Low-reflectivity spraying extinction paint as well as preparation device and use method thereof

Abstract

The invention discloses low-reflectivity spray matting paint as well as a preparation device and a use method thereof, and relates to the technical field of matting paint preparation. The invention relates to a water-based paint which comprises the following components: 30-50% of modified polyurethane acrylate, 5-15% of porous silicon dioxide, 0.1-0.5% of nano carbon black, 1-3% of fluorine modified wax powder and 31.5-63.9% of a quick-drying mixed solvent. The delustering paint has the advantages that pore filling and light absorption are synergistically realized through the composite delustering agent, the reflectivity is relatively low, the hydrophobicity, the adhesive force and the wear resistance are excellent, the construction is simple and convenient, and the preparation device has the advantages of dispersion stage, parameter integration, quantitative index construction, real-time monitoring, closed-loop control and grinding stage. A pore retention rate and energy density gradient collaborative optimization and reflectivity prediction stage is introduced, real-time prediction correction and control of a whole-process model are achieved, the problems of decentralized monitoring, fineness pore contradiction and parameter performance disjunction are solved, and the process efficiency and stability are improved.

Description

Technical Field

[0001] This invention relates to the field of matte paint preparation technology, specifically to a low-reflectivity spray matte paint, its preparation apparatus, and its application method. Background Technology

[0002] Matte varnish is a type of paint that eliminates the gloss of chemical fibers. It has a different refractive index than chemical fibers, thus reducing the reflectivity of light. Low-reflectivity matte varnishes are widely used in fields with stringent requirements for surface gloss and anti-reflective properties, such as optical instruments, display devices, and precision machinery. In existing technologies, the preparation of low-reflectivity matte varnishes mainly relies on physical matting, nanomaterial modification, or multilayer interference coatings. These methods can achieve a certain degree of low-reflection effect under laboratory conditions.

[0003] Currently, existing matte paints mainly reduce the gloss of the paint film by adding matting agents, such as silica and wax powder, thereby achieving a certain anti-reflective effect. However, the reflectivity of traditional matte paints is still relatively high, usually between 5% and 15%, which is difficult to meet the requirements of high-reflectivity application scenarios. Existing technologies for reducing reflectivity mainly include physical matting, nanomaterial modification, and multilayer interference coating. Physical matting scatters light by roughening the surface, nanomaterial modification uses nanoparticles, such as nano-silica and carbon black, to enhance light absorption, and multilayer interference coating reduces reflection through the principle of optical interference. However, these methods have the following problems: physical matting easily leads to a decrease in the mechanical properties of the paint film, uneven dispersion of nanomaterials affects the stability of the coating, and multilayer coating is complex and costly.

[0004] In summary, the existing matte paints still have a high reflectivity, which is difficult to meet the ultra-low reflectivity requirement of less than 2%. Excessive addition of existing matting agents can easily lead to a decrease in paint film strength and easy peeling. Furthermore, nanomaterials are prone to agglomeration, which affects the uniformity of spraying. Complex structure coatings, such as multi-layer interference, are difficult to apply and are not suitable for large-area applications.

[0005] Existing matte varnish preparation equipment lacks the means to achieve precise control and management throughout the entire preparation process. For example, the uniformity of the dispersion of nano carbon black in the resin matrix may be judged mainly through offline detection, such as electron microscopy, which cannot provide real-time feedback on the dispersion process. This leads to lag in the adjustment of dispersion parameters, such as ultrasonic power and temperature, which may result in over-dispersion or under-dispersion. Furthermore, the dispersion effect lacks a unified quantitative index, making it difficult to guide the process consistency control of different batches.

[0006] Furthermore, the grinding of porous silica must simultaneously meet the standards of fineness less than or equal to 15μm and porosity greater than or equal to 60%. However, in the existing technology, the control of fineness and pore structure is independent of each other. For example, if the fineness is reduced by extending the grinding time, the pore structure may be destroyed simultaneously. There is a lack of control logic that can quantify the balance between the two, resulting in the ground material either having insufficient fineness or destroyed pore structure, making it impossible to balance light scattering and spraying performance.

[0007] The reflectivity of low-reflection matte paint is determined by multiple parameters, including dispersion uniformity, grinding fineness, viscosity adjustment, and coating film thickness. These parameters have complex coupling relationships. For example, uneven dispersion can lead to particle agglomeration during grinding, which in turn affects the viscosity adjustment. Existing technologies may not be able to predict reflectivity through real-time parameter coupling calculations. Instead, they can only determine whether the reflectivity meets the standard through offline detection after preparation. This results in a long process debugging cycle and a potentially high scrap rate, making it impossible to achieve a direct correlation and real-time adjustment between parameters and performance. Summary of the Invention

[0008] The purpose of this invention is to provide a low-reflectivity spray matte paint, its preparation apparatus, and its usage method, thereby solving the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] In a first aspect, the present invention provides a low-reflectivity spray matte paint, which, by weight percentage, comprises the following components:

[0011] Resin matrix: 30-50% modified polyurethane acrylate;

[0012] Composite matting agent: 5-15% porous silica, 0.1-0.5% nano carbon black;

[0013] Light scattering modifier: 1-3% fluorine-modified wax powder;

[0014] Solvent system: 31.5%-63.9% fast-drying mixed solvents.

[0015] Optionally, the fast-drying mixed solvent is specifically acetone to butyl acetate in a weight ratio of 3:7.

[0016] In a second aspect, the present invention provides an apparatus for preparing a low-reflectivity spray matte varnish, comprising a dispersion component, a mixing and grinding component, a viscosity adjustment component, and a preparation control component;

[0017] The dispersion component is used to ultrasonically disperse nano-carbon black and part of the resin matrix.

[0018] The mixing and grinding assembly is used to add porous silica and fluorine-modified wax powder to the material processed by the dispersing assembly to mix the material and grind it to a certain fineness.

[0019] The viscosity adjustment component is used to add the remaining resin matrix and fast-drying mixed solvent to the material processed by the mixing and grinding component, and to mix and stir it to finally adjust the viscosity to the range applicable to spraying.

[0020] The preparation control component is located in the control cabinet and is used to predict the dispersion effect, analyze the grinding efficiency and predict the reflectivity based on multiple factors, and to centrally control the dispersion component, the mixing and grinding component and the viscosity adjustment component based on the dispersion effect prediction, grinding efficiency analysis and reflectivity prediction.

[0021] Optionally, the distributed component includes:

[0022] A dispersion tank, wherein an observation window is provided on the side of the dispersion tank and an outlet pipe is provided at the bottom of the dispersion tank;

[0023] The hybrid grinding assembly includes:

[0024] A grinding mill, wherein both sides of the grinding mill are provided with a feed port, and an electromagnetic flow meter is installed on each feed port;

[0025] The dispersion tank, the grinding mill, and the viscosity adjustment component are connected in sequence by a conveying pipeline, and a pump is installed in the conveying pipeline to drive the flow of materials.

[0026] Optionally, the preparation control component includes:

[0027] Data acquisition module: used to acquire dispersion processing data, mixing and grinding data, and viscosity adjustment information data during the preparation of matte varnish;

[0028] Data processing module: Used to input the data obtained from the data acquisition module, clean and normalize the data, and synchronize all data with the system clock so that the processed data can be input into the analysis and processing module;

[0029] Analysis and processing module: includes dispersion effect submodule, mixed grinding analysis submodule, and reflectivity prediction and adjustment submodule;

[0030] The dispersion effect submodule outputs a dispersion effect index based on the initial resin viscosity, ultrasonic power, dispersion system temperature, dispersion time, dispersion uniformity correction coefficient, resin activity index, and ultrasonic cavitation intensity in the dispersion processing data, combined with the resin activity correction coefficient and temperature decay coefficient.

[0031] The hybrid grinding analysis submodule outputs a grinding efficiency factor based on the ball mill speed, grinding time, porosity retention rate, grinding media size, grinding torque, grinding energy density gradient, and current grinding fineness value from the hybrid grinding data, combined with the dispersion effect index.

[0032] The reflectivity prediction and adjustment submodule outputs a reflectivity prediction and adjustment coefficient and a solvent adjustment amount based on the real-time viscosity value, target viscosity value, root mean square roughness of the coating surface, median surface roughness, and minimum requirement factor for resin curing degree in the viscosity adjustment information data, combined with the dispersion effect index.

[0033] Adjustment and management module: The dispersion effect index, grinding efficiency factor reflectance prediction, adjustment coefficient and solvent adjustment amount are input into the adjustment and management module. The adjustment and management module adjusts and controls the operation of the dispersion component, the mixing and grinding component and the viscosity adjustment component based on the input data.

[0034] Optionally, the processing flow of the dispersion effect submodule is as follows:

[0035] Ⅰ: The temperature value and initial resin viscosity of the dispersion system are normalized and multiplied to form the denominator for the analysis of dispersion effect;

[0036] II: By multiplying the ultrasonic power by the dispersion time and combining the dispersion uniformity correction coefficient with the resin activity index, the influence of the initial dispersion state of nano carbon black and the activity of resin hydroxyl functional groups on the dispersion effect is analyzed. Ultrasonic cavitation intensity is also introduced as a molecular factor for analyzing multiple influencing factors of dispersion effect.

[0037] III: By combining the temperature of the dispersion system with the temperature decay coefficient in the form of a negative exponent, the nonlinear inhibitory effect of temperature on the dispersion effect is simulated, and the dispersion effect index is finally output.

[0038] Optionally, the processing flow of the hybrid grinding analysis submodule is as follows:

[0039] Ⅰ: The dispersion effect index is introduced into this mixed grinding analysis submodule, and combined with ball mill speed, grinding time, and porosity retention rate, to analyze the grinding efficiency through the actual operating parameters during the mixed grinding process;

[0040] II: Multiply the grinding media size value and grinding torque to reflect the viscosity change of the material indirectly;

[0041] III: By combining the power of the grinding mill with the material flow rate, the grinding energy density gradient is analyzed, and then divided by the critical threshold of the grinding energy density gradient to normalize the analysis of the grinding energy density.

[0042] IV: Normalize the current grinding fineness value with the target grinding fineness to incorporate the real-time fineness of solid particles in the raw material into this mixed grinding analysis submodule, and finally output the grinding efficiency factor.

[0043] Optionally, the processing flow of the reflectivity prediction and adjustment submodule is as follows:

[0044] Ⅰ: Introduce the dispersion effect index and the current grinding fineness value into this reflectivity prediction and adjustment submodule, and combine them with the baseline value of the dispersion effect index and the upper limit value of the grinding fineness, respectively, so as to incorporate the dispersion effect submodule and the mixed grinding analysis submodule into the reflectivity prediction and adjustment analysis process;

[0045] II: The real-time viscosity value, root mean square roughness of the coating surface, and resin curing degree are combined with the target viscosity value, median surface roughness value, and minimum resin curing degree requirement factor, respectively, and weighted summation is used to consider the prediction and adjustment of the reflectance of the matting agent under multiple factors, and the reflectance prediction and adjustment coefficient is output.

[0046] III: The solvent adjustment amount is output based on the difference between the reflectance prediction and adjustment coefficient and the baseline value of the reflectance prediction and adjustment coefficient, combined with the influence adjustment ratio coefficient.

[0047] Thirdly, the present invention provides a method for using a low-reflectivity spray matte paint, comprising the following steps:

[0048] Surface treatment involves sandblasting the metal substrate to achieve a cleanliness level of Sa2.5, and chemical passivation of the non-metallic substrate.

[0049] For the spraying operation, the low-reflectivity matte paint is fed into the spraying equipment, and a spray gun with a nozzle diameter of 1.3-1.5mm is used. Then, the spraying air pressure is set to 0.4-0.6MPa to spray the substrate, and the film thickness is controlled to be 20-30μm.

[0050] The curing process involves hot air curing of the sprayed metal substrate, maintaining a temperature of 80℃ for 30 minutes. For the sprayed non-metallic substrate, UV curing is performed, with the UV energy being greater than or equal to 800 mJ / cm². 2 .

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] I. This low-reflectivity spray matte paint and its application method achieve pore filling and light absorption through the synergistic effect of porous silica and nano carbon black in the composite matting agent. The introduction of fluorine-containing segments enhances the hydrophobicity of the substrate surface after the matte paint is applied. Through optimization of the spraying process, a specific film thickness is matched with curing conditions to avoid matting agent sedimentation. As a result, the reflectivity of this low-reflectivity spray matte paint is ≤1.5%, and it has good adhesion and abrasion resistance. It is also suitable for conventional spraying equipment and is easy to apply.

[0053] II. In the preparation device for this low-reflectivity spray matte varnish, the dispersion effect submodule outputs a dispersion effect index. The dispersion effect index integrates parameters such as ultrasonic power, dispersion temperature, resin activity index, and ultrasonic cavitation intensity to construct a quantitative index of dispersion uniformity. This solves the problems of real-time monitoring of dispersion effect and lack of unified standards in the existing technology. The dispersion effect submodule can reflect the dispersion state of nano carbon black in real time and achieve closed-loop control of the dispersion process by adjusting parameters such as ultrasonic power and temperature, ensuring the consistency of dispersion effect between different batches and providing a stable material basis for subsequent grinding.

[0054] Third, in the preparation device of this low-reflectivity spray matte paint, the grinding efficiency factor is output through the hybrid grinding analysis submodule. The grinding efficiency factor introduces the porosity retention rate and grinding energy density gradient, and incorporates the grinding fineness and pore structure into the same control logic. This solves the contradiction between the independent control of fineness and pore protection in the prior art. By adjusting parameters such as ball mill speed and grinding time, the hybrid grinding analysis submodule can ensure that the fineness meets the standard while ensuring that the pore retention rate is qualified. It achieves the synergistic optimization of light scattering efficiency and spray uniformity during the grinding process, and avoids the increase in reflectivity caused by pore damage or the spray defects caused by excessive fineness.

[0055] IV. In this low-reflectivity spray matte varnish preparation device, the reflectivity prediction and adjustment submodule outputs reflectivity prediction and adjustment coefficients. The reflectivity prediction coefficients integrate parameters from multiple stages, such as dispersion effect, grinding fineness, viscosity adjustment, surface roughness, and resin curing degree, and construct a quantitative correlation model between the parameters of the entire process of dispersion, grinding, viscosity adjustment, and curing and reflectivity. This solves the problem of the disconnect between parameters and performance in the existing technology. The reflectivity prediction and adjustment submodule can predict the reflectivity under the current parameter combination in real time, and achieve pre-correction of reflectivity by adjusting parameters such as solvent addition and curing temperature. This avoids waste products caused by substandard reflectivity after preparation, and significantly improves process debugging efficiency and product performance stability. Attached Figure Description

[0056] Figure 1 This is a schematic diagram of the composition and structure of the apparatus for preparing low-reflectivity spray matte varnish.

[0057] Figure 2 This is an axonometric view of the apparatus for preparing low-reflectivity spray matte varnish;

[0058] Figure 3 This is a schematic diagram of the preparation control component in the low-reflectivity spray matte varnish preparation apparatus.

[0059] Figure 4 This is a schematic diagram of the data acquisition module in the control assembly of this fabrication.

[0060] Figure 5 This is a schematic diagram of the analysis and processing module in the preparation control assembly.

[0061] In the diagram: 1. Dispersion assembly; 2. Mixing and grinding assembly; 3. Dispersion tank; 4. Observation window; 5. Outlet pipeline; 6. Grinding mill; 7. Material inlet; 8. Electromagnetic flow meter; 9. Control cabinet. Detailed Implementation

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] Please see Figures 1 to 5 This embodiment provides a low-reflectivity spray matte paint, which, by weight percentage, consists of the following components:

[0064] Resin matrix: 30-50% modified polyurethane acrylate;

[0065] Composite matting agent: 5-15% porous silica, 0.1-0.5% nano carbon black;

[0066] Light scattering modifier: 1-3% fluorine-modified wax powder;

[0067] Solvent system: 31.5%-63.9% fast-drying mixed solvent;

[0068] The fast-drying mixed solvent is specifically acetone and butyl acetate in a weight ratio of 3:7.

[0069] Based on the above, this embodiment uses modified polyurethane acrylate to achieve a balance between hardness and flexibility. The average particle size of the porous silica is 3-5 μm, and the porosity is ≥60%. The particle size of the nano carbon black is 20-50 nm, accounting for 0.1-0.5%. The use of fluorine-modified wax powder reduces surface energy and enhances diffuse reflection.

[0070] This low-reflectivity spray matte paint utilizes the synergistic effect of porous silica and nano carbon black to achieve pore filling and light absorption. By introducing fluorine-containing segments to enhance surface hydrophobicity, the contact angle is increased to ≥110°. Through specific film thickness and curing conditions, matting agent sedimentation is avoided. The reflectivity of this low-reflectivity spray matte paint is ≤1.5%, with a wavelength of 400-700nm.

[0071] Please see Figures 1 to 5 This embodiment provides an apparatus for preparing a low-reflectivity spray matte varnish, including a dispersion component 1, a mixing and grinding component 2, a viscosity adjustment component, and a preparation control component;

[0072] Dispersion component 1 is used for ultrasonic dispersion of nano-carbon black and part of the resin matrix.

[0073] Distributed component 1 includes:

[0074] The dispersion tank 3 has an observation window 4 on its side and an outlet pipe 5 at its bottom.

[0075] The mixing and grinding component 2 is used to add porous silica and fluorine-modified wax powder to the material processed by the dispersing component 1 to mix the material and grind it to a certain fineness;

[0076] The hybrid grinding component 2 includes:

[0077] The grinding mill 6 has a feed inlet 7 on both sides, and an electromagnetic flow meter 8 is installed on each feed inlet 7.

[0078] The viscosity adjustment component is used to add the remaining resin matrix and fast-drying mixed solvent to the material after it has been processed by the mixing and grinding component 2, and to mix and stir it to finally adjust the viscosity to the range applicable to spraying.

[0079] The dispersion tank 3, the grinding mill 6, and the viscosity adjustment component are connected in sequence by a conveying pipeline, and a pump is installed in the conveying pipeline to drive the flow of materials.

[0080] The preparation control component is set in the control cabinet 9, which is used to predict the dispersion effect, analyze the grinding efficiency and predict the reflectivity based on multiple factors, and to centrally control the dispersion component 1, the mixing and grinding component 2 and the viscosity adjustment component based on the dispersion effect prediction, grinding efficiency analysis and reflectivity prediction.

[0081] The preparation of the control components includes:

[0082] Data acquisition module: used to acquire dispersion processing data, mixing and grinding data, and viscosity adjustment information data during the preparation of matte varnish;

[0083] Data processing module: Used to input the data obtained from the data acquisition module, clean and normalize the data, and synchronize all data with the system clock so that the processed data can be input into the analysis and processing module;

[0084] Analysis and processing module: includes dispersion effect submodule, mixed grinding analysis submodule, and reflectivity prediction and adjustment submodule;

[0085] Dispersion effect submodule: Based on the initial resin viscosity, ultrasonic power, dispersion system temperature, dispersion time, dispersion uniformity correction coefficient, resin activity index, and ultrasonic cavitation intensity in the dispersion processing data, and combined with the resin activity correction coefficient and temperature decay coefficient, the dispersion effect index is output.

[0086] Hybrid Grinding Analysis Submodule: Based on the ball mill speed, grinding time, porosity retention rate, grinding media size, grinding torque, grinding energy density gradient, and current grinding fineness value in the hybrid grinding data, and combined with the dispersion effect index, it outputs a grinding efficiency factor;

[0087] The reflectivity prediction and adjustment submodule is based on the real-time viscosity value, target viscosity value, root mean square roughness of the coating surface, median surface roughness, and minimum requirement factor for resin curing degree in the viscosity adjustment information data, and combines the dispersion effect index to output the reflectivity prediction and adjustment coefficient and solvent adjustment amount.

[0088] Adjustment and management module: The dispersion effect index, grinding efficiency factor, reflectivity prediction and adjustment coefficient, and solvent adjustment amount are input into the adjustment and management module. Based on the input data, the adjustment and management module adjusts and controls the operation of dispersion component 1, mixing and grinding component 2 and viscosity adjustment component.

[0089] Based on the above, this implementation achieves closed-loop control throughout the entire process by coupling micro-dispersion, meso-structure, and macro-performance across scales. In the dispersion stage, it provides uniformly dispersed initial materials for grinding, avoiding grinding difficulties or functional failures caused by carbon black agglomeration. It achieves a balance between fineness and structure based on the dispersion effect index (DA), providing intermediate products with both light scattering and processability for viscosity adjustment. The reflectivity prediction and adjustment submodule integrates the results of the grinding efficiency factor and the dispersion effect index, linking viscosity adjustment and curing parameters, and ultimately outputting a stable product with satisfactory reflectivity and adhesion. This breaks through the limitations of traditional segmented control of dispersion, grinding, and spraying, achieving precise control with designable microstructure, predictable macro-performance, and adjustable parameters throughout the entire process. It solves the core problems of high reflectivity, poor mechanical properties, and batch instability in traditional matte paints.

[0090] The dispersion effect index (DA) is an input parameter for calculating the grinding efficiency factor (GB). Good dispersion effect can reduce the energy demand in the grinding stage, such as reducing the ball mill speed or time, and avoid low grinding efficiency and pore damage due to poor dispersion. Conversely, if the dispersion effect index (DA) < 1.0, it indicates insufficient dispersion, and the grinding efficiency factor (GB) will automatically decrease because carbon black agglomerates increase grinding resistance. At this time, the control system needs to retrospectively adjust the dispersion parameters, such as increasing the ultrasonic power, to form a linkage optimization between dispersion and grinding.

[0091] The calculation result of the grinding efficiency factor GB directly affects the reflectivity prediction and adjustment coefficient RC. When the grinding fineness meets the standard and the porosity retention rate is high, RC is more likely to stabilize at 1.0±0.05. If the grinding efficiency factor GB is abnormal, RC will increase due to the decrease in light scattering efficiency. At this time, the control system needs to adjust the grinding energy density gradient and correct the dispersion parameter through RC feedback to form a closed loop between grinding and reflectivity.

[0092] When the RC value is abnormal, not only are the viscosity adjustment parameters adjusted, but the target values ​​of the dispersion effect index DA and the grinding efficiency factor GB are also optimized in reverse to achieve dynamic adaptation of the parameters throughout the process and ensure long-term stable operation of the system.

[0093] See Figure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 5 The processing flow of the dispersion effect submodule is as follows:

[0094] Ⅰ: The temperature value and initial resin viscosity of the dispersion system are normalized and multiplied to form the denominator for the analysis of dispersion effect;

[0095] II: By multiplying the ultrasonic power by the dispersion time and combining the dispersion uniformity correction coefficient with the resin activity index, the influence of the initial dispersion state of nano carbon black and the activity of resin hydroxyl functional groups on the dispersion effect is analyzed. Ultrasonic cavitation intensity is also introduced as a molecular factor for analyzing multiple influencing factors of dispersion effect.

[0096] III: By combining the temperature of the dispersion system with the temperature decay coefficient in the form of a negative exponent, the nonlinear inhibitory effect of temperature on the dispersion effect is simulated, and the dispersion effect index is finally output.

[0097] It is worth noting that the calculation process for the dispersion effect submodule is as follows:

[0098]

[0099] in:

[0100] DA stands for Dispersion Performance Index, which characterizes the dispersion quality of carbon black in the pre-dispersion stage and serves as a correction term for grinding efficiency.

[0101] DAT refers to the temperature of the dispersion system, which is the temperature of the material during the dispersion process. Too high a temperature will cause the resin to cross-link, which will affect the subsequent mixing. Too low a temperature will reduce the dispersion efficiency. The acquisition method is to use an infrared temperature sensor to measure the surface temperature of the material in a non-contact manner. The acquisition position can be above the inner wall of the dispersion tank, which can be flush with the liquid surface of the material to avoid high temperature interference from the ultrasonic probe.

[0102] This parameter is normalized by dividing the collected material temperature by the reference temperature of 25℃.

[0103] The dispersion system temperature DAT serves as both the denominator and the exponential decay term. When this parameter is too high, with the dispersion system temperature DAT exceeding 30°C, it will simultaneously increase the denominator and weaken the exponential term, leading to a significant decrease in the dispersion effect index DA. When the dispersion system temperature DAT is less than 20°C, the initial resin viscosity DAE increases, which means the denominator increases, thereby reducing the dispersion effect index DA and decreasing the dispersion efficiency.

[0104] The dispersion system temperature DAT is controlled in real time by an infrared temperature sensor to ensure that the temperature is within the golden range of 20-30℃, so as to avoid abnormal dispersion effect index DA due to temperature fluctuations and ensure the stability of dispersion effect. Temperature stability is the key to batch consistency of low reflectivity.

[0105] DAE refers to the initial resin viscosity, which is the initial viscosity of the resin matrix without the addition of carbon black. It serves as a benchmark value for dispersion effect. The data can be collected offline using a rotational viscometer before dispersion and then input into the preparation control component as a fixed parameter, such as approximately DAE 800-1000 mPa·s at 25°C.

[0106] This parameter is normalized by dividing the collected initial viscosity by the baseline value of 900 mPa·s.

[0107] The initial resin viscosity DAE is used as the denominator. When it is too high, it will increase the denominator and reduce the dispersion effect index DA, indicating that the dispersion resistance is large. When it is too low, the resin's ability to encapsulate carbon black will decrease, and the dispersion effect index DA will also be weakened, indicating that the carbon black is prone to sedimentation.

[0108] By measuring the initial resin viscosity (DAE) as the benchmark parameter for calculating the dispersion performance index (DA), it is ensured that the initial state of the resin matrix meets the dispersion requirements. Viscosity matching is a prerequisite for ultrasonic dispersion efficiency and avoids dispersion failure due to defects in the resin itself.

[0109] DPP refers to ultrasonic power, which is the actual power output of the ultrasonic disperser. It determines the intensity of cavitation effect and affects the dispersion efficiency of nano carbon black. The acquisition method is to use a power sensor connected in series in the power line of the ultrasonic equipment to monitor the active power in real time. The acquisition location can be inside the control cabinet 9 used to control the operation of the dispersion components. The sensor is connected in parallel with the power module.

[0110] This parameter can be normalized by dividing the power output of the ultrasonic disperser by the reference power of 0.35kW.

[0111] As a molecular core variable, ultrasonic power DPP directly determines the energy input intensity. When ultrasonic power DPP increases, within the range of 0.3-0.4kW, it will improve the calculated dispersion effect index DA, reflecting the enhanced ability of ultrasound to break up nano carbon black agglomerates. When ultrasonic power DPP is too small, the dispersion effect index DA is too low, indicating insufficient dispersion.

[0112] By adjusting the ultrasonic power DPP, the dispersion effect index DA is ensured to be within a reasonable range of 1.2-1.8, thus avoiding the decrease in light absorption efficiency of subsequent coatings due to uneven carbon black dispersion. Carbon black agglomerates are prone to forming light reflection points, increasing reflectivity, which is the primary prerequisite for low reflectivity.

[0113] DAL refers to dispersion time, which is the duration of ultrasonic dispersion. In this implementation, it is set to a fixed 30 minutes to calculate the cumulative energy input. The acquisition method can be triggered by the built-in timer of the PLC system, starting the timer from the start of ultrasonic transmission. The acquisition location can be in the central processing unit of control cabinet 9.

[0114] Dispersion time (DAL) serves as the time dimension of cumulative energy input, ensuring that ultrasonic energy fully acts on carbon black agglomerates. Insufficient dispersion time (DAL) will lead to a low dispersion effect index (DA), meaning incomplete dispersion.

[0115] By strictly controlling the dispersion time (DAL) using PLC, dispersion failure due to insufficient time is avoided, ensuring the stability of the dispersion effect between batches and providing a uniform precursor for the subsequent grinding stage.

[0116] DAA refers to the dispersion uniformity correction factor, which is the initial dispersion state of nano-carbon black. It can be calculated by measuring the particle size distribution using a laser particle size analyzer. The data collection method can be to use an online laser particle size analyzer to perform real-time sampling and analysis of the dispersed raw material liquid. It is an indicator of particle size distribution uniformity, with a range of 0.8-1.2. The sampling location can be at the bottom outlet pipe 5 of the dispersion tank 3, with a bypass sampling loop (i.e., equipped with an automatic valve) set up, sampling once every 5 minutes without affecting the main dispersion process. It can be calculated using the following formula:

[0117] DAA = D50 / D90;

[0118] in:

[0119] D50 refers to the value that 50% of the particles in a particle group have a diameter smaller than this value, which is the median particle size and reflects the concentration trend of particle distribution.

[0120] D90 indicates that 90% of the particles are smaller than this value, which reflects the proportion of large particles;

[0121] Further details are as follows:

[0122] Online laser particle size analyzers work by light scattering: when a laser passes through a dispersion, particles of different sizes scatter light at different angles and intensities. Smaller particles have larger scattering angles, while larger particles have smaller scattering angles. After the instrument's detector receives the scattered light signal, it calculates the particle size distribution using Mie scattering theory and directly outputs the characteristic parameters D50 and D90. The data can be updated every 5-10 seconds.

[0123] The higher the dispersion uniformity correction coefficient (DAA) value is calculated by measuring the particle size distribution with a laser particle size analyzer, the narrower the particle size distribution and the larger the dispersion effect index (DA) is, reflecting better dispersion uniformity. The lower the dispersion uniformity correction coefficient (DAA) is, the lower the dispersion effect index (DA) is, indicating that there may be large particle agglomerates.

[0124] The introduction of the dispersion uniformity correction factor DAA is used to correct the dispersion effect index DA in real time, so as to avoid misjudgment of dispersion effect caused by the initial carbon black particle size fluctuation, and ensure that the dispersion effect index DA can truly reflect the actual dispersion state of nano carbon black. Uniform dispersion is the microscopic basis for reducing reflectivity.

[0125] DAC stands for Resin Activity Index, which reflects the activity of the hydroxyl functional groups in the resin. It indicates the chemical compatibility between the hydroxyl groups in the modified polyurethane acrylate and the surface of nano-carbon black. A higher value indicates better wettability. The data is collected in real-time by monitoring the characteristic absorption peaks of the dispersion using a near-infrared spectroscopy sensor, specifically the hydroxyl groups at 3600-3200 cm⁻¹. -1 The absorbance ranges from 0.6 to 0.9. The sampling position is at the observation window 4 on the top of the dispersion tank 3. The sensor probe is connected via optical fiber and non-contact detection is used to avoid contact with the highly corrosive dispersion liquid.

[0126] The hydroxyl groups in the modified polyurethane acrylate can form hydrogen bonds with the carboxyl groups on the surface of nano carbon black, which improves compatibility and reduces agglomeration. The higher the resin activity index DAC, the stronger the hydrogen bonding effect and the more uniform the carbon black dispersion.

[0127] The resin activity index DAC is a molecular correction term. The higher the value, the closer it is to 0.9, the better the compatibility between the resin and carbon black, the more stable the adsorption on the carbon black surface, and the larger the calculated dispersion effect index DA. Conversely, if the resin activity index DAC < 0.6, the dispersion effect index DA is weakened, indicating poor compatibility of the dispersion system.

[0128] This dispersion effect submodule introduces a chemical compatibility quantification index, breaking through the limitations of traditional methods that rely solely on physical dispersion. It uses the resin activity index DAC to play a regulatory role, analyzes dispersion stability at the molecular level, and avoids reflectivity rebound caused by carbon black agglomeration in the later stages.

[0129] DAD refers to ultrasonic cavitation intensity, which is the energy of the microjets generated by the collapse of ultrasonic cavitation bubbles. It directly affects the breaking efficiency of nano-carbon black agglomerates. The acquisition method is to attach a sound pressure sensor to the surface of the ultrasonic probe to detect the sound pressure amplitude, with the unit being Pa and the range being 0.7-1.3.

[0130] The ultrasonic cavitation intensity DAD can be normalized by dividing the collected sound pressure amplitude by the reference sound pressure.

[0131] This is achieved using a piezoelectric sound pressure sensor, which is attached to the metal housing of the ultrasonic probe of the ultrasonic disperser. It directly receives the sound pressure signal generated by the probe's vibration. After being converted into an electrical signal by a signal amplifier, the real-time sound pressure amplitude is read via data acquisition to calculate the cavitation intensity. The reference sound pressure is taken as the sound pressure value at which the dispersion effect is optimal; in this embodiment, it is set to 1.2 × 10⁻⁶. 5 Pa;

[0132] The ultrasonic cavitation intensity DAD reflects the microjets generated by the collapse of cavitation bubbles. When the ultrasonic cavitation intensity DAD is in the range of 0.7-1.3, the dispersion index DA increases with the increase of ultrasonic cavitation intensity DAD. When the ultrasonic cavitation intensity DAD > 1.3, it may lead to resin degradation, and the dispersion index DA will decrease instead. By monitoring the ultrasonic cavitation intensity DAD in real time through a sound pressure sensor and dynamically adjusting the ultrasonic amplitude, it is possible to ensure that the cavitation intensity is in a balance range between effectively breaking up agglomerates and not damaging the resin structure. This is the core energy parameter for nanoscale dispersion.

[0133] DK1 refers to the resin activity correction coefficient, which is the resin hydroxyl functional group activity correction coefficient. Its significance is to couple the hydroxyl activity index (DAC) and the dispersion uniformity correction coefficient (DAA). In this embodiment, DK1 is set to 0.3.

[0134] DK2 refers to the temperature decay coefficient, which is used to quantify the inhibitory effect of temperature increase on dispersion effect. Because excessive temperature can lead to resin crosslinking or carbon black agglomeration, in this embodiment, DK2 is set to 0.025. When the temperature rises from 25°C to 35°C, the dispersion effect index DA decreases by about 20%.

[0135] e -Dk2×DAT It plays a role in suppressing the nonlinear effect of temperature on dispersion. Increased temperature will accelerate the thermal motion of resin molecules, which may lead to carbon black agglomeration or local cross-linking of resin. This effect is not a linear relationship, and the negative exponential form can more accurately characterize it. When the dispersion system temperature DAT exceeds 30℃, the exponential term decays rapidly, and the dispersion effect index DA value decreases significantly, thereby triggering the cooling system to adjust and avoid excessive heating.

[0136] Based on the above, this implementation quantifies the dispersion uniformity of nano carbon black in the resin matrix. By coupling chemical compatibility, resin hydroxyl activity and physical dispersion strength, and ultrasonic cavitation effect, it determines whether the dispersion system has reached a nanoscale uniform dispersion microstate, providing a dispersion quality benchmark for the subsequent grinding stage. The dispersion effect index DA value directly determines the initial material state of the grinding stage, such as the size of carbon black agglomerates, to avoid a decrease in grinding efficiency or over-grinding due to poor dispersion.

[0137] The calculation of this dispersion effect sub-model ensures that the nano carbon black is uniformly distributed in the resin, avoiding the formation of light absorption blind zones by agglomerates, thereby stabilizing the light absorption efficiency. At the same time, through the adsorption of the resin hydroxyl activity and the carbon black surface, the long-term stability of the dispersion system is improved, so as to avoid secondary agglomeration of carbon black during storage.

[0138] When the dispersion effect index DA < 1.0, it indicates insufficient dispersion and carbon black agglomeration. The overall control components will increase the ultrasonic cavitation intensity, that is, increase the ultrasonic power or adjust the amplitude of the amplitude transformer, and at the same time increase the resin hydroxyl activity index, that is, adjust the resin batch through infrared spectroscopy feedback.

[0139] When the dispersion effect index DA > 2.0, it indicates that the dispersion is excessive and the resin is degraded. To reduce the ultrasonic power and start the cooling system, the jacket water temperature is adjusted by the temperature sensor to prevent the resin from cross-linking due to excessive cavitation energy.

[0140] This embodiment introduces the resin activity index DAC, which quantifies the surface adsorption capacity of resin and carbon black through infrared spectroscopy. This solves the problem of insufficient power but poor dispersion in traditional dispersion, which is caused by poor compatibility between resin and carbon black. In this embodiment, the energy of microjets generated by the collapse of cavitation bubbles is measured by acoustic pressure sensor to quantify the dispersion driving force, so as to avoid the energy waste or resin degradation caused by relying solely on power in the traditional method. Insufficient cavitation intensity will result in ineffective dispersion, while excessive cavitation intensity will result in resin chain breakage.

[0141] See Figure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 5 The processing flow of the mixed grinding analysis submodule is as follows:

[0142] Ⅰ: The dispersion effect index is introduced into this mixed grinding analysis submodule, and combined with ball mill speed, grinding time, and porosity retention rate, to analyze the grinding efficiency through the actual operating parameters during the mixed grinding process;

[0143] II: Multiply the grinding media size value and grinding torque to reflect the viscosity change of the material indirectly;

[0144] Ⅲ: By combining the power of the grinding mill 6 with the material conveying flow rate, the grinding energy density gradient is analyzed, and then divided by the critical threshold of the grinding energy density gradient to normalize the analysis of the grinding energy density.

[0145] IV: Normalize the current grinding fineness value with the target grinding fineness to incorporate the real-time fineness of solid particles in the raw material into this mixed grinding analysis submodule, and finally output the grinding efficiency factor;

[0146] It is worth noting that the calculation process of the mixed grinding analysis submodule is as follows:

[0147]

[0148] in:

[0149] GB refers to the grinding efficiency factor.

[0150] GBA refers to the ball mill speed, which is the speed of the stirring shaft of the grinding mill 6, to determine the kinetic energy of the grinding media. The acquisition method is to install an encoder on the output shaft of the motor of the grinding mill 6 to provide real-time feedback of the speed. The acquisition position can be at the flange connecting the motor and the reducer of the sand mill 6, using a non-contact magnetoelectric encoder.

[0151] This parameter is normalized by dividing the collected stirring speed by the base speed of 1350 rpm.

[0152] By adjusting the grinding efficiency factor GB using a servo motor, the grinding efficiency and pore protection are balanced. The pores of porous silica are the core structure for light scattering. This is to avoid a decrease in light scattering efficiency due to improper rotation speed, as pore damage may increase reflectivity by 0.3%-0.5%.

[0153] α refers to the weighting factor of the grinding energy density gradient term, which is used to adjust the degree of influence of the grinding energy density gradient GBF on the grinding efficiency factor GB;

[0154] β refers to the weighting factor for the grinding fineness value;

[0155] GBB refers to grinding time, which is the cumulative grinding time of the material in the sand mill. It is used to control the fineness to meet the standard. The data can be collected by a timer in a PLC system, starting from when the material is pumped into the sand mill. The data collection point can be a flow switch, such as an electromagnetic flow meter 8, installed at the feed inlet of the mill 6 to trigger the timing start.

[0156] The grinding time GBB is controlled by PLC to ensure that grinding is completed within a balanced time that achieves the fineness target and retains the porosity, thus avoiding the deterioration of the coating's light scattering performance due to time loss.

[0157] GBC refers to porosity retention rate, which is the degree to which the pore structure is retained after grinding porous silica. This influencing factor directly affects light scattering efficiency. The sampling method can be to use a specific surface area analyzer to sample the slurry after grinding and measure the nitrogen adsorption-desorption curve to calculate the porosity change. The sampling location can be an automatic sampling valve set in the circulation pipeline of the grinder 6, and sampling can be performed once every 10 minutes. The sample volume is ≤5mL and does not affect the main process.

[0158] It should be noted that the specific surface area analyzer measures the specific surface area (RRAA) of porous silica in the ground slurry using a nitrogen adsorption-desorption curve. Combined with the specific surface area (RRAB) of the original porous silica, the result is approximately 300 μm². 2 / g and the original porosity, which is greater than or equal to 60%, calculate the porosity after grinding = (RRAA / RRAB) × original porosity, and the final porosity retention rate GBC = porosity after grinding / original porosity;

[0159] Pore ​​retention GBC is used as a molecular correction term. The higher the value, the larger the calculated polishing efficiency factor GB, which reflects that the pore structure of porous silica is more completely preserved. The smaller the pore retention GBC, the more the polishing efficiency factor GB is reduced, indicating pore damage, which means a decrease in light scattering efficiency.

[0160] This submodule quantifies porosity as a grinding efficiency indicator, breaking through the traditional limitation of only focusing on macroscopic fineness, ensuring the core light scattering function of porous silica, and the pore structure is the key light scattering carrier to achieve low reflectivity.

[0161] GBD refers to the grinding media size value, which is the diameter of the zirconia beads. This parameter affects the crushing force on porous silica to avoid excessive damage to the pores.

[0162] This parameter is calculated based on the collected grinding media size / reference value of 0.8m.

[0163] The grinding media size value GBD is used as a denominator to ensure that the grinding media exerts a moderate crushing force on porous silica, avoiding abnormal grinding efficiency factor GB due to improper media size. The grinding media size value GBD provides a stable grinding media benchmark for the calculation of grinding efficiency factor GB, ensuring consistent grinding conditions between batches.

[0164] GBE refers to the grinding torque, which is the resistance torque on the stirring shaft of the grinder 6. It reflects the change in slurry viscosity. The higher the viscosity, the greater the torque. The acquisition method is to install a torque sensor at the bearing seat of the stirring shaft to measure the shaft end torque. The acquisition position can be at the input end of the stirring shaft of the grinder 6, at the flexible coupling connected to the motor, to sense the torque through strain gauges.

[0165] This parameter is normalized by dividing the collected torque value by the reference torque of 150 N·m.

[0166] By monitoring the grinding torque GBE in real time through a torque sensor, the changes in slurry viscosity can be indirectly reflected, avoiding fluctuations in grinding efficiency due to abnormal viscosity. A stable grinding torque GBE is a prerequisite for the accurate calculation of the grinding efficiency factor GB.

[0167] GBF refers to the grinding energy density gradient, which is the difference in energy input per unit volume of slurry. It helps prevent carbon black oxidation caused by localized overheating. When the grinding energy density gradient GBF = 50 kJ / cm², the desired effect is achieved. 3 The critical threshold can be calculated using the power sensor and flow sensor of the grinder 6. The data collection point can be an electromagnetic flow meter 8 installed at the feed inlet of the grinder 6 to measure the volumetric flow rate, which is then linked with the power data for calculation. The specific calculation formula is as follows:

[0168] GBF = (Real-time power × GBB) / (Material volumetric flow rate × GBB);

[0169] Calculating the energy density gradient using power and flow sensors can prevent carbon black oxidation caused by excessive local energy. For example, carbon black is easily oxidized at temperatures above 200°C, and concentrated energy can cause localized high temperatures.

[0170] This item As a correction term, GBF is closer to the critical threshold GBG of the grinding energy density gradient, which is 50 kJ / cm². 3 The closer this term is to 0, the significantly lower the calculated grinding efficiency factor GB, indicating excessive local energy, which is the risk of pore damage. When the grinding energy density gradient GBF is less than 0.8 times the critical threshold GBG of the grinding energy density gradient, this term approaches 1, the grinding efficiency factor GB is not suppressed, and thus it is in the safe grinding range.

[0171] By controlling the uniformity of grinding energy distribution through the grinding energy density gradient GBF, the oxidation of nano carbon black caused by local overheating can be avoided, which may reduce light absorption efficiency, increase reflectivity, and cause the collapse of porous silica pores, that is, reduce light scattering efficiency.

[0172] GBG refers to the critical threshold of the grinding energy density gradient, in kJ / L, which is the upper limit of energy at which the porous structure of silica begins to break down. In this embodiment, it is set to 50 kJ / L. When the critical threshold of the grinding energy density gradient GBF is greater than 50 kJ / L, the porosity retention rate GBC < 80%, and the light scattering efficiency decreases.

[0173] GBI refers to the target grinding fineness, in μm, which is the maximum allowable particle size of solid particles in the slurry. It is set to 15μm according to the process requirements. The D90 is measured to be ≤15μm by a laser particle size analyzer to ensure that there is no clogging and the light scattering is uniform during spraying.

[0174] GBH refers to the current grinding fineness value, which is the real-time fineness of solid particles in the slurry. The target is GBH≤15μm, which can be measured by an online particle size analyzer. The data acquisition method is an online laser particle size analyzer, which is installed at the outlet of the grinder 6 in the same dispersion stage to monitor the D90 particle size in real time, representing the coarsest particle size.

[0175] As a correction term, the closer GBH is to GBI, the closer this term approaches 0, and the lower the calculated grinding efficiency factor GB, indicating that grinding is nearing completion. If GBH > GBI, this term is negative, and the grinding efficiency factor GB is significantly weakened, indicating that grinding is not up to standard. The ratio of GBH to GBI is used to evaluate the grinding progress in real time to ensure that the fineness of the slurry after grinding is ≤15μm, because excessively coarse particles will cause abnormal surface roughness of the coating and increased reflectivity. The design is intended to convert excessive fineness into a negative feedback signal that reduces the grinding efficiency factor GB, which facilitates the control system to trigger adjustment commands to reduce the rotation speed and extend the grinding time, in accordance with closed-loop control logic.

[0176] When the porosity retention rate GBC < 85% is detected, indicating porosity destruction, the hybrid grinding analysis submodule automatically triggers a decrease in the grinding energy density gradient GBF. This can be achieved by reducing the ball mill speed by 100 rpm and simultaneously increasing the ultrasonic cavitation intensity DAD through the dispersion effect submodule to increase the ultrasonic power by 20W to compensate for insufficient dispersion caused by the decrease in grinding intensity. Then, the coating surface root mean square roughness RCB is fed back through the reflectivity prediction adjustment submodule to ensure that the surface roughness is still controlled within 50-80 nm.

[0177] Based on the above, this embodiment balances the contradiction between grinding fineness and porous structure protection. It quantifies the structural integrity of porous silica by quantifying the porosity retention rate and controls the uniform input of grinding energy through energy density gradient control to avoid local over-grinding that damages the pores. It breaks through the traditional grinding control logic that only focuses on fineness and incorporates structural function into the grinding quality evaluation system. It provides an intermediate product with both fineness and light scattering ability for the viscosity adjustment stage. This embodiment ensures that the porous silica still maintains a porosity of ≥60% after grinding, which is the core structural basis for light scattering. At the same time, the fineness is controlled within 15μm to avoid excessively large particles causing roughness on the paint film surface. Through energy density gradient control, it avoids oxidation of nano carbon black due to local overheating during the grinding process, because oxidation will reduce light absorption efficiency.

[0178] If the grinding efficiency factor GB < 0.85, it indicates that the grinding efficiency is low and the fineness does not meet the standard. Under the premise of ensuring that the porosity retention rate exceeds 85%, the ball mill speed should be increased, and the energy density gradient should be reduced to increase the cooling water flow rate and avoid local overheating.

[0179] If the grinding efficiency factor GB > 1.15, it indicates over-grinding and pore damage. Reduce the ball mill speed and extend the grinding time. This can be adjusted through feedback from the torque sensor to avoid the impact of the media on the pore structure due to excessive speed.

[0180] This hybrid grinding analysis submodule aims to achieve a balance between efficiency and functionality in the grinding process by simultaneously achieving the desired fineness and structural integrity. It calculates the porosity change before and after grinding using specific surface area, and for the first time incorporates structural integrity into the grinding control, thereby ensuring the light scattering efficiency of porous silica. By coupling power and flow rate, it calculates the energy input per unit volume of material, quantifying energy uniformity and preventing carbon black oxidation or pore collapse caused by excessively high local energy levels, as traditional grinding only controls the total energy and ignores energy distribution.

[0181] See Figure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 5 The processing flow of the reflectivity prediction and adjustment submodule is as follows:

[0182] Ⅰ: Introduce the dispersion effect index and the current grinding fineness value into this reflectivity prediction and adjustment submodule, and combine them with the baseline value of the dispersion effect index and the upper limit value of the grinding fineness, respectively, so as to incorporate the dispersion effect submodule and the mixed grinding analysis submodule into the reflectivity prediction and adjustment analysis process;

[0183] II: The real-time viscosity value, root mean square roughness of the coating surface, and resin curing degree are combined with the target viscosity value, median surface roughness value, and minimum resin curing degree requirement factor, respectively, and weighted summation is used to consider the prediction and adjustment of the reflectance of the matting agent under multiple factors, and the reflectance prediction and adjustment coefficient is output.

[0184] III: Based on the difference between the reflectance prediction and adjustment coefficient and the baseline value of the reflectance prediction and adjustment coefficient, and combined with the influence adjustment ratio coefficient, the solvent adjustment amount is output;

[0185] It is worth noting that the calculation process of the reflectivity prediction and adjustment submodule is as follows:

[0186]

[0187] VV = KKL × (RC - RCQA);

[0188] in:

[0189] RC refers to the reflectivity prediction and adjustment coefficient. Reflectivity is predicted in real time using the RC value. When RC deviates from [0.95, 1.05], dynamic adjustment of the corresponding process parameters is triggered to ensure that the final reflectivity is ≤1.5%. The following is an example illustrating a scenario:

[0190] Scenario 1, the reflectance prediction and adjustment coefficient RC = 1.12. Since it is greater than 1.05, the predicted reflectance = 1.7% → the reflectance needs to be reduced.

[0191] Scenario 2, the reflectance prediction and adjustment coefficient RC = 0.92. Since it is less than 0.95, the predicted reflectance = 1.2%, but it may be accompanied by insufficient film thickness → the reflectance needs to be increased to the target range;

[0192] The dynamic adjustment strategy will be explained using scenario 1 as an example.

[0193] By increasing the solvent addition amount VV to reduce the real-time viscosity value RCA, the uniformity of spray atomization is improved, thereby reducing the root mean square roughness RCB of the coating surface, and ultimately bringing the reflectivity prediction and adjustment coefficient RC back to 1.0. The following will explain the steps in more detail:

[0194] S1: Calculate the solvent adjustment amount VV;

[0195] VV=0.8×(1.12-1.0)=0.096m;

[0196] At this point, the analysis and processing module and the regulation and management module instruct the viscosity adjustment component to open the solvent valve and add 0.096 mL of solvent.

[0197] S2: If the current grinding fineness value GBH = 16μm, which is greater than 15μm, then the weighting factor R2 of the grinding fineness contributes too much. The speed of the sand mill needs to be reduced by 100rpm, that is, from 1500 to 1400rpm, and the grinding time GBB = 5min should be extended to ensure that the current grinding fineness value GBH ≤ 15μm.

[0198] S3: If the dispersion effect index DA = 1.3, which is less than 1.5, the contribution of the dispersion effect weighting factor R1 is insufficient. In the next batch of pre-dispersion, the ultrasonic cavitation intensity DAD can be increased by 20W to improve the uniformity of carbon black dispersion.

[0199] S4: If the resin curing degree RCC = 93%, which is less than 95%, the curing degree weighting factor R5 is too low. The curing oven temperature is changed from 60℃ to 65℃, and the curing time is changed from 15min to 18min to ensure that the resin curing degree RCC is ≥ 95% and to avoid surface defects caused by insufficient curing.

[0200] When the reflectance prediction and adjustment coefficient RC > 1.05, the reflectance is too high, which may be due to the following conditions: the current grinding fineness value GBH > 15μm, the real-time viscosity value RCA > 50s, and the root mean square roughness of the coating surface RCB > 80nm.

[0201] The corresponding measures are to reduce the sand mill speed by 100 rpm, increase the solvent adjustment amount VV, and increase the spraying pressure by 0.1 MPa;

[0202] When the reflectance prediction and adjustment coefficient RC < 0.95, the reflectance is low, which may be due to the real-time viscosity value RCA < 40s, resulting in a thin film due to sagging, and the dispersion effect index DA > 1.8, which may be due to excessive dispersion of carbon black.

[0203] The corresponding measures are to reduce the solvent adjustment amount (VV), reduce the ultrasonic power by 30W, and reduce the stirring speed by 200 rpm.

[0204] When the fluctuation of the reflectance prediction and adjustment coefficient RC is greater than ±0.3, it indicates batch-to-batch reflectance fluctuation, which may indicate resin activity index DAC fluctuation, indicating resin batch differences. The grinding energy density gradient GBF is unstable, indicating uneven grinding energy.

[0205] The corresponding measures are to correct the resin activity index DAC by infrared spectroscopy and to adjust the cooling water flow rate of the grinding mill 9 to ±5L / min;

[0206] VV refers to the solvent adjustment amount, measured in mL. It is used to correct viscosity during the viscosity adjustment stage. The amount of solvent that needs to be added or reduced during the viscosity adjustment stage can be collected in real time at the solvent inlet of the viscosity adjustment component using a mass flow meter. The PLC controls the valve opening according to the solvent adjustment amount VV command, with an adjustment resolution of 0.1 mL / s.

[0207] KKL refers to the proportionality coefficient of influence, which is set to 0.8 mL in this embodiment. 1 This is used to ensure the accuracy of viscosity adjustment;

[0208] The solvent adjustment amount VV converts the deviation between the reflectance prediction and the adjustment coefficient RC into a specific amount of solvent. If the reflectance prediction and adjustment coefficient RC > 1.05, it indicates that the reflectance is too high → the solvent adjustment amount VV is positive, which is used to increase the solvent, reduce the viscosity, and improve atomization. If the reflectance prediction and adjustment coefficient RC < 0.95, it indicates that the reflectance is too low → the solvent adjustment amount VV is negative, which is used to reduce the solvent, increase the viscosity, and avoid sagging.

[0209] By adjusting the solvent amount VV, a closed-loop adjustment of reflectivity prediction and adjustment coefficient RC value is achieved, transforming the abstract deviation between reflectivity prediction and adjustment coefficient RC into an executable solvent addition command, ensuring that the viscosity of the slurry stabilizes within the target range after viscosity adjustment, and ultimately controlling the reflectivity within the target range.

[0210] DAS refers to the baseline value of the dispersion effect index, which is the ideal control threshold. In this embodiment, it is set to 1.5.

[0211] GBI refers to the upper limit of the grinding fineness, which is the ideal control value. In this embodiment, it is set to 15μm.

[0212] RCA refers to the real-time viscosity value, which is the viscosity of the slurry after adjustment. It can be detected by the Fore-4 cup for 40-50 seconds to determine the spraying atomization effect. It can also be used in conjunction with an online viscometer to measure the shear viscosity by rotating the blade. The blade is installed horizontally at the conveying pipeline from the outlet of the grinder 6 to the spraying equipment, and the insertion depth of the rotating blade is 1 / 3 of the pipeline diameter, so as not to affect the material flow.

[0213] The Forecast-4 cup is an offline testing method used in laboratories or production sampling to calibrate online viscometers. 100mL samples are taken from each batch, and the viscosity of the Forecast-4 cup is measured and compared with the data from the online viscometer to correct calculation errors.

[0214] An online viscometer is used for real-time monitoring and dynamic adjustment of the solvent volume to correspond to the viscosity adjustment stage, ensuring that the viscosity remains stable at 40-50s, according to Forte 4 cup at 25°C.

[0215] The online viscometer is installed at the horizontal section of the outlet pipe at the bottom of the viscosity adjustment assembly. The probe is inserted to a depth of 1 / 3 of the pipe diameter, opposite to the direction of the agitator, to avoid eddy current interference from agitation and to ensure stable data.

[0216] The closer the real-time viscosity value RCA is to the target viscosity value RCAS, the closer this term is to R3×1.0, and the closer the calculated result of the reflectance prediction and adjustment coefficient RC is to the target value. A real-time viscosity value RCA > 50s or a real-time viscosity value RCA < 40s will cause this term to deviate, and the reflectance prediction and adjustment coefficient RC will be abnormal. That is, a real-time viscosity value RCA that is too high indicates poor atomization and increased reflectance, while a real-time viscosity value RCA that is too low indicates sagging, low reflectance but insufficient film thickness. The solvent adjustment amount VV is adjusted in real time by adjusting the real-time viscosity value RCA to ensure that the slurry viscosity is in the optimal atomization range of 40-50s during spraying, because viscosity matching is the key to spray uniformity and directly affects the surface roughness and reflectance of the coating.

[0217] RCAS stands for target viscosity value, specifically the target viscosity value for Fork-4 cup.

[0218] RCB refers to the root mean square roughness of the coating surface, which is the degree of microscopic undulation on the surface of the cured paint film. It affects the diffuse reflection efficiency and can be detected offline by a laser confocal microscope. That is, one test panel is sprayed for each batch and used as a feedback parameter. The sampling location can be set at the end of the spraying production line, where an automatic sampling robotic arm is set up to transfer the test panel to the testing station. This parameter is not a real-time parameter and is updated once every 30 minutes to correct the RC calculation of subsequent batches.

[0219] RCBS refers to the median surface roughness, which balances diffuse reflection and gloss. In this embodiment, it is set to 65nm.

[0220] RCC refers to the degree of resin curing, which is the degree of completion of the cross-linking reaction of the resin matrix. The target is ≥95%, which affects adhesion and abrasion resistance. The sampling method is to take samples of the cured paint film using a differential scanning calorimeter and measure the reaction enthalpy change. RCC = (residual enthalpy change / total enthalpy change) × 100%. The sampling location can be at the exit of the curing oven, at the same station as the root mean square roughness (RCB) detection of the coating surface. The robotic arm takes samples synchronously. This parameter is an offline parameter and is tested once per batch.

[0221] Differential scanning calorimeters measure the heat change of a sample during programmed heating and the temperature difference with a reference, directly outputting the enthalpy change value. Uncured resin releases heat during the curing reaction, while fully cured resin has no exothermic peak. Enthalpy change = total heat released during curing reaction - remaining heat released, which can be automatically integrated and calculated by the differential scanning calorimeter software. Resin curing degree RCC = (enthalpy change / total heat released) × 100%.

[0222] The closer the resin cure degree RCC is to the minimum requirement factor RCCS, the closer this factor is to R5×1.0, and the more stable the calculated result of reflectivity prediction and adjustment coefficient RC. Resin cure degree RCC > 80nm indicates that the surface is too rough, or resin cure degree RCC < 50nm indicates that the surface is too smooth, which will lead to abnormal reflectivity prediction and adjustment coefficient RC. This is because excessive roughness leads to reflectivity fluctuations, and excessive smoothness leads to increased specular reflection. The spraying pressure and curing temperature are corrected by the resin cure degree RCC feedback. For example, if the resin cure degree RCC > 80nm, the spraying pressure is increased by 0.1MPa to refine the atomized particles and ensure that the micro-morphology of the coating surface is in the optimal range of diffuse reflection. This is the optical basis for achieving a surface reflectivity ≤ 1.5%.

[0223] RCCS stands for the minimum required resin curing factor, ensuring adhesion ≥4B;

[0224] 4B is the cross-cut adhesion rating in ASTM D3359. A cross-cut test is used to draw 100 1mm x 1mm squares on the paint film surface. 3M tape is then applied and quickly peeled off. The extent of square detachment is observed. 4B indicates a detachment area of ​​<5%, meaning only a few squares at the edges or corners have come off. This demonstrates strong adhesion between the paint film and the substrate, meeting the requirements for good adhesion.

[0225] RCQA refers to the baseline value of the reflectance prediction and adjustment coefficient, which is set to 1.0 in this embodiment;

[0226] R1 refers to the weighting factor of the dispersion effect. In this embodiment, it is set to 0.4, which has the highest weight, because the dispersion of carbon black directly determines light absorption.

[0227] R2 refers to the weighting factor of grinding fineness, which is set to 0.35 in this embodiment. It has a secondary weight and affects the uniformity of light scattering.

[0228] R3 refers to the viscosity weighting factor, which is set to 0.15 in this embodiment, affecting the spray atomization effect;

[0229] R4 refers to the roughness weighting factor, which is set to 0.15 in this embodiment, affecting the diffuse reflection efficiency;

[0230] R5 refers to the weighting factor of the degree of curing, which is set to 0.15 in this embodiment, affecting hardness and surface stability;

[0231] Based on the above, this implementation couples multi-stage parameters to achieve synergistic optimization of dispersion, grinding, viscosity adjustment, and curing. It integrates parameters from the entire process of dispersion, grinding, viscosity adjustment, and curing to predict the reflectivity and mechanical properties of the coating film in real time, establishes a quantitative correlation between microstructure and macroscopic properties, and achieves a leap from single parameter adjustment to a closed-loop quality control throughout the entire process. By using the deviation between the reflectivity prediction and adjustment coefficient RC value and the target value, it drives the dynamic correction of preceding process parameters, such as dispersion power, grinding speed, and solvent volume, avoiding the problems of post-processing inspection and batch rework in traditional processes. It directly correlates with core performance indicators such as reflectivity ≤1.5% and adhesion ≥4B, ensuring that the product simultaneously meets the dual requirements of ultra-low reflectivity and mechanical performance. Through the coupled control of surface roughness and curing degree, it avoids reflectivity fluctuations caused by abnormal microstructure.

[0232] If the reflectance prediction and adjustment coefficient RC > 1.05 and the predicted reflectance > 1.5%, the viscosity is reduced by increasing the solvent adjustment amount VV during the viscosity adjustment stage to ensure uniform spray atomization. At the same time, the curing temperature is increased, and the surface roughness is avoided by adjusting the resin curing degree RCC feedback.

[0233] If the reflectance prediction and adjustment coefficient RC < 0.95 and the predicted reflectance < 1.2%, it may be accompanied by insufficient film thickness. To address this, the solvent adjustment amount VV should be reduced to increase viscosity, while the spraying pressure should be lowered. The film thickness should be adjusted based on feedback from the film thickness sensor to avoid uneven film thickness caused by sagging.

[0234] The root mean square roughness (RCB) of the coating surface is quantified by laser confocal microscopy to quantify the microscopic undulations of the paint film, and the diffuse reflection efficiency is correlated with the microscopic morphology. This solves the problem that the traditional method of only looking at the macroscopic film thickness may result in the reflectivity meeting the standard but the wear resistance being insufficient. The resin curing degree (RCC) quantifies the degree of resin crosslinking and incorporates mechanical properties into the reflectivity prediction to ensure that the reflectivity meets the standard.

[0235] Further explanation is needed regarding the calculation of reflectivity prediction and adjustment coefficient RC, which affects the weighting factor α of the grinding energy density gradient term in the hybrid grinding analysis submodule. This parameter is used to adjust the influence of the grinding energy density gradient GBF in the hybrid grinding analysis submodule, thereby achieving an iterative system. The specific processing procedure is as follows:

[0236] First, the iterative formula is as follows:

[0237] α k+1 =α k -JA×(RC) k -RCQA)×sign(RC k -RCQA);

[0238] Then, set the iteration termination condition, and perform iteration convergence based on three termination conditions. The iteration terminates and achieves convergence when any one of the following three conditions is met:

[0239] Termination condition 1: The maximum number of iterations is 6;

[0240] Termination condition two: The condition is satisfied for three consecutive iterations |RC k -RCQA|≤0.05;

[0241] Termination condition three: |α k+1 -α k |≤0.02 indicates that the change in α tends to stabilize;

[0242] in:

[0243] α k+1 The weighting factor refers to the grinding energy density gradient term after the (k+1)th iteration;

[0244] α k The weighting factor refers to the grinding energy density gradient term after the k-th iteration;

[0245] JA refers to the iteration step size, which is set to 0.1 in this embodiment to ensure convergence stability;

[0246] RC k Refers to the reflectance prediction and adjustment coefficient after the k-th iteration;

[0247] RCQA refers to the baseline value of the reflectance prediction and adjustment coefficient, which is set to 1.0 in this embodiment;

[0248] sign(RC k -RCQA) refers to the sign function, RC k - When RCQA > 0, it is 1, RC k -RCQA < 0 is -1;

[0249] It should be noted that:

[0250] If RC k >RCQA indicates that the predicted reflectivity is too high. For example, if RC = 1.1, by reducing α, the weight of the grinding energy density gradient GBF term is reduced, which weakens the inhibitory effect of energy input on the grinding efficiency factor GB, causing GB to increase → grinding efficiency to improve → the current grinding fineness value GBH to decrease, thus causing RC to decrease.

[0251] If RC k<RCQA indicates that the predicted reflectivity is too low. For example, if RC = 0.9, the weight of the α-enhanced grinding energy density gradient GBF term is increased to suppress excessive grinding energy input and avoid insufficient film thickness caused by the current grinding fineness value GBH being too small, thus causing RC to rise.

[0252] Based on the above, in this implementation, in the traditional open-loop control, the fluctuation of the grinding energy density gradient GBF causes a batch deviation of ±0.15 in the reflectivity prediction and adjustment coefficient RC. After iterative optimization, the deviation of the reflectivity prediction and adjustment coefficient RC can be controlled within a certain range to meet the stringent requirements of military and high-end display industries for ultra-low reflectivity. This iterative form can reduce the sensitivity to batch differences in raw materials and can achieve synergistic optimization of energy, fineness, and reflectivity. This makes the dispersion effect submodule, the mixed grinding analysis submodule, and the reflectivity prediction and adjustment submodule in this preparation control component form a cyclical, iterative, related, and intertwined system.

[0253] Please see Figures 1 to 5 This embodiment provides a method for applying low-reflectivity matte paint, including the following steps:

[0254] Surface treatment involves sandblasting the metal substrate to achieve a cleanliness level of Sa2.5, and chemical passivation of the non-metallic substrate.

[0255] For the spraying operation, the low-reflectivity matte paint is fed into the spraying equipment, and a spray gun with a nozzle diameter of 1.3-1.5mm is used. Then, the spraying air pressure is set to 0.4-0.6MPa to spray the substrate, and the film thickness is controlled to be 20-30μm.

[0256] The curing process involves hot air curing of the sprayed metal substrate, maintaining a temperature of 80℃ for 30 minutes. For the sprayed non-metallic substrate, UV curing is performed, with the UV energy being greater than or equal to 800 mJ / cm². 2 .

[0257] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A low reflectance spray matting paint, characterized by, According to the weight percentage, it is composed of the following components: Resin matrix: modified polyurethane acrylate 30-50%; Composite matting agent: porous silica 5-15%, nano carbon black 0.1-0.5%; Light scattering modifier: fluorine modified wax powder 1-3%; Solvent system: fast-drying mixed solvent 31.5%-63.9%.

2. The low reflectance spray matting paint of claim 1, wherein, The fast-drying mixed solvent includes acetone and butyl acetate, and the weight ratio of acetone to butyl acetate is 3:

7.

3. A device for preparing a low reflectance spray matting paint according to claim 1, characterized in that, The preparation device includes a dispersion assembly (1), a mixing and grinding assembly (2), a viscosity adjusting assembly, and a preparation control assembly; The dispersion assembly (1) is used for ultrasonic dispersion of nano carbon black and part of the resin matrix; The mixing and grinding assembly (2) is used for adding porous silica and fluorine modified wax powder to the material treated by the dispersion assembly (1) to mix and grind the material to a certain fineness; The viscosity adjusting assembly is used for adding the remaining resin matrix and fast-drying mixed solvent to the material treated by the mixing and grinding assembly (2) and mixing and stirring to finally adjust the viscosity to the spraying applicable range; The preparation control assembly is arranged in a control cabinet (9) and is used for dispersion effect prediction, grinding efficiency analysis, and reflectivity prediction according to multiple source factors, and centralized control of the dispersion assembly (1), the mixing and grinding assembly (2), and the viscosity adjusting assembly based on the dispersion effect prediction, the grinding efficiency analysis, and the reflectivity prediction.

4. The apparatus for preparing a low reflectance, sprayable flatting paint according to claim 3, wherein: The dispersion assembly (1) includes: A dispersion tank (3) is provided with an observation window (4) on the side, and an outlet pipeline (5) is arranged at the bottom of the dispersion tank (3); The mixing and grinding assembly (2) includes: A grinder (6) is provided with a material port (7) on both sides, and an electromagnetic flowmeter (8) is installed on the material port (7); The dispersion tank (3), the grinder (6), and the viscosity adjusting assembly are sequentially connected through a conveying pipeline, and a pump body device is arranged in the conveying pipeline to drive the material flow.

5. The apparatus for preparing a low reflective spray matting paint according to claim 3, wherein: The preparation control assembly includes: A data acquisition module is used to acquire dispersion processing data, mixing and grinding data, and viscosity adjusting information data in the preparation process of the matting paint; A data processing module is used to input the data acquired by the data acquisition module, clean and normalize the data, and synchronize all data with the system clock, so as to input the processed data into an analysis processing module; The analysis processing module includes a dispersion effect submodule, a mixing and grinding analysis submodule, and a reflectivity prediction and adjustment submodule; The dispersion effect submodule is based on the initial resin viscosity, ultrasonic power, dispersion system temperature value, dispersion time, dispersion uniformity correction coefficient, resin activity index, and ultrasonic cavitation intensity in the dispersion processing data, and combines the resin activity correction coefficient and the temperature attenuation coefficient to output the dispersion effect index; The mixing and grinding analysis submodule is based on the ball mill rotating speed, grinding time, pore retention rate, grinding medium size value, grinding torque, grinding energy density gradient, and current grinding fineness value in the mixing and grinding data, and combines the dispersion effect index to output the grinding efficiency factor; The reflectivity prediction and adjustment submodule outputs a reflectivity prediction and adjustment coefficient and a solvent adjustment amount based on the real-time viscosity value, target viscosity value, root mean square roughness of the coating surface, median surface roughness, and minimum requirement factor for resin curing degree in the viscosity adjustment information data, combined with the dispersion effect index. Adjustment and management module: The dispersion effect index, grinding efficiency factor, reflectance prediction and adjustment coefficient, and solvent adjustment amount are input into the adjustment and management module. The adjustment and management module adjusts and controls the operation of the dispersion component (1), the mixing and grinding component (2), and the viscosity adjustment component based on the input data.

6. The apparatus for preparing a low reflective spray matting paint according to claim 1, wherein: The processing flow of the dispersion effect submodule is as follows: Ⅰ: The temperature value and initial resin viscosity of the dispersion system are normalized and multiplied to form the denominator for the analysis of dispersion effect; II: By multiplying the ultrasonic power by the dispersion time and combining the dispersion uniformity correction coefficient with the resin activity index, the influence of the initial dispersion state of nano carbon black and the activity of resin hydroxyl functional groups on the dispersion effect is analyzed. Ultrasonic cavitation intensity is also introduced as a molecular factor for analyzing multiple influencing factors of dispersion effect. III: By combining the temperature of the dispersion system with the temperature decay coefficient in the form of a negative exponent, the nonlinear inhibitory effect of temperature on the dispersion effect is simulated, and the dispersion effect index is finally output.

7. The apparatus for preparing low-reflectivity spray matte varnish according to claim 1, characterized in that: The processing flow of the hybrid grinding analysis submodule is as follows: Ⅰ: The dispersion effect index is introduced into this mixed grinding analysis submodule, and combined with ball mill speed, grinding time, and porosity retention rate, to analyze the grinding efficiency through the actual operating parameters during the mixed grinding process; II: Multiply the grinding media size value and grinding torque to reflect the viscosity change of the material indirectly; Ⅲ: By combining the power of the grinding mill (6) with the material conveying flow rate, the grinding energy density gradient is analyzed, and then divided by the critical threshold of the grinding energy density gradient to normalize the analysis of the grinding energy density. IV: Normalize the current grinding fineness value with the target grinding fineness to incorporate the real-time fineness of solid particles in the raw material into this mixed grinding analysis submodule, and finally output the grinding efficiency factor.

8. The apparatus for preparing a low reflective spray matting paint according to claim 1, wherein: The processing flow of the reflectivity prediction and adjustment submodule is as follows: Ⅰ: Introduce the dispersion effect index and the current grinding fineness value into this reflectivity prediction and adjustment submodule, and combine them with the baseline value of the dispersion effect index and the upper limit value of the grinding fineness, respectively, so as to incorporate the dispersion effect submodule and the mixed grinding analysis submodule into the reflectivity prediction and adjustment analysis process; II: The real-time viscosity value, root mean square roughness of the coating surface, and resin curing degree are combined with the target viscosity value, median surface roughness value, and minimum resin curing degree requirement factor, respectively, and weighted summation is used to consider the prediction and adjustment of the reflectance of the matting agent under multiple factors, and the reflectance prediction and adjustment coefficient is output. III: The solvent adjustment amount is output based on the difference between the reflectance prediction and adjustment coefficient and the baseline value of the reflectance prediction and adjustment coefficient, combined with the influence adjustment ratio coefficient.

9. A method of using the low reflectance spray matting paint of claim 1, wherein, Includes the following steps: Surface treatment involves sandblasting the metal substrate to achieve a cleanliness level of Sa2.5, and chemical passivation of the non-metallic substrate. For the spraying operation, the low-reflectivity matte paint is fed into the spraying equipment, and a spray gun with a nozzle diameter of 1.3-1.5mm is used. Then, the spraying air pressure is set to 0.4-0.6MPa to spray the substrate, and the film thickness is controlled to be 20-30μm. The solidification operation is that the metal base material after spraying is heat air solidified and meets the holding condition of holding at 80℃ for 30min, and the non-metal base material after spraying is UV solidified and meets the condition that the energy of UV is greater than or equal to 800mJ / cm 2 .

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