Optimization design method and equipment of dust removal system and storage medium

Abstract

The invention discloses an optimization design method and equipment for a dust removal system and a storage medium. The method comprises the following steps: constructing a fluid region model of the dust removal system, and inputting process parameters as boundary conditions; performing flow field and particle phase simulation calculation on the model to obtain a pole piece pressure difference and particle distribution result so as to represent the risk of pole piece shaking or waste belt breakage; and if the result does not meet the preset requirement, adjusting the three-dimensional structure model or the process parameters and repeating simulation until the requirement is met. By establishing the closed-loop optimization design method, quantitative evaluation of the pneumatic stability of the pole piece and accurate prediction of splashing particle distribution are achieved, and a traditional experimental verification means can be replaced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of laser processing auxiliary system design, in particular to an optimization design method and device of a dust removal system and a storage medium. BACKGROUND

[0002] Laser cutting technology is widely used in tab material processing due to its high energy density and accuracy. In the laser cutting process, the dust removal system is crucial to maintaining the cleanliness of the processing environment. However, as a flexible and thin material, the tab is prone to shaking due to the pressure difference caused by uneven airflow in the two side cavities under the action of the dust removal wind pressure. This shaking can cause abnormal distribution of laser energy, resulting in unstable or overburning of the local cutting seam, directly affecting product quality.

[0003] At the same time, if the waste generated by cutting is not properly pulled by the dust removal wind, it is easy to cause abnormal pressure difference between the front and back, leading to waste belt breakage and pipeline blockage, thereby worsening the dust removal effect. In addition, high energy density processing is inevitably accompanied by metal spatter. If the spatter particles fall back to the tab surface or are rolled in with the belt, they may pierce the separator and cause serious safety hazards such as internal short circuit of the battery. The existing technology usually relies on experimental trial-and-error method to adjust the dust removal structure or process parameters. This method is long in cycle, high in cost and cannot quantitatively evaluate the internal flow field details.

[0004] Therefore, how to establish a closed-loop optimization design method that can quantitatively evaluate the aerodynamic stability of the tab and accurately predict the distribution of spatter particles, thereby replacing the traditional experimental verification method, has become a technical problem to be solved. SUMMARY

[0005] The main purpose of the present application is to provide an optimization design method, device and storage medium of a dust removal system, aiming to establish a closed-loop optimization design method that can quantitatively evaluate the aerodynamic stability of the tab and accurately predict the distribution of spatter particles, thereby replacing the traditional experimental verification method.

[0006] In order to achieve the above purpose, the present application provides an optimization design method of a dust removal system, which comprises the following steps: Step S1: constructing a fluid region model of a laser cutting dust removal system, the laser cutting dust removal system comprising a laser inlet, a return air inlet, a negative pressure dust removal inlet and a tab located in a laser cutting region; Step S2: obtaining process parameters of the laser cutting dust removal system and inputting the process parameters as boundary conditions into the fluid region model; Step S3: performing flow field and particle phase simulation calculation on the fluid region model to obtain tab pressure difference results and particle distribution results; the tab pressure difference results are used to represent the shaking risk or waste belt breakage risk of the tab; Step S4: judging whether the pole piece pressure difference result and the particle distribution result meet the preset requirement; Step S5: if not, adjusting the three-dimensional structure model of the laser cutting dust removal system or the process parameters, and repeating the simulation calculation and judgment steps until the pole piece pressure difference result and the particle distribution result meet the preset requirement.

[0007] Preferably, in step S4, the specific steps of judging whether the pole piece pressure difference result meets the preset requirement include: calculating the left-right side pressure difference of the pole piece, if the left-right side pressure difference is greater than a first threshold value, it is determined that the shaking risk exists; calculating the front-back side pressure difference of the pole piece, if the front-back side pressure difference is greater than a second threshold value, it is determined that the waste strip breaking risk exists; wherein the first threshold value is 30 Pa, and the second threshold value is 50 Pa.

[0008] Preferably, in step S3, when obtaining the particle distribution result, the number of particles passing through the boundary is counted by using a user-defined field function, and the specific steps include: defining a scalar field function, setting the function value to 1 when the region index number corresponds to the index number of the boundary to be counted, and otherwise setting the function value to 0; establishing a cell counter and creating a monitor based on the cell counter for monitoring the number of particles passing through the boundary to be counted in each time step; creating a statistical data report to count and accumulate the total number of particles passing through the boundary to be counted in each time step in real time by using the monitor.

[0009] Preferably, in step S3, the particle phase simulation calculation adopts a Lagrangian multiphase model, and the gravity model, the drag force model, the shear lift force model and the two-way coupling model are enabled.

[0010] Preferably, in step S2, when inputting the process parameters as boundary conditions into the fluid region model, the specific steps include: setting the laser inlet and the return air outlet as pressure inlets, and setting the relative pressure value to zero; setting the negative pressure dust removal outlet as a velocity outlet, and inputting the flow rate value in the process parameters; setting the surfaces other than the laser inlet, the return air outlet and the negative pressure dust removal outlet as walls.

[0011] Preferably, in step S3, the physical model selected for the flow field simulation calculation further includes a three-dimensional model, a gas model, a separated flow model, an implicit unsteady model and a K-epsilon turbulent flow model.

[0012] Preferably, before step S3, the simulation calculation further includes the step of dividing the grid of the fluid region model: adopting a surface quadrilateral master-controlled polyhedral grid for division; and performing grid encryption processing on the splash area, the pole piece area and the inlet and outlet area of the laser cutting dust removal system.

[0013] Preferably, in step S1, the step of constructing the fluid region model of the laser cutting dust removal system comprises: establishing a three-dimensional solid model of the laser cutting dust removal system; generating the fluid region model from the three-dimensional solid model by using the subtraction operation of Boolean operation; and segmenting the fluid region model into independent parts corresponding to the negative pressure dust removal port, the laser inlet, the return air port, the splashing area and the tab area.

[0014] Preferably, in step S2, the process parameters are obtained by the online monitoring device or the handheld measuring instrument in a stable state to obtain the mean value of the flow, flow rate or pressure value.

[0015] Preferably, the laser cutting dust removal system comprises a semi-closed space, and the semi-closed space is communicated with the external environment only through the laser inlet and the return air port.

[0016] The application also discloses an electronic device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the optimization design method of the dust removal system according to any one of the above.

[0017] The application also discloses a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the optimization design method of the dust removal system according to any one of the above.

[0018] The above technical solution has the following advantages: By constructing an accurate fluid region model and inputting actual process parameters as boundary conditions, the tab pressure difference results and particle distribution results representing the tab shaking risk and waste strip breaking risk are directly obtained by using flow field and particle phase simulation calculation, and are judged and iteratively optimized according to specific pressure difference threshold and particle statistical data. The method can quantitatively evaluate the aerodynamic performance and dust removal efficiency of the dust removal system without physical trial and error, accurately identify and eliminate the tab shaking and breaking risks, shorten the design optimization cycle, reduce the research and development cost, and effectively improve the tab cutting quality and the safety of the battery product. BRIEF DESCRIPTION OF DRAWINGS

[0019] The application will be described in detail below with reference to specific embodiments and drawings, in which: Figure 1 The flowchart of the optimization design method of the dust removal system provided for the embodiments of the application.

[0020] Figure 2 The overall flow field streamline diagram of the laser cutting dust removal system provided for the embodiments of the application. Figure 3 The pressure difference distribution cloud diagram of the tab surface provided for the embodiments of the application. Figure 4 This is a schematic diagram of particle distribution in a laser cutting dust removal system provided in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the following specific embodiments are only used to explain the invention and do not constitute a limitation thereof.

[0022] Example 1 like Figure 1 As shown, this embodiment provides an optimized design method for a dust removal system. Addressing the technical problems in existing laser cutting processes, such as electrode vibration affecting cutting accuracy due to improper dust removal air pressure configuration or unreasonable structural design, and the easy breakage and blockage of pipelines by cutting waste, this embodiment proposes a closed-loop optimization scheme based on fluid simulation and specific pressure difference index determination. This method establishes an accurate physical model to convert the mechanical stability risk of the electrode into a pressure difference index in the fluid domain for quantitative evaluation. Simultaneously, combined with a custom particle statistics method, it achieves dual optimization of dust removal effect and process stability.

[0023] This optimization design method first requires the construction and processing of a physical model. In step S1, a fluid region model of the laser cutting dust removal system is constructed. Specifically, a 3D model of the laser cutting dust removal system is first created in 3D modeling software, such as SolidWorks or Catia. This laser cutting dust removal system mainly consists of a semi-enclosed space, a laser inlet, a return air inlet, a negative pressure dust removal port, and electrodes located in the laser cutting area. To ensure the directionality and effectiveness of the dust removal flow field, the semi-enclosed space is only connected to the external environment through the laser inlet and the return air inlet, forming a specific negative pressure flow field environment. After obtaining the 3D model, it is converted into a common format of 3D software, such as Parasolid or Step format, for subsequent processing.

[0024] Next, using Boolean subtraction, a fluid region model for simulation is generated from the 3D model. To facilitate subsequent setting of physical properties and data monitoring for different regions, the fluid region model is divided into independent components corresponding to the negative pressure dust removal port, laser inlet, return air inlet, splash area, and electrode area. Mesh generation is then performed. To balance computational accuracy and efficiency, this embodiment uses a polyhedral mesh dominated by surface quadrilaterals for mesh generation, with particular mesh refinement applied to the splash area, electrode area, and inlet / outlet areas where flow field changes drastically or where high interest is required.

[0025] After the model is built, step S2 is entered to obtain the process parameters of the laser cutting dust removal system, and the process parameters are input as boundary conditions into the fluid region model. To ensure the authenticity of the simulation, the process parameters are obtained by online monitoring equipment or handheld measuring instruments in the stable state to obtain the average value of the flow, flow rate or pressure value. In the specific boundary condition setting, considering the working principle of the system, the laser inlet and the return air outlet are set as pressure inlets, and the relative pressure value is set to zero because they are connected to the atmosphere; the negative pressure dust removal port is set as a velocity outlet, and the actual flow rate value in the process parameters is input; the surfaces other than the laser inlet, the return air outlet and the negative pressure dust removal port are set as walls.

[0026] Step S3 involves the core simulation calculation process. In this embodiment, physical models including a three-dimensional model, a gas model, a separated flow model, an implicit unsteady model and a K-epsilon turbulence model are used for flow field solving. Figure 2 The overall streamline diagram of the dust removal structure is shown. At the same time, in order to simulate the metal splash particles generated by cutting, the Lagrangian multiphase model is used for particle phase simulation calculation, and the gravity model, the drag force model, the shear lift force model and the two-way coupling model are enabled to truly reflect the force and motion trajectory of the particles in the complex flow field.

[0027] In the simulation calculation process, obtaining the pole piece pressure difference result and the particle distribution result is a key link of this embodiment. Unlike the prior art which only focuses on the flow field velocity vector, this embodiment particularly extracts the pressure distribution data on the surface of the pole piece. The pole piece pressure difference result is used to represent the risk of pole piece shaking or waste strip breaking, as shown in Figure 3 At the same time, as shown in Figure 4 When obtaining the particle distribution result, in order to solve the problem that the conventional simulation software cannot directly and accurately count the number of particles passing through a specific boundary, this embodiment uses the method of customizing a field function. The specific steps include: defining a scalar field function, and the function expression is: $RegionIndex == <region_number>? 1:0 Wherein, $RegionIndex represents a region index variable, and <region_number> represents the index number corresponding to the boundary to be counted; through the function, when the region index number corresponds to the index number of the boundary to be counted, the function value is 1, otherwise it is 0; a cell counter is established, and a monitor is created based on the cell counter to monitor the number of particles passing through the boundary to be counted in each time step; a statistical data report is created, and the monitor is used to real-time count and accumulate the total number of particles passing through the boundary to be counted in each time step. This accurate quantitative statistics provides solid data support for the evaluation of dust removal efficiency.

[0028] Step S4 is to determine the simulation results. It is determined whether the pole piece pressure difference result and the particle distribution result meet the preset requirements. Specifically, for the pole piece stability, the left and right side pressure difference of the pole piece is calculated. If the left and right side pressure difference is greater than a first threshold value, it is determined that there is the shaking risk. Shaking will cause uneven distribution of laser energy, affecting cutting quality. The front and back side pressure difference of the pole piece is calculated. If the front and back side pressure difference is greater than a second threshold value, it is determined that there is the waste strip breaking risk. Broken waste is easy to block the pipeline. In this embodiment, based on a large amount of experimental data and engineering experience, the first threshold value is set to 30 Pa, and the second threshold value is set to 50 Pa. Through the two quantitative indicators, the hidden danger that may cause process defects in system design can be accurately identified.

[0029] Finally, step S5 is performed. If the above indicators do not meet the preset requirements, the three-dimensional model of the laser cutting dust removal system or the process parameters is adjusted. The adjustment methods include but are not limited to changing the angle of the flow guide plate, adjusting the size of the air port, or modifying the dust removal wind speed, etc. After adjustment, the above modeling, setting, simulation calculation and determination steps are repeated until the pole piece pressure difference result and the particle distribution result meet the preset requirements. At this time, the final simulation model or process parameter is output as the optimization design result. This method replaces the traditional experimental trial and error method, significantly shortening the development and rectification cycle of the dust removal system.

[0030] Embodiment 2 This embodiment provides an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the optimization design method of the dust removal system described in embodiment 1 can be implemented. Through the electronic device, the engineering designer can automatically complete the whole process from grid division, solution calculation to result post-processing, and quickly verify different design schemes.

[0031] Embodiment 3 This embodiment provides a computer readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the optimization design method of the dust removal system described in embodiment 1 is implemented. The storage medium can be conveniently transferred between different computing terminals, so that the above optimization design method can be widely deployed in related industrial design scenarios.

[0032] The above-described embodiments are only preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Any equivalent replacement or transformation made by those skilled in the art based on the present application is within the protection scope of the present application. The protection scope of the present application is subject to the claims.

Claims

1. An optimization design method for a dust removal system, characterized in that, The method includes the following steps: Step S1: Construct a fluid region model of the laser cutting dust removal system, which includes a laser inlet, a return air inlet, a negative pressure dust removal inlet, and an electrode located in the laser cutting region; Step S2: Obtain the process parameters of the laser cutting dust removal system, and input the process parameters as boundary conditions into the fluid region model; Step S3: Perform flow field and particle phase simulation calculations on the fluid region model to obtain electrode pressure difference results and particle distribution results; the electrode pressure difference results are used to characterize the electrode vibration risk or waste material breakage risk; Step S4: Determine whether the electrode pressure difference result and the particle distribution result meet the preset requirements; Step S5: If not satisfied, adjust the three-dimensional structural model of the laser cutting dust removal system or the process parameters, and repeat the above simulation calculation and judgment steps until the electrode pressure difference result and the particle distribution result meet the preset requirements.

2. The optimization design method for the dust removal system as described in claim 1, characterized in that, In step S4, the specific steps for determining whether the electrode pressure difference result meets the preset requirements include: calculating the left and right side pressure difference of the electrode; if the left and right side pressure difference is greater than a first threshold, it is determined that there is a risk of vibration; calculating the front and rear side pressure difference of the electrode; if the front and rear side pressure difference is greater than a second threshold, it is determined that there is a risk of waste material breakage; wherein, the first threshold is 30 Pa and the second threshold is 50 Pa.

3. The optimization design method for the dust removal system as described in claim 1, characterized in that, In step S3, when obtaining the particle distribution result, a custom field function is used to count the number of particles passing through the boundary. The specific steps include: defining a scalar field function, setting the function value to 1 when the region index number corresponds to the index number of the boundary to be counted, and 0 otherwise; establishing a cell counter, and creating a monitor based on the cell counter to monitor the number of particles passing through the boundary to be counted in each time step; creating a statistical data report, and using the monitor to count and accumulate the total number of particles passing through the boundary to be counted in each time step in real time.

4. The optimization design method for the dust removal system as described in claim 1, characterized in that, In step S3, the particle phase simulation calculation adopts the Lagrange multiphase model and enables the gravity model, drag model, shear lift model and two-way coupling model.

5. The optimization design method for a dust removal system as described in claim 1, characterized in that, In step S2, when the process parameters are input as boundary conditions into the fluid region model, the following steps are included: setting the laser inlet and the return air inlet as pressure inlets and setting the relative pressure value to zero; setting the negative pressure dust removal port as a velocity outlet and inputting the flow velocity value in the process parameters; and setting the surfaces other than the laser inlet, the return air inlet, and the negative pressure dust removal port as walls.

6. The optimization design method for a dust removal system as described in claim 1, characterized in that, In step S3, the physical models selected for the flow field simulation calculation also include three-dimensional models, gas models, separated flow models, implicit unsteady-state models, and K-epsilon turbulence models.

7. The optimization design method for a dust removal system as described in claim 1, characterized in that, Before performing simulation calculations in step S3, the process also includes meshing the fluid region model: using a polyhedral mesh dominated by a surface quadrilateral; and performing mesh refinement processing on the splash area, electrode area, and inlet / outlet area of ​​the laser cutting dust removal system.

8. The optimization design method for a dust removal system as described in claim 1, characterized in that, In step S1, the step of constructing the fluid region model of the laser cutting dust removal system includes: establishing a three-dimensional solid model of the laser cutting dust removal system; using Boolean subtraction operation to generate the fluid region model from the three-dimensional solid model; and dividing the fluid region model into independent components corresponding to the negative pressure dust removal port, the laser inlet, the return air port, the splash area, and the electrode area.

9. The optimization design method for a dust removal system as described in claim 1, characterized in that, In step S2, the process parameters are obtained by acquiring the mean values ​​of flow rate, flow velocity, or pressure under stable conditions using online monitoring equipment or handheld measuring instruments.

10. The optimization design method for a dust removal system as described in claim 1, characterized in that, The laser cutting dust removal system includes a semi-enclosed space, which is connected to the external environment only through the laser inlet and the return air vent.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the optimization design method for the dust removal system as described in any one of claims 1 to 10.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the optimization design method for the dust removal system as described in any one of claims 1 to 10.

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