CN114605687B - Preparation method of anti-aging polyester film material - Google Patents

Preparation method of anti-aging polyester film material Download PDF

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Publication number
CN114605687B
CN114605687B CN202210518225.0A CN202210518225A CN114605687B CN 114605687 B CN114605687 B CN 114605687B CN 202210518225 A CN202210518225 A CN 202210518225A CN 114605687 B CN114605687 B CN 114605687B
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aging
polyester film
color
polyester
film
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CN114605687A (en
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李沅鸿
闫银凤
吴齐
樊鹏鹏
赵越超
李海良
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Henan Yinjinda New Materials Co ltd
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Henan Yinjinda New Materials Co ltd
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    • C—CHEMISTRY; METALLURGY
    • C08—ORGANIC MACROMOLECULAR COMPOUNDS; THEIR PREPARATION OR CHEMICAL WORKING-UP; COMPOSITIONS BASED THEREON
    • C08J—WORKING-UP; GENERAL PROCESSES OF COMPOUNDING; AFTER-TREATMENT NOT COVERED BY SUBCLASSES C08B, C08C, C08F, C08G or C08H
    • C08J5/00—Manufacture of articles or shaped materials containing macromolecular substances
    • C08J5/18—Manufacture of films or sheets
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29B—PREPARATION OR PRETREATMENT OF THE MATERIAL TO BE SHAPED; MAKING GRANULES OR PREFORMS; RECOVERY OF PLASTICS OR OTHER CONSTITUENTS OF WASTE MATERIAL CONTAINING PLASTICS
    • B29B9/00—Making granules
    • B29B9/02—Making granules by dividing preformed material
    • B29B9/06—Making granules by dividing preformed material in the form of filamentary material, e.g. combined with extrusion
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
    • B29C48/00—Extrusion moulding, i.e. expressing the moulding material through a die or nozzle which imparts the desired form; Apparatus therefor
    • B29C48/03—Extrusion moulding, i.e. expressing the moulding material through a die or nozzle which imparts the desired form; Apparatus therefor characterised by the shape of the extruded material at extrusion
    • B29C48/09—Articles with cross-sections having partially or fully enclosed cavities, e.g. pipes or channels
    • B29C48/10—Articles with cross-sections having partially or fully enclosed cavities, e.g. pipes or channels flexible, e.g. blown foils
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
    • B29C48/00—Extrusion moulding, i.e. expressing the moulding material through a die or nozzle which imparts the desired form; Apparatus therefor
    • B29C48/25—Component parts, details or accessories; Auxiliary operations
    • B29C48/92—Measuring, controlling or regulating
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
    • B29C55/00—Shaping by stretching, e.g. drawing through a die; Apparatus therefor
    • B29C55/28—Shaping by stretching, e.g. drawing through a die; Apparatus therefor of blown tubular films, e.g. by inflation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N17/00—Investigating resistance of materials to the weather, to corrosion, or to light
    • G01N17/004—Investigating resistance of materials to the weather, to corrosion, or to light to light
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N25/00—Investigating or analyzing materials by the use of thermal means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
    • B29C2948/00—Indexing scheme relating to extrusion moulding
    • B29C2948/92—Measuring, controlling or regulating
    • B29C2948/92323—Location or phase of measurement
    • B29C2948/92447—Moulded article
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
    • B29C2948/00—Indexing scheme relating to extrusion moulding
    • B29C2948/92—Measuring, controlling or regulating
    • B29C2948/92504—Controlled parameter
    • B29C2948/92704—Temperature
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    • C08—ORGANIC MACROMOLECULAR COMPOUNDS; THEIR PREPARATION OR CHEMICAL WORKING-UP; COMPOSITIONS BASED THEREON
    • C08J—WORKING-UP; GENERAL PROCESSES OF COMPOUNDING; AFTER-TREATMENT NOT COVERED BY SUBCLASSES C08B, C08C, C08F, C08G or C08H
    • C08J2323/00—Characterised by the use of homopolymers or copolymers of unsaturated aliphatic hydrocarbons having only one carbon-to-carbon double bond; Derivatives of such polymers
    • C08J2323/02—Characterised by the use of homopolymers or copolymers of unsaturated aliphatic hydrocarbons having only one carbon-to-carbon double bond; Derivatives of such polymers not modified by chemical after treatment
    • C08J2323/04—Homopolymers or copolymers of ethene
    • C08J2323/06—Polyethene
    • C—CHEMISTRY; METALLURGY
    • C08—ORGANIC MACROMOLECULAR COMPOUNDS; THEIR PREPARATION OR CHEMICAL WORKING-UP; COMPOSITIONS BASED THEREON
    • C08J—WORKING-UP; GENERAL PROCESSES OF COMPOUNDING; AFTER-TREATMENT NOT COVERED BY SUBCLASSES C08B, C08C, C08F, C08G or C08H
    • C08J2327/00—Characterised by the use of homopolymers or copolymers of compounds having one or more unsaturated aliphatic radicals, each having only one carbon-to-carbon double bond, and at least one being terminated by a halogen; Derivatives of such polymers
    • C08J2327/02—Characterised by the use of homopolymers or copolymers of compounds having one or more unsaturated aliphatic radicals, each having only one carbon-to-carbon double bond, and at least one being terminated by a halogen; Derivatives of such polymers not modified by chemical after-treatment
    • C08J2327/04—Characterised by the use of homopolymers or copolymers of compounds having one or more unsaturated aliphatic radicals, each having only one carbon-to-carbon double bond, and at least one being terminated by a halogen; Derivatives of such polymers not modified by chemical after-treatment containing chlorine atoms
    • C08J2327/06—Homopolymers or copolymers of vinyl chloride
    • C—CHEMISTRY; METALLURGY
    • C08—ORGANIC MACROMOLECULAR COMPOUNDS; THEIR PREPARATION OR CHEMICAL WORKING-UP; COMPOSITIONS BASED THEREON
    • C08J—WORKING-UP; GENERAL PROCESSES OF COMPOUNDING; AFTER-TREATMENT NOT COVERED BY SUBCLASSES C08B, C08C, C08F, C08G or C08H
    • C08J2367/00—Characterised by the use of polyesters obtained by reactions forming a carboxylic ester link in the main chain; Derivatives of such polymers
    • C—CHEMISTRY; METALLURGY
    • C08—ORGANIC MACROMOLECULAR COMPOUNDS; THEIR PREPARATION OR CHEMICAL WORKING-UP; COMPOSITIONS BASED THEREON
    • C08K—Use of inorganic or non-macromolecular organic substances as compounding ingredients
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    • C08K3/18—Oxygen-containing compounds, e.g. metal carbonyls
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    • C08K3/22—Oxides; Hydroxides of metals
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    • C08K5/00—Use of organic ingredients
    • C08K5/04—Oxygen-containing compounds
    • C08K5/13—Phenols; Phenolates
    • C08K5/132—Phenols containing keto groups, e.g. benzophenones
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    • C08—ORGANIC MACROMOLECULAR COMPOUNDS; THEIR PREPARATION OR CHEMICAL WORKING-UP; COMPOSITIONS BASED THEREON
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    • C08K5/34—Heterocyclic compounds having nitrogen in the ring
    • C08K5/3412—Heterocyclic compounds having nitrogen in the ring having one nitrogen atom in the ring
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    • C08K5/00—Use of organic ingredients
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    • C08K5/3475—Five-membered rings condensed with carbocyclic rings
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Abstract

The preparation method of the anti-aging polyester film material comprises the steps of preparing 0.1-0.2% of W944, 0.1-0.2% of UV329, 0.1-0.2% of UV531, 0.4-0.5% of zinc stearate, 0.4-0.5% of zeolite and 0.05% of TiO 2 And a polyester; extruding and blowing the master batch to further stretch to obtain an anti-aging polyester film; the polyester film is placed in an aging environment with an ultraviolet light source, an infrared light source and a heat source for aging, and the polyester film is sent into a neural network model after an image of the polyester film is collected to judge the film aging resistance and the consistency of the aging resistance; and optimizing the components, the proportion and the preparation process of the anti-aging polyester material master batch according to the judgment result. Thereby, the anti-aging polyester material can be optimizedThe preparation process of the polyester material can obtain the polyester material with better light stability and thermal stability.

Description

Preparation method of anti-aging polyester film material
Technical Field
The invention belongs to the field of polymer film materials, and particularly relates to a preparation method of an anti-aging film material.
Background
The polyester film can be influenced by temperature and illumination to cause aging in actual use, and the principle is that the illumination and the temperature cause the change in the polyester material. After the film is aged, the optical property and the mechanical property of the film are greatly changed, for example, the color is yellowed, the film is embrittled or cracked, and the like, so that the film is inconvenient to use.
Therefore, how to improve the ageing resistance of the polyester film is a problem to be solved in the development process of the polyester material. In the prior art, the light stability and the heat stability of the polyester film are improved by adding a light stabilizer and a heat stabilizer. However, the existing design ideas are considered independently for light stability and thermal stability, that is, to achieve a certain light stability, a certain amount of light stabilizer with a plurality of components is required to be added; to achieve a certain thermal stability, a certain amount of a heat stabilizer comprising several components is added, but the relationship between the two that affect each other is less considered. However, the effects of light and heat on the properties of the film material components, as well as on the properties of the various stabilizers, are not negligible. Therefore, it is an urgent need to select suitable light stabilizers and heat stabilizers to achieve better aging resistance by combining them.
In addition, at present, the optimization of the polyester preparation process generally needs to consider the anti-aging performance of the polyester material, but no one has proposed to detect the consistency of the anti-aging performance, and although materials with good anti-aging performance are obtained in some preparation processes, the anti-aging performance of materials prepared in different batches is not consistent (although the materials meet the requirements). This can cause inconvenience in some applications. For example, different batches of products may have different aging degrees when used in the same place, which may cause inconvenience in use.
In the test of the ageing resistance of the polyester material, the accurate detection of the ageing resistance is necessary for optimizing the component proportion. According to the results of the analysis and aging detection, scientific researchers can improve the types and the proportions of various additives of the polyester material, so that a better aging resistant effect is achieved. Complex equipment is required to achieve a certain degree of accuracy, which prevents the online use of the detection process. This is because the aging test of polyester materials is usually carried out on particles, which is difficult to test. On the other hand, the current anti-aging detection needs a plurality of technologies such as optics, mechanics, thermodynamics and the like. The aging performance detection is also provided in the prior art in an image acquisition mode, but the algorithm is complex, the precision is not high, and the practicability cannot be realized.
Disclosure of Invention
To solve one or more of the above problems, and the problems and effects mentioned in the embodiments, the following solutions are proposed:
a method for preparing an anti-aging polyester film material,
(I) preparation step of anti-aging polyester film material master batch
Step 1: fully mixing the following components to obtain a mixture A;
the weight ratio of each component is as follows:
GW944： 0.1%-0.2%
UV329： 0.1-0.2%
UV531：0.1-0.2%
zinc stearate: 0.4 to 0.5 percent
Zeolite: 0.4 to 0.5 percent
TiO 2 ：0.05%
Polyester: the rest(s)
Step 2: cooling the mixture A and stirring at a high speed;
step 3, extruding and granulating the mixture A obtained in the step 2 in an extruder to obtain polymer master batches;
(II) optimization of anti-aging polyester film material master batch
In order to detect the anti-aging parameters of the master batch material to optimize the preparation process of the master batch, the method further comprises the following steps:
extruding and blowing the master batch obtained in the step, and further stretching to obtain an anti-aging polyester film;
the polyester film is placed in an aging environment with an ultraviolet light source, an infrared light source and a heat source for aging, and the polyester film is sent into a neural network model after an image of the polyester film is collected to judge the film aging resistance and the consistency of the aging resistance;
and optimizing the components, the proportion and the preparation process of the anti-aging polyester material master batch according to the judgment result.
The mixing operation in the step 1 is realized by stirring for 10-30min at the temperature of 90-120 ℃.
The completion temperature in step 2 is 60-80 ℃.
The stirring speed in the step 2 is 300-600 rpm.
The temperature of the extruder in step 3 is controlled as follows: the temperature of the rear section of the machine barrel is controlled to be 150-160 ℃, the temperature of the front section is controlled to be 160-170 ℃, and the temperature of the machine head is controlled to be 170-175 ℃.
The film blowing temperature is controlled at 160-180 ℃.
The polyester is PVC, PE or LDPE.
And when the judgment is carried out, the input image is an image before color change, the identification result is output as the color before color change according to the neural network model, the next step is carried out, and otherwise, the detection is stopped or the whole detection result is invalid.
The input image is a color-changed image, and the color-changed image is output according to the neural network model, so that the polyester material is judged to be qualified in anti-aging performance and good in product consistency; if the identification result is an abnormal color, the abnormal condition needs to be further judged.
The above determining the abnormal condition includes: sending a plurality of collected images collected before the images after color change are collected into a neural network model, and if a plurality of identification results are output and comprise the color after color change, judging that the anti-aging performance of the polyester material is unqualified; otherwise, judging that the ageing resistance of the polyester material is qualified, but the product consistency is not good.
The invention has the advantages that:
1. provides a preparation method of a polyester material, which combines the consideration of the influence of the photo-aging effect on a heat stabilizer and the influence of the thermal aging effect on a light stabilizer, thereby optimizing the components and the proportion, organically combining various additives and simultaneously realizing excellent photo-aging resistance and thermal aging resistance.
2. The invention provides a method for optimizing the components, the proportion and the process of a polyester material by detecting the ageing resistance of the polyester material. In order to avoid the detection difficulty, the polyester material master batch is prepared into a film, so that the detection is convenient. In addition, the consistency of the aging resistance and the aging resistance is particularly required to be detected, so that the product quality and the application range of the material master batch are improved.
3. The invention adopts a mode of combining machine vision and a neural network model, improves the detection accuracy through the feature extraction optimization of data preprocessing and the optimization of a network model classifier structure, and particularly can simultaneously detect the performance and consistency of products.
Detailed Description
Preparation method of (I) anti-aging polyester material
Step 1: stirring the following components for 10-30min at 90-120 ℃ to realize the full mixing of the components to obtain a mixture A.
The weight ratio of each component is as follows:
GW944： 0.1%-0.2%
UV329： 0.1-0.2%
UV531：0.1-0.2%
zinc stearate: 0.4 to 0.5 percent
Zeolite: 0.4 to 0.5 percent
TiO 2 ：0.05%
Polyester: the rest(s)
In addition, no more than 0.1% of additional additives may also be added according to actual needs.
Step 2: and cooling the mixture A, and stirring at a high speed to complete the temperature of 60-80 ℃. The preferred stirring speed is 300-600 rpm.
And 3, extruding and granulating the mixture A obtained in the step in an extruder. Wherein the temperature of the extruder is controlled as follows: the temperature of the rear section of the machine barrel is controlled to be 150-160 ℃, the temperature of the front section is controlled to be 160-170 ℃, and the temperature of the machine head is controlled to be 170-175 ℃, so that the polymer master batch is obtained.
Wherein, TiO 2 The particles can prevent ultraviolet light from entering the material by refracting and reflecting the ultraviolet light, but excessive TiO 2 The particles affect the thermal stabilization effect, so the proportion needs to be optimized. UV329, UV531, which have relatively similar molecular weights and complementary absorption wavelengths, are preferred to better cooperate with each other to assist GW944 in resisting ultraviolet light degradation of the polyester film. Meanwhile, in actual use, in addition to the polyester film being deteriorated by ultraviolet rays, the polyester is also deteriorated by heat caused by light and other factors. Moreover, excessive heat may deteriorate the performance of the light stabilizer and the light absorber, which may adversely affect the ultraviolet ray resistance of the polyester composite film. GW944, UV329, UV531 and TiO are selected for the purpose 2 And the components can obtain higher thermal stability through proportioning, and the failure caused by heat is avoided. At the same time, the selection of zinc stearate and zeolite further reduces the effect of thermal effects on the above ingredients and the polyester film. Therefore, the components and the proportion are one contribution of the invention.
The polyester may be PVC, PE, LDPE, and the like.
Thus obtaining the anti-aging polyester material. However, in order to accurately detect whether the prepared material meets the anti-aging requirement, so as to further adjust the components, the proportion and the preparation process of the material, the material master batch needs to be formed into a film, so that the anti-aging detection process can be facilitated. Therefore, a film formation step can also be added.
And 4, step 4: and (3) extruding and blowing the master batch obtained in the step, controlling the film blowing temperature at 160-180 ℃, and further stretching to obtain the anti-aging polyester film.
Sample 1 preparation method
Step 1: stirring the following components for 10-30min at 90 ℃ to realize full mixing of the components to obtain a mixture A.
The weight ratio of each component is as follows:
GW944： 0.2%
UV329： 0.2%
UV531：0.2%
zinc stearate: 0.4 percent
Zeolite: 0.4 percent
TiO 2 ：0.05%
Polyester: the rest(s)
Step 2: and cooling the mixture A, and stirring at a high speed to complete the reaction at 70 ℃. The stirring speed was 450 rpm.
And 3, extruding and granulating the mixture A obtained in the step in an extruder. Wherein the temperature of the extruder is controlled as follows: the temperature of the rear section of the machine barrel is controlled to be 150-160 ℃, the temperature of the front section is controlled to be 160-170 ℃, and the temperature of the machine head is controlled to be 170-175 ℃, so that the polymer master batch is obtained.
And 4, step 4: and (3) extruding and blowing the master batch obtained in the step, controlling the film blowing temperature at 170 ℃, and further stretching to obtain the anti-aging polyester film.
Sample 2 preparation method
Step 1: stirring the following components for 30min at 110 ℃ to realize full mixing of the components to obtain a mixture A.
The weight ratio of each component is as follows:
GW944： 0.1%
UV329： 0.15%
UV531：0.2%
zinc stearate: 0.5 percent
Zeolite: 0.5 percent
TiO 2 ：0.05%
Polyester: the rest(s)
Step 2: and cooling the mixture A, and stirring at a high speed to finish the temperature of 80 ℃. The stirring speed was 500 rpm.
And 3, extruding and granulating the mixture A obtained in the step in an extruder. Wherein the temperature of the extruder is controlled as follows: the temperature of the rear section of the machine barrel is controlled to be 150-160 ℃, the temperature of the front section is controlled to be 160-170 ℃, and the temperature of the machine head is controlled to be 170-175 ℃, so that the polymer master batch is obtained.
And 4, step 4: and (3) extruding and blowing the master batch obtained in the step, controlling the film blowing temperature at 180 ℃, and further stretching to obtain the anti-aging polyester film.
Sample 3 preparation method
Step 1: stirring the following components for 30min at 110 ℃ to realize full mixing of the components to obtain a mixture A.
The weight ratio of each component is as follows:
GW944： 0.1%
UV329： 0.1%
UV531：0.1%
zinc stearate: 0.45 percent
Zeolite: 0.45 percent
TiO 2 ：0.0.05%
Polyester: the rest(s)
Step 2: and cooling the mixture A, and stirring at a high speed to finish the temperature of 80 ℃. The stirring speed was 500 rpm.
And 3, extruding and granulating the mixture A obtained in the step in an extruder. Wherein the temperature of the extruder is controlled as follows: the temperature of the rear section of the machine barrel is controlled to be 150-160 ℃, the temperature of the front section is controlled to be 160-170 ℃, and the temperature of the machine head is controlled to be 170-175 ℃, so that the polymer master batch is obtained.
And 4, step 4: and (3) extruding and blowing the master batch obtained in the step, controlling the film blowing temperature at 180 ℃, and further stretching to obtain the anti-aging polyester film.
The sample is tested to obtain
Figure DEST_PATH_IMAGE001
(II) detection of anti-aging film material
After the anti-aging film is prepared, anti-aging inspection can be performed on the prepared film to verify the anti-aging performance of the polyester material. This is because the anti-aging performance of the granular material is not easily detected, and therefore, it is necessary to perform detection after processing the granular material into a film. This operation can be performed at the experimental stage to adjust the process parameters, such as the composition ratio of the masterbatch, the temperature in the preparation process, etc. It will be appreciated that it may also be performed during product line inspection to determine product quality. The present invention is not limited to this. The detection method is not limited to be applied to the PVC, PE and LDPE materials prepared by the method, and can also be used for detecting other anti-aging polyester materials. However, it is understood that the following inspection method is more suitable for the polyester film produced by the above method, and the effect is more excellent. Although the present invention is intended to test the aging resistance of polyester materials, the test object is in the form of a film, and therefore, the test for polyester films is hereinafter referred to as "test".
The detection device comprises a light source, a heat source, a camera and a processor.
Wherein the light source is used to irradiate the anti-aging film to cause optical instability of the anti-aging film, thereby causing discoloration.
The heat source is used for heating the anti-aging film, so that the anti-aging film is caused to be thermally unstable, and the color is changed.
The camera is used for shooting an image before color change of the anti-aging film and an image after color change after a period of time.
The processor is used for controlling the on-off of the light source and the camera and is also used for recording a time stamp when the camera takes a picture. The processor is also configured to process the images captured by the camera as follows. It will be appreciated that the processor may be a site processor disposed in the field or may be a remote server.
The method comprises the following steps: setting a detection environment
And starting the camera to acquire the image of the film in the natural environment. And continuously acquiring film images in the process of gradual aging of the film under the action of a light source and a heat source.
And starting a light source to irradiate the film. Preferably, the light source is a composite light source of an ultraviolet light source and an infrared light source. This is because not only light aging can occur by light irradiation, but thermal instability can also occur by thermal action. And this thermal instability can adversely affect the effectiveness of the photoaging component in the polyester film, which can accelerate photoaging. Therefore, the heating effect generated by simulating illumination by the infrared light source is specially designed.
And starting a heat source to heat the environment of the film, so as to simulate the temperature aging environment. Preferably, the heat source can adopt a hot air device, the flow of heat in a natural environment can be simulated, and the defect of local too fast temperature rise caused by simple heat baking is avoided.
And sending the image collected by the camera to a processor for processing. The processor may be a site processor or a remote server.
Step two: collecting images and detecting anti-aging performance
The invention adopts a mode of combining machine vision and a neural network model to detect the aging resistance, and a specific algorithm is as follows.
Step 1: and carrying out aging operations such as illumination, temperature rise and the like on the anti-aging film, and carrying out relevant image acquisition.
First, an image of a film which is in a natural state and is not subjected to an aging operation is captured by a camera.
And secondly, starting a light source and a heat source to carry out aging operation, and continuously acquiring the image of the film.
And thirdly, setting a timer by using the processor, and timing from the start of the camera, wherein the timing time is a preset value T.
Step 2: and color separation and color feature extraction of the collected images (images before and after color change).
And (3) separating the color features of the image collected in the step (1), so that the intelligent algorithm can detect the change of the color more easily, and further extracting the color features.
The image collected by the camera in the step 1 is a standard three-channel digital color image, and three channels of the image respectively represent three primary color channels of red, green and blue. Suppose that
Figure 756771DEST_PATH_IMAGE002
Representing an image
Figure DEST_PATH_IMAGE003
The hue of the image is calculated as follows:
Figure DEST_PATH_IMAGE005
in the formula (I), the compound is shown in the specification,
Figure 549278DEST_PATH_IMAGE006
representing an inverse cosine function, mod represents a modulo operation,
Figure DEST_PATH_IMAGE007
representing the hue of the image. It will be appreciated that the above description has been made
Figure 532277DEST_PATH_IMAGE002
For each pixel
Figure 927487DEST_PATH_IMAGE002
The value, and therefore the above calculation, results in a tone matrix for the image, i.e. the tone value for each pixel of the image. Let the two-dimensional space size of the image be
Figure 210700DEST_PATH_IMAGE008
，
Figure DEST_PATH_IMAGE009
The number of columns and rows of the image, respectively
Figure 236425DEST_PATH_IMAGE007
Is one
Figure 503459DEST_PATH_IMAGE008
A matrix of sizes. Will matrix
Figure 171200DEST_PATH_IMAGE007
Decomposing according to the following steps:
Figure DEST_PATH_IMAGE011
Figure 43341DEST_PATH_IMAGE012
the columns of the matrix h. Definition of
Figure DEST_PATH_IMAGE013
A square matrix U:
Figure DEST_PATH_IMAGE015
Figure 239967DEST_PATH_IMAGE016
and p and q are row and column coordinates of the elements in the matrix. And:
Figure 463138DEST_PATH_IMAGE018
defining a matrix:
Figure 200150DEST_PATH_IMAGE020
f is the product of matrix U and matrix h, which is one
Figure 926798DEST_PATH_IMAGE008
A matrix of sizes. Take the first row of the matrix F as one
Figure 356642DEST_PATH_IMAGE022
Vector of dimensions, called color feature vector of image X
Figure DEST_PATH_IMAGE023
. The meaning of matrix F is the frequency domain response of matrix U, by converting the image matrix into the frequency domain, separating the low frequency part of the image from the high frequency part response, and extracting the phase of the image with the film areaThe frequency component with the highest relevance, namely the first component, can remove the noise influence, and simultaneously reduce the data dimension and reduce the calculation amount.
By mapping image X to color feature vectors
Figure 67109DEST_PATH_IMAGE023
Data volume of single picture color feature
Figure 342233DEST_PATH_IMAGE024
Vitamin is reduced to
Figure 183107DEST_PATH_IMAGE022
Dimension, greatly reduced the data volume of color characteristic, help to improve the computational efficiency.
And step 3: image analysis and detection step for aging condition of anti-aging film
And establishing an intelligent analysis model, identifying and classifying the characteristics of the anti-aging film, and outputting a classification result as a detection result.
The anti-aging film is characterized in that various properties of the film are changed after the film is illuminated and heated, and the color change is the most obvious characteristic in visual expression. The degree of aging of the film can be recognized as a color change. The aging resistance of the film is judged by detecting the color change degree of the film aged for a preset time under the conditions of preset illumination and temperature.
The color features to be identified or detected are divided into three dimensions, which respectively represent three categories of (T =0 moment) before color change, (T = T moment) after color change and abnormal color tones.
And (3) according to the definition, establishing a model to map the color feature vector in the step (2) to the three-dimensional vector. Having an input sample space of
Figure 518273DEST_PATH_IMAGE022
And (3) a color feature vector space is maintained, the category space is a color feature space, and the number of categories is 3. In general, high-dimensional vector spaces are linearly indivisible, and therefore, it is necessary to build a non-linear classifier.
Order:
Figure 716036DEST_PATH_IMAGE026
defining:
Figure 794851DEST_PATH_IMAGE028
the upper type
Figure DEST_PATH_IMAGE029
Is about
Figure 964932DEST_PATH_IMAGE023
Is a linear function of (a) is,
Figure 736579DEST_PATH_IMAGE030
in order to be a linear weight, the weight of the weight,
Figure DEST_PATH_IMAGE031
in order to be a linear offset,
Figure 890480DEST_PATH_IMAGE032
representing the coordinates in the input vector space,
Figure DEST_PATH_IMAGE033
representing by coordinates
Figure DEST_PATH_IMAGE035
As the coordinate offset of the center, j is the classifier channel coordinate,
Figure 445089DEST_PATH_IMAGE036
indicates that there are 8 classifiers
Figure DEST_PATH_IMAGE037
Each classifier
Figure 469677DEST_PATH_IMAGE038
Is the same as the output dimension, so
Figure DEST_PATH_IMAGE039
Presentation classifier
Figure 615487DEST_PATH_IMAGE038
Is output for the dimension corresponding to the ith dimension of its input.
Each of the 8 classifiers is used for capturing a feature distribution related to a certain class of features in the color feature vector. The 8 classifiers are designed, so that the classification result is more accurate on the basis of ensuring certain redundancy.
Since the color feature vector space is linearly inseparable, an accurate classification result cannot be obtained only by using the linear classifier. Therefore, the nonlinear classification function is further designed:
Figure DEST_PATH_IMAGE041
function(s)
Figure 522263DEST_PATH_IMAGE042
Mapping an input linear space to a target non-linear space, a linear classifier
Figure DEST_PATH_IMAGE043
Nonlinear classifier
Figure 942880DEST_PATH_IMAGE042
The combination of the color feature vector space and the color feature vector space can process the condition that the color feature vector space is not separable, and the robustness of the classifier is improved. Parameter(s)
Figure DEST_PATH_IMAGE045
And the nonlinear function is not continuous, so that the robustness of the model to noise is further improved.
Figure 618712DEST_PATH_IMAGE045
The values can be obtained through a large number of experiments
Figure 201003DEST_PATH_IMAGE046
。
The tone matrix of the image comprises two parts of important information related to the film, wherein firstly, the color distribution of the image presents different distribution characteristics before and after color change; the second is the spatial structure information of the image, i.e. the relative position relationship of different colors in the image, such as the difference of the colors in the center and the periphery of the film. The first type of information is modeled by equation (7), and further, the second type of information is further modeled.
Defining:
Figure 126234DEST_PATH_IMAGE048
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE049
represents the output of equation (7)
Figure 553804DEST_PATH_IMAGE043
Maximum of every third adjacent element in the sequence to reduce noise effects; defining:
Figure DEST_PATH_IMAGE051
in the formula (I), the compound is shown in the specification,
Figure 818564DEST_PATH_IMAGE052
and
Figure 571756DEST_PATH_IMAGE049
is a vector of equal dimensions and is,
Figure DEST_PATH_IMAGE053
to represent
Figure 984283DEST_PATH_IMAGE049
The ith element of (1)
Figure 215544DEST_PATH_IMAGE052
To (1) a
Figure DEST_PATH_IMAGE055
Linear weight relationships between individual elements. The model defined by (10) is used to model the relative positional relationship of the colors.
Defining:
Figure DEST_PATH_IMAGE057
in the formula (I), the compound is shown in the specification,
Figure 865968DEST_PATH_IMAGE058
for the output of the previous step, the subscript l denotes
Figure 258903DEST_PATH_IMAGE052
J represents the ordinal number of the classifier corresponding to equation (7),
Figure DEST_PATH_IMAGE059
、
Figure 627568DEST_PATH_IMAGE060
、
Figure DEST_PATH_IMAGE061
in order to be a linear weight parameter,
Figure 193678DEST_PATH_IMAGE062
、
Figure DEST_PATH_IMAGE063
、
Figure 698609DEST_PATH_IMAGE064
is a linear bias parameter.
Figure DEST_PATH_IMAGE065
、
Figure 262446DEST_PATH_IMAGE066
、
Figure DEST_PATH_IMAGE067
Is the corresponding output value.
The model defined by the formula (11) summarizes the results of the plurality of classifier sequences, thereby establishing the relationship between the classifier sequences, and maps the relationship to a three-dimensional vector space so as to correspond to the color features to be detected.
Further, in order to make the output value of the color feature correspond to the range of [0, 1] defined above, thereby facilitating the direct application of the detection result, a value range remapping method is adopted, defining:
Figure DEST_PATH_IMAGE069
after the above formula remapping, the output in formula (11)
Figure 118406DEST_PATH_IMAGE070
Is mapped to
Figure DEST_PATH_IMAGE071
And is made of
Figure 691470DEST_PATH_IMAGE071
The value range is [0, 1]]。
Figure 50907DEST_PATH_IMAGE072
Representing a natural exponential function.
When in use
Figure DEST_PATH_IMAGE073
When the color is changed, the state of the image corresponding to the current input color feature vector is represented as a color before color change; when in use
Figure 68802DEST_PATH_IMAGE074
When the color is changed, the state of the image corresponding to the current input color feature vector is represented as a color after the color is changed; when in use
Figure DEST_PATH_IMAGE075
Representing images corresponding to the current input colour feature vectorThe state is an abnormal tone.
Figure 146480DEST_PATH_IMAGE076
Express get
Figure DEST_PATH_IMAGE077
Maximum value of (2). Therefore, the detection of the color change characteristic of the anti-aging film based on the color characteristics in the image is realized.
Before the detection is carried out by adopting the models described in the above equations (6) to (12), the parameters of the models are determined through a learning process and are respectively listed in the equations (6) to (12), namely the linear bias parameters and the linear weight parameters. Preparing a plurality of images corresponding to three types of images before color change, after color change and abnormal color tones as learning samples; for the learning sample image, the output characteristics corresponding to the sample can be determined according to the following rules, if the sample corresponds to the color before color change, the values of the output characteristics are [1, 0, 0], if the sample corresponds to the color after color change, the values of the output characteristics are [0, 1, 0], and if the sample corresponds to the abnormal color, the values of the output characteristics are [0, 0, 1 ]. Extracting color feature vectors by the method in the step 2, substituting the color feature vectors into the models (6) - (12) in the step 3, and solving the parameters by adopting the conventional algorithm (such as BP algorithm).
And 4, step 4: determination of anti-aging performance of film
And (3) when the input image is an image at the time of t =0, namely the image before color change, outputting the identification result as the color before color change according to the model in the step 3, and carrying out the next step, otherwise, stopping detection or enabling the whole detection result to be invalid.
The input image is the image at the time of T = T, namely the image after color change, and the identification result is the color after color change according to the model output in the step 3, so that the film is judged to be qualified in anti-aging performance and good in product consistency; if the identification result is an abnormal color, the abnormal condition needs to be further judged. This is because the abnormal color at this time means that there are two cases: at a certain moment before the picture with the abnormal color is collected, the stability of the film reaches a preset value (consistent with the preset color-changed image), so that the color at the moment is darker than the color-changed image. This indicates premature aging of the film. Secondly, at a certain moment after the picture with the abnormal color is collected, the stability of the film can reach a preset value (consistent with the preset color-changed image), so that the color at the moment is lighter than the color-changed image, which means that the film has smaller preset aging degree in the preset time, which is in accordance with the product requirement (better performance), but the film is different from the situation that most films just conform to the preset aging degree, and the occurrence of the individual situation indicates that the product consistency is poor.
At the moment, judging whether the results of 'color after color change' are included in the plurality of identification results output by the step 3 of the plurality of images acquired before the moment, and if yes, judging that the anti-aging performance of the film is unqualified; otherwise, judging the film to be qualified in anti-aging performance, but not good in product consistency. Sending a plurality of collected images collected before the moment (before the images after color change are collected) into a neural network model, and judging that the anti-aging performance of the polyester material is unqualified if a plurality of identification results are output and comprise the color after color change; otherwise, judging that the ageing resistance of the polyester material is qualified, but the product consistency is not good.
In summary, the criteria for determining aging resistance are: and the color of the polyester film after being changed in the preset time T meets the preset color, and the anti-aging performance meets the requirement. The consistency judgment standard is as follows: all test batches met the above criteria. Poor consistency is indicated if there is a better individual performance. If there is a worse performance, the performance is not satisfactory.
And optimizing the components, the proportion and the preparation process of the anti-aging polyester material according to the judgment result.
According to a large number of experiments, compared with the existing image algorithm, the method has the advantages that the accuracy is higher by 35%, the calculation time is shortened by 12%, and the resource occupancy rate is reduced by 26%. The accuracy of the invention can reach more than 97.4 percent, and the invention can be widely applied to production practice.
It will be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been shown and described in detail herein, many other variations or modifications can be made, which are consistent with the principles of this invention, and which are directly determined or derived from the disclosure herein, without departing from the spirit and scope of the invention. Accordingly, the scope of the invention should be understood and interpreted to cover all such other variations or modifications.

Claims (6)

1. The preparation method of the anti-aging polyester film material is characterized by comprising the following steps:
(I) preparation step of anti-aging polyester film material master batch
Step 1: fully mixing the following components to obtain a mixture A;
the weight ratio of each component is as follows:
GW944： 0.1%-0.2%
UV329： 0.1-0.2%
UV531：0.1-0.2%
zinc stearate: 0.4 to 0.5 percent
Zeolite: 0.4 to 0.5 percent
TiO 2 ：0.05%
Polyester: the rest(s)
Step 2: cooling the mixture A and stirring at a high speed;
step 3, extruding and granulating the mixture A obtained in the step 2 in an extruder to obtain polymer master batches;
(II) optimization of anti-aging polyester film material master batch
In order to detect the anti-aging parameters of the master batch material to optimize the preparation process of the master batch, the method further comprises the following steps:
extruding and blowing the master batch obtained in the step, and further stretching to obtain an anti-aging polyester film;
the polyester film is placed in an aging environment with an ultraviolet light source, an infrared light source and a heat source for aging, and the polyester film is sent into a neural network model after an image of the polyester film is collected to judge the film aging resistance and the consistency of the aging resistance;
optimizing the components, the proportion and the preparation process of the anti-aging polyester film material master batch according to the judgment result;
wherein, classifier includes in the neural network model:
Figure DEST_PATH_IMAGE002
wherein
Figure DEST_PATH_IMAGE004
In order to be a color feature vector,
Figure DEST_PATH_IMAGE006
in order to be a linear weight, the weight of the weight,
Figure DEST_PATH_IMAGE008
in order to be a linear bias, the bias is,
Figure DEST_PATH_IMAGE010
representing by coordinates
Figure DEST_PATH_IMAGE012
As the coordinate offset of the center, j is the classifier channel coordinate,
Figure DEST_PATH_IMAGE014
indicates that there are 8 classifiers
Figure DEST_PATH_IMAGE016
；
Furthermore, a non-linear classification function is defined:
Figure DEST_PATH_IMAGE018
；
wherein the parameters
Figure DEST_PATH_IMAGE020
Non-continuously transforming the non-linear function;
the distinguishing method comprises the following steps: when the judgment is carried out, the input image is an image before color change, the identification result is output as the color before color change according to the neural network model, the next step is carried out, and otherwise, the detection is stopped or the whole detection result is invalid; the input image is a color-changed image, and the color-changed image is output according to the neural network model, so that the polyester material is judged to be qualified in anti-aging performance and good in product consistency; if the identification result is an abnormal color, further judging the abnormal condition; the above determining the abnormal condition includes: sending a plurality of acquired images acquired before the images after color change are acquired into a neural network model, and if a plurality of identification results are output and comprise 'the color after color change', judging that the ageing resistance of the polyester material is unqualified; otherwise, judging that the ageing resistance of the polyester material is qualified, but the product consistency is not good.
2. The preparation method of the anti-aging polyester film material as claimed in claim 1, characterized in that: the mixing operation in the step 1 is realized by stirring for 10-30min at the temperature of 90-120 ℃.
3. The preparation method of the anti-aging polyester film material as claimed in claim 1, wherein: the completion temperature in step 2 is 60-80 ℃.
4. The preparation method of the anti-aging polyester film material as claimed in claim 1, wherein: the stirring speed in the step 2 is 300-600 rpm.
5. The preparation method of the anti-aging polyester film material as claimed in claim 1, wherein: the temperature of the extruder in step 3 is controlled as follows: the temperature of the rear section of the machine barrel is controlled to be 150-160 ℃, the temperature of the front section is controlled to be 160-170 ℃, and the temperature of the machine head is controlled to be 170-175 ℃.
6. The preparation method of the anti-aging polyester film material as claimed in claim 1, characterized in that: the film blowing temperature is controlled at 160-180 ℃.
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