A surface modification system for anti-aging tape film

Abstract

The present invention relates to the field of surface modification technology, specifically to a surface modification system for an aging-resistant adhesive tape film, the system comprising an adhesive blending module, a substrate processing module, a coating uniformity monitoring module and a quality assessment module, the adhesive blending module calculates the initial reaction rate based on the temperature, pressure and chemical concentration data collected by real-time sensors. The present invention realizes fine control of production conditions by real-time monitoring of key parameters in the production process of the adhesive tape film. This dynamic optimization of the production process of the adhesive tape film adjusts production parameters through real-time data, significantly improves production efficiency and product reliability, and uses a predictive model to analyze the potential impact of parameter changes on substrate processing, which can foresee potential problems, thereby reducing production waste and reducing costs. By recording the adjusted parameter configuration and performing performance testing and statistical analysis based on the data, the product development cycle is shortened, ensuring the long-term stability and wide applicability of the adhesive tape film.

Description

Technical Field

[0001] The invention relates to the technical field of surface modification, in particular to a surface modification system for an anti-aging adhesive tape film. Background Art

[0002] The field of surface modification technology involves changing the chemical and physical properties of the material surface to enhance the functionality or adaptability of the material. The surface structure is changed by coating, chemical treatment, physical plating or polymerization reaction to improve the material's resistance to environmental factors (such as UV radiation, wear and corrosion) or improve the material's contact properties with other substances (such as adhesion, friction coefficient and wettability). Common methods include plasma treatment, chemical vapor deposition, electroplating and self-assembled monolayer technology.

[0003] Among them, the surface modification system of aging-resistant tape film is a system specially designed to improve the durability and long-term performance of tape film. The system enhances its environmental aging resistance, such as resistance to UV radiation, temperature changes and mechanical wear, by applying specific chemical or physical treatments on the surface of the tape film. The modified tape film has a wide range of uses and is commonly found in the construction, automotive, electronics and packaging industries.

[0004] Existing technologies, such as chemical coating or physical plating, lack the ability to control and adjust variables in the production process in real time, and it is difficult to immediately evaluate the material properties after processing, resulting in quality fluctuations in tape films during mass production. The lack of real-time monitoring causes slight changes in processing conditions to cause uneven coating, affecting the performance of the tape film. This limitation is particularly evident in applications that require long-term stability, such as in the high-end electronics and automotive industries. Inconsistent tape film quality also leads to serious performance issues and rising costs. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a surface modification system for an aging-resistant adhesive tape film.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: a surface modification system for an aging-resistant adhesive tape film, the system comprising:

[0007] The adhesive preparation module calculates the initial reaction rate and determines the initial sensitivity index of the adhesive based on the temperature, pressure and chemical concentration data collected by real-time sensors. It also analyzes the parameter sensitivity based on the index, identifies the key parameters that affect the tape film modification effect, and generates a key parameter configuration table.

[0008] The substrate processing module adjusts the temperature, pressure and chemical concentration based on the key parameter configuration table, uses polynomial regression to predict the effect of parameter changes on substrate processing, and conducts batch tests to adjust and determine the optimal operating parameters to generate an optimized configuration of operating parameters;

[0009] The coating uniformity monitoring module monitors the temperature, pressure and chemical concentration in the tape film production based on the optimized configuration of the operating parameters, records key real-time data, compares it with the operating parameters, and adjusts the production line parameters to maintain the coating uniformity, and generates parameter adjustment records;

[0010] The quality assessment module collects samples of finished tape films based on the parameter adjustment records, performs performance tests, including adhesion tests and aging resistance tests, and performs statistical analysis to compare expectations with performance differences and generate performance difference analysis results.

[0011] The present invention is improved in that the calculation step of the initial reaction rate is specifically as follows:

[0012] Based on the temperature, pressure and chemical concentration data collected by real-time sensors, use the formula:

[0013]

[0014] Generate the initial reaction rate value IR, where k represents the reaction rate constant, E a represents activation energy, R represents gas constant, T represents temperature, C represents chemical concentration, n represents concentration reaction order, γ represents pressure sensitivity coefficient, δ represents pressure regulation baseline, and P represents real-time pressure;

[0015] Based on the initial value of the reaction rate, combined with the pressure, the formula:

[0016]

[0017] Perform pressure adjustment to obtain an adjusted reaction rate AR, where α and β are pressure adjustment parameters, P is the real-time pressure, μ represents the weight for adjusting the reaction rate, and IR is the initial value of the reaction rate;

[0018] Based on the adjusted reaction rate, it is compared with the average reaction rate of historical data, using the formula:

[0019]

[0020] Generate the initial sensitivity index SI, where AR represents the adjusted reaction rate, AHR represents the historical average reaction rate, ξ represents the sensitivity reduction coefficient, and ∈ represents the sensitivity-adjusted baseline.

[0021] The present invention is improved in that the steps of obtaining the key parameter configuration table are specifically as follows:

[0022] Based on the indicators, the parameter sensitivity is analyzed using the formula:

[0023]

[0024] Generate sensitivity analysis results S x ,in, represents the partial derivative of SI with respect to parameter x, where x is the current parameter value, including temperature, pressure and chemical concentration, SI is the initial sensitivity index, λ is the nonlinear adjustment factor, and η and θ are adjustment parameters;

[0025] According to the sensitivity coefficient, the key parameters that affect the film are identified, and each key parameter is analyzed to determine the degree of influence and adjustment requirements during the tape film modification process, and generate a key parameter configuration table.

[0026] The present invention is improved in that the step of predicting the effect of the parameter change on the substrate treatment is specifically:

[0027] Based on the key parameter configuration table, temperature, pressure and chemical concentration are selected as independent variables, and a polynomial regression model is established in combination with historical data. The formula is:

[0028] Y=β0+β1T+β2P+β3C+β4T 2 +β5P 2 +β6C 2

[0029] Where Y is the substrate treatment effect, β0, β1, β2, β3, β4, β5 and β6 are model parameters, T is temperature, P is pressure, and C is chemical concentration;

[0030] The polynomial regression model was fitted using the collected data, and the model parameters were calculated using the least squares method:

[0031]

[0032] in, is the model regression coefficient obtained from data fitting, X is the design matrix including independent variables, Y is the response variable vector, X T Design the transpose of the matrix for X;

[0033] Using the model regression coefficients, the potential results of substrate treatment under new operating conditions are predicted using the formula:

[0034]

[0035] Among them, Y pred is the substrate treatment effect predicted by the regression model, X is the design matrix including the independent variables, are the model regression coefficients.

[0036] The present invention is improved in that the step of obtaining the optimized configuration of the operating parameters is specifically as follows:

[0037] Based on the described impact on substrate processing, a decision tree algorithm was used to identify key influencing factors using the formula:

[0038]

[0039] Among them, Gain(S i ,A) indicates the use of parameter A in the data set S i The information gain on i YesS i The weight factor of For the dataset S i The standard deviation, S i is a subset of the data set, and n is the number of subsets;

[0040] According to the identified key parameters, optimize the parameter configuration, using the formula:

[0041]

[0042] Generate optimized parameter settings, where C new and C old are the costs before and after adjustment, ΔC represents the cost change, T is the temperature parameter used to control the optimization process, κ is the adjustment factor used to adjust the optimization sensitivity, α z For the learning rate, adjust the step size;

[0043] Based on the optimized parameter settings, a configuration table is developed using the formula:

[0044]

[0045] Get the operation parameter optimization configuration Config final , among which, OP j is the optimized parameter, v j is the weight coefficient of each parameter, which is used to calculate the effect of the configuration, and m is the number of parameters after optimization.

[0046] The present invention is improved in that the steps of monitoring the temperature, pressure and chemical concentration are specifically as follows:

[0047] Based on the optimized configuration of the operating parameters, sensors are installed on the production line to monitor the temperature, pressure and chemical concentration in real time, and the data is preliminarily processed. The formula is:

[0048]

[0049] Generate standardized data X raw , where T, P and C represent temperature, pressure and chemical concentration respectively, k1, k2 and k3 are adjustment coefficients used for preliminary conversion and standardization of data, and C sat is the saturation concentration of the chemical agent, used to nonlinearize the concentration data;

[0050] The standardized data is analyzed to identify abnormalities or deviations from operating parameters using the formula:

[0051]

[0052] The degree of deviation of the normal operating parameters ΔX is obtained to determine whether adjustment is needed, where X norm is the expected data under normal operating conditions, d is the adjustment factor used to adjust the sensitivity of the response, X raw is the standardized data, ΔX′ is X raw and X norm The difference.

[0053] The present invention is improved in that the step of obtaining the parameter adjustment record is specifically as follows:

[0054] Based on the key real-time data, parameter status evaluation is performed using the formula:

[0055]

[0056] Generate parameter state index S, where X i Represents the real-time measurement value of key parameters, q i is the critical coefficient of the corresponding parameter, σ i is the standard deviation of parameter measurements, used to normalize the data, and n represents the total number of parameters;

[0057] Based on the parameter status index, an adjustment plan is formulated using the formula:

[0058]

[0059] Get the adjusted parameter value P new , where P old is the original parameter value, δ is the adjustment strength coefficient, S max is the theoretical maximum value of the parameter state index, S is the parameter state index;

[0060] According to the adjusted parameter value, the adjustment activity is recorded in the following format:

[0061] Record = {P old , P new , DR}

[0062] Generate parameter adjustment record Record, where P old is the original parameter value, P new is the adjusted parameter value, and DR is the reason for adjustment.

[0063] The present invention is improved in that the step of obtaining the performance difference analysis result is specifically as follows:

[0064] Based on the parameter adjustment records, samples of finished tape films were collected from the production line, using the formula:

[0065]

[0066] Get the required number of samples N, where μ is the number of samples in the standard batch, ∈ is used to adjust the proportion of additional collected samples, and γ is a fixed increment;

[0067] The collected samples were subjected to viscosity test and aging resistance test, using the formula:

[0068]

[0069] Get the performance evaluation result P, where V i is the viscosity test value of the i-th sample, A i is the aging test value of the i-th sample, ω V and ω A are the weights of the two tests respectively, and N is the number of samples required;

[0070] Use statistical analysis to compare test results to expected performance and identify key performance differences using the formula:

[0071]

[0072] Get the performance difference analysis result D, where E i is the expected performance indicator, σ is the standard deviation of the performance data, λ is a constant used to adjust the error sensitivity, P is the performance evaluation result, and N is the required number of samples.

[0073] Compared with the prior art, the advantages and positive effects of the present invention are:

[0074] In the present invention, by real-time monitoring of key parameters in the tape film production process, fine control of production conditions is achieved. This dynamic optimization of the tape film production process adjusts production parameters through real-time data, significantly improving production efficiency and product reliability. The potential impact of parameter changes on substrate processing is analyzed using a predictive model, which can foresee potential problems, thereby reducing production waste and lowering costs. By recording the adjusted parameter configuration and performing performance testing and statistical analysis based on the data, the product development cycle is shortened, ensuring the long-term stability and wide applicability of the tape film. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A module diagram of a surface modification system for an anti-aging tape film is proposed for the present invention;

[0076] Figure 2 A calculation flow chart of the initial reaction rate in a surface modification system of an aging-resistant adhesive tape film is proposed for the present invention;

[0077] Figure 3 The present invention proposes a flow chart for obtaining a key parameter configuration table in a surface modification system for an aging-resistant adhesive tape film;

[0078] Figure 4 The present invention proposes a flow chart for predicting the effect of parameter changes on substrate treatment in a surface modification system of an aging-resistant adhesive tape film;

[0079] Figure 5 The present invention proposes a flow chart for obtaining the optimal configuration of operating parameters in a surface modification system for an aging-resistant adhesive tape film;

[0080] Figure 6 The present invention proposes a monitoring flow chart of temperature, pressure and chemical agent concentration in a surface modification system of an aging-resistant adhesive tape film;

[0081] Figure 7 The present invention proposes a flow chart for obtaining parameter adjustment records in a surface modification system for an aging-resistant adhesive tape film;

[0082] Figure 8 The present invention proposes a flow chart for obtaining performance difference analysis results in a surface modification system of an aging-resistant adhesive tape film. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0084] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0085] Example

[0086] See also Figure 1 The present invention provides a technical solution: a surface modification system for an aging-resistant adhesive tape film comprises:

[0087] The adhesive preparation module calculates the initial reaction rate and determines the initial sensitivity index of the adhesive based on the temperature, pressure and chemical concentration data collected by real-time sensors. It also analyzes the parameter sensitivity based on the index, identifies the key parameters that affect the tape film modification effect, and generates a key parameter configuration table.

[0088] The substrate processing module adjusts the temperature, pressure and chemical concentration based on the key parameter configuration table, uses polynomial regression to predict the impact of parameter changes on substrate processing, and conducts batch tests to adjust and determine the optimal operating parameters and generate an optimized configuration of operating parameters;

[0089] The coating uniformity monitoring module monitors the temperature, pressure and chemical concentration in the tape film production based on the optimized configuration of the operating parameters, records the key real-time data, compares it with the operating parameters, adjusts the production line parameters to maintain the coating uniformity, and generates parameter adjustment records;

[0090] The quality assessment module collects samples of finished tape films based on parameter adjustment records, conducts performance tests, including adhesion tests and aging resistance tests, and performs statistical analysis to compare expectations with performance differences and generate performance difference analysis results.

[0091] The key parameter configuration table includes parameter identification, sensitivity adjustment information, and control priority. The operating parameter optimization configuration includes temperature control adjustment range, pressure adjustment range, and chemical ratio information. The parameter adjustment record includes time point adjustment results and parameter adjustment results. The performance difference analysis results include adhesion change results, durability change results, and performance deviation index.

[0092] See also Figure 2 , the calculation steps of the initial reaction rate are as follows:

[0093] Based on the temperature, pressure and chemical concentration data collected by real-time sensors, use the formula:

[0094]

[0095] Generate the initial reaction rate value IR, where k represents the reaction rate constant, E a represents activation energy, R represents gas constant, T represents temperature, C represents chemical concentration, n represents concentration reaction order, γ represents pressure sensitivity coefficient, δ represents pressure regulation baseline, and P represents real-time pressure;

[0096] Based on the initial value of the reaction rate, combined with the pressure, the formula:

[0097]

[0098] Perform pressure adjustment to obtain an adjusted reaction rate AR, where α and β are pressure adjustment parameters, P is the real-time pressure, μ represents the weight for adjusting the reaction rate, and IR is the initial value of the reaction rate;

[0099] Based on the adjusted reaction rate, it is compared with the average reaction rate of historical data using the formula:

[0100]

[0101] Generate the initial sensitivity index SI, where AR represents the adjusted reaction rate, AHR represents the historical average reaction rate, ξ represents the sensitivity reduction coefficient, and ∈ represents the sensitivity-adjusted baseline.

[0102] Assume the following data:

[0103] k is assumed to be 0.1min -1 ;

[0104] E a Assume a value of 50 kl / mol;

[0105] The assumed value of R is 8.314 J / (mol·K);

[0106] The value of T is assumed to be 298K (room temperature);

[0107] C assumes a concentration of 1 mol / L;

[0108] n is assumed to be 1;

[0109] γ is assumed to be 0.05;

[0110] δ is assumed to be 1 atm;

[0111] P is assumed to be 2atm.

[0112] Calculate the exponential part:

[0113]

[0114] e -20.2 ≈1.83×10 -9

[0115] Calculate the pressure adjustment term:

[0116]

[0117] Calculate the total initial reaction rate IR:

[0118] IR=0.1·1.83×10 -9 1 1 1.06≈1.94×10 -10 min -1

[0119] The result represents the reaction rate per minute under the given reaction conditions.

[0120] Assumed parameters:

[0121] α is assumed to be 0.1;

[0122] β is assumed to be 1 atm;

[0123] μ is assumed to be 2.

[0124] Calculate the pressure adjustment:

[0125]

[0126] Calculate the adjusted reaction rate AR:

[0127] AR=1.94×10 -10 1.033≈2.00×10 -10 min -1

[0128] The results represent the reaction rate per minute after adjusting for pressure.

[0129] Assume data:

[0130] AHR is assumed to be 2.1×10 -10 min -1 ;

[0131] ξ is assumed to be 0.01;

[0132] ∈ is assumed to be 1×10 -10 .

[0133] Calculate the ratio:

[0134]

[0135] Calculate the desensitization term:

[0136]

[0137] Calculate the initial sensitivity index SI:

[0138] SI=0.952·0.995≈0.947

[0139] The results represent an index of sensitivity relative to the historical average reaction rate, with lower values ​​indicating that the reaction is less sensitive to environmental changes.

[0140] See also Figure 3 , the specific steps for obtaining the key parameter configuration table are:

[0141] Based on the indicators, the parameter sensitivity is analyzed using the formula:

[0142]

[0143] Generate sensitivity analysis results S x ,in, represents the partial derivative of SI with respect to parameter x, indicating the effect of a small change in x on SI, where x is the current parameter value, including temperature, pressure, and chemical concentration, SI is the initial sensitivity index, λ is the nonlinear adjustment factor, and η and θ are adjustment parameters;

[0144] According to the sensitivity coefficient, the key parameters that affect the film are identified, and each key parameter is analyzed to determine the degree of influence and adjustment requirements during the tape film modification process, and a key parameter configuration table is generated.

[0145] Assume the following data:

[0146] xTake temperature T as an example, assuming T = 300K;

[0147] λ is assumed to be 2;

[0148] Assume η = 0.1 and θ = 100;

[0149] Assume SI = 1.5;

[0150] Assume that the partial derivative with respect to temperature is 0.05 as obtained through sensitivity analysis.

[0151] Compute the first part of the sensitivity coefficient:

[0152]

[0153] Apply a nonlinear adjustment factor:

[0154] (10) λ =10 2 =100

[0155] Compute the logarithmic term:

[0156]

[0157] Integrate the calculation results:

[0158] S x =100 0.0003 = 0.03

[0159] The calculated S x =0.03, indicating that the influence of temperature on the sensitivity index SI is small, which means that the influence of temperature on the system within the current range is not very critical, and there is no need to give priority to temperature adjustment.

[0160] See also Figure 4 , the specific steps for predicting the effect of parameter changes on substrate processing are:

[0161] Based on the key parameter configuration table, temperature, pressure and chemical concentration are selected as independent variables, and a polynomial regression model is established in combination with historical data. The formula is:

[0162] Y=β0+β1T+β2P+β3C+β4T 2 +β5P 2 +β6C 2

[0163] Where Y is the substrate treatment effect, β0, β1, β2, β3, β4, β5 and β6 are model parameters, T is temperature, P is pressure, and C is chemical concentration;

[0164] Use the collected data to fit a polynomial regression model and calculate the model parameters using the least squares method:

[0165]

[0166] in, is the model regression coefficient obtained from data fitting, X is the design matrix including independent variables, Y is the response variable vector, X T Design the transpose of the matrix for X;

[0167] Using the model regression coefficients, the potential outcomes of substrate treatment under new operating conditions are predicted using the formula:

[0168]

[0169] Among them, Y pred is the substrate treatment effect predicted by the regression model, X is the design matrix including the independent variables, are the model regression coefficients.

[0170] Assuming that data has been collected, including temperature T, pressure P, chemical concentration C and the corresponding substrate treatment effect Y, use the following hypothetical data:

[0171] Temperature T: 280K, 300K, 320K;

[0172] Pressure P: 10atm, 12atm, 14atm;

[0173] Chemical concentration C: 0.1mol / L, 0.15mol / L, 0.2mol / L;

[0174] Substrate treatment effect Y: 1.5, 1.7, 1.9.

[0175] The design matrix X and response variable Y are set up as follows:

[0176]

[0177] Using formula

[0178] Calculate X T X, assuming X T X turns out to be a 7×7 matrix;

[0179] Calculate X T X ∧ {-1}), (X T X) -1 The inverse matrix is ​​assumed to be a 7×7 matrix, each element of which is based on the original X T X gets;

[0180] Calculate X T Y, assuming the result is a 7x1 vector;

[0181] Using the above inverse matrix and vector product we get

[0182] is a regression coefficient vector, each coefficient corresponds to a term in the model, and the coefficient describes the influence of the corresponding variable (such as temperature, pressure, chemical concentration and its square term) on the substrate treatment effect.

[0183] Use the obtained Make predictions about the effects of substrate treatment under new conditions:

[0184] Assume a new behavior of X [1, 310, 13, 0.18, 96100, 169, 0.0324], use Calculate and get Y pred ;

[0185] Final value Y predRepresents the predicted substrate treatment effect at 310K temperature, 13atm pressure and 0.18mol / L chemical concentration.

[0186] See also Figure 5 , the specific steps for obtaining the optimized configuration of operation parameters are:

[0187] Based on the impact on substrate treatment, a decision tree algorithm was used to identify key influencing factors using the formula:

[0188]

[0189] Among them, Gain(S i ,A) indicates the use of parameter A in the data set S i The information gain on i YesS i The weight factor is used to adjust the contribution importance of each subset. For the dataset S i The standard deviation of S is used to standardize the information gain. i It is a subset of the data set, representing data with different parameter combinations, and n is the number of subsets;

[0190] According to the identified key parameters, optimize the parameter configuration, using the formula:

[0191]

[0192] Generate optimized parameter settings, where C new and C old are the costs before and after adjustment, ΔC represents the cost change, T is the temperature parameter used to control the optimization process, κ is the adjustment factor used to adjust the optimization sensitivity, α z For the learning rate, adjust the step size;

[0193] Based on the optimized parameter settings, a configuration table is developed using the formula:

[0194]

[0195] Get the operation parameter optimization configuration Config final , among which, OP j is the optimized parameter, v j is the weight coefficient of each parameter, which is used to calculate the effect of the configuration, and m is the number of parameters after optimization.

[0196] Assume the following data:

[0197] Suppose there are three data subsets, and the weight w of each set is i They are 0.3, 0.5, and 0.2 respectively;

[0198] Information gain Gain(S i ,A) is 0.4, 0.6, 0.2;

[0199] Standard Deviation They are 0.1, 0.2, and 0.1 respectively.

[0200] Calculation process:

[0201]

[0202] Importance=0.3·4+0.5·3+0.2·2

[0203] Importance=1.2+1.5+0.4=3.1

[0204] The results show that the importance score of the comprehensive analysis of each subset data is 3.1, which can be used to evaluate the effectiveness of the overall parameter configuration.

[0205] Assume the following data:

[0206] Assumption C old =100, ΔC=-10, α z =0.1, T=1, κ=0.5.

[0207] Calculation process:

[0208]

[0209] C new =100-0.1(-10·e 2 )

[0210] By adjusting T and κ to obtain an operational C new value.

[0211] Assume the following data:

[0212] Assume that there are three optimized parameters, namely temperature T, pressure P and chemical concentration C. The optimized parameter values ​​are 310K, 15atm, 0.25mol / L, and the corresponding weight coefficients are v j They are 0.5, 0.3 and 0.2 respectively.

[0213] Calculation process:

[0214] Config final =(310·0.5)+(15·0.3)+(0.25·0.2)

[0215] Config final =155+4.5+0.05

[0216] Config final =159.55

[0217] Calculation result Config final =159.55 is a composite value, which is usually used to quantify the overall effect of the optimized parameter configuration. The composite value can be used to compare the effectiveness of different parameter configurations, or directly used as a comprehensive indicator to evaluate operational performance.

[0218] See also Figure 6 The specific steps for monitoring temperature, pressure and chemical concentration are as follows:

[0219] Based on the optimized configuration of operating parameters, sensors are installed on the production line to monitor temperature, pressure and chemical concentration in real time, and the data is preliminarily processed. The formula is:

[0220]

[0221] Generate standardized data X raw , where T, P and C represent temperature, pressure and chemical concentration respectively, k1, k2 and k3 are adjustment coefficients used for preliminary conversion and standardization of data, and C sat is the saturation concentration of the chemical agent, used to nonlinearize the concentration data;

[0222] The standardized data is analyzed to identify anomalies or deviations from operating parameters using the formula:

[0223]

[0224] The degree of deviation of the normal operating parameters ΔX is obtained to determine whether adjustment is needed, where X norm is the expected data under normal operating conditions, d is the adjustment factor used to adjust the sensitivity of the response, X raw is the standardized data, ΔX′ is X raw and X norm The difference.

[0225] Assume the following data:

[0226] T assumes real-time measurement is 350K;

[0227] P is assumed to be 120 atm in real time;

[0228] C assumes real-time measurement is 0.3 mol / L;

[0229] C sat Assume 1.0 mol / L;

[0230] Assume k1=0.01, k2=0.05, k3=1.

[0231] Calculation process:

[0232]

[0233] X raw =1225+0.5477+0.2308

[0234] X raw =1252.7775

[0235] Calculations show that the synthetic monitoring value adjusted by the conversion factor is 1252.7775, which is used to reflect the current status of the production environment.

[0236] Assume the following data:

[0237] X raw Obtained from the above;

[0238] X norm Assume 1250;

[0239] d is assumed to be 0.1.

[0240] Compute the direct difference:

[0241] ΔX′=1252.7775-1250=2.7775

[0242] Calculate the nonlinear adjustment value:

[0243]

[0244] Use the approximation e -2.7775 ≈0.062:

[0245]

[0246] ΔX=2.7775+0.1×0.944

[0247] ΔX=2.7775+0.0944

[0248] ΔX=2.8719

[0249] The calculated result ΔX indicates the deviation from normal operating parameters and takes into account nonlinear adjustments.

[0250] See also Figure 7 , the specific steps for obtaining parameter adjustment records are:

[0251] Based on key real-time data, parameter status assessment is performed using the formula:

[0252]

[0253] Generate parameter state index S, where X i Represents the real-time measurement value of key parameters, q i is the criticality coefficient of the corresponding parameter, adjusting its contribution to the overall evaluation index, σ i is the standard deviation of parameter measurements, used to normalize the data, and n represents the total number of parameters;

[0254] Based on the parameter status index, an adjustment plan is formulated using the formula:

[0255]

[0256] Get the adjusted parameter value P new , where P old is the original parameter value, δ is the adjustment strength coefficient, S max is the theoretical maximum value of the parameter state index, S is the parameter state index;

[0257] According to the adjusted parameter values, the adjustment activities are recorded in the following format:

[0258] Record = {P old , P new , DR}

[0259] Generate parameter adjustment record Record, where P old is the original parameter value, P new is the adjusted parameter value, and DR is the reason for adjustment.

[0260] Assume the following data:

[0261] X1=150, X2=75;

[0262] q1=0.5,q2=1.5;

[0263] σ1=20,σ2=10.

[0264] Calculation process:

[0265]

[0266] S=0.5·exp(-28.125)+1.5·exp(-28.125)

[0267] S≈1.348×10 -12

[0268] The value S represents the parameter status index after comprehensive consideration of importance and deviation, which is very small, indicating that the parameter deviates significantly from the normal range.

[0269] Assume the following data:

[0270] P old Assume it is 100;

[0271] δ is assumed to be 5;

[0272] S max Assume 1;

[0273] S is obtained from above.

[0274] Calculation process:

[0275]

[0276] P new =100+5·cos90.00000009545)

[0277] P new =100+5·0.999999999999

[0278] P new ≈105

[0279] New parameter value P new It indicates that the parameters have been adjusted very slightly, with almost no change, because S is very small, indicating that the current operation is very close to the preset standard.

[0280] See also Figure 8 , the specific steps for obtaining the performance difference analysis results are:

[0281] Based on the parameter adjustment records, samples of finished tape films are collected from the production line, using the formula:

[0282]

[0283] Get the required number of samples N, where μ is the number of samples in the standard batch, ∈ is used to adjust the proportion of additional collected samples, and γ is a fixed increment to ensure that the additional collected samples can cover unexpected losses;

[0284] The collected samples were subjected to viscosity test and aging resistance test, using the formula:

[0285]

[0286] Get the performance evaluation result P, where V i is the viscosity test value of the i-th sample, A i is the aging test value of the i-th sample, ω V and ω A are the weights of the two tests respectively, and N is the number of samples required;

[0287] Use statistical analysis to compare test results to expected performance and identify key performance differences using the formula:

[0288]

[0289] Get the performance difference analysis result D, where E i is the expected performance indicator, σ is the standard deviation of the performance data, λ is a constant used to adjust the error sensitivity, P is the performance evaluation result, and N is the required number of samples.

[0290] Assume the following data:

[0291] μ is assumed to be 100 samples;

[0292] ∈ is assumed to be 0.10 (10%);

[0293] γ is assumed to be 5.

[0294] Calculation process:

[0295]

[0296] N = 115

[0297] The value N represents the total number of samples after taking into account the additional collection ratio and the safety margin, ensuring that there are enough samples for effective performance testing.

[0298] Assume the following data:

[0299] Assume ω V =0.6,ω A =0.4;

[0300] Assume V i =80, A i =70.

[0301] Calculation process:

[0302]

[0303] P=76

[0304] The average performance index P is 76, which represents the weighted result of two different tests, adhesion and aging resistance.

[0305] Assume the following data:

[0306] E i Assume 75;

[0307] σ is assumed to be 5;

[0308] λ is assumed to be 1.

[0309] Calculation process:

[0310]

[0311] D=2.366

[0312] The performance variance analysis result D indicates the degree of difference between the actual test results and the expected performance, showing a slight deviation that requires further production adjustments or quality control measures.

[0313] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A surface modification system for an aging-resistant adhesive tape film, characterized in that: The system comprises: The adhesive preparation module calculates the initial reaction rate and determines the initial sensitivity index of the adhesive based on the temperature, pressure and chemical concentration data collected by real-time sensors. It also analyzes the parameter sensitivity based on the index, identifies the key parameters that affect the tape film modification effect, and generates a key parameter configuration table. The substrate processing module adjusts the temperature, pressure and chemical concentration based on the key parameter configuration table, uses polynomial regression to predict the effect of parameter changes on substrate processing, and conducts batch tests to adjust and determine the optimal operating parameters to generate an optimized configuration of operating parameters; The coating uniformity monitoring module monitors the temperature, pressure and chemical concentration in the tape film production based on the optimized configuration of the operating parameters, records key real-time data, compares it with the operating parameters, and adjusts the production line parameters to maintain the coating uniformity, and generates parameter adjustment records; The quality assessment module collects samples of finished tape films based on the parameter adjustment records, performs performance tests, including adhesion tests and aging resistance tests, and performs statistical analysis to compare expectations with performance differences and generate performance difference analysis results.

2. The surface modification system of the aging-resistant tape film according to claim 1, characterized in that: The calculation steps of the initial reaction rate are specifically as follows: Based on the temperature, pressure and chemical concentration data collected by real-time sensors, use the formula: Generate the initial reaction rate value IR, where k represents the reaction rate constant, E a represents activation energy, R represents gas constant, T represents temperature, C represents chemical concentration, n represents concentration reaction order, γ represents pressure sensitivity coefficient, δ represents pressure regulation baseline, and P represents real-time pressure; Based on the initial value of the reaction rate, combined with the pressure, the formula: Perform pressure adjustment to obtain an adjusted reaction rate AR, where α and β are pressure adjustment parameters, P is the real-time pressure, μ represents the weight for adjusting the reaction rate, and IR is the initial value of the reaction rate; Based on the adjusted reaction rate, it is compared with the average reaction rate of historical data, using the formula: Generate the initial sensitivity index SI, where AR represents the adjusted reaction rate, AHR represents the historical average reaction rate, ξ represents the sensitivity reduction coefficient, and ∈ represents the sensitivity-adjusted baseline.

3. The surface modification system of the anti-aging tape film according to claim 1, characterized in that: The steps for obtaining the key parameter configuration table are specifically as follows: Based on the indicators, the parameter sensitivity is analyzed using the formula: Generate sensitivity analysis results S x ,in, represents the partial derivative of SI with respect to parameter x, where x is the current parameter value, including temperature, pressure and chemical concentration, SI is the initial sensitivity index, λ is the nonlinear adjustment factor, and η and θ are adjustment parameters; According to the sensitivity coefficient, the key parameters that affect the film are identified, and each key parameter is analyzed to determine the degree of influence and adjustment requirements during the tape film modification process, and generate a key parameter configuration table.

4. The surface modification system of the anti-aging tape film according to claim 1, characterized in that: The steps for predicting the effect of the parameter changes on substrate processing are specifically as follows: Based on the key parameter configuration table, temperature, pressure and chemical concentration are selected as independent variables, and a polynomial regression model is established in combination with historical data. The formula is: Y=β0+β1T+β2P+β3C+β4T 2 +β5P 2 +β6C 2 Where Y is the substrate treatment effect, β0, β1, β2, β3, β4, β5 and β6 are model parameters, T is temperature, P is pressure, and C is chemical concentration; The polynomial regression model was fitted using the collected data, and the model parameters were calculated using the least squares method: in, is the model regression coefficient obtained from data fitting, X is the design matrix including independent variables, Y is the response variable vector, X T Design the transpose of the matrix for X; Using the model regression coefficients, the potential results of substrate treatment under new operating conditions are predicted using the formula: Among them, Y pred is the substrate treatment effect predicted by the regression model, X is the design matrix including the independent variables, are the model regression coefficients.

5. The surface modification system of the anti-aging tape film according to claim 1, characterized in that: The steps for obtaining the optimized configuration of the operating parameters are specifically as follows: Based on the described impact on substrate processing, a decision tree algorithm was used to identify key influencing factors using the formula: Among them, Gain(S i ,A) indicates the use of parameter A in the data set S i The information gain on i YesS i The weight factor of For the dataset S i The standard deviation, S i is a subset of the data set, and n is the number of subsets; According to the identified key parameters, optimize the parameter configuration, using the formula: Generate optimized parameter settings, where C new and C old are the costs before and after adjustment, ΔC represents the cost change, T is the temperature parameter used to control the optimization process, κ is the adjustment factor used to adjust the optimization sensitivity, α z For the learning rate, adjust the step size; Based on the optimized parameter settings, a configuration table is developed using the formula: Get the operation parameter optimization configuration Config final , among which, OP j is the optimized parameter, v j is the weight coefficient of each parameter, which is used to calculate the effect of the configuration, and m is the number of parameters after optimization.

6. The surface modification system of the anti-aging tape film according to claim 1, characterized in that: The monitoring steps of the temperature, pressure and chemical concentration are specifically as follows: Based on the optimized configuration of the operating parameters, sensors are installed on the production line to monitor the temperature, pressure and chemical concentration in real time, and the data is preliminarily processed. The formula is: Generate standardized data X raw , where T, P and C represent temperature, pressure and chemical concentration respectively, k1, k2 and k3 are adjustment coefficients used for preliminary conversion and standardization of data, and C sat is the saturation concentration of the chemical agent, used to nonlinearize the concentration data; The standardized data is analyzed to identify abnormalities or deviations from operating parameters using the formula: The degree of deviation of the normal operating parameters ΔX is obtained to determine whether adjustment is needed, where X norm is the expected data under normal operating conditions, d is the adjustment factor used to adjust the sensitivity of the response, X raw is the standardized data, ΔX′ is X raw and X norm The difference.

7. The surface modification system of the anti-aging tape film according to claim 1, characterized in that: The steps for obtaining the parameter adjustment record are specifically as follows: Based on the key real-time data, parameter status evaluation is performed using the formula: Generate parameter state index S, where X i Represents the real-time measurement value of key parameters, q i is the critical coefficient of the corresponding parameter, σ i is the standard deviation of parameter measurements, used to normalize the data, and n represents the total number of parameters; Based on the parameter status index, an adjustment plan is formulated using the formula: Get the adjusted parameter value P new , where P old is the original parameter value, δ is the adjustment strength coefficient, S max is the theoretical maximum value of the parameter state index, S is the parameter state index; According to the adjusted parameter value, the adjustment activity is recorded in the following format: Record={P old ,P new ,DR} Generate parameter adjustment record Record, where P old is the original parameter value, P new is the adjusted parameter value, and DR is the reason for adjustment.

8. The surface modification system of the anti-aging tape film according to claim 1, characterized in that: The steps for obtaining the performance difference analysis results are specifically as follows: Based on the parameter adjustment records, samples of finished tape films were collected from the production line, using the formula: Get the required number of samples N, where μ is the number of samples in the standard batch, ∈ is used to adjust the proportion of additional collected samples, and γ is a fixed increment; The collected samples were subjected to viscosity test and aging resistance test, using the formula: Get the performance evaluation result P, where V i is the viscosity test value of the i-th sample, A i is the aging test value of the i-th sample, ω V and ω A are the weights of the two tests respectively, and N is the number of samples required; Use statistical analysis to compare test results to expected performance and identify key performance differences using the formula: Get the performance difference analysis result D, where E i is the expected performance indicator, σ is the standard deviation of the performance data, λ is a constant used to adjust the error sensitivity, P is the performance evaluation result, and N is the required number of samples.

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