A Coating Device Adjustment Method for Coating Processing and a Coating Device

Abstract

The present application relates to a coating device adjustment method and coating device for coating processing, and relates to the field of coating coating processing technology, which includes: analyzing the substrate type and sample thickness of the sample to be processed, and matching the target coating thickness corresponding to the substrate type; analyzing according to the target coating thickness and sample thickness to determine the coating parameters, the coating parameters including coating supply pressure, sample tension, and coating speed; generating multiple sets of simulated coating parameters based on the coating parameters and a preset coating optimization strategy, and performing coating processing to obtain multiple sets of coating simulation samples; performing thickness detection on the multiple sets of coating simulation samples to determine the optimal coating simulation sample closest to the target coating thickness, and marking the optimal coating parameters; and coating the sample to be processed based on the optimal coating parameters. The present application has the effect of improving the thickness consistency when coating products with different substrates.

Description

Technical Field

[0001] The present application relates to the technical field of film coating processing, and in particular to a coating device adjustment method and a coating device for film coating processing. Background Art

[0002] Coating processing is a treatment process that covers the surface of an object with a coating or film. It can form a coating or film with specific functions on the surface of the soil to protect the object from the influence of external environmental factors.

[0003] In the related technology, during the coating process, the coating equipment controls the feeding mechanism, coating mechanism, transmission mechanism and control system, and the control system detects various parameters in the coating process and makes corresponding adjustments, so that the feeding mechanism can stably provide coating materials and the coating mechanism can stably perform coating processing. For some flexible moisture-proof paper, the moisture-proof material will be compounded with the paper as a coating through hot pressing during processing.

[0004] Regarding the above-mentioned related technologies, when paper is hot-pressed and laminated with moisture-proof materials, the product changes. In addition to the moisture-proof materials, different coating materials will have different usage amounts during the lamination process, causing the paper to have different thickness changes during the continuous lamination process. However, the related technologies lack thickness control during the coating process of different base materials, so some products with high requirements for thickness consistency cannot meet the processing requirements well. Summary of the Invention

[0005] In order to facilitate the coating process of products with different substrates and to maintain thickness consistency as needed, the present application provides a method for adjusting a coating device for coating processing.

[0006] In a first aspect, the present application provides a method for adjusting a coating device for film coating, which adopts the following technical solution:

[0007] A method for adjusting a coating device for film coating processing, comprising:

[0008] Analyze the substrate type and sample thickness of the sample to be processed and match the target coating thickness corresponding to the substrate type;

[0009] Analyze the target coating thickness and sample thickness to determine coating parameters, including coating supply pressure, sample tension, and coating speed;

[0010] Generate multiple sets of simulated coating parameters based on coating parameters and preset coating optimization strategies, and perform coating processing to obtain multiple sets of coating simulation samples;

[0011] Perform thickness testing on multiple groups of coating simulation samples to determine the optimal coating simulation sample that is closest to the target coating thickness, and mark the optimal coating parameters;

[0012] The samples to be processed were coated based on the optimal coating parameters.

[0013] By adopting the above technical solution, through the generation and screening of multiple sets of simulation parameters, the number of trial and error times is significantly reduced, the efficiency of coating parameter optimization is improved, and the target thickness is matched according to the characteristics of the substrate, ensuring the consistency of coating thickness on different flexible substrates (such as paper and cloth), and solving the problem of uneven thickness caused by substrate differences in traditional methods.

[0014] Optionally, the coating optimization strategy includes:

[0015] According to the substrate type, query the preset historical coating parameter database for approximate coating parameters whose matching degree of the substrate physical property parameters is not less than a preset threshold;

[0016] Perform parameter difference analysis based on the approximate coating parameters and the coating parameters of the sample to determine the parameter adjustment benchmark unit;

[0017] Multiple adjacent groups of coating parameters are generated as simulated coating parameters with the parameter adjustment reference unit as the step size.

[0018] By adopting the above technical solution and utilizing intelligent retrieval of historical process data and parameter difference analysis, the initial parameter adjustment benchmark can be quickly determined, shortening the debugging cycle of new substrates. Through the iterative generation of adjacent parameter groups, a wider process window can be covered, enhancing adaptability to complex substrates (such as multi-layer composite materials) and reducing coating defects caused by empirical parameter deviations.

[0019] Optionally, when generating simulated coating parameters, the following are also included:

[0020] Establish a substrate deformation-coating thickness transfer function model and analyze the pressure compensation of the coating supply system;

[0021] Pressure regulation of the paint supply system based on the pressure compensation amount;

[0022] The substrate deformation-coating thickness transfer function model is as follows:

[0023] ;

[0024] in, Indicates the pressure compensation amount, Indicates the set curvature compensation gain, Indicates the curvature radius of the substrate when it is bent. represents the curvature gradient of the local deformation zone of the sample, represents the set sample strain rate damping coefficient, represents the detected microstrain on the sample surface, Indicates the real-time strain rate of the substrate.

[0025] By adopting the above technical solution and using real-time detection of substrate curvature gradient and strain rate to accurately calculate the pressure compensation amount, the model effectively suppresses coating thickness fluctuations caused by substrate bending or tensile deformation, reduces the standard deviation of lateral thickness, and improves applicability to high-tension coating scenarios.

[0026] Optionally, a laser displacement sensor array is preset to perform real-time deformation parameter detection on the sample coating surface, and a correlation model between the stress-strain field of the substrate and the coating thickness is established to analyze and obtain the coating thickness deviation value;

[0027] Based on the coating thickness deviation value, the corresponding compensation parameters in the preset compensation database are matched, including tension and coating speed, and the coating device is adjusted according to the compensation parameters;

[0028] The analytical model of the coating thickness deviation value is analyzed using the following formula:

[0029] ;

[0030] in, Indicates the coating thickness deviation value, represents the detected microstrain on the sample surface, It represents the strain rate of the substrate obtained by time series analysis of the strain sensor, is the maximum surface stress value of the substrate, The corresponding thickness coupling coefficient for the substrate is found in the table. Look up the stress sensitivity coefficient corresponding to the substrate table.

[0031] By employing this technical solution, real-time closed-loop control of coating thickness is achieved by dynamically analyzing the effects of stress and strain rate on thickness deviation. Intelligent matching of the compensation parameter library accelerates thickness deviation correction, making it suitable for high-speed coating lines (such as roll-to-roll coating) and reducing batch defects caused by substrate vibration.

[0032] Optionally, detecting multimodal environmental parameters of the sample coating area, including ambient humidity, ambient temperature, and electrostatic voltage field strength;

[0033] Based on multimodal environmental parameters, a paint viscosity-environmental parameter simulation equation is established to analyze and obtain the viscosity adjustment value of the paint supply system;

[0034] Dynamically adjusting the paint viscosity of the paint supply system based on the viscosity adjustment amount;

[0035] The coating viscosity-environmental parameter simulation equation is as follows:

[0036] ;

[0037] in, For real-time paint viscosity, Indicates the set base viscosity, Indicates the ambient temperature of the coating area being tested. Indicates the set reference temperature, represents the temperature sensitivity coefficient, represents the detected electric field gradient in the coating area, Indicates the electric field coupling coefficient of the coating obtained by looking up the table.

[0038] By adopting this technical solution, the coating rheological properties can be precisely controlled through real-time feedback of ambient temperature, humidity, and electric field strength. This solution solves the problem of sudden changes in coating fluidity in high humidity environments, increases viscosity control precision, and reduces coating orange peel or sagging defects caused by environmental fluctuations.

[0039] Optionally, when dynamic coating viscosity adjustment is performed, the following is also included:

[0040] Collect the coating thickness of the sample and divide it into multiple independent temperature zones according to the thickness, and match the infrared irradiation intensity corresponding to the coating thickness in the independent temperature zones;

[0041] The independent temperature zones are dried based on the infrared irradiation intensity, and the hot air system is controlled to form a laminar flow field on the sample surface. The angle between the airflow direction of the laminar flow field and the sample movement direction is within a preset acute angle range.

[0042] By adopting the above technical solution, the zoned temperature control strategy (strong radiation in thick areas and weak radiation in thin areas) compensates for the difference in drying shrinkage, improves the uniformity of coating thickness, and inhibits coating migration through laminar flow field design to reduce edge thickening.

[0043] Optionally, also include:

[0044] Analyze sample substrates and coatings using a preset evaluation strategy to obtain coating compatibility parameters;

[0045] Dynamically adjust the coating roller pressure based on coating compatibility parameters and monitor the cavity defect density at the composite interface in real time;

[0046] A coupling control model of the pressure roller and the conveying speed is established based on the cavity defect density, and the coating roller adjustment parameters are generated for adjustment.

[0047] By adopting this technical solution, when abnormal thickness fluctuations are detected, the airflow field distribution is optimized in real time through an angle feedback mechanism, stabilizing the thickness variation range within ±0.5μm. This solution solves the problem of thickness drift caused by sudden changes in coating speed (such as during start-up and stop phases).

[0048] Optionally, the coupling control model is as follows:

[0049] ;

[0050] in, Indicates the real-time roller pressure detected by the coating roller. Indicates the set reference roller pressure. represents the set adhesion work weight coefficient, represents the real-time adhesion work obtained by analysis, represents the reference adhesion work, Indicates the set cross-section strength weight coefficient, represents the real-time interface bonding strength obtained and set experimentally, Indicates the benchmark interface bonding strength set according to process standards.

[0051] By adopting the above technical solution, the pressure and vibration mode of the pressure roller are adjusted in real time, so that the cavity defect density is reduced, which helps to improve the adhesion between the coating and the substrate, thereby helping to improve the composite stability between the coating layer and the substrate.

[0052] In a second aspect, the present application provides a coating device for film processing, which adopts the following technical solution:

[0053] A coating device for film coating, comprising:

[0054] The feeding module mixes the coating material evenly and then continuously outputs it for the sample to be coated;

[0055] The coating module receives the coating material from the feeding module and performs surface coating treatment on the sample according to the optimal coating parameters, so that a coating layer is formed on the surface of the sample;

[0056] The transmission module provides transmission force for the transmission roller that conveys the sample, keeping the sample under the tension and coating speed required by the coating parameters for coating;

[0057] The control and adjustment module dynamically optimizes and adjusts the coating parameters to maintain a consistent thickness of the coated sample.

[0058] By adopting the above technical solution, the coating device for film processing uses the coating module to perform coating processing with the optimal coating parameters, and cooperates with the transmission module to adjust the coating tension and coating speed of the sample, so that the coating thickness formed remains uniform and consistent. At the same time, by dynamically optimizing and adjusting the coating parameters, it helps to adapt to changes generated during the coating process and maintain the coating consistency of the sample.

[0059] In summary, this application includes at least one of the following beneficial technical effects:

[0060] 1. By generating and screening multiple sets of simulation parameters, the number of trial and error times is significantly reduced, the efficiency of coating parameter optimization is improved, and the target thickness is matched according to substrate characteristics to ensure consistent coating thickness on different flexible substrates (such as paper and cloth), solving the uneven thickness problem caused by substrate differences in traditional methods.

[0061] 2. Utilize intelligent retrieval of historical process data and parameter difference analysis to quickly determine the initial parameter adjustment benchmark, shorten the commissioning cycle for new substrates, and iteratively generate adjacent parameter groups to cover a wider process window, enhance adaptability to complex substrates (such as multi-layer composites), and reduce coating defects caused by empirical parameter deviations;

[0062] 3. By using real-time detection of substrate curvature gradient and strain rate to accurately calculate the pressure compensation amount, this model effectively suppresses coating thickness fluctuations caused by substrate bending or tensile deformation, reducing the standard deviation of lateral thickness. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a method flow chart of steps S100 to S500 in this application.

[0064] Figure 2 It is a method flow chart of steps S301 to S303 in this application.

[0065] Figure 3 It is a method flow chart of steps S3031 to S3032 in this application.

[0066] Figure 4 It is a method flow chart of steps S501 to S502 in this application.

[0067] Figure 5 It is a method flow chart of steps S503 to S505 in this application.

[0068] Figure 6 It is a method flow chart of steps S5051 to S5052 in this application.

[0069] Figure 7 It is a method flow chart of steps S5053 to S5054 in this application.

[0070] Figure 8 It is a method flow chart of steps S600 to S602 in this application.

[0071] Figure 9 This is a schematic diagram of the overall structure of a coating device for film processing in the present application. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-9 It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0073] The embodiments of the present invention are described in further detail below with reference to the accompanying drawings.

[0074] The embodiments of the present application disclose a method for adjusting a coating device for film processing. By generating and screening multiple sets of simulation parameters, the number of trial and error times is significantly reduced, the efficiency of coating parameter optimization is improved, and the target thickness is matched in combination with the substrate characteristics to ensure the consistency of coating thickness for different flexible substrates, thereby solving the problem of uneven thickness caused by substrate differences in traditional methods.

[0075] Reference Figure 1 The method flow of the coating device adjustment method for film coating processing includes the following steps:

[0076] Step S100: Analyze the substrate type and sample thickness of the sample to be processed, and match the target coating thickness corresponding to the substrate type;

[0077] During the film coating process, the substrate type and thickness of the sample being processed are key initial parameters that influence the coating effect. Substrate types may include films of various materials, such as polypropylene (PP), polyethylene (PE), and polyester (PET). Each substrate has different physical and chemical properties, such as surface tension, flexibility, and heat resistance. These properties directly determine the required coating function and thickness requirements. For example, for packaging substrates requiring high barrier properties, a thicker coating layer may be required to achieve a good barrier effect; whereas for substrates that require lightness, thinness, and flexibility, the target coating thickness is relatively thin.

[0078] Accurately measuring sample thickness is the basis for subsequent parameter determination. Using high-precision thickness measuring instruments, such as laser thickness gauges and contact thickness gauges, multi-point measurements are taken on the sample to be processed and the average value is taken to ensure the accuracy of the thickness data. Based on the substrate type, a preset substrate-target coating thickness database is queried. This database stores the optimal coating thickness range for different substrates in different application scenarios. This data is derived from extensive historical experiments and production experience and can provide a scientific reference for the target coating thickness of the current sample to be processed. For example, when the substrate is detected to be PET film with a thickness of 50μm, database matching determines that its target coating thickness for food packaging applications is 10-15μm.

[0079] Step S200: Analyze the target coating thickness and the sample thickness to determine coating parameters, including coating supply pressure, sample tension, and coating speed;

[0080] After determining the target coating thickness and sample thickness, further analysis and determination of specific coating parameters are required. The paint supply pressure determines the flow rate and pressure stability of the paint from the supply system to the coating head, directly affecting the uniformity and thickness of the coating. High supply pressure may result in excessive paint supply, causing a thick coating or sagging; insufficient pressure may lead to insufficient coating thickness or discontinuous coating. Based on the target coating thickness and the substrate's liquid absorption properties, a fluid dynamics model is used to preliminarily calculate the required paint supply pressure range. The sample thickness is also used to assess the substrate's load-bearing capacity to avoid deformation caused by excessive pressure.

[0081] Sample tension is a crucial parameter for ensuring smooth sample delivery during the coating process. Appropriate tension prevents wrinkling, stretching, or sagging during coating, ensuring accurate coating placement and uniform coating. The amount of tension is closely related to the sample's material, thickness, and coating speed. For thinner or more flexible substrates, less tension is required to avoid damage from excessive stretching. For thicker or more rigid substrates, increased tension is required to ensure smooth delivery. By establishing a mathematical model that correlates sample tension with the mechanical properties of the substrate, combined with the target coating thickness and sample thickness, the optimal sample tension value can be calculated.

[0082] Determining the coating speed requires a comprehensive consideration of both production efficiency and coating quality. A higher coating speed improves production efficiency but may result in insufficient time for the coating to level on the substrate surface, affecting coating uniformity. A lower coating speed, while beneficial for coating leveling, can reduce production efficiency. Based on the target coating thickness and the coating's viscosity characteristics, experimental data fitting or empirical formulas can be used to determine the coating speed range that allows for efficient production while ensuring coating quality. For example, for high-viscosity coatings and thicker target coating thicknesses, a lower coating speed is required to ensure adequate coating leveling. For low-viscosity coatings and thinner target coating thicknesses, a higher coating speed can be appropriate.

[0083] Step S300: generating multiple sets of simulated coating parameters based on the coating parameters and the preset coating optimization strategy, and performing coating processing to obtain multiple sets of coating simulation samples;

[0084] In order to find the best combination of coating parameters, it is necessary to generate multiple sets of simulated coating parameters based on the preliminarily determined coating parameters and the preset coating optimization strategy. The coating optimization strategy first queries the preset historical coating parameter database according to the substrate type. The database stores successful parameter cases for coating similar substrates in the past, including the specific values of parameters such as coating supply pressure, sample tension, coating speed, and the corresponding coating effect evaluation. By comparing the physical properties of the current substrate with those in historical cases, such as density, elastic modulus, surface roughness, etc., approximate coating parameters with a physical performance parameter matching degree not lower than the preset threshold are screened out as a reference, where the preset threshold is the difference range of the physical performance parameters. For example, in the density parameter, the density difference between the two substrates is 1.2 grams per cubic centimeter, and the threshold range is 1.5 grams per cubic centimeter. The physical performance parameters of the two substrates and the preset threshold are in an approximate range.

[0085] Then, a parameter difference analysis is performed based on the approximate coating parameters and the currently preliminarily determined coating parameters to determine the parameter adjustment reference unit. The parameter adjustment reference unit is the minimum adjustment amount set based on the degree of influence of the coating parameters on the coating effect. For example, the adjustment reference unit of the coating supply pressure can be set to 0.1MPa, the adjustment reference unit of the sample tension to 1N / m, and the adjustment reference unit of the coating speed to 0.5m / min. Using these reference units as units, multiple adjacent groups of coating parameters are generated around the preliminarily determined coating parameters. For example, based on a coating supply pressure of 0.5MPa, multiple groups of pressure parameters such as 0.4MPa, 0.5MPa, and 0.6MPa are generated. At the same time, similar adjustments are made to the sample tension and coating speed to form multiple different sets of simulated coating parameter combinations.

[0086] When generating simulated coating parameters, the effect of substrate deformation on coating thickness also needs to be considered. After generating multiple sets of simulated coating parameters, the coating apparatus is used to coat the sample to be processed, resulting in multiple sets of simulated coating samples. During the coating process, experimental conditions are strictly controlled to ensure the independence and repeatability of each set of simulated coating parameters, enabling accurate testing and analysis of the simulated coating samples. The specific coating optimization strategy for generating multiple sets of simulated coating parameters will be further explained later.

[0087] Step S400: performing thickness detection on multiple groups of coating simulation samples to determine the optimal coating simulation sample closest to the target coating thickness, and marking the optimal coating parameters;

[0088] Thickness testing is performed on multiple sets of prepared coating simulation samples. High-precision thickness testing equipment, such as non-contact optical thickness gauges and eddy current thickness gauges, is used to measure the thickness of each sample at multiple locations to determine the distribution of coating thickness. The average thickness of each sample is calculated, along with thickness uniformity indicators such as standard deviation and coefficient of variation, to comprehensively assess the accuracy and uniformity of the coating thickness.

[0089] The average thickness of each coating simulation sample is compared with the target coating thickness, and the thickness deviation value is calculated. The smaller the thickness deviation value, the closer the coating effect of the sample is to the target requirement. At the same time, considering the thickness uniformity index, the sample with the smallest thickness deviation and good thickness uniformity is selected as the optimal coating simulation sample. For example, among multiple groups of simulation samples, the average thickness of sample A is 12μm, the target coating thickness is 13μm, the deviation is 1μm, and the thickness standard deviation is 0.5μm; the average thickness of sample B is 13.5μm, the deviation is 0.5μm, and the thickness standard deviation is 1.0μm. Although the average thickness deviation of sample B is small, due to its poor thickness uniformity, sample A is finally selected as the optimal coating simulation sample.

[0090] After determining the optimal coating simulation sample, the corresponding coating parameter combination, including coating supply pressure, sample tension, coating speed, etc., is marked as the optimal coating parameters. These optimal coating parameters are verified through actual coating simulation and testing. They can achieve a coating effect closest to the target coating thickness under the current substrate type and sample thickness conditions, providing a reliable parameter basis for subsequent actual coating production.

[0091] Step S500: performing coating treatment on the sample to be processed based on the optimal coating parameters.

[0092] In the actual coating production process, the sample to be processed is coated based on the optimal coating parameters obtained by marking. In order to ensure the stability and coating quality of the coating process, the coating process needs to be monitored and controlled in real time.

[0093] Reference Figure 2 , coating optimization strategies include:

[0094] Step S301: querying a preset historical coating parameter database for approximate coating parameters whose matching degree of the physical property parameters of the substrate is not less than a preset threshold value according to the substrate type;

[0095] In the film coating industry, a pre-set historical coating parameter database is the result of long-term production practice and experimental data. Its core value lies in improving the efficiency and accuracy of coating parameter design through data reuse. This database is built by associating and storing a large number of substrate physical property parameters with corresponding coating process parameters. Specifically, it includes key physical indicators such as substrate material type (such as PP, PE, PET, aluminum foil composite film), density, elastic modulus, surface roughness, water absorption, surface tension, and the corresponding historically successful coating parameters such as coating supply pressure, sample tension, and coating speed.

[0096] After identifying the substrate type of the sample to be processed (e.g., through spectral analysis, material label reading, etc.), the system first extracts the physical performance parameters of the substrate, such as the thickness, density, surface roughness, and other indicators of the substrate in real time through a sensor array. Subsequently, a multi-dimensional similarity matching algorithm (such as Euclidean distance, cosine similarity, etc.) is used to search the historical database for historical cases that most closely match the physical properties of the current substrate. Taking surface tension as an example, if the current surface tension of a PET substrate is 42mN / m, and the database contains three sets of historical data with surface tensions of 40mN / m, 42mN / m, and 45mN / m, the system will prioritize the case with a surface tension of 42mN / m as a basic reference, while also incorporating cases in the adjacent range (e.g., ±5mN / m) as auxiliary references to form a set of candidate approximate coating parameters.

[0097] To ensure matching accuracy, the database typically uses a hierarchical index structure. First, a primary classification is performed by material type (such as plastic film, metal foil, and paper-based materials). Secondary classification is performed by key dimensions of physical performance parameters (such as mechanical properties, surface properties, and thermal properties). Finally, parameter thresholds are set (e.g., elastic modulus difference ≤ 10%, surface roughness difference ≤ 5%) to screen out approximate coating parameters with a physical performance parameter match of at least the preset threshold. For example, for a 50μm thick PET substrate, the system will prioritize historical coating parameters from the database for PET materials, thicknesses in the 45-55μm range, and surface tensions of 40-45mN / m. This creates a preliminary list of approximate parameters, providing a data foundation for subsequent parameter adjustments.

[0098] Step S302: performing parameter difference analysis based on the approximate coating parameters and the coating parameters of the sample to determine a parameter adjustment reference unit;

[0099] After obtaining the approximate coating parameters, a differential analysis is performed between them and the initially determined coating parameters for the current sample (e.g., the initial parameters calculated in step S200 based on the target thickness and sample thickness) to clarify the adjustment direction and minimum adjustment step size for each parameter, i.e., the parameter adjustment benchmark unit. The core of parameter differential analysis is to quantify the impact of different parameters on the coating effect and determine the appropriate adjustment granularity based on the control accuracy and process stability requirements of the production equipment.

[0100] First, a parameter influencing factor matrix is established, and weight coefficients are assigned to the three core parameters of coating supply pressure, sample tension, and coating speed. The setting of the weight coefficients is based on the orthogonal experimental method or the response surface analysis method. The weight of each parameter on key indicators such as coating thickness uniformity, adhesion, and drying speed is obtained by fitting historical data. For example, it has been experimentally verified that the influence weight of coating supply pressure on coating thickness is 0.4, the influence weight of sample tension is 0.3, and the influence weight of coating speed is 0.3. Subsequently, the absolute difference and relative difference between the approximate parameters and the initial parameters of the current sample are calculated. For example, if the coating supply pressure of the approximate parameter is 0.6MPa and the current initial parameter is 0.5MPa, the absolute difference is 0.1MPa and the relative difference is 20%.

[0101] The determination of the reference unit for parameter adjustment requires a balance between process accuracy and production efficiency. For paint supply pressure, the reference unit is set to 0.05 MPa, considering that the pump's control accuracy is 0.05 MPa and that even small changes in pressure can significantly affect coating thickness. For sample tension, the reference unit is set to 1 N / m, as the minimum adjustment step of the tension control system is 1 N / m and excessive tension fluctuations can easily lead to substrate stretching and deformation. For coating speed, the reference unit is set to 0.5 m / min, considering that the motor speed control accuracy is 0.1 m / min and that speed changes have a nonlinear effect on coating drying (using empirical formulas to avoid oversensitivity).

[0102] During the difference analysis process, a correction factor for the parameter coupling effect must also be introduced. For example, when the paint supply pressure and coating speed change simultaneously, their effects on coating thickness are not simply linearly superimposed, but rather interact. A coupling correction function is derived by fitting historical data, and the single parameter difference is corrected to ensure that the reference unit setting can reflect the parameter linkage effect in the actual process. Ultimately, through comprehensive weight calculation, equipment accuracy constraints, and coupling effect corrections, the adjustment reference unit for each parameter is determined, providing a standardized adjustment step for generating simulated coating parameters.

[0103] Step S303: generating a plurality of adjacent groups of coating parameters as simulated coating parameters using the parameter adjustment reference unit as a unit.

[0104] After determining the reference units for parameter adjustment, the initial coating parameters of the current sample are used as the center, and the reference units of each parameter are used as the step size to generate multiple adjacent groups of simulated coating parameters in the three-dimensional parameter space (pressure, tension, and speed). The generation strategy uses orthogonal experimental design or uniform design to ensure that the parameter combinations fully cover the potential optimal solution range while avoiding redundant experiments.

[0105] Specifically, for the coating supply pressure P (initial value P0, reference unit ΔP), five sets of pressure parameters are generated: P0-2ΔP, P0-ΔP, P0, P0+ΔP, and P0+2ΔP. For the sample tension T (initial value T0, reference unit ΔT), three sets of tension parameters are generated: T0-ΔT, T0, and T0+ΔT. For the coating speed V (initial value V0, reference unit ΔV), three sets of speed parameters are generated: V0-ΔV, V0, and V0+ΔV. Through these combinations and permutations, 5×3×3=45 sets of simulated coating parameters are generated (the step size range can be adjusted to meet actual needs, such as ±1Δ or ±2Δ). This design ensures comprehensive coverage of the parameter space while keeping the number of experiments within a reasonable range.

[0106] The generated sets of simulated coating parameters must be accompanied by parameter labels to clearly define the adjustment direction and amplitude of each parameter (e.g., "+1ΔP" indicates a one-base-unit pressure increase). These parameters should be grouped and managed according to their degree of difference to facilitate the orderly conduct of subsequent coating simulation experiments. For example, parameter combinations can be divided into "pressure-sensitive groups," "tension-sensitive groups," and "speed-sensitive groups." The impact of changes in different parameter dimensions on coating quality can be specifically analyzed to improve the targeted nature of parameter optimization.

[0107] The simulated coating parameter set formed through the above steps is based on both historical successful experience and the specific characteristics of the current sample. It also considers the influence of parameter linkage and substrate dynamic deformation. This provides a scientific and reasonable parameter combination for subsequent coating simulation experiments, ensuring the efficient screening of optimal coating parameters and achieving dual optimization of coating quality and production efficiency. The entire process embodies an intelligent adjustment strategy that combines data-driven and model-assisted methods, deeply integrating empirical knowledge with real-time detection data, and providing a systematic solution for precise coating in film processing.

[0108] Reference Figure 3 , when generating simulation coating parameters, also includes:

[0109] Step S3031: establishing a substrate deformation-coating thickness transfer function model and analyzing to obtain the pressure compensation amount of the coating supply system;

[0110] During the coating process, dynamic deformation of the substrate is a key factor affecting coating thickness uniformity. When the substrate bends, stretches, or locally wrinkles under tension, changes in its surface morphology directly lead to uneven coating distribution. To accurately quantify the impact of this deformation on coating thickness, a substrate deformation-coating thickness transfer function model is required. This model captures the dynamic relationship between substrate geometry and mechanical response, providing a theoretical basis for real-time compensation of coating supply pressure.

[0111] Step S3032: regulating the pressure of the paint supply system based on the pressure compensation amount;

[0112] When the substrate experiences local bending or abnormal strain rate during the coating process, the system automatically calculates the pressure compensation amount ΔP and makes real-time corrections to each set of simulated pressure parameters to ensure that the simulation parameters can truly reflect the actual coating pressure requirements of the substrate's dynamic deformation.

[0113] The substrate deformation-coating thickness transfer function model is as follows:

[0114] ;

[0115] in, It represents the pressure compensation amount, which is the additional pressure value that needs to be applied to the coating supply system to offset the influence of substrate deformation on coating thickness. The unit is MPa.

[0116] Indicates the set curvature compensation gain, which is the additional pressure value that needs to be applied to the coating supply system to offset the influence of substrate deformation on coating thickness. The unit is MPa.

[0117] Indicates the curvature radius of the substrate when it is bent.

[0118] The curvature gradient represents the localized deformation zone of the sample, indicating the degree of local curvature of the substrate along the coating direction (x-axis). This gradient is calculated using second-order difference calculations using real-time surface profile data collected by a laser displacement sensor array. When the substrate exhibits convex curvature, the curvature gradient is positive, requiring increased pressure compensation; when the substrate exhibits concave curvature, the curvature gradient is negative, requiring decreased pressure compensation.

[0119] This represents the set sample strain rate damping coefficient, reflecting the hysteresis effect of the substrate's dynamic strain on pressure compensation, obtained by fitting the tensile test (for example, Kd for PE substrates is typically 0.5-0.7 MPa・s / με).

[0120] represents the detected microstrain on the sample surface, It represents the real-time strain rate of the substrate, which is monitored in real time by a microstrain sensor attached to the surface of the substrate. It characterizes the tensile or compressive strain of the substrate per unit time, with the unit being με / s.

[0121] The model application and compensation calculation process are as follows:

[0122] 1. Real-time deformation data collection: A laser displacement sensor array (accuracy ±1μm) is deployed 50-100mm upstream of the coating head. The surface of the substrate is scanned at a frequency of 200Hz to obtain multi-point height data along the width direction (y-axis) and construct a real-time 3D profile model.

[0123] 2. Curvature gradient calculation: Perform polynomial fitting on the profile data and calculate the second-order spatial derivative d²R / dx² of the local curvature radius R through the second-order derivative to identify the curved areas of the substrate (such as edge waves, middle relaxation and other typical deformations).

[0124] 3. Dynamic monitoring of strain rate: Install strain gauge sensors on the substrate tension roller and guide roller to collect the longitudinal strain ε of the substrate in real time. Calculate the strain rate dε / dt through time series analysis to capture the instantaneous stretching or contraction caused by tension fluctuations during high-speed coating.

[0125] 4. Compensation Coupling Calculation: Substitute the curvature gradient and strain rate into the transfer function model and weightedly calculate the total pressure compensation ΔP. For example, if a positive curvature gradient (convex bend) of 0.02 m² is detected in the middle of the substrate and the strain rate is 5 με / s, and if Kp = 1.0 and Kd = 0.6, then ΔP = 1.0 × 0.02 + 0.6 × 5 = 3.02 MPa, indicating that an increase of 3.02 MPa in supply pressure is required to offset the insufficient coating distribution caused by the substrate protrusion.

[0126] Reference Figure 4 , when coating the sample to be processed, it also includes:

[0127] Step S501: a preset laser displacement sensor array is used to perform real-time deformation parameter detection on the sample coating surface, and a correlation model between the stress-strain field of the substrate and the coating thickness is established to analyze and obtain a coating thickness deviation value;

[0128] Step S502: matching corresponding compensation parameters in a preset compensation database based on the coating thickness deviation value, including tension and coating speed, and adjusting the coating device according to the compensation parameters;

[0129] During the coating process, the dynamic stress-strain state of the substrate directly affects the uniformity of the coating. Step S501 achieves real-time quantitative analysis of coating thickness deviation by building a high-precision monitoring system and physical model. Specifically, it includes the following core steps:

[0130] 1. Construction of real-time deformation parameter detection system

[0131] Sensor Array Layout: High-precision laser displacement sensors (±5μm accuracy, sampling frequency ≥1kHz) are deployed directly below and 50mm downstream of the coating head, spaced 10-20mm apart across the width of the substrate (transversely). This forms a detection matrix covering the entire width of the substrate. The sensors utilize the principle of triangulation, emitting laser beams and receiving diffusely reflected signals to acquire the three-dimensional coordinate data (x, y, z) of each point on the substrate surface in real time, thereby constructing a dynamic deformation profile.

[0132] Detection parameter definition:

[0133] surface microstrain : By comparing the change in the longitudinal (travel direction) length of the substrate before and after coating, combined with the guide roller spacing, it is calculated to reflect the degree of stretching or compression of the substrate;

[0134] strain rate : Perform first-order difference calculation on the time series strain data to characterize the dynamic rate of substrate deformation;

[0135] Maximum surface stress :The elastic modulus E of the substrate material and the strain The product of is combined with finite element simulation or historical data fitting to determine the maximum stress distribution on the substrate surface during the coating process.

[0136] 2. Correlation model construction and deviation value calculation

[0137] A correlation model between substrate stress-strain field and coating thickness is established, and its core expression is:

[0138] ;

[0139] in, Indicates the coating thickness deviation value, which represents the difference between the measured coating thickness and the target thickness (unit: μm). A positive value indicates too thick, and a negative value indicates too thin.

[0140] represents the detected microstrain on the sample surface, It represents the strain rate of the substrate obtained by time series analysis of the strain sensor, is the maximum surface stress value of the substrate.

[0141] The strain-thickness coupling coefficient corresponding to the substrate lookup table is the substrate characteristic parameter calibrated by orthogonal experiment, reflecting the influence weight of strain rate on coating thickness.

[0142] The stress sensitivity coefficient corresponding to the substrate lookup table is the substrate characteristic parameter calibrated by orthogonal experiment, which reflects the influence weight of strain rate on coating thickness.

[0143] After obtaining the real-time thickness deviation, step S502 realizes dynamic optimization of coating process parameters through a data-driven closed-loop compensation mechanism to ensure that the coating thickness is stable within the target range.

[0144] Reference Figure 5 , when coating the sample to be processed based on the optimal coating parameters, it also includes:

[0145] Step S503: detecting multimodal environmental parameters of the sample coating area, including ambient humidity, ambient temperature, and electrostatic voltage field strength;

[0146] During the coating process, fluctuations in environmental parameters can significantly affect the fluidity and coating uniformity of the coating. Step S503 builds a high-precision environmental monitoring network to achieve real-time quantitative detection of humidity, temperature, and static voltage fields, providing a data basis for subsequent viscosity adjustment.

[0147] The multimodal sensor layout and detection principle include ambient temperature, humidity, and electrostatic field strength detection. To measure ambient temperature, three platinum resistance temperature sensors (PT100, accuracy ±0.1°C) are evenly distributed 200mm above the coating area, covering the left, center, and right sides of the substrate width. They collect real-time air temperature and average it to avoid measurement errors caused by localized heat sources (such as drying lamps). To measure ambient humidity, a capacitive humidity sensor (accuracy ±2%RH) is integrated with the temperature sensor to simultaneously measure relative humidity. Using a dew point calculation model, the humidity signal is converted into an absolute humidity parameter (g / m³), which more directly affects paint viscosity. To measure electrostatic field strength, non-contact electrostatic field sensors (range ±20kV / m, resolution 0.1kV / m) are installed on both sides of the coating head to measure the electric field gradient ∇E in the coating area. Since friction between the high-speed substrate and the equipment easily generates static electricity, resulting in uneven distribution of paint particles, accurate measurement of the electric field gradient is crucial for compensating for electrostatic interference.

[0148] Step S504: establishing a paint viscosity-environmental parameter simulation equation based on the multimodal environmental parameters to analyze and obtain a viscosity adjustment value of the paint supply system;

[0149] Step S505: dynamically adjusting the coating viscosity of the coating supply system based on the viscosity adjustment amount;

[0150] The coating viscosity-environmental parameter simulation equation is established, which integrates the coupled effects of temperature, humidity, and electrostatic voltage field on viscosity. Its mathematical expression is:

[0151] ;

[0152] in, It is the real-time paint viscosity, reflecting the paint flow resistance in the current environment; Indicates the set reference viscosity, which is set to the standard viscosity value under the conditions of 25°C, 0 kV / m electric field, and 50% RH, calibrated by a Brookfield viscometer (accuracy ±1%); Indicates the ambient temperature of the coating area being tested. Indicates the set reference temperature, usually set to 25℃; It represents the temperature sensitivity coefficient, a thermodynamic parameter determined by the coating formulation, which characterizes the rate at which temperature changes affect viscosity; represents the detected electric field gradient in the coating area, It represents the electric field coupling coefficient of the coating obtained by looking up the table. It is a thermodynamic parameter determined by the coating formula and characterizes the rate at which temperature changes affect viscosity.

[0153] Dynamically adjust the actuator and control logic, and the adjustment control steps are as follows:

[0154] First, the temperature control module integrates electric heating / cooling devices (power density 50W / m²) in the paint storage tank and delivery pipelines to adjust the paint temperature in real time based on ΔT. For example, when η(t) > η_target, the temperature is increased to reduce viscosity. The temperature adjustment rate is controlled within 1°C / min to prevent sudden temperature changes from affecting paint stability. η_target represents the ideal viscosity value preset according to coating process requirements, that is, the viscosity of the paint under optimal coating conditions.

[0155] Secondly, the electrostatic field regulation module applies a reverse electric field through an electrostatic eliminator (such as an ion wind wand) to offset the effects of the ambient static voltage field. When ∇E > 1 kV / m, the eliminator is activated to stabilize ∇E below 0.5 kV / m. This is combined with paint conductivity adjustment (by adding an antistatic agent) to achieve electric field compensation for viscosity. Finally, closed-loop feedback control is implemented: an online viscometer (such as a rotary viscosity sensor with a response time of ≤10 seconds) is installed at the paint pump outlet to monitor the adjusted viscosity in real time, forming a "detection-calculation-adjustment-feedback" closed loop to ensure that the deviation between η(t) and η_target is ≤±2%.

[0156] Furthermore, a multi-parameter coordinated adjustment strategy is included, including indirect humidity compensation. Although the simulation equations don't directly include humidity parameters, high humidity environments can easily cause water-based coatings to absorb moisture and dilute, reducing viscosity. Using a preset humidity-temperature compensation mapping table, the system automatically increases the temperature adjustment threshold by 0.5°C when humidity exceeds 70% RH to offset the effect of moisture absorption on viscosity.

[0157] Coating Speed Interaction: When viscosity adjustment causes a significant change in coating fluidity (e.g., Δη > 10%), the coating speed is fine-tuned (with an adjustment range of ±5 m / min) to ensure a stable coating volume per unit time and avoid thickness fluctuations. Δη represents the difference between the actual and target viscosity, quantifying the degree of viscosity deviation.

[0158] In summary, steps S503-S505 build an intelligent coating system with strong environmental adaptability through dynamic adjustment driven by multimodal environmental perception and viscosity model, achieving a technological breakthrough from "fixed parameter production" to "real-time environmental adaptation", and providing a systematic solution for high-precision coating processing under complex working conditions.

[0159] Reference Figure 6 , when dynamic coating viscosity adjustment is performed, it also includes:

[0160] Step S5051: collecting the coating thickness of the sample and dividing it into multiple independent temperature zones according to the thickness, and matching the infrared irradiation intensity corresponding to the coating thickness in the independent temperature zones;

[0161] During the coating process, coating thickness often varies across different areas, necessitating precise drying to ensure coating quality. In step S5051, the sample's coating thickness is first accurately measured using high-precision thickness measurement equipment, such as a laser thickness gauge. The gauge measures at regular intervals along the sample's coated surface, acquiring numerous thickness data points to create a distribution map of coating thickness.

[0162] Based on this thickness data, the system divides the sample surface into multiple independent temperature zones. This segmentation is based on thickness variations, typically assigning zones where the thickness parameter matches a preset threshold or higher. This allows for more targeted drying based on the characteristics of each zone. For example, thicker coating areas, which contain more coating and require more heat for drying, are assigned to one temperature zone, while thinner areas are assigned to another.

[0163] Next, the system matches the corresponding infrared radiation intensity to each independent temperature zone. This matching process is based on a model established through extensive experimental data and experience. Different coating thicknesses require different amounts of heat. Experiments determine the infrared radiation intensity required to achieve optimal drying results at different thicknesses and store this data in a database. Once the thickness of each temperature zone is determined, the system quickly searches the database and matches the corresponding infrared radiation intensity to ensure that the coating in each temperature zone receives the appropriate drying treatment. This prevents both incomplete drying due to insufficient heat and damage to the coating or substrate due to excessive heat.

[0164] Step S5052: Dry the independent temperature zones based on the infrared irradiation intensity, and control the hot air system to form a laminar flow field on the sample surface. The angle between the airflow direction of the laminar flow field and the sample movement direction is within a preset acute angle range.

[0165] After determining the infrared irradiation intensity for each independent temperature zone, the actual drying process begins. The infrared drying device emits infrared light to each temperature zone according to the set intensity. Infrared light is directly absorbed by the paint, converting the light energy into heat energy, which quickly evaporates the solvent in the paint and dries the coating. Temperature control is crucial in this process. The system monitors temperature changes within the temperature zones in real time and adjusts the power of the infrared irradiation device to maintain a stable drying temperature.

[0166] At the same time, the hot air system begins operating, creating a laminar flow field on the sample surface. This helps improve drying efficiency and coating quality. Specially designed air ducts and fans ensure that hot air is blown evenly and steadily onto the sample surface, creating a laminar flow. The angle between the laminar flow direction and the sample's movement is strictly controlled within a preset acute angle range. This angle is typically determined by the coating's characteristics, substrate material, and production process requirements, and is typically between 30° and 60°.

[0167] Reference Figure 7 , when controlling the hot air system to form a laminar flow field on the sample surface, it also includes:

[0168] Step S5053: analyzing the thickness variation interval when the coating thickness changes, and issuing a laminar flow angle feedback adjustment instruction when the thickness variation interval exceeds the set stable threshold interval;

[0169] During the drying process, coating thickness changes as the drying process progresses. To ensure uniform drying and consistent coating quality, real-time monitoring of coating thickness changes is necessary. The system continuously analyzes collected coating thickness data and calculates a thickness variation range. This value reflects the range of coating thickness fluctuations over a given period of time.

[0170] A pre-set stability threshold is also established based on production process requirements and product quality standards. If the thickness variation value is within the stability threshold, the drying process is normal and the coating thickness variation is within an acceptable range. However, if the thickness variation value exceeds the set stability threshold, it indicates that the drying process is abnormal and may affect the coating quality.

[0171] At this point, the system immediately issues a laminar flow angle feedback adjustment command. This command is based on a deep understanding of the drying process and a precise grasp of the effects of the laminar flow field. Because the angle of the laminar flow field has a significant impact on the drying effect, adjusting the laminar flow angle can change the contact between the hot air and the sample surface and the efficiency of heat transfer, thereby affecting the drying speed and thickness change of the coating, thereby correcting any anomalies in the drying process.

[0172] Step S5054: Based on the laminar flow angle feedback instruction, the inclination angle of the laminar flow field is increased or decreased until the thickness variation interval value is within the stable threshold interval.

[0173] Upon receiving the laminar flow angle feedback adjustment command, the hot air system will adjust the laminar flow field's tilt angle. If the thickness variation range is too large, it means that the drying speed in a certain area is too fast or too slow, and the hot air may not be effective in that area. In this case, the laminar flow field's tilt angle will be adjusted up or down according to the specific situation.

[0174] For example, if the coating in a certain area dries too quickly, resulting in excessive thickness changes, the inclination angle of the laminar flow field should be appropriately lowered to reduce the impact of hot air and heat transfer in the area, thereby slowing down the drying speed. Conversely, if the drying speed in a certain area is too slow, the inclination angle of the laminar flow field should be increased to enhance the effect of hot air and speed up the drying speed.

[0175] While adjusting the laminar flow angle, the system continuously monitors changes in coating thickness and calculates the thickness variation range. Adjustment of the laminar flow angle only stops when the thickness variation range returns to the stable threshold. This feedback mechanism enables real-time, dynamic optimization of the drying process, ensuring uniformity and stability of coating thickness, and improving product quality and production efficiency.

[0176] Reference Figure 8 , also includes:

[0177] Step S600: Analyzing the sample substrate and the coating using a preset evaluation strategy to obtain coating compatibility parameters;

[0178] In the coating process, the compatibility between the substrate and the coating directly affects the bonding strength and defect rate of the composite interface. Step S600 establishes a compatibility evaluation system through multi-dimensional detection and quantitative analysis, specifically including: 1. Evaluation strategy and detection dimensions

[0179] The pre-defined evaluation strategy integrates physicochemical property testing and interfacial interaction analysis. Core testing items include:

[0180] Surface physical properties:

[0181] Surface tension (γs): The surface tension of the substrate is tested by the pendant drop method (contact angle meter), and the surface tension of the coating (γl) is measured by the platinum plate method. When the difference between the two is ≤5mN / m, the compatibility is excellent;

[0182] Surface roughness (Ra): Atomic force microscope (AFM) scans the substrate surface. The Ra value affects the wetting and spreading of the coating. Usually Ra ≤ 100nm is required to ensure interface adhesion.

[0183] Chemical Compatibility:

[0184] Solubility parameter (δ): Calculate the difference in Hildebrand solubility parameters between the substrate (e.g., PET’s δ = 21.9 (J / cm³)¹ / ²) and the coating (e.g., polyurethane adhesive’s δ = 20.5 (J / cm³)¹ / ²). When Δδ ≤ 3, interfacial diffusion is good.

[0185] Functional group matching: FTIR analyzes the reactivity of substrate surface functional groups (such as ester groups in PET) and coating active groups (such as isocyanate groups), and quantifies the matching coefficient through changes in peak intensity;

[0186] Interfacial adhesion work (Wa): Calculated based on the Young-Dupré equation Wa = γs + γl − γsl (γsl is the interfacial tension) combined with contact angle measurements. A higher Wa indicates a stronger interfacial bonding force.

[0187] 2. Compatibility parameter generation process

[0188] Sample pretreatment: The substrate was cut into 50 mm × 50 mm specimens, and the coating was prepared into a standard coating film (thickness 50 μm);

[0189] Multi-instrument testing: basic data is obtained through surface tension meter, AFM, and FTIR in sequence, and then imported into the compatibility assessment algorithm;

[0190] Weighted calculation: The surface tension difference (weight 40%), solubility parameter difference (30%), and adhesion work (30%) were normalized to generate a comprehensive compatibility parameter Cp (0-100, the higher the value, the better the compatibility).

[0191] Step S601: dynamically adjusting the coating roller pressure based on the coating compatibility parameter and monitoring the cavity defect density of the composite interface in real time;

[0192] 1. Pressure dynamic adjustment logic

[0193] The pressure adjustment mapping relationship is established according to the compatibility parameter Cp:

[0194] When Cp≥80 (high compatibility): the coating roller pressure adopts the reference value P0 (e.g. 50kN) to avoid deformation of the substrate due to excessive pressure;

[0195] When 60≤Cp<80 (medium compatibility): increase the pressure by 5%-10% (e.g. P=P0+5kN) to enhance the interface contact to compensate for the insufficient bonding force;

[0196] When Cp<60 (low compatibility): start pressure adaptive adjustment and dynamically optimize in combination with real-time defect data (see step S602).

[0197] Pressure regulation is achieved through a servo hydraulic system with an accuracy of ±1% and a response time of ≤100ms, ensuring that pressure changes are synchronized with the substrate movement.

[0198] 2. Real-time monitoring of cavity defects

[0199] Use machine vision inspection system to monitor composite interface online:

[0200] Hardware configuration: A linear array CCD camera (resolution 12μm / pixel) is deployed along the coating width direction, and is combined with a backlight module to capture interface images in real time;

[0201] Defect recognition algorithm: Based on the YOLO model of deep learning, it identifies cavity defects with a diameter ≥ 50 μm and calculates the defect density (numbers / m²). When the defect density is greater than 10 / m², the coupling control model is adjusted (step S602).

[0202] Step S602: establishing a coupling control model of the pressure roller and the conveying speed based on the cavity defect density, and generating a coating roller adjustment parameter for adjustment.

[0203] The coupling control model is as follows:

[0204] ;

[0205] in, Indicates the real-time roller pressure detected by the coating roller. Indicates the set reference roller pressure. represents the set adhesion work weight coefficient, represents the real-time adhesion work obtained by analysis, represents the reference adhesion work, Indicates the set cross-section strength weight coefficient, represents the real-time interface bonding strength obtained and set experimentally, Indicates the benchmark interface bonding strength set according to process standards.

[0206] The adjustment parameters are generated and executed as follows:

[0207] Model input update: real-time synchronization of cavity defect density, Wa (test value in step S600), and Kb (measured value of peeling force);

[0208] Pressure-velocity decoupling regulation:

[0209] When the defect density is greater than 15 / m², increase the roller pressure (by 2kN each time) and reduce the conveying speed by 10% (reducing the composite length per unit time and increasing the pressing time).

[0210] When the defect density is 10-15 / m², use the coupling parameters calculated by the model (such as P=1.1P0, velocity V=0.95V0) to avoid over-adjustment of a single parameter;

[0211] Closed-loop feedback verification: After adjustment, the defect density is continuously monitored. If it does not decrease within 30 seconds, the second-level adjustment is triggered (the pressure is increased to 5kN and the speed is reduced to 15%) until the defect density is ≤10 / m².

[0212] Reference Figure 9 Based on the same inventive concept, an embodiment of the present invention provides a coating device for film processing, and the above coating device adjustment method is applied, including:

[0213] The feeding module mixes the coating material evenly and then continuously outputs it for the sample to be coated;

[0214] Among them, the feeding module includes a feeding system consisting of a feeding rack and a PLC control system. The PLC control system receives control signals to adjust the feeding speed of the coated product, so that the flexible cloth or paper has a certain tension, which facilitates the uniform adhesion of the coating during subsequent coating processing.

[0215] The coating module receives the coating material from the feeding module and performs surface coating treatment on the sample according to the optimal coating parameters, so that a coating layer is formed on the surface of the sample;

[0216] The coating module consists of a coating system, which is equipped with a paint spraying device and a spraying parameter control system. The spraying parameter control system can call corresponding coating parameters according to different coating materials and different coating products, thereby adjusting the coating uniformity of the coating products.

[0217] The transmission module provides transmission force for the transmission roller that conveys the sample, keeping the sample under the tension and coating speed required by the coating parameters for coating;

[0218] The control and adjustment module dynamically optimizes and adjusts the coating parameters to maintain a consistent thickness of the coated sample. By testing the coated sample, corresponding feedback adjustment parameters are generated to feedback adjust the coating system to maintain the optimal coating effect.

[0219] In addition, it also includes a drying system and a winding system. The drying system is a wind drying and infrared drying device, which can realize wind drying and infrared drying to meet different drying processing requirements, so that the product can be dried quickly and effectively.

[0220] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the division of the above-mentioned functional modules is only used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working processes of the above-mentioned systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0221] An embodiment of the present invention provides a computer-readable storage medium storing a computer program capable of being loaded by a processor and executing a method for adjusting a coating device for film coating processing.

[0222] Computer storage media include, for example, various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0223] Based on the same inventive concept, an embodiment of the present invention provides an intelligent terminal including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute a method for adjusting a coating device for film coating processing.

[0224] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the division of the above-mentioned functional modules is only used as an example for illustration. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working processes of the above-mentioned systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0225] The above are all preferred embodiments of the present application and are not intended to limit the scope of protection of this application. Unless otherwise specified, any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features. In other words, unless otherwise specified, each feature is merely an example of a series of equivalent or similar features.

Claims

1. A method for adjusting a coating device for coating processing, characterized in that: include: Analyze the substrate type and sample thickness of the sample to be processed and match the target coating thickness corresponding to the substrate type; Analyze the target coating thickness and sample thickness to determine coating parameters, including coating supply pressure, sample tension, and coating speed; Based on the coating parameters and the preset coating optimization strategy, multiple sets of simulated coating parameters are generated, and coating treatment is performed to obtain multiple sets of coating simulation samples; the coating optimization strategy includes: According to the substrate type, query the preset historical coating parameter database for approximate coating parameters whose matching degree of the substrate physical property parameters is not less than a preset threshold; Perform parameter difference analysis based on the approximate coating parameters and the coating parameters of the sample to determine the parameter adjustment benchmark unit; generating a plurality of adjacent groups of coating parameters as simulated coating parameters using a parameter adjustment reference unit as a unit; When generating simulation coating parameters, it also includes: Establish a substrate deformation-coating thickness transfer function model and analyze the pressure compensation of the coating supply system; Pressure regulation of the paint supply system based on the pressure compensation amount; The substrate deformation-coating thickness transfer function model is as follows: ； in, Indicates the pressure compensation amount, Indicates the set curvature compensation gain, Indicates the curvature radius of the substrate when it is bent. represents the curvature gradient of the local deformation zone of the sample, represents the set sample strain rate damping coefficient, represents the detected microstrain on the sample surface, Indicates the real-time strain rate of the substrate; Perform thickness testing on multiple groups of coating simulation samples to determine the optimal coating simulation sample that is closest to the target coating thickness, and mark the optimal coating parameters; The samples to be processed were coated based on the optimal coating parameters.

2. The method for adjusting a coating device for coating processing according to claim 1, characterized in that: When coating the sample to be processed, it also includes: A preset laser displacement sensor array is used to detect the real-time deformation parameters of the sample coating surface, and a correlation model between the substrate stress-strain field and the coating thickness is established to analyze and obtain the coating thickness deviation value; Based on the coating thickness deviation value, the corresponding compensation parameters in the preset compensation database are matched, including tension and coating speed, and the coating device is adjusted according to the compensation parameters; The analytical model of the coating thickness deviation value is analyzed using the following formula: ； in, Indicates the coating thickness deviation value, represents the detected microstrain on the sample surface, It represents the strain rate of the substrate obtained by time series analysis of the strain sensor, is the maximum surface stress value of the substrate, The corresponding thickness coupling coefficient for the substrate is found in the table. Look up the stress sensitivity coefficient corresponding to the substrate table.

3. The method for adjusting a coating device for coating processing according to claim 2, characterized in that: When coating the sample to be processed based on the optimal coating parameters, it also includes: Detect multimodal environmental parameters in the sample coating area, including ambient humidity, ambient temperature, and electrostatic voltage field strength; Based on multimodal environmental parameters, a paint viscosity-environmental parameter simulation equation is established to analyze and obtain the viscosity adjustment value of the paint supply system; Dynamically adjusting the paint viscosity of the paint supply system based on the viscosity adjustment amount; The coating viscosity-environmental parameter simulation equation is as follows: ； in, For real-time paint viscosity, Indicates the set base viscosity, Indicates the ambient temperature of the coating area being tested. Indicates the set reference temperature, represents the temperature sensitivity coefficient, represents the detected electric field gradient in the coating area, Indicates the electric field coupling coefficient of the coating obtained by looking up the table.

4. A coating device adjustment method for coating according to claim 3, characterized in that: When dynamic paint viscosity adjustment is performed, it also includes: Collect the coating thickness of the sample and divide it into multiple independent temperature zones according to the thickness, and match the infrared irradiation intensity corresponding to the coating thickness in the independent temperature zones; The independent temperature zones are dried based on the infrared irradiation intensity, and the hot air system is controlled to form a laminar flow field on the sample surface. The angle between the airflow direction of the laminar flow field and the sample movement direction is within a preset acute angle range.

5. The method for adjusting a coating device for coating processing according to claim 4, characterized in that: When controlling the hot air system to form a laminar flow field on the sample surface, it also includes: Analyze the thickness change interval value when the coating thickness changes. When the thickness change interval value exceeds the set stable threshold range, issue a laminar angle feedback adjustment instruction; The inclination angle of the laminar flow field is increased or decreased based on the laminar flow angle feedback instruction until the thickness change interval value is within the stable threshold range.

6. The method for adjusting a coating device for coating processing according to claim 1, characterized in that: Also includes: Analyze sample substrates and coatings using a preset evaluation strategy to obtain coating compatibility parameters; Dynamically adjust the coating roller pressure based on coating compatibility parameters and monitor the cavity defect density at the composite interface in real time; A coupling control model of the pressure roller and the conveying speed is established based on the cavity defect density, and the coating roller adjustment parameters are generated for adjustment.

7. A coating device adjustment method for coating according to claim 6, characterized in that: The coupling control model is as follows: ； in, Indicates the real-time roller pressure detected by the coating roller. Indicates the set reference roller pressure. represents the set adhesion work weight coefficient, represents the real-time adhesion work obtained by analysis, represents the reference adhesion work, Indicates the set cross-section strength weight coefficient, represents the real-time interface bonding strength obtained and set experimentally, Indicates the benchmark interface bonding strength set according to process standards.

8. A coating device for film processing, using the coating device adjustment method for film processing according to any one of claims 1 to 7, characterized in that: include: The feeding module mixes the coating material evenly and then continuously outputs it for the sample to be coated; The coating module receives the coating material from the feeding module and performs surface coating treatment on the sample according to the optimal coating parameters, so that a coating layer is formed on the surface of the sample; The transmission module provides transmission force for the transmission roller that conveys the sample, keeping the sample under the tension and coating speed required by the coating parameters for coating; The control and adjustment module dynamically optimizes and adjusts the coating parameters to maintain a consistent thickness of the coated sample.

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