Low-index double-sided antistatic silicone protective film and preparation process thereof

Abstract

This invention relates to the field of silicone protective film technology, and more particularly to a low-index double-sided antistatic silicone protective film and its preparation process. By precisely controlling the thickness of the antistatic layer through double-sided corona treatment, dust removal and static elimination, and micro-gravure coating, combined with an organic silicone adhesive formulation and multi-stage temperature zone curing, the peel force fluctuations caused by uneven curing stress of the organic silicone layer after release film lamination are significantly reduced, resulting in a substantial improvement in the product's peel force stability. This effectively avoids defects caused by excessively high or low peel forces in traditional processes. Simultaneously, by constructing a database of the entire preparation process and introducing data mining algorithms for association rule analysis, the intrinsic correlation between key process parameters and product performance indicators can be systematically revealed, thereby generating objective process parameter optimization schemes. This overcomes the limitations of existing technologies that rely on experience for process parameters, ensuring the consistency and reproducibility of product performance between batches.

Description

Technical Field

[0001] This invention relates to the field of silicone protective film technology, and in particular to a low-index double-sided antistatic silicone protective film and its preparation process. Background Technology

[0002] Low-index double-sided antistatic silicone protective film is a type of functional protective material widely used in optoelectronic displays, precision electronic component manufacturing processes, and optical film processing. This type of protective film typically uses optical-grade polyethylene terephthalate (PET) film as the substrate, coated with an antistatic layer and an organosilicon pressure-sensitive adhesive layer, giving it excellent optical transmittance, high-temperature resistance, chemical solvent resistance, and re-peelability.

[0003] However, when preparing double-sided antistatic silicone protective films using existing technologies, the peel force is prone to large fluctuations due to uneven curing stress of the silicone layer after the release film is bonded. Furthermore, the coating process parameters rely on experience for adjustment, making it difficult to guarantee product stability. Summary of the Invention

[0004] The purpose of this invention is to provide a low-index double-sided antistatic silicone protective film and its preparation process, which solves the technical problems in the prior art where the peel force fluctuates greatly due to uneven curing stress of the silicone layer after the release film is bonded, and the coating process parameters rely on experience for adjustment, making it difficult to ensure product stability.

[0005] To achieve the above objectives, the present invention provides a preparation process, comprising:

[0006] Optical grade polyethylene terephthalate film substrates are subjected to double-sided corona treatment, followed by dust removal and static electricity removal treatment.

[0007] An antistatic coating liquid is prepared by mixing conductive polymer, dispersant, coupling agent and organic solvent in a preset ratio, followed by high-speed dispersion and filtration.

[0008] An antistatic coating liquid was prepared by applying it to both sides of a substrate using a micro-gravure coating method. After coating, the coating was dried and cured in an oven to form an antistatic layer with a thickness of 0.05-0.20 μm on both sides of the substrate.

[0009] Vinyl silicone oil, hydrogen-containing silicone oil, organosilicon resin, platinum catalyst, antistatic filler and organic solvent are mixed and stirred evenly according to the formula to obtain organosilicon adhesive;

[0010] The prepared silicone adhesive is coated on the antistatic layer on one side of the substrate, with the coating thickness controlled at 5-50μm, and then cured by heating in a multi-temperature zone oven to form a silicone layer.

[0011] The release film is bonded to the surface of the formed silicone layer, pressed by a pressing roller, and then wound up.

[0012] Collect process parameter data and corresponding product performance test data during the preparation process to construct a database of the entire preparation process;

[0013] Data mining algorithms were used to perform association rule analysis on the entire process database of the manufacturing process, to uncover the correlation between various process parameters and product performance indicators, and to generate process parameter optimization schemes based on the mining results.

[0014] The specific process for applying the antistatic layer on both sides is as follows:

[0015] First, microgravure coating is applied to the first side of the substrate, and then it is dried and cured in an oven at 60-100℃ before being rolled up.

[0016] Then, a micro-gravure coating is applied to the second side of the substrate, and after drying and curing in an oven at 60-100℃, it is rolled up.

[0017] The total thickness of the double-sided antistatic layer is 0.10-0.35 μm, and the surface resistivity of the single-sided antistatic layer is 10. 5 -10 8 Ω.

[0018] Data mining algorithms include:

[0019] Extract the batch preparation process parameter data and corresponding product performance test data from the whole process database to construct a process-performance data matrix.

[0020] After preprocessing the process-performance data matrix, a frequent pattern mining algorithm is used to scan the process-performance data matrix. The discretized intervals of each process parameter are used as itemset elements to filter frequent itemsets with support higher than the preset minimum support threshold.

[0021] Association rules are generated based on the selected frequent itemsets. The antecedent of each association rule is a process parameter condition itemset, and the consequent is a product performance range itemset. Strong association rules with confidence scores higher than the preset minimum confidence score threshold are selected.

[0022] Extract the rules whose consequents correspond to the optimal product performance range from the strongly correlated rules, and output the process parameter combination corresponding to the antecedent of the rule as the process parameter optimization scheme.

[0023] The generated process parameter optimization scheme is divided into multiple progressive adjustment gradients for step-by-step trial production verification. After each gradient verification is completed, product performance data is collected and compared with the data of the previous gradient. When the difference comparison result meets the preset threshold, the next gradient is executed. When the difference comparison result exceeds the preset threshold, an anomaly is determined and the process parameters of the previous gradient are automatically rolled back.

[0024] The silicone adhesive comprises, by weight, 110 parts of vinyl silicone oil, 1.0-6.0 parts of hydrogen-containing silicone oil, 15-40 parts of silicone resin, 0.2-1.2 parts of platinum catalyst, 0.8-4.0 parts of antistatic filler, and 150-450 parts of organic solvent; the antistatic filler is at least one of carbon nanotubes, nano-graphite powder, or ionic liquid.

[0025] In the step of “collecting process parameter data and corresponding product performance test data of the preparation process and constructing a database of the entire preparation process”, the substrate parameters, antistatic coating liquid formulation parameters, antistatic layer coating process parameters, silicone adhesive formulation parameters, silicone layer coating process parameters, curing temperature parameters, environmental parameters and product performance test data of the preparation process are stored in a distributed storage manner and stored in a structured manner according to time series.

[0026] The conductive polymer is at least one of a polythiophene-based conductive polymer, a polyaniline-based conductive polymer, or a polypyrrole-based conductive polymer; the dispersing agent is at least one of a polyvinylpyrrolidone, polyvinyl alcohol, or polyethylene glycol; and the coupling agent is a silane coupling agent or a titanate coupling agent.

[0027] This invention also provides a low-index double-sided antistatic silicone protective film, prepared using the process described above.

[0028] From the inside out, the layers are: a first antistatic layer, a PET substrate, a second antistatic layer, an silicone layer, and a release film. The thickness of both the first and second antistatic layers is 0.05-0.20 μm on one side, and their surface resistivity is 10⁻⁶. 5 -10 8 Ω, the thickness of the silicone layer is 5-50μm.

[0029] This invention discloses a low-index double-sided antistatic silicone protective film and its preparation process. By precisely controlling the thickness of the antistatic layer through double-sided corona treatment, dust removal and static elimination, and micro-gravure coating, combined with an organic silicone adhesive formulation and multi-segment temperature curing, the peel force fluctuations caused by uneven curing stress of the organic silicone layer after release film bonding are significantly reduced. This greatly improves the peel force stability of the product and effectively avoids defects caused by excessively high or low peel force in traditional processes. Simultaneously, by constructing a database of the entire preparation process and introducing data mining algorithms for association rule analysis, the inherent correlation between key process parameters (such as corona power, coating thickness, curing temperature gradient, etc.) and product performance indicators can be systematically revealed. This generates objective process parameter optimization schemes, overcoming the limitations of existing technologies that rely on experience for process parameters, and ensuring the consistency and reproducibility of product performance between batches. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0031] Figure 1 This is a flowchart of the preparation process of the present invention. Detailed Implementation

[0032] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, but should not be construed as limiting the present invention.

[0033] Please refer to Figure 1 , Figure 1 This is a flowchart of the preparation process of the present invention. An embodiment of the present invention provides a preparation process, including:

[0034] S1. The optical grade polyethylene terephthalate film substrate is subjected to double-sided corona treatment, followed by dust removal and static electricity removal treatment.

[0035] In this specific embodiment, the dust removal process combines dust removal by sticky rollers with dust removal by ion air knives, and the static electricity removal process uses an AC corona discharge static electricity removal device or a pulsed DC static electricity removal device.

[0036] S2. The conductive polymer, dispersant, coupling agent and organic solvent are mixed in a preset ratio, and the mixture is dispersed at high speed and filtered to obtain an antistatic coating liquid.

[0037] In this specific embodiment, the conductive polymer is at least one of a polythiophene-based conductive polymer, a polyaniline-based conductive polymer, or a polypyrrole-based conductive polymer; the dispersing agent is at least one of a polyvinylpyrrolidone, polyvinyl alcohol, or polyethylene glycol; and the coupling agent is a silane coupling agent or a titanate coupling agent.

[0038] S3. The antistatic coating liquid prepared by micro-gravure coating is applied to both sides of the substrate. After coating, it is dried and cured in an oven to form an antistatic layer with a thickness of 0.05-0.20μm on both sides of the substrate.

[0039] For this specific embodiment, the specific process for applying the antistatic layer on both sides is as follows:

[0040] First, microgravure coating is applied to the first side of the substrate, and then it is dried and cured in an oven at 60-100℃ before being rolled up.

[0041] Then, a micro-gravure coating is applied to the second side of the substrate, and after drying and curing in an oven at 60-100℃, it is rolled up.

[0042] The total thickness of the double-sided antistatic layer is 0.10-0.35 μm, and the surface resistivity of the single-sided antistatic layer is 10. 5 -10 8 Ω.

[0043] S4. Mix vinyl silicone oil, hydrogen-containing silicone oil, organosilicon resin, platinum catalyst, antistatic filler and organic solvent according to the formula and stir evenly to obtain organosilicon adhesive;

[0044] In this specific embodiment, the silicone adhesive comprises, by weight, 110 parts of vinyl silicone oil, 1.0-6.0 parts of hydrogen-containing silicone oil, 15-40 parts of silicone resin, 0.2-1.2 parts of platinum catalyst, 0.8-4.0 parts of antistatic filler, and 150-450 parts of organic solvent; the antistatic filler is at least one of carbon nanotubes, nano-graphite powder, or ionic liquid.

[0045] S5. Apply the prepared silicone adhesive to the antistatic layer on one side of the substrate, with the coating thickness controlled at 5-50μm, and cure it in a multi-temperature zone oven to form a silicone layer.

[0046] S6. Lay the release film onto the surface of the formed silicone layer, press it with a pressing roller, and then roll it up.

[0047] S7. Collect process parameter data and corresponding product performance test data during the preparation process, and construct a database of the entire preparation process;

[0048] In this specific implementation, in the step of "collecting process parameter data and corresponding product performance test data of the preparation process and constructing a database of the entire preparation process", the substrate parameters, antistatic coating liquid formulation parameters, antistatic layer coating process parameters, silicone adhesive formulation parameters, silicone layer coating process parameters, curing temperature parameters, environmental parameters and product performance test data of the preparation process are stored in a distributed storage manner and stored in a structured manner according to time series.

[0049] S8. Use data mining algorithms to perform association rule analysis on the entire process database of the preparation process, mine the correlation between each process parameter and product performance indicators, and generate process parameter optimization schemes based on the mining results.

[0050] For this specific implementation method, the data mining algorithm includes:

[0051] Extract the batch preparation process parameter data and corresponding product performance test data from the whole process database to construct a process-performance data matrix.

[0052] After preprocessing the process-performance data matrix, a frequent pattern mining algorithm is used to scan the process-performance data matrix. The discretized intervals of each process parameter are used as itemset elements to filter frequent itemsets with support higher than the preset minimum support threshold.

[0053] Association rules are generated based on the selected frequent itemsets. The antecedent of each association rule is a process parameter condition itemset, and the consequent is a product performance range itemset. Strong association rules with confidence scores higher than the preset minimum confidence score threshold are selected.

[0054] Extract the rules whose consequents correspond to the optimal product performance range from the strongly correlated rules, and output the process parameter combination corresponding to the antecedent of the rule as the process parameter optimization scheme.

[0055] The generated process parameter optimization scheme is broken down into multiple progressive adjustment gradients for step-by-step trial production verification. After each gradient verification is completed, product performance data is collected and compared with the data of the previous gradient. When the difference comparison result meets the preset threshold, the next gradient is executed. When the difference comparison result exceeds the preset threshold, an anomaly is determined and the process parameters of the previous gradient are automatically rolled back.

[0056] Data preprocessing includes data cleaning, missing value imputation, outlier removal, and process parameter discretization; the minimum support threshold is 0.05-0.15, and the minimum confidence threshold is 0.70-0.90.

[0057] Secondly, the operating conditions for double-sided corona treatment are: corona power of 3-8kW, processing speed of 10-50m / min, surface tension of both sides of the substrate after corona treatment ≥42dyn / cm, online surface tension detection of the substrate after double-sided corona treatment to ensure that the surface tension of both sides of the substrate is ≥42dyn / cm, if the detection fails, it is returned for re-corona treatment or rejected as waste.

[0058] Meanwhile, the multi-zone oven includes at least three temperature zones: the first zone has a temperature of 60-90℃, the second zone has a temperature of 90-120℃, and the third zone has a temperature of 120-150℃. The coating thickness of the silicone layer is 5-50μm.

[0059] Furthermore, the coating speed of the microgravure coating is 5-30 m / min, and the screen line count of the microgravure roller is 100-300 lines / inch.

[0060] By precisely controlling the thickness of the antistatic layer through double-sided corona treatment, dust removal and static electricity elimination, and micro-gravure coating, combined with the silicone adhesive formulation and multi-segment temperature zone curing, the peel force fluctuation caused by uneven curing stress of the silicone layer after release film lamination is significantly reduced, greatly improving the peel force stability of the product and effectively avoiding defects caused by excessively high or low peel force in traditional processes. At the same time, by constructing a database of the entire preparation process and introducing data mining algorithms for association rule analysis, the intrinsic relationship between key process parameters (such as corona power, coating thickness, curing temperature gradient, etc.) and product performance indicators can be systematically revealed, thereby generating objective process parameter optimization schemes. This overcomes the limitations of process parameters relying on experience in existing technologies and ensures the consistency and reproducibility of product performance between batches.

[0061] The above-disclosed embodiments are merely one or more preferred embodiments of this application and should not be construed as limiting the scope of this application. Those skilled in the art can understand that all or part of the processes for implementing the above embodiments and equivalent changes made in accordance with the claims of this application still fall within the scope of this application.

Claims

1. A preparation process, characterized in that, include: Optical grade polyethylene terephthalate film substrates are subjected to double-sided corona treatment, followed by dust removal and static electricity removal treatment. An antistatic coating liquid is prepared by mixing conductive polymer, dispersant, coupling agent and organic solvent in a preset ratio, followed by high-speed dispersion and filtration. An antistatic coating liquid was prepared by applying it to both sides of a substrate using a micro-gravure coating method. After coating, the coating was dried and cured in an oven to form an antistatic layer with a thickness of 0.05-0.20 μm on both sides of the substrate. Vinyl silicone oil, hydrogen-containing silicone oil, organosilicon resin, platinum catalyst, antistatic filler and organic solvent are mixed and stirred evenly according to the formula to obtain organosilicon adhesive; The prepared silicone adhesive is coated on the antistatic layer on one side of the substrate, with the coating thickness controlled at 5-50μm, and then cured by heating in a multi-temperature zone oven to form a silicone layer. The release film is bonded to the surface of the formed silicone layer, pressed by a pressing roller, and then wound up. Collect process parameter data and corresponding product performance test data during the preparation process to construct a database of the entire preparation process; Data mining algorithms were used to perform association rule analysis on the entire process database of the manufacturing process, to uncover the correlation between various process parameters and product performance indicators, and to generate process parameter optimization schemes based on the mining results.

2. The preparation process according to claim 1, characterized in that, The specific process for applying a double-sided antistatic coating is as follows: First, microgravure coating is applied to the first side of the substrate, and then it is dried and cured in an oven at 60-100℃ before being rolled up. Then, a micro-gravure coating is applied to the second side of the substrate, and after drying and curing in an oven at 60-100℃, it is rolled up. The total thickness of the double-sided antistatic layer is 0.10-0.35 μm, and the surface resistivity of the single-sided antistatic layer is 10. 5 -10 8 Ω.

3. The preparation process according to claim 2, characterized in that, Data mining algorithms include: Extract the batch preparation process parameter data and corresponding product performance test data from the whole process database to construct a process-performance data matrix. After preprocessing the process-performance data matrix, a frequent pattern mining algorithm is used to scan the process-performance data matrix. The discretized intervals of each process parameter are used as itemset elements to filter frequent itemsets with support higher than the preset minimum support threshold. Association rules are generated based on the selected frequent itemsets. The antecedent of each association rule is a process parameter condition itemset, and the consequent is a product performance range itemset. Strong association rules with confidence scores higher than the preset minimum confidence score threshold are selected. Extract the rules whose consequents correspond to the optimal product performance range from the strongly correlated rules, and output the process parameter combination corresponding to the antecedent of the rule as the process parameter optimization scheme.

4. The preparation process according to claim 3, characterized in that, The generated process parameter optimization scheme is broken down into multiple progressive adjustment gradients for step-by-step trial production verification. After each gradient verification is completed, product performance data is collected and compared with the data of the previous gradient. When the difference comparison result meets the preset threshold, the next gradient is executed. When the difference comparison result exceeds the preset threshold, an anomaly is determined and the process parameters of the previous gradient are automatically rolled back.

5. The preparation process according to claim 4, characterized in that, The silicone adhesive comprises, by weight, 110 parts of vinyl silicone oil, 1.0-6.0 parts of hydrogen-containing silicone oil, 15-40 parts of silicone resin, 0.2-1.2 parts of platinum catalyst, 0.8-4.0 parts of antistatic filler, and 150-450 parts of organic solvent; the antistatic filler is at least one of carbon nanotubes, nano-graphite powder, or ionic liquid.

6. The preparation process according to claim 5, characterized in that, In the step "Collect process parameter data and corresponding product performance test data of the preparation process, and construct a database of the entire preparation process", the substrate parameters, antistatic coating liquid formulation parameters, antistatic layer coating process parameters, silicone adhesive formulation parameters, silicone layer coating process parameters, curing temperature parameters, environmental parameters and product performance test data of the preparation process are stored in a distributed storage manner and stored in a structured manner according to time series.

7. The preparation process according to claim 6, characterized in that, The conductive polymer is at least one of polythiophene-based conductive polymer, polyaniline-based conductive polymer, or polypyrrole-based conductive polymer; the dispersing agent is at least one of polyvinylpyrrolidone, polyvinyl alcohol, or polyethylene glycol; and the coupling agent is a silane coupling agent or a titanate coupling agent.

8. A low-index double-sided antistatic silicone protective film, prepared using the preparation process described in claim 7, characterized in that, From the inside out, the layers are: a first antistatic layer, a PET substrate, a second antistatic layer, an silicone rubber layer, and a release film. The thickness of both the first and second antistatic layers is 0.05-0.20 μm on one side, and their surface resistivity is 10⁻⁶. 5 -10 8 Ω, the thickness of the silicone layer is 5-50μm.

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