Adjusting method of coating device for coating processing and coating device

Abstract

The invention relates to a coating device adjusting method for coating processing and a coating device, and relates to the technical field of coating processing of coatings, and the coating device adjusting method comprises the following steps: analyzing a base material type and a sample thickness of a sample to be processed, and matching a target coating thickness corresponding to the base material type; analyzing according to the target coating thickness and the sample thickness to determine coating parameters, wherein the coating parameters comprise coating supply pressure, sample tension and coating speed; generating a plurality of groups of simulation coating parameters based on the coating parameters and a preset coating optimization strategy, and carrying out coating treatment to obtain a plurality of groups of coating simulation samples; performing thickness detection on the multiple groups of coating simulation samples to determine an optimal coating simulation sample closest to the target coating thickness, and marking to obtain an optimal coating parameter; and carrying out coating treatment on the to-be-processed sample based on the optimal coating parameters. The coating device has the effect that the thickness consistency is kept when different base material products are subjected to coating processing.

Description

Technical Field

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

[0002] Laminating processing is a treatment process for coating or covering the surface of an object, which can form a coating or film with specific functions on the surface of the object to protect the object from the influence of external environmental factors.

[0003] In the related art, during the laminating processing, the coating equipment controls the feeding mechanism, the coating mechanism, the transmission mechanism, and the control system, etc. The control system detects various parameters during the coating process and makes corresponding adjustments, so that the feeding mechanism can stably supply the coating material and the coating mechanism can stably perform the coating processing. For some flexible moisture-proof papers, during processing, the moisture-proof material is compounded with the paper as a coating by means of hot pressing.

[0004] In view of the above related art, when the paper is hot-pressed and compounded with the moisture-proof material, the product is changed. Except for the moisture-proof material, different coating materials have different dosages during the compounding process, resulting in different thickness changes of the paper during continuous compounding. However, in the related art, there is a lack of thickness control when coating and processing products with different substrates, so that some products with high requirements for thickness consistency cannot meet the processing requirements well. Summary of the Invention

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

[0006] In a first aspect, the present application provides a method for adjusting a coating device for laminating processing, adopting the following technical solution: A method for adjusting a coating device for laminating processing includes: 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 according to the target coating thickness and the sample thickness to determine the coating parameters, and the coating parameters include the coating supply pressure, the sample tension, and the coating speed; Generate multiple groups of simulated coating parameters based on the coating parameters and a preset coating optimization strategy, and perform coating processing to obtain multiple groups of coated simulation samples; Detect the thickness of multiple groups of coated simulation samples to determine the optimal coated simulation sample closest to the target coating thickness, and mark to obtain the best coating parameters; Perform coating processing on the sample to be processed based on the best coating parameters.

[0007] By adopting the above technical solution, through the generation and screening of multiple groups of simulation parameters, the number of trial and error is significantly reduced, the efficiency of coating parameter optimization is improved, and the target thickness is matched with the substrate characteristics to ensure the coating thickness consistency of different flexible substrates (such as paper and fabric), solving the problem of uneven thickness caused by substrate differences in traditional methods.

[0008] Optionally, the coating optimization strategy includes: Query the approximate coating parameters in the preset historical coating parameter database with a substrate physical property parameter matching degree not lower than the preset threshold according to the substrate type; Perform parameter difference analysis based on the approximate coating parameters and the coating parameters of the sample to determine the parameter adjustment reference unit; Generate adjacent multiple groups of coating parameters as simulated coating parameters with the parameter adjustment reference unit as the step size.

[0009] By adopting the above technical solution, through the intelligent retrieval of historical process data and parameter difference analysis, the initial parameter adjustment reference is quickly determined, the debugging cycle of new substrates is shortened, a wider process window is covered through the iterative generation of adjacent parameter groups, the adaptability to complex substrates (such as multi-layer composite materials) is enhanced, and coating defects caused by empirical parameter deviation are reduced.

[0010] Optionally, when generating simulated coating parameters, it further includes: Establish a substrate deformation - coating thickness transfer function model and analyze to obtain the pressure compensation amount of the coating supply system; Adjust the pressure of the coating supply system based on the pressure compensation amount; The substrate deformation - coating thickness transfer function model is as follows: ; Wherein, represents the pressure compensation amount, represents the set curvature compensation gain, represents the radius of curvature when the substrate bends, represents the curvature gradient of the local deformation area of the sample, represents the set sample strain rate damping coefficient, represents the detected microstrain on the sample surface, represents the real-time strain rate of the substrate.

[0011] By adopting the above technical solution, the pressure compensation amount is accurately calculated by using the real-time detection of the substrate curvature gradient and strain rate. This model effectively suppresses the coating thickness fluctuation caused by the bending or stretching deformation of the substrate, reduces the standard deviation of the transverse thickness, and improves the applicability to high-tension coating scenarios.

[0012] Optionally, 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 for analysis to 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 analysis model of the coating thickness deviation value is analyzed by the following formula: ; Wherein, represents the coating thickness deviation value, represents the micro-strain detected on the sample surface, represents the strain rate obtained by the substrate through time series analysis of the strain sensor, is the maximum stress value on the surface of the substrate, is the stress-thickness coupling coefficient corresponding to the substrate by looking up the table, is the stress sensitivity coefficient corresponding to the substrate by looking up the table.

[0013] By adopting the above technical solution, according to the influence of the dynamic analysis stress and strain rate on the thickness deviation, the real-time closed-loop control of the coating thickness is realized. The intelligent matching of the compensation parameter library improves the thickness deviation correction speed, is applicable to high-speed coating production lines (such as roll-to-roll coating), and reduces the batch defects caused by the vibration of the substrate.

[0014] Optionally, the multi-modal environmental parameters of the sample coating area are detected, including environmental humidity, environmental temperature and static voltage field strength; Based on the multi-modal environmental parameters, a coating viscosity-environmental parameter simulation equation is established for analysis to obtain the viscosity adjustment amount of the coating supply system; Based on the viscosity adjustment amount, the dynamic coating viscosity of the coating supply system is adjusted; The coating viscosity-environmental parameter simulation equation is as follows: ; Wherein, is the real-time coating viscosity, represents the set reference viscosity, represents the detected environmental temperature of the coating area, represents the set reference temperature, represents the temperature sensitivity coefficient, represents the detected electric field gradient of the coating area, represents the electric field coupling coefficient of the coating obtained by looking up the table.

[0015] By adopting the above technical solution, through the real-time feedback of environmental temperature and humidity and electric field strength, the rheological properties of the coating are precisely regulated. This solution solves the problem of sudden change in the fluidity of the coating in a high-humidity environment, increases the precision of viscosity control, and reduces the coating orange peel or sagging defects caused by environmental fluctuations.

[0016] Optionally, when performing dynamic coating viscosity adjustment, it further includes: Collect the coating thickness of the sample and divide it into multiple independent temperature zones according to different thicknesses, and match the infrared irradiation intensity corresponding to the coating thickness in the independent temperature zones; Dry the independent temperature zones based on the infrared irradiation intensity, and at the same time control the hot air system to form a laminar flow field on the surface of the sample, and the included angle range between the air flow direction of the laminar flow field and the sample movement direction is within a preset acute angle range.

[0017] By adopting the above technical solution, the zoning temperature control strategy (strong radiation in thick areas and weak radiation in thin areas) compensates for the drying shrinkage difference, improves the coating thickness uniformity, and suppresses the coating migration through the laminar flow field design, reducing the edge thickening phenomenon.

[0018] Optionally, it further includes: Analyze the sample substrate and the coating with a preset evaluation strategy to obtain the coating compatibility parameters; Dynamically adjust the pressure of the coating roller based on the coating compatibility parameters and monitor the cavity defect density of the composite interface in real time; Establish a coupling control model of the pressure roller pressure and the conveying speed based on the cavity defect density, and generate coating roller adjustment parameters for adjustment.

[0019] By adopting the above technical solution, when detecting abnormal thickness fluctuations, the air flow field distribution is optimized in real time through the angle feedback mechanism, so that the thickness change range is stabilized within ±0.5 μm. This solution solves the thickness drift problem caused by sudden changes in the coating speed (such as during start-stop stages).

[0020] Optionally, the coupling control model is as follows: ; Wherein, represents the real-time pressure roller pressure detected by the coating roller, represents the set reference pressure roller pressure, represents the set adhesion work weight coefficient, represents the real-time adhesion work obtained through analysis, represents the reference adhesion work, represents the set cross-section strength weight coefficient, represents the real-time interface bonding strength obtained through experiments and set, represents the reference interface bonding strength set according to the process standard.

[0021] By adopting the above technical solution, the pressure of the pressure roller and the vibration mode are adjusted in real time, so that the density of cavity defects is reduced, which helps to improve the adhesion between the coating and the substrate, thereby contributing to improving the composite stability between the coating layer and the substrate.

[0022] In a second aspect, the present application provides a coating device for film coating processing, adopting the following technical solution: A coating device for film coating processing, comprising: A feeding module that continuously outputs the coating material after mixing it evenly for use in coating processing of samples; A coating module that 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; A transmission module that provides driving force for the transmission roller for transmitting the sample, and keeps the sample under the tension and coating speed required by the coating parameters for coating treatment; A control and adjustment module that dynamically optimizes and adjusts the coating parameters to keep the samples after coating treatment having a consistent thickness.

[0023] By adopting the above technical solution, the coating device for film coating processing performs coating treatment according to the optimal coating parameters through the coating module, and cooperates with the transmission module to adjust the coating tension and coating speed of the sample, so that the formed coating thickness maintains uniform consistency. At the same time, by dynamically optimizing and adjusting the coating parameters, it helps to adapt to the changes generated during the coating process and maintain the consistent coating effect of the sample.

[0024] In summary, the present application includes at least one of the following beneficial technical effects: 1. By generating and screening multiple groups of simulation parameters, the number of trial-and-error times is significantly reduced, the efficiency of optimizing coating parameters is improved, and the target thickness is matched with the substrate characteristics to ensure the consistency of the coating thickness of different flexible substrates (such as paper and cloth), solving the problem of uneven thickness caused by substrate differences in traditional methods; 2. By using the intelligent retrieval of historical process data and the analysis of parameter differences, the initial parameter adjustment benchmark is quickly determined, the debugging cycle of new substrates is shortened, and a wider process window is covered through the iterative generation of adjacent parameter groups, enhancing the adaptability to complex substrates (such as multi-layer composite materials) and reducing coating defects caused by empirical parameter deviations; 3. By using real-time detection of the curvature gradient and strain rate of the substrate to accurately calculate the pressure compensation amount, this model effectively suppresses the coating thickness fluctuation caused by the bending or stretching deformation of the substrate, reducing the standard deviation of the lateral thickness. Description of the Drawings

[0025] Figure 1 It is a flowchart of the method of steps S100 to S500 in the present application.

[0026] Figure 2 It is the flowchart of the method in steps S301 to S303 in this application.

[0027] Figure 3 It is the flowchart of the method in steps S3031 to S3032 in this application.

[0028] Figure 4 It is the flowchart of the method in steps S501 to S502 in this application.

[0029] Figure 5 It is the flowchart of the method in steps S503 to S505 in this application.

[0030] Figure 6 It is the flowchart of the method in steps S5051 to S5052 in this application.

[0031] Figure 7 It is the flowchart of the method in steps S5053 to S5054 in this application.

[0032] Figure 8 It is the flowchart of the method in steps S600 to S602 in this application.

[0033] Figure 9 It is the schematic diagram of the overall structure of a coating device for coating processing in this application. Detailed implementation manners

[0034] In order to make the purpose, technical solutions and advantages of this application clearer, the following Figures 1-9 are further described in detail in conjunction with the appended

[0035] and embodiments. It should be understood that the specific embodiments described here are only used to explain this application and are not used to limit this application.

[0036] The embodiment of this application discloses a method for adjusting a coating device for coating processing. By generating and screening multiple groups of simulation parameters, the number of trial and error times is significantly reduced, the efficiency of optimizing coating parameters is improved, the target thickness is matched in combination with the substrate characteristics, and the coating thickness consistency of different flexible substrates is ensured, so as to solve the problem of uneven thickness caused by substrate differences in the traditional method.

[0037] Referring to Figure 1 , the method flow of the method for adjusting a coating device for coating processing includes the following steps: 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; During the coating process, the substrate type and thickness of the sample to be processed are key initial parameters that affect the coating effect. The substrate type may include films of different materials, such as polypropylene (PP), polyethylene (PE), polyester (PET), etc. The physical and chemical properties of each substrate, such as surface tension, flexibility, heat resistance, etc., are different. These properties directly determine the function and thickness requirements of the required coating. For example, for packaging substrates that require high barrier performance, a relatively thick coating layer may be needed to achieve good barrier effects; while for substrates that require thin, light, and flexible properties, the target coating thickness is relatively thin.

[0038] The accurate measurement of the sample thickness is the basis for subsequent parameter determination. Through high-precision thickness measurement instruments, such as laser thickness gauges, contact thickness gauges, etc., the sample to be processed is measured at multiple points and the average value is taken to ensure the accuracy of the thickness data. According to the substrate type, query the preset substrate-target coating thickness database. This database stores the optimal coating thickness ranges of different substrates in different application scenarios. These data are obtained through a large number of historical experiments and production experience summaries, and can provide a scientific reference for the target coating thickness of the current sample to be processed. For example, when it is detected that the substrate is a PET film with a thickness of 50 μm, through database matching, the target coating thickness in food packaging applications is determined to be 10 - 15 μm.

[0039] Step S200: Analyze according to the target coating thickness and sample thickness to determine the coating parameters. The coating parameters include paint supply pressure, sample tension, and coating speed; After determining the target coating thickness and sample thickness, it is necessary to further analyze and determine the specific coating parameters. The paint supply pressure determines the flow rate and pressure stability of the paint from the supply system to the coating head, and directly affects the uniformity and thickness of the coating. A relatively high supply pressure may cause excessive paint supply, resulting in an overly thick coating or sagging; while insufficient pressure may lead to an insufficient coating thickness or discontinuous coating. According to the target coating thickness and the liquid absorption performance of the substrate, initially calculate the required paint supply pressure range through a hydrodynamic model, and at the same time evaluate the bearing capacity of the substrate in combination with the sample thickness to avoid substrate deformation caused by excessive pressure.

[0040] Sample tension is an important parameter to ensure smooth operation of the sample during the coating process. Appropriate tension can prevent the sample from wrinkling, stretching or relaxing during the coating process, thereby ensuring the accuracy of the coating position and the uniformity of the coating. The magnitude of the tension is closely related to the material, thickness and coating speed of the sample. For thinner or more flexible substrates, a smaller tension needs to be applied to avoid damage to the substrate due to excessive stretching; for thicker or more rigid substrates, the tension needs to be appropriately increased to ensure smooth transportation. By establishing a mathematical model of sample tension and substrate mechanical properties, combined with the target coating thickness and sample thickness, the optimal sample tension value is calculated.

[0041] The determination of coating speed requires comprehensive consideration of production efficiency and coating quality. A higher coating speed can improve production efficiency, but may result in insufficient leveling time of the coating on the substrate surface, thus affecting the uniformity of the coating; a lower coating speed is beneficial to coating leveling, but will reduce production efficiency. According to the target coating thickness and the viscosity characteristics of the coating, the coating speed range that can achieve efficient production while ensuring the coating quality is determined through experimental data fitting or empirical formulas. For example, for high-viscosity coatings and thicker target coating thicknesses, a lower coating speed needs to be selected to ensure that the coating can be fully leveled; for low-viscosity coatings and thinner target coating thicknesses, the coating speed can be appropriately increased.

[0042] Step S300: generating multiple sets of simulated coating parameters based on coating parameters and a preset coating optimization strategy, and performing coating processing to obtain multiple sets of coating simulation samples; In order to find the best coating parameter combination, 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 specific values ​​of parameters such as coating supply pressure, sample tension, coating speed, and corresponding coating effect evaluation. By comparing the physical properties of the current substrate with those of the substrate in historical cases, such as density, elastic modulus, surface roughness, etc., the approximate coating parameters whose physical performance parameter matching degree is not less than the preset threshold are selected as a reference, where the preset threshold is the difference interval 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 interval is 1.5 grams per cubic centimeter. The physical performance parameters of the two substrates and the preset threshold are in an approximate range.

[0043] Then, based on the approximate coating parameters and the currently preliminarily determined coating parameters, parameter difference analysis is carried out to determine the parameter adjustment reference unit. The parameter adjustment reference unit is the minimum adjustment amount set according to the influence degree of the coating parameters on the coating effect. For example, the adjustment reference unit of the coating supply pressure can be set to 0.1 MPa, the adjustment reference unit of the sample tension is 1 N / m, and the adjustment reference unit of the coating speed is 0.5 m / min. Taking these reference units as the unit, multiple adjacent groups of coating parameters are generated around the preliminarily determined coating parameters. For example, on the basis of the coating supply pressure of 0.5 MPa, multiple groups of pressure parameters such as 0.4 MPa, 0.5 MPa, and 0.6 MPa are generated. At the same time, similar adjustments are made to the sample tension and the coating speed to form multiple different combinations of simulated coating parameters.

[0044] In the process of generating the simulated coating parameters, the influence of the substrate deformation on the coating thickness also needs to be considered. After generating multiple groups of simulated coating parameters, the coating device is used to perform coating treatment on the sample to be processed, and multiple groups of coated simulated samples are obtained. During the coating process, the experimental conditions are strictly controlled to ensure the independence and repeatability of each group of simulated coating parameters, so as to accurately detect and analyze the coated simulated samples in the follow-up. The specific coating optimization strategy for generating multiple groups of simulated coating parameters will be further elaborated in the follow-up.

[0045] Step S400: Detect the thickness of multiple groups of coated simulated samples to determine the optimal coated simulated sample closest to the target coating thickness, and mark the best coating parameters; Detect the thickness of the prepared multiple groups of coated simulated samples. Use high-precision thickness detection equipment, such as non-contact optical thickness gauges, eddy current thickness gauges, etc., to measure the thickness at multiple positions of each sample to obtain the distribution of the coating thickness. Calculate the average thickness and thickness uniformity indexes of each sample, such as standard deviation, coefficient of variation, etc., to comprehensively evaluate the accuracy and uniformity of the coating thickness.

[0046] Compare the average thickness of each coated simulated sample with the target coating thickness and calculate the thickness deviation value. 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, select the sample with the smallest thickness deviation and good thickness uniformity as the optimal coated simulated sample. For example, among multiple groups of simulated samples, the average thickness of sample A is 12 μm, the target coating thickness is 13 μm, the deviation is 1 μm, and the thickness standard deviation is 0.5 μm; the average thickness of sample B is 13.5 μm, the deviation is 0.5 μm, and the thickness standard deviation is 1.0 μm. Although the average thickness deviation of sample B is smaller, due to its poor thickness uniformity, finally sample A is selected as the optimal coated simulated sample.

[0047] After determining the optimal coating simulation sample, mark the corresponding coating parameter combination of the sample, including coating supply pressure, sample tension, coating speed, etc., as the optimal coating parameters. These optimal coating parameters have been verified through actual coating simulation and testing, and can achieve the 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.

[0048] Step S500: Perform coating treatment on the sample to be processed based on the optimal coating parameters.

[0049] During the actual coating production process, perform coating treatment on the sample to be processed based on the optimal coating parameters obtained by marking. In order to ensure the stability of the coating process and the coating quality, it is necessary to monitor and control the coating process in real time.

[0050] Refer to Figure 2 , the coating optimization strategy includes: Step S301: Query the approximate coating parameters in the preset historical coating parameter database whose matching degree of substrate physical property parameters is not lower than the preset threshold according to the substrate type; In the field of coating processing, the preset historical coating parameter database is the crystallization 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. The construction of this database is based on the associated storage of a large amount of substrate physical property parameters and corresponding coating process parameters, specifically including key physical indicators such as the material type of the substrate (such as PP, PE, PET, aluminum foil composite film, etc.), density, elastic modulus, surface roughness, water absorption rate, surface tension, etc., and the historical successful coating parameters such as coating supply pressure, sample tension, coating speed, etc. matching them.

[0051] After identifying the substrate type of the sample to be processed (such as through spectral analysis, material label reading, etc.), the system first extracts the physical property parameters of the substrate, for example, by using a sensor array to detect indicators such as the thickness, density, and surface roughness of the substrate in real time. Subsequently, a multi-dimensional similarity matching algorithm (such as Euclidean distance, cosine similarity, etc.) is used to retrieve the historical cases in the historical database that are closest to the current substrate physical properties. Taking surface tension as an example, if the surface tension of the current PET substrate is 42 mN / m, and there are three groups of historical data with surface tensions of 40 mN / m, 42 mN / m, and 45 mN / m in the database, the system will preferentially select the case with a surface tension of 42 mN / m as the basic reference, and at the same time include the cases in the adjacent interval (such as ±5 mN / m) as auxiliary references to form a candidate set of approximate coating parameters.

[0052] To ensure the matching accuracy, the database usually adopts a hierarchical index structure. First, it conducts a primary classification according to the material type (such as plastic film, metal foil, paper-based material), and then a secondary classification according to the key dimensions of physical property parameters (such as mechanical properties, surface properties, thermal properties). Finally, through parameter threshold setting (such as elastic modulus difference ≤ 10%, surface roughness difference ≤ 5%), approximate coating parameters with a physical property parameter matching degree not lower than the preset threshold are screened out. For example, for a PET substrate with a thickness of 50μm, the system will first retrieve the historical coating parameters in the historical database with the material being PET, the thickness in the range of 45 - 55μm, and the surface tension of 40 - 45mN / m to form a preliminary approximate parameter list, providing a data basis for subsequent parameter adjustment.

[0053] Step S302: Perform parameter difference analysis based on the approximate coating parameters and the coating parameters of the sample to determine the parameter adjustment reference unit; After obtaining the approximate coating parameters, it is necessary to perform difference analysis with the coating parameters preliminarily determined for the current sample (such as the initial parameters calculated based on the target thickness and the sample thickness in step S200) to clarify the adjustment direction and the minimum adjustment step of each parameter, that is, the parameter adjustment reference unit. The core of the parameter difference analysis is to quantify the influence degree of different parameters on the coating effect, and determine a reasonable adjustment granularity in combination with the control accuracy and process stability requirements of the production equipment.

[0054] First, establish a parameter influence factor matrix and assign weight coefficients to the three core parameters of paint supply pressure, sample tension, and coating speed respectively. The setting of the weight coefficients is based on the orthogonal experiment method or the response surface analysis method, and the influence weights of each parameter on key indicators such as coating thickness uniformity, adhesion, and drying speed are obtained through fitting historical data. For example, through experimental verification, the influence weight of the paint supply pressure on the coating thickness is 0.4, the influence weight of the sample tension is 0.3, and the influence weight of the coating speed is 0.3. Subsequently, calculate the absolute difference and relative difference between the approximate parameters and the initial parameters of the current sample. For example, the paint supply pressure of the approximate parameter is 0.6MPa, the current initial parameter is 0.5MPa, the absolute difference is 0.1MPa, and the relative difference is 20%.

[0055] The determination of the reference unit for parameter adjustment needs to consider both process accuracy and production efficiency. For the coating supply pressure, considering that the control accuracy of the pump body is 0.05 MPa and a slight change in pressure can significantly affect the coating thickness, the reference unit is set to 0.05 MPa; for the sample tension, since the minimum adjustment step of the tension control system is 1 N / m and excessive tension fluctuations are likely to cause substrate stretching deformation, the reference unit is set to 1 N / m; for the coating speed, considering that the control accuracy of the motor speed is 0.1 m / min and the impact of speed changes on the coating drying effect has non-linear characteristics, the reference unit is set to 0.5 m / min (combined with empirical formulas to avoid over-sensitivity).

[0056] During the difference analysis process, it is also necessary to introduce a parameter coupling effect correction coefficient. For example, when the coating supply pressure and the coating speed change simultaneously, their effects on the coating thickness are not simply linearly superimposed, but there is an interaction. The coupling correction function is obtained by fitting historical data to correct the single-parameter difference, ensuring that the setting of the reference unit can reflect the parameter linkage effect in the actual process. Finally, through comprehensive weight calculation, equipment accuracy constraint, and coupling effect correction, the reference unit for adjusting each parameter is determined, providing a standardized adjustment step for generating simulated coating parameters.

[0057] Step S303: Generate multiple adjacent sets of coating parameters as simulated coating parameters in units of the reference unit for parameter adjustment.

[0058] After determining the reference unit for parameter adjustment, centered on the initial coating parameters of the current sample and with the reference unit of each parameter as the step size, multiple adjacent sets of simulated coating parameters are generated in the three-dimensional parameter space (pressure, tension, speed). The generation strategy adopts orthogonal experimental design or uniform design method to ensure that the parameter combinations can comprehensively cover the potential optimal solution area while avoiding redundant experiments.

[0059] Specifically, for the coating supply pressure P (initial value P0, reference unit ΔP), five groups of pressure parameters P0 - 2ΔP, P0 - ΔP, P0, P0 + ΔP, P0 + 2ΔP are generated; for the sample tension T (initial value T0, reference unit ΔT), three groups of tension parameters T0 - ΔT, T0, T0 + ΔT are generated; for the coating speed V (initial value V0, reference unit ΔV), three groups of speed parameters V0 - ΔV, V0, V0 + ΔV are generated. Through combination and arrangement, 5×3×3 = 45 groups of simulated coating parameters are formed (the step size range can be adjusted according to actual needs, such as ±1Δ or ±2Δ). This design can not only ensure the coverage of the parameter space but also control the number of experiments within a reasonable range.

[0060] The generated multiple sets of simulated coating parameters shall be accompanied by parameter tags to clarify the adjustment direction and amplitude of each parameter (e.g., "+1ΔP" indicates that the pressure increases by one reference unit), and they shall be grouped and managed according to the parameter difference degree to facilitate the orderly development of subsequent coating simulation experiments. For example, the parameter combinations are divided into "pressure-sensitive group", "tension-sensitive group", and "speed-sensitive group", and special analysis is carried out on the influence of changes in different parameter dimensions on the coating quality to improve the pertinence of parameter optimization.

[0061] Through the above steps, the set of simulated coating parameters formed is based on historical successful experiences, combined with the specific characteristics of the current sample, and also considers the parameter linkage effect and the influence of substrate dynamic deformation, providing a scientific and reasonable parameter combination for subsequent coating simulation experiments, ensuring that the best coating parameters can be efficiently screened out and realizing the dual optimization of coating quality and production efficiency. The whole process reflects an intelligent adjustment strategy that combines data-driven and model-assisted, deeply integrating empirical knowledge with real-time detection data, and providing a systematic solution for precise coating in film coating processing.

[0062] Refer to Figure 3 , when generating simulated coating parameters, it also includes: Step S3031: Establish a substrate deformation - coating thickness transfer function model and analyze to obtain the pressure compensation amount of the coating supply system; During the film coating processing, the dynamic deformation of the substrate is one of the key factors affecting the coating thickness uniformity. When the substrate is bent, stretched or locally wrinkled under the action of tension, the change in its surface morphology will directly lead to uneven distribution during coating. To accurately quantify the influence of this deformation on the coating thickness, a substrate deformation - coating thickness transfer function model needs to be established. This model provides a theoretical basis for the real-time compensation of the coating supply pressure by capturing the dynamic relationship between the geometric characteristics and mechanical response of the substrate.

[0063] Step S3032: Adjust the pressure of the coating supply system based on the pressure compensation amount; When local bending or abnormal strain rate occurs in the substrate during coating, the system automatically calculates the pressure compensation amount ΔP and makes real-time corrections to each group of simulated pressure parameters to ensure that the simulated parameters can truly reflect the actual demand of the substrate dynamic deformation for the coating pressure.

[0064] The substrate deformation - coating thickness transfer function model is as follows: ; Among them, 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 the coating thickness, with the unit of MPa.

[0065] It represents the set curvature compensation gain, which is the additional pressure value to be applied to the coating supply system to offset the influence of the substrate deformation on the coating thickness, with the unit of MPa.

[0066] It represents the radius of curvature when the substrate bends. It represents the curvature gradient of the local deformation area of the sample, characterizing the local bending degree of the substrate along the coating direction (x-axis). The surface profile data of the substrate is collected in real time through a laser displacement sensor array and obtained by second-order difference calculation. When the substrate shows a convex bend, the curvature gradient is positive and pressure compensation needs to be increased; when it is a concave bend, it is negative and pressure compensation needs to be reduced.

[0067] It represents the set sample strain rate damping coefficient, reflecting the hysteresis effect of the substrate dynamic strain on the pressure compensation, obtained by fitting through a tensile experiment (for example, the Kd of a PE substrate is usually 0.5 - 0.7 MPa·s / με).

[0068] It represents the detected microstrain on the sample surface. It represents the real-time strain rate of the substrate, monitored in real time through a microstrain sensor pasted on the substrate surface, characterizing the tensile or compressive strain of the substrate per unit time, with the unit of με / s.

[0069] Among them, the model application and compensation amount calculation process are as follows: 1. Real-time acquisition of deformation data: Deploy a laser displacement sensor array (with an accuracy of ±1μm) at a position 50 - 100 mm upstream of the coating head, scan the substrate surface at a frequency of 200 Hz, obtain the height data of multiple points along the width direction (y-axis), and construct a real-time three-dimensional contour model.

[0070] 2. Curvature gradient calculation: Perform polynomial fitting on the contour data, calculate the second spatial derivative d²R / dx² of the local radius of curvature R through the second derivative, and identify the bending area of the substrate (such as typical deformations like edge waves and middle relaxation).

[0071] 3. Dynamic monitoring of strain rate: Install strain gauge sensors at the substrate tension roller and guide roller, collect the longitudinal strain ε of the substrate in real time, calculate the strain rate dε / dt through time series analysis, and capture the instantaneous tension or contraction caused by tension fluctuations during high-speed coating.

[0072] 4. Compensation amount coupling calculation: Substitute the curvature gradient and strain rate into the transfer function model, and calculate the total pressure compensation amount ΔP by weighting. For example, when a positive curvature gradient (convex bending) of 0.02 m⁻² appears in the middle of the substrate and the strain rate is 5 με / s, if Kp = 1.0 and Kd = 0.6, then ΔP = 1.0×0.02 + 0.6×5 = 3.02 MPa, indicating that a supply pressure of 3.02 MPa needs to be increased to offset the insufficient coating distribution caused by the protrusion of the substrate.

[0073] Referring to Figure 4 , when performing a coating treatment on the sample to be processed, it further includes: Step S501: Preset a laser displacement sensor array to detect the real-time deformation parameters of the coated surface of the sample, and establish a correlation model between the substrate stress-strain field and the coating thickness for analysis to obtain the coating thickness deviation value; Step S502: Based on the coating thickness deviation value, match the corresponding compensation parameters in the preset compensation database, including tension and coating speed, and adjust the coating device according to the compensation parameters; During the coating process, the dynamic stress-strain state of the substrate directly affects the uniformity of the coating. Step S501 realizes the real-time quantitative analysis of the coating thickness deviation by constructing a high-precision monitoring system and a physical model, which specifically includes the following core links: 1. Construction of a real-time deformation parameter detection system Sensor array layout: At a position 50 mm directly below and downstream of the coating head, deploy high-precision laser displacement sensors (accuracy ±5 μm, sampling frequency ≥1 kHz) at intervals of 10 - 20 mm along the width direction (transverse direction) of the substrate to form a detection matrix covering the entire width. The sensors use the triangulation principle, emit laser beams and receive diffuse reflection signals to obtain the three-dimensional coordinate data (x, y, z) of each point on the substrate surface in real time, and construct a dynamic deformation profile.

[0074] Definition of detection parameters: Surface microstrain : Calculated by comparing the longitudinal (traveling direction) length changes of the substrate before and after coating, combined with the distance between the guide rollers, reflecting the degree of stretching or compression of the substrate; Strain rate : Perform a first-order difference calculation on the strain data in the time series to characterize the dynamic rate of substrate deformation; Surface maximum stress : Determine the maximum stress distribution on the substrate surface during the coating process by multiplying the elastic modulus E of the substrate material by the strain and combining finite element simulation or historical data fitting.

[0075] 2. Construction of a correlation model and calculation of deviation values Establish a correlation model between the substrate stress-strain field and the coating thickness, and its core expression is: ; Among them, represents the coating thickness deviation value, which is the difference between the measured coating thickness and the target thickness (unit: μm). A positive value indicates over-thickness, and a negative value indicates under-thickness.

[0076] represents the micro-strain detected on the surface of the sample, represents the strain rate obtained by the substrate through time series analysis of the strain sensor, is the maximum stress value on the surface of the substrate.

[0077] is the stress-thickness coupling coefficient corresponding to the substrate by looking up the table, which is a substrate characteristic parameter calibrated through orthogonal experiments and reflects the influence weight of the strain rate on the coating thickness.

[0078] is the stress sensitivity coefficient corresponding to the substrate by looking up the table, which is a substrate characteristic parameter calibrated through orthogonal experiments and reflects the influence weight of the strain rate on the coating thickness.

[0079] After obtaining the real-time thickness deviation, step S502 realizes the dynamic optimization of the coating process parameters through a data-driven closed-loop compensation mechanism to ensure that the coating thickness is stable within the target range.

[0080] Refer to Figure 5 When coating the sample to be processed based on the optimal coating parameters, it also includes: Step S503: Detect the multi-modal environmental parameters in the coated area of the sample, including environmental humidity, environmental temperature, and static voltage field strength; During the coating process, the fluctuations of environmental parameters will significantly affect the fluidity and coating uniformity of the coating. Step S503 realizes the real-time quantitative detection of humidity, temperature, and static voltage field by constructing a high-precision environmental monitoring network, providing a data basis for subsequent viscosity adjustment.

[0081] Among them, the multi-modal sensor layout and detection principle include ambient temperature detection, ambient humidity detection, and static voltage field strength detection. When detecting the ambient temperature, 3 groups of platinum resistance temperature sensors (PT100, accuracy ±0.1°C) are evenly deployed 200 mm above the coating area, covering the left, middle, and right areas in the width direction of the substrate. The air temperature is collected in real time and averaged to avoid detection deviation caused by local heat sources (such as drying lamps). When detecting the ambient humidity, a capacitive humidity sensor (accuracy ±2%RH) is used and integrated with the temperature sensor to synchronously detect the relative humidity data. Through the dew point calculation model, the humidity signal is converted into an absolute humidity parameter (g / m³) that has a more direct impact on the coating viscosity. When detecting the static voltage field strength, non-contact electrostatic field sensors (range ±20 kV / m, resolution 0.1 kV / m) are installed on both sides of the coating head to detect the electric field gradient ∇E in the coating area. Since the high-speed moving substrate and the equipment are prone to generate static electricity during friction, resulting in uneven distribution of coating particles, the accurate measurement of the electric field gradient is the key to electrostatic interference compensation.

[0082] Step S504: Establish a coating viscosity - environmental parameter simulation equation based on the multi-modal environmental parameters and analyze to obtain the viscosity adjustment amount of the coating supply system; Step S505: Dynamically adjust the coating viscosity of the coating supply system based on the viscosity adjustment amount; Establish a coating viscosity - environmental parameter simulation equation, comprehensively considering the coupling effects of temperature, humidity, and static voltage field on viscosity. Its mathematical expression is: ; Where, is the real-time coating viscosity, reflecting the flow resistance of the coating under the current environment; represents the set reference viscosity, set as 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%); represents the detected ambient temperature of the coating area, represents the set reference temperature, usually set as 25°C; represents the temperature sensitivity coefficient, a thermodynamic parameter determined by the coating formula, characterizing the rate of change of viscosity with temperature; represents the detected electric field gradient in the coating area, represents the electric field coupling coefficient of the coating obtained by looking up the table, a thermodynamic parameter determined by the coating formula, characterizing the rate of change of viscosity with temperature.

[0083] Dynamic adjustment of the actuator and control logic, the adjustment control steps are as follows: First, the temperature regulation module: Integrate an electric heating / cooling device (power density 50 W / m²) in the coating storage tank and the conveying pipeline, and adjust the coating temperature in real time according to ΔT. For example, when η(t) > η_target, increase the temperature to reduce the viscosity, and control the temperature regulation rate within 1 °C / min to avoid sudden temperature changes affecting the coating stability, where η_target represents the ideal viscosity value preset according to the coating process requirements, that is, the viscosity of the coating in the optimal coating state.

[0084] Second, the electrostatic field regulation module: Apply a reverse electric field through an electrostatic eliminator (such as an ion wind rod) to offset the influence of the ambient static voltage field. When ∇E > 1 kV / m is detected, start the eliminator to stabilize ∇E below 0.5 kV / m, and cooperate with the adjustment of the coating conductivity (adding antistatic agents) to achieve the electric field compensation of the viscosity. Finally, the closed-loop feedback control: Install an on-line viscometer (such as a rotary viscometer, response time ≤ 10 s) at the outlet of the coating pump to monitor the adjusted viscosity value in real time, form a "detection - calculation - regulation - feedback" closed loop, and ensure that the deviation between η(t) and η_target is ≤ ±2%.

[0085] In addition, a multi-parameter collaborative regulation strategy is also included. Among them, humidity indirect compensation: Although the simulation equation does not directly include the humidity parameter, a high-humidity environment is likely to cause water-based coatings to absorb moisture and dilute, resulting in a decrease in viscosity. The system automatically increases the temperature regulation threshold by 0.5 °C when the humidity > 70%RH through a preset humidity-temperature compensation mapping table to offset the influence of moisture absorption on the viscosity.

[0086] Coating speed linkage: When the viscosity adjustment causes a significant change in the coating fluidity (such as Δη > 10%), synchronously fine-tune the coating speed (adjustment range ±5 m / min) to ensure the stability of the coating amount per unit time and avoid thickness fluctuations. Among them, Δη represents the difference between the real-time viscosity and the target viscosity, which is used to quantify the degree of viscosity deviation.

[0087] In summary, steps S503 - S505 construct an intelligent coating system with strong environmental adaptability through multi-modal environmental perception and dynamic regulation driven by the viscosity model, achieving a technical breakthrough from "fixed-parameter production" to "real-time environmental adaptation", and providing a systematic solution for high-precision coating processing under complex working conditions.

[0088] Refer to Figure 6 , when performing dynamic coating viscosity adjustment, it also includes: Step S5051: Collect the coating thickness of the sample and divide it into multiple independent temperature zones according to different thicknesses, and match the infrared irradiation intensity corresponding to the coating thickness in the independent temperature zone; In the coating process of film processing, there are often differences in the coating thickness in different areas, which requires precise drying treatment to ensure the coating quality. In step S5051, first, a high-precision thickness detection device, such as a laser thickness gauge, is used to accurately collect the coating thickness of the sample. The thickness gauge will measure along the coating surface of the sample at a certain interval to obtain a large number of thickness data points, thereby drawing a distribution map of the coating thickness.

[0089] Based on these thickness data, the system will divide the surface of the sample into multiple independent temperature zones. The basis for division is the change in thickness. Usually, the areas with a thickness parameter matching degree not lower than the preset threshold are divided into one temperature zone, so that more targeted drying treatment can be carried out according to the characteristics of different temperature zones. For example, for the thicker coating area, since it contains more coating materials and requires more heat for drying, it will be separately divided into one temperature zone; while the thinner area is divided into another temperature zone.

[0090] Next, the corresponding infrared irradiation intensity needs to be matched for each independent temperature zone. This matching process is based on a model established from a large amount of experimental data and experience. Different coating thicknesses have different heat requirements. Through experiments, the infrared irradiation intensity required to achieve the best drying effect at different thicknesses is determined, and these data are stored in the database. When the thickness of each temperature zone is determined, the system can quickly query and match the corresponding infrared irradiation intensity from the database to ensure that the coating in each temperature zone can be dried appropriately, avoiding insufficient drying due to insufficient heat and preventing damage to the coating or substrate due to excessive heat.

[0091] Step S5052: Dry the independent temperature zones based on the infrared irradiation intensity, and at the same time control the hot air system to form a laminar flow field on the surface of the sample. The included angle range between the air flow direction of the laminar flow field and the sample movement direction is within the preset acute angle range.

[0092] After determining the infrared irradiation intensity of each independent temperature zone, it enters the actual drying process. The infrared drying device will emit infrared rays to each temperature zone according to the set intensity. The infrared rays can be directly absorbed by the coating, converting light energy into heat energy, thereby quickly evaporating the solvent in the coating and achieving the drying of the coating. In this process, temperature control is crucial. The system will monitor the temperature change in the temperature zone in real time and maintain a stable drying temperature by adjusting the power of the infrared irradiation device.

[0093] Meanwhile, the hot air system starts to work, forming a laminar flow field on the surface of the sample. The formation of the laminar flow field helps to improve the drying efficiency and coating quality. Through specially designed air ducts and fans, the hot air blows towards the surface of the sample in a uniform and stable manner, forming a laminar state. The angle between the air flow direction of the laminar flow field and the sample movement direction is strictly controlled within a preset acute angle range. Generally, this acute angle is determined according to the characteristics of the coating, the material of the substrate, and the requirements of the production process, usually between 30° and 60°.

[0094] Refer to Figure 7 , when controlling the hot air system to form a laminar flow field on the surface of the sample, it also includes: Step S5053: Analyze the thickness change interval value when the coating thickness changes. When the thickness change interval value exceeds the set stable threshold interval, issue a laminar flow angle feedback adjustment instruction; During the drying process, the coating thickness will change with the degree of drying. To ensure the uniformity of drying and the stability of the coating quality, it is necessary to monitor the change of the coating thickness in real time. The system will continuously analyze the collected coating thickness data and calculate the thickness change interval value. This value reflects the fluctuation range of the coating thickness within a certain period of time.

[0095] At the same time, a stable threshold interval is preset, which is determined according to the requirements of the production process and the product quality standard. If the thickness change interval value is within the stable threshold interval, it indicates that the drying process is normal and the change of the coating thickness is within an acceptable range; but when the thickness change interval value exceeds the set stable threshold interval, it indicates that the drying process has an abnormality, which may affect the quality of the coating.

[0096] At this time, the system will immediately issue a laminar flow angle feedback adjustment instruction. The issuance of this instruction is based on an in-depth understanding of the drying process and an accurate grasp of the role of the laminar flow field. Because the angle of the laminar flow field has an important impact on the drying effect. By adjusting the angle of the laminar flow field, the contact mode between the hot air and the surface of the sample and the heat transfer efficiency can be changed, thereby affecting the drying speed and thickness change of the coating, so as to correct the abnormality in the drying process.

[0097] Step S5054: Adjust the inclination angle of the laminar flow field up or down based on the laminar flow angle feedback instruction until the thickness change interval value is within the stable threshold interval.

[0098] When receiving the laminar flow angle feedback adjustment instruction, the hot air system will adjust the inclination angle of the laminar flow field. If the thickness change interval value is too large, it means that the drying speed in a certain area is too fast or too slow, and the effect of the hot air in this area may not be ideal. At this time, according to the specific situation, the inclination angle of the laminar flow field is adjusted up or down.

[0099] For example, if the coating in a certain area dries too quickly, resulting in excessive thickness variation, the tilt angle of the laminar flow field is appropriately reduced to reduce the impact of hot air and heat transfer in that area, slowing down the drying speed; conversely, if the drying speed in a certain area is too slow, the tilt angle of the laminar flow field is increased to enhance the effect of hot air and accelerate the drying speed.

[0100] During the process of adjusting the angle of the laminar flow field, the system continuously monitors the change in coating thickness and calculates the thickness change interval value. The adjustment of the laminar flow field angle stops only when the thickness change interval value returns to the stable threshold range. Through such a feedback adjustment mechanism, the drying process can be optimized in real time and dynamically, ensuring the uniformity and stability of the coating thickness, and improving the product quality and production efficiency.

[0101] Refer to Figure 8 , and it also includes: Step S600: Analyze the sample substrate and the coating with a preset evaluation strategy to obtain the coating compatibility parameters; In the encapsulation 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 The preset evaluation strategy integrates the detection of physical and chemical properties and the analysis of interfacial interactions. The core detection items include: Surface physical properties: Surface tension (γs): The surface tension of the substrate is detected by the pendant drop method (contact angle measuring instrument), and the surface tension of the coating (γl) is determined by the platinum plate method. When the difference between the two is ≤5 mN / m, the compatibility is relatively good; Surface roughness (Ra): The surface of the substrate is scanned by an atomic force microscope (AFM). The Ra value affects the wetting and spreading of the coating. Usually, Ra≤100 nm is required to ensure interface fitting; Chemical compatibility: Solubility parameter (δ): Calculate the difference in Hildebrand solubility parameters between the substrate (such as δ = 21.9 (J / cm³)¹ / ² for PET) and the coating (such as δ = 20.5 (J / cm³)¹ / ² for polyurethane adhesive). When Δδ≤3, the interfacial diffusivity is good; Functional group matching degree: The reaction activity of the functional groups on the substrate surface (such as the ester group of PET) and the reactive groups of the coating (such as the isocyanate group) is analyzed by Fourier transform infrared spectroscopy (FTIR), and the matching coefficient is quantified through the change in peak intensity; Interfacial adhesion work (Wa): According to the Young-Dupré equation Wa = γs + γl−γsl (γsl is the interfacial tension), it is calculated by combining the contact angle measurement value. The higher the Wa, the stronger the interfacial bonding force.

[0102] 2. Compatibility Parameter Generation Process Sample Pretreatment: Cut the substrate into 50mm×50mm specimens and prepare the coating into a standard coating film (thickness 50μm); Multi-instrument Joint Measurement: Obtain basic data through a surface tension meter, AFM, and FTIR in sequence, and import them into the compatibility evaluation algorithm; Weighted Calculation: Normalize the surface tension difference (weight 40%), solubility parameter difference (30%), and adhesion work (30%) to generate a comprehensive compatibility parameter Cp (0 - 100, the higher the value, the better the compatibility).

[0103] Step S601: Dynamically adjust the pressure of the coating roller based on the coating compatibility parameter and real-time monitor the cavity defect density of the composite interface; 1. Pressure Dynamic Adjustment Logic Establish a pressure adjustment mapping relationship according to the compatibility parameter Cp: When Cp≥80 (high compatibility): The pressure of the coating roller adopts the reference value P0 (such as 50kN) to avoid substrate deformation caused by excessive pressure; When 60≤Cp<80 (medium compatibility): The pressure is increased by 5% - 10% (such as P = P0 + 5kN) to enhance interface contact to make up for insufficient bonding force; When Cp<60 (low compatibility): Activate the pressure adaptive adjustment and dynamically optimize it in combination with real-time defect data (see Step S602).

[0104] The pressure adjustment is realized through a servo hydraulic system with an accuracy of ±1% and a response time of ≤100ms to ensure that the pressure change is synchronized with the substrate movement.

[0105] 2. Real-time Monitoring of Cavity Defects Use a machine vision detection system to conduct on-line monitoring of the composite interface: Hardware Configuration: Deploy a linear array CCD camera (resolution 12μm / pixel) along the coating width direction, cooperate with a backlight illumination module, and collect interface images in real time; Defect Recognition Algorithm: Based on the YOLO model of deep learning, identify cavity defects with a diameter ≥50μm and calculate the defect density (number / m²). When the defect density > 10 number / m², trigger the coupling control model adjustment (Step S602).

[0106] Step S602: Establish a coupling control model of the pressure roller pressure and the conveying speed based on the cavity defect density, and generate coating roller adjustment parameters for adjustment.

[0107] The coupling control model is as follows: ; Where, Indicates the real-time pressure roller pressure detected by the coating roller, Indicates the set reference pressure roller pressure, Indicates the set adhesion work weight coefficient, Indicates the real-time adhesion work obtained through analysis, Indicates the reference adhesion work, Indicates the set cross-sectional strength weight coefficient, Indicates the real-time interfacial bonding strength obtained through experiments and set, Indicates the reference interfacial bonding strength set according to the process standard.

[0108] Adjustment parameter generation and execution are as follows: Model input update: Real-time synchronization of cavity defect density, Wa (detection value in step S600), and Kb (measured peeling force value); Pressure-velocity decoupling adjustment: When the defect density > 15 pieces / m², give priority to increasing the pressure roller pressure (increase by 2 kN each time), and at the same time reduce the conveying speed by 10% (reduce the composite length per unit time and increase the pressing time); When the defect density is between 10 - 15 pieces / m², use the coupling parameters calculated by the model (such as P = 1.1P0, speed V = 0.95V0) to avoid excessive adjustment of a single parameter; Closed-loop feedback verification: Continuously monitor the defect density after adjustment. If it does not decrease within 30 seconds, trigger secondary adjustment (the pressure increase amplitude increases to 5 kN, and the speed reduction amplitude decreases to 15%) until the defect density ≤ 10 pieces / m².

[0109] Refer to Figure 9 , based on the same inventive concept, the embodiment of the present invention provides a coating device for coating processing, applying the above coating device adjustment method for coating processing, including: A feeding module that continuously outputs the coating material after mixing it evenly for use in coating processing of samples; Among them, the feeding module includes a feeding system composed of a feeding rack and a PLC control system. The PLC control system receives a control signal to adjust the feeding speed of the coating product, so that the flexible fabric or paper has a certain tension, facilitating uniform attachment of the coating during subsequent coating processing.

[0110] A coating module that 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 coating module is composed of a coating system. The coating system is provided with a coating 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, so as to adjust the coating uniformity of the coating product.

[0111] The driving module provides driving force for the driving roller that conveys the sample, and keeps the sample under the tension and coating speed required by the coating parameters for coating treatment; The control and adjustment module dynamically optimizes and adjusts the coating parameters to keep the sample after coating treatment having a consistent thickness. By detecting the coated sample, corresponding feedback adjustment parameters are generated to feedback-adjust the coating system to maintain the optimal coating effect of the coating parameters.

[0112] In addition, it further includes a drying system and a winding system. The drying system is a wind drying and infrared drying device, which can realize wind drying treatment and infrared drying treatment, meet different drying treatment requirements, and enable the product to be dried quickly and effectively.

[0113] Those skilled in the art can clearly understand that for the convenience and conciseness of description, only the above division of each functional module is used as an example. In practical applications, the above functions can be allocated to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0114] The embodiment of the present invention provides a computer-readable storage medium storing a computer program that can be loaded and executed by a processor for the adjustment method of the coating device for film coating processing.

[0115] Computer storage media include, for example: various media such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs that can store program codes.

[0116] Based on the same inventive concept, the embodiment of the present invention provides an intelligent terminal including a memory and a processor, and a computer program that can be loaded and executed by the processor for the adjustment method of the coating device for film coating processing is stored on the memory.

[0117] Those skilled in the art can clearly understand that for the convenience and conciseness of description, only the above division of each functional module is used as an example. In practical applications, the above functions can be allocated to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0118] The above are all preferred embodiments of the present application, and do not limit the protection scope of the present application accordingly. Any feature disclosed in this specification (including the abstract and drawings), unless specifically described, can be replaced by other equivalent or similar-purpose alternative features. That is, unless specifically described, each feature is only an example in a series of equivalent or similar features.

Claims

1. A coating device adjustment method for coating processing, characterized in that, Including: 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 according to the target coating thickness and sample thickness to determine coating parameters, and the coating parameters include coating supply pressure, sample tension, and coating speed; Generate multiple groups of simulated coating parameters based on the coating parameters and a preset coating optimization strategy, and perform coating treatment to obtain multiple groups of coated simulation samples; Perform thickness detection on multiple groups of coated simulation samples to determine the optimal coated simulation sample closest to the target coating thickness, and mark to obtain the best coating parameters; Perform coating treatment on the sample to be processed based on the best coating parameters.

2. The adjusting method of a coating device for coating processing according to claim 1, wherein, The coating optimization strategy includes: Query the approximate coating parameters in the preset historical coating parameter database with a substrate physical property parameter matching degree not lower than the preset threshold according to the substrate type; Perform parameter difference analysis based on the approximate coating parameters and the coating parameters of the sample to determine the parameter adjustment reference unit; Generate adjacent multiple groups of coating parameters as simulated coating parameters in units of the parameter adjustment reference unit.

3. A coating device adjustment method for coating processing according to claim 2, characterized in that, When generating simulated coating parameters, it also includes: Establish a substrate deformation - coating thickness transfer function model, and analyze to obtain the pressure compensation amount of the coating supply system; Adjust the pressure of the coating supply system based on the pressure compensation amount; The substrate deformation - coating thickness transfer function model is as follows: ； Among them, represents the pressure compensation amount, represents the set curvature compensation gain, represents the radius of curvature when the substrate bends, represents the curvature gradient of the local deformation area of the sample, represents the set sample strain rate damping coefficient, represents the detected microstrain on the surface of the sample, represents the real-time strain rate of the substrate.

4. A coating device adjustment method for coating processing according to claim 1, characterized in that, When performing coating treatment on the sample to be processed, it also includes: Preset a laser displacement sensor array to detect the real-time deformation parameters of the sample coating surface, and establish an association model between the substrate stress - strain field and the coating thickness for analysis to obtain the coating thickness deviation value; Match the corresponding compensation parameters in the preset compensation database based on the coating thickness deviation value, including tension and coating speed, and adjust the coating device according to the compensation parameters; The analysis model of the coating thickness deviation value is analyzed using the following formula: ； Among them, represents the coating thickness deviation value, represents the micro-strain on the surface of the detected sample, represents the strain rate obtained by time series analysis of the substrate through the strain sensor, is the maximum surface stress value of the substrate, is the stress-thickness coupling coefficient corresponding to the substrate by looking up the table, is the stress sensitivity coefficient corresponding to the substrate by looking up the table.

5. A coating device adjustment method for coating processing according to claim 4, characterized in that, When performing coating treatment on the sample to be processed based on the best coating parameters, it also includes: Detect the multi-modal environmental parameters of the sample coating area, including environmental humidity, environmental temperature, and static voltage field intensity; Establish a coating viscosity - environmental parameter simulation equation based on the multi-modal environmental parameters for analysis to obtain the viscosity adjustment amount of the coating supply system; Perform dynamic coating viscosity adjustment on the coating supply system based on the viscosity adjustment amount; The coating viscosity - environmental parameter simulation equation is as follows: ； Among them, is the real-time coating viscosity, represents the set reference viscosity, represents the detected environmental temperature of the coating area, represents the set reference temperature, represents the temperature sensitivity coefficient, represents the detected electric field gradient in the coating area, represents the electric field coupling coefficient of the coating obtained by looking up the table.

6. A method for adjusting a coating device for coating film processing according to claim 5, characterized in that When performing dynamic coating viscosity adjustment, it also includes: Collect the coating thickness of the sample and divide it into multiple independent temperature zones according to different thicknesses, and match the infrared irradiation intensity corresponding to the coating thickness in the independent temperature zones; Dry the independent temperature zones based on the infrared irradiation intensity, and at the same time control the hot air system to form a laminar flow field on the sample surface, and the included angle range between the air flow direction of the laminar flow field and the sample movement direction is within a preset acute angle range.

7. A coating device adjustment method for coating processing according to claim 6, 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 interval, issue a laminar flow angle feedback adjustment instruction; Adjust the inclination angle of the laminar flow field up or down based on the laminar flow angle feedback instruction until the thickness change interval value is within the stable threshold interval.

8. A coating device adjustment method for coating processing according to claim 1, characterized in that, It also includes: Analyze the sample substrate and the coating with a preset evaluation strategy to obtain the coating compatibility parameters; Dynamically adjust the pressure of the coating roller based on the coating compatibility parameters and monitor the cavity defect density of the composite interface in real time; Establish a coupling control model of the pressure roller pressure and the conveying speed based on the cavity defect density, and generate coating roller adjustment parameters for adjustment.

9. A method for adjusting a coating device for film coating processing according to claim 8, characterized in that, The coupling control model is as follows: ； Among them, represents the real-time pressure roller pressure detected by the coating roller, represents the set reference pressure roller pressure, represents the set adhesion work weight coefficient, represents the real-time adhesion work obtained through analysis, represents the reference adhesion work, represents the set cross-sectional strength weight coefficient, represents the real-time interfacial bonding strength obtained and set through experiments, represents the reference interfacial bonding strength set according to the process standard.

10. A coating device for film coating processing, which applies the adjusting method for the coating device for film coating processing according to any one of claims 1-9, is characterized in that, Including: A feeding module that continuously outputs the coating material after mixing it evenly for the sample to be used for coating processing; A coating module that 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; A transmission module that provides driving force for the transmission roller for the sample to keep the sample under the tension and coating speed required by the coating parameters for coating treatment; A control and adjustment module that dynamically optimizes and adjusts the coating parameters to keep the samples after coating treatment having a consistent thickness.

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