An optimization method for plasma rotating electrode atomization process

Abstract

The invention discloses an optimization method for a plasma rotating electrode atomization process, and relates to the technical field of powder metallurgy. The invention establishes a two-dimensional fluid slice model of a plasma rotating electrode, divides the two-dimensional fluid slice model into a plurality of unstructured quadrilateral grids, simulates the volume change of a metal bar during plasma rotating electrode atomization in the two-dimensional grid, and determines the liquid film thickness of the metal bar and the melt velocity at the edge of the bar under the two-dimensional model; simultaneously, a three-dimensional fluid model is established, and the liquid film thickness and the melt velocity at the edge of the bar under the two-dimensional model are introduced into the three-dimensional fluid model for simulation, and the flow velocity distribution and the fragmentation distribution of the metal bar during plasma rotating electrode atomization under the three-dimensional model are determined, and the liquid fragmentation under the high rotation speed of the plasma rotating electrode is accurately observed according to the flow velocity distribution and the fragmentation distribution, and compared with the material analysis or empirical analysis adopted by conventional means, the optimal process parameters can be obtained quickly and accurately.

Description

Technical Field

[0001] The invention relates to the technical field of powder metallurgy, and in particular to an optimization method for a plasma rotating electrode atomization process. Background Art

[0002] In the plasma rotating electrode atomization powder making process, different atomization modes correspond to different critical conditions for droplet formation, and further determine the powder particle morphology and size distribution; in the droplet breakup mode, the adhesion liquid between the primary particles and the liquid film will form secondary particles with a much smaller diameter, and the particle size distribution presents a typical bimodal shape; in the liquid film breakup mode, the distribution and movement of the film liquid are unstable, and a large number of irregular particles or flaky powders are formed after breakage; only when it is stable in the liquid phase breakup mode can metal powders with high sphericity and fine particle size be obtained.

[0003] In plasma rotating electrode atomization, a large amount of molten metal liquid flows radially outward under the action of centrifugal force, forming a liquid line at the edge, and further evenly breaks into a string of droplets, and finally cools and spheroidizes into powder; the rotation speed of the plasma rotating electrode atomization rod is usually 20000~50000RPM, the molten metal liquid breaks before forming a liquid line, and a new molten pool is continuously formed on the end face of the rod, and the depth and curvature of the molten pool will change with the rod material, melting current and feed speed, etc. The molten pool morphology directly affects the thickness and distribution of the molten liquid film, and then affects the formation of the liquid line breakage pattern; therefore, in view of the high rotation speed and complex molten pool morphology of the plasma rotating electrode atomization rod, and because the breakage process is difficult to observe at high speed, the current observation of liquid breakage under high rotation speed of the plasma rotating electrode often relies on material analysis or experience, and it is difficult to obtain the best process parameters in the whole process. Summary of the invention

[0004] The embodiment of the present invention provides an optimization method for a plasma rotating electrode atomization process, which can solve the problem in the prior art that the observation of liquid breakage under high rotation speed of a plasma rotating electrode often depends on material analysis or experience, and it is difficult to obtain the best process parameters in the whole process.

[0005] The embodiment of the present invention provides a method for optimizing a plasma rotating electrode atomization process, comprising the following steps:

[0006] Obtaining material property data of molten metal materials;

[0007] Establishing a two-dimensional fluid slice model of a plasma rotating electrode; dividing the two-dimensional fluid slice model into a plurality of quadrilateral grids;

[0008] According to the material property data including density, viscosity and surface tension, as well as the metal bar rotation speed moving wall and fluid velocity in the plasma rotating electrode, the volume change of the metal bar in each quadrilateral grid when it is atomized by the plasma rotating electrode is simulated; when the change is stable, the liquid film thickness of the metal bar and the melt velocity at the edge of the bar are obtained;

[0009] The method of obtaining the liquid film thickness of the metal bar and the melt speed at the edge of the bar comprises the following steps:

[0010] Use the CFD modeling method SpaceClaim to draw the geometric shape and physical characteristics of the plasma rotating electrode, establish a two-dimensional fluid slice model of the plasma rotating electrode based on the geometric shape and physical characteristics, and set the inlet and outlet fluid velocities, turntable rotation speed and wall contact angle characteristics as boundary conditions of the two-dimensional fluid slice model;

[0011] Import the 2D fluid slice model into WorkBench Ansys Mesh and create corresponding boundary conditions; divide the 2D fluid slice model into multiple discrete unit unstructured quadrilateral meshes;

[0012] According to the characteristics and requirements of the plasma rotating electrode, the VOF model is selected, the VOF model parameters are set, the implicit volume force method is used, the fluid is set to two-phase flow, the main phase is set to argon, and the second phase is set to molten metal;

[0013] In the VOF model, the metal bar rotation speed moving wall and fluid velocity are set as boundary conditions. At the same time, according to the density, viscosity and surface tension in the metal material physical property data, the volume change of the metal bar in each unstructured quadrilateral grid when it is atomized by the plasma rotating electrode is simulated. When the change is stable, it is analyzed in the Tecplot software to obtain the liquid film thickness of the metal bar and the melt velocity at the edge of the bar.

[0014] A three-dimensional circular model of a plasma rotating electrode is established, and the three-dimensional circular model is divided into ten equal parts, and one of the parts is determined as a three-dimensional fluid model of the plasma rotating electrode; wherein two side surfaces of the three-dimensional fluid model are set as periodic boundaries, and the melt velocity of the metal bar edge is determined within the periodic boundary conditions;

[0015] According to the rotating wall surface and the thickness of the liquid film of the metal bar, the fluid velocity distribution and the fragmentation distribution of the metal bar on the three-dimensional fluid model when the metal bar is atomized by the plasma rotating electrode are simulated; and according to the fluid velocity distribution and the fragmentation distribution, the particle size distribution cloud map of the metal bar is obtained;

[0016] According to the particle size distribution cloud map of the metal bar material, the particle amount of the metal bar material after atomization and the area of ​​each particle are obtained; according to the particle amount and the area of ​​each particle, the process parameters of the plasma rotating electrode atomization process are adjusted to optimize the plasma rotating electrode atomization process.

[0017] Preferably, the obtaining of material property data of the molten metal material comprises:

[0018] Select the database in JMatpro software and input the alloy composition of the metal material;

[0019] Selecting a temperature range of the molten metal material to obtain a temperature viscosity change curve, a temperature density change curve, and a surface tension change curve of the molten metal material;

[0020] The material property data of the molten metal material is obtained according to the temperature viscosity change curve, the temperature density change curve and the surface tension change curve of the molten metal material.

[0021] Preferably, the step of obtaining a particle size distribution cloud diagram of the metal bar material comprises:

[0022] According to the geometric shape and physical characteristics of the plasma rotating electrode, SpaceClaim was used to model a 1 / 10 circle and determine it as a three-dimensional fluid model of the plasma rotating electrode;

[0023] The two sides of the three-dimensional fluid model are set as periodic boundaries, and the melt velocity at the edge of the metal bar is determined within the periodic boundary conditions. At the same time, the fluid flow velocity distribution and fragmentation distribution of the metal bar on the three-dimensional fluid model when the metal bar is atomized by the plasma rotating electrode are simulated in the VOF model according to the moving wall and liquid film thickness of the metal bar rotation speed. When the changes are stable, the Tecplot processing tool is used to analyze the fluid flow velocity distribution and fragmentation distribution, and the particle size distribution cloud map of the metal bar is determined according to the fluid flow velocity distribution and fragmentation distribution.

[0024] Preferably, obtaining the amount of particles of the metal bar material after atomization and the area of ​​each particle includes:

[0025] Import the particle size distribution cloud map of the metal bar into ImageJ software and transform the cloud map into a high-contrast image with obvious marked particles;

[0026] Set the scale of the recognition image, use image processing to mark the particle size, and use the Analyze Particle function to count the particle area.

[0027] Preferably, the process parameters of adjusting the plasma rotating electrode atomization process include:

[0028] The speed and feed rate of the plasma rotating electrode are adjusted according to the particle quantity and the area of ​​each particle, and the simulation is performed again to obtain the changed liquid film thickness and particle size, and the influence of different process parameters on the particle size distribution is obtained;

[0029] According to the influence of different process parameters on particle size distribution, if the average particle size decreases with the increase of rotation speed and the decrease of melting rate, it is considered that the optimized process parameter adjustment can promote the reduction of particle diameter and make the particle size reach the target diameter; if the average particle size does not decrease significantly with the increase of rotation speed and the decrease of melting rate, it is considered that the optimized process parameter adjustment can promote the change of particle diameter and cannot obtain the target size; through multiple orthogonal experimental comparisons, the optimal process parameters for plasma rotating electrode process optimization are obtained.

[0030] The embodiment of the present invention provides a method for optimizing a plasma rotating electrode atomization process. Compared with the prior art, the method has the following beneficial effects:

[0031] The present invention establishes a two-dimensional fluid slice model of a plasma rotating electrode, divides the two-dimensional fluid slice model into a plurality of unstructured quadrilateral grids, simulates the volume change of a metal bar when the plasma rotating electrode is atomized in the two-dimensional grid, and determines the liquid film thickness of the metal bar and the melt speed at the edge of the bar under the two-dimensional model; simultaneously, a three-dimensional fluid model is established, and the liquid film thickness and the melt speed at the edge of the bar under the two-dimensional model are introduced into the three-dimensional fluid model for simulation, and the flow velocity distribution and the fragmentation distribution of the metal bar when the plasma rotating electrode is atomized in the three-dimensional model are determined, and the liquid fragmentation under the high rotation speed of the plasma rotating electrode is accurately observed according to the flow velocity distribution and the fragmentation distribution; the present invention can accurately simulate the liquid fragmentation under the high rotation speed of the plasma rotating electrode through two-dimensional simulation combined with three-dimensional simulation, and is easy to observe. Compared with the material analysis or empirical analysis adopted by conventional means, the present invention can quickly and accurately obtain the optimal process parameters in the process of plasma rotating electrode atomization, so that the plasma rotating electrode atomization is more widely used. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A schematic diagram of the overall process of an optimization method for a plasma rotating electrode atomization process provided by an embodiment of the present invention;

[0033] Figure 2 A schematic diagram of a curve of thermophysical properties of H13 steel calculated using JMatPro for an optimization method of a plasma rotating electrode atomization process provided by an embodiment of the present invention;

[0034] Figure 3 A schematic diagram of a two-dimensional fluid domain CAD modeling of an optimization method for a plasma rotating electrode atomization process provided by an embodiment of the present invention;

[0035] Figure 4 A schematic diagram of a two-dimensional fluid domain mesh of an optimization method for a plasma rotating electrode atomization process provided by an embodiment of the present invention;

[0036] Figure 5 A two-dimensional simulation output H13 liquid phase volume fraction result cloud diagram of an optimization method for a plasma rotating electrode atomization process provided in an embodiment of the present invention;

[0037] Figure 6 A schematic diagram of a simplified three-dimensional simulation model of an optimization method for a plasma rotating electrode atomization process provided by an embodiment of the present invention;

[0038] Figure 7 A cloud diagram of the volume fraction of liquid phase of particle crushing in a three-dimensional simulation result of an optimization method of a plasma rotating electrode atomization process provided by an embodiment of the present invention;

[0039] Figure 8 A schematic diagram of particle analysis extracted by ImageJ software of an optimization method of a plasma rotating electrode atomization process provided by an embodiment of the present invention;

[0040] Fig. 9 A schematic diagram of the final particle size distribution and accumulation of H13 steel at a feed rate of 7 mm / s at a rotation speed of 10,000 according to an optimization method of a plasma rotating electrode atomization process provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0042] See also Figure 1 The embodiment of the present invention provides a method for optimizing a plasma rotating electrode atomization process, comprising the following steps:

[0043] Step S1: Use JMatpro software to calculate the material properties of molten metal at high temperature and output the required parameters such as viscosity and surface tension. Specifically:

[0044] Step S11: Input alloy composition; select the required database in JMatpro software and input the alloy composition of the metal material.

[0045] Step S12: setting environmental conditions; selecting a calculation temperature range, and calculating a physical property parameter variation curve.

[0046] Step S13: Output the result; electrode target temperature, obtain physical parameters such as viscosity and surface tension on the curve, and convert the units.

[0047] Step S2: Extract the material thermal property data from step S1, optimize the data and input it into 2D Fluent for calculation. Specifically:

[0048] Step S21: perform fluid slice modeling and define inlets, outlets and boundaries.

[0049] Step S22: Meshing: Meshing is performed in WorkBench to divide the established fluid slice model into a number of discrete unit unstructured quadrilateral meshes, and the generated meshes are exported.

[0050] Step S23: Material property setting: Use the rotational axis symmetric model to set material properties, including density, viscosity and surface tension, and input the physical property parameters obtained in step S13.

[0051] Step S24: Model setting: open the VOF model, set argon as the main phase, set the metal melt as the secondary phase, and use the SST-K omega turbulence model.

[0052] Step S25: setting boundary conditions; setting the bar speed to move the wall, and setting the fluid speed.

[0053] Step S26: Run the simulation; set the time step within the limit, start the simulation, stop the simulation after the operation is stable, and check the results.

[0054] Step S27: Output the results; analyze them in Tecplot software to obtain the liquid film thickness and the melt speed at the edge of the bar.

[0055] Step S3: Input the two-dimensional results into the three-dimensional Fluent for crushing and particle size calculation. Specifically:

[0056] Step S31: Establish a model; use SpaceClaim to model a 1 / 10 circle and input Flunet Meshing to perform meshing.

[0057] Step S32: Set boundary conditions; open periodic boundaries, set material properties, and input physical property parameters obtained in step S13.

[0058] Step S33: Set the bar speed to move the wall, and input the two-dimensional bar edge speed result into the three-dimensional boundary condition.

[0059] Step S34: Run the simulation; first set the time step, start the simulation, and after the operation is stable, stop the simulation and check the results.

[0060] Step S35: output the results; first use the Tecplot post-processing tool to analyze the simulation results; then use VOF to analyze the fluid velocity distribution and fragmentation conditions based on the simulation results, determine the uniformity and effect of the particle size distribution, and then output the analysis results.

[0061] Step S4: Import the Fluent calculation results into ImageJ software for particle size statistical analysis. Specifically:

[0062] Step S41: Save the marked liquid phase cloud image and import it into ImageJ software.

[0063] Step S42: Image processing: Processing the image into a high-contrast image suitable for marking particles.

[0064] Step S43: Particle size statistics: set the scale, use image processing to mark the particle size, and use the AnalyzeParticle function to count the particle area, export the results, calculate the particle diameter in Excel, and draw a graph for analysis.

[0065] Step S5: extract the simulation cloud map and data of the simulation output result of step 4, optimize the data and simulate again. Specifically:

[0066] Step S51: adjusting key process parameters such as the rotation speed and feed rate of the plasma rotating electrode.

[0067] Step S52: After adjusting the key process parameters such as the rotation speed and feed rate of the plasma rotating electrode, an orthogonal experimental comparison is performed.

[0068] Step S53: According to the orthogonal experiment comparison results of adjusting key process parameters such as the rotation speed and feed rate of the plasma rotating electrode, the simulation accuracy is verified to obtain the optimal process parameters.

[0069] Wherein, step S21 is specifically as follows:

[0070] Using the CFD modeling method SpaceClaim, the geometry and physics of the plasma rotating electrode are mapped.

[0071] Set the 2D simulation boundary conditions, including the inlet and outlet fluid velocities, the rotating disk rotation speed, and the wall contact angle characteristics.

[0072] Wherein, step S22 is specifically as follows:

[0073] Import the geometry model in WorkBench Ansys Mesh and create boundary conditions as needed.

[0074] Meshing is performed to divide the fluid domain geometric model into discrete small units for numerical calculation.

[0075] Export the generated mesh for subsequent simulations.

[0076] Wherein, step S23 is specifically as follows:

[0077] Open the Fluent material database and copy the argon material parameters.

[0078] Define new molten metal material to input JMatPro calculation results.

[0079] Wherein, step S24 is specifically as follows:

[0080] According to the characteristics and requirements of the plasma rotating electrode, the corresponding simulation model type is selected, including the VOF model and the turbulence model. The turbulence model is the SST-K omega model.

[0081] Set the VOF parameters, use the implicit volume force method, set the fluid to two-phase flow, set the main phase to argon, and use molten metal as the second phase.

[0082] Adjust model parameters.

[0083] Wherein, step S31 is specifically as follows:

[0084] 1 / 10 circle modeling using SpaceClaim.

[0085] Enter Flunet Meshing to perform meshing.

[0086] Wherein, step S32 is specifically as follows:

[0087] Turn on periodic boundaries and set the two side surfaces as periodic boundaries.

[0088] Open the Fluent material database and copy the argon material parameters.

[0089] Define new molten metal material to input JMatPro calculation results.

[0090] Set the VOF model parameters.

[0091] Inlet boundary condition input 2D simulation results.

[0092] Wherein, step S5 is specifically as follows:

[0093] By analyzing the particle size generated by ImageJ in step S4, exporting the data, adjusting the rotation speed or flow rate parameters according to the data results, and simulating and recording the changed liquid film thickness and particle size again, the optimal model for the change of process parameters is found.

[0094] Compare the liquid film thickness and particle size affected by changing the rotation speed and flow rate parameters in the previous step, and evaluate the effect of different process parameters on particle size distribution.

[0095] By comparing the particle size statistics obtained after changing the rotation speed or melting flow rate parameters in the first step, the influence of different rotation speeds and rod melting rates on the particle size distribution is obtained. If the average particle size decreases with the increase of the rotation speed and the decrease of the melting rate, it is considered that the optimized process parameter adjustment can promote the reduction of the particle diameter and the particle size reaches the target diameter; if the average particle size does not decrease significantly with the increase of the rotation speed and the decrease of the melting rate, it is considered that the optimized process parameter adjustment can promote the particle diameter but does not reach the target size; by comparing the simulation graphic data output by multiple numerical simulations, the optimal parameters for optimizing the plasma rotating electrode process are obtained.

[0096] Specifically:

[0097] Step 1: Use JMatpro software to calculate the material properties of H13 steel at 1600℃ and output the required viscosity, surface tension and other parameters; Figure 2 shown.

[0098] ① Input alloy composition; select the required database in JMatpro software and input the alloy composition of H13 material, as shown in Table 1.

[0099]

[0100] ② Set environmental conditions; select the calculation temperature range and calculate the physical property parameter change curve.

[0101] ③Output results: Click on the target temperature to obtain physical parameters such as viscosity and surface tension on the curve, and convert units.

[0102] Step 2: Extract the material thermal property data from the first step, optimize the data, and input it into 2D Fluent for calculation.

[0103] ① Perform fluid slice modeling and define entrances, exits and boundaries.

[0104] a. Using the CFD modeling method SpaceClaim, the geometry and physical properties of the plasma rotating electrode are plotted, such as Figure 3 shown.

[0105] b. Set the two-dimensional simulation boundary conditions, including the inlet and outlet fluid velocities, the turntable rotation speed, and the wall contact angle characteristics.

[0106] ② Meshing: Perform meshing in WorkBench, divide the established fluid slice model into several discrete unit unstructured quadrilateral meshes, and export the generated mesh, such as Figure 4shown.

[0107] a. Import the geometry model in WorkBench Ansys Mesh and create boundary conditions as needed.

[0108] b. Meshing: dividing the fluid domain geometric model into discrete small units for numerical calculation.

[0109] c. Export the generated mesh for subsequent simulation.

[0110] ③Set boundary conditions, set the bar speed to move the wall, and set the fluid speed.

[0111] a. In the Fluent material database, copy the Argon material parameters.

[0112] b. Define a new molten metal material and input the thermophysical parameters of H13 at 1600℃ calculated by JMatPro.

[0113] ④Model setting: Open the VOF model, set argon as the main phase, set H13 melt as the secondary phase, and use the SST-Komega turbulence model.

[0114] a. According to the characteristics and requirements of the plasma rotating electrode, select the corresponding simulation model type, including VOF model and turbulence model. The turbulence model is the SST-K Omega model.

[0115] b. Set the VOF parameters, use the implicit volume force method, set the fluid to two-phase flow, set the main phase to argon, and use molten H13 as the second phase.

[0116] ⑤ Run the simulation; first set the time step, start the simulation, and after the operation is stable, stop the simulation and view the results.

[0117] ⑥ Output results; Analyze in Tecplot software to obtain the liquid film thickness and the melt speed at the edge of the bar. Figure 5 shown.

[0118] Step 3: Input the 2D results into the 3D Fluent for crushing and particle size calculation.

[0119] ① Build the model; use SpaceClaim to model a 1 / 10 circle and input Flunet Meshing to divide the mesh. Figure 6 shown.

[0120] a. Use SpaceClaim to model a 1 / 10 circle.

[0121] b. Enter Flunet Meshing to perform meshing.

[0122] ② Set boundary conditions; open periodic boundaries, set material properties, and input H13 physical property parameters.

[0123] a. Turn on periodic boundaries and set the two sides as periodic boundaries.

[0124] b. Open the Fluent material database and copy the argon material parameters.

[0125] c. Define new molten metal material and input the H13 result calculated by JMatPro.

[0126] d. Set VOF model parameters.

[0127] e. Inlet boundary condition input 2D simulation results.

[0128] ③ Set the bar speed to move the wall and input the two-dimensional bar edge speed results into the three-dimensional boundary conditions.

[0129] ④ Run the simulation; first set the time step, start the simulation, and after the operation is stable, stop the simulation and view the results.

[0130] ⑤ Output results; first use the Tecplot post-processing tool to analyze the simulation results; then analyze the fluid velocity distribution and fragmentation through the VOF simulation results to determine the uniformity and effect of the particle size distribution: Output the analysis results, such as Figure 7 shown.

[0131] Step 4: Import the Fluent calculation results into ImageJ software for particle size statistical analysis.

[0132] ①Save the marked liquid phase cloud image and import it into ImageJ software.

[0133] ② Image processing: Process the image into a high-contrast image suitable for marking particles.

[0134] ③ Particle size statistics: set the scale, use image processing to mark the particle size, and use the Analyze Particle function to count the particle area, export the results, calculate the particle diameter in Excel, and draw a picture for analysis, such as Figure 8 shown.

[0135] Step 5: Extract the simulation cloud map and data of the simulation output results of step 4, optimize the data and simulate again.

[0136] ① Adjust key process parameters such as the rotation speed and feed rate of the plasma rotating electrode.

[0137] ② Analyze the particle size generated by ImageJ, export the data, adjust the rotation speed or flow rate parameters according to the data results, simulate and record the changed liquid film thickness and particle size again, and find the best model for process parameter changes.

[0138] ③ Compare the liquid film thickness and particle size affected by changing the rotation speed and flow rate parameters, and evaluate the influence of different process parameters on particle size distribution.

[0139] ④ Compare the particle size statistics obtained after changing the rotation speed or melting flow rate parameters, and obtain the influence of different rotation speeds and bar melting rates on the particle size distribution. If the average particle size decreases with the increase of the rotation speed and the slowdown of the melting rate, it is believed that the optimized process parameter adjustment can promote the reduction of the particle size and the particle size reaches the target diameter; if the average particle size does not decrease significantly with the increase of the rotation speed and the slowdown of the melting rate, it is believed that the optimized process parameter adjustment can promote the particle size without obtaining the target size; compare the simulation graphic data output by multiple numerical simulations, and obtain the H13 optimal parameters for the plasma rotating electrode process optimization, such as Fig. 9 shown.

[0140] The present invention can accurately predict the particle size distribution of plasma rotating electrode atomized powder under the condition of high rotation speed of the plasma rotating electrode and inability to observe liquid breakage. The optimized process is simple, fast and has high usability.

[0141] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for optimizing a plasma rotating electrode atomization process, characterized in that: The following steps are involved: Obtaining material property data of molten metal materials; Establishing a two-dimensional fluid slice model of a plasma rotating electrode; dividing the two-dimensional fluid slice model into a plurality of quadrilateral grids; According to the material property data including density, viscosity and surface tension, as well as the metal bar rotation speed moving wall and fluid velocity in the plasma rotating electrode, the volume change of the metal bar in each quadrilateral grid when it is atomized by the plasma rotating electrode is simulated; when the change is stable, the liquid film thickness of the metal bar and the melt velocity at the edge of the bar are obtained; The method of obtaining the liquid film thickness of the metal bar and the melt speed at the edge of the bar comprises the following steps: Use the CFD modeling method SpaceClaim to draw the geometric shape and physical characteristics of the plasma rotating electrode, establish a two-dimensional fluid slice model of the plasma rotating electrode based on the geometric shape and physical characteristics, and set the inlet and outlet fluid velocities, turntable rotation speed and wall contact angle characteristics as boundary conditions of the two-dimensional fluid slice model; Import the 2D fluid slice model into WorkBench Ansys Mesh and create corresponding boundary conditions; divide the 2D fluid slice model into multiple discrete unit unstructured quadrilateral meshes; According to the characteristics and requirements of the plasma rotating electrode, the VOF model is selected, the VOF model parameters are set, the implicit volume force method is used, the fluid is set to two-phase flow, the main phase is set to argon, and the second phase is set to molten metal; In the VOF model, the metal bar rotation speed moving wall and fluid velocity are set as boundary conditions. At the same time, according to the density, viscosity and surface tension in the metal material physical property data, the volume change of the metal bar in each unstructured quadrilateral grid when it is atomized by the plasma rotating electrode is simulated. When the change is stable, it is analyzed in the Tecplot software to obtain the liquid film thickness of the metal bar and the melt velocity at the edge of the bar. A three-dimensional circular model of a plasma rotating electrode is established, and the three-dimensional circular model is divided into ten equal parts, and one of the parts is determined as a three-dimensional fluid model of the plasma rotating electrode; wherein two side surfaces of the three-dimensional fluid model are set as periodic boundaries, and the melt velocity of the metal bar edge is determined within the periodic boundary conditions; According to the rotating wall surface and the thickness of the liquid film of the metal bar, the fluid velocity distribution and the fragmentation distribution of the metal bar on the three-dimensional fluid model when the metal bar is atomized by the plasma rotating electrode are simulated; and according to the fluid velocity distribution and the fragmentation distribution, the particle size distribution cloud map of the metal bar is obtained; According to the particle size distribution cloud map of the metal bar material, the particle amount of the metal bar material after atomization and the area of ​​each particle are obtained; according to the particle amount and the area of ​​each particle, the process parameters of the plasma rotating electrode atomization process are adjusted to optimize the plasma rotating electrode atomization process.

2. The method for optimizing a plasma rotating electrode atomization process according to claim 1, characterized in that: The obtaining of material property data of the molten metal material comprises: Select the database in JMatpro software and input the alloy composition of the metal material; Selecting a temperature range of the molten metal material to obtain a temperature viscosity change curve, a temperature density change curve, and a surface tension change curve of the molten metal material; The material property data of the molten metal material is obtained according to the temperature viscosity change curve, the temperature density change curve and the surface tension change curve of the molten metal material.

3. The method for optimizing a plasma rotating electrode atomization process according to claim 1, characterized in that: The step of obtaining a particle size distribution cloud diagram of the metal bar material comprises: According to the geometric shape and physical characteristics of the plasma rotating electrode, SpaceClaim was used to model a 1 / 10 circle and determine it as a three-dimensional fluid model of the plasma rotating electrode; The two sides of the three-dimensional fluid model are set as periodic boundaries, and the melt velocity at the edge of the metal bar is determined within the periodic boundary conditions. At the same time, the fluid flow velocity distribution and fragmentation distribution of the metal bar on the three-dimensional fluid model when the metal bar is atomized by the plasma rotating electrode are simulated in the VOF model according to the moving wall and liquid film thickness of the metal bar rotation speed. When the changes are stable, the Tecplot processing tool is used to analyze the fluid flow velocity distribution and fragmentation distribution, and the particle size distribution cloud map of the metal bar is determined according to the fluid flow velocity distribution and fragmentation distribution.

4. The method for optimizing a plasma rotating electrode atomization process according to claim 1, characterized in that: The method of obtaining the particle quantity and the area of ​​each particle of the metal bar after atomization includes: Import the particle size distribution cloud map of the metal bar into ImageJ software and transform the cloud map into a high-contrast image with obvious marked particles; Set the scale of the recognition image, use image processing to mark the particle size, and use the Analyze Particle function to count the particle area.

5. The method for optimizing a plasma rotating electrode atomization process according to claim 1, characterized in that: The process parameters of the plasma rotating electrode atomization process are adjusted, including: The speed and feed rate of the plasma rotating electrode are adjusted according to the particle quantity and the area of ​​each particle, and the simulation is performed again to obtain the changed liquid film thickness and particle size, and the influence of different process parameters on the particle size distribution is obtained; According to the influence of different process parameters on particle size distribution, if the average particle size decreases with the increase of rotation speed and the decrease of melting rate, it is considered that the optimized process parameter adjustment can promote the reduction of particle diameter and make the particle size reach the target diameter; if the average particle size does not decrease significantly with the increase of rotation speed and the decrease of melting rate, it is considered that the optimized process parameter adjustment can promote the change of particle diameter and cannot obtain the target size; through multiple orthogonal experimental comparisons, the optimal process parameters for plasma rotating electrode process optimization are obtained.

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