Online surface activating treatment method for biaxially oriented PET (Polyethylene Terephthalate) highlight film

Abstract

The invention discloses an online surface activation treatment method for a biaxially oriented PET (Polyethylene Terephthalate) highlight film, which relates to the technical field of plastic processing and comprises the following steps: summarizing a parameter guide set and a corresponding activation defect multi-dimensional fitting function to obtain an activation defect model; taking the characterization parameters of which the actual values exceed the corresponding normal ranges as target characterization parameters; obtaining a suspected activation defect type; forming a to-be-verified activation defect combination; decomposing the actual value of the target characterization parameter; calculating a difference coefficient of the activation defect combination to be verified; and treating the PET highlight film according to the abnormal degree of the actual activation defect type. According to the method, a parameter guiding set of activation defect types is obtained through construction, an activation defect multi-dimensional fitting function of the parameter guiding set is formed, a difference coefficient of a to-be-verified activation defect combination is calculated, and an activation defect condition which is most consistent with reality is selected from various conditions, so that the types of the activation defects and the abnormal degree of the activation defects are determined.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of plastic processing, in particular to an online surface activation treatment method for bidirectional stretching PET high-gloss film. BACKGROUND

[0002] The high-gloss film is a film composite product with glossiness exceeding 100, which is mainly applied to the fields of building materials and home decoration. The structure of the high-gloss film is composed of a PET film, a surface treatment agent, ink, an adhesive and a PVC film. The surface activation treatment of the PET high-gloss film mainly improves the adhesion and surface performance through a surface treatment process. Different treatment methods with different parameters exist according to the different surface conditions of the high-gloss film. The high-gloss film with a surface meeting the requirements does not need to be treated, and the high-gloss film with a surface not meeting the requirements needs to be treated according to the actual situation.

[0003] The surface activation condition of the high-gloss film is influenced by multiple parameters in parallel, and multiple abnormal degrees of different activation defects may exist in parallel, so it is difficult to determine the accurate condition of the activation defects, thereby leading to the difficulty in targeted treatment. SUMMARY

[0004] To solve the above technical problems, the application provides an online surface activation treatment method for bidirectional stretching PET high-gloss film, which solves the problems in the background art.

[0005] To achieve the above purposes, the application adopts the technical scheme of: An online surface activation treatment method for bidirectional stretching PET high-gloss film, comprising: obtaining at least one activation defect type, each activation defect type containing only one activation defect reason, and obtaining a treatment scheme with an abnormal degree of the activation defect type as a reference value in advance; obtaining at least one characteristic parameter of the activation condition of the PET high-gloss film, and obtaining a normal range of the characteristic parameter when the activation condition of the PET high-gloss film is normal; constructing a parameter-oriented set of the activation defect type, the parameter-oriented set being composed of the characteristic parameter, forming an activation defect multi-dimensional fitting function of the parameter-oriented set, and obtaining an activation defect model by summarizing the parameter-oriented set and the corresponding activation defect multi-dimensional fitting function; performing a patrol inspection on the actual activation condition of the surface of the PET high-gloss film to obtain actual values of the characteristic parameters; regarding the characteristic parameter with an actual value exceeding the corresponding normal range as a target characteristic parameter; obtaining at least one suspected activation defect type based on the target characteristic parameter and the parameter-oriented set; forming at least one to-be-verified activation defect combination based on the suspected activation defect type; Decompose the actual value of the target characterization parameter based on the suspected activation defect type in the activation defect combination to be verified; Calculate the gap coefficient of the activation defect combination to be verified based on the decomposition result of the actual value of the target characterization parameter; Take the activation defect type in the activation defect combination to be verified with the minimum gap coefficient as the actual activation defect type, and process the PET high light film according to the abnormality degree of the actual activation defect type.

[0006] Preferably, the parameter-oriented set of the activation defect type obtained by the construction comprises the following steps: Collect the characterization parameters that exceed the normal range of the characterization parameter when the activation defect type occurs each time into a first preliminary-oriented set respectively; Collect the characterization parameters that exceed the normal range of the characterization parameter when the activation defect type does not occur into a second preliminary-oriented set; Take the intersection of at least one first preliminary-oriented set to obtain a preliminary-oriented set; Take the difference set of the preliminary-oriented set to the second preliminary-oriented set to obtain a parameter-oriented set.

[0007] Preferably, the activation defect multi-dimensional fitting function for forming the parameter-oriented set comprises the following steps: Based on the historical detection data, obtain the value range of the characterization parameter in the parameter-oriented set that exceeds the normal range, equally space the value range of the characterization parameter in the parameter-oriented set to obtain at least one identification point, and the identification point corresponds to the characterization parameter; Randomly take the value of the corresponding identification point of the characterization parameter to form a first assignment scheme of the parameter-oriented set; Take the fixed value of the corresponding identification point of the characterization parameter to form a second assignment scheme of the parameter-oriented set; Take the superimposed value of the value of the characterization parameter in the first assignment scheme and the value of the characterization parameter in the second assignment scheme to form a third assignment scheme; Under the condition that the parameter-oriented set adopts the third assignment scheme, take the mean of all abnormality degrees of the activation defect type to obtain a first abnormality degree; Under the condition that the parameter-oriented set adopts the second assignment scheme, take the mean of all abnormality degrees of the activation defect type to obtain a second abnormality degree; Take the difference between the first abnormality degree and the second abnormality degree to obtain a third abnormality degree; Pair and fit the value of the characterization parameter in the first assignment scheme with the third abnormality degree to obtain an activation defect multi-dimensional fitting function, wherein the value of the characterization parameter in the first assignment scheme is the independent variable, and the third abnormality degree is the dependent variable.

[0008] Preferably, the step of obtaining at least one suspected activation defect type based on the target characterization parameter and the parameter-oriented set comprises the following steps: When all the characterization parameters in the parameter-oriented set are the target characterization parameter, the parameter-oriented set is taken as the target parameter-oriented set, otherwise, no processing is performed, and the activation defect type corresponding to the target parameter-oriented set is taken as the suspected activation defect type.

[0009] Preferably, the step of forming at least one to-be-verified activation defect combination based on the suspected activation defect type comprises the following steps: All combination possibilities of the at least one suspected activation defect type are obtained, respectively, as suspected activation defect type combinations; The parameter-oriented sets corresponding to the suspected activation defect types in the suspected activation defect type combination are taken as a union set to obtain a target-oriented set; When the target-oriented set exactly contains all the target characterization parameters, the suspected activation defect type combination is taken as the to-be-verified activation defect combination, otherwise, no processing is performed.

[0010] Preferably, the step of decomposing the actual value of the target characterization parameter based on the suspected activation defect types in the to-be-verified activation defect combination comprises the following steps: Part of the actual value of the target characterization parameter beyond the normal range is taken as a feature value; An abnormality degree range of the suspected activation defect type is obtained, the abnormality degree range of the suspected activation defect type is equally spaced to obtain at least one test point; The abnormality degree of the suspected activation defect type is randomly taken as the value of the test point, and each taking value mode is respectively taken as an abnormal value taking scheme of the to-be-verified activation defect combination; A proportion coefficient of the suspected activation defect type is formed, and a decomposition coefficient of the suspected activation defect type relative to the target characterization parameter is formed based on the proportion coefficient; The feature value, the abnormality degree of the suspected activation defect type in the abnormal value taking scheme, and the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter are multiplied to obtain an out-of-characterization value of the suspected activation defect type relative to the target characterization parameter, and the out-of-characterization value of the suspected activation defect type relative to the target characterization parameter corresponds to the abnormal value taking scheme.

[0011] Preferably, the step of forming the proportion coefficient of the suspected activation defect type comprises the following steps: An intersection of abnormality ranges of all suspected activation defect types is obtained to obtain a feature range, and at least one feature point is uniformly taken in the feature range; The value of the feature point is substituted into an activation defect multi-dimensional fitting function corresponding to the suspected activation defect type as a constraint condition of the parameter-oriented set corresponding to the suspected activation defect type; The remaining suspected activation defect types except the suspected activation defect type are recorded as characteristic suspected activation defect types; The constraint condition of the suspected activation defect type is combined with the constraint condition of the suspected activation defect type of each feature to obtain a simultaneous constraint condition of at least one suspected activation defect type; Under the simultaneous constraints of the suspected activation defect type, integrating the characterization parameters in the parameter-guided set corresponding to the suspected activation defect type to obtain a first integral value; Accumulating at least one first integral value of at least one feature point to obtain a second integral value; Accumulating the second integral values ​​of all suspected activation defect types to obtain a third integral value; The second integral value is compared with the third integral value to obtain a proportionality coefficient of the suspected activation defect type.

[0012] Preferably, forming the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter comprises the following steps: When the parameter-guided set of the suspected activation defect type includes the target characterization parameter, the suspected activation defect type is used as the target suspected activation defect type, the proportional coefficients of the target suspected activation defect type are accumulated to obtain a comprehensive coefficient, and the proportional coefficient of the target suspected activation defect type is divided by the comprehensive coefficient to obtain a decomposition coefficient of the suspected activation defect type relative to the target characterization parameter; When the parameter-guided set of the suspected activation defect type does not include the target characterization parameter, the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter is equal to 0.

[0013] Preferably, the calculation of the gap coefficient of the activation defect combination to be verified based on the decomposition result of the actual value of the target characterization parameter includes the following steps: Substituting the exceeded characterization value of the target characterization parameter of the suspected activation defect type in the abnormal value scheme into the corresponding activation defect multidimensional fitting function to obtain the predicted abnormality degree of the suspected activation defect type; Calculating the variance of the abnormality degree of the suspected activation defect type in the abnormality value scheme and the predicted abnormality degree of the suspected activation defect type to obtain the deviation coefficient of the abnormality value scheme for the activation defect combination to be verified; The minimum value of the deviation coefficient of the abnormal value scheme of the activation defect combination to be verified is used as the gap coefficient of the activation defect combination to be verified.

[0014] Preferably, the processing of the PET high-gloss film according to the abnormal degree of the actual activation defect type includes the following steps: The actual activation defect type of the target to-be-verified activation defect combination is taken as a target to-be-verified activation defect combination, and the abnormal value scheme of the target to-be-verified activation defect combination with the minimum bias coefficient is taken as a target abnormal value scheme; The value of the abnormal degree of the actual activation defect type in the target abnormal value scheme is taken as the abnormal degree of the actual activation defect type; The abnormal degree of the actual activation defect type is divided by a reference value to obtain a processing coefficient, and the processing coefficient is multiplied by parameters in a processing scheme corresponding to the actual activation defect type respectively and is summarized to obtain a processing correction scheme; The processing correction schemes of all actual activation defect types are summarized to obtain an actual processing scheme, and the PET high light film is processed according to the actual processing scheme.

[0015] Compared with the prior art, the beneficial effects of the present application are that: By constructing the parameter-oriented set of the activation defect type, forming the activation defect multi-dimensional fitting function of the parameter-oriented set, decomposing the actual value of the target characteristic parameter and calculating the gap coefficient of the to-be-verified activation defect combination, the actual value of the characteristic parameter is obtained, the suspected activation defect type is determined, and the situation of the activation defect is predicted based on this. Through the prediction and comparison of various situations, the activation defect situation most consistent with the actual situation is selected from various situations, so that the type of the activation defect is determined, and the degree of the activation defect type is also determined. Therefore, the detection result of the activation defect can be used for targeted activation treatment. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 It is a flowchart of the online surface activation treatment method of the bidirectional stretching PET high light film of the present application; Figure 2 It is a flowchart of the construction of the parameter-oriented set of the activation defect type of the present application; Figure 3 It is a flowchart of the formation of the activation defect multi-dimensional fitting function of the parameter-oriented set of the present application; Figure 4 It is a flowchart of forming at least one to-be-verified activation defect combination based on the suspected activation defect type of the present application; Figure 5 It is a flowchart of decomposing the actual value of the target characteristic parameter based on the suspected activation defect type in the to-be-verified activation defect combination of the present application; Figure 6 It is a flowchart of forming the proportion coefficient of the suspected activation defect type of the present application; Figure 7A flowchart for forming a suspected activation defect type of the present application relative to a target characterization parameter decomposition coefficient; Figure 8 A flowchart for calculating the gap coefficient of the to-be-verified activation defect combination based on the decomposition result of the actual value of the target characterization parameter of the present application; Figure 9 A flowchart for processing the PET high light film according to the abnormality degree of the actual activation defect type of the present application. DETAILED DESCRIPTION

[0017] The following description is used to disclose the present application so that those skilled in the art can implement the present application. The preferred embodiments in the following description are only as examples, and other obvious variations can be thought of by those skilled in the art.

[0018] REFERENCE Figure 1 As shown, a bidirectional stretching PET high light film online surface activation treatment method comprises: Obtain at least one activation defect type, each activation defect type only contains one activation defect reason, and obtain an abnormality degree of the activation defect type as a reference value of the treatment scheme; Obtain at least one characterization parameter of the activation condition of the PET high light film, and obtain a normal range of the characterization parameter when the activation condition of the PET high light film is normal; Construct a parameter-oriented set of the activation defect type, the parameter-oriented set is composed of the characterization parameter, form an activation defect multi-dimensional fitting function of the parameter-oriented set, and obtain an activation defect model by summarizing the parameter-oriented set and the corresponding activation defect multi-dimensional fitting function; Inspect the actual activation condition of the surface of the PET high light film to obtain the actual value of the characterization parameter; The characterization parameter whose actual value exceeds the corresponding normal range is taken as a target characterization parameter; Based on the target characterization parameter and the parameter-oriented set, at least one suspected activation defect type is obtained; Based on the suspected activation defect type, at least one to-be-verified activation defect combination is formed; Based on the suspected activation defect type in the to-be-verified activation defect combination, the actual value of the target characterization parameter is decomposed; Based on the decomposition result of the actual value of the target characterization parameter, the gap coefficient of the to-be-verified activation defect combination is calculated; The activation defect type in the to-be-verified activation defect combination with the minimum gap coefficient is taken as an actual activation defect type, and the PET high light film is processed according to the abnormality degree of the actual activation defect type.

[0019] In this solution, it is necessary to determine the type of activation defects and determine the abnormal degree of the type of activation defects in the case of multiple activation defects appearing in parallel and the abnormal degree of the activation defects being unknown. However, since both the type of activation defects and the abnormal degree are unknown, it is necessary to first generate possible combination cases and make predictions for each combination case, and then compare to obtain the combination case that is most consistent with the actual case; The surface activation treatment generally has corona treatment, coating method and flame treatment, in addition to some other treatment methods. When the surface activation requirements are different, the corresponding treatment parameter settings are also different. Therefore, the type of activation defects is set in advance, and the parameter range of each type of activation defects is small. Therefore, when the actual existing type of activation defects is identified, the predetermined treatment scheme is used as a reference for treatment, and the error caused thereby is acceptable.

[0020] The activation defect generally does not refer to the production defect of the film itself, but refers to the fact that, in the process of coating the film, the hot melt adhesive layer or the coating layer of the PET high gloss film fails to achieve an ideal molten bonding state due to improper process parameter setting. Since the PET high gloss film is coated on various substrates, the coating parameters need to be changed accordingly, otherwise, activation defects are likely to occur. The types of activation defects include poor adhesion, poor gloss, white spots, and bubbles or orange peel texture. The bubbles or orange peel texture can cause insufficient smoothness. The reasons for poor adhesion are as follows: 1. The heating temperature is too low: The heating temperature does not reach the melting point of the adhesive layer, and the adhesive layer cannot be fully melted and flowed. 2. The coating speed is too fast: The film passes under the hot roller for too short a time, and the heat cannot be transferred to the adhesive layer in time. 3. The pressure is insufficient: Even if the adhesive is melted, insufficient pressure cannot make it fully adhere to and penetrate the printed matter. The reasons for poor gloss are as follows: 1. The heating temperature is too low: The adhesive layer cannot be fully melted and form a smooth mirror surface. 2. The pressure is insufficient: which also causes insufficient flow leveling of the adhesive layer. 3. The quality of the film itself is poor: the adhesive layer or coating layer formula is problematic, and the activation window is narrow. The reasons for white spots are consistent with those for poor adhesion. The reasons for bubbles or orange peel texture are as follows: 1. The heating temperature is too high: which causes the adhesive layer to be over-melted and even decomposed to produce gas, or causes the water in the paper to evaporate rapidly. 2. Uneven heating: the surface temperature of the hot roller is inconsistent, and the activation of some areas is excessive. 3. The speed is too slow: which causes excessive activation due to the long time spent at high temperature. In order to identify all the activation defect types, it is necessary to determine the characterization parameters of the activation defect types, as follows: First, the characterization parameters used in PET high light film detection are determined, including daN value, gloss at an incident angle of 20°, haze, light transmittance, surface tension, surface hardness, thickness, elastic modulus, white proportion, and transparent profile proportion; In the data comparison of various activation defect types, the main characterization parameters of each activation defect type can be found, as follows: The characterization parameter of poor adhesion is daN value, which is detected using a daN pen and daN liquid set. Depending on the different uses of PET high light film, the lower limit of daN value is also different. Generally, the lower limit of daN value is set between 40-42 mN / m. For some special applications (such as car stickers), the lower limit of daN value is set to 44 mN / m or higher. The characterization parameter of poor gloss is gloss at an incident angle of 20°. The lower limit of gloss is 85 GU, and the gloss is detected using a gloss meter. The characterization parameter of white spots is white proportion, which is the area proportion of white pixel points identified by image recognition. The proportion has a lower limit, which can be set by empirical data. The characterization parameter of bubbles or orange peel texture is transparent profile proportion, which is the transparent profile existing in the high light film identified by image recognition. The bubble or orange peel texture at the high light film will produce a different color from other locations, resulting in a boundary line, thereby forming a profile. However, the profile here is different from the white spot. The white spot is opaque, while this place is transparent, so it can form the characterization parameter of bubbles or orange peel texture. The proportion has a lower limit, which can be set by empirical data. Therefore, they are respectively used as the parameter guidance set of each activation defect type. When adjusting the parameters of the activation treatment, the amplitude of the parameter change should be controlled as much as possible. If the parameter is increased too much, the energy consumption will increase. For example, the white spot defect is caused by too low heating temperature, too fast film coating speed, and insufficient pressure. The current temperature, film coating speed, and pressure are recorded. The temperature, film coating speed, and pressure are increased to the extent that there are no white spots. The increased temperature, film coating speed, and pressure are recorded. The average temperature, film coating speed, and pressure are obtained by averaging the current temperature, film coating speed, and pressure and the increased temperature, film coating speed, and pressure. If there are no white spots under the average temperature, film coating speed, and pressure, the average temperature, film coating speed, and pressure are updated. The updated values are the average of the average temperature, film coating speed, and pressure and the current temperature, film coating speed, and pressure. If there are white spots under the average temperature, film coating speed, and pressure, the values before updating the average temperature, film coating speed, and pressure are used as the target temperature, film coating speed, and pressure, respectively. The target temperature, film coating speed, and pressure are used for processing. Similar processing is performed for other defects such as poor gloss, white spots, and bubbles or orange peel texture. However, in actual situations, multiple defect types may exist simultaneously. Therefore, the parameters that need to be adjusted may overlap. For example, the white spot and the poor adhesion both have temperature, film coating speed, and pressure as the adjustment parameters. The larger value of the processing temperature for the white spot and the processing temperature for the poor adhesion is used as the temperature for processing. Similar processing is performed for the film coating speed and the pressure. Then the processing can be completed. For more simultaneous activation defect types, similar processing is also performed. That is, when the processing parameters of different activation defect types overlap, the largest value of the processing parameters is used as the final parameter. This can meet the processing requirements and control the amplitude of the parameters.

[0021] Referring to Figure 2 The parameter-oriented set of activation defect types is constructed by the following steps: The characteristic parameters that exceed the normal range of the characteristic parameters when the activation defect type occurs each time are collected into a first preliminary-oriented set. The characteristic parameters that exceed the normal range of the characteristic parameters when the activation defect type does not occur are collected into a second preliminary-oriented set. The intersection of at least one first preliminary-oriented set is obtained to obtain a preliminary-oriented set. The difference set of the preliminary-oriented set and the second preliminary-oriented set is obtained to obtain a parameter-oriented set.

[0022] The parameter-oriented set of the activation defect type mainly collects the characteristic parameters that are abnormal when the activation defect type is abnormal, so that the suspected activation defect type that may be abnormal can be determined by comparing the target characteristic parameters, and the range of subsequent identification can be narrowed. However, since the abnormal characteristic parameters identified each time during the identification of the activation defect type may be caused by not only a single activation defect type but also multiple activation defect types, it is difficult to determine the characteristic parameters corresponding to the activation defect type. Here, the two cases of the presence and absence of the activation defect type are compared to exclude the influence of other activation defect types, so as to determine the characteristic parameters corresponding to the activation defect type, and thus the parameter-oriented set of the activation defect type is generated.

[0023] Referring to Figure 3 As shown in the figure, the multi-dimensional fitting function of the activation defect forming the parameter-oriented set includes the following steps: Based on the historical detection data, the value range of the characteristic parameter in the parameter-oriented set that exceeds the normal range is obtained, the value range of the characteristic parameter in the parameter-oriented set is equally spaced, at least one identification point is obtained, and the identification point corresponds to the characteristic parameter; The characteristic parameter takes the value of the corresponding identification point at random to form the first assignment scheme of the parameter-oriented set; The characteristic parameter takes the value of the corresponding fixed identification point to form the second assignment scheme of the parameter-oriented set; The characteristic parameter takes the superimposed value of the value of the characteristic parameter in the first assignment scheme and the value of the characteristic parameter in the second assignment scheme to form the third assignment scheme; Under the condition that the parameter-oriented set adopts the third assignment scheme, the mean value of all abnormal degrees of the activation defect type is taken to obtain the first abnormal degree; Under the condition that the parameter-oriented set adopts the second assignment scheme, the mean value of all abnormal degrees of the activation defect type is taken to obtain the second abnormal degree; The first abnormal degree is subtracted from the second abnormal degree to obtain the third abnormal degree; The value of the characteristic parameter in the first assignment scheme is paired with the third abnormal degree and fitted to obtain the multi-dimensional fitting function of the activation defect, wherein the value of the characteristic parameter in the first assignment scheme is the independent variable, and the third abnormal degree is the dependent variable.

[0024] Here, since the characterization parameter is the comprehensive result of the conditions of multiple activation defects, and the stripping of the influence of other activation defects is extremely difficult, because the abnormal degree of the remaining activation defects must be accurately determined, and the influence value in the characterization parameter is determined according to the abnormal degree, and then the characterization parameter is corrected to obtain a value affected only by a single activation defect type, therefore, the third assignment scheme and the second assignment scheme are set, the difference between the values of the corresponding characterization parameters in the third assignment scheme and the second assignment scheme is exactly equal to the value of the corresponding characterization parameter in the first assignment scheme, so the third abnormal degree obtained by subtracting the second abnormal degree from the first abnormal degree corresponds to the value of the characterization parameter in the first assignment scheme. Under the condition that the parameter-oriented set adopts the third assignment scheme, the abnormal degrees of all activation defect types are obtained, and in multiple acquisitions, the comprehensive of the abnormal conditions of the remaining activation defect types can be regarded as fixed. Under the condition that the parameter-oriented set adopts the second assignment scheme, the abnormal degrees of all activation defect types are obtained, and in multiple acquisitions, the comprehensive of the abnormal conditions of the remaining activation defect types can also be regarded as fixed. The two fixed conditions are basically consistent, so the influence of the abnormal conditions of the remaining activation defect types on the parameter-oriented set is consistent. Therefore, the difference between the values of the corresponding characterization parameters in the third assignment scheme and the second assignment scheme can eliminate the influence of the abnormal conditions of the remaining activation defect types on the parameter-oriented set. Thus, the value of the characterization parameter in the first assignment scheme is only affected by the current single activation defect type, and therefore the activation defect multidimensional fitting function can be used to characterize the abnormal degree of the activation defect type corresponding to the parameter-oriented set.

[0025] Based on the target characterization parameter and the parameter-oriented set, at least one suspected activation defect type is obtained, including the following steps: When the characterization parameters in the parameter-oriented set are all target characterization parameters, the parameter-oriented set is taken as the target parameter-oriented set, otherwise, no processing is performed, and the activation defect type corresponding to the target parameter-oriented set is taken as the suspected activation defect type.

[0026] Since the target characterization parameter is the parameter that is abnormal due to the presence of the activation defect type, but the abnormality of the target characterization parameter may be caused by another activation defect type, not by this activation defect type, so it can only be taken as a suspected activation defect type, which needs to be further determined in the subsequent process.

[0027] Referring to Figure 4 As shown in the figure, based on the suspected activation defect type, at least one to-be-verified activation defect combination is formed, including the following steps: All combinations of at least one suspected activation defect type are obtained, respectively as suspected activation defect type combinations; Taking the union of the parameter-oriented sets corresponding to the suspected activation defect types in the suspected activation defect type combination, a target-oriented set is obtained; When the target-oriented set contains all the target characteristic parameters, the suspected activation defect type combination is taken as the activation defect combination to be verified, otherwise, no processing is performed.

[0028] For a suspected activation defect type, the actual activation defect type that occurs can be one of all possible combinations thereof. In order to identify each case, the activation defect combination to be verified needs to be formed in advance, but the target-oriented set must contain all the target characteristic parameters, otherwise, the target-oriented set is inconsistent with all the target characteristic parameters, which is the case. The first is that there are characteristic parameters different from all the target characteristic parameters in the target-oriented set, which are referred to as primary characteristic parameters, which are inconsistent with the actual inspection situation because all the target characteristic parameters contain all the abnormal characteristic parameters, but the primary characteristic parameters are also abnormal parameters but are not included. The second is that there are parameters different from the elements in the target-oriented set among all the target characteristic parameters, which are referred to as primary target characteristic parameters. However, the primary target characteristic parameters are caused by a certain activation defect type in the suspected activation defect type combination once they occur. When the target-oriented set of the suspected activation defect type combination does not contain the primary target characteristic parameters, it means that the activation defect type does not cause the primary target characteristic parameters, and thus does not meet the requirements.

[0029] Referring to Figure 5 As shown in the figure, based on the suspected activation defect types in the activation defect combination to be verified, the actual values of the target characteristic parameters are decomposed, including the following steps: The part of the actual value of the target characteristic parameter that exceeds the normal range is taken as a characteristic value; The abnormality range of the suspected activation defect type is obtained, the abnormality range of the suspected activation defect type is equally spaced, and at least one test point is obtained; The abnormality degree of the suspected activation defect type is randomly taken as the value of the test point, and each taking value mode is respectively taken as an abnormal value scheme of the activation defect combination to be verified; A proportion coefficient of the suspected activation defect type is formed, and based on the proportion coefficient, a decomposition coefficient of the suspected activation defect type relative to the target characteristic parameter is formed; The characteristic value, the abnormality degree of the suspected activation defect type in the abnormal value scheme, and the decomposition coefficient of the suspected activation defect type relative to the target characteristic parameter are multiplied to obtain the exceeding characteristic value of the target characteristic parameter of the suspected activation defect type, and the exceeding characteristic value of the target characteristic parameter corresponds to the abnormal value scheme.

[0030] In the decomposition, the decomposition needs to be carried out according to the abnormal degree of the suspected activation defect type. In the decomposition, firstly, the proportion coefficient of the suspected activation defect type is determined, which characterizes the influence degree of the suspected activation defect type, and in addition, the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter is formed, because the target characterization parameter is not affected by all suspected activation defect types, so only the relevant suspected activation defect type is determined. The decomposition coefficient is determined, thereby the exceeding characterization value of the target characterization parameter can be determined. It should be noted here that since the abnormal degree of the suspected activation defect type is unknown, in order to identify, all abnormal value schemes need to be formed in advance, and in each abnormal value scheme, the abnormal degree of the suspected activation defect type is determined, and then the required combination is selected according to the deduction.

[0031] Referring to Figure 6 As shown, the proportion coefficient of the suspected activation defect type includes the following steps: Take the intersection of the abnormal range of all suspected activation defect types to obtain a feature range, and uniformly take at least one feature point in the feature range; Substitute the value of the feature point into the activation defect multi-dimensional fitting function corresponding to the suspected activation defect type as a constraint condition of the parameter-oriented set corresponding to the suspected activation defect type; The remaining suspected activation defect types except the suspected activation defect type are recorded as characteristic suspected activation defect types; The constraint condition of the suspected activation defect type is combined with the constraint condition of the characteristic suspected activation defect type to obtain the combined constraint condition of at least one suspected activation defect type; Integrate the characterization parameter in the parameter-oriented set corresponding to the suspected activation defect type under the combined constraint condition of the suspected activation defect type to obtain a first integral value; Accumulate at least one first integral value of at least one feature point to obtain a second integral value; Accumulate the second integral values of all suspected activation defect types to obtain a third integral value; The second integral value is compared with the third integral value to obtain the proportion coefficient of the suspected activation defect type.

[0032] Here, the calculation depends on the combination of the constraint conditions of the suspected activation defect type and the remaining suspected activation defect types. In essence, it is an equation of two multivariate unknowns, which limits the value of the unknowns, and then the integral value under the combined constraint condition can be obtained by limiting. Through the synthesis of multiple integral values, the relative relationship between the suspected activation defect type and the remaining suspected activation defect types is more reliable.

[0033] Referring to Figure 7As shown, the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter comprises the following steps: When the parameter-oriented set of the suspected activation defect type contains the target characterization parameter, then the suspected activation defect type is taken as the target suspected activation defect type, the proportional coefficient of the target suspected activation defect type is accumulated to obtain a comprehensive coefficient, and the proportional coefficient of the target suspected activation defect type divided by the comprehensive coefficient obtains the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter; When the parameter-oriented set of the suspected activation defect type does not contain the target characterization parameter, then the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter is equal to 0.

[0034] Referring to Figure 8 As shown, based on the decomposition result of the actual value of the target characterization parameter, the gap coefficient of the to-be-verified activation defect combination is calculated, comprising the following steps: The exceeding characterization value of the target characterization parameter of the suspected activation defect type in the abnormal value scheme is substituted into the corresponding activation defect multi-dimensional fitting function to obtain the predicted abnormal degree of the suspected activation defect type; The variance of the value of the abnormal degree of the suspected activation defect type in the abnormal value scheme and the predicted abnormal degree of the suspected activation defect type is calculated to obtain the deviation coefficient of the abnormal value scheme of the to-be-verified activation defect combination; The minimum value of the deviation coefficient of the abnormal value scheme of the to-be-verified activation defect combination is taken as the gap coefficient of the to-be-verified activation defect combination.

[0035] The basis for screening is that, if the abnormal value scheme is set in the to-be-verified activation defect combination, then the abnormality in the to-be-verified activation defect combination is assumed to be determined, but according to the actual value of the target characterization parameter, the abnormal degree can be predicted again according to the abnormal degree, and the predicted abnormal degree must be as small as possible from the setting of the abnormal value scheme in the to-be-verified activation defect combination. If it is very large, it means that the assumption is problematic, and the situation that is problematic is extremely unlikely in the actual situation, so the situation with the smallest gap between the assumption and the predicted situation is screened as the actual situation, and thus the result obtained has high reliability.

[0036] Referring to Figure 9 As shown, according to the abnormal degree of the actual activation defect type, the PET high light film is processed, comprising the following steps: The to-be-verified activation defect combination generating the actual activation defect type is taken as the target to-be-verified activation defect combination, and the abnormal value scheme of the target to-be-verified activation defect combination with the minimum deviation coefficient is taken as the target abnormal value scheme; The value of the abnormal degree of the actual activation defect type in the target abnormal value scheme is taken as the abnormal degree of the actual activation defect type; The abnormal degree of the actual activation defect type is divided by the reference value to obtain a processing coefficient, the processing coefficient is multiplied by the parameters in the processing scheme corresponding to the actual activation defect type respectively and is summarized to obtain a processing correction scheme; The processing correction schemes of all actual activation defect types are summarized to obtain an actual processing scheme, and the PET high light film is processed according to the actual processing scheme.

[0037] Since the parameter in the processing scheme is the scheme when the abnormal degree of the activation defect type is equal to the reference value, the parameter needs to be corrected to obtain the processing correction scheme, and the actual activation defect type can be processed using the processing correction scheme.

[0038] Further, the scheme further proposes a storage medium having a computer readable program stored thereon, and the computer readable program is called to execute the above-mentioned online surface activation processing method of the bidirectional stretching PET high light film.

[0039] It can be understood that the storage medium can be a magnetic medium, for example, a floppy disk, a hard disk, a magnetic tape, an optical medium, for example, a DVD, or a semiconductor medium, for example, a solid state disk (SSD) and the like.

[0040] In summary, the advantages of the present application are that: by constructing the parameter-oriented set of the activation defect type, forming the activation defect multi-dimensional fitting function of the parameter-oriented set, decomposing and calculating the gap coefficient of the to-be-verified activation defect combination for the actual value of the target characterization parameter, determining the suspected activation defect type according to the actual value of the actual acquired characterization parameter, and predicting the activation defect condition based thereon, and by comparing and predicting various conditions, the activation defect condition most consistent with the actual condition is selected from various conditions, so that the type of the activation defect is determined, and the degree of the activation defect type is also determined, thereby the detection result of the activation defect can be used for targeted activation processing.

[0041] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection claimed by the present application is defined by the appended claims and their equivalents.

Claims

1. A method for online surface activation treatment of a biaxially oriented PET high-gloss film, characterized in that: include: Obtain at least one activation defect type, each activation defect type including only one activation defect cause, and pre-obtain a treatment plan using the abnormality degree of the activation defect type as a baseline value; Obtaining at least one parameter characterizing activation of the PET high-gloss film, and obtaining a normal range of the parameter characterizing normal activation of the PET high-gloss film; Constructing a parameter-guided set of activation defect types, the parameter-guided set is composed of characterization parameters, forming a multidimensional fitting function of activation defects of the parameter-guided set, and summarizing the parameter-guided set and its corresponding multidimensional fitting function of activation defects to obtain an activation defect model; Conduct inspections on the actual activation status of the PET high-gloss film surface to obtain the actual values ​​of the characterization parameters; The characterization parameter whose actual value exceeds the corresponding normal range is used as the target characterization parameter; At least one suspected activation defect type is obtained based on the target characterization parameter and the parameter-guided set; Based on the suspected activation defect type, forming at least one activation defect combination to be verified; Decomposing the actual values ​​of the target characterization parameters based on the suspected activation defect types in the activation defect combination to be verified; Based on the decomposition results of the actual values ​​of the target characterization parameters, the gap coefficient of the activation defect combination to be verified is calculated; The activation defect type in the activation defect combination to be verified with the smallest gap coefficient is taken as the actual activation defect type, and the PET high-gloss film is processed according to the abnormality degree of the actual activation defect type.

2. The online surface activation treatment method for a biaxially oriented PET high-gloss film according to claim 1, characterized in that: The construction of a parameter-guided set of activation defect types comprises the following steps: Each time an activation defect type occurs, the characterization parameters that exceed the normal range of the characterization parameters are summarized as a first guided preparation set; When the activation defect type does not occur, the characterization parameters that are beyond the normal range of the characterization parameters are summarized as a second guided preparation set; Taking an intersection of at least one first guide preliminary set to obtain a preliminary guide set; The initial guide set is subtracted from the second guide preparation set to obtain a parameter guide set.

3. The online surface activation treatment method for a biaxially oriented PET high-gloss film according to claim 2, characterized in that: The activation defect multidimensional fitting function of the parameter-guided set is formed, comprising the following steps: Based on historical detection data, obtaining a value range of a characterization parameter in a parameter-guided set that exceeds a normal range, dividing the value range of the characterization parameter in the parameter-guided set into equal intervals to obtain at least one identification point, where the identification point corresponds to the characterization parameter; The characterization parameter randomly takes the value of the corresponding identification point to form a first assignment scheme of the parameter-oriented set; The characterization parameters all take fixed values ​​of the corresponding identification points, forming a second assignment scheme of the parameter-oriented set; The characterization parameter takes the superposition value of the value of the characterization parameter in the first assignment scheme and the value of the characterization parameter in the second assignment scheme to form a third assignment scheme; Under the condition that the parameter-guided set adopts the third assignment scheme, the first abnormality degree is obtained by taking the average of all abnormality degrees of the activation defect type; Under the condition that the parameter-guided set adopts the second assignment scheme, the average of all abnormality degrees of the activation defect type is taken to obtain the second abnormality degree; The third abnormality degree is obtained by subtracting the first abnormality degree from the second abnormality degree; The value of the characterization parameter in the first assignment scheme is paired and fitted with the third abnormality degree to obtain a multidimensional fitting function of activation defects, wherein the value of the characterization parameter in the first assignment scheme is the independent variable and the third abnormality degree is the dependent variable.

4. The online surface activation treatment method for a biaxially oriented PET high-gloss film according to claim 3, characterized in that: The obtaining of at least one suspected activation defect type based on the target characterization parameter and the parameter guide set comprises the following steps: When the characterization parameters in the parameter-oriented set are all target characterization parameters, the parameter-oriented set is used as the target parameter-oriented set; otherwise, no processing is performed, and the activation defect type corresponding to the target parameter-oriented set is used as the suspected activation defect type.

5. The online surface activation treatment method for a biaxially oriented PET high-gloss film according to claim 4, characterized in that: The forming of at least one activation defect combination to be verified based on the suspected activation defect type comprises the following steps: Obtain all possible combinations of at least one suspected activation defect type as suspected activation defect type combinations; Taking the union of the parameter guidance sets corresponding to the suspected activation defect types in the suspected activation defect type combination to obtain a guidance target set; When the guided target set happens to contain all target characterization parameters, the suspected activation defect type combination is taken as the activation defect combination to be verified; otherwise, no processing is performed.

6. The online surface activation treatment method for a biaxially oriented PET high-gloss film according to claim 5, characterized in that: Decomposing the actual value of the target characterization parameter based on the suspected activation defect type in the activation defect combination to be verified includes the following steps: The part of the actual value of the target characterization parameter that exceeds the normal range is taken as the characteristic value; Obtaining an abnormality degree range of a suspected activation defect type, dividing the abnormality degree range of the suspected activation defect type into equal intervals to obtain at least one test point; The abnormality degree of the suspected activation defect type randomly takes the value of the test point, and each value selection method is used as the abnormal value selection scheme for the activation defect combination to be verified; forming a proportionality coefficient of the suspected activation defect type, and forming a decomposition coefficient of the suspected activation defect type relative to the target characterization parameter based on the proportionality coefficient; The abnormal degree of the suspected activation defect type in the characteristic value and abnormal value scheme is multiplied by the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter to obtain the excess characterization value of the target characterization parameter of the suspected activation defect type. The excess characterization value of the target characterization parameter corresponds to the abnormal value scheme.

7. The online surface activation treatment method for a biaxially oriented PET high-gloss film according to claim 6, characterized in that: The forming of the proportionality coefficient of the suspected activation defect type comprises the following steps: Take the intersection of all abnormal ranges of suspected activation defect types to obtain a characteristic range, and evenly select at least one characteristic point in the characteristic range; Substituting the value of the characteristic point into the activation defect multidimensional fitting function corresponding to the suspected activation defect type as the constraint condition of the parameter-guided set corresponding to the suspected activation defect type; The remaining suspected activation defect types except the suspected activation defect type are recorded as characteristic suspected activation defect types; The constraint condition of the suspected activation defect type is combined with the constraint condition of the suspected activation defect type of each feature to obtain a simultaneous constraint condition of at least one suspected activation defect type; Under the simultaneous constraints of the suspected activation defect type, integrating the characterization parameters in the parameter-guided set corresponding to the suspected activation defect type to obtain a first integral value; Accumulating at least one first integral value of at least one feature point to obtain a second integral value; Accumulating the second integral values ​​of all suspected activation defect types to obtain a third integral value; The second integral value is compared with the third integral value to obtain a proportionality coefficient of the suspected activation defect type.

8. The online surface activation treatment method for a biaxially oriented PET high-gloss film according to claim 7, characterized in that: The forming of the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter comprises the following steps: When the parameter-guided set of the suspected activation defect type includes the target characterization parameter, the suspected activation defect type is used as the target suspected activation defect type, the proportional coefficients of the target suspected activation defect type are accumulated to obtain a comprehensive coefficient, and the proportional coefficient of the target suspected activation defect type is divided by the comprehensive coefficient to obtain a decomposition coefficient of the suspected activation defect type relative to the target characterization parameter; When the parameter-guided set of the suspected activation defect type does not include the target characterization parameter, the decomposition coefficient of the suspected activation defect type relative to the target characterization parameter is equal to 0.

9. The online surface activation treatment method for a biaxially oriented PET high-gloss film according to claim 8, characterized in that: The calculation of the gap coefficient of the activation defect combination to be verified based on the decomposition result of the actual value of the target characterization parameter includes the following steps: Substituting the exceeded characterization value of the target characterization parameter of the suspected activation defect type in the abnormal value scheme into the corresponding activation defect multidimensional fitting function to obtain the predicted abnormality degree of the suspected activation defect type; Calculating the variance of the abnormality degree of the suspected activation defect type in the abnormality value scheme and the predicted abnormality degree of the suspected activation defect type to obtain the deviation coefficient of the abnormality value scheme for the activation defect combination to be verified; The minimum value of the deviation coefficient of the abnormal value scheme of the activation defect combination to be verified is used as the gap coefficient of the activation defect combination to be verified.

10. The online surface activation treatment method for a biaxially oriented PET high-gloss film according to claim 9, characterized in that: The processing of the PET high-gloss film according to the abnormal degree of the actual activation defect type includes the following steps: The activation defect combination to be verified that produces the actual activation defect type is used as the target activation defect combination to be verified, and the abnormal value scheme of the target activation defect combination to be verified with the smallest deviation coefficient is used as the target abnormal value scheme; The value of the abnormality degree of the actual activation defect type in the target abnormality value scheme is used as the abnormality degree of the actual activation defect type; The abnormality degree of the actual activation defect type is divided by the reference value to obtain the treatment coefficient, and the treatment coefficient is multiplied by the parameters in the treatment plan corresponding to the actual activation defect type and summarized to obtain the treatment correction plan; The treatment and correction plans for all actual activation defect types are summarized to obtain the actual treatment plan, and the PET high-gloss film is treated according to the actual treatment plan.

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