Apparatus and method for designing multilayer film

Abstract

Disclosed are an apparatus and a method for designing a multilayer film. The apparatus for designing a multilayer film may: model a laminated structure of a multilayer film to be designed; collect dart strength data regarding a single-layer film that constitutes the laminated structure as stress strain data; read a value previously stored in a storage space accessible by the apparatus for designing a multilayer film; obtain a feature configuration mode for designating a different feature configuration scheme according to the value that has been read; configure multiple physical indices selected from the dart strength data and the thickness of the single-layer film as features, according to the feature configuration mode; predict the dart strength of the multilayer film by using a machine learning model selected from multiple supervised learning models and trained by using the features as independent variables and using the dart strength of the multilayer film as a target variable; and generate design data regarding the multilayer film by combining prediction values regarding different characteristics and the dart strength's prediction value.

Description

Design device and method for multilayer films

[0001] Cross-citation with related applications

[0002] This application claims the benefit of priority to Korean Patent Application No. 10-2023-0116487, filed September 1, 2023, and Korean Patent Application No. 10-2024-0117595, filed August 30, 2024, the entire contents of which are incorporated herein by reference.

[0003] The disclosure relates to a device and method for designing a multilayer film.

[0004] Polymer films are thin, flat structures made from polymer materials. Polymers can have a molecular structure in the form of long chains of repeating monomers. The thickness of polymer films can vary, reaching up to millimeters (mm). Polymer films offer numerous advantages, including flexibility, lightness, cost-effectiveness, and processability. Specifically, polymer films are highly flexible and can be manufactured in a variety of shapes and sizes, allowing them to be applied to a variety of product designs. Furthermore, their lightweight nature makes them suitable for products requiring portability, reducing transportation and handling costs. Furthermore, they can be manufactured using relatively inexpensive raw materials and are capable of mass production, making them cost-effective. Furthermore, polymer films can be applied to various manufacturing and processing techniques, such as thermal processing, extrusion, and bonding, allowing for easy adjustment of product shape, size, and thickness. Furthermore, polymer films possess high durability and strength, and some exhibit excellent optical properties, excellent water resistance, and, if desired, breathability.

[0005] A multilayer film can be composed of two or more layers of polymers or other materials. This multilayer structure can enhance the performance of the overall film by combining the unique properties of each layer. For example, one layer may be used to enhance durability and strength, another layer may provide water resistance or moisture repellency, and yet another layer may provide resistance to specific chemicals. By combining layers with different properties, multilayer films can adapt to a wider range of applications and specific requirements than single-layer films.

[0006] The task to be solved is to provide a design device and method for a multilayer film that can predict the properties of a multilayer film using only information about the single-layer films that constitute the multilayer film when designing the multilayer film.

[0007] According to one embodiment, a device for designing a multilayer film is a device for designing a multilayer film, which designs the multilayer film based on the physical properties of the multilayer film predicted only with information about a single-layer film by executing a program code loaded in one or more memory devices through one or more processors, wherein the program code is executed to model a laminated structure of a multilayer film to be designed, collect drop strength data for a single-layer film forming the laminated structure as stress-strain data, read a value stored in advance in a storage space accessible to the device for designing the multilayer film, and obtain a feature setting mode for designating a different feature setting method according to the read value, and according to the feature setting mode, set a plurality of physical indices selected from the drop strength data and the thickness of the single-layer film as features, and select at least one from a plurality of supervised learning models capable of regression analysis as a machine learning model, and use the machine learning model learned by using the feature as an independent variable and the drop strength of the multilayer film as a target variable to predict the drop strength of the multilayer film, and To meet the design requirements of the film, design data for the multilayer film can be generated by combining the predicted values ​​for other properties and the predicted values ​​for the drop strength.

[0008] In some embodiments, when the feature setting mode includes a first feature setting mode, setting as a feature may include setting as a feature at least some of Yield Point, Yield Stress, Resilience, Strain Hardening, Failure, Strain Hardening Modulus, Flow Energy, Strain Hardening Energy, Toughness, 1% Modulus, and 2% Modulus, and the thickness of the single-layer film.

[0009] In some embodiments, selecting the machine learning model comprises selecting any one of a Partial Least Squares (PLS) model, a Lasso regression model, and a random forest model as the machine learning model, wherein the machine learning model selected from the PLS model, the Lasso regression model, and the random forest model may be trained to receive a vector generated from at least some of the yield point, the yield stress, the elasticity, the strain hardening, the failure, the strain hardening coefficient, the flow energy, the strain hardening energy, the toughness, the 1% modulus, and the 2% modulus, and the thickness of the single-layer film, and to predict the drop strength of the multilayer film.

[0010] In some embodiments, when the feature setting mode includes a second feature setting mode, setting as a feature may include setting as a feature at least some of Yield Stress, Yield Strain, Necking Stress, Necking Strain, Breaking Stress and Breaking Strain, and the thickness of the single-layer film.

[0011] In some embodiments, selecting the machine learning model comprises selecting any one of a Partial Least Squares (PLS) model, a Lasso regression model, and a random forest model as the machine learning model, wherein the machine learning model selected from the PLS model, the Lasso regression model, and the random forest model may be trained to receive a vector generated from at least some of the yield stress, the yield strain, the necking point stress, the necking point strain, the fracture stress, and the fracture strain, and the thickness of the single-layer film, and to predict the drop strength of the multilayer film.

[0012] In some embodiments, when the feature setting mode includes a third feature setting mode, setting the feature may include setting the yield stress, the yield strain, and the yield stress divided by the yield strain (Yield Stress / Yield Strain) as features, and the thickness of the single-layer film.

[0013] In some embodiments, selecting the machine learning model includes selecting any one of a Partial Least Squares (PLS) model, a Lasso regression model, and a random forest model as the machine learning model, and the machine learning model selected from the PLS model, the Lasso regression model, and the random forest model may be trained to receive the yield stress, the yield strain, and an index obtained by dividing the yield stress by the yield strain, and a vector generated from the thickness of the single-layer film as input and to predict the drop strength of the multilayer film.

[0014] In some embodiments, generating design data for the multilayer film may include combining a predicted value for at least one of stiffness, strength, strain, tear strength, high-velocity drop strength, sealing initiation temperature (SIT), haze, moisture permeability, and air permeability for the multilayer film with a predicted value of the drop strength, and generating the design data based on the combination of predicted values ​​if the multilayer film designed based on the combination of predicted values ​​satisfies the design requirement, and redesigning the multilayer film by changing the combination to another combination if the multilayer film designed based on the combination of predicted values ​​does not satisfy the design requirement.

[0015] In some embodiments, the multilayer film is modeled as a multilayer film model, wherein the multilayer film comprises a first monolayer film and a second monolayer film laminated to each other, wherein the multilayer film model comprises n (where n is a multiple of 6) model layers laminated to each other, wherein the first monolayer film corresponds to two or more of the model layers, and the second monolayer film corresponds to two or more other model layers of the model layers.

[0016] In some embodiments, the program code may be executed to further display the model layer on a display device and provide a user interface that allows a user to set a model layer corresponding to the first single-layer film and a model layer corresponding to the second single-layer film.

[0017] According to one embodiment, a method for designing a multilayer film is provided, which is performed by a computing device including one or more processors and one or more memory devices, for designing a multilayer film based on properties of the multilayer film predicted only from information about a single-layer film, the method comprising: modeling a laminated structure of a multilayer film to be designed; collecting drop strength data for a single-layer film forming the laminated structure as stress-strain data; acquiring a feature setting mode for reading a value stored in advance in a storage space accessible to the computing device and designating a different feature setting method according to the read value; setting a plurality of physical indices selected from the drop strength data and the thickness of the single-layer film as features according to the feature setting mode; selecting at least one as a machine learning model from a plurality of supervised learning models capable of regression analysis; predicting the drop strength of the multilayer film using the machine learning model learned by using the feature as an independent variable and the drop strength of the multilayer film as a target variable; And, to meet the design requirements of the multilayer film, the method may include a step of generating design data for the multilayer film by combining predicted values ​​for other characteristics and predicted values ​​of the drop strength.

[0018] In some embodiments, when the feature setting mode includes a first feature setting mode, the step of setting as a feature may include the step of setting as a feature at least some of Yield Point, Yield Stress, Resilience, Strain Hardening, Failure, Strain Hardening Modulus, Flow Energy, Strain Hardening Energy, Toughness, 1% Modulus, and 2% Modulus, and the thickness of the single-layer film.

[0019] In some embodiments, the step of selecting as the machine learning model includes the step of selecting as the machine learning model any one of a Partial Least Squares (PLS) model, a Lasso regression model, and a random forest model, and the machine learning model selected from the PLS model, the Lasso regression model, and the random forest model may be trained to receive a vector generated from at least some of the yield point, the yield stress, the elasticity, the strain hardening, the failure, the strain hardening coefficient, the flow energy, the strain hardening energy, the toughness, the 1% modulus, and the 2% modulus, and the thickness of the single-layer film, and to predict the drop strength of the multilayer film.

[0020] In some embodiments, when the feature setting mode includes a second feature setting mode, the step of setting as a feature may include the step of setting as a feature at least some of a yield stress, a yield strain, a necking stress, a necking strain, a breaking stress, and a breaking strain, and a thickness of the single-layer film.

[0021] In some embodiments, the step of selecting the machine learning model comprises the step of selecting any one of a Partial Least Squares (PLS) model, a Lasso regression model, and a random forest model as the machine learning model, and the machine learning model selected from the PLS model, the Lasso regression model, and the random forest model may be trained to receive a vector generated from at least some of the yield stress, the yield strain, the necking point stress, the necking point strain, the fracture stress, and the fracture strain, and the thickness of the single-layer film, and to predict the drop strength of the multilayer film.

[0022] In some embodiments, when the feature setting mode includes a third feature setting mode, the step of setting as a feature may include the step of setting as a feature a yield stress, a yield strain, and an index of yield stress divided by yield strain (Yield Stress / Yield Strain), and a thickness of the single-layer film.

[0023] In some embodiments, the step of selecting as the machine learning model includes the step of selecting as the machine learning model any one of a Partial Least Squares (PLS) model, a Lasso regression model, and a random forest model, and the machine learning model selected from the PLS model, the Lasso regression model, and the random forest model may be trained to receive as input the yield stress, the yield strain, and an index obtained by dividing the yield stress by the yield strain, and a vector generated from the thickness of the single-layer film, and to predict the drop strength of the multilayer film.

[0024] In some embodiments, the step of generating design data for the multilayer film may include: combining a predicted value for at least one of stiffness, strength, strain, tear strength, high-velocity drop strength, sealing initiation temperature (SIT), haze, moisture permeability, and air permeability for the multilayer film with a predicted value of the drop strength; generating the design data based on the combination of predicted values ​​when the multilayer film designed based on the combination of predicted values ​​satisfies the design requirement; and redesigning the multilayer film by changing the combination to another combination when the multilayer film designed based on the combination of predicted values ​​does not satisfy the design requirement.

[0025] In some embodiments, the multilayer film is modeled as a multilayer film model, wherein the multilayer film comprises a first monolayer film and a second monolayer film laminated to each other, wherein the multilayer film model comprises n (where n is a multiple of 6) model layers laminated to each other, wherein the first monolayer film corresponds to two or more of the model layers, and the second monolayer film corresponds to two or more other model layers of the model layers.

[0026] In some embodiments, the method may further include the step of displaying the model layer on a display device and providing a user interface that allows a user to set a model layer corresponding to the first single-layer film and a model layer corresponding to the second single-layer film.

[0027] According to embodiments, when designing a multilayer film, the properties of the multilayer film can be predicted using only information about the single-layer films constituting the multilayer film, and by considering the prediction results regarding other properties together, a multilayer film that can satisfy given design requirements can be designed.

[0028] Figure 1 is a block diagram illustrating a design device for a multilayer film according to one embodiment.

[0029] FIG. 2 is a diagram illustrating stress strain data for a single-layer film according to one embodiment.

[0030] Figure 3 is a flowchart illustrating a design method of a multilayer film according to one embodiment.

[0031] Figure 4 is a block diagram illustrating a design device for a multilayer film according to one embodiment.

[0032] Figure 5 is a block diagram illustrating a design device for a multilayer film according to one embodiment.

[0033] Figures 6 and 7 are drawings for explaining a user interface according to one embodiment.

[0034] FIG. 8 is a drawing for explaining a computing device according to one embodiment.

[0035] Below, with reference to the attached drawings, embodiments of the present invention are described in detail so that those skilled in the art can easily implement the present invention. However, the present invention may be implemented in various different forms and is not limited to the embodiments described herein. In addition, in the drawings, parts irrelevant to the description are omitted for clarity of description, and similar parts are designated with similar reference numerals throughout the specification.

[0036] Throughout the specification and claims, whenever a part is referred to as "comprising" a component, this does not exclude other components, but rather includes other components, unless otherwise stated. Terms including ordinal numbers, such as "first," "second," etc., may be used to describe various components, but these components are not limited by these terms. These terms are used solely to distinguish one component from another.

[0037] Terms such as "...part," "...unit," and "module" described in the specification may refer to a unit capable of processing at least one function or operation described in the specification, which may be implemented by hardware or a circuit, software, or a combination of hardware or a circuit and software. In addition, at least some of the configurations or functions of the design device and method for a multilayer film according to the embodiments described below may be implemented by a program or software, and the program or software may be stored in a computer-readable medium.

[0038] FIG. 1 is a block diagram for explaining a design device for a multilayer film according to one embodiment, and FIG. 2 is a diagram for explaining stress strain data for a single-layer film according to one embodiment.

[0039] Referring to FIG. 1, a design device (10) for a multilayer film according to an embodiment can execute program codes or instructions loaded in one or more memory devices through one or more processors. For example, the design device (10) for a multilayer film can be implemented as a computing device (50) as described below with reference to FIG. 8. In this case, one or more processors may correspond to the processor (510) of the computing device (50), and one or more memory devices may correspond to the memory (520) of the computing device (50). The program codes or instructions can be executed by one or more processors to predict the properties of a multilayer material, i.e., a multilayer film, using only information about a single-layer film, and design the multilayer film based on the predicted properties. In this specification, the term "module" is used to logically distinguish these functions performed by the program codes or instructions.

[0040] The design device (10) for a multilayer film may include all or at least a part of a multilayer film modeling module (110), a data collection module (120), a feature setting module (130), a model selection module (140), a learning module (150), a prediction module (160), and a design data generation module (162). For example, the learning module (150) may be implemented within the design device (10) for a multilayer film. Alternatively, for example, the learning module (150) may be implemented outside the design device (10) for a multilayer film, and the design device (10) for a multilayer film may be provided with a machine learning model learned from the learning module (150).

[0041] The multilayer film modeling module (110) can model the laminated structure of a multilayer film to be designed. The multilayer film may include a plurality of single-layer films. Here, the plurality of single-layer films may include a certain type of single-layer film and a different type of single-layer film, and the unique characteristics of the single-layer film may differ depending on the type. In addition, the stacking order of the single-layer films within the multilayer film may also vary. The multilayer film modeling module (110) can model the multilayer film by setting the type of single-layer film, the stacking order of the single-layer film, etc. for the multilayer film whose properties are to be predicted.

[0042] In some embodiments, the multilayer film modeling module (110) may model a multilayer film including k single-layer films (where k is an integer greater than or equal to 2). For example, the multilayer film modeling module (110) may model a multilayer film in which a first film layer, a second film layer, ..., and a k-th film layer are sequentially laminated from the bottom. Features set by the feature setting module (130) described below may be applied to each of the k single-layer films forming the multilayer film.

[0043] In some embodiments, the multilayer film modeling module (110) can model a multilayer film including six single-layer films. For example, the multilayer film modeling module (110) can model a multilayer film in which a first film layer, a second film layer, a third film layer, a fourth film layer, a fifth film layer, and a sixth film layer are sequentially laminated from the bottom.

[0044] The data collection module (120) can collect drop strength data for a single-layer film forming a laminated structure modeled by the multilayer film modeling module (110). Here, drop strength can refer to the strength that a material (here, a single-layer film) can withstand when subjected to a physical impact. The drop strength can be used as an indicator for measuring the extent of damage to a single-layer film, for example, when a weight is dropped from a predetermined height. The multilayer film design device (10) can predict the drop strength of a multilayer film through machine learning based on the drop strength data for each of a plurality of single-layer films forming a laminated structure.

[0045] In some embodiments, the data collection module (120) may collect drop strength data for a single-layer film as stress-strain data. The stress-strain data may represent mechanical properties of the single-layer film, and, referring to FIG. 2, may be expressed in the form of a stress-strain curve. The stress-strain curve may represent the stress generated when a force is applied to the single-layer film, and how the single-layer film is deformed as a result.

[0046] In some embodiments, the stress-strain data may include at least one of a yield point, yield stress, resilience, strain hardening, failure, strain hardening modulus, flow energy, strain hardening energy, toughness, 1% modulus, and 2% modulus. Here, the yield point represents the point at which a material transitions from an elastic deformation region to an inelastic deformation region, after which the material may not fully return to its original shape when the applied stress is removed. The yield stress is the stress value at the yield point, and a higher value may indicate that the material can withstand a greater stress before deforming. Resilience is the area under the stress-strain curve up to the yield point, and may be a measure of how well a material can return to its original shape when the stress is removed after being deformed under a certain stress. Strain hardening refers to the phenomenon in which a material strengthens as strain increases to withstand additional stress after the yield point, while failure can indicate the point at which the material can no longer withstand stress and breaks. The strain hardening coefficient is represented by the slope of the stress-strain curve in the strain-hardening region and can be used as an indicator of how quickly a material strengthens. The flow energy is the area under the stress-strain curve from the yield point to strain hardening, and the strain hardening energy can represent the area under the stress-strain curve from strain hardening to failure. Toughness is the total area under the stress-strain curve and indicates how much energy a material can absorb, and the 1% modulus and 2% modulus can represent the stress at 1% and 2% strain, respectively.The values ​​of 1% modulus and 2% modulus can be used as measurements of strength within a range that does not exceed the elastic range of the material.

[0047] In some embodiments, the data collection module (120) may collect drop strength data for a single-layer film through a test according to "Method A" of ASTM D1709. Here, the test may evaluate the impact strength or toughness of the single-layer film using a freely falling dart. Further details regarding ASTM D1709 may be referenced from ASTM standard documents, and thus are not described herein.

[0048] In some embodiments, the data collection module (120) may acquire stress-strain curves as data based on the results of uniaxial tensile tests performed in the Machine Direction (MD) and the Transverse Direction (TD). Here, MD may be the direction in which the film moves between the rollers of the manufacturing machine during the film manufacturing process, and TD may be the direction orthogonal to the rollers of the manufacturing machine, i.e., perpendicular to MD. Meanwhile, the uniaxial tensile test may be performed by measuring the resistance when a single-layer film is stretched in the uniaxial direction. In this case, the data collection module (120) may collect data regarding the stress-strain curves through a test according to ASTM D882. For more detailed information regarding ASTM D882, reference may be made to the ASTM standard document, and therefore, a description thereof will be omitted herein.

[0049] The feature setting module (130) can perform feature setting according to a plurality of feature setting modes. The plurality of feature setting modes are stored and managed as predetermined values ​​in a storage space accessible to the processor of the design device (10) for a multilayer film, and the design device (10) for a multilayer film can read the values ​​stored in advance in the storage space and determine a feature setting mode for specifying a different feature setting method according to the read values.

[0050] In some embodiments, when the feature setting mode is the first feature setting mode, the feature setting module (130) may set a plurality of physical indices selected from the drop strength data for a single-layer film and the thickness of the single-layer film as features. In machine learning, a feature is an input variable used to predict a target to be predicted, i.e., a physical property of a multilayer film, and may mean an individual independent variable of data. A feature provides information related to a dependent variable or target variable to be predicted within a dataset and may have a significant impact on the performance of a machine learning model (20). The learning module (150) may use features to learn the machine learning model (20). At this time, the features constitute a portion of the data input to the machine learning model (20), and the machine learning model (20) may learn patterns based on the data and perform predictions. In particular, feature engineering, which selects or transforms features, may be an important process for improving the performance of the machine learning model (20). That is, selecting appropriate features or appropriately transforming features can have a great impact on the high performance of the machine learning model (20).

[0051] In some embodiments, the feature setting module (130) may set some of the yield point, yield stress, elasticity, strain hardening, fracture, strain hardening coefficient, flow energy, strain hardening energy, toughness, 1% modulus, and 2% modulus, and the thickness of the single-layer film as features. That is, the feature setting module (130) may set only some of the stress-strain data collected by the data collection module (120) and the thickness of the single-layer film as features. When a multilayer film including k single-layer films is modeled by the multilayer film modeling module (110), the learning module (150) may apply features including only some of the stress-strain data collected by the data collection module (120) for the first film layer among the k single-layer films and the thickness of the single-layer film. In addition, the learning module (150) may select only a portion of the stress strain data collected by the data collection module (120) for the second film layer among the k single-layer films and apply a feature including the thickness of the single-layer film. In addition, the learning module (150) may select only a portion of the stress strain data collected by the data collection module (120) for the k-th film layer and apply a feature including the thickness of the single-layer film to perform learning of the machine learning model (20). At this time, learning may be performed by applying the same type of feature to all of the k single-layer films, or alternatively, learning may be performed by applying a different type of feature to one film layer among the k single-layer films from the other film layers. This embodiment may be adopted in an environment where computing resources for performing learning and prediction for the machine learning model (20) are limited.

[0052] In some embodiments, the feature setting module (130) may set all of the yield point, yield stress, elasticity, strain hardening, fracture, strain hardening coefficient, flow energy, strain hardening energy, toughness, 1% modulus, and 2% modulus, and the thickness of the single-layer film as features. That is, the feature setting module (130) may set all of the stress-strain data collected by the data collection module (120) and the thickness of the single-layer film as features. When a multilayer film including k single-layer films is modeled by the multilayer film modeling module (110), the learning module (150) may apply features including all of the stress-strain data collected by the data collection module (120) for the first film layer among the k single-layer films, and the thickness of the single-layer film. In addition, the learning module (150) can apply all of the stress strain data collected by the data collection module (120) for the second film layer among the k single-layer films and a feature including the thickness of the single-layer film. In addition, the learning module (150) can proceed with learning of the machine learning model (20) by applying all of the stress strain data collected by the data collection module (120) for the k-th film layer and a feature including the thickness of the single-layer film. The embodiment can be adopted in an environment with sufficient computing resources for performing learning and prediction for the machine learning model (20).

[0053] In some embodiments, the stress strain data may include first stress strain data and second stress strain data. The first stress strain data may represent mechanical properties of the single-layer film in a first direction, and the second stress strain data may represent mechanical properties of the single-layer film in a second direction perpendicular to the first direction. Specifically, the first direction may be MD, and the second direction may be TD. The feature setting module (130) may set a plurality of physical indicators selected from at least one of the first stress strain data and the second stress strain data as features.

[0054] In some embodiments, the feature setting module (130) may set as features some of the yield point, yield stress, elasticity, strain hardening, failure, strain hardening coefficient, flow energy, strain hardening energy, toughness, 1% modulus, and 2% modulus for the first direction, some of the yield point, yield stress, elasticity, strain hardening, failure, strain hardening coefficient, flow energy, strain hardening energy, toughness, 1% modulus, and 2% modulus for the second direction, and the thickness of the single-layer film. That is, the feature setting module (130) may set as features only some of the first stress strain data collected by the data collection module (120), only some of the second stress strain data, and the thickness of the single-layer film. When a multilayer film including k single-layer films is modeled by the multilayer film modeling module (110), the learning module (150) can select only a portion of the first stress strain data collected by the data collection module (120) for the first film layer among the k single-layer films, select only a portion of the second stress strain data, and apply the thickness of the single-layer film as features. In addition, the learning module (150) can select only a portion of the first stress strain data collected by the data collection module (120) for the second film layer among the k single-layer films, select only a portion of the second stress strain data, and apply features including the thickness of the single-layer film. In addition, the learning module (150) can proceed with learning of the machine learning model (20) by applying features including only a portion of the first stress strain data collected by the data collection module (120) for the k-th film layer, select only a portion of the second stress strain data, and apply features including the thickness of the single-layer film.

[0055] In some embodiments, the feature setting module (130) may set as features all of the yield point, yield stress, elasticity, strain hardening, failure, strain hardening coefficient, flow energy, strain hardening energy, toughness, 1% modulus, and 2% modulus for the first direction, and all of the yield point, yield stress, elasticity, strain hardening, failure, strain hardening coefficient, flow energy, strain hardening energy, toughness, 1% modulus, and 2% modulus for the second direction, and the thickness of the single-layer film. That is, the feature setting module (130) may set as features all of the first stress strain data collected by the data collection module (120), all of the second stress strain data, and the thickness of the single-layer film. When a multilayer film including k single-layer films is modeled by the multilayer film modeling module (110), the learning module (150) can apply all of the first stress strain data, all of the second stress strain data, and the thickness of the single-layer film collected by the data collection module (120) for the first film layer among the k single-layer films as features. In addition, the learning module (150) can apply a feature including all of the first stress strain data, all of the second stress strain data, and the thickness of the single-layer film collected by the data collection module (120) for the second film layer among the k single-layer films. In addition, the learning module (150) can proceed with learning of the machine learning model (20) by applying a feature including all of the first stress strain data, all of the second stress strain data, and the thickness of the single-layer film collected by the data collection module (120) for the k-th film layer.

[0056] In some other embodiments, when the feature setting mode is the second feature setting mode, the feature setting module (130) may set yield stress, yield strain, necking stress, necking strain, breaking stress, and breaking strain as features. Here, yield stress, yield strain, necking strain, and breaking stress may be features corresponding to yield stress, yield point, strain hardening, and failure of the stress-strain data described above. Meanwhile, necking point stress and breaking strain may be newly designated features analyzed from the stress-strain data. Here, yield stress may represent the stress at the yield point, which is the point where the material exceeds the elastic limit and permanent deformation begins, that is, the point where the material moves from an elastic deformation region to an inelastic deformation region, and yield strain may represent the strain corresponding to the yield stress. Necking stress refers to the stress at the point where the cross-section of a material begins to narrow locally (the necking point), after which the material's load capacity typically decreases rapidly. Necking strain can be used to indicate the strain at the necking point. Breaking stress refers to the stress at which a material finally breaks, at which point it can no longer support the load. Breaking strain can be used to indicate the strain corresponding to the breaking stress.

[0057] In some embodiments, the feature setting module (130) may set at least some of the yield stress, the yield strain, the necking point stress, the necking point strain, the fracture stress, and the fracture strain for each of the first direction and the second direction, and the thickness of the single-layer film as features. When a multilayer film including k single-layer films is modeled by the multilayer film modeling module (110), the learning module (150) may apply a feature including, for a first film layer among the k single-layer films, at least some of the yield stress, the yield strain, the necking point stress, the necking point strain, the fracture stress, and the fracture strain for the first direction, and at least some of the yield stress, the yield strain, the necking point stress, the necking point strain, the fracture stress, and the fracture strain for the second direction, and the thickness of the single-layer film. In addition, the learning module (150) may apply features including at least some of the yield stress, the yield strain, the necking point stress, the necking point strain, the fracture stress, and the fracture strain for the first direction, and at least some of the yield stress, the yield strain, the necking point stress, the necking point strain, the fracture stress, and the fracture strain for the second direction, and the thickness of the single-layer film, for the second film layer among the k single-layer films. In addition, the learning module (150) may proceed with learning of the machine learning model (20) by applying features including at least some of the yield stress, the yield strain, the necking point stress, the necking point strain, the fracture stress, and the fracture strain for the first direction, and at least some of the yield stress, the yield strain, the necking point stress, the necking point strain, the fracture stress, and the fracture strain for the second direction, and the thickness of the single-layer film, for the k-th film layer.At this time, learning may proceed by applying the same type of feature to all k single-layer films, or alternatively, learning may proceed by applying a different type of feature to one film layer among the k single-layer films from the other film layers.

[0058] In some other embodiments, when the feature setting mode is the third feature setting mode, the feature setting module (130) may set yield stress, yield strain, and an index of yield stress divided by yield strain (Yield Stress / Yield Strain) as features. Specifically, the feature setting module (130) may set all of the yield stress, yield strain, and index of yield stress divided by yield strain for each of the first direction and the second direction, and the thickness of the single-layer film as features. When a multilayer film including k single-layer films is modeled by the multilayer film modeling module (110), the learning module (150) may apply, to a first film layer among the k single-layer films, features including all indices of yield stress, yield strain, and yield stress divided by yield strain for a first direction, all indices of yield stress, yield strain, and yield stress divided by yield strain for a second direction, and a thickness of the single-layer film. In addition, the learning module (150) may apply, to a second film layer among the k single-layer films, features including all indices of yield stress, yield strain, and yield stress divided by yield strain for a first direction, all indices of yield stress, yield strain, and yield stress divided by yield strain for a second direction, and a thickness of the single-layer film. In addition, the learning module (150) can proceed with learning of the machine learning model (20) by applying features including, for the k-th film layer, all indices of yield stress, yield strain, and yield stress divided by yield strain for the first direction, all indices of yield stress, yield strain, and yield stress divided by yield strain for the second direction, and the thickness of the single-layer film.

[0059] For example, when the multilayer film modeling module (110) models a multilayer film including six single-layer films, seven features can be applied to one film layer: all indices of yield stress, yield strain, and yield stress divided by yield strain for the first direction, all indices of yield stress, yield strain, and yield stress divided by yield strain for the second direction, and the thickness of the single-layer film. Since there are six layers in total, a total of 42 features can be applied.

[0060] The model selection module (140) can select a machine learning model to be used to predict the physical properties of the multilayer film, i.e., the impact strength. Specifically, the model selection module (140) can select at least one machine learning model from among a plurality of supervised learning models capable of regression analysis for predicting the impact strength of the multilayer film. Supervised learning is a method of training a model using labeled training data, and in supervised learning, the model learns the relationship between input variables and target variables, thereby being able to predict the value of the target variable for new input data. In some embodiments, the model selection module (140) can select a supervised learning model capable of regression analysis from among Partial Least Squares (PLS), Linear Regression, Polynomial Regression, Ridge Regression, Lasso Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, and Neural Networks. The PLS model can be used to model the relationship between multiple independent variables and multiple dependent variables (or target variables), and can find independent variables that are determined to be important for predicting the dependent variable. In linear regression, the model learns a linear relationship between input variables and the target variable, and in polynomial regression, the model can learn a complex relationship by extending linear regression to include higher-order terms of input variables. Ridge regression and Lasso regression add regularization to linear regression to prevent overfitting and control the complexity of the model. Logistic regression is a classification algorithm for solving binary classification problems, but it can be used in regression analysis because it predicts the outcome as a probability.Decision trees, random forests, and gradient boosting learn complex nonlinear relationships and can be used for regression problems as well as classification. Neural networks can solve various types of regression problems, and deep learning, in particular, can be very effective for learning complex nonlinear relationships. Of course, the supervised learning models capable of regression analysis that can be selected by the model selection module (140) are not limited to those listed above, and other types of models not listed may be selected based on specific implementation objectives, implementation environments, required performance, etc.

[0061] The learning module (150) can train a machine learning model (20) using the features set by the feature setting module (130) as independent variables and the drop strength of the multilayer film as a target variable. Accordingly, the machine learning model (20) can be trained as a model capable of predicting the properties of the multilayer film for new data, i.e., property data on the single-layer film, by learning the relationship between the features and the target variables. In particular, the learning module (150) can use different learning methods depending on the model selected by the model selection module (140).

[0062] The prediction module (160) can predict the impact strength of a multilayer film using the machine learning model (20) learned by the learning module (150). That is, the prediction module (160) can transmit new data, i.e., property data regarding a single-layer film, to the machine learning model (20) and obtain a prediction result regarding the impact strength of the multilayer film.

[0063] Specifically, the model selection module (140) can perform model selection according to a plurality of model selection modes. The plurality of model selection modes are stored and managed as predetermined values ​​in a storage space accessible to the processor of the design device (10) of the multilayer film, and the design device (10) of the multilayer film can read the values ​​stored in advance in the storage space and determine a model selection mode for designating a different model selection method according to the read values.

[0064] In some embodiments, when the model selection mode is the first model selection mode, the model selection module (140) may select the PLS model as the machine learning model to be used to predict the drop strength of the multilayer film. As described above, the features set by the feature setting module (130) depending on the feature setting mode may be combined into a form such as a single vector and used as input to the PLS model. The drop strength of the multilayer film is set as the target variable, and the learning module (150) may train the PLS model in a direction that maximizes the correlation between the input features and the target variable. At this time, the learning module (150) optimizes the "n_components" parameter so that the PLS model can properly explain the data, and the optimal "n_components" value may be determined through cross-validation. Through the PLS model trained in this way, the drop strength of the multilayer film can be predicted by inputting features for a new single-layer film.

[0065] In some other embodiments, when the model selection mode is the second model selection mode, the model selection module (140) may select a Lasso regression model as a machine learning model to be used to predict the crash strength of a multilayer film. As described above, features set by the feature setting module (130) depending on the feature setting mode may be combined into a form such as a single vector and used as input to the Lasso regression model. The crash strength of the multilayer film may be set as a target variable, and the learning module (150) may train the Lasso regression model in a way that induces the model to select only important features through L1 regularization (regularization that adds the sum of the absolute values ​​of the regression coefficients to the objective function as a penalty). At this time, the learning module (150) optimizes the "alpha" parameter for adjusting the regularization strength during model training, and the optimal "alpha" value may be determined through cross-validation. Through the Lasso regression model trained in this way, the crash strength of a new single-layer film may be predicted by inputting features for the multilayer film.

[0066] In some other embodiments, when the model selection mode is the third model selection mode, the model selection module (140) may select a random forest model as a machine learning model to be used to predict the crash strength of a multilayer film. As described above, features set by the feature setting module (130) according to the feature setting mode may be combined into a form such as a single vector and used as input to the random forest model. The crash strength of the multilayer film may be set as a target variable, and the learning module (150) may train the random forest model by setting hyperparameters such as "n_estimators," "max_depth," and "min_samples_split." Through the random forest model trained in this way, features for a new single-layer film may be input to predict the crash strength of the multilayer film.

[0067] The design data generation module (162) can generate design data for the multilayer film by combining predicted values ​​for other characteristics with predicted values ​​for drop weight strength obtained through the prediction module (160) to meet design requirements of the multilayer film. In some embodiments, the other characteristics can include at least one of stiffness, strength, strain, tear strength, high-speed drop weight strength, sealing initiation temperature (SIT), haze, moisture permeability, and air permeability for the multilayer film.

[0068] The design data generation module (162) can first combine the predicted value for at least one of the stiffness, strength, strain, tear strength, high-speed drop strength, seal initiation temperature, transparency, moisture permeability, and air permeability of the multilayer film with the predicted value of the drop strength. The design requirements for the multilayer film to be designed can include requirements for ensuring mechanical properties, thermal properties, optical properties, barrier properties, and chemical properties to secure desired performance. If the multilayer film designed based on the first combination of the predicted values ​​satisfies the design requirements, the design data generation module (140) can generate design data based on the first combination. In this case, the design data can include specific predicted values.

[0069] In contrast, if the multilayer film designed based on the first combination of predicted values ​​does not meet the design requirements, the design data generation module (140) can redesign the multilayer film by changing the first combination to another combination. That is, the design data generation module (140) can secondarily combine the predicted value for at least one of the stiffness, strength, strain, tear strength, high-speed drop strength, seal initiation temperature, transparency, moisture permeability, and air permeability of the multilayer film with the predicted value of the drop strength so as to have a different combination from the first combination. At this time, the predicted value of the drop strength in the first combination and the predicted value of the drop strength in the second combination may be different values.

[0070] If the multilayer film designed based on the secondary combination of predicted values ​​satisfies the design requirements, the design data generation module (140) can generate design data based on the secondary combination. Alternatively, if the multilayer film designed based on the secondary combination of predicted values ​​also does not satisfy the design requirements, the design data generation module (140) can repeat the process of redesigning the multilayer film by changing the secondary combination to another combination.

[0071] According to this embodiment, when designing a multilayer film, the properties of the multilayer film can be predicted using only information about the single-layer film constituting the multilayer film. In particular, by performing learning and prediction using various indices of the drop strength stress strain data for the single-layer film and the thickness of the single-layer film as features, the drop strength of the multilayer film can be predicted with high accuracy. In addition, by varying the number of indices selected as features when performance is prioritized and the number of indices selected as features when computing resources are saved, flexible design is possible according to the implementation purpose and environment of the design device for the multilayer film. Furthermore, by considering the prediction results regarding other characteristics of the multilayer film together with the prediction results of the drop strength, a multilayer film that can satisfy given design requirements can be designed.

[0072] Figure 3 is a flowchart illustrating a design method of a multilayer film according to one embodiment.

[0073] Referring to FIG. 3, a method for designing a multilayer film according to an embodiment includes a step of modeling a laminated structure of a multilayer film to be designed (S301), a step of collecting drop strength data for a single-layer film forming the laminated structure (S302), a step of reading a value stored in advance in a storage space and obtaining a feature setting mode for designating a different feature setting method according to the read value (S303), a step of setting a plurality of physical indices selected from the drop strength data and the thickness of the single-layer film as features according to the feature setting mode (S304), a step of selecting at least one machine learning model from among a plurality of supervised learning models capable of regression analysis (S305), a step of training a machine learning model using features as independent variables and the drop strength of the multilayer film as a target variable (S306), a step of predicting the drop strength of the multilayer film using the trained machine learning model (S307), and a step of generating design data for the multilayer film by combining predicted values ​​for other characteristics and predicted values ​​of the drop strength so as to satisfy design requirements of the multilayer film. It may include step (S308).

[0074] For more specific details regarding the design method of a multilayer film, reference can be made to and application of the description of the embodiments described in this specification, so redundant descriptions are omitted here.

[0075] Figure 4 is a block diagram illustrating a design device for a multilayer film according to one embodiment.

[0076] Referring to FIG. 4, a design device (10) for a multilayer film according to an embodiment may include all or at least a part of a multilayer film modeling module (110), a data collection module (120), a feature setting module (130), a model selection module (140), a learning module (150), a prediction module (160), a design data generation module (162), a correlation coefficient analysis module (170), and a PLS (Partial Least Squares) analysis module (180). For example, the learning module (150) may be implemented within the design device (10) for a multilayer film. Alternatively, for example, the learning module (150) may be implemented outside the design device (10) for a multilayer film, and the design device (10) for a multilayer film may be provided with a machine learning model learned from the learning module (150). As for the multilayer film modeling module (110), the data collection module (120), the feature setting module (130), the model selection module (140), the learning module (150), the prediction module (160), and the design data generation module (162), unless they are contradictory or inconsistent with what is described in the present embodiment, the description of the multilayer film modeling module (110), the data collection module (120), the feature setting module (130), the model selection module (140), the learning module (150), the prediction module (160), and the design data generation module (162) described above with reference to FIGS. 1 and 2 may be referred to, and thus, any redundant description will be omitted here.

[0077] The correlation coefficient analysis module (170) can analyze the correlation coefficient between mechanical properties in stress-strain data, and the PLS analysis module (180) can predict the drop strength value of a multilayer film using PLS analysis according to the correlation coefficient analyzed by the correlation coefficient analysis module (170). PLS analysis can be used to model the relationship between multiple independent variables and multiple dependent variables (or target variables), and can find independent variables that are determined to be important for predicting the dependent variables. Specifically, the correlation coefficient analysis module (170) can construct independent variables and dependent variables from various indices of the drop strength stress-strain data for a single-layer film and find the component with the largest covariance. From this, a new subspace is generated, in which the relationship between the independent variables and the dependent variables can be most greatly expressed. The PLS analysis module (180) can find new components by repeatedly adjusting variables in proportion to each covariance for the new subspace until the desired number of components is extracted. In this way, the properties of a multilayer film can be predicted together with or instead of the prediction module (160).

[0078] In some embodiments, the correlation coefficient analysis module (170) may be implemented to be logically separate from the feature setting module (130), or may be implemented to be logically included in the feature setting module (130). When the correlation coefficient analysis module (170) is implemented to be logically included in the feature setting module (130), when setting a plurality of physical indices selected from the drop strength data for a single-layer film and the thickness of the single-layer film as features, the correlation coefficient between the mechanical properties of the stress-strain data can be analyzed as preprocessing for data dimensionality reduction.

[0079] Meanwhile, in some embodiments, the PLS analysis module (180) may be implemented to be logically separate from the model selection module (140), or may be implemented to be logically included in the model selection module (140). When the PLS analysis module (180) is implemented to be logically included in the model selection module (140), when selecting a machine learning model to be used for predicting the fall strength, a model performing PLS analysis may be selected.

[0080] According to this embodiment, the accuracy of predicting the drop strength of a multilayer film can be further improved by performing separate analysis on indicators with high collinearity among various indicators of the drop strength stress strain data for a single-layer film.

[0081] Figure 5 is a block diagram illustrating a design device for a multilayer film according to one embodiment.

[0082] Referring to FIG. 5, the design device (10) for a multilayer film may include all or at least a portion of a multilayer film modeling module (110), a data collection module (120), a feature setting module (130), a model selection module (140), a learning module (150), a prediction module (160), a design data generation module (162), and a model layer setting interface providing module (190). For example, the learning module (150) may be implemented within the design device (10) for a multilayer film. Alternatively, for example, the learning module (150) may be implemented outside the design device (10) for a multilayer film, and the design device (10) for a multilayer film may be provided with a machine learning model learned from the learning module (150). As for the multilayer film modeling module (110), the data collection module (120), the feature setting module (130), the model selection module (140), the learning module (150), the prediction module (160), and the design data generation module (162), unless they are contradictory or inconsistent with what is described in the present embodiment, the description of the multilayer film modeling module (110), the data collection module (120), the feature setting module (130), the model selection module (140), the learning module (150), the prediction module (160), and the design data generation module (162) described above with reference to FIGS. 1 and 2 may be referred to, and thus, any redundant description will be omitted here.

[0083] The multilayer film modeled in the multilayer film modeling module (110) can be modeled as a multilayer film model. Here, the multilayer film model can be in the form of including a plurality of empty slots that are arranged in a vertical direction continuously. Specifically, the multilayer film model can include n slots (n is a multiple of 6) that are laminated to each other, i.e., model layers. In addition, when the multilayer film includes a first single-layer film and a second single-layer film that are laminated to each other, the first single-layer film can correspond to two or more model layers among the model layers, and the second single-layer film can correspond to the other two or more model layers among the model layers. The data collection module (120) can collect drop strength data for the first single-layer film and the second single-layer film based on such a multilayer film model.

[0084] The model layer setting interface providing module (190) can enable a user to set the model layer as described above. To this end, the model layer setting interface providing module (190) can display the model layer on a display device and provide a user interface that allows the user to set the model layer corresponding to the first single-layer film and the model layer corresponding to the second single-layer film.

[0085] Figures 6 and 7 are drawings for explaining a user interface according to one embodiment.

[0086] Referring to FIG. 6, the model layer setting interface providing module (190) can present the first to sixth model layers, which are expressed as n (n is a multiple of 6) stacked slots, to the user through the display device. The user can input, through the user interface, information about how many model layers one single-layer film occupies, the stacking order between the single-layer films, and the types and unique characteristics of the single-layer films set to which model layers. For example, as illustrated in the drawing, the user can input information that the first single-layer film and the second single-layer film each occupy three model layers, and the second single-layer film is stacked on the first single-layer film, by assigning the first single-layer film to the first to third model layers and the second single-layer film to the fourth to sixth model layers. Although not illustrated, the user can also input information about the types and other unique characteristics of the first and second single-layer films, respectively.

[0087] Referring to FIG. 7, the user can input information that the first to third monolayer films each occupy two model layers, the second monolayer film is laminated on the first monolayer film, and the third monolayer film is laminated on the second monolayer film by assigning the first monolayer film to the first to second model layers, the second monolayer film to the third to fourth model layers, and the third monolayer film to the fifth to sixth model layers. Although not shown, the user can also input the type and other special details of each of the first to third monolayer films.

[0088] According to the present embodiment, the feature setting module (130) can set features based on the same six-layer model layer structure in both cases, for example, when there are two types of single-layer films forming a multilayer film and three types of single-layer films forming a multilayer film. Accordingly, the learning module (150) can train the machine learning model (20) by using features for the same number of model layer structures as independent variables and the drop strength of the multilayer film as a target variable, regardless of the number of types of single-layer films forming a multilayer film. Accordingly, since the machine learning model (20) can be applied regardless of the number of types of single-layer films forming a multilayer film, it is possible to reduce implementation complexity and computational cost while maintaining high prediction accuracy compared to cases where different layer structures must be trained for each type of single-layer films forming a multilayer film.

[0089] FIG. 8 is a drawing for explaining a computing device according to one embodiment.

[0090] Referring to FIG. 8, the design device and method of a multilayer film according to the embodiments can be implemented using a computing device (50). This computing device (50) can be implemented as various types of electronic devices, servers, or similar devices, and its functions can be implemented through a combination of software and hardware.

[0091] The computing device (50) may include at least one of a processor (510), a memory (530), a user interface input device (540), a user interface output device (550), and a storage device (560) that communicate via a bus (520). The computing device (50) may also include a network interface (570) electrically connected to a network (40). The network interface (570) may transmit or receive signals to or from other entities via the network (40).

[0092] The processor (510) may be implemented as various types of computing devices, such as an MCU (Micro Controller Unit), an AP (Application Processor), a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an NPU (Neural Processing Unit), a QPU (Quantum Processing Unit), etc. The processor (510) is a semiconductor device that executes instructions stored in a memory (530) or a storage device (560), and may play a core role in the system. The program code and data stored in the memory (530) or the storage device (560) instruct the processor (510) to perform a specific task, thereby enabling the overall operation of the system. The processor (510) may be configured to implement various functions and methods described above with reference to FIGS. 1 to 7.

[0093] The memory (530) and storage device (560) may include various types of volatile or non-volatile storage media for storing and accessing data of the system. For example, the memory (530) may include read-only memory (ROM) (531) and random access memory (RAM) (532). In some embodiments, the memory (530) may be built into the processor (510), in which case the data transfer speed between the memory (530) and the processor (510) may be very fast. In other embodiments, the memory (530) may be located external to the processor (510), in which case the memory (530) may be connected to the processor (510) through various data buses or interfaces. Such connection may be made through various means already known in the art, for example, a Peripheral Component Interconnect Express (PCIe) interface for high-speed data transfer or a memory controller.

[0094] In some embodiments, at least some components or functions of the device and method for designing a multilayer film according to the embodiments may be implemented as a program or software running on a computing device (50), and the program or software may be stored on a computer-readable medium. Specifically, the computer-readable medium according to one embodiment may be a program recorded thereon for causing a computer including a processor (510) that executes a program or instructions stored in a memory (530) or a storage device (560) to execute steps included in the method for designing a multilayer film according to the embodiments.

[0095] In some embodiments, at least some of the components or functions of the design devices and methods of multilayer films according to the embodiments may be implemented using hardware or circuitry of the computing device (50), or may be implemented as separate hardware or circuitry that can be electrically connected to the computing device (50).

[0096] According to an embodiment, when designing a multilayer film, the properties of the multilayer film can be predicted using only information about the single-layer film constituting the multilayer film. In particular, by performing learning and prediction using various indices of the drop strength stress strain data for the single-layer film and the thickness of the single-layer film as features, the drop strength of the multilayer film can be predicted with high accuracy. In addition, by varying the number of indices selected as features when performance is prioritized and the number of indices selected as features when computing resources are saved, flexible design is possible according to the implementation purpose and environment of the design device for the multilayer film. Furthermore, by considering the prediction results regarding other characteristics of the multilayer film together with the prediction results of the drop strength, a multilayer film that can satisfy given design requirements can be designed.

[0097] Although the embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements made by a person of ordinary skill in the art to which the present invention pertains using the basic concept of the present invention defined in the following claims also fall within the scope of the present invention.

Claims

1. A device for designing a multilayer film that designs a multilayer film based on the properties of the multilayer film predicted only with information about a single-layer film by executing a program code loaded into one or more memory devices through one or more processors, The above program code is executed, Modeling the laminated structure of the multilayer film to be designed, The drop strength data for the single-layer film forming the above laminated structure is collected as stress-strain data, The design device of the above multilayer film reads a value stored in advance in a storage space accessible to it, and obtains a feature setting mode for specifying a different feature setting method according to the read value. According to the above feature setting mode, a plurality of physical indices selected from the above drop strength data and the thickness of the single-layer film are set as features, Select at least one machine learning model from among multiple supervised learning models capable of regression analysis, The machine learning model learned with the above features as independent variables and the drop strength of the multilayer film as a target variable is used to predict the drop strength of the multilayer film, In order to meet the design requirements of the multilayer film, design data for the multilayer film is generated by combining the predicted values ​​for other characteristics and the predicted values ​​for the drop strength. Design device for multilayer films.

2. In paragraph 1, If the above feature setting mode includes the first feature setting mode, setting it as the feature is A design device for a multilayer film, comprising setting at least some of Yield Point, Yield Stress, Resilience, Strain Hardening, Failure, Strain Hardening Modulus, Flow Energy, Strain Hardening Energy, Toughness, 1% Modulus, and 2% Modulus, and the thickness of the single layer film as features.

3. In paragraph 2, Choosing the above machine learning model is, Including selecting one of the PLS (Partial Least Squares) model, the Lasso regression model, and the random forest model as the machine learning model, The machine learning model selected from the above PLS model, Lasso regression model, and random forest model is, A design device for a multilayer film, which receives a vector generated from at least some of the yield point, the yield stress, the elasticity, the strain hardening, the failure, the strain hardening coefficient, the flow energy, the strain hardening energy, the toughness, the 1% coefficient, and the 2% coefficient, and the thickness of the single-layer film, and is trained to predict the drop strength of the multilayer film.

4. In paragraph 1, If the above feature setting mode includes a second feature setting mode, setting it as a feature is A design device for a multilayer film, comprising setting at least some of yield stress, yield strain, necking stress, necking strain, breaking stress and breaking strain as features, and the thickness of the single layer film.

5. In paragraph 4, Choosing the above machine learning model is, Including selecting one of the PLS (Partial Least Squares) model, the Lasso regression model, and the random forest model as the machine learning model, The machine learning model selected from the above PLS model, Lasso regression model, and random forest model is, A design device for a multilayer film, which receives at least some of the yield stress, the yield strain, the necking point stress, the necking point strain, the breaking stress and the breaking strain, and a vector generated from the thickness of the single-layer film, and learns to predict the drop strength of the multilayer film.

6. In paragraph 1, If the above feature setting mode includes a third feature setting mode, setting it as a feature is A design device for a multilayer film, comprising setting yield stress, yield strain, and an index of yield stress divided by yield strain (Yield Stress / Yield Strain) and the thickness of the single-layer film as features.

7. In paragraph 6, Choosing the above machine learning model is, Including selecting one of the PLS (Partial Least Squares) model, the Lasso regression model, and the random forest model as the machine learning model, The machine learning model selected from the above PLS model, Lasso regression model, and random forest model is, A design device for a multilayer film, which receives the yield stress, the yield strain, and the yield stress divided by the yield strain, and a vector generated from the thickness of the single-layer film, and learns to predict the drop strength of the multilayer film.

8. In paragraph 1, Generating design data for the above multilayer film is as follows: Combining the predicted value of the drop strength with the predicted value of at least one of the properties of stiffness, strength, strain, tear strength, high-speed drop strength, sealing initiation temperature (SIT), haze, moisture permeability, and air permeability for the multilayer film, If the multilayer film designed based on the above combination of predicted values ​​satisfies the above design requirements, the design data is generated based on the above combination, A design device for a multilayer film, comprising redesigning the multilayer film by changing the combination to another combination when the multilayer film designed based on the above combination of predicted values ​​does not satisfy the above design requirements.

9. In paragraph 1, The above multilayer film is modeled as a multilayer film model, The above multilayer film includes a first single-layer film and a second single-layer film laminated to each other, The above multilayer film model comprises n (n is a multiple of 6) model layers stacked on top of each other, A design device for a multilayer film, wherein the first single-layer film corresponds to two or more of the model layers, and the second single-layer film corresponds to two or more other model layers among the model layers.

10. In paragraph 9, The above program code is executed and further, A design device for a multilayer film, which displays the model layer on a display device and provides a user interface that allows a user to set a model layer corresponding to the first single-layer film and a model layer corresponding to the second single-layer film.

11. A design method of a multilayer film, which designs the multilayer film based on the properties of the multilayer film predicted only with information about the single-layer film, performed by a computing device including one or more processors and one or more memory devices, A step of modeling the laminated structure of the multilayer film to be designed; A step of collecting drop strength data for a single-layer film forming the above laminated structure as stress-strain data; A step of reading a value pre-stored in a storage space accessible by the computing device and obtaining a feature setting mode for specifying a different feature setting method according to the read value; A step of setting a plurality of physical indices selected from the fall strength data and the thickness of the single-layer film as features according to the above feature setting mode; A step of selecting at least one machine learning model from among multiple supervised learning models capable of regression analysis; A step of predicting the fall strength of the multilayer film using the learned machine learning model with the above features as independent variables and the fall strength of the multilayer film as a target variable; and In order to meet the design requirements of the multilayer film, a step of generating design data for the multilayer film by combining predicted values ​​for other characteristics and predicted values ​​of the drop strength is included. Design method of multilayer films.

12. In paragraph 11, If the above feature setting mode includes the first feature setting mode, the step of setting the feature is: A method for designing a multilayer film, comprising the step of setting at least some of Yield Point, Yield Stress, Resilience, Strain Hardening, Failure, Strain Hardening Modulus, Flow Energy, Strain Hardening Energy, Toughness, 1% Modulus, and 2% Modulus, and the thickness of the single layer film as features.

13. In paragraph 12, The steps for selecting the above machine learning model are: A step of selecting one of a PLS (Partial Least Squares) model, a Lasso regression model, and a random forest model as the machine learning model is included. The machine learning model selected from the above PLS model, Lasso regression model, and random forest model is, A design method for a multilayer film, wherein at least some of the yield point, the yield stress, the elasticity, the strain hardening, the failure, the strain hardening coefficient, the flow energy, the strain hardening energy, the toughness, the 1% coefficient, and the 2% coefficient, and a vector generated from the thickness of the single-layer film are input, and the method is learned to predict the drop strength of the multilayer film.

14. In paragraph 11, If the above feature setting mode includes a second feature setting mode, the step of setting the feature is: A method for designing a multilayer film, comprising the step of setting at least some of yield stress, yield strain, necking stress, necking strain, breaking stress and breaking strain, and the thickness of the single layer film as features.

15. In paragraph 14, The steps for selecting the above machine learning model are: A step of selecting one of a PLS (Partial Least Squares) model, a Lasso regression model, and a random forest model as the machine learning model is included. The machine learning model selected from the above PLS model, Lasso regression model, and random forest model is, A design method for a multilayer film, wherein at least some of the yield stress, the yield strain, the necking point stress, the necking point strain, the fracture stress and the fracture strain, and a vector generated from the thickness of the single-layer film are input, and the method is learned to predict the drop strength of the multilayer film.

16. In paragraph 11, If the above feature setting mode includes a third feature setting mode, the step of setting the feature is: A design method for a multilayer film, comprising a step of setting yield stress, yield strain, and an index of yield stress divided by yield strain (Yield Stress / Yield Strain), and a thickness of the single-layer film as features.

17. In paragraph 16, The steps for selecting the above machine learning model are: A step of selecting one of a PLS (Partial Least Squares) model, a Lasso regression model, and a random forest model as the machine learning model is included. The machine learning model selected from the above PLS model, Lasso regression model, and random forest model is, A design method for a multilayer film, wherein the method is learned to predict the drop strength of the multilayer film by inputting the yield stress, the yield strain, and the index obtained by dividing the yield stress by the yield strain, and a vector generated from the thickness of the single-layer film.

18. In paragraph 11, The step of generating design data for the above multilayer film is as follows: A step of combining a predicted value for at least one of the properties of stiffness, strength, strain, tear strength, high-speed drop strength, sealing initiation temperature (SIT), haze, moisture permeability, and air permeability for the multilayer film with a predicted value of the drop strength; If the multilayer film designed based on the above combination of predicted values ​​satisfies the above design requirements, a step of generating the design data based on the above combination; and A method for designing a multilayer film, comprising the step of redesigning the multilayer film by changing the combination to another combination, if the multilayer film designed based on the above combination of predicted values ​​does not satisfy the above design requirements.

19. In paragraph 11, The above multilayer film is modeled as a multilayer film model, The above multilayer film includes a first single-layer film and a second single-layer film laminated to each other, The above multilayer film model comprises n (n is a multiple of 6) model layers stacked on top of each other, A method for designing a multilayer film, wherein the first single-layer film corresponds to two or more model layers among the model layers, and the second single-layer film corresponds to two or more other model layers among the model layers.

20. In paragraph 19, A method for designing a multilayer film, further comprising the step of displaying the model layer on a display device and providing a user interface that allows a user to set a model layer corresponding to the first single-layer film and a model layer corresponding to the second single-layer film.

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