Blending condition setting method, blending condition setting program, blending condition setting device, and polyester resin film manufacturing system

Abstract

To provide a trained model generation method capable of efficiently setting a composition condition of a resin film, a composition condition setting method, a trained model generation program, a composition condition setting program, a training device, a composition condition setting device, a method for manufacturing a resin film, a device for manufacturing a resin film and a resin film.SOLUTION: A trained model generation method according to the present invention causes a computer to generate a trained model by learning using learning data with composition conditions including the physical properties and composition ratio of a thermoplastic resin constituting a resin film as explanatory variables and the properties of the resin film as objective variables.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a trained model generation method, a blending condition setting method, a trained model generation program, a blending condition setting program, a learning device, a blending condition setting device, a resin film manufacturing method, a resin film manufacturing system, and a resin film. [Background technology]

[0002] Resin films are films that have excellent heat resistance, flame retardancy, chemical resistance, electrical insulation, moist heat resistance, mechanical strength, dimensional stability, etc. Such resin films are produced, for example, by biaxial stretching, in which resin chips are melted, stretched in one direction, and then stretched in a direction perpendicular to the melted resin chips (see, for example, Patent Documents 1 and 2). This biaxial stretching results in a film with increased strength in both the longitudinal and transverse directions. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2001-287266 [Patent Document 2] Japanese Patent Application Laid-Open No. 2011-148186 Summary of the Invention [Problem to be solved by the invention]

[0004] In order to obtain the resin film described above with the desired physical properties, the compounding ratios and other compounding conditions are designed by engineers through extensive trial and error, and there has been a desire to improve the efficiency of this design.

[0005] The present invention has been made in consideration of the above, and aims to provide a trained model generation method, a blending condition setting method, a trained model generation program, a blending condition setting program, a learning device, a blending condition setting device, a resin film manufacturing method, a resin film manufacturing system, and a resin film, which can efficiently set the blending conditions of a resin film. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the objectives, the trained model generation method of the present invention generates a trained model by having a computer learn using training data in which the compounding conditions, including the physical properties and compounding ratio of the thermoplastic resin that constitutes the resin film, are used as explanatory variables, and the physical properties of the resin film are used as objective variables.

[0007] In addition, in the trained model generation method according to the present invention, in the above invention, the physical properties of the thermoplastic resin include the intrinsic viscosity and solution haze of the thermoplastic resin.

[0008] In addition, in the trained model generation method according to the present invention, in the above invention, the physical properties of the resin film include the intrinsic viscosity and film haze of the resin film.

[0009] In addition, in the trained model generation method according to the present invention, in the above invention, the thermoplastic resin includes recycled resin.

[0010] In addition, in the trained model generation method according to the present invention, in the above invention, the thermoplastic resin is a polyester resin.

[0011] In addition, in the trained model generation method of the present invention, in the above invention, the resin film is a biaxially oriented polyester film.

[0012] In addition, the compounding condition setting method of the present invention involves a computer inputting the physical properties of the target resin film into a search section including a trained model generated by learning using learning data in which compounding conditions including the physical properties and compounding ratio of the thermoplastic resin constituting the resin film are used as explanatory variables and the physical properties of the resin film are used as target variables, and setting the compounding conditions based on the output results of the search section.

[0013] In addition, in the blending condition setting method of the present invention, in the above invention, the step of setting the blending conditions selects, from among the blending conditions, a blending condition with a large proportion of recycled resin when there are multiple blending conditions in the output result.

[0014] In the method for setting blending conditions according to the present invention, the physical properties of the thermoplastic resin include the intrinsic viscosity and solution haze of the thermoplastic resin.

[0015] In the blending condition setting method according to the present invention, the physical properties of the resin film include the intrinsic viscosity and film haze of the resin film.

[0016] In the blending condition setting method according to the present invention, the thermoplastic resin includes a recycled resin.

[0017] In the method for setting blending conditions according to the present invention, the thermoplastic resin is a polyester resin.

[0018] Further, in the method for setting blending conditions according to the present invention, the resin film is a biaxially oriented polyester film.

[0019] In addition, the trained model generation program of the present invention causes a computer to execute a trained model generation step of generating a trained model by learning using training data in which the compounding conditions, including the physical properties and compounding ratio of the thermoplastic resin that constitutes the resin film, are explanatory variables and the physical properties of the resin film are objective variables.

[0020] In addition, the blending condition setting program of the present invention causes a computer to execute a blending condition setting step in which the physical properties of the target resin film are input to a search section including a trained model generated by learning using learning data in which blending conditions including the physical properties and blending ratio of the thermoplastic resin constituting the resin film are used as explanatory variables and the physical properties of the resin film are used as target variables, and the blending conditions are set based on the output results of the search section.

[0021] In addition, the learning device of the present invention is equipped with a learning unit that generates a trained model by learning using learning data in which the physical properties and blending conditions including the blending ratio of the thermoplastic resin that constitutes the resin film are used as explanatory variables, and the physical properties of the resin film are used as objective variables.

[0022] In addition, the blending condition setting device of the present invention has a trained model generated by learning using training data in which blending conditions including the physical properties and blending ratio of the thermoplastic resin constituting the resin film are used as explanatory variables and the physical properties of the resin film are used as target variables, and is equipped with a search unit that inputs the physical properties of the target resin film into the trained model to search for blending conditions suitable for the physical properties, and a setting unit that sets blending conditions based on the output results of the learning search unit.

[0023] Furthermore, the method for producing a resin film according to the present invention produces a resin film in accordance with the blending conditions of the thermoplastic resin set by the blending condition setting method according to the above invention.

[0024] In addition, the resin film manufacturing system of the present invention includes the blending condition setting device of the above invention, and a film manufacturing device that manufactures a resin film in accordance with the blending conditions of the thermoplastic resin set by the blending condition setting device.

[0025] The resin film according to the present invention is produced by mixing the thermoplastic resins according to the blending conditions set by the blending condition setting method according to the above invention, and molding the mixture into a sheet. [Effects of the Invention]

[0026] According to the present invention, the compounding conditions for the resin film can be set efficiently. [Brief explanation of the drawings]

[0027] [Figure 1] FIG. 1 is a diagram showing a schematic configuration of a compounding condition setting system according to an embodiment of the present invention. [Figure 2] FIG. 2 is a diagram for explaining the flow of the compounding condition setting process performed by the compounding condition setting system according to one embodiment of the present invention. [Figure 3] FIG. 3 is a diagram for explaining an example of a manufacturing method using biaxial stretching. [Figure 4] FIG. 4 is a block diagram showing the configuration of a learning device provided in a blending condition setting system according to an embodiment of the present invention. [Figure 5] FIG. 5 is a block diagram showing the configuration of a blending condition setting device provided in a blending condition setting system according to an embodiment of the present invention. [Figure 6] FIG. 6 is a flowchart showing an outline of the learning process performed by the learning device according to an embodiment of the present invention. [Figure 7] FIG. 7 is a flowchart showing an outline of the blending condition setting process performed by the blending condition setting device according to one embodiment of the present invention. [Figure 8] FIG. 8 is a flowchart showing an outline of a blending condition setting process performed by a blending condition setting device according to a modified embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0028] Hereinafter, an embodiment of the compounding condition setting system according to the present invention will be described in detail with reference to the drawings. However, the present invention is not limited to this embodiment.

[0029] (Embodiment) 1 is a diagram showing a schematic configuration of a blending condition setting system according to one embodiment of the present invention. The blending condition setting system 1 includes a learning device 2 that creates training data and generates a trained model trained using the created training data, a blending condition setting device 3 that sets blending conditions that will give the resin film properties desired by the user using the trained model generated by the learning device 2, a display device 4 that displays information including the setting results of the blending condition setting device 3, and an input device 5.

[0030] The blending conditions set by the blending condition setting device 3 include the types of raw materials (resins) that make up the resin film and their blending ratios.

[0031] Examples of resins that constitute the resin film include thermoplastic resins. Examples of thermoplastic resins include polyester resins. Thermoplastic resins include new resins that have not been subjected to processing such as hot melt molding or heat treatment, and recycled resins that have been subjected to processing such as hot melt molding or heat treatment. Recycled resins can be used, for example, from process waste materials generated during commercialization or discarded or recovered products. The form of the resin is not limited, and it can be used in the form of melt-extruded pellets or compressed flakes.

[0032] 2 is a diagram illustrating the flow of a blending condition setting process performed by a blending condition setting system according to an embodiment of the present invention. The learning device 2 generates a trained model 100 by learning using training data LD. The blending condition setting device 3 inputs physical property conditions IP indicating the desired physical properties into the trained model 100 to obtain blending conditions (predicted conditions), and sets and outputs blending conditions OP that satisfy the physical properties (predictions) of the resin film. Alternatively, the blending condition setting device 3 inputs physical property conditions IP indicating the desired physical properties into the trained model 100 to obtain blending conditions (prediction conditions), and outputs the obtained one or more blending conditions OP.

[0033] Here, an example of a method for producing a resin film will be described with reference to Fig. 3. Fig. 3 is a diagram for explaining an example of a production method using biaxial stretching. In Fig. 3, the longitudinal direction of the film along which the resin is fed in the treatment step is defined as the X-axis direction, the width direction of the resin film (the same as the rotation axis of the roll that feeds it to the next step) is defined as the Y-axis direction, and the height direction perpendicular to the X-axis and Y-axis directions is defined as the Z-axis direction.

[0034] The resin film manufacturing apparatus 200 includes a manufacturing unit 201 and a control unit 202 . The blending condition setting device 3 and the manufacturing device 200 constitute a resin film manufacturing system. The manufacturing device 200 is not limited to a manufacturing unit that manufactures a resin film by biaxial stretching, such as the manufacturing unit 201 shown in FIG.

[0035] The manufacturing section 201 includes a vacuum drying section 211 that removes moisture from the pellets (resin), a storage section 212 that stores the pellets after vacuum drying, an extrusion section 213 that melts and extrudes the pellets sent from the storage section 212, a filtration filter 214 that filters the molten resin sent from the extrusion section 213, a die 215 that forms the filtered molten resin into a sheet, a casting drum 216 that wraps the sheet-like resin from the die 215 around and cools and solidifies it, a first stretching section 217 that stretches the sheet-like resin in the X-axis direction, a second stretching section 218 that stretches in the Y-axis direction perpendicular to the stretching direction by the first stretching section 217, a transfer conveying section 219 that conveys the biaxially stretched film that has passed through the second stretching section 218 while cooling it, and a winding section 220 that winds up the stretched resin sent from the transfer conveying section 219.

[0036] The extrusion section 213 mixes and melts the pellets and forces them through a die 215 . The casting drum 216 rotates around an axis in the Y-axis direction, cools and solidifies the resin, and sends the sheet-shaped resin in the film longitudinal direction (X-axis direction).

[0037] The first stretching section 217 stretches the sheet-shaped resin sent from the casting drum 216 in the longitudinal direction of the film (X-axis direction) using a plurality of rolls while heating as necessary, to form a uniaxially stretched film.

[0038] The second stretching section 218 stretches the uniaxially stretched film (221) sent from the first stretching section 217 in a direction perpendicular to the longitudinal direction of the film using clips while heating as necessary. Here, the film is stretched in the Y-axis direction (the film width direction, the direction of the roll rotation axis) to form a biaxially stretched film.

[0039] The winding section 220 is a roll that winds up the biaxially stretched film after stretching in the second stretching section 218 via the transfer conveying section 219. Through this series of processes, multiple resin raw materials are mixed, melted, stretched, and formed into a sheet, producing a biaxially stretched resin film that is stretched in two perpendicular directions. Because the biaxially stretched resin film is stretched in different directions, it exhibits higher isotropy than a uniaxially stretched film, and exhibits, for example, excellent dimensional stability.

[0040] The control unit 202 controls the operation of the production unit 201. For example, when production conditions for biaxial stretching, such as the roll temperature and rotation speed, are set in the blending condition setting device 3, the control unit 202 acquires the blending conditions and drives the production unit 201 in accordance with the production conditions. The control unit 202 is a computer configured using one or more pieces of hardware, such as a CPU (Central Processing Unit), GPU (Graphics Processing Unit), ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), and the like, and memory in which various programs and the like are pre-installed.

[0041] Next, the configuration of the learning device 2 will be described with reference to Fig. 4. The learning device 2 is electrically connected to the blending condition setting device 3. Fig. 4 is a block diagram showing the configuration of the learning device provided in a blending condition setting system according to one embodiment of the present invention. The learning device 2 has a learning data generation unit 21, a learning unit 22, a control unit 23, and a memory unit 24.

[0042] The learning data generation unit 21 generates, from the data stored in the memory unit 24, data that associates the blending conditions, which consist of the types and physical properties of multiple raw materials that make up the resin film and the blending ratios of the raw materials, with the physical properties of the resin film manufactured under those blending conditions, as learning data. In other words, the learning data is data on resin films whose physical properties are known. Note that the learning data generation unit 21 may extract data according to conditions input via the input device 5 and generate the learning data. The learning data generating unit 21 generates learning data using the compounding conditions, including the physical properties and compounding ratio of the thermoplastic resin that constitutes the resin film, as explanatory variables, and the physical properties of the resin film corresponding to the compounding conditions as objective variables. In this case, the blending conditions such as the type of raw material, physical properties, blending ratio, etc. may be quantified or visualized. In addition, a plurality of blending conditions may be combined and quantified or visualized to be expressed as a single blending condition. For example, if there are 40 possible combinations of ingredient types and blending ratios, one way to express this is to assign a number corresponding to each combination, obtain a number between 1 and 40, and then quantify this number as a single blending condition related to ingredient types and blending ratios.Using the above-mentioned representation makes it possible to reduce the number of explanatory variables in the generated training data and reduce multicollinearity, leading to the construction of a highly accurate trained model. The physical properties of the resin film may be expressed numerically or as an image. Furthermore, when a plurality of physical properties of the resin film are set, these physical properties may be combined and expressed as a single physical property of the resin film. The experimental data stored in the memory unit 24, which contains a mixture of formulation conditions and physical properties, is divided into explanatory variables (x1, x2, ...) and a response variable (y) in the learning data generation unit 21. In this case, there may be multiple types of formulation conditions used as explanatory variables, and each formulation condition is associated with a physical property. Note that multiple formulation conditions may be collectively treated as one. Furthermore, the explanatory variables and the response variable may be stored separately in the memory unit in advance.

[0043] Here, the known compounding ratio of a resin film refers to data accumulated in the past that expresses the composition ratio of raw materials in a resin film in terms of weight ratio, and includes the resin ratio and additive ratio as necessary.

[0044] The physical properties used as training data include the physical properties of raw materials and the physical properties of resin films. The physical properties of raw materials include intrinsic viscosity, solution haze (turbidity), and color tone. The physical properties of resin films include intrinsic viscosity, film haze (turbidity), color tone, thickness, and mechanical strength. When manufactured using biaxial stretching, the mechanical strength includes the strength in each stretching direction. Furthermore, for recycled resins, the details of previous treatments (heating conditions, etc.) may also be included as physical properties. In this case, even if a new resin and a recycled resin are the same product, they may differ from each other in at least some of their physical properties, such as intrinsic viscosity, solution haze (turbidity), and color tone. In particular, recycled resins that have been recycled repeatedly may have relatively large differences in physical properties, such as intrinsic viscosity, film haze (turbidity), and color tone, compared to new resins.

[0045] The raw materials for resin films are a mixture of multiple resins depending on the desired physical properties. For example, even when a film is made using polyester resin, there are different types of polyester resin raw materials, such as polyethylene terephthalate (PET), polyethylene naphthalate (PEN), polytrimethylen terephthalate (PTT), and polybutylene terephthalate (PBT). Furthermore, even the same polyester resin can have different physical properties depending on whether it is recycled or not.

[0046] An example of raw material parameters will now be described with reference to Table 1. Table 1 shows an example of the physical properties (intrinsic viscosity, solution haze (turbidity), color tone (b value)) of polyester resin raw materials, and whether they can be recycled. In Table 1, No. 1 and No. 2 show the physical properties of new resins, and No. 3 to No. 7 show the physical properties of recycled resins. As shown in Table 1, even if the polyester resin is the same, the physical properties will differ depending on the type. [Table 1]

[0047] The learning unit 22 performs learning using the learning data to generate a trained model. A known learning method can be adopted for the learning performed by the learning unit 22. Examples of statistical models adopted for learning include a simple linear regression model, Ridge regression, Lasso regression, Elastic Net regression, general additive model, random forest regression, rule fit regression, gradient boosting tree, extra tree, support vector regression, Gaussian process regression, k-nearest neighbor regression, kernel ridge regression, and neural network.

[0048] For example, when the learning unit 22 generates a trained model by learning using regularization, the learning unit 22 provides multiple candidate values ​​for the hyperparameters of the trained model, performs learning for each of the provided candidate values ​​of the hyperparameters, generates a trained model for the target variable (physical properties of the resin film), and stores the trained model in the storage unit 24. The learning unit 22 then calculates prediction errors by cross-validation or holdout validation using the training data for the models obtained by learning using each candidate value, and selects the trained model that provides the smallest prediction error. The hyperparameters referred to here are parameters that are set in advance by the learning unit 22 for learning, and include, for example, regularization coefficients. In addition, in the case of a trained model using a neural network, the hyperparameters also include the number of layers of the neural network.

[0049] The control unit 23 controls the overall operation of the learning device 2 .

[0050] The storage unit 24 stores various programs for operating the learning device 2 and data including various parameters necessary for the operation of the learning device 2. The various programs include a training data generation program that generates training data for generating a trained model, and a trained model generation program that generates a trained model by training using the training data. The various parameters include hyperparameters and parameters acquired by the learning unit 22 through training. The storage unit 24 also stores data for configuring the training data (for example, the above-mentioned blending conditions and physical properties).

[0051] The storage unit 24 is configured using a ROM (Read Only Memory) in which various programs etc. are pre-installed, a RAM (Random Access Memory) for storing calculation parameters and data for each process, an HDD (Hard Disk Drive), an SSD (Solid State Drive), etc.

[0052] The various programs can also be widely distributed by recording them on computer-readable recording media such as HDDs, flash memories, CD-ROMs, DVD-ROMs, Blu-ray (registered trademark), etc. The communication network referred to here is configured using, for example, an existing public line network, a LAN (Local Area Network), a WAN (Wide Area Network), etc., and may be wired or wireless.

[0053] The learning device 2 having the above functional configuration is a computer configured using one or more pieces of hardware such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), and an FPGA (Field Programmable Gate Array).

[0054] Next, the blending condition setting device 3 will be described with reference to Fig. 5. Fig. 5 is a block diagram showing the configuration of the blending condition setting device provided in a blending condition setting system according to one embodiment of the present invention. The blending condition setting device 3 is electrically connected to the learning device 2 and the display device 4. The blending condition setting device 3 has a setting unit 31, a search unit 32, a control unit 33, and a memory unit 34.

[0055] The blending condition setting device 3 uses the physical properties to be predicted and the trained model acquired from the learning device 2 to set blending conditions including the types of raw materials and their blending ratios that are suitable for the conditions.

[0056] The setting unit 31 inputs the set and input physical properties to the search unit 32 including a trained model, and sets the candidate blending conditions output from the search unit 32 as the blending conditions. The blending conditions set at this time may be the most suitable blending condition among multiple blending conditions that satisfy the set physical property conditions, or may be all blending conditions that satisfy the physical property conditions.

[0057] The search unit 32 extracts optimal blending conditions by searching for candidate blending conditions using a trained model based on the physical properties input from the setting unit 31, and outputs the optimal blending conditions to the setting unit 31. Here, searching for candidate blending conditions using a trained model means inputting candidate blending conditions generated based on a search algorithm described below into the trained model, and extracting optimal blending conditions for achieving the physical properties input from the setting unit 31 from the physical properties of the resin film output from the trained model.

[0058] Here, the optimal blending conditions refer to a state in which the target physical property is at its most desirable value. In the present embodiment, for example, in the case of strength, this is synonymous with maximizing or bringing the property closest to a set value. If there is a desirable range, the physical property must fall within that range. Furthermore, if the set conditions include multiple types of physical properties, candidate blending conditions are extracted based on the difference between the respective physical property values, the priority assigned to each physical property, etc.

[0059] Search algorithms for obtaining optimal formulation conditions include a grid search method, which selects formulation conditions from a trained model that satisfy the target range of physical properties for the desired resin film, and a random search method, which determines a predetermined upper limit for the number of searches and randomly extracts formulation conditions to obtain formulation conditions that maximize or minimize physical properties. Another search method that can be used is the Bayesian optimization method, which selects and evaluates extreme values ​​in accumulated data within a confidence interval represented by the average physical property value ± prediction error, taking into account the prediction error of the physical properties, and then repeats the search. Another search method that can be used is the genetic algorithm selection method, which selects formulation conditions close to the desired physical properties and repeatedly searches for formulation conditions while changing some of those formulation conditions. Here, when implementing search algorithms that require a starting point before search, such as the Bayesian optimization method or the genetic algorithm selection method, the starting point can be data generated by grid search or random plotting, or the training data used to generate the trained model.

[0060] The control unit 33 controls the overall operation of the blending condition setting device 3. The control unit 33 has a display control unit 331 that displays the setting results of the setting unit 31 on the display device 4. The display control unit 331 may also display information on the raw materials, the raw materials, predicted physical properties, etc. on the display device 4 in addition to the setting results.

[0061] The storage unit 34 stores various programs for operating the blending condition setting device 3 and data including various parameters necessary for the operation of the blending condition setting device 3. The various programs include a blending condition setting program executed using a trained model. The storage unit 34 may also store a trained model. In this case, the trained model may be updated in synchronization with the learning device 2. The storage unit 34 is configured using a ROM in which various programs are pre-installed, and a RAM, HDD, SSD, etc. that store calculation parameters and data for each process.

[0062] The various programs can be recorded on computer-readable recording media such as HDDs, flash memories, CD-ROMs, DVD-ROMs, and Blu-ray (registered trademark) and widely distributed. The blending condition setting device 3 can also acquire the various programs via a communication network. The communication network here can be configured using, for example, an existing public line network, LAN, WAN, or the like, and can be wired or wireless.

[0063] The blending condition setting device 3 having the above-described functional configuration is a computer configured using one or more pieces of hardware such as a CPU, a GPU, an ASIC, an FPGA, and the like.

[0064] The display device 4 is a display made of liquid crystal or organic EL (Electro Luminescence), and is electrically connected to the blending condition setting device 3. The display device 4 acquires and displays display data output from the blending condition setting device 3 under the control of the display control unit 331. The display device 4 may also have an audio output function such as a speaker.

[0065] The input device 5 receives input of various information including information such as set mechanical property values ​​related to the process of setting the blending conditions, and outputs the received information to the learning device 2 and the blending condition setting device 3. The input device 5 is configured using a user interface such as a keyboard, mouse, microphone, and touch panel.

[0066] Next, the flow of the learning process performed by the learning device 2 will be described with reference to FIG. 6. FIG. 6 is a flowchart showing an outline of the learning process performed by a learning device according to an embodiment of the present invention. First, the learning device 2 generates a data set to be used for learning with reference to the storage unit 24 (step S11). Here, the learning data generation unit 21 generates a data set that pairs the blending conditions, including the types, physical properties, and blending ratios of raw materials in the resin film, with the physical properties of the resin film manufactured under those blending conditions. This data set is used as learning data for generating a trained model. The learning data generation unit 21 generates learning data for each resin film.

[0067] The learning unit 22 generates a trained model by performing machine learning using the training data generated in step S11 (step S12). The learning unit 22 performs learning using the compounding conditions, including the physical properties and compounding ratio of the thermoplastic resin that constitutes the resin film, as explanatory variables and the physical properties of the resin film as objective variables, to generate a trained model.

[0068] Thereafter, the blending conditions, which are the optimum types of raw materials for the resin film suited to the physical properties to be set and their blending ratios, are set in the blending condition setting device 3. Fig. 7 is a flowchart showing an outline of the blending condition setting process performed by the blending condition setting device according to one embodiment of the present invention.

[0069] The setting unit 31 acquires the trained model from the learning device 2 and inputs the physical properties of the target resin film to the search unit 32 (step S21). The search unit 32 receives the set physical properties treated as the target variables during learning and outputs the blending conditions. When the physical properties of the target resin film are input to the search unit in this way, blending conditions (prediction conditions) suitable for those physical properties are output. The target physical properties input at this time are based on, for example, information input by the user via the input unit of the blending condition setting device 3. For example, a compounding condition using raw material A and raw material B, where the compounding ratio is A:B=○○(%):△△%(%), is set and output. At this time, the ratio of recycled resin to the total amount of raw materials (recycle rate) may be displayed. The trained model may output the compounding condition in a digitized or visualized form, and the setting unit 31 may convert it to represent the compounding condition.

[0070] The setting unit 31 sets the blending conditions based on the prediction results of the search unit 32 (step S22). In this embodiment, when there are multiple blending conditions for the target physical property, the setting unit 31 may set one optimal blending condition that is closest to the target physical property, may set blending conditions that satisfy the target physical property or its allowable setting range, or may select and set the top several blending conditions that satisfy the target physical property. For example, when priorities are set for each type of physical property, the blending conditions are preferentially selected based on the physical property with the highest priority and the condition that is closest to the target physical property.

[0071] When the display control unit 331 acquires the setting result from the setting unit 31, it outputs the setting result to the display device 4 and performs display control to display the setting result on the display device 4. The display device 4 displays the blending conditions including the types of raw materials of the optimal resin film that are predicted to exhibit the target physical properties and their blending ratios.

[0072] Thereafter, for example, raw materials are prepared according to the blending conditions set by the setting method, and a resin film is produced using the production apparatus 200 described above.

[0073] In the embodiment described above, the compounding conditions for a resin film having a desired physical property are set by inputting the desired physical property into a trained model generated by training using the types, physical properties, and blending ratios of raw materials as explanatory variables and the desired physical property as a response variable. According to this embodiment, the compounding conditions for a resin film can be set efficiently.

[0074] In addition, in the embodiment, since blending conditions including recycled resins as well as new resins can be searched for as raw materials for the resin film and blending conditions including the recycled resins can be set, the recycling rate of raw materials in manufacturing can be improved. In this case, when selecting raw materials, the priority of recycled resins can be set higher than the priority of new resins.

[0075] In the embodiment, the objective variables are described as the physical properties of the resin film, and the explanatory variables are the physical properties and blending conditions of the raw materials. However, in addition to these, the objective variables may further include spectral intensity, process condition values ​​during manufacturing (e.g., stretching ratio, etc.), and measurement condition values.

[0076] In this case, the spectral intensity refers to the intensity of the spectrum at each wavelength obtained by spectral measurement of the resin film, and examples of such measurements include spectral measurements using light sources in each wavelength region, such as infrared light, near-infrared light, visible light, ultraviolet light, and X-rays.

[0077] The process condition values ​​refer to control parameters for producing a resin film, and include, for example, production conditions such as temperature when biaxial stretching is used, resin feed speed (roll rotation speed), and clip spacing in the film width direction. Furthermore, when isotropy or the like is specified as the target physical property, the necessity of biaxial stretching, etc. may be included in the compounding conditions.

[0078] The physical quantities may also include values ​​that can be calculated from the physical properties or characteristics inherent to each raw material of the resin film and its blending conditions, as well as physical quantities such as molecular weight, glass transition point, melting point, etc. Here, the physical quantities inherent to each raw material can be calculated from the amount of specific functional groups or the number of partial structures contained in each raw material, or the chemical structure.

[0079] Furthermore, when a contribution derived from the chemical structure of the resin components contained in the resin film is expected, further improvement in the accuracy of the trained model can be expected by using as explanatory variables not only the compounding ratio of the components but also the amount of specific functional groups, the number of partial structures, and related values ​​calculated based on the chemical structural formula of the components, or molecular weights that can be calculated from the chemical structure of each component, or physical quantities including physical property values ​​such as glass transition point and melting point.

[0080] (Variation) Next, a modified example of the embodiment will be described with reference to Fig. 8. In the modified example, the compounding condition setting process is different from the above-described embodiment. The processes and system configuration other than the compounding condition setting process are the same as those of the embodiment, so the description will be omitted.

[0081] 8 is a flowchart showing an outline of a blending condition setting process performed by a blending condition setting device according to a modified embodiment of the present invention. Similar to step S21, the setting unit 31 acquires a trained model from the learning device 2 and inputs target physical properties to the search unit 32 (step S31). When the physical properties of the target resin film are input to the search unit 32, blending conditions (prediction conditions) suitable for those physical properties are output.

[0082] The setting unit 31 determines whether the combination conditions output by the trained model are multiple (step S32). If the setting unit 31 determines that there are not multiple combination conditions (step S32: No), it proceeds to step S34. On the other hand, if the setting unit 31 determines that there are multiple combination conditions (step S32: Yes), it proceeds to step S33.

[0083] In step S33, the setting unit 31 calculates the recycle rate for each blending condition and selects the blending condition with the highest recycle rate. The setting unit 31, for example, calculates the proportion of recycled resin in the raw materials to be blended and sets this as the recycle rate. The proportion is calculated, for example, by adding up the blending rate or mass % of recycled resin. After calculating the recycle rate, the setting unit 31 proceeds to step S34.

[0084] In step S34, the setting unit 31 sets the compounding conditions. In this modified example, if there is only one compounding condition in step S32, the setting unit 31 sets that compounding condition, and if a compounding condition is selected in step S33, the setting unit 31 sets the selected compounding condition.

[0085] Thereafter, when the display control unit 331 acquires the setting result from the setting unit 31, it outputs the setting result to the display device 4 and performs display control to display the setting result on the display device 4. The display device 4 displays the blending conditions including the types of raw materials of the optimal resin film that are predicted to exhibit the physical properties of the target resin film and their blending ratios.

[0086] Thereafter, for example, raw materials are prepared according to the blending conditions set by the setting method, and a resin film is produced using the production apparatus 200 described above.

[0087] In the modified example described above, as in the embodiment, the compounding conditions for a resin film having target physical properties are set using a trained model generated by training using the types, physical properties, and compounding ratios of raw materials as explanatory variables and the physical properties of the resin film as target variables. According to this modified example, the compounding conditions for the resin film can be set efficiently.

[0088] In addition, in the modified example, the compounding conditions are set so that the proportion or amount of recycled resin used as the raw material for the resin film is high, thereby improving the recycling rate of raw materials in manufacturing.

[0089] (Other embodiments) Although the embodiments of the present invention have been described above, the present invention should not be limited to the above-described embodiments. For example, the compounding condition setting device may have the function of a learning unit. In this case, the compounding condition setting device not only sets the compounding conditions to be set, but also sequentially updates the trained model. [Explanation of symbols]

[0090] 1. Mixing condition setting system 2 Learning device 3 Mixing condition setting device 4 Display device 5 Input Devices 21 Learning data generation unit 22 Learning Department 23, 33, 202 Control section 24, 34 Storage section 31 Setting section 32 Search Department 200 Manufacturing equipment 201 Manufacturing Department 331 Display control unit

Claims

1. The computer Inputting the physical properties of a target polyester resin film into a search unit including a trained model generated by training using training data in which the physical properties and blending conditions, including the blending ratio, of a polyester resin including a recycled resin constituting a polyester resin film are used as explanatory variables, and the physical properties of the polyester resin film are used as objective variables; When a plurality of blending conditions are present in the output result of the search unit, a blending condition having a high proportion of recycled resin is selected from among the blending conditions, and the selected blending condition is set. How to set compounding conditions.

2. On the computer, a blending condition setting step of inputting the physical properties of the target polyester resin film into a search unit including a trained model generated by learning using learning data in which blending conditions including the physical properties and blending ratio of polyester resins including recycled resins constituting the polyester resin film are used as explanatory variables and the physical properties of the polyester resin film are used as objective variables, and selecting, from among the blending conditions and in which the proportion of recycled resin is high, a blending condition in which the selected blending condition is set; A compounding condition setting program that executes the above.

3. a search unit that has a trained model generated by training using training data in which the physical properties and blending conditions, including the blending ratio, of the polyester resin, including the recycled resin, that constitutes the polyester resin film are used as explanatory variables, and the physical properties of the polyester resin film are used as objective variables, and that inputs the physical properties of the target polyester resin film into the trained model to search for blending conditions that are suitable for the physical properties; a setting unit that selects a blending condition having a high proportion of recycled resin from among the blending conditions when a plurality of blending conditions are present in the output result of the search unit, and sets the selected blending condition; A blending condition setting device comprising:

4. The blending condition setting device according to claim 3; a film manufacturing device that manufactures a polyester resin film in accordance with the blending conditions of the polyester resin set by the blending condition setting device; A polyester resin film manufacturing system comprising:

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