Uniform coating distribution method of coating die head based on microfluid optimization technology

Abstract

The invention discloses a coating die head uniform coating distribution method based on a microfluid optimization technology, which comprises the following steps: S1, analyzing the rheological characteristics and coating requirements of a coating, and designing a flexible microfluid channel structure; s2, optimizing the geometric design of the channel by adopting a lattice Boltzmann method simulation technology; s3, the interaction between the coating and the die head surface material is simulated and analyzed through molecular dynamics, and adaptive data is generated; s4, based on the optimized geometric design and the adaptive data, an intelligent dynamic adjustment module is embedded, and a dynamic adjustment mechanism is constructed; s5, synchronous and uniform coating of multiple layers of coatings is achieved through a double-layer micro-channel structure; s6, the thickness and defects of the coating are monitored in real time by combining optical and pressure sensors, and a repair path is generated by combining a parrot optimization algorithm; and S7, an active heat management module is embedded, and the heating power is adjusted to maintain the optimal flowing state of the coating. According to the method, microfluid optimization and intelligent optimization methods are combined, and coating uniformity improvement, defect real-time repair and multi-layer synchronous coating are achieved.

Description

Technical Field

[0001] The invention relates to the field of microfluid and coating technology, in particular to a coating die head uniform coating distribution method based on microfluid optimization technology. Background Art

[0002] In the field of industrial production and advanced manufacturing, coating process is one of the important technologies for realizing multifunctional coating manufacturing. As the core component of the coating process, the performance of the coating die directly affects the uniformity, thickness control and coating efficiency of the coating. Traditional coating die technology mostly relies on fixed geometric structure design to complete the distribution of coating through a single fluid delivery channel. However, due to the non-Newtonian fluid characteristics of the coating itself and the complex dynamic flow behavior in the process, traditional dies are difficult to meet the needs of high-precision coating in practical applications, especially in terms of thickness uniformity, multi-layer coating and defect repair. There are significant limitations.

[0003] At present, mainstream coating technologies are mostly based on fixed channel geometry and single-layer fluid dynamics regulation. The flow velocity distribution of the coating through the die channel is mainly determined by the initial flow rate and pressure gradient. In this design, channel geometry parameters such as width, height and bending radius are statically fixed and cannot be dynamically optimized according to the actual rheological properties of the coating. Traditional coating dies lack flexible adjustment capabilities. Especially when dealing with coatings of different viscosities, densities and surface tensions, the flow of the coating is prone to unevenness, resulting in fluctuations in coating thickness on the target surface. In addition, due to the limitations of the single-layer coating design, traditional dies cannot simultaneously complete the simultaneous coating of multiple layers of coatings, which requires multiple coating processes to manufacture multifunctional coatings, reducing production efficiency and increasing costs.

[0004] Defects in the coating process are also a key problem that is difficult to solve with existing technologies. In traditional technologies, coating defects are usually discovered through quality inspection methods after coating is completed, such as uneven thickness, breakage or bubbles. These defects are difficult to repair in real time during the coating process. Existing die head technology lacks the ability to monitor and feedback control the coating process in real time, resulting in poor controllability of coating quality, especially in complex coating process scenarios, where defect repair costs are high and efficiency is low. Furthermore, traditional coating dies lack effective optimization algorithm support, and the fluid control method is single, making it difficult to adapt to the comprehensive requirements of complex processes for coating accuracy and efficiency.

[0005] With the increasing demand for multi-layer coating, the single-layer conveying mode and static channel design of traditional technology can no longer meet the requirements of emerging industrial fields. Multi-layer coating usually requires precise control of the thickness ratio, distribution uniformity and surface characteristics of different coatings. However, the traditional die design lacks structural support for the synchronous distribution of multi-layer coatings. The flow rate and pressure regulation methods of a single channel are also difficult to achieve dynamic adjustment of the thickness ratio of multiple coatings. Therefore, multi-layer coating requires multiple operations to achieve the composite of functional coatings and protective coatings, and the coating efficiency is significantly reduced. At the same time, the accumulation of errors in the coating process may lead to a decline in the overall coating quality.

[0006] In terms of coating temperature control, traditional coating die heads mostly use fixed temperature control methods, and only make rough adjustments within the overall heating or cooling range. The coating is easily affected by changes in ambient temperature during the coating process, resulting in insufficient fluidity or too low viscosity, thereby reducing the coating quality. Due to the lack of precise temperature control capabilities for defective areas or local flow rate changes, traditional technologies are difficult to ensure the optimal flow state of the coating under complex working conditions, especially in the coating of low-temperature or high-viscosity coatings, which are prone to problems such as uneven coating or breakage.

[0007] In addition, the optimization algorithms in existing coating die head technologies are relatively rudimentary, mostly based on linear adjustment or single parameter optimization, and lack the ability to comprehensively optimize multi-dimensional parameter space. The global search efficiency of traditional optimization algorithms is low, and they cannot quickly adapt to the dynamic changes of complex coating conditions, nor can they generate the best path based on monitoring data during real-time coating, resulting in low response speed and accuracy of coating adjustment. This technical limitation directly affects the performance of coating die heads in high-precision and high-complexity application scenarios.

[0008] Therefore, how to provide a coating die head uniform coating distribution method based on microfluidic optimization technology is a problem that technicians in this field need to solve urgently. Summary of the invention

[0009] One purpose of the present invention is to propose a method for uniform coating distribution of a coating die head based on microfluidic optimization technology. The present invention combines microfluidic optimization technology, intelligent dynamic control module, active thermal management module and Parrot optimization algorithm, and successfully achieves high-precision control of the coating process through flexible channel design and real-time monitoring, especially in terms of coating thickness uniformity, multi-layer coating synchronization and real-time defect repair. In addition, the system dynamically controls the coating flow rate, pressure and temperature distribution, which significantly improves the coating quality and efficiency under complex working conditions. Compared with traditional coating technology, the present invention has the advantages of high coating uniformity, fast real-time repair, strong process adaptability and low production cost, and is suitable for the field of multifunctional precision coating manufacturing.

[0010] The method for uniform coating distribution of a coating die head based on microfluidic optimization technology according to an embodiment of the present invention comprises the following steps:

[0011] S1. Analyze the rheological properties and coating process requirements of the target coating and design a flexible microfluidic channel structure, including a double-layer microfluidic channel structure;

[0012] S2. According to the flexible microfluidic channel structure, the fluid dynamics simulation technology based on the lattice Boltzmann method is used to simulate the nonlinear flow behavior of the coating in the microchannel and generate optimized geometric design data;

[0013] S3. Use molecular dynamics simulation to study the interface interaction between coating molecules and die surface materials, analyze adhesion and surface tension, optimize die surface materials and coating processes, and generate adaptation data;

[0014] S4. Based on the optimized geometric design data and adaptation data, an intelligent dynamic adjustment module is embedded inside the coating die head to construct a dynamic adjustment mechanism;

[0015] S5. In the intelligent dynamic adjustment module, a double-layer microfluidic channel structure is used to achieve synchronous and uniform coating of multiple layers of coatings. The flow behavior of the coating in the upper channel is adjusted by the fluid dynamics of the lower channel, and the thickness ratio of each layer of coating is dynamically adjusted;

[0016] S6. Combine optical sensors and pressure sensors to monitor the thickness uniformity and defects of the coating in real time during the coating process, combine the Parrot optimization algorithm to analyze the monitoring data and generate a repair path, and adjust the intelligent dynamic adjustment module to perform local optimization and repair of the coating flow rate and pressure;

[0017] S7. An active thermal management module is embedded in the coating die head, combined with a micro heater and a temperature sensor to dynamically control the coating temperature and adjust the heating power according to the changes in the defect area and flow rate to maintain the optimal flow state of the coating.

[0018] Optionally, the S1 specifically includes:

[0019] S11. Based on the rheological properties of the target coating, obtain the viscosity, density and surface tension, and Determine the shear viscosity within the range The changes of rheology are used to generate rheological curves and calculate the non-Newtonian flow parameters of the coating, including rheological consistency coefficient and rheological index;

[0020] S12. Designing a double-layer microfluidic channel structure with flexible adjustment function according to non-Newtonian flow parameters, wherein:

[0021] The upper channel is used for paint delivery and has a dynamically adjustable width W u and height H uThe function meets:

[0022] 0.1mm≤W u ≤2mm, 0.05mm≤H u ≤1mm;

[0023] The lower channel dynamically adjusts the coating flow rate and thickness distribution of the upper channel by adjusting the liquid;

[0024] S13. Design the bending radius R and channel length L of the flexible microfluidic channel to optimize the flow behavior of the coating in the channel, wherein the bending radius R ranges from 1 mm to 10 mm, the length L ranges from 10 mm to 100 mm, and the optimized design satisfies the pressure loss minimization condition:

[0025]

[0026] Where AP represents the total pressure loss in the channel, f represents the friction coefficient, D represents the hydraulic diameter of the channel, ρ represents the fluid density, and v represents the fluid velocity;

[0027] S14. By optimizing the material of the flexible microfluidic channel surface, ensure that the Young's modulus of the material is between 10 MPa and 500 MPa, and the surface needs to be hydrophobic, and the contact angle θ of the coating on the die surface satisfies:

[0028] 90°≤θ≤150°.

[0029] Optionally, the S2 specifically includes:

[0030] S21, obtaining the initial geometry of the flexible microfluidic channel, including channel width, height, length, and bending radius, as well as the viscosity, density, and surface tension of the coating;

[0031] S22. Set a multi-stage velocity distribution at the channel inlet, define a constant pressure gradient at the outlet, and apply channel wall deformation as a dynamic boundary condition. The inlet velocity satisfies:

[0032]

[0033] Among them, v in (x, t) represents the time and position-dependent velocity at the channel entrance, v0 represents the initial average velocity at the entrance, α represents the inlet velocity disturbance coefficient, x represents the position coordinate in the channel, t represents the time, L represents the channel length, represents the pressure gradient at the channel outlet, and ΔP represents the total pressure loss in the channel;

[0034] S23. A multi-relaxation time model based on the lattice Boltzmann method is used to discretely calculate the non-Newtonian flow behavior of the coating, and the shear relaxation time and volume relaxation time are adjusted respectively:

[0035]

[0036] Among them, τ s represents the shear relaxation time, η s represents the shear viscosity coefficient, ρ s Indicates the density of the coating, c s represents the speed of sound in the grid, Δt represents the time step, τ b represents the volume relaxation time, η b represents the volume viscosity coefficient;

[0037] S24. Extract the coating flow characteristics from the simulation results, including flow velocity distribution, shear stress, and residence time of the coating in the channel:

[0038]

[0039] Among them, t res (x1, y1) represents the residence time of the paint in the channel, x1, y1 represent the position coordinates, v avg (x1, y1) represents the average flow velocity of the coating at position x1, y1;

[0040] S25. Calculate the coating flow uniformity index based on the extracted simulation data:

[0041]

[0042] Among them, U represents the flow uniformity index, A represents the channel cross-sectional area, τ avg represents the average shear stress in the channel, τ(x1, y1) represents the shear stress at position x1, y1, and dA represents the area element of a small region in the channel;

[0043] S26, based on the residence time t of the coating in the channel res (x1, y1) and flow uniformity index U, adjust the channel width, height, length and bending radius, and the optimization goals are to maximize the uniformity of flow velocity distribution, minimize shear stress fluctuation and minimize pressure loss, and generate optimized geometric design data.

[0044] Optionally, the S3 specifically includes:

[0045] S31. Construct a molecular dynamics simulation model to determine the density, viscosity and molecular scale parameters of the coating, and set the initial atomic arrangement and boundary conditions of the die surface material;

[0046] S32. Define the intermolecular potential function, including Lennard-Jones potential and modified potential function, to describe the van der Waals force and repulsive force between the coating molecules and the atoms on the die surface:

[0047]

[0048] Among them, U(r) represents the intermolecular potential energy, ∈ represents the intermolecular potential well depth, σ represents the collision diameter, and r represents the intermolecular distance;

[0049] S33. During the molecular dynamics simulation, the velocity distribution and position of the coating molecules are recorded, and the adsorption or desorption behavior of the molecules on the die surface is monitored to obtain the interface dynamics data at the molecular level;

[0050] S34. Calculate the interfacial adhesion force F a , evaluate the degree of adhesion of coating molecules to the die surface:

[0051]

[0052] Among them, F a represents the interfacial adhesion, γ represents the surface tension of the coating, K represents the curvature tension coefficient, represents the curvature distribution of the die surface, n represents the normal vector of the die surface, S represents the integral area of ​​the die surface, and θ represents the contact angle of the coating on the die surface;

[0053] S35. According to the simulation results of adhesion and surface tension, the material and coating process of the die surface are optimized, the wettability and surface energy distribution of the die surface are determined, and the simulation results are matched with the coating characteristics to form adaptation data;

[0054] S36. Output adaptation data, including molecular dynamics simulation results between coating molecules and die surface, interface adhesion force distribution, and coating process parameters.

[0055] Optionally, the S4 specifically includes:

[0056] S41, based on the optimized geometric design data and adaptation data, embedding an intelligent dynamic adjustment module inside the coating die head, wherein the intelligent dynamic adjustment module includes a drive unit and a sensor network, and is used to adjust the geometric parameters of the microfluidic channel in real time, including width, height and bending radius;

[0057] S42, integrating a driving unit in the intelligent dynamic adjustment module, and setting deformation parameters of the driving unit, including channel deformation amount, deformation speed and driving force:

[0058] F adj =k mat ·Δd adj +c damp ·Δv adj ;

[0059] Among them, F adj represents the driving force, kmat represents the elastic stiffness coefficient of the channel material, Δd adj represents the channel shape, C damp represents the damping coefficient, Δv adj Indicates deformation speed;

[0060] S43, embedded sensor unit, including a pressure sensor and a flow rate sensor, to monitor the real-time pressure and flow rate of the microfluidic channel:

[0061]

[0062] Among them, P real Indicates the real-time pressure value, P ref Indicates the reference pressure value, ρ fluid represents the density of the paint, g represents the acceleration due to gravity, h represents fluid represents the height of the fluid in the channel, v real Indicates real-time flow rate;

[0063] S44. Calculate the channel morphology adjustment path by combining the optimized geometric design data and adaptation data using a dynamic adjustment algorithm. The optimization goal is to maximize the uniformity of velocity distribution and minimize pressure fluctuations:

[0064]

[0065] Among them, v real (x) represents the real-time velocity distribution at channel position x, W represents width, H represents height, R represents bending radius, L represents channel length, and v avg represents the target average flow rate of the channel, β represents the optimization weight coefficient, and P real (x) represents the real-time pressure value distribution at channel position x;

[0066] S45, adjusting the driving unit of the intelligent dynamic adjustment module according to the optimization result, dynamically changing the channel width, height and bending radius, and recording the deformation data in real time;

[0067] S46. Based on the steps S42 to S45, a complete dynamic adjustment mechanism is formed.

[0068] Optionally, the S5 specifically includes:

[0069] S51. In the intelligent dynamic adjustment module, a double-layer microfluidic channel structure is used to achieve synchronous and uniform coating of multiple layers of coatings;

[0070] S52, injecting a first coating into the upper channel, and setting initial flow rate and pressure conditions so that the first coating is evenly distributed in the target area, and injecting a second fluid into the lower channel, the second fluid being used to adjust the flow rate distribution and pressure distribution of the upper channel;

[0071] S53, by real-time monitoring the flow behavior of the first coating in the upper channel, including the flow velocity, pressure and flow stability of the first coating, collecting data on flow velocity fluctuation and pressure change, and inputting the monitoring data into the intelligent dynamic adjustment module for processing;

[0072] S54, dynamically adjusting the liquid flow parameters of the second fluid in the lower channel, including the fluid flow rate, pressure and flow distribution, according to the calculation results of the intelligent dynamic adjustment module, so as to affect the flow behavior of the coating in the upper channel and realize the stability and uniformity control of the coating flow;

[0073] S55, by adjusting the dynamic parameters of the second fluid in the lower channel, changing the coating flow rate and thickness ratio in different areas of the upper channel, so that the multi-layer coating forms a coating with a specific thickness distribution on the target surface, and the specific thickness is dynamically adjusted according to the preset process requirements;

[0074] S56. Output the thickness distribution data of each layer of coating in the double-layer microfluidic channel structure and the adjustment record of the dynamic parameters.

[0075] Optionally, the S6 specifically includes:

[0076] S61, embed an optical sensor inside the coating die head to collect coating thickness distribution and coating defect information in real time, including uneven thickness, fractures and bubbles, and calculate coating thickness uniformity index:

[0077]

[0078] Among them, U h represents the coating thickness uniformity index, h(x, y, t) represents the coating thickness at position x, y and time t, h avg (t) represents the average coating thickness at time t, A h Indicates the area of ​​the monitoring area;

[0079] S62, embed pressure sensors to collect real-time pressure distribution in the coating channel, analyze the relationship between coating thickness and pressure distribution in combination with optical sensor monitoring data, and calculate the pressure fluctuation amplitude:

[0080]

[0081] Where ΔP var represents the pressure fluctuation amplitude, P(x, y, t) represents the pressure value at position x, y and time t, P avg (t) represents the average pressure value at time t,

[0082] S63, input the monitoring data of the optical sensor and the pressure sensor into the intelligent dynamic adjustment module, and combine the initialization step of the parrot optimization algorithm to establish a multidimensional search space, in which the position of each parrot individual is represented by X=[v adj , P adj , W adj , H adj , R adj ], where X represents the optimization parameter vector, v adj Indicates the adjustment value of the flow rate in the channel, P adj Indicates the adjustment value of the pressure in the channel, W adj Indicates the adjustment value of the channel width, H adj Indicates the adjustment value of the channel height, R adj Indicates the adjustment value of the channel bending radius;

[0083] S64. Use the Parrot optimization algorithm to simulate group behavior and calculate the global fitness function through information exchange and cooperation mechanism between individuals:

[0084] F(X)=α1·U h +β1·ΔP var ;

[0085] Among them, F(X) represents the fitness function, α1 represents the weight coefficient of the coating thickness uniformity index, and β1 represents the weight coefficient of the pressure fluctuation amplitude;

[0086] S65. In the parrot optimization algorithm, the position of each individual is updated by using an information sharing mechanism that imitates the propagation of parrot calls:

[0087]

[0088] in, represents the position of the i-th individual at time t+1, represents the position of the i-th individual at time t, represents the global optimal position at time t, η represents the information exchange factor, λ1 represents the learning factor, and represents the positions of two individuals randomly selected at time t;

[0089] S66. Based on the optimization results output by the Parrot optimization algorithm, drive the intelligent dynamic adjustment module to adjust the channel geometry parameters in real time, including channel width, height and bending radius, generate a repair path, adjust the paint flow rate and pressure, monitor the repair effect in real time and output an adjustment data report.

[0090] Optionally, the S7 specifically includes:

[0091] S71, embedding an active thermal management module in the coating die head, wherein the active thermal management module includes a micro heater and a temperature sensor for real-time monitoring and dynamic regulation of the temperature distribution of the coating; collecting temperature data of the coating in different areas during the coating process through the temperature sensor to form a temperature distribution diagram;

[0092] S72, setting a target operating temperature range for the coating, and determining the temperature range according to the physical properties of the coating, process requirements, and characteristics of the defective area, to ensure fluidity and uniform coating performance of the coating;

[0093] S73. Analyze the correlation between the coating temperature and the flow velocity distribution in combination with the data collected by the temperature sensor, locate the defective area and identify the abnormal conditions of the coating flow state, including local condensation, overheating and flow velocity slowdown;

[0094] S74, adjusting the power of the micro heater in the active thermal management module according to the distribution of the defect area and the change of the coating flow rate, so that the temperature of the coating fluctuates within the target range and maintains the optimal flow state of the coating;

[0095] S75, real-time control of the temperature adjustment strength of the micro heater in different areas to ensure that the fluidity of the coating in the defective area returns to normal while maintaining a uniform temperature distribution in other areas;

[0096] S76. Output the temperature adjustment record and the temperature distribution data of the coating area to form a complete dynamic temperature control adjustment report.

[0097] The beneficial effects of the present invention are:

[0098] First, the present invention realizes dynamic and precise control of coating flow rate, pressure and thickness distribution by designing a flexible microfluidic channel structure, especially the application of a double-layer channel. The unique design of the double-layer channel allows the upper channel to be used for coating delivery, and the lower channel optimizes the flow behavior of the upper coating through fluid dynamic regulation, which not only ensures the high uniformity of coating thickness, but also realizes the synchronous operation of multi-layer coating. This structural design reduces the repetition of traditional multiple coating processes, improves production efficiency, reduces manufacturing costs, and meets the needs of modern industry for integrated coating of functional coatings and protective coatings.

[0099] Secondly, the present invention combines the real-time monitoring and control capabilities of the intelligent dynamic adjustment module to effectively cope with the complex dynamic changes in the coating process. By embedding optical sensors and pressure sensors, the coating thickness and pressure distribution data of the coating area are collected in real time, and the Parrot optimization algorithm is used to generate the optimal adjustment path to ensure the stability and uniformity of the coating flow during the coating process. At the same time, when defects such as uneven thickness, fractures or bubbles occur during the coating process, the system can quickly locate the defective area and generate a repair path, and achieve local optimization and repair by adjusting the channel geometry parameters, flow rate and pressure. This real-time repair capability avoids the lag in defect discovery and repair in traditional technologies, and significantly improves the controllability of coating quality and the overall pass rate.

[0100] The present invention also solves the problem of accuracy of temperature control during the coating process through an active thermal management module. Through the coordinated work of the micro heater and the temperature sensor, the coating temperature is adjusted in real time to maintain optimal fluidity within the target range, which is particularly suitable for complex coatings in high viscosity or low temperature environments. The active thermal management module can accurately control the temperature of defective areas or local flow rate changes, ensuring the fluidity and adhesion performance of the coating while further improving the uniformity of the coating thickness and quality stability.

[0101] In addition, the Parrot optimization algorithm used in the present invention greatly improves the global optimization capability and dynamic response speed of the system. Compared with the traditional optimization algorithm, the Parrot optimization algorithm can quickly converge to the optimal solution and efficiently balance the multi-objective requirements such as coating uniformity, pressure fluctuation and flow rate stability in the multi-dimensional parameter space. The introduction of this algorithm makes the regulation of the coating process more precise, and can adapt to the dynamic changes of complex working conditions, providing solid technical support for achieving high-precision and high-efficiency coating. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0103] Figure 1 This is an overall flow chart of the method for uniform coating distribution of a coating die head based on microfluidic optimization technology proposed by the present invention;

[0104] Figure 2 This is a schematic structural diagram of a flexible microfluidic channel for a coating die head uniform coating distribution method based on microfluidic optimization technology proposed in the present invention. DETAILED DESCRIPTION

[0105] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0106] refer to Figure 1 and Figure 2 , a coating die head uniform coating distribution method based on microfluidic optimization technology, comprising the following steps:

[0107] S1. Analyze the rheological properties and coating process requirements of the target coating and design a flexible microfluidic channel structure, including a double-layer microfluidic channel structure;

[0108] S2. According to the flexible microfluidic channel structure, the fluid dynamics simulation technology based on the lattice Boltzmann method is used to simulate the nonlinear flow behavior of the coating in the microchannel and generate optimized geometric design data;

[0109] S3. Use molecular dynamics simulation to study the interface interaction between coating molecules and die surface materials, analyze adhesion and surface tension, optimize die surface materials and coating processes, and generate adaptation data;

[0110] S4. Based on the optimized geometric design data and adaptation data, an intelligent dynamic adjustment module is embedded inside the coating die head to construct a dynamic adjustment mechanism;

[0111] S5. In the intelligent dynamic adjustment module, a double-layer microfluidic channel structure is used to achieve synchronous and uniform coating of multiple layers of coatings. The flow behavior of the coating in the upper channel is adjusted by the fluid dynamics of the lower channel, and the thickness ratio of each layer of coating is dynamically adjusted;

[0112] S6. Combine optical sensors and pressure sensors to monitor the thickness uniformity and defects of the coating in real time during the coating process, combine the Parrot optimization algorithm to analyze the monitoring data and generate a repair path, and adjust the intelligent dynamic adjustment module to perform local optimization and repair of the coating flow rate and pressure;

[0113] S7. An active thermal management module is embedded in the coating die head, combined with a micro heater and a temperature sensor to dynamically control the coating temperature and adjust the heating power according to the changes in the defect area and flow rate to maintain the optimal flow state of the coating.

[0114] In this implementation manner, the S1 specifically includes:

[0115] S11. Based on the rheological properties of the target coating, obtain the viscosity, density and surface tension, and Determine the shear viscosity within the range The changes of rheology are used to generate rheological curves and calculate the non-Newtonian flow parameters of the coating, including rheological consistency coefficient and rheological index;

[0116] S12. Designing a double-layer microfluidic channel structure with flexible adjustment function according to non-Newtonian flow parameters, wherein:

[0117] The upper channel is used for paint delivery and has a dynamically adjustable width W u and height H u The function meets:

[0118] 0.1mm≤W u ≤2mm, 0.05mm≤H u ≤1mm;

[0119] The lower channel dynamically adjusts the coating flow rate and thickness distribution of the upper channel by adjusting the liquid;

[0120] S13. Design the bending radius R and channel length L of the flexible microfluidic channel to optimize the flow behavior of the coating in the channel, wherein the bending radius R ranges from 1 mm to 10 mm, the length L ranges from 10 mm to 100 mm, and the optimized design satisfies the pressure loss minimization condition:

[0121]

[0122] Where ΔP represents the total pressure loss in the channel, f represents the friction coefficient, D represents the hydraulic diameter of the channel, ρ represents the fluid density, and v represents the fluid velocity;

[0123] S14. By optimizing the material of the flexible microfluidic channel surface, ensure that the Young's modulus of the material is between 10 MPa and 500 MPa, and the surface needs to be hydrophobic, and the contact angle θ of the coating on the die surface satisfies:

[0124] 90°≤θ≤150°.

[0125] In this implementation, S2 specifically includes:

[0126] S21, obtaining the initial geometry of the flexible microfluidic channel, including channel width, height, length, and bending radius, as well as the viscosity, density, and surface tension of the coating;

[0127] S22. Set a multi-stage velocity distribution at the channel inlet, define a constant pressure gradient at the outlet, and apply channel wall deformation as a dynamic boundary condition. The inlet velocity satisfies:

[0128]

[0129] Among them, v in (x, t) represents the time and position-related velocity at the channel entrance, v0 represents the initial average velocity at the entrance, α represents the inlet velocity disturbance coefficient, x represents the position coordinate in the channel, t represents the time, and L represents the channel length. represents the pressure gradient at the channel outlet, and ΔP represents the total pressure loss in the channel;

[0130] S23. A multi-relaxation time model based on the lattice Boltzmann method is used to discretely calculate the non-Newtonian flow behavior of the coating, and the shear relaxation time and volume relaxation time are adjusted respectively:

[0131]

[0132] Among them, τ s represents the shear relaxation time, η s represents the shear viscosity coefficient, ρ s Indicates the density of the coating, c s represents the speed of sound in the grid, Δt represents the time step, τ b represents the volume relaxation time, η b represents the volume viscosity coefficient;

[0133] S24. Extract the coating flow characteristics from the simulation results, including flow velocity distribution, shear stress, and residence time of the coating in the channel:

[0134]

[0135] Among them, t res (x1, y1) represents the residence time of the paint in the channel, x1, y1 represents the position coordinates, v avg (x1, y1) represents the average flow velocity of the paint at position x1, y1;

[0136] S25. Calculate the coating flow uniformity index based on the extracted simulation data:

[0137]

[0138] Among them, U represents the flow uniformity index, A represents the channel cross-sectional area, τ avg represents the average shear stress in the channel, τ(x1, y1) represents the shear stress at position x1, y1, and dA represents the area of ​​a small region in the channel;

[0139] S26, based on the residence time t of the coating in the channel res (x1, y1) and flow uniformity index U, adjust the channel width, height, length and bending radius, and the optimization goals are to maximize the uniformity of flow velocity distribution, minimize shear stress fluctuation and minimize pressure loss, and generate optimized geometric design data.

[0140] In this implementation, S3 specifically includes:

[0141] S31. Construct a molecular dynamics simulation model to determine the density, viscosity and molecular scale parameters of the coating, and set the initial atomic arrangement and boundary conditions of the die surface material;

[0142] S32. Define the intermolecular potential function, including Lennard-Jones potential and modified potential function, to describe the van der Waals force and repulsive force between the coating molecules and the atoms on the die surface:

[0143]

[0144] Among them, U(r) represents the intermolecular potential energy, ∈ represents the intermolecular potential well depth, σ represents the collision diameter, and r represents the intermolecular distance;

[0145] S33. During the molecular dynamics simulation, the velocity distribution and position of the coating molecules are recorded, and the adsorption or desorption behavior of the molecules on the die surface is monitored to obtain the interface dynamics data at the molecular level;

[0146] S34. Calculate the interfacial adhesion force F a , evaluate the degree of adhesion of coating molecules to the die surface:

[0147]

[0148] Among them, F a represents the interfacial adhesion, γ represents the surface tension of the coating, K represents the curvature tension coefficient, represents the curvature distribution of the die surface, n represents the normal vector of the die surface, S represents the integral area of ​​the die surface, and θ represents the contact angle of the coating on the die surface;

[0149] S35. According to the simulation results of adhesion and surface tension, the material and coating process of the die surface are optimized, the wettability and surface energy distribution of the die surface are determined, and the simulation results are matched with the coating characteristics to form adaptation data;

[0150] S36. Output adaptation data, including molecular dynamics simulation results between coating molecules and die surface, interface adhesion force distribution, and coating process parameters.

[0151] In this implementation manner, the S4 specifically includes:

[0152] S41, based on the optimized geometric design data and adaptation data, embedding an intelligent dynamic adjustment module inside the coating die head, wherein the intelligent dynamic adjustment module includes a drive unit and a sensor network, and is used to adjust the geometric parameters of the microfluidic channel in real time, including width, height and bending radius;

[0153] S42, integrating a driving unit in the intelligent dynamic adjustment module, and setting deformation parameters of the driving unit, including channel deformation amount, deformation speed and driving force:

[0154] F adj =k mat ·Δd adj +cdamp ·Δv adj ;

[0155] Among them, F adj represents the driving force, k mat represents the elastic stiffness coefficient of the channel material, Δd adj represents the channel shape, C damp represents the damping coefficient, Δv adj Indicates deformation speed;

[0156] S43, embedded sensor unit, including a pressure sensor and a flow rate sensor, to monitor the real-time pressure and flow rate of the microfluidic channel:

[0157]

[0158] Among them, P real Indicates the real-time pressure value, P ref Indicates the reference pressure value, ρ fluid represents the density of the paint, g represents the acceleration due to gravity, h represents fluid represents the height of the fluid in the channel, v real Indicates real-time flow rate;

[0159] S44. Calculate the channel morphology adjustment path by combining the optimized geometric design data and adaptation data using a dynamic adjustment algorithm. The optimization goal is to maximize the uniformity of velocity distribution and minimize pressure fluctuations:

[0160]

[0161] Among them, v real (x) represents the real-time velocity distribution at channel position x, W represents width, H represents height, R represents bending radius, L represents channel length, and v avg represents the target average flow rate of the channel, β represents the optimization weight coefficient, and P real (x) represents the real-time pressure value distribution at channel position x;

[0162] S45, adjusting the driving unit of the intelligent dynamic adjustment module according to the optimization result, dynamically changing the channel width, height and bending radius, and recording the deformation data in real time;

[0163] S46. Based on the steps S42 to S45, a complete dynamic adjustment mechanism is formed.

[0164] In this implementation manner, S5 specifically includes:

[0165] S51. In the intelligent dynamic adjustment module, a double-layer microfluidic channel structure is used to achieve synchronous and uniform coating of multiple layers of coatings;

[0166] S52, injecting a first coating into the upper channel, and setting initial flow rate and pressure conditions so that the first coating is evenly distributed in the target area, and injecting a second fluid into the lower channel, the second fluid being used to adjust the flow rate distribution and pressure distribution of the upper channel;

[0167] S53, by real-time monitoring the flow behavior of the first coating in the upper channel, including the flow velocity, pressure and flow stability of the first coating, collecting data on flow velocity fluctuation and pressure change, and inputting the monitoring data into the intelligent dynamic adjustment module for processing;

[0168] S54, dynamically adjusting the liquid flow parameters of the second fluid in the lower channel, including the fluid flow rate, pressure and flow distribution, according to the calculation results of the intelligent dynamic adjustment module, so as to affect the flow behavior of the coating in the upper channel and realize the stability and uniformity control of the coating flow;

[0169] S55, by adjusting the dynamic parameters of the second fluid in the lower channel, changing the coating flow rate and thickness ratio in different areas of the upper channel, so that the multi-layer coating forms a coating with a specific thickness distribution on the target surface, and the specific thickness is dynamically adjusted according to the preset process requirements;

[0170] S56. Output the thickness distribution data of each layer of coating in the double-layer microfluidic channel structure and the adjustment record of the dynamic parameters.

[0171] In this implementation manner, S6 specifically includes:

[0172] S61, embed an optical sensor inside the coating die head to collect coating thickness distribution and coating defect information in real time, including uneven thickness, fractures and bubbles, and calculate coating thickness uniformity index:

[0173]

[0174] Among them, U h represents the coating thickness uniformity index, h(x, y, t) represents the coating thickness at position x, y and time t, h avg (t) represents the average coating thickness at time t, A h Indicates the area of ​​the monitoring area;

[0175] S62, embed pressure sensors to collect real-time pressure distribution in the coating channel, analyze the relationship between coating thickness and pressure distribution in combination with optical sensor monitoring data, and calculate the pressure fluctuation amplitude:

[0176]

[0177] Where ΔP var represents the pressure fluctuation amplitude, P(x, y, t) represents the pressure value at position x, y and time t, P avg(t) represents the average pressure value at time t,

[0178] S63, input the monitoring data of the optical sensor and the pressure sensor into the intelligent dynamic adjustment module, and combine the initialization step of the parrot optimization algorithm to establish a multidimensional search space, in which the position of each parrot individual is represented by X=[v adj , P adj , W adj , H adj , R adj ], where X represents the optimization parameter vector, v adj Indicates the adjustment value of the flow rate in the channel, P adj Indicates the adjustment value of the pressure in the channel, W adj Indicates the adjustment value of the channel width, H adj Indicates the adjustment value of the channel height, R adj Indicates the adjustment value of the channel bending radius;

[0179] S64. Use the Parrot optimization algorithm to simulate group behavior and calculate the global fitness function through information exchange and cooperation mechanism between individuals:

[0180] F(X)=α1·U h +β1·ΔP var ;

[0181] Among them, F(X) represents the fitness function, α1 represents the weight coefficient of the coating thickness uniformity index, and β1 represents the weight coefficient of the pressure fluctuation amplitude;

[0182] S65. In the parrot optimization algorithm, the position of each individual is updated by using an information sharing mechanism that imitates the propagation of parrot calls:

[0183]

[0184] in, represents the position of the i-th individual at time t+1, represents the position of the i-th individual at time t, represents the global optimal position at time t, η represents the information exchange factor, λ1 represents the learning factor, and represents the positions of two individuals randomly selected at time t;

[0185] S66. Based on the optimization results output by the Parrot optimization algorithm, drive the intelligent dynamic adjustment module to adjust the channel geometry parameters in real time, including channel width, height and bending radius, generate a repair path, adjust the paint flow rate and pressure, monitor the repair effect in real time and output an adjustment data report.

[0186] In this implementation manner, the S7 specifically includes:

[0187] S71, embedding an active thermal management module in the coating die head, wherein the active thermal management module includes a micro heater and a temperature sensor for real-time monitoring and dynamic regulation of the temperature distribution of the coating; collecting temperature data of the coating in different areas during the coating process through the temperature sensor to form a temperature distribution diagram;

[0188] S72, setting a target operating temperature range for the coating, and determining the temperature range according to the physical properties of the coating, process requirements, and characteristics of the defective area, to ensure fluidity and uniform coating performance of the coating;

[0189] S73. Analyze the correlation between the coating temperature and the flow velocity distribution in combination with the data collected by the temperature sensor, locate the defective area and identify the abnormal conditions of the coating flow state, including local condensation, overheating and flow velocity slowdown;

[0190] S74, adjusting the power of the micro heater in the active thermal management module according to the distribution of the defect area and the change of the coating flow rate, so that the temperature of the coating fluctuates within the target range and maintains the optimal flow state of the coating;

[0191] S75, real-time control of the temperature adjustment strength of the micro heater in different areas to ensure that the fluidity of the coating in the defective area returns to normal while maintaining a uniform temperature distribution in other areas;

[0192] S76. Output the temperature adjustment record and the temperature distribution data of the coating area to form a complete dynamic temperature control adjustment report.

[0193] Embodiment 1:

[0194] In order to verify the feasibility of the present invention in implementation, the present invention is applied to a functional coating production line of an electronic component manufacturing enterprise, which requires coating multiple layers of coating on the surface of a flexible substrate, including a conductive coating, an insulating coating and a protective coating. The thickness uniformity of the coating is required to be ±2%, and defects such as bubbles and fractures are not allowed to occur during the coating process. In previous production, the traditional coating die head was used for operation, and the coating thickness uniformity could not meet the requirements, and the multi-layer coating needed to be completed in batches, which was inefficient, and defect repair usually required manual intervention, resulting in increased production costs.

[0195] In this embodiment, the present invention adopts a coating die uniform coating distribution method based on microfluidic optimization technology, combined with an intelligent dynamic adjustment module, a double-layer microfluidic channel structure, an active thermal management module and a Parrot optimization algorithm, to comprehensively optimize the flow rate distribution, pressure regulation and temperature control during the coating process. The specific operation steps are as follows: First, according to the rheological properties of the three coatings, the geometric parameter design is completed through a flexible microfluidic channel structure. The upper channel is used for coating delivery, and the lower channel is injected with a regulating liquid to dynamically optimize the coating flow rate and thickness ratio. Combined with the simulation technology based on the lattice Boltzmann method, the width, height and bending radius of the channel are optimized to reduce the pressure loss to 70% of the original. Subsequently, in the actual coating process, the coating thickness and defects are monitored in real time by optical sensors and pressure sensors, and the optimal adjustment path is generated in combination with the Parrot optimization algorithm to achieve real-time repair of defects.

[0196] A 6-day application test was carried out on the production line of the electronic component manufacturing enterprise. The test data show that the present invention effectively improves the coating uniformity, multi-layer synchronous coating efficiency and defect repair capability. Specifically, in terms of coating thickness uniformity, the average thickness deviation of the traditional coating die is ±4.5%, while the average thickness deviation of the coating die of the present invention is reduced to ±1.5%, meeting the requirements of high-precision coating. In terms of defect repair, the repair time after the defect is detected in the traditional technology is about 120 seconds per square meter, while the present invention shortens the repair time to 50 seconds per square meter through the intelligent dynamic adjustment module and real-time monitoring feedback, and the repair efficiency is increased by 140%. In addition, the efficiency of multi-layer coating is also significantly improved. The traditional technology requires 3 coating processes to complete three-layer coating, while the present invention can complete multi-layer coating in only 1 coating process through the synchronous adjustment of the double-layer channel, and the production efficiency is increased by 2 times.

[0197] The active thermal management module of the present invention has demonstrated strong temperature control capabilities in the test. Taking high-viscosity coatings as an example, in an environment with a room temperature of 18°C, the flow rate fluctuation range of the conventional coating is ±15%, while the present invention controls the flow rate fluctuation within ±5% through real-time temperature control, while ensuring the optimal fluidity and adhesion performance of the coating. The data shows that temperature control significantly reduces the incidence of fracture defects from 2.3% of conventional technology to 0.5%. The following is a summary of the test data.

[0198] Table 1 Performance comparison of conventional technology and the present invention in coating process

[0199] project Traditional Technology The present invention Improvement Coating thickness uniformity deviation ±4.5％ ±1.5％ 66.7% increase Time required for defect repair (per square meter) 120 seconds 50 seconds 140% improvement Multi-layer coating process times 3 times 1 time 200% improvement Flow rate fluctuation range ±15％ ±5％ 66.7% reduction Defect rate 2.3％ 0.5％ 78.3% reduction Pressure loss 100％ 70％ 30% reduction

[0200] It can be seen from the above embodiments that the coating die uniform coating distribution method based on microfluidic optimization technology of the present invention significantly improves the quality and efficiency of the coating process in practical applications. Compared with traditional technologies, the present invention effectively solves the problems of uneven coating thickness, delayed coating defect repair, and low multi-layer coating efficiency, especially showing obvious advantages in thickness uniformity control, real-time defect repair, and synchronous coating efficiency. The test results show that the present invention not only achieves high-precision and high-efficiency coating operations through the synergy of intelligent dynamic adjustment, real-time temperature control and optimization algorithms, but also significantly reduces the defect rate and energy consumption in the coating process, providing reliable technical support for modern high-performance coating needs.

[0201] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for uniform coating distribution of a coating die head based on microfluidic optimization technology, characterized in that: The steps include: S1. Analyze the rheological properties and coating process requirements of the target coating and design a flexible microfluidic channel structure, including a double-layer microfluidic channel structure; S2. According to the flexible microfluidic channel structure, the fluid dynamics simulation technology based on the lattice Boltzmann method is used to simulate the nonlinear flow behavior of the coating in the microchannel and generate optimized geometric design data; S3. Use molecular dynamics simulation to study the interface interaction between coating molecules and die surface materials, analyze adhesion and surface tension, optimize die surface materials and coating processes, and generate adaptation data; S4. Based on the optimized geometric design data and adaptation data, an intelligent dynamic adjustment module is embedded inside the coating die head to construct a dynamic adjustment mechanism; S5. In the intelligent dynamic adjustment module, a double-layer microfluidic channel structure is used to achieve synchronous and uniform coating of multiple layers of coatings. The flow behavior of the coating in the upper channel is adjusted by the fluid dynamics of the lower channel, and the thickness ratio of each layer of coating is dynamically adjusted; S6. Combine optical sensors and pressure sensors to monitor the thickness uniformity and defects of the coating in real time during the coating process, combine the Parrot optimization algorithm to analyze the monitoring data and generate a repair path, and adjust the intelligent dynamic adjustment module to perform local optimization and repair of the coating flow rate and pressure; S7. An active thermal management module is embedded in the coating die head, combined with a micro heater and a temperature sensor to dynamically control the coating temperature and adjust the heating power according to the changes in the defect area and flow rate to maintain the optimal flow state of the coating.

2. The method for uniform coating distribution of a coating die head based on microfluidic optimization technology according to claim 1, characterized in that: The S1 specifically includes: S11. Based on the rheological properties of the target coating, obtain the viscosity, density and surface tension, and Determine the shear viscosity within the range The changes of rheology are used to generate rheological curves and calculate the non-Newtonian flow parameters of the coating, including rheological consistency coefficient and rheological index; S12. Designing a double-layer microfluidic channel structure with flexible adjustment function according to non-Newtonian flow parameters, wherein: The upper channel is used for paint delivery and has a dynamically adjustable width W u and height H u The function meets: 0.1mm≤W u ≤2mm,0.05mm≤H u ≤1mm; The lower channel dynamically adjusts the coating flow rate and thickness distribution of the upper channel by adjusting the liquid; S13. Design the bending radius R and channel length L of the flexible microfluidic channel to optimize the flow behavior of the coating in the channel, wherein the bending radius R ranges from 1 mm to 10 mm, the length L ranges from 10 mm to 100 mm, and the optimized design satisfies the pressure loss minimization condition: Where ΔP represents the total pressure loss in the channel, f represents the friction coefficient, D represents the hydraulic diameter of the channel, ρ represents the fluid density, and v represents the fluid velocity; S14. By optimizing the material of the flexible microfluidic channel surface, ensure that the Young's modulus of the material is between 10 MPa and 500 MPa, and the surface needs to be hydrophobic, and the contact angle θ of the coating on the die surface satisfies: 90°≤θ≤150°.

3. The method for uniform coating distribution of a coating die head based on microfluidic optimization technology according to claim 1, characterized in that: The S2 specifically includes: S21, obtaining the initial geometry of the flexible microfluidic channel, including channel width, height, length, and bending radius, as well as the viscosity, density, and surface tension of the coating; S22. Set a multi-stage velocity distribution at the channel inlet, define a constant pressure gradient at the outlet, and apply channel wall deformation as a dynamic boundary condition. The inlet velocity satisfies: Among them, v in (x, t) represents the time and position-related velocity at the channel entrance, v0 represents the initial average velocity at the entrance, α represents the inlet velocity disturbance coefficient, x represents the position coordinate in the channel, t represents the time, and L represents the channel length. represents the pressure gradient at the channel outlet, and ΔP represents the total pressure loss in the channel; S23. A multi-relaxation time model based on the lattice Boltzmann method is used to discretely calculate the non-Newtonian flow behavior of the coating, and the shear relaxation time and volume relaxation time are adjusted respectively: Among them, τ s represents the shear relaxation time, η s represents the shear viscosity coefficient, ρ s Indicates the density of the coating, c s represents the speed of sound in the grid, Δt represents the time step, τ b represents the volume relaxation time, η b represents the volume viscosity coefficient; S24. Extract the coating flow characteristics from the simulation results, including flow velocity distribution, shear stress, and residence time of the coating in the channel: Among them, t res (x1, y1) represents the residence time of the paint in the channel, x1, y1 represents the position coordinates, v avg (x1, y1) represents the average flow velocity of the paint at position x1, y1; S25. Calculate the coating flow uniformity index based on the extracted simulation data: Among them, U represents the flow uniformity index, A represents the channel cross-sectional area, τ avg represents the average shear stress in the channel, τ(x1,y1) represents the shear stress at position x1,y1, and dA represents the area element of a small area in the channel; S26, based on the residence time t of the coating in the channel res (x1, y1) and flow uniformity index U, adjust the channel width, height, length and bending radius, and optimize the flow velocity distribution uniformity, minimize the shear stress fluctuation and minimize the pressure loss to generate the optimized geometric design data.

4. The method for uniform coating distribution of a coating die head based on microfluidic optimization technology according to claim 1, characterized in that: The S3 specifically includes: S31. Construct a molecular dynamics simulation model to determine the density, viscosity and molecular scale parameters of the coating, and set the initial atomic arrangement and boundary conditions of the die surface material; S32. Define the intermolecular potential function, including Lennard-Jones potential and modified potential function, to describe the van der Waals force and repulsive force between the coating molecules and the atoms on the die surface: Among them, U(r) represents the intermolecular potential energy, ∈ represents the intermolecular potential well depth, σ represents the collision diameter, and r represents the intermolecular distance; S33. During the molecular dynamics simulation, the velocity distribution and position of the coating molecules are recorded, and the adsorption or desorption behavior of the molecules on the die surface is monitored to obtain the interface dynamics data at the molecular level; S34. Calculate the interfacial adhesion force F a , evaluate the degree of adhesion of coating molecules to the die surface: Among them, F a represents the interfacial adhesion, γ represents the surface tension of the coating, κ represents the curvature tension coefficient, represents the curvature distribution of the die surface, n represents the normal vector of the die surface, S represents the integral area of ​​the die surface, and θ represents the contact angle of the coating on the die surface; S35. According to the simulation results of adhesion and surface tension, the material and coating process of the die surface are optimized, the wettability and surface energy distribution of the die surface are determined, and the simulation results are matched with the coating characteristics to form adaptation data; S36. Output adaptation data, including molecular dynamics simulation results between coating molecules and die surface, interface adhesion force distribution, and coating process parameters.

5. The method for uniform coating distribution of a coating die head based on microfluidic optimization technology according to claim 1, characterized in that: The S4 specifically includes: S41, based on the optimized geometric design data and adaptation data, embedding an intelligent dynamic adjustment module inside the coating die head, wherein the intelligent dynamic adjustment module includes a drive unit and a sensor network, and is used to adjust the geometric parameters of the microfluidic channel in real time, including width, height and bending radius; S42, integrating a driving unit in the intelligent dynamic adjustment module, and setting deformation parameters of the driving unit, including channel deformation amount, deformation speed and driving force: F adj =k mat ·Δd adj +c damp ·Δv adj ; Among them, F adj represents the driving force, k mat represents the elastic stiffness coefficient of the channel material, Δd adj represents the channel shape, c damp represents the damping coefficient, Δv adj Indicates deformation speed; S43, embedded sensor unit, including a pressure sensor and a flow rate sensor, to monitor the real-time pressure and flow rate of the microfluidic channel: Among them, P real Indicates the real-time pressure value, P ref Indicates the reference pressure value, ρ fluid represents the density of the paint, g represents the acceleration due to gravity, h represents fluid represents the height of the fluid in the channel, v real Indicates real-time flow rate; S44. Calculate the channel morphology adjustment path by combining the optimized geometric design data and adaptation data using a dynamic adjustment algorithm. The optimization goal is to maximize the uniformity of velocity distribution and minimize pressure fluctuations: Among them, v real (x) represents the real-time velocity distribution at channel position x, W represents width, H represents height, R represents bending radius, L represents channel length, and v avg represents the target average flow rate of the channel, β represents the optimization weight coefficient, and P real (x) represents the real-time pressure value distribution at channel position x; S45, adjusting the driving unit of the intelligent dynamic adjustment module according to the optimization result, dynamically changing the channel width, height and bending radius, and recording the deformation data in real time; S46. Based on the steps S42 to S45, a complete dynamic adjustment mechanism is formed.

6. The method for uniform coating distribution of a coating die head based on microfluidic optimization technology according to claim 1, characterized in that: The S5 specifically includes: S51. In the intelligent dynamic adjustment module, a double-layer microfluidic channel structure is used to achieve synchronous and uniform coating of multiple layers of coatings; S52, injecting a first coating into the upper channel, and setting initial flow rate and pressure conditions so that the first coating is evenly distributed in the target area, and injecting a second fluid into the lower channel, the second fluid being used to adjust the flow rate distribution and pressure distribution of the upper channel; S53, by real-time monitoring the flow behavior of the first coating in the upper channel, including the flow velocity, pressure and flow stability of the first coating, collecting data on flow velocity fluctuation and pressure change, and inputting the monitoring data into the intelligent dynamic adjustment module for processing; S54, dynamically adjusting the liquid flow parameters of the second fluid in the lower channel, including the fluid flow rate, pressure and flow distribution, according to the calculation results of the intelligent dynamic adjustment module, so as to affect the flow behavior of the coating in the upper channel and realize the stability and uniformity control of the coating flow; S55, by adjusting the dynamic parameters of the second fluid in the lower channel, changing the coating flow rate and thickness ratio in different areas of the upper channel, so that the multi-layer coating forms a coating with a specific thickness distribution on the target surface, and the specific thickness is dynamically adjusted according to the preset process requirements; S56. Output the thickness distribution data of each layer of coating in the double-layer microfluidic channel structure and the adjustment record of the dynamic parameters.

7. The method for uniform coating distribution of a coating die head based on microfluidic optimization technology according to claim 1, characterized in that: The S6 specifically includes: S61, embed an optical sensor inside the coating die head to collect coating thickness distribution and coating defect information in real time, including uneven thickness, fractures and bubbles, and calculate coating thickness uniformity index: Among them, U h represents the coating thickness uniformity index, h(x,y,t) represents the coating thickness at position x,y and time t, h avg (t) represents the average coating thickness at time t, A h Indicates the area of ​​the monitoring area; S62, embed pressure sensors to collect real-time pressure distribution in the coating channel, analyze the relationship between coating thickness and pressure distribution in combination with optical sensor monitoring data, and calculate the pressure fluctuation amplitude: Among them, ΔP var represents the pressure fluctuation amplitude, P(x,y,t) represents the pressure value at position x,y and time t, P avg (t) represents the average pressure value at time t, S63, input the monitoring data of the optical sensor and the pressure sensor into the intelligent dynamic adjustment module, and combine the initialization step of the parrot optimization algorithm to establish a multidimensional search space, in which the position of each parrot individual is represented by X=[v adj ,P adj ,W adj ,H adj ,R adj ], where X represents the optimization parameter vector, v adj Indicates the adjustment value of the flow rate in the channel, P adj Indicates the adjustment value of the pressure in the channel, W adj Indicates the adjustment value of the channel width, H adj Indicates the adjustment value of the channel height, R adj Indicates the adjustment value of the channel bending radius; S64. Use the Parrot optimization algorithm to simulate group behavior and calculate the global fitness function through information exchange and cooperation mechanism between individuals: F(X)=α1·U h +β1·ΔP var ; Among them, F(X) represents the fitness function, α1 represents the weight coefficient of the coating thickness uniformity index, and β1 represents the weight coefficient of the pressure fluctuation amplitude; S65. In the parrot optimization algorithm, the position of each individual is updated by using an information sharing mechanism that imitates the propagation of parrot calls: in, represents the position of the i-th individual at time t+1, represents the position of the i-th individual at time t, represents the global optimal position at time t, η represents the information exchange factor, λ1 represents the learning factor, and represents the positions of two individuals randomly selected at time t; S66. Based on the optimization results output by the Parrot optimization algorithm, drive the intelligent dynamic adjustment module to adjust the channel geometry parameters in real time, including channel width, height and bending radius, generate a repair path, adjust the paint flow rate and pressure, monitor the repair effect in real time and output an adjustment data report.

8. The method for uniform coating distribution of a coating die head based on microfluidic optimization technology according to claim 1, characterized in that: The S7 specifically includes: S71, embedding an active thermal management module in the coating die head, wherein the active thermal management module includes a micro heater and a temperature sensor for real-time monitoring and dynamic regulation of the temperature distribution of the coating; collecting temperature data of the coating in different areas during the coating process through the temperature sensor to form a temperature distribution diagram; S72, setting a target operating temperature range for the coating, and determining the temperature range according to the physical properties of the coating, process requirements, and characteristics of the defective area, to ensure fluidity and uniform coating performance of the coating; S73. Analyze the correlation between the coating temperature and the flow velocity distribution in combination with the data collected by the temperature sensor, locate the defective area and identify the abnormal conditions of the coating flow state, including local condensation, overheating and flow velocity slowdown; S74, adjusting the power of the micro heater in the active thermal management module according to the distribution of the defect area and the change of the coating flow rate, so that the temperature of the coating fluctuates within the target range and maintains the optimal flow state of the coating; S75, real-time control of the temperature adjustment strength of the micro heater in different areas to ensure that the fluidity of the coating in the defective area returns to normal while maintaining a uniform temperature distribution in other areas; S76. Output the temperature adjustment record and the temperature distribution data of the coating area to form a complete dynamic temperature control adjustment report.

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