions, thus affecting the quality and performance of the final product. The present invention makes the formulation of process parameters more scientific and accurate by means of computer simulation technology and numerical analysis methods, fully considering the interactions between materials. (3) The present invention can accurately simulate the heat transfer between the printing material and the substrate material by implementing multi-material heat conduction simulation in Fluent. In addition, the present invention introduces an adaptive mesh generation technology, which can dynamically adjust the mesh density around the heat source, improve the calculation accuracy of key areas, and ensure more accurate temperature field simulation. Through the dynamic modeling of the Gaussian heat source UDF program, the change of the heat source during the electron beam scanning process is simulated, further enhancing the authenticity of the simulation results. It is applicable not only to the case where the substrate and the printing material are the same, but also to heat transfer between substrates and printing samples of different materials, and can help researchers deeply analyze key process parameters such as the molten pool morphology, thermal stress, and cooling rate.

[0061] Those skilled in the art should understand that the embodiments of the present invention may provide a method, a system, or a computer program product. Therefore, the present invention may be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the processFigure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0063] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0064] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0065] It should be pointed out that the specific implementation methods described above can enable those skilled in the art to understand the invention more comprehensively, but do not limit the invention in any way. Therefore, although the invention has been described in detail in this specification and embodiments, those skilled in the art should understand that the invention can still be modified or replaced by equivalents; and all technical solutions and improvements that do not deviate from the spirit and scope of the invention are included in the protection scope of the patent for the invention. Any figure mark in the claims should not be regarded as limiting the claims involved. Any simple change or equivalent replacement of the technical solution that can be obviously obtained by any technician familiar with the field within the technical scope disclosed in the present invention belongs to the protection scope of the present invention.

Claims

1. A method for optimizing process parameters of temperature field of electron beam selective melting based on fluent, characterized in that: The following steps are involved: Construct a 3D geometric model of the metal component sample and output the corresponding geometric file; Meshing the geometry file to obtain a mesh file; Obtain the composition of the printing material alloy and calculate the material properties; Importing the grid file and material property parameters into Fluent, importing the UDF program for simulating the dynamics of the heat source into Fluent for simulation, dynamically updating the position and power distribution of the heat source, simulating the actual heating process of the electron beam, and outputting simulation data; The simulation data are analyzed to obtain the temperature distribution of different regions, and the simulation process parameters for the next step are optimized according to the temperature distribution.

2. According to the method for optimizing the temperature field process parameters of electron beam selective melting based on fluent in claim 1, it is characterized in that: The UDF program for simulating the dynamics of the heat source is written according to the power distribution of the heat source, and the Gaussian heat source UDF program is used, which is specifically: Among them, Q is the total power of the heat source, x0 and y0 are the center positions of the heat source, and r0 is the characteristic radius of the heat source.

3. The method for optimizing process parameters of temperature field of electron beam selective melting based on fluent according to claim 2 is characterized in that: During the UDF program processing, the heat source is scanned along the x-axis direction, and the position and power of the heat source are dynamically updated according to the time step to simulate the actual electron beam melting process and the change of the temperature field.

4. The method for optimizing the temperature field process parameters of electron beam selective melting based on FLUENT according to claim 1 is characterized in that: Meshing the geometry file includes the following steps: Import the geometry file into the meshing software, perform structured division on the heat source surface, which includes the solid, the fluid interface and the heat source area, use a hexahedral mesh to adapt to the solid domain, and encrypt the mesh of the heat source action area and the solid and fluid interface area; Based on the change of temperature field, the density of the grid is dynamically adjusted through adaptive grid generation technology. The adaptive generation technology sets the temperature gradient threshold of the heat source, identifies the area that needs to be encrypted, and dynamically adjusts the grid according to the change of temperature field after each time step; After completing the mesh division, the mesh file is obtained.

5. The method for optimizing process parameters of temperature field of electron beam selective melting based on FLUENT according to claim 4 is characterized in that: The Fluent simulation process also includes setting boundary conditions and time steps. The boundary conditions are specifically selected as the heat source surface, the heat source surface is used as the heat flux boundary condition, the UDF program is applied to the heat source surface, and the heat source data is transferred; the time step is set according to the movement of the heat source and the power change.

6. The method for optimizing process parameters of temperature field of electron beam selective melting based on fluent according to claim 1, characterized in that: The obtaining of the composition of the printing material alloy and the calculation of the material physical property parameters specifically involves inputting the composition of the material alloy into the material performance simulation software Jmatpro for calculation to obtain the material physical property parameters; wherein the material physical property parameters specifically include viscosity, thermal conductivity, density, heat of fusion, solidus temperature and liquidus temperature.

7. The method for optimizing process parameters of temperature field of electron beam selective melting based on FLUENT according to claim 1, characterized in that: The output simulation data specifically includes temperature field data, heat source movement trajectory and power change data.

8. The method for optimizing process parameters of temperature field of electron beam selective melting based on FLUENT according to claim 1, characterized in that: The simulation data are analyzed to obtain the temperature distribution of different areas. Specifically, the simulation data are imported into the visualization software Tecplot to analyze and calculate the temperature distribution of different areas, display the cloud map of the temperature field and the temperature change curve map, and optimize the simulation parameters for the next step according to the cloud map of the temperature field and the temperature change curve map.

Images