A method for optimizing design of magnetic powder core density and yield based on discrete element simulation

Abstract

The application relates to a kind of based on discrete element simulation magnetic powder core density and yield optimization design method, belong to magnetic powder core production simulation design technical field.The method constructs magnetic powder particle system model through discrete element software, and realizes density and yield double target optimization in magnetic powder core production simultaneously.Through the filling process of multiple particle size metal powder simulated by discrete element software, the optimal grading scheme of magnetic powder core is obtained by accurately calculating the grading porosity and theoretical density;The simulation range is extended to the dynamic storage hopper discharging link, and the segregation mechanism of multiple particle size powder is objectively revealed by extracting the particle velocity field, position distribution and falling trajectory in the whole cycle of discharging;At the same time, based on the change of particle proportion in the specific discharging process, a segregation quantification calculation index is constructed, which provides reliable data support for accurately evaluating and improving the discharging uniformity of production equipment.

Description

Technical Field

[0001] This invention belongs to the field of magnetic powder core production simulation design technology, specifically involving a magnetic powder core density and yield optimization design method based on discrete element method (DEM) simulation. This method establishes a discrete element model of the magnetic powder system to achieve visualized analysis and optimization of magnetic powder core density distribution and particle segregation, guiding the magnetic powder core molding process and structural design. Background Technology

[0002] With the development of electronic devices towards higher frequencies, miniaturization, and higher integration, molded inductors are widely used due to their excellent electromagnetic compatibility and structural stability. As the core component of molded inductors, the density and uniformity of the magnetic powder core directly determine the permeability, energy loss, and operational stability of the device. The preparation process of the magnetic powder core typically includes key steps such as powder mixing, molding, and heat treatment. Among these, the particle size distribution and mixing uniformity of the powder are decisive factors affecting magnetic properties and production yield. Currently, traditional particle size distribution design mainly relies on empirical formulas and repeated experiments to determine the optimal ratio through trial and error, which is time-consuming, costly, and inefficient. Furthermore, different particle sizes of metal powder are prone to segregation during transport and molding, leading to localized density inhomogeneities and causing defects such as performance fluctuations, cracking, and delamination. Therefore, it is urgent to establish a unified modeling framework and quantitative evaluation system to achieve synergistic optimization of magnetic powder core density and yield.

[0003] The Discrete Element Method (DEM), as an important numerical simulation tool for studying the behavior of particulate systems, can accurately describe the contact, collision, and energy transfer processes between particles, and has significant advantages in fields such as powder packing, flow, and compression molding. Its basic principle is to discretize the material system into multiple particle units with specific particle size, density, and mechanical properties. By establishing a contact mechanics model between particles (such as the Hertz-Mindlin nonlinear contact model), and iteratively solving for the particle motion state based on Newton's second law and force-displacement relationships, a refined simulation of the microscopic dynamics of particles and the macroscopic compactness of the system can be achieved. Its governing equations are as follows:

[0004]

[0005]

[0006] Where m represents the particle mass, u represents the particle translational velocity, and F n,pq and F t,pq Let p and q represent the normal and tangential forces from particle p to q, respectively, and M represent the rotational velocity of the particle. t,pq and M r,pqThese represent the torques generated by the tangential force and sliding friction of the particles, respectively, with I representing the moment of inertia. Discrete element method (DEM) simulation can effectively analyze particle packing density, porosity, and contact characteristics under different particle sizes, morphologies, and proportions when designing gradation schemes, providing a quantitative basis for gradation optimization. Simultaneously, by simulating the hopper discharge, conveying, and mold cavity filling processes, the mechanism of particle segregation formation and its impact on density distribution can be intuitively revealed.

[0007] However, existing research mainly focuses on the theoretical density calculation of powder packing processes, and has not yet achieved synergistic modeling and prediction of gradation optimization and segregation control. For example, although patent CN119480363A improves magnetic properties by adjusting the powder ratio, its method mainly relies on experimental trial and error, resulting in a long development cycle and high cost; patent CN115935771A uses the discrete element method to calculate the theoretical density of powder, but does not consider gravity or vibration segregation caused by differences in particle properties during production, making it difficult to predict the resulting density unevenness and structural defects; although the anti-segregation hopper structure proposed by patent CN222571165U can improve particle distribution to a certain extent, parameter optimization still relies on mechanical structure trial and error, and due to differences in particle systems, it is difficult to directly apply to the production of magnetic powder cores. In summary, current technologies lack a digital modeling method for the production process of magnetic powder cores based on discrete element simulation, which can simultaneously realize the calculation of gradation density, prediction of segregation behavior, and optimization of structural parameters in the same simulation system, so as to achieve synergistic optimization of magnetic powder core density improvement and yield control, and provide systematic theoretical guidance and engineering support for magnetic powder core production. Summary of the Invention

[0008] The purpose of this invention is to address the problems existing in the background technology by proposing a method for optimizing the core density and yield of magnetic powder based on discrete element simulation.

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0010] A method for optimizing the core density and yield of magnetic powder based on discrete element method (DEM) simulation includes the following steps:

[0011] Step 1. Based on the material properties of the required metal particles, mold, and storage hopper, set the basic material parameters and contact parameters of the metal particles, mold, and storage hopper;

[0012] Step 2. Based on the required microstructure of the metal particles, define matching particle shapes in the discrete element method software; obtain the particle size-to-mass ratio data of the actual metal particles, and import the particle size-to-mass ratio data of different metal particles into the discrete element method software;

[0013] Step 3. Based on the basic material parameters of the mold and the storage hopper, set the geometric model of the mold and the storage hopper; set up the pellet factory, and simulate the generation process of metal particles with different properties based on the basic material parameters and particle shape of the metal particles; set the gradation ratio of the generated metal particles (the mass ratio of metal particles of different sizes) based on the particle size mass ratio data of the metal particles.

[0014] Step 4. Select the contact mechanics model, set the simulation mesh size, calculate the Rayleigh time step of the solver, and then run the simulation through the engine.

[0015] Step 5. Start the solver to simulate the filling process of metal particles with different gradation ratios in the mold. After the simulation is completed, extract the filling density data in the mold. By comparing the simulated filling density results of metal particles with different gradation ratios in multiple groups, output the optimal gradation ratio of metal particles.

[0016] Step 6. After obtaining the optimal gradation ratio, generate metal particles in the discrete element method software according to the optimal gradation ratio and fill the hopper; after filling, perform the discharge operation, extract the particle velocity field, position distribution and falling trajectory of the entire discharge cycle according to the basic material parameters and contact parameters of the hopper and metal particles, and display the above results; and statistically analyze the segregation of the target metal particles under different discharge processes.

[0017] Step 7. Set the segregation threshold (20%) for the target metal particles. If the segregation obtained in Step 6 is less than the segregation threshold, then output the optimal hopper structure.

[0018] If the segregation degree obtained in step 6 exceeds the segregation degree threshold, the structural parameters of the storage hopper are optimized, and the material discharge simulation in step 6 is re-executed until the segregation degree meets the threshold requirement, and the optimal hopper structure is output.

[0019] The optimal storage hopper structure parameters obtained in step 7 and the optimal gradation ratio obtained in step 5 are directly applied to the internal structure modification of the storage hopper equipment in the production line, as well as the gradation of metal particles before feeding.

[0020] Furthermore, in step 1, the basic material parameters include the density, Poisson's ratio, and shear modulus of the metal particles, the mold, and the storage hopper, with the density set within a range of 6500 kg / m³. 3 ~8500 kg / m 3The Poisson's ratio is set in the range of 0.20 to 0.35, and the shear modulus is set in the range of 50 GPa to 100 GPa. The contact parameters include the coefficient of recovery, rolling friction coefficient, and static friction coefficient between metal particles, between metal particles and the mold, and between metal particles and the storage hopper. The coefficient of recovery is set in the range of 0.10 to 0.30, the rolling friction coefficient is set in the range of 0.005 to 0.05, and the static friction coefficient is set in the range of 0.10 to 0.50.

[0021] Furthermore, the particle shape described in step 2 adopts one or more combinations of a multi-spherical agglomeration model, a spherical model, or a polyhedral model, and the particle size mass ratio data of the actual metal particles used are tested using a particle size analyzer.

[0022] Preferably, the geometric model in step 3 includes the shape and size of the mold and the storage hopper, and the model shape includes a ring, a cylinder or a cuboid.

[0023] Furthermore, the gradation ratio of the metal particles in step 3 is dynamically controlled by setting the generation rate of each particle factory.

[0024] Furthermore, in step 4, the contact mechanics model is configured differently based on the characteristics of different metal particles. For metal particles without obvious adhesion characteristics, the Hertz-Mindlin (no slip) model is used; for metal particles with adhesive properties, the Hertz-Mindlin with JKR model is used. The simulation mesh size in step 4 ranges from 2.5R to 10R (R is the minimum particle radius). The Rayleigh time step calculation needs to consider the basic material parameters of the metal particles, such as the minimum particle radius, material density, shear modulus, and Poisson's ratio, and its selection range is 20% to 30%. The running engine is GPUCUDA Solver.

[0025] Furthermore, in step 5, the filling density value is extracted using the Density Sensor post-processing tool based on the basic material parameters and contact parameters of the metal particles and the mold.

[0026] Furthermore, in step 5, the gradation ratio corresponding to the maximum fill density value is the optimal gradation ratio of the metal particles. In step 6, the particle velocity field, position distribution, and falling trajectory throughout the entire feeding cycle are obtained using Geometry Bin.

[0027] Furthermore, the segregation degree described in step 6 is defined as:

[0028] Segregation = |(Current percentage - Initial percentage) / Initial percentage| × 100%

[0029] The current percentage is the mass percentage of the target metal particles in the hopper during a specific feeding process, and the initial percentage is the mass percentage of the target metal particles in the hopper when it is filled but not fed. The feeding process can be set to feed 10%, 20%, 30%, ..., 100%.

[0030] Furthermore, in step 7, the structural parameters of the storage hopper include the shape of the storage hopper and the size parameters of the conical fluid-changing device added inside the storage hopper; wherein, the height of the conical fluid-changing device is set to 60~110 mm, the radius is 20~40 mm, and the cone angle is 30°~60°.

[0031] This invention also provides a magnetic powder core production system, which is obtained by modifying the parameters output in coordination with the optimization design method described above; the production system includes a batching unit and a storage hopper device, the batching unit is used to accurately batch metal particles before feeding according to the output optimal gradation ratio; the storage hopper device is equipped with a fluid modification device inside, the fluid modification device has a height of 60~110 mm, a radius of 20~40 mm, and a cone angle of 30°~60°.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] 1. This invention provides an efficient scheme for optimizing the gradation of magnetic powder cores: Addressing the problems of traditional powder gradation methods, which rely heavily on physical experimental trial-and-error, resulting in long cycles and high costs, this invention establishes a discrete element simulation model incorporating the material's micromechanical response and contact parameters to simulate the generation and dynamic filling process of multiple sets of metal particles with different properties. This method can calculate and select the optimal gradation ratio corresponding to the highest filling density in a virtual environment, effectively replacing tedious experimental methods and reducing the R&D cost and trial-and-error cycle of material formulation development.

[0034] 2. Quantitative assessment and prediction of powder segregation behavior has been achieved: Existing gradation designs often neglect the uniformity issue in actual production. This invention extends the simulation scope to the dynamic material discharge stage of the storage hopper. By extracting the particle velocity field, position distribution, and falling trajectory throughout the discharge cycle, the segregation mechanism of multi-size powders is objectively revealed. Simultaneously, a quantitative calculation index for segregation is constructed based on the change in particle proportion during a specific discharge process, providing reliable data support for accurately assessing and improving the discharge uniformity of production equipment.

[0035] 3. A synergistic optimization strategy for material proportioning and equipment structure is provided: This invention comprehensively considers the static formulation design and dynamic physical flow process of magnetic powder cores, and outputs the optimal gradation ratio obtained from simulation and the targeted optimized anti-segregation structural parameters (such as the modified fluid size added inside the storage hopper) as a complete process parameter combination. This method achieves synergistic control from two dimensions: powder gradation at the material source and material discharge and conveying during the production process. It effectively suppresses the segregation phenomenon of metal particles during flow, thereby improving the final molding quality and production yield of magnetic powder cores. Attached Figure Description

[0036] Figure 1 This is a flowchart of the magnetic powder core density and yield optimization design method of the present invention;

[0037] Figure 2 A schematic diagram of graded particle filling;

[0038] Figure 3 A comparison chart of gradation simulation and experimental results;

[0039] Figure 4 This is a schematic diagram of the hopper segregation simulation.

[0040] Figure 5 This is a velocity cloud map and material distribution map of particles during the hopper discharge process;

[0041] Figure 6 This is a schematic diagram of a conical fluid remodeling process;

[0042] Figure 7 The figure shows the results of the segregation suppression effect of the conical modified fluid. Detailed Implementation

[0043] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. This embodiment is based on the technical solution of the present invention and provides detailed implementation methods and specific operating procedures, but the present invention is not limited to the following embodiments.

[0044] like Figure 1 As shown, a method for optimizing the core density and yield of magnetic powder based on discrete element simulation includes the following steps:

[0045] Step 1. Based on the material properties of the required metal particles, mold, and storage hopper, set the basic material parameters and contact parameters for the metal particles, mold, and storage hopper. The basic material parameters include the density, Poisson's ratio, and shear modulus of the metal particles, mold, and storage hopper. The density is set within the range of 6500 kg / m³ to 8500 kg / m³, the Poisson's ratio within the range of 0.20 to 0.35, and the shear modulus within the range of 50 GPa to 100 GPa. The contact parameters include the coefficient of restitution, rolling friction coefficient, and static friction coefficient between metal particles, between metal particles and the mold, and between metal particles and the storage hopper. The coefficient of restitution is set within the range of 0.10 to 0.30, the rolling friction coefficient within the range of 0.005 to 0.05, and the static friction coefficient within the range of 0.10 to 0.50.

[0046] Step 2. Based on the required microstructure of the metal particles, define matching particle shapes in the discrete element method (DIM) software. The particle shapes can adopt multi-sphere agglomeration models (such as double-sphere or triple-sphere models), spherical, or polyhedral models. Obtain the particle size mass ratio data of the actual metal particles through equipment such as particle size analyzers, and import the particle size mass ratio data of different metal particles into the DIM software to accurately reproduce the particle size distribution of the powder used in production.

[0047] Step 3. Based on the basic material parameters of the mold and the storage hopper, set the geometric model of the mold and the storage hopper, including the model shape and size of the mold and the storage hopper. The shape of the mold includes ring, cylinder and cuboid. Set up the particle factory and simulate the generation process of metal particles with different properties according to the basic material parameters and particle shape of the metal particles. Set the gradation ratio of the generated metal particles (the mass ratio of metal particles of different sizes) according to the particle size mass ratio data of the metal particles. Specifically, set up independent particle factories for metal particles of different materials or different particle size ranges, and dynamically control and simulate multiple sets of gradient-changing metal particle mixing gradation ratios by setting the generation rate of each particle factory.

[0048] Step 4. Select differentiated contact mechanics models based on the characteristics of different metal particles. For metal particles without obvious adhesion characteristics, use the Hertz-Mindlin (no slip) model; for metal particles with adhesive properties (such as metal particles coated with resin), use the Hertz-Mindlin with JKR model. Then, based on the minimum particle radius R set in Step 2, divide the simulation mesh size (mesh size includes 2.5R~10R), and calculate the Rayleigh time step of the solver by combining the minimum particle radius, material density, shear modulus, and Poisson's ratio, using the following formula. The Rayleigh time step range is 20%~30%. Select a suitable runtime engine (such as GPU CUDA Solver) for simulation.

[0049]

[0050] Step 5. Start the solver to simulate the filling process of metal particles with different gradation ratios in the mold. After the particle system reaches a stable and static state, use a post-processing tool (such as Density Sensor) with the same geometry as the mold to extract the filling density data in the mold. By comparing the simulated filling density results of metal particles with different gradation ratios in multiple sets, the optimal gradation ratio of metal particles is output.

[0051] Step 6. After obtaining the optimal gradation ratio, generate metal particles according to the optimal gradation ratio in the discrete element method software and fill the hopper; after filling, perform the discharge operation, and extract the particle velocity field, position distribution and falling trajectory of the entire discharge cycle using software analysis tools (such as Geometry Bin) based on the basic material parameters and contact parameters of the hopper and metal particles, and display the above results; statistically analyze the segregation of the target metal particles under different discharge processes;

[0052] Step 7. Set the segregation threshold (20%) for the target metal particles. If the segregation obtained in Step 6 is less than the segregation threshold, then output the optimal hopper structure.

[0053] If the segregation degree obtained in step 6 exceeds the segregation degree threshold, the structural parameters of the storage hopper are optimized, and the material discharge simulation in step 6 is re-executed until the segregation degree meets the threshold requirement, and the optimal hopper structure is output.

[0054] The degree of segregation is defined as follows:

[0055] Segregation = |(Current percentage - Initial percentage) / Initial percentage| × 100%

[0056] The current percentage is the mass percentage of the target metal particles in the hopper during a specific feeding process, and the initial percentage is the mass percentage of the target metal particles in the hopper when it is filled but not fed. The feeding process can be set to feed 10%, 20%, 30%, ..., 100%.

[0057] The structural parameters of the storage hopper include the shape of the storage hopper and the size parameters of the conical fluid conversion device added inside the storage hopper; wherein, the height of the conical fluid conversion device is set to 60~110 mm, the radius is 20~40 mm, and the cone angle is 30°~60°.

[0058] The optimal storage hopper structure parameters obtained in step 7 and the optimal gradation ratio obtained in step 5 are directly applied to the internal structure modification of the storage hopper equipment in the subsequent production line of the enterprise, as well as the gradation process of metal particles of different sizes before feeding, thereby achieving a dual improvement in the core density and production yield of the magnetic powder prepared from metal particles.

[0059] Example

[0060] Step 1. Based on the material properties of the required metal particles, mold, and storage hopper (in this embodiment, the mold and storage hopper use the same material properties), set the basic material parameters and contact parameters of the metal particles, mold, and storage hopper. To accurately simulate the micromechanical behavior of the particles, the basic material parameters include the density, Poisson's ratio, and shear modulus of the metal particles, mold, and storage hopper. In this embodiment, the metal particles are iron-silicon metal powder and carbonyl iron powder, with densities of 6950 kg / m³, respectively. 3 With 7630 kg / m 3 The Poisson's ratios are 0.28 and 0.3, and the shear moduli are 70 GPa and 75 GPa, respectively. The mold and the storage hopper are made of the same metal material with a density of 7850 kg / m³. 3 The Poisson's ratio is 0.3, and the shear modulus is 85 GPa. The contact parameters include the coefficient of recovery, rolling friction coefficient, and static friction coefficient between metal particles, between metal particles and the mold, and between metal particles and the storage hopper. The coefficient of recovery between metal particles is 0.15, the rolling friction coefficient is 0.01, and the static friction coefficient is 0.3. The coefficient of recovery between metal particles and the mold and the storage hopper is 0.13, the rolling friction coefficient is 0.01, and the static friction coefficient is 0.25.

[0061] Step 2. Based on the required microstructure of the metal particles, define matching particle shapes in the discrete element method (DIM) software, and set the shape and particle size distribution of the metal particles. In this embodiment, the shape of the metal particles is set to spherical. Obtain the particle size mass ratio data of the actual metal particles through a particle size analyzer, and import the particle size mass ratio data of different metal particles into the DIM software to accurately reproduce the particle size distribution of the powder used in production.

[0062]

[0063] Step 3. Based on the basic material parameters of the mold and the storage hopper, set the geometric models of the mold and the storage hopper; use 3D modeling software to create 3D models of the mold and hopper that match the actual project requirements and import them into discrete element method (DEM) software. The mold is a ring-shaped mold, modeled and imported from the modeling software, with an outer diameter to inner diameter ratio of 20:6. The mold model is shown below. Figure 2 As shown. The parameters of the storage hopper are: upper port diameter 700 mm, lower discharge port diameter 50 mm, a virtual baffle is set at the lower discharge port to simulate the opening and closing of the storage hopper, and the overall height of the storage hopper is 700 mm.

[0064] A pelletizing plant is set up to generate metal particles. In this embodiment, the pelletizing plant is set up as a plane, located on top of the mold and the storage hopper. Pelletizing plants are set up separately for the two types of metal particles mentioned above to simulate the generation and accumulation of particles. The generation rate of each pelletizing plant is set in the software to dynamically control the gradation ratio when the metal particles are mixed. In this embodiment, the generation rates of iron-silicon metal powder and carbonyl iron powder are set according to the gradation ratio gradient, which are 9:1, 8:2, 7:3, 6:4, 5:5, and 4:6, respectively.

[0065] Step 4. Different contact mechanics models are selected for different metal particle characteristics. In this embodiment, the Hertz-Mindlin (no slip) contact model is selected. Subsequently, the simulation mesh size is divided based on the minimum particle radius R set in Step 2. In this embodiment, the mesh size is set to 3R. Then, the solution time is set. The first-order Euler time integration method is selected to set the time step. Based on the minimum particle radius, material density, shear modulus, and Poisson's ratio set above, the Rayleigh time step is calculated. In this embodiment, the Rayleigh step is set to 20%, the simulation time is set to 5 s, and the data is saved every 0.2 s. To speed up the calculation, GPU CUDA Solver is selected as the simulation engine.

[0066] Step 5. Start the solver to simulate the packing and filling process of metal particles with different gradations in the mold. After the particle system reaches a stable and static state, use the software's built-in post-processing tool, Density Sensor, to extract the filling situation in the mold. The geometry of Density Sensor is set to match the mold. Analyze the packing density value of the metal particles. By comparing the packing density values ​​of metal particles with different gradations and verifying the loose density of the gradation ratio in actual experiments, plot the following: Figure 3 The relationship curve between fine powder content and filling rate / loose density is shown, thus allowing for the screening and determination of the optimal gradation ratio of metal particles in this system. This result demonstrates that this discrete element method can effectively simulate the gradation ratio scheme of metal particles.

[0067] Step 6. In this embodiment, metal particles are generated according to the optimal gradation ratio obtained in Step 5 and the hopper is filled. After filling, the virtual baffle at the lower discharge port of the storage hopper described in Step 3 is removed to open the discharge port, thereby simulating the dynamic discharge process in the storage hopper. The velocity field, position distribution, and falling trajectory of the metal particles throughout the entire discharge cycle are extracted using the Geometry Bin tool built into the software. By statistically analyzing the ratio deviation between iron-silicon metal powder (large particles) and carbonyl iron powder (small particles) in the mold at different times, the degree of segregation and the critical position of segregation during the hopper discharge process are quantitatively evaluated. The spatial layering extraction diagram of this process is shown in Figure 1. Figure 4 As shown.

[0068] Figure 5 The image shows the velocity cloud map of metal particles and the material distribution map during the material discharge process from the storage hopper. (The image is obtained through...) Figure 5 The visualization results allow for direct observation of the "center-edge" velocity difference in the hopper during the discharge process, as well as the resulting segregation phenomenon of small particles (carbonyl iron powder) seeping and accumulating towards the center. This enables accurate determination of the specific area and morphology of segregation.

[0069] Step 7. In this embodiment, based on the segregation phenomenon obtained in Step 6, the structure of the storage hopper is specifically optimized. By remodeling, a modified fluid structure is added inside the storage hopper to break the "center-edge" velocity difference in Step 6. The final modified fluid geometry parameters used in this embodiment are: height 80 mm, radius 30 mm, and cone angle 50°. The modified fluid structure and dimensioned schematic diagram are shown below. Figure 6 As shown in the figure. The discharge simulation was re-executed using a hopper containing the modified fluid, and the comparison curves of its segregation suppression effect are shown in the figure. Figure 7 As shown. Figure 7The vertical axis represents the ratio of the current proportion of metal particles to the initial proportion. (For clarity, this embodiment uses the proportion of iron-silicon metal powder.) The deviation of this ratio from 100% represents the segregation degree. In this embodiment, the segregation degree is defined as:

[0070] Segregation = |(Current percentage of iron-silicon metal powder - Initial percentage of iron-silicon metal powder) / Initial percentage of iron-silicon metal powder| × 100%.

[0071] Among them, the current proportion of iron-silicon metal powder is the mass proportion of iron-silicon metal powder in the hopper during a specific feeding process, and the initial proportion of iron-silicon metal powder is the mass proportion of iron-silicon metal powder before the filling is completed and before feeding.

[0072] Simulation results show that the addition of this fluid significantly suppressed particle segregation. This was achieved when the segregation rate reached 20% (i.e., Figure 7 The ordinate of the curve (reduced to 80%) is considered the critical point at which significant segregation occurs. In a standard hopper without the modified fluid, this critical point is reached at 85.9% of the discharge process; however, with the optimized modified fluid, this critical point is significantly delayed to 98.5% of the discharge process. This quantitative result confirms the effectiveness of the internal structural design of this embodiment in improving the uniformity of magnetic powder discharge.

[0073] Step 8. In this embodiment, the modified fluid structure parameters with excellent segregation suppression effect obtained in Step 7 (i.e., height 80 mm, radius 30 mm, cone angle 50°) are finally extracted, as well as the optimal powder gradation ratio verified in Step 5 (i.e., the mass ratio of large iron-silicon metal powder particles to small carbonyl iron powder particles according to the corresponding gradient). In this embodiment, the above-mentioned quantified parameters are output as the optimal process combination and applied to the internal structure modification of the hopper equipment of the subsequent magnetic powder core production line of the enterprise, as well as the precise batching of powders of different particle sizes before feeding.

[0074] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, several equivalent substitutions or obvious modifications can be made without departing from the concept of the present invention, and all such modifications, achieving the same performance or purpose, should be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing the design of magnetic powder core density and yield based on discrete element simulation, characterized in that, Includes the following steps: Step 1. Based on the material properties of the required metal particles, mold, and storage hopper, set the basic material parameters and contact parameters of the metal particles, mold, and storage hopper; Step 2. Define the particle shape in the discrete element method software according to the required microstructure of the metal particles; obtain the particle size-to-mass ratio data of the metal particles, and import the particle size-to-mass ratio data of different metal particles into the discrete element method software; Step 3. Based on the basic material parameters of the mold and the storage hopper, set the geometric model of the mold and the storage hopper; set up the pellet factory, and simulate the generation process of metal particles with different properties based on the basic material parameters and particle shape of the metal particles; set the gradation ratio of the generated metal particles based on the particle size and mass ratio data of the metal particles. Step 4. Select the contact mechanics model, set the simulation mesh size, calculate the Rayleigh time step of the solver, and then run the simulation through the engine. Step 5. Start the solver to simulate the filling process of metal particles with different gradation ratios in the mold. After the simulation is completed, extract the filling density data in the mold. By comparing the simulated filling density results of metal particles with different gradation ratios in multiple sets, output the optimal gradation ratio of metal particles.

2. The magnetic powder core density and yield optimization design method based on discrete element simulation according to claim 1, characterized in that, After obtaining the optimal gradation ratio, metal particles are generated in the discrete element method software according to the optimal gradation ratio and the hopper is filled. After filling, the material is discharged. The particle velocity field, position distribution and falling trajectory of the entire discharge cycle are extracted and displayed based on the basic material parameters and contact parameters of the hopper and metal particles. The segregation of the target metal particles under different discharge processes is statistically analyzed. Set a segregation threshold for the target metal particles. If the segregation is less than the segregation threshold, the optimal hopper structure is output. If the segregation exceeds the segregation threshold, the structural parameters of the storage hopper are optimized, and the feeding simulation is re-executed until the segregation meets the threshold requirement, and the optimal hopper structure is output.

3. The magnetic powder core density and yield optimization design method based on discrete element simulation according to claim 1, characterized in that, In step 1, the basic material parameters include the density, Poisson's ratio, and shear modulus of the metal particles, the mold, and the storage hopper. The contact parameters include the coefficient of restitution, rolling friction coefficient, and static friction coefficient between metal particles, between metal particles and the mold, and between metal particles and the storage hopper.

4. The magnetic powder core density and yield optimization design method based on discrete element simulation according to claim 1, characterized in that, Step 2 describes the particle shape as one or more of a multi-spherical agglomeration model, a spherical model, or a polyhedral model, and uses a particle size analyzer to test the particle size-to-mass ratio data of the metal particles.

5. The magnetic powder core density and yield optimization design method based on discrete element simulation according to claim 1, characterized in that, The geometric model described in step 3 includes the shape and size of the mold and the storage hopper. The model shape includes a ring, a cylinder or a cuboid.

6. The magnetic powder core density and yield optimization design method based on discrete element simulation according to claim 1, characterized in that, The gradation ratio of the metal particles in step 3 is dynamically controlled by setting the generation rate of each particle factory.

7. The magnetic powder core density and yield optimization design method based on discrete element simulation according to claim 1, characterized in that, In step 4, the contact mechanics model is set differently according to the characteristics of different metal particles. For metal particles without obvious adhesion characteristics, the Hertz-Mindlin model is selected; for metal particles with adhesion characteristics, the Hertz-Mindlin with JKR model is selected. The simulation mesh size ranges from 2.5R to 10R, where R is the minimum particle radius; the Rayleigh time step ranges from 20% to 30%.

8. The magnetic powder core density and yield optimization design method based on discrete element simulation according to claim 2, characterized in that, The segregation degree is defined as: Segregation = |(Current percentage - Initial percentage) / Initial percentage| × 100% The current percentage refers to the mass percentage of the target metal particles in the hopper during a specific material discharge process, while the initial percentage refers to the mass percentage of the target metal particles in the hopper when the hopper is filled but not yet discharged.

9. The magnetic powder core density and yield optimization design method based on discrete element simulation according to claim 2, characterized in that, The structural parameters of the storage hopper include the shape of the storage hopper and the size parameters of the conical fluid conversion device added inside the storage hopper; wherein, the height of the conical fluid conversion device is set to 60~110 mm, the radius is 20~40 mm, and the cone angle is 30°~60°.

10. A magnetic powder core production system, comprising a batching unit and a storage hopper, wherein the batching unit batches metal particles before feeding according to the optimal gradation ratio output in the design method of claim 2, and the structural parameters of the storage hopper are the optimal hopper structure output in the design method of claim 2.

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