Apparatus and method for designing multilayer film

Abstract

An apparatus and a method for designing a multilayer film are disclosed. The apparatus for designing a multilayer film models the stack structure of a multilayer film being designed, collects stress strain data for a monolayer film forming the stack structure, reads a value previously stored in a storage space accessible by the apparatus for designing a multilayer film, obtains a feature setting mode for designating different feature setting methods according to the read value, calculates, according to the feature setting mode, strain energy from a plurality of physical indices selected from the stress strain data, sets the strain energy as a feature, selects, as a machine learning model, at least one of a plurality of supervised learning models capable of performing regression analysis, predicts the falling weight strength of the multilayer film by using the machine learning model trained using the feature as an independent variable and the falling weight strength of the multilayer film as a target variable, and in order to satisfy design requirements of the multilayer film, combines a predicted value for other characteristics and the predicted value of the falling weight strength such that design data for the multilayer film can be generated.

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. 10-2023-0129784, filed September 26, 2023, and Korean Patent Application No. 10-2024-0130233, filed September 25, 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 drop strength of a multilayer film using only information about the single-layer films constituting the multilayer film when designing the multilayer film, and design the multilayer film according to given design requirements.

[0007] According to one embodiment, a design device for a multilayer film is a design device for a multilayer film that designs the multilayer film based on the physical properties of the multilayer film predicted only with information about the 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 the laminated structure of the multilayer film to be designed, collect stress-strain data for the single-layer film forming the laminated structure, read a value stored in advance in a storage space accessible to the design device for the multilayer film, obtain a feature setting mode for designating a different feature setting method according to the read value, calculate strain energy from a plurality of physical indices selected from the stress-strain data according to the feature setting mode, set the strain energy as a feature, select at least one machine learning model from a plurality of supervised learning models capable of regression analysis, use the feature as an independent variable, and predict the drop strength of the multilayer film using the machine learning model learned by using the drop strength of the multilayer film as a target variable, and satisfy the design requirements of the multilayer film. To satisfy this, design data for the multilayer film can be generated by combining predicted values ​​for other characteristics and 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 a yield stress, a yield strain, a necking point stress, a necking point strain, a breaking stress, and a breaking strain, and a strain energy calculated from 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 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 receives 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, a vector generated from strain energy calculated 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 a vector generated from a thickness of the single-layer film, and may be trained 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 a yield stress, a yield strain, a necking stress, a necking strain, a breaking stress and a breaking strain, and a strain energy calculated from the yield stress, the yield strain, the necking stress, the necking strain, the breaking stress and the breaking strain, and a thickness of the single-layer film.

[0011] 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 a vector generated from the yield stress, the yield strain, the necking point stress, the necking point strain, the fracture stress, and the fracture strain, a vector generated from strain energy calculated from 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, and to predict the drop strength of the multilayer film.

[0012] In some embodiments, the strain energy is a first strain energy (W yield ) and setting it as the feature, the first strain energy (W yield ) may include setting the feature by calculating it according to the following mathematical formula:

[0013]

[0014] Here, σ y is the yield stress, and ε y is the yield strain, and σ br is the above-mentioned fracture stress, d is the joint diameter, t is the thickness of the measurement sample, and a is a constant determined experimentally.

[0015] In some embodiments, the strain energy is a second strain energy (W neck ) and setting it as the feature, the second strain energy (W neck ) may include setting the feature by calculating it according to the following mathematical formula:

[0016]

[0017] Here, σ n is the stress at the necking point, and ε n is the strain at the necking point, and ε y is the yield strain, and σ br is the above-mentioned fracture stress, and σ y is the yield stress, d is the joint diameter, t is the thickness of the measurement sample, and b is a constant determined experimentally.

[0018] In some embodiments, the strain energy is a third strain energy (W str-hard ) and setting it as the feature, the third strain energy (W str-hard ) may include setting the feature by calculating it according to the following mathematical formula:

[0019]

[0020] Here, ε br is the above fracture strain, and ε n is the strain at the necking point, and σ br is the above-mentioned fracture stress, and σ n is the necking point stress, L is the final stretching propagation length, t is the thickness of the measurement sample, and c is a constant determined experimentally.

[0021] In some embodiments, the strain energy is a fourth strain energy (W elast ) and setting it as the feature, the fourth strain energy (W elast ) may include setting the feature by calculating it according to the following mathematical formula:

[0022]

[0023] Here, σ y is the yield stress, and ε y is the yield strain, d is the diameter of the joint, D is the diameter of the fixed disk, L is the final stretching propagation length, t is the thickness of the measurement sample, and e is a constant determined experimentally.

[0024] 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 ​​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] 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 the multilayer film to be designed; collecting stress-strain data on a single-layer film forming the laminated structure; 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; calculating strain energy from a plurality of physical indices selected from the stress-strain data according to the feature setting mode and setting the strain energy as a feature; selecting at least one machine learning model from a plurality of supervised learning models capable of regression analysis; predicting a 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.

[0026] 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 a yield stress, a yield strain, a necking point stress, a necking point strain, a breaking stress, and a breaking strain, and a strain energy calculated from 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 the thickness of the single-layer film.

[0027] 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, wherein the machine learning model selected from the PLS model, the Lasso regression model, and the random forest model receives 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, a vector generated from strain energy calculated 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 a vector generated from a thickness of the single-layer film, and may be trained to predict the drop strength of the multilayer film.

[0028] 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 a yield stress, a yield strain, a necking point stress, a necking point strain, a breaking stress and a breaking strain, and a strain energy calculated from the yield stress, the yield strain, the necking point stress, the necking point strain, the breaking stress and the breaking strain, and a thickness of the single-layer film.

[0029] 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 predict the drop strength of the multilayer film by receiving a vector generated from the yield stress, the yield strain, the necking point stress, the necking point strain, the fracture stress, and the fracture strain, a vector generated from strain energy calculated from 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.

[0030] In some embodiments, the strain energy is a first strain energy (W yield ) and the step of setting the feature comprises the first strain energy (W yield ) may include a step of calculating and setting the feature according to the following mathematical formula:

[0031]

[0032] Here, σ y is the yield stress, and ε y is the yield strain, and σ br is the above-mentioned fracture stress, d is the joint diameter, t is the thickness of the measurement sample, and a is a constant determined experimentally.

[0033] In some embodiments, the strain energy is a second strain energy (W neck ) and the step of setting the feature comprises the second strain energy (W neck ) may include a step of calculating and setting the feature according to the following mathematical formula:

[0034]

[0035] Here, σ n is the stress at the necking point, and ε n is the strain at the necking point, and ε y is the yield strain, and σ br is the above-mentioned fracture stress, d is the joint diameter, and σ y is the yield stress, t is the thickness of the measurement sample, and b is a constant determined experimentally.

[0036] In some embodiments, the strain energy is a third strain energy (W str-hard ) and the step of setting the feature includes the third strain energy (W str-hard ) may include a step of calculating and setting the feature according to the following mathematical formula:

[0037]

[0038] Here, ε br is the above fracture strain, and ε n is the strain at the necking point, and σ br is the above-mentioned fracture stress, and σ nis the necking point stress, L is the final stretching propagation length, t is the thickness of the measurement sample, and c is a constant determined experimentally.

[0039] In some embodiments, the strain energy is a fourth strain energy (W elast ) and the step of setting the feature includes the fourth strain energy (W elast ) may include a step of calculating and setting the feature according to the following mathematical formula:

[0040]

[0041] Here, σ y is the yield stress, and ε y is the yield strain, d is the diameter of the joint, D is the diameter of the fixed disk, L is the final stretching propagation length, t is the thickness of the measurement sample, and e is a constant determined experimentally.

[0042] 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 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.

[0043] According to embodiments, when designing a multilayer film, the drop strength 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 characteristics together, a multilayer film that can meet given design requirements can be designed.

[0044] Figure 1 is a block diagram illustrating a design device for a multilayer film according to one embodiment.

[0045] FIG. 2 is a diagram illustrating stress strain data for a single-layer film according to one embodiment.

[0046] Figures 3 and 4 are drawings for explaining the fall strength measurement environment data according to one embodiment.

[0047] Figure 5 is a flowchart illustrating a design method of a multilayer film according to one embodiment.

[0048] FIG. 6 is a drawing for explaining a computing device according to one embodiment.

[0049] 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 practice them. 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.

[0050] 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.

[0051] 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.

[0052] FIG. 1 is a block diagram for explaining a design device for a multilayer film according to one embodiment, FIG. 2 is a diagram for explaining stress strain data for a single-layer film according to one embodiment, and FIGS. 3 and 4 are diagrams for explaining drop strength measurement environment data according to one embodiment.

[0053] 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. 6. 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, executed by one or more processors, can predict the drop strength of a multilayer film using only information about a single-layer film constituting the multilayer film, and design the multilayer film according to given design requirements. In this specification, the term "module" is used to logically distinguish these functions performed by the program codes or instructions.

[0054] A design device (10) for a multilayer film according to one embodiment can predict the drop strength of a film without conducting a separate drop test. Drop strength can refer to the strength that a material, in this case a film, can withstand when subjected to a physical impact. Drop strength can be used as an indicator for measuring the extent of damage to a film when, for example, a weight is dropped from a predetermined height. The design device (10) for a multilayer film can predict the drop strength of a multilayer film through machine learning based on experimental result data measured for a polymer film in a manner other than a drop test. To this end, the design device (10) for a multilayer film can 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 (170). For example, the learning module (150) may be implemented within the design device (10) of the multilayer film. Alternatively, for example, the learning module (150) may be implemented outside the design device (10) of the multilayer film, and the design device (10) of the multilayer film may be provided with a machine learning model learned from the learning module (150).

[0055] 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.

[0056] 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.

[0057] 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.

[0058] The data collection module (120) can collect stress-strain data for a single-layer film forming a laminated structure modeled by the multilayer film modeling module (110). Here, the stress-strain data can represent mechanical properties of the film (i.e., the single-layer film forming the multilayer film), and with reference to FIG. 2, can be expressed in the form of a stress-strain curve. The stress-strain curve can represent the stress generated when a force is applied to the film, and how the film is deformed as a result.

[0059] In some embodiments, the data collection module (120) can collect stress-strain data obtained through a Universal Testing Machine (UTM) experiment on a single-layer film forming a multilayer film. The UTM experiment can be designed to perform various mechanical tests on a material, and can measure the load value when work is applied to a specimen for a certain time, force, and direction, and a load corresponding to the force is generated. That is, experiments such as compression, tension, and bending can be performed by applying a force to a specimen and measuring the load value until the specimen deforms. The data collection module (120) can collect the results obtained through such a UTM experiment on a film specimen as stress-strain data.

[0060] 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.

[0061] 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.

[0062] In some embodiments, the stress-strain data may include at least one of Yield Stress, Yield Strain, Necking Stress, Necking Strain, Breaking Stress, and Breaking Strain. Here, Yield Stress may represent the stress at the yield point, which is the point where the material begins to permanently deform beyond its elastic limit, i.e., the point where the material transitions from an elastic deformation region to an inelastic deformation region, and Yield Strain may represent the strain corresponding to the Yield Stress. Necking Point Stress represents the stress at the point where the cross-section of the material begins to narrow locally (the necking point), after which the load capacity of the material may typically decrease rapidly. Necking Point Strain may represent the strain at the necking point. Breaking Stress represents the stress at the point where the material finally breaks, at which point the material can no longer support the load. Breaking Strain may represent the strain corresponding to the Breaking Stress.

[0063] Referring to Figure 2 together, exemplary points (P) corresponding to yield stress and yield strain on the stress-strain curve y ), exemplary points corresponding to necking point stress and necking point strain (P n ), and exemplary points corresponding to the breaking stress and breaking strain (P br ) can be determined.

[0064] 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.

[0065] The feature setting module (130) can calculate strain energy from a plurality of physical indices selected from stress strain data according to the feature setting mode, and set the strain energy as a feature. In machine learning, a feature is an input variable used to predict a target to be predicted, i.e., the properties of a multilayer film, and can mean individual independent variables of data. A feature provides information related to a dependent variable or target variable to be predicted within a dataset, and can have a significant impact on the performance of a machine learning model (20). The learning module (150) can use features to learn a machine learning model (20). At this time, the feature constitutes a portion of the data input to the machine learning model (20), and the machine learning model (20) can learn a pattern based on the data and perform a prediction. In particular, feature engineering, which selects or transforms features, can 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).

[0066] In some embodiments, when the feature setting mode includes the first feature setting mode, the feature setting module (130) may set as features at least some of yield stress, yield strain, necking point stress, necking point strain, fracture stress, and fracture strain, and strain energy calculated from at least some of yield stress, yield strain, necking point stress, necking point strain, fracture stress, and fracture strain, and the thickness of the single-layer film. That is, the feature setting module (130) may set as features at least some of the stress strain data collected by the data collection module (120), strain energy calculated from at least some of the stress strain data collected by the data collection module (120), 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) may apply features including only a portion of the stress strain data collected by the data collection module (120) for the first film layer among the k single-layer films, strain energy calculated from at least a portion of the stress strain data collected by the data collection module (120), and the thickness of the single-layer film. In addition, the learning module (150) may apply features including 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, strain energy calculated from at least a portion of the stress strain data collected by the data collection module (120), and 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 stress strain data collected by the data collection module (120) for the k-th film layer, strain energy calculated from at least a portion of the stress strain data collected by the data collection module (120), and the thickness of the single-layer film.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 of the k single-layer films compared to 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.

[0067] In some embodiments, when the feature setting mode includes the second feature setting mode, the feature setting module (130) may set as features the yield stress, the yield strain, the necking point stress, the necking point strain, the fracture stress and the fracture strain, the strain energy calculated from 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. That is, the feature setting module (130) may set as features all of the stress strain data collected by the data collection module (120), the strain energy calculated from the stress strain data collected by the data collection module (120), 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 features including all stress strain data collected by the data collection module (120) for the first film layer among the k single-layer films, strain energy calculated from the stress strain data collected by the data collection module (120), and the thickness of the single-layer film. In addition, the learning module (150) can apply features including all stress strain data collected by the data collection module (120) for the second film layer among the k single-layer films, strain energy calculated from the stress strain data collected by the data collection module (120), and the thickness of the single-layer film. In addition, the learning module (150) can perform learning of the machine learning model (20) by applying features including all of the stress strain data collected by the data collection module (120) for the k-th film layer, the strain energy calculated from the stress strain data collected by the data collection module (120), and the thickness of the single-layer film. At this time, learning may be performed by applying the same type of feature to all 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 can be adopted in an environment with sufficient computing resources for performing learning and prediction for a machine learning model (20).

[0068] 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.

[0069] In some embodiments, when the feature setting mode includes the third feature setting mode, the feature setting module (130) may set as features 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 strain energy calculated 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 for the first direction, and strain energy calculated 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 for the second direction, and strain energy calculated 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 for the second direction, and a thickness of the single-layer film. That is, the feature setting module (130) can set as features at least some of the first stress strain data collected by the data collection module (120), strain energy calculated from at least some of the first stress strain data collected by the data collection module (120), at least some of the second stress strain data collected by the data collection module (120), strain energy calculated from at least some of the second stress strain data collected by the data collection module (120), 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) may apply features including 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, strain energy calculated from at least a portion of the first stress strain data collected by the data collection module (120), only a portion of the second stress strain data collected by the data collection module (120), strain energy calculated from at least a portion of the second stress strain data collected by the data collection module (120), and a thickness of the single-layer film. Additionally, the learning module (150) may apply features including 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, strain energy calculated from at least a portion of the first stress strain data collected by the data collection module (120), only a portion of the second stress strain data collected by the data collection module (120), strain energy calculated from at least a portion of the second stress strain data collected by the data collection module (120), 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 only some of the first stress strain data collected by the data collection module (120) for the k-th film layer, strain energy calculated from at least some of the first stress strain data collected by the data collection module (120), only some of the second stress strain data collected by the data collection module (120), strain energy calculated from at least some of the second stress strain data collected by the data collection module (120), and the thickness of the single-layer film.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 of the k single-layer films compared to 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.

[0070] In some embodiments, when the feature setting mode includes the fourth feature setting mode, the feature setting module (130) may set as features 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, the strain energy calculated from 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, 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, the strain energy calculated from 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. That is, the feature setting module (130) can set all of the first stress strain data collected by the data collection module (120), the strain energy calculated from the first stress strain data collected by the data collection module (120), all of the second stress strain data collected by the data collection module (120), the strain energy calculated from the second 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) can apply features including all of the first stress strain data collected by the data collection module (120) for the first film layer among the k single-layer films, strain energy calculated from the first stress strain data collected by the data collection module (120), all of the second stress strain data collected by the data collection module (120), strain energy calculated from the second stress strain data collected by the data collection module (120), and the thickness of the single-layer film.Additionally, the learning module (150) may apply features including all of the first stress strain data collected by the data collection module (120) for the second film layer among the k single-layer films, strain energy calculated from the first stress strain data collected by the data collection module (120), all of the second stress strain data collected by the data collection module (120), strain energy calculated from the second stress strain data collected by the data collection module (120), and the thickness of the single-layer film. In addition, the learning module (150) may perform learning of the machine learning model (20) by applying features including all of the first stress strain data collected by the data collection module (120) for the k-th film layer, the strain energy calculated from the first stress strain data collected by the data collection module (120), all of the second stress strain data collected by the data collection module (120), the strain energy calculated from the second stress strain data collected by the data collection module (120), and the thickness of the single-layer film. 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. The embodiment may be adopted in an environment having sufficient computing resources for performing learning and prediction for the machine learning model (20).

[0071] For example, when the multilayer film modeling module (110) models a multilayer film including six monolayer films, all of the indices of yield stress, yield strain, necking point stress, necking point strain, fracture stress, and fracture strain for the first direction, and four indices of strain energy calculated from yield stress, yield strain, necking point stress, necking point strain, fracture stress, and fracture strain for the first direction, and all of the indices of yield stress, yield strain, necking point stress, necking point strain, fracture stress, and fracture strain for the second direction, and four indices of strain energy calculated from yield stress, yield strain, necking point stress, necking point strain, fracture stress, and fracture strain for the second direction, and 21 features of the thickness of the monolayer film can be applied, and since there are six layers in total, a total of 126 features can be applied.

[0072] In some embodiments, the feature setting module (130) obtains a first strain energy (W) from the stress strain data. yield ) can be set as a feature by calculating the first strain energy (W yield ) can be calculated according to the following mathematical formula (1).

[0073] Mathematical formula (1)

[0074]

[0075] Here, σ y is the yield stress among the stress-strain data, and ε y is the yield strain among the stress-strain data, and σ among the stress-strain data br may be the breaking stress. These variables may correspond to experimental results measured by methods other than the drop test described above.

[0076] Meanwhile, in order to calculate strain energy from stress strain data, variables related to the environmental settings of a drop test according to a predetermined standard may be additionally used for a film whose drop strength is to be predicted. Here, the standard may represent an ASTM (American Society for Testing and Materials) standard, or may represent an arbitrary standard that is customized in consideration of the learning performance of the machine learning model (20). Specifically, as variables related to the environmental settings during the drop test, d is the diameter of the weight joint, t is the thickness of the measurement sample, and a may be a constant experimentally determined in consideration of the experimental environment or the product testing environment. Referring also to FIG. 3, the joint diameter d may refer to the diameter of a circular area that is joined to an additional film when a weight is dropped from a predetermined height. The weight includes a fixed disk having a diameter D, the area indicated by l is the area where stretching is propagated, and the area indicated by L represents the area (or length) where stretching is ultimately propagated. The variables related to the environmental settings of these drop experiments (d, D, l, L, t, etc.) can be appropriately adjusted to improve the accuracy of the drop strength to be ultimately predicted.

[0077] In some embodiments, the feature setting module (130) obtains a second strain energy (W) from the stress strain data. neck ) can be set as a feature by calculating the second strain energy (W neck ) can be calculated according to the following mathematical formula (2).

[0078] Mathematical formula (2)

[0079]

[0080] Here, σ n is the necking point stress among the stress-strain data, and ε n is the necking point strain among the stress-strain data, and εy is the yield strain among the stress-strain data, and σ br is the fracture stress among the stress-strain data, and σ y is the yield stress among the stress-strain data, and these variables may correspond to experimental result data measured in a manner other than the drop test as described above.

[0081] Meanwhile, as variables related to the environmental settings of the drop experiment, d may be the diameter of the drop joint, t may be the thickness of the measurement sample, and b may be a constant experimentally determined considering the experimental environment or product testing environment.

[0082] In some embodiments, the feature setting module (130) obtains a third strain energy (W) from the stress strain data. str-hard ) can be set as a feature by calculating the third strain energy (W str-hard ) can be calculated according to the following mathematical formula (3).

[0083] Mathematical formula (3)

[0084]

[0085] Here, ε br is the fracture strain among the stress-strain data, and ε n is the necking point strain among the stress-strain data, and σ br is the fracture stress among the stress-strain data, and σ n The stress-strain data may be the necking point stress. These variables may correspond to experimental results measured by methods other than the drop test as described above.

[0086] Meanwhile, as variables related to the environmental settings of the drop experiment, L is the final stretching propagation length, t is the thickness of the measurement sample, and c can be a constant experimentally determined by considering the experimental environment or product test environment.

[0087] In some embodiments, the feature setting module (130) obtains a fourth strain energy (W) from the stress strain data. elast ) can be set as a feature by calculating the fourth strain energy (W elast ) can be calculated according to the following mathematical formula (4).

[0088] Mathematical formula (4)

[0089]

[0090] Here, σ y is the yield stress among the stress-strain data, and ε y may be the yield strain among the stress-strain data. These variables may correspond to experimental result data measured by methods other than the drop test as described above.

[0091] Meanwhile, as variables related to the environmental settings of the drop experiment, d is the diameter of the drop joint, D is the diameter of the fixed disk, L is the final stretching propagation length, t is the thickness of the measurement sample, and e can be a predetermined constant.

[0092] In some embodiments, the final stretching propagation length L may be calculated from experimental data measured by methods other than the drop experiment. Specifically, the final stretching propagation length L may be calculated according to the following mathematical expression (5).

[0093] Mathematical formula (5)

[0094]

[0095] Here, σ br is the fracture stress among the stress-strain data, and σ y is the yield stress among the stress-strain data, and d may be the joint diameter.

[0096] In some embodiments, the feature setting module (130) sets the first strain energy (W yield ), second strain energy (W neck), third strain energy (W str-hard ) and the fourth strain energy (W elast ) can all be set as features. The learning module (150) can proceed with learning of the machine learning model (20) based on the features set in this way. This embodiment can be adopted in an environment with sufficient computing resources for performing learning and prediction for the machine learning model (20).

[0097] Alternatively, in some other embodiments, the feature setting module (130) may set the first strain energy (W yield ), second strain energy (W neck ), third strain energy (W str-hard ) and the fourth strain energy (W elast ) can be set as features. The learning module (150) can proceed with learning of the machine learning model (20) based on the features set in this way. This embodiment can be adopted in an environment where computing resources for performing learning and prediction for the machine learning model (20) are limited.

[0098] In some embodiments, the feature setting module (130) may additionally set yield stress, yield strain, necking point stress, necking point strain, fracture stress, and fracture strain as features. That is, the feature setting module (130) may also set yield stress, yield strain, necking point stress, necking point strain, fracture stress, and fracture strain selected from the stress-strain data, along with strain energy calculated from the stress-strain data, as features.

[0099] In some embodiments, the feature setting module (130) may additionally set the thickness of a single-layer film forming a multilayer film as a feature. That is, the feature setting module (130) may also set the thickness of the single-layer film as a feature along with the strain energy calculated from stress strain data.

[0100] In some embodiments, the feature setting module (130) sets a first strain energy (W) for the first direction yield ), second strain energy (W neck ), third strain energy (W str-hard ) and the fourth strain energy (W elast ) and at least one or all of the first strain energy (W) in the second direction yield ), second strain energy (W neck ), third strain energy (W str-hard ) and the fourth strain energy (W elast ) can be set as features. That is, the feature setting module (130) can set as features at least some or all of the strain energies calculated from the first stress strain data collected by the data collection module (120), and at least some or all of the strain energies calculated from the second stress strain data, optionally together with yield stress, yield strain, necking point stress, necking point strain, fracture stress, and fracture strain, and optionally together with the thickness of the single-layer film.

[0101] 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, in which the model learns the relationship between input variables and target variables, and thereby can 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 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 to identify independent variables deemed important for predicting the dependent variable. In linear regression, the model learns the linear relationship between input variables and the target variable, while in polynomial regression, the model can learn complex relationships 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 model complexity. Logistic regression is a classification algorithm for binary classification problems, but because it predicts outcomes as probabilities, it can be used in regression analysis.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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] In some embodiments, a machine learning model selected from among a PLS model, a Lasso regression model, and a random forest model may be trained to predict the drop strength of a multilayer film by receiving as input 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, a vector generated from strain energy calculated 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 a vector generated from the thickness of the single-layer film.

[0110] In some other embodiments, a machine learning model selected from among a PLS model, a Lasso regression model, and a random forest model may be trained to predict the drop strength of a multilayer film by receiving as input a vector generated from yield stress, yield strain, necking point stress, necking point strain, fracture stress, and fracture strain, a vector generated from strain energy calculated from yield stress, yield strain, necking point stress, necking point strain, fracture stress, and fracture strain, and a vector generated from the thickness of a single-layer film.

[0111] The design data generation module (170) can first combine predicted values ​​for at least one of stiffness, strength, strain, tear strength, high-speed drop strength, seal initiation temperature, transparency, moisture permeability, and air permeability for the multilayer film with predicted values ​​for the drop strength. The design requirements for the multilayer film to be designed may 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 predicted values ​​satisfies the design requirements, the design data generation module (170) can generate design data based on the first combination. In this case, the design data may include specific predicted values.

[0112] 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 (170) can redesign the multilayer film by changing the first combination to another combination. That is, the design data generation module (170) 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.

[0113] If the multilayer film designed based on the secondary combination of predicted values ​​satisfies the design requirements, the design data generation module (170) 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 (170) can repeat the process of redesigning the multilayer film by changing the secondary combination to another combination.

[0114] According to the present embodiment, the drop strength of a multilayer film can be predicted with high accuracy by learning and predicting using strain energy as a feature, which is calculated from stress strain data measured by a method other than a drop test on a single-layer film forming a multilayer film, without directly performing a drop test on the film to measure the drop strength. 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 of 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.

[0115] FIG. 5 is a flowchart illustrating a design method of a multilayer film of a multilayer material according to one embodiment.

[0116] Referring to FIG. 5, 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 (S501), a step of collecting stress strain data for a single-layer film forming the laminated structure (S502), a step of reading a value stored in advance in a storage space accessible to a design device of the multilayer film and obtaining a feature setting mode for designating a different feature setting method according to the read value (S503), a step of calculating strain energy from a plurality of physical indices selected from the stress strain data according to the feature setting mode and setting the strain energy as a feature (S504), a step of selecting at least one machine learning model among a plurality of supervised learning models capable of regression analysis (S505), 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 (S506), a step of predicting the drop strength of the multilayer film using the trained machine learning model (S507), and a step of combining predicted values ​​for other characteristics and predicted values ​​of the drop strength to satisfy the design requirements of the multilayer film. It may include a step (S508) of generating design data for a multilayer film.

[0117] For more specific details regarding the design method of a multilayer film, please refer to the description of the embodiments described in this specification and the disclosed feature configuration can be applied, so a redundant description is omitted here.

[0118] FIG. 6 is a drawing for explaining a computing device according to one embodiment.

[0119] Referring to FIG. 6, 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.

[0120] 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).

[0121] 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 5.

[0122] 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.

[0123] 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 device and method for designing a multilayer film according to the embodiments.

[0124] 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).

[0125] According to embodiments, the drop strength of a multilayer film can be predicted with high accuracy by learning and predicting using strain energy as a feature, which is calculated from stress strain data measured by a method other than a drop test on a single-layer film forming a multilayer film, without directly conducting a drop test on the film to measure the drop strength. 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 and method 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.

[0126] 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, Collect stress-strain data for the single-layer film forming the above laminated structure, 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, strain energy is calculated from a plurality of physical indices selected from the stress strain data, and the strain energy is set as a feature. 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 At least some of the Yield Stress, Yield Strain, Necking Stress, Necking Strain, Breaking Stress and Breaking Strain, Strain energy calculated 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; A design device for a multilayer film, comprising setting the thickness of the single-layer film as a feature.

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 vector generated from 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, A vector generated from strain energy calculated 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, Input a vector generated from the thickness of the above single-layer film, A design device for a multilayer film, which 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 Yield Stress, Yield Strain, Necking Stress, Necking Strain, Breaking Stress and Breaking Strain, The strain energy calculated from the yield stress, the yield strain, the necking point stress, the necking point strain, the breaking stress, and the breaking strain, A design device for a multilayer film, comprising setting the thickness of the single-layer film as a feature.

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 vector generated from the yield stress, the yield strain, the necking point stress, the necking point strain, the breaking stress, and the breaking strain, A vector generated from the strain energy calculated from the yield stress, the yield strain, the necking point stress, the necking point strain, the breaking stress, and the breaking strain, Input a vector generated from the thickness of the above single-layer film, A design device for a multilayer film, which is trained to predict the drop strength of the multilayer film.

6. In paragraph 2, The above strain energy is the first strain energy (W yield ) and Setting the above features is: The first strain energy (W) yield ) is set as the above feature by calculating it according to the following mathematical formula, and a design device for a multilayer film: Here, σ y is the yield stress, and ε y is the yield strain, and σ br is the above-mentioned fracture stress, d is the joint diameter, t is the thickness of the measurement sample, and a is a constant determined experimentally.

7. In paragraph 2, The above strain energy is the second strain energy (W neck ) and Setting the above features is: The second strain energy (W) neck ) is set as the above feature by calculating it according to the following mathematical formula, and a design device for a multilayer film: Here, σ n is the stress at the necking point, and ε n is the strain at the necking point, and ε y is the yield strain, and σ br is the above-mentioned fracture stress, and σ y is the yield stress, d is the joint diameter, t is the thickness of the measurement sample, and b is a constant determined experimentally.

8. In paragraph 2, The above strain energy is the third strain energy (W str-hard ) and Setting the above features is: The third strain energy (W) str-hard ) is set as the above feature by calculating it according to the following mathematical formula, and a design device for a multilayer film: Here, ε br is the above fracture strain, and ε n is the strain at the necking point, and σ br is the above-mentioned fracture stress, and σ n is the necking point stress, L is the final stretching propagation length, t is the thickness of the measurement sample, and c is a constant determined experimentally.

9. In paragraph 2, The above strain energy is the fourth strain energy (W elast ) and Setting the above features is: The fourth strain energy (W) elast ) is set as the above feature by calculating it according to the following mathematical formula, and a design device for a multilayer film: Here, σ y is the yield stress, and ε y is the yield strain, d is the diameter of the joint, D is the diameter of the fixed disk, L is the final stretching propagation length, t is the thickness of the measurement sample, and e is a constant determined experimentally.

10. 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.

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 stress strain data for a single-layer film forming the above laminated structure; 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 calculating strain energy from a plurality of physical indices selected from the stress strain data according to the above feature setting mode, and setting the strain energy as a feature; 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: At least some of the Yield Stress, Yield Strain, Necking Stress, Necking Strain, Breaking Stress and Breaking Strain, Strain energy calculated 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; A method for designing a multilayer film, comprising the step of setting the thickness of the single-layer film as a feature.

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 vector generated from 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, A vector generated from strain energy calculated 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, Input a vector generated from the thickness of the above single-layer film, A design method for a multilayer film, wherein 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: Yield Stress, Yield Strain, Necking Stress, Necking Strain, Breaking Stress and Breaking Strain, The strain energy calculated from the yield stress, the yield strain, the necking point stress, the necking point strain, the breaking stress, and the breaking strain, A method for designing a multilayer film, comprising the step of setting the thickness of the single-layer film as a feature.

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 vector generated from the yield stress, the yield strain, the necking point stress, the necking point strain, the breaking stress, and the breaking strain, A vector generated from the strain energy calculated from the yield stress, the yield strain, the necking point stress, the necking point strain, the breaking stress, and the breaking strain, Input a vector generated from the thickness of the above single-layer film, A design method for a multilayer film, wherein the method is learned to predict the drop strength of the multilayer film.

16. In paragraph 12, The above strain energy is the first strain energy (W yield ) and The steps to set the above features are: The first strain energy (W) yield ) is set as the feature by calculating it according to the following mathematical formula, and a design method for a multilayer film: Here, σ y is the yield stress, and ε y is the yield strain, and σ br is the above-mentioned fracture stress, d is the joint diameter, t is the thickness of the measurement sample, and a is a constant determined experimentally.

17. In paragraph 12, The above strain energy is the second strain energy (W neck ) and The steps to set the above features are: The second strain energy (W) neck ) is set as the feature by calculating it according to the following mathematical formula, and a design method for a multilayer film: Here, σ n is the stress at the necking point, and ε n is the strain at the necking point, and ε y is the yield strain, and σ br is the above-mentioned fracture stress, d is the joint diameter, and σ y is the yield stress, t is the thickness of the measurement sample, and b is a constant determined experimentally.

18. In paragraph 12, The above strain energy is the third strain energy (W str-hard ) and The steps to set the above features are: The third strain energy (W) str-hard ) is set as the feature by calculating it according to the following mathematical formula, and a design method for a multilayer film: Here, ε br is the above fracture strain, and ε n is the strain at the necking point, and σ br is the above-mentioned fracture stress, and σ n is the necking point stress, L is the final stretching propagation length, t is the thickness of the measurement sample, and c is a constant determined experimentally.

19. In paragraph 12, The above strain energy is the fourth strain energy (W elast ) and The steps to set the above features are: The fourth strain energy (W) elast ) is set as the feature by calculating it according to the following mathematical formula, and a design method for a multilayer film: Here, σ y is the yield stress, and ε y is the yield strain, d is the diameter of the joint, D is the diameter of the fixed disk, L is the final stretching propagation length, t is the thickness of the measurement sample, and e is a constant determined experimentally.

20. 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.

Images