Coating die head parameter optimization method

Abstract

The invention provides a coating die head parameter optimization method. The coating die head parameter optimization method comprises the following steps: S1, obtaining structure parameters of a coating die head; s2, establishing an inner flow field multiphase transient flow field model of the coating die head according to the structure parameters; s3, structure parameters are optimized based on the internal flow field multiphase transient flow field model, and optimized structure parameters are obtained; s4, establishing an external flow field multiphase transient flow field model of the coating die head based on the optimized structure parameters; and S5, optimizing the process parameters based on the external flow field multiphase transient flow field model, and obtaining optimized process parameters. According to the technical scheme, the method for optimizing the parameters of the coating die head can solve the problem that the coating consistency and stability are poor due to the fact that an optimization method in the prior art cannot meet various process requirements at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery manufacturing, and in particular to a method for optimizing coating die head parameters. Background Art

[0002] In lithium-ion battery manufacturing, the coating process is a crucial step influencing battery performance and consistency. As a core component of coating equipment, the design and optimization of the coating die directly impacts the thickness consistency and surface quality of the coating film. Traditional coating die design relies primarily on experience and experimentation, manually adjusting die structural and process parameters to achieve the desired coating effect.

[0003] However, with the advancement of lithium-ion battery technology, traditional methods have struggled to meet the high-precision and high-consistency requirements. Furthermore, the experimental debugging process is time-consuming, labor-intensive, and costly. Some existing research has used simulation technology to assist in the design and optimization of coating dies, but most of these studies focus on optimizing a single parameter, such as optimizing the die cavity structure to improve coating uniformity or adjusting the slit outlet size to reduce coating defects. This single-parameter optimization approach often fails to simultaneously meet multiple process requirements, resulting in poor coating consistency and stability. Summary of the Invention

[0004] The main purpose of the present invention is to provide a coating die head parameter optimization method, which can solve the problem that the optimization method using the existing technology cannot meet multiple process requirements at the same time, resulting in poor coating consistency and stability.

[0005] In order to achieve the above-mentioned objectives, the present invention provides a coating die parameter optimization method, comprising: S1: obtaining the structural parameters of the coating die; S2: establishing an internal flow field multiphase transient flow field model of the coating die according to the structural parameters; S3: optimizing the structural parameters based on the internal flow field multiphase transient flow field model, and obtaining preferred structural parameters; S4: establishing an external flow field multiphase transient flow field model of the coating die based on the preferred structural parameters; S5: optimizing the process parameters based on the external flow field multiphase transient flow field model, and obtaining preferred process parameters.

[0006] Through the above settings, structural parameters and process parameters can be optimized simultaneously to meet multiple process goals, such as consistency of wet film thickness, uniformity of flow rate and surface quality. This can effectively solve the problem that traditional optimization methods cannot meet multiple process requirements at the same time, significantly improve the consistency and stability of the coating, and further enhance the overall performance of lithium batteries.

[0007] Furthermore, the steps of establishing a multiphase transient flow field model of the internal flow field of the coating die head according to the structural parameters include: importing the structural parameters into simulation software; and using the VOF method in the simulation software to simulate the interface flow of the slurry and air.

[0008] Through the above settings, different combinations of structural parameters and process parameters can be quickly evaluated in a virtual environment, significantly reducing the number of experiments and development time.

[0009] Furthermore, the steps of optimizing the structural parameters based on the internal flow field multiphase transient flow field model include: defining the structural parameters as the first design variable; setting the value range of the first design variable; obtaining first combination data of the first design variable within the value range of the first design variable; importing the first combination data into the simulation software for simulation to obtain simulation results; constructing a response surface model according to the simulation results; and using a multi-objective genetic algorithm based on the response surface model to obtain optimal structural parameters.

[0010] Through the above settings, multiple design options can be quickly evaluated in a virtual environment without the need for actual physical experiments, which can significantly shorten the design cycle. Through the post-processing function of the simulation software, the flow field data during the optimization process can be visualized, and the flow changes of the fluid in the mold cavity before and after optimization can be directly displayed. This visualization can provide the design team with intuitive feedback on the optimization effect, further enhancing the accuracy of design decisions.

[0011] Furthermore, based on the response surface model, the step of using a multi-objective genetic algorithm to obtain the optimal structural parameters includes: constructing a first objective function, the first objective function includes: f1 = Min[Var(V out )] and f2=Max(a), where V out is the discharge flow rate of the coating die head, a is the uniformity of the discharge flow rate of the coating die head; based on the response surface model, with the first objective function as the termination condition, a multi-objective genetic algorithm is used to obtain the optimal structural parameters.

[0012] Through the above settings, the performance of different structural parameter combinations can be quickly evaluated based on model prediction, avoiding a large amount of actual simulation calculations and significantly improving the efficiency of the optimization process.

[0013] Furthermore, based on the response surface model, the steps of obtaining the optimal structural parameters by using the multi-objective genetic algorithm include: constructing a fitness function, the fitness function is: f(x) = ω1Var(V out )+ω2(1-a), where ω1 and ω2 are weight factors. Based on the response surface model and taking the fitness function as the termination condition, a multi-objective genetic algorithm is used to obtain the optimal structural parameters.

[0014] Through the above settings, by defining the fitness function and adjusting the weighting factors, the multi-objective genetic algorithm can find the optimal balance between multiple objectives, such as minimizing the variance of the discharge flow rate and maximizing the uniformity of the coating die discharge flow rate, ensuring that the coating effect is optimized simultaneously in multiple dimensions. The use of the response surface model enables the multi-objective genetic algorithm to quickly evaluate the performance of parameter combinations without the need for actual experiments or simulation calculations, significantly improving the efficiency and speed of optimization. The combination of the response surface model and the multi-objective genetic algorithm reduces the number of experiments and simulations, reducing the time and economic costs of coating die optimization.

[0015] Furthermore, the step of establishing a multiphase transient flow field model of the external flow field of the coating die head based on the preferred structural parameters includes: establishing a three-dimensional model of the coating die head according to the preferred structural parameters; importing the three-dimensional model into the simulation software to establish a multiphase transient flow field model of the external flow field.

[0016] With this setup, the 3D model verified by simulation can be directly used in preparation for the production phase. For example, it can be sent to the manufacturer for mold fabrication, ensuring that the coating die produced is exactly the same as the optimized design without the need for additional modification or debugging.

[0017] Furthermore, the steps of optimizing the process parameters based on the external flow field multiphase transient flow field model include: defining the process parameters as a second design variable; setting a value range of the second design variable; obtaining second combination data of the second design variable within the value range of the second design variable; constructing a second objective function, the second objective function including: f3=Min[Var(T m )] and f4=Max(d), where T m is the coating thickness, d is the coating thickness consistency; the second combination data and the second objective function are imported into the simulation software; the second objective function is used as the termination condition, and the multi-objective genetic algorithm is used to obtain the optimal process parameters.

[0018] The above settings can reduce the number of parameter adjustments during the actual production process, avoiding the reduction in production efficiency and increase in costs caused by repeated adjustments to process parameters. The multi-objective genetic algorithm uses an adaptive crossover and mutation strategy to optimize process parameters. The optimization process also considers the dynamic changes in the actual production environment, making the optimization results more adaptable and robust, ultimately achieving efficient, stable, and precise control of the coating process. This method can significantly reduce experimental debugging time and production costs, and is suitable for the optimization and application of various lithium battery coating processes. The parameter search range during the optimization process is dynamically adjusted to improve optimization efficiency and avoid local optimal solutions.

[0019] Furthermore, the coating die head parameter optimization method further includes: if the obtained preferred process parameters are not within the value range of the second design variable, repeating S1 to S5 until the obtained preferred process parameters are within the value range of the second design variable.

[0020] Through the above settings, the process parameters are limited to a feasible range, ensuring that the optimization results can be applied in the actual production environment.

[0021] Furthermore, the structural parameters include the size of the gasket, the size of the baffle, and the size of the cavity of the coating die.

[0022] Through the above arrangement, the distribution uniformity of the fluid inside the coating die head can be significantly improved.

[0023] Furthermore, the process parameters include the discharge flow rate of the coating die head, the moving speed of the workpiece to be coated, and the distance from the discharge port of the coating die head to the surface of the workpiece to be coated.

[0024] The above settings can significantly improve coating accuracy and uniformity. Optimizing the distance between the coating die outlet and the surface of the part to be coated ensures that the slurry is applied smoothly and evenly to the substrate surface, improving the surface quality and consistency of the coating.

[0025] By applying the technical solution of the present invention, the structural parameters of the coating die are obtained, including but not limited to the mold cavity size, gasket size, baffle size and position, etc., to provide basic data for the subsequent establishment of an internal flow field multiphase transient flow field model. Then, based on the structural parameters collected in S1, an internal flow field multiphase transient flow field model of the coating die is established to simulate the flow process of the slurry inside the coating die, including flow velocity distribution, pressure change and interface behavior. Based on the internal flow field multiphase transient flow field model, the structural parameters are optimized and the preferred structural parameters are obtained. Based on the preferred structural parameters, an external flow field multiphase transient flow field model of the coating die is established. Based on the external flow field multiphase transient flow field model, the process parameters are optimized and the preferred process parameters are obtained. Compared with traditional coating die optimization methods, they often only focus on a single goal, such as only optimizing structural parameters without considering process parameters, or only adjusting process parameters during the experiment without optimizing the die structure. The coating die parameter optimization method of the present application can simultaneously optimize structural parameters and process parameters to meet multiple process goals, such as consistency of wet film thickness, uniformity of flow rate and surface quality. It can effectively solve the problem that traditional optimization methods cannot meet multiple process requirements at the same time, significantly improve the consistency and stability of coating, and further enhance the overall performance of lithium batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0027] Figure 1 A flow chart of the coating die head parameter optimization method of the present invention is shown;

[0028] Figure 2 A schematic diagram of the internal flow channel structure of a coating die head according to an embodiment of the present invention is shown;

[0029] Figure 3 shows a grid distribution diagram of a coating die head according to one embodiment of the present invention;

[0030] Figure 4 A schematic diagram showing the simulated internal flow field distribution of a multiphase transient flow field model of the internal flow field of a coating die head according to an embodiment of the present invention is shown;

[0031] Figure 5 A schematic diagram showing the simulated outlet flow velocity distribution of a multiphase transient flow field model of the internal flow field of a coating die head according to one embodiment of the present invention is shown;

[0032] Figure 6 A schematic diagram of a simulated three-dimensional structure of a multiphase transient flow field model of an external flow field of a coating die head according to another embodiment of the present invention is shown;

[0033] Figure 7 A schematic diagram of a simulation grid model of a multiphase transient flow field model of an external flow field of a coating die head according to another embodiment of the present invention is shown;

[0034] Figure 8 A schematic diagram showing the simulation flow field results of a multiphase transient flow field model of the external flow field of a coating die head according to an embodiment of the present invention is shown.

[0035] The above drawings include the following reference numerals:

[0036] 10, first simulation exit; 20, second simulation exit; 30, third simulation exit; 40, fourth simulation exit. DETAILED DESCRIPTION

[0037] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0038] like Figure 1As shown, the present invention provides a coating die parameter optimization method, which includes: S1: obtaining structural parameters of the coating die; S2: establishing an internal flow field multiphase transient flow field model of the coating die according to the structural parameters; S3: optimizing the structural parameters based on the internal flow field multiphase transient flow field model, and obtaining preferred structural parameters; S4: establishing an external flow field multiphase transient flow field model of the coating die based on the preferred structural parameters; S5: optimizing process parameters based on the external flow field multiphase transient flow field model, and obtaining preferred process parameters.

[0039] In this embodiment, the structural parameters of the coating die are obtained, including but not limited to the mold cavity size, gasket size, baffle size and position, etc., to provide basic data for the subsequent establishment of the internal flow field multiphase transient flow field model. Then, based on the structural parameters collected in S1, the internal flow field multiphase transient flow field model of the coating die is established to simulate the flow process of the slurry inside the coating die, including flow velocity distribution, pressure change and interface behavior. Based on the internal flow field multiphase transient flow field model, the structural parameters are optimized and the preferred structural parameters are obtained. Based on the preferred structural parameters, the external flow field multiphase transient flow field model of the coating die is established. At the same time, the initial process parameter combination needs to be imported into the external flow field multiphase transient flow field model. Then, based on the external flow field multiphase transient flow field model, the process parameters are optimized and the preferred process parameters are obtained.

[0040] The step of obtaining the structural parameters of the coating die head also includes: initializing the structural parameters, and then setting the parameter range of the structural parameters (defining the range of the maximum and minimum values ​​of the structural parameters to be optimized and the interval of the values), and the specific structural parameters include but are not limited to the mold cavity size, gasket size, and the size and position of the baffle. The step of optimizing the process parameters based on the external flow field multiphase transient flow field model also includes: initializing the process parameters, and then setting the parameter range of the process parameters (defining the range of the maximum and minimum values ​​of the process parameters to be optimized and the interval of the values), and the specific process parameters include but are not limited to the inlet flow rate, the movement speed of the electrode, the gap between the slit outlet and the electrode, etc.

[0041] Compared with traditional coating die optimization methods that often focus on a single goal, such as optimizing only structural parameters without considering process parameters, or adjusting process parameters only during the experiment without optimizing the die structure, the coating die parameter optimization method of this application can simultaneously optimize structural parameters and process parameters to meet multiple process goals, such as consistency of wet film thickness, uniformity of flow rate, and surface quality. It can effectively solve the problem that traditional optimization methods cannot meet multiple process requirements at the same time, significantly improve the consistency and stability of coating, and further enhance the overall performance of lithium batteries.

[0042] It should be noted that the internal flow field and external flow field in this application are defined as follows: the internal flow field refers to the entire flow process of the slurry from the feed port of the coating die head into the interior of the die head, through the mold cavity, gaskets, baffles and other structures, and finally reaching the slit outlet. This process mainly involves the fluid behavior inside the coating die head, including flow rate distribution, pressure changes, etc., and is a key stage that affects the uniform distribution of the slurry and the consistency of the flow rate. The external flow field refers to the process of spraying the slurry from the slit outlet of the coating die head to the surface of the part to be coated (such as copper foil or aluminum foil). This process involves the flow and deposition behavior of the slurry from the slit outlet of the die head to the part to be coated (such as copper foil or aluminum foil), which directly affects the thickness and consistency of the coated wet film, as well as the surface quality of the coated wet film.

[0043] In one embodiment, when establishing the multiphase transient flow field model of the internal flow field and the multiphase transient flow field model of the external flow field, the VOF model is used in combination with the surface tension model. Through the VOF model, the interfacial flow between the slurry (liquid phase) and the air (gas phase) can be tracked, thereby accurately simulating the movement of the multiphase fluid in the coating die, mainly including the flow velocity distribution and pressure changes; it should be noted that the VOF model, the full name of which is the Volume of Fluid model, is a numerical model used in computational fluid dynamics (CFD) to simulate free surface flow and multiphase fluid interface tracking. The VOF model belongs to the prior art, and the calculation principle and method will not be repeated here.

[0044] The surface tension model is Among them, σ is the surface tension coefficient, n is the interface normal vector, and the surface tension model can take into account the capillary effect at the interface, that is, the influence of surface tension on the dynamic behavior of the interface (such as the formation, rupture or merger of droplets), and thus can analyze the distribution of the slurry inside the die head and the discharge condition of the external flow field (discharge stability).

[0045] In one embodiment of the present invention, the step of establishing a multiphase transient flow field model of the internal flow field of the coating die head according to the structural parameters includes: importing the structural parameters into simulation software; and using the VOF method in the simulation software to simulate the interface flow of the slurry and air.

[0046] In this example, the structural parameters were imported into the simulation software. The VOF multiphase flow model was then selected within the simulation software, and the model parameters were adjusted, including the solution method for the volume fraction equation, the time step, the mesh quality requirements, and the surface tension model parameters. The initial conditions of the VOF model were set, such as the initial distribution of the slurry and air, and the interfacial flow between the slurry and air was simulated.

[0047] Simulation results from simulation software can reveal the relationship between structural parameters and flow field performance, providing guidance for coating die design optimization. Adjusting coating dies and process parameters in actual production typically requires extensive trial-and-error experiments, which is time-consuming and costly. Simulation technology, however, allows for rapid evaluation of different structural and process parameter combinations in a virtual environment, significantly reducing the number of experiments and development time.

[0048] It should be noted that the VOF (Volume of Fluid) method is used to simulate the interfacial flow between slurry and air. The VOF method includes basic equations, which include the mass conservation equation and the momentum conservation equation: Where, ρ is the fluid density; v is the slurry flow velocity vector; p is the pressure; μ is the fluid dynamic viscosity; g is the gravitational acceleration; F s is the surface tension.

[0049] In one embodiment, the simulation software is CAE simulation software.

[0050] In one embodiment of the present invention, the steps of optimizing structural parameters based on the internal flow field multiphase transient flow field model include: defining the structural parameters as a first design variable; setting a value range of the first design variable; obtaining first combination data of the first design variable within the value range of the first design variable; importing the first combination data into simulation software for simulation to obtain simulation results; constructing a response surface model based on the simulation results; and using a multi-objective genetic algorithm based on the response surface model to obtain optimal structural parameters.

[0051] In this embodiment, the structural parameters are defined as the first design variables, and the structural parameters will be used as the first design variables of the multi-objective genetic algorithm for the subsequent optimization process. Set the value range of the first design variable: According to the design experience and physical limitations of the coating die head, a reasonable value range is set for each structural parameter (first design variable). Then, an orthogonal design experiment can be used to obtain different combinations of the first design variable within the value range. For example, the structural parameters include cavity length, gasket thickness, and the length of the baffle. Then, corresponding value ranges are set for the cavity length, gasket thickness, and the length of the baffle respectively. Then, the combination of the cavity length, gasket thickness, and the baffle within the value range is obtained, that is, the first combination data is obtained. The first combination data is imported into the simulation software for simulation, and the simulation results are obtained. Then, a response surface model is constructed based on the simulation results. Based on the response surface model, a multi-objective genetic algorithm is used to obtain the optimal structural parameters. The response surface model (RSM) is a statistical modeling technique used to approximately describe the relationship between the design variables and their responses (i.e., the optimization objective function). Based on the simulation results, RSM is constructed, and complex fluid mechanics behavior can be simplified into a mathematical relationship between structural parameters and key performance indicators. Based on the response surface model, a multi-objective genetic algorithm was used to obtain the optimal structural parameters.

[0052] By defining structural parameters as design variables and building a response surface model based on simulation, multiple design options can be quickly evaluated in a virtual environment without the need for actual physical experiments, which can significantly shorten the design cycle. Through the post-processing function of the simulation software, the flow field data during the optimization process can be visualized, and the flow changes of the fluid in the mold cavity before and after optimization can be intuitively displayed. This visualization can provide the design team with intuitive feedback on the optimization effect and further enhance the accuracy of design decisions.

[0053] In one embodiment of the present invention, based on the response surface model, the step of using a multi-objective genetic algorithm to obtain optimal structural parameters includes: constructing a first objective function, the first objective function including: f1 = Min[Var(V out )] and f2=Max(a), where V out is the discharge flow rate of the coating die head, a is the uniformity of the discharge flow rate of the coating die head; based on the response surface model, with the first objective function as the termination condition, a multi-objective genetic algorithm is used to obtain the optimal structural parameters.

[0054] In this embodiment, a first objective function is constructed to evaluate the quality of the structural parameter combination. The first objective function includes the minimum variance of the discharge flow rate and the maximum uniformity of the discharge flow rate of the coating die head. The multi-objective genetic algorithm is an evolutionary algorithm that can simultaneously optimize multiple objective functions and find the optimal balance between them. Based on the response surface model, with the first objective function as the termination condition, the process of using the multi-objective genetic algorithm to obtain the optimal structural parameters is as follows: a set of initial populations containing random structural parameter combinations is generated, the first objective function value of each individual (i.e., structural parameter combination) is predicted using the constructed response surface model, and its performance on multiple objectives is evaluated. Based on the first objective function value, the parent individual is selected according to the selection strategy of the multi-objective genetic algorithm (such as non-dominated sorting and crowding calculation); the offspring is generated through crossover and mutation operations to explore the design space and improve the population diversity. The evaluation, selection, crossover and mutation process is repeated until the termination condition is met. After the optimization process is completed, the multi-objective genetic algorithm will provide a set of Pareto optimal solutions, each of which represents the optimal structural parameter combination under the constraints of the first objective function (including flow rate and flow rate uniformity objectives). By constructing a response surface model, the multi-objective genetic algorithm can quickly evaluate the performance of different structural parameter combinations based on model predictions, avoiding a large amount of actual simulation calculations and significantly improving the efficiency of the optimization process.

[0055] For ease of understanding, the logical relationship between the first objective function, the response surface model, and the target genetic algorithm is briefly explained. The response surface model is an approximate model used to quickly evaluate the objective function, and the multi-objective genetic algorithm is an evolutionary algorithm used to search for the optimal solution. The first objective function is the evaluation indicator that connects the two. In the entire optimization process, a response surface model is first constructed based on the simulation experimental data, and then the model is used to repeatedly calculate and compare the objective function values ​​under different design variable combinations in the iterative evolution of the genetic algorithm to find the optimal solution. It should be noted that the surface flow velocity of the slit outlet of the coating die head has a higher consistency of flow velocity as the smaller the standard deviation of the flow velocity. The outlet stability is the pressure difference between the slurry inlet and the slit outlet of the coating die head. The smaller the pressure difference, the more stable it is. The outlet flow velocity and its consistency are used as the objective function to define its target range. The outlet flow velocity depends on the specific process requirements. Different processes correspond to different outlet flow velocity consistency, which refers to the standard deviation of the outlet flow velocity.

[0056] In one embodiment, the target value of the first objective function is set to a range of values: the standard deviation of the outlet surface flow rate is ≤0.003, which can control the fluctuation of the discharge flow rate; the maximum inlet and outlet pressure difference is ≤0.2MPa, which can avoid excessive energy loss and improve flow stability.

[0057] In one embodiment of the present invention, based on the response surface model, the step of using a multi-objective genetic algorithm to obtain optimal structural parameters includes: constructing a fitness function, the fitness function is: f(x) = ω1Var(V out )+ω2(1-a), where ω1 and ω2 are weight factors. Based on the response surface model and taking the fitness function as the termination condition, a multi-objective genetic algorithm is used to obtain the optimal structural parameters.

[0058] In this embodiment, the setting of the weight factor needs to be adjusted according to the specific application requirements. By defining the fitness function and adjusting the weight factor, the multi-objective genetic algorithm can find the optimal balance between multiple objectives such as the minimum variance of the discharge flow rate and the maximum uniformity of the discharge flow rate of the coating die, ensuring that the coating effect is optimized simultaneously in multiple dimensions. The use of the response surface model enables the multi-objective genetic algorithm to quickly evaluate the performance of the parameter combination without the need for actual experiments or simulation calculations, significantly improving the efficiency and speed of optimization. The combination of the response surface model and the multi-objective genetic algorithm reduces the number of experiments and simulations, and reduces the time cost and economic cost in the coating die optimization process.

[0059] It should be noted that the fitness function of this application is constructed based on multiple objective functions, and a weight is assigned to each objective.

[0060] In one embodiment of the present invention, the step of establishing a multiphase transient flow field model of the external flow field of the coating die head based on the preferred structural parameters includes: establishing a three-dimensional model of the coating die head according to the preferred structural parameters; importing the three-dimensional model into the simulation software to establish a multiphase transient flow field model of the external flow field.

[0061] In this example, the multiphase transient flow model of the external flow field provides accurate predictions of fluid dynamics, including detailed information about the slurry flow from the slit outlet to the substrate surface. The 3D model, validated by simulation, can be directly used in production preparation. For example, it can be sent to a manufacturer for mold fabrication, ensuring that the resulting coating die is identical to the optimized design without the need for additional modification or debugging.

[0062] In the existing technology, simulation technology is used to assist in the design and optimization of coating dies, and most of them only optimize a single parameter, for example, optimizing the cavity structure to improve coating uniformity, or adjusting the slit outlet size to reduce coating defects. This single parameter optimization method is often unable to meet multiple process requirements at the same time, resulting in poor consistency and stability in the coating process. Especially for the needs of multi-objective optimization, for example, while ensuring the uniformity of the coating film thickness, ensuring the consistency of the flow rate and the surface quality of the film layer, the existing optimization methods often find it difficult to find the best parameter combination that takes into account all aspects of performance. In addition, traditional simulation technology has great limitations in terms of computational complexity and time cost, and it is difficult to complete the iterative optimization of complex structures and multiple process parameters within a reasonable time.

[0063] In order to solve the above problem, in one embodiment of the present invention, the steps of optimizing the process parameters based on the external flow field multiphase transient flow field model include: defining the process parameters as a second design variable; setting the value range of the second design variable; obtaining second combination data of the second design variable within the value range of the second design variable; constructing a second objective function, the second objective function including: f3=Min[Var(T m )] and f4=Max(d), where T m is the coating thickness, d is the coating thickness consistency; the second combination data and the second objective function are imported into the simulation software; the second objective function is used as the termination condition, and the multi-objective genetic algorithm is used to obtain the optimal process parameters.

[0064] In this embodiment, within the value range of the second design variable, a plurality of combination data of the second design variables are generated by experimental design methods (such as orthogonal design, Latin hypercube sampling, etc.) or random sampling, and each combination represents a specific set of process parameter settings. The second combination data and the second objective function are imported into the multiphase transient flow field model of the external flow field, and the coating process is simulated and analyzed using simulation software. The multi-objective genetic algorithm is integrated in the simulation software or called through an external script. The multi-objective genetic algorithm will look for the Pareto optimal solution in the combination data of the second design variable, that is, the process parameter combination that finds the best balance point between the two objectives of coating thickness and thickness consistency. Optimization based on simulation software can reduce the number of parameter debugging times in the actual production process, and avoid the reduction in production efficiency and increase in cost due to repeated adjustment of process parameters.

[0065] A multi-objective genetic algorithm employs an adaptive crossover and mutation strategy to optimize process parameters. This strategy, while factoring in dynamic changes in the actual production environment, results in highly adaptable and robust optimization results, ultimately achieving efficient, stable, and precise control of the coating process. This method significantly reduces experimental debugging time and production costs, making it suitable for optimizing and applying various lithium battery coating processes. The parameter search range is dynamically adjusted during the optimization process to improve optimization efficiency and avoid local optimal solutions.

[0066] It should be noted that regarding the coating thickness, Min refers to the minimum deviation between the simulation result and the target result of the coating thickness. For different production lines or production requirements, there is a certain coating wet film thickness requirement, such as 180±2um, f3=Min[Var(T m The goal is to keep the wet film thickness derived from external flow field simulation as close as possible to the target of 180 μm. This thickness depends on the process requirements and varies with different production processes, but is generally around 180 μm. Thickness consistency is the standard deviation of thickness, which generally does not exceed 0.4%.

[0067] In one embodiment of the present invention, the coating die head parameter optimization method further includes: if the obtained preferred process parameters are not within the value range of the second design variable, repeating S1 to S5 until the obtained preferred process parameters are within the value range of the second design variable.

[0068] In this example, limiting the process parameters to an implementable range ensures that the optimization results can be applied in a real-world production environment. For example, if the optimal process parameters exceed the actual operating range, even if they theoretically provide better coating results, they may not be achieved in actual production. Through repeated optimization, the process parameters can be gradually adjusted until they meet actual production conditions, ensuring the practicality of the optimization solution.

[0069] It should be noted that the optimization process of this application uses parallel computing technology for non-dominated sorting and congestion calculation to accelerate the optimization process and maintain the diversity of the solution set. Specifically, the following three methods are mainly used: 1) CPU-based parallel computing: multi-threaded parallel computing accelerates the VOF solution process; 2) GPU-based parallel computing: GPU-accelerated interface reconstruction, mesh encryption, pressure correction and other processes to improve simulation speed; 3) software-built parallel computing function: using the software's built-in parallel function, multi-threaded SMP optimizes resource utilization.

[0070] The optimization process considers the dynamic changes in the actual production environment, including the impact of environmental factors such as workshop temperature and humidity on coating results, thereby improving the adaptability of the optimization results under actual production conditions. The optimized coating die and process parameters are verified in actual production to ensure the stability and consistency of the coating process. The results of these experimental verifications are then applied to large-scale production. The effects of temperature and humidity are considered in the simulation. The inputs to the simulation analysis include external environment settings, generally set to room temperature and dry environment; however, different temperature and humidity environments can be set.

[0071] In one embodiment of the present invention, the structural parameters include the size of the gasket, the size of the baffle, and the size of the cavity of the coating die.

[0072] In this embodiment, the structural parameters also include the position of the baffle. Optimizing the gasket size, baffle size, and mold cavity size can significantly improve the uniformity of fluid distribution within the coating die. For example, adjusting the gasket thickness can control the pressure distribution within the coating die, thereby affecting the flow and distribution of the slurry. Optimizing the size and position of the baffle can adjust the slurry flow path, avoiding localized excessively high or low flow rates. Precise mold cavity dimensions ensure stable slurry flow within the die, reducing fluctuations and turbulence, ultimately achieving a more uniform coating effect.

[0073] It should be noted that the size of the baffle refers to the length and width of the baffle.

[0074] In one embodiment of the present invention, the process parameters include the discharge flow rate of the coating die head, the moving speed of the workpiece to be coated, and the distance from the discharge port of the coating die head to the surface of the workpiece to be coated.

[0075] In this embodiment, optimizing the discharge flow rate and the speed of the coated object significantly improves coating accuracy and uniformity. Optimizing the distance from the coating die outlet to the surface of the coated object ensures smooth and even application of the slurry to the substrate surface, improving the surface quality and consistency of the coating.

[0076] It should be noted that, based on the optimized structural and process parameters, actual coating experiments were conducted to measure the thickness uniformity and flow rate consistency of the coating film to verify the reliability of the optimization results. The coating die and process parameters verified through the experiments were applied to large-scale production. By comparing the production data before and after optimization, it was demonstrated that the method of this application significantly improved the stability and production efficiency of the coating process.

[0077] In summary, this application adopts the VOF multiphase flow model, which can accurately simulate the flow behavior of the slurry in the coating die. By refining the flow field analysis, the flow velocity distribution and pressure changes during the coating process are accurately predicted, effectively improving the uniformity and consistency of the coated wet film. The multi-objective genetic algorithm adopts an adaptive crossover and mutation strategy, which can dynamically adjust the search range of parameters during the optimization process, thereby improving the optimization efficiency and avoiding falling into the local optimal solution. This strategy enables the structural parameters and process parameters of the coating die to achieve global optimal optimization in a shorter time. The non-dominated sorting and congestion calculation in the optimization process of this application adopts parallel computing technology, which greatly improves the calculation speed and processing efficiency. Through parallel processing, the Pareto optimal solution set can be quickly generated under complex working conditions, providing a more flexible optimization solution for different production needs. Dynamic simulation of the actual production environment is added to the optimization process, such as real-time simulation of changes in workshop temperature and humidity, to improve the adaptability of the optimization results. By considering the impact of environmental factors on the coating effect, the process parameters are further optimized, making the coating quality more stable and reliable under actual production conditions.

[0078] like Figure 2 The figure shows a schematic diagram of the internal flow channel structure of the coating die head of the first embodiment. The coating die head has a first simulation outlet 10, a second simulation outlet 20, a third simulation outlet 30 and a fourth simulation outlet 40; Figure 3 The grid distribution diagram of the coating die head of Example 1 is shown, which can be used for CAE simulation analysis; Figure 4 Schematic diagram of the internal flow field distribution of the coating die head of Example 1 is shown; Figure 5 The schematic diagram of the simulated outlet velocity distribution of the multiphase transient flow field model of the internal flow field of the coating die head of Example 1 is shown, wherein the red line corresponds to the outlet velocity distribution of the first simulated outlet 10, the blue line corresponds to the outlet velocity distribution of the fourth simulated outlet 40, the purple line corresponds to the outlet velocity distribution of the second simulated outlet 20, and the yellow line corresponds to the outlet velocity distribution of the third simulated outlet 30, wherein the horizontal axis is the width value of the outlet on the X axis, the vertical axis is the velocity value in the Y axis direction, and the X and Y directions are Figure 4 You can see in; Figure 6 A schematic diagram of a simulated three-dimensional structure of a multiphase transient flow field model of an external flow field of a coating die head in Example 2 is shown; Figure 7 A schematic diagram of a simulation grid model of a multiphase transient flow field model of the external flow field of the coating die head of Example 2 is shown, which is used for CAE simulation analysis; Figure 8 A schematic diagram of the simulation flow field results of the multiphase transient flow field model of the external flow field of the coating die head is shown, which can be used to understand the flow state of the slurry, where red represents slurry and blue represents air.

[0079] The beneficial effects of this application are as follows:

[0080] 1) Significantly improve coating quality: By adopting the VOF multiphase flow model, the slurry flow behavior in the coating die can be accurately simulated, the structural parameters of the coating die can be optimized, and the flow velocity distribution and pressure changes during the coating process can be ensured to be more uniform, thereby significantly improving the thickness consistency and surface quality of the coated wet film.

[0081] 2) This application introduces a multi-objective genetic algorithm with an adaptive crossover and mutation strategy. By dynamically adjusting the parameter search range, it achieves global optimization of structural and process parameters, avoiding the local optimal solution problem that can occur in traditional optimization methods. This optimization strategy not only improves optimization efficiency but also significantly shortens the development cycle, saving time and costs.

[0082] 3) By incorporating dynamic simulation of the actual production environment into the optimization process, the impact of environmental factors such as workshop temperature and humidity changes on the coating effect was taken into account, and the process parameters were further optimized, making the optimization results more adaptable and stable under different production conditions, ensuring high-quality output of the coating process.

[0083] 4) The use of parallel computing technology for non-dominated sorting and congestion calculation greatly improves the calculation speed during the optimization process. It can quickly generate Pareto optimal solution sets under complex working conditions, provide flexible optimization solutions for different production needs, and effectively respond to diverse challenges in production.

[0084] 5) By combining simulation technology with a multi-objective genetic algorithm (MOGA), multiple iterative optimizations can be performed in a virtual environment, reducing the number and cost of experimental debugging in actual production and improving overall production efficiency and economic benefits.

[0085] 6) Comprehensive optimization of process parameters using a multi-objective genetic algorithm improves the controllability of process parameters during the coating process. The optimized process parameters can be maintained highly consistent throughout the production process, reducing product quality fluctuations caused by process parameter fluctuations.

[0086] 7) The optimization method of this application is highly flexible and can quickly adjust the design and process parameters of the coating die according to different product specifications and production requirements. This makes it suitable not only for standardized production but also for the special needs of customized production, thus broadening its application range.

[0087] From the above description, it can be seen that the above-mentioned embodiments of the present invention achieve the following technical effects: obtaining the structural parameters of the coating die, including but not limited to the cavity size, gasket size, baffle size and position, etc., to provide basic data for the subsequent establishment of the internal flow field multiphase transient flow field model, and then based on the structural parameters collected in S1, establishing the internal flow field multiphase transient flow field model of the coating die to simulate the flow process of the slurry inside the coating die, including flow velocity distribution, pressure change and interface behavior. Based on the internal flow field multiphase transient flow field model, the structural parameters are optimized and the preferred structural parameters are obtained. Based on the preferred structural parameters, the external flow field multiphase transient flow field model of the coating die is established. Based on the external flow field multiphase transient flow field model, the process parameters are optimized and the preferred process parameters are obtained. Compared with traditional coating die optimization methods, they often only focus on a single goal, such as only optimizing structural parameters without considering process parameters, or only adjusting process parameters during the experiment without optimizing the die structure. The coating die parameter optimization method of the present application can simultaneously optimize structural parameters and process parameters to meet multiple process goals, such as consistency of wet film thickness, uniformity of flow rate and surface quality. It can effectively solve the problem that traditional optimization methods cannot meet multiple process requirements at the same time, significantly improve the consistency and stability of coating, and further enhance the overall performance of lithium batteries.

[0088] Obviously, the embodiments described above are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0089] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, tasks, devices, components and / or combinations thereof.

[0090] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A coating die head parameter optimization method, characterized in that: include: S1: Obtain the structural parameters of the coating die; S2: establishing a multiphase transient flow field model of the internal flow field of the coating die head according to the structural parameters; S3: Optimizing the structural parameters based on the internal flow field multiphase transient flow field model to obtain optimal structural parameters; S4: establishing a multiphase transient flow field model of the external flow field of the coating die head based on the preferred structural parameters; S5: Optimizing process parameters based on the external flow field multiphase transient flow field model to obtain optimal process parameters.

2. The coating die head parameter optimization method according to claim 1, characterized in that, The step of establishing a multiphase transient flow field model of the internal flow field of the coating die head according to the structural parameters comprises: Importing the structural parameters into simulation software; The VOF method is used in the simulation software to simulate the interfacial flow between the slurry and the air.

3. The coating die head parameter optimization method according to claim 2, characterized in that, The step of optimizing the structural parameters based on the internal flow field multiphase transient flow field model includes: defining the structural parameter as a first design variable; Setting the value range of the first design variable; Acquire first combination data of the first design variable within a value range of the first design variable; Importing the first combined data into the simulation software to perform simulation and obtain simulation results; constructing a response surface model based on the simulation results; Based on the response surface model, a multi-objective genetic algorithm is used to obtain the optimal structural parameters.

4. The coating die head parameter optimization method according to claim 3, characterized in that, Based on the response surface model, the step of using a multi-objective genetic algorithm to obtain the optimal structural parameters includes: Construct a first objective function, wherein the first objective function includes: f1=Min[Var(V out )] and f2=Max(a), where V out is the discharge flow rate of the coating die head, and a is the uniformity of the discharge flow rate of the coating die head; Based on the response surface model and taking the first objective function as a termination condition, a multi-objective genetic algorithm is used to obtain the optimal structural parameters.

5. The coating die head parameter optimization method according to claim 4, characterized in that, Based on the response surface model, the step of using a multi-objective genetic algorithm to obtain the optimal structural parameters includes: Construct a fitness function, the fitness function is: f(x) = ω1Var(V out )+ω2(1-a), where ω1 and ω2 are weight factors; Based on the response surface model and taking the fitness function as a termination condition, a multi-objective genetic algorithm is adopted to obtain the optimal structural parameters.

6. The coating die head parameter optimization method according to any one of claims 1 to 5, characterized in that: The step of establishing a multiphase transient flow field model of the external flow field of the coating die head based on the preferred structural parameters comprises: Establishing a three-dimensional model of the coating die according to the preferred structural parameters; The three-dimensional model is imported into simulation software to establish the multiphase transient flow field model of the external flow field.

7. The coating die head parameter optimization method according to claim 6, characterized in that: The step of optimizing the process parameters based on the external flow field multiphase transient flow field model includes: defining the process parameter as a second design variable; Setting the value range of the second design variable; Acquire second combination data of the second design variable within the value range of the second design variable; Construct a second objective function, wherein the second objective function includes: f3=Min[Var(T m )] and f4=Max(d), where T m is the coating thickness, d is the coating thickness consistency; Importing the second combined data and the second objective function into the simulation software; Taking the second objective function as a termination condition, a multi-objective genetic algorithm is used to obtain the optimal process parameters.

8. The coating die head parameter optimization method according to claim 7, characterized in that: The coating die head parameter optimization method further includes: if the obtained preferred process parameters are not within the value range of the second design variable, repeating S1 to S5 until the obtained preferred process parameters are within the value range of the second design variable.

9. The coating die head parameter optimization method according to any one of claims 1 to 4, characterized in that: The structural parameters include the size of the gasket, the size of the baffle block, and the size of the die cavity of the coating die head.

10. The coating die head parameter optimization method according to any one of claims 1 to 4, characterized in that: The process parameters include the discharge flow rate of the coating die head, the moving speed of the workpiece to be coated, and the distance from the discharge port of the coating die head to the surface of the workpiece to be coated.

Images