Apparatus and method for designing a multilayer film

Abstract

An apparatus and method for designing a multilayer film are disclosed. The apparatus for designing a multilayer film may perform the following operations: modeling a laminated structure of a multilayer film to be designed; collecting stress-strain data on the single-layer film forming the laminated structure; reading a value pre-stored in a storage space accessible to a device for designing the multilayer film, and obtaining a feature setting mode for specifying different feature setting modes according to the read value; according to a characteristic setting mode, calculating strain energy according to a plurality of physical indexes selected from the stress-strain data, and setting the strain energy as a characteristic; selecting at least one of a plurality of supervised learning models capable of performing regression analysis as a machine learning model; predicting a dart impact strength of the multilayer film by using a machine learning model learned by using the feature as an independent variable and a dart impact strength of the multilayer film as a target variable; and generating design data on the multilayer film by combining the predicted values of the other characteristics and the predicted value of the falling dart impact strength so as to meet design requirements of the multilayer film.

Description

Technical Field

[0001] Cross-reference to related applications

[0002] This application claims priority and benefit to Korean Patent Application No. 10-2023-0129784, filed with the Korean Intellectual Property Office on September 26, 2023, and Korean Patent Application No. 10-2024-0130233, filed with the Korean Intellectual Property Office on September 25, 2024, the entire contents of which are incorporated herein by reference.

[0003] This disclosure relates to apparatus and methods for designing multilayer films. Background Technology

[0004] Polymer films are thin, flat structures made of polymer materials. Here, the polymer can have a long-chain molecular structure with repeating units called monomers linked within these chains. The thickness of polymer films can be set in various units, down to millimeters (mm). Polymer films offer a variety of advantages, including flexibility, lightweight, cost-effectiveness, and processability. Specifically, polymer films, due to their excellent flexibility, can be manufactured in various shapes and sizes, making them suitable for a wide range of product designs. Furthermore, the lightweight nature of polymer films makes them suitable for products requiring portability, thereby reducing transportation and handling costs. Additionally, polymer films are cost-effective because they can be manufactured using relatively inexpensive raw materials and can be mass-produced. Moreover, because polymer films can be applied to various manufacturing and processing techniques, such as heat treatment, extrusion, and bonding, the shape, size, and thickness of products can be easily adjusted. Additionally, polymer films possess high durability and strength, and some exhibit excellent optical properties, are waterproof, and can be breathable if desired.

[0005] Multilayer membranes can be composed of two or more layers of polymers or other materials. This multilayer structure can improve the overall performance of the membrane by combining the unique properties of each layer. For example, one layer can be used to improve durability and strength, another layer can provide water or moisture resistance, and yet another layer can provide resistance to certain chemicals. In this way, by combining layers with different properties, multilayer membranes can be adapted to a wider range of applications and specific requirements than single-layer membranes. Summary of the Invention

[0006] Technical issues

[0007] The objective is to provide an apparatus and method for designing multilayer membranes that can predict the dart impact strength of a multilayer membrane using only information about the single-layer membranes constituting the multilayer membrane and design the multilayer membrane according to given design requirements.

[0008] Technical solution

[0009] An apparatus for designing multilayer membranes according to an embodiment is used to design multilayer membranes by executing program code loaded on one or more memory devices by one or more processors, based on physical properties of the multilayer membrane predicted solely from information about a single-layer membrane, wherein the program code is configured to perform the following operations when executed: modeling the laminated structure of the multilayer membrane to be designed; collecting stress-strain data about the single-layer membrane forming the laminated structure; reading values ​​pre-stored in storage space accessible to the apparatus for designing the multilayer membrane, and obtaining a feature setting pattern for specifying different feature setting methods based on the read values; calculating strain energy based on the feature setting pattern and multiple physical indices selected from the stress-strain data, and setting the strain energy as a feature; selecting at least one machine learning model from multiple supervised learning models capable of performing regression analysis; predicting the dart impact strength of the multilayer membrane using a machine learning model learned by using the feature as an independent variable and the dart impact strength of the multilayer membrane as a target variable; and generating design data for the multilayer membrane by combining the predicted values ​​of other characteristics and the predicted value of the dart impact strength to meet the design requirements of the multilayer membrane.

[0010] In some embodiments, when the feature setting mode may include a first feature setting mode, setting a feature may include setting at least one of yield stress, yield strain, necking stress, necking strain, fracture stress and fracture strain, strain energy calculated based on at least one of yield stress, yield strain, necking stress, necking strain, fracture stress and fracture strain, and the thickness of the monolayer film.

[0011] In some implementations, selection as a machine learning model may include selecting 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 is learned by receiving vectors generated from at least some of yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain, vectors generated from strain energy calculated based on at least some of yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain, and vectors generated from the thickness of the monolayer membrane.

[0012] In some implementations, when the feature setting mode may include a second feature setting mode, setting a feature may include setting the following as features: yield stress, yield strain, necking stress, necking strain, fracture stress and fracture strain, strain energy calculated based on yield stress, yield strain, necking stress, necking strain, fracture stress and fracture strain, and the thickness of the monolayer film.

[0013] In some implementations, selection as a machine learning model may include selecting 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, lasso regression model, and random forest model is learned by receiving the following to predict the dart impact strength of the multilayer membrane: a vector generated from yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain; a vector generated from strain energy calculated from yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain; and a vector generated from the thickness of the single-layer membrane.

[0014] In some implementations, the strain energy may include a first strain energy (W). yield ), and wherein, setting a feature may include calculating the first strain energy (W) according to the following formula. yield ), and the calculated first strain energy (W) yield Set as a feature,

[0015]

[0016] Where, σ y ε represents the yield stress. y σ represents the yield strain. br Let represent the fracture stress, d represent the contact diameter of the falling dart, t represent the thickness of the measured sample, and a represent an experimentally determined constant.

[0017] In some implementations, the strain energy may include a second strain energy (W). neck ), and wherein, setting the feature may include calculating the second strain energy (W) according to the following formula. neck ), and the calculated second strain energy (W) neck Set as a feature,

[0018]

[0019] Where, σ n Represents necking stress, ε n ε represents necking strain. y σ represents the yield strain. br σ represents the fracture stress. yLet represent the yield stress, d represent the contact diameter of the dart, t represent the thickness of the measured sample, and b represent an experimentally determined constant.

[0020] In some implementations, the strain energy may include a third strain energy (W). str-hard ), and wherein, setting as a feature may include calculating the third strain energy (W) according to the following formula. str-hard ), and the calculated third strain energy (W) str-hard Set as a feature,

[0021]

[0022] Where, ε br ε represents fracture strain. n σ represents the necking strain. br σ represents the fracture stress. n Let L represent the necking stress, L represent the final tensile propagation length, t represent the thickness of the measured sample, and c represent an experimentally determined constant.

[0023] In some implementations, the strain energy may include a fourth strain energy (W). elast ), and wherein, setting as a feature may include calculating the fourth strain energy (W) according to the following formula. elast ), and the calculated fourth strain energy (W) elast Set as a feature,

[0024]

[0025] Where, σ y ε represents the yield stress. y Let represent the yield strain, d represent the dart contact diameter, D represent the diameter of the fixed disk, L represent the final tensile propagation length, t represent the thickness of the measured sample, and e represent an experimentally determined constant.

[0026] In some implementations, generating design data for the multilayer membrane may include: combining predicted values ​​of at least one of the following characteristics of the multilayer membrane: stiffness, strength, strain, tear strength, high-speed dart impact strength, seal initiation temperature (SIT), haze, moisture permeability, and air permeability, with a predicted value of dart impact strength; generating design data based on the combination when the multilayer membrane designed based on the combination of predicted values ​​meets the design requirements; and redesigning the multilayer membrane by changing the combination to another combination when the multilayer membrane designed based on the combination of predicted values ​​does not meet the design requirements.

[0027] A method for designing a multilayer membrane according to an embodiment is executed by a computing device including one or more processors and one or more memory devices to design a multilayer membrane based on physical properties of the multilayer membrane predicted solely from information about a single-layer membrane. The method may include: modeling the laminated structure of the multilayer membrane to be designed; collecting stress-strain data about the single-layer membrane forming the laminated structure; reading values ​​pre-stored in storage accessible to the computing device and obtaining a feature setting pattern for specifying different feature settings based on the read values; calculating strain energy based on the feature setting pattern and multiple physical indices selected from the stress-strain data, and setting the strain energy as a feature; selecting at least one machine learning model from multiple supervised learning models capable of performing regression analysis; predicting the dart impact strength of the multilayer membrane using a machine learning model learned by using the feature as an independent variable and the dart impact strength of the multilayer membrane as a target variable; and generating design data for the multilayer membrane by combining the predicted values ​​of other characteristics and the predicted value of the dart impact strength to meet the design requirements of the multilayer membrane.

[0028] In some embodiments, when the feature setting mode may include a first feature setting mode, setting a feature may include setting at least one of yield stress, yield strain, necking stress, necking strain, fracture stress and fracture strain, strain energy calculated based on at least one of yield stress, yield strain, necking stress, necking strain, fracture stress and fracture strain, and the thickness of the monolayer film.

[0029] In some implementations, selection as a machine learning model may include selecting 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 is learned by receiving vectors generated from at least some of yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain, vectors generated from strain energy calculated based on at least some of yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain, and vectors generated from the thickness of the monolayer membrane.

[0030] In some implementations, when the feature setting mode may include a second feature setting mode, setting a feature may include setting the following as features: yield stress, yield strain, necking stress, necking strain, fracture stress and fracture strain, strain energy calculated based on yield stress, yield strain, necking stress, necking strain, fracture stress and fracture strain, and the thickness of the monolayer film.

[0031] In some implementations, selection as a machine learning model may include selecting 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, lasso regression model, and random forest model is learned by receiving the following to predict the dart impact strength of the multilayer membrane: a vector generated from yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain; a vector generated from strain energy calculated from yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain; and a vector generated from the thickness of the single-layer membrane.

[0032] In some implementations, the strain energy may include a first strain energy (W). yield ), and wherein, setting a feature may include calculating the first strain energy (W) according to the following formula. yield ), and the calculated first strain energy (W) yield Set as a feature,

[0033]

[0034] Where, σ y ε represents the yield stress. y σ represents the yield strain. br Let represent the fracture stress, d represent the contact diameter of the falling dart, t represent the thickness of the measured sample, and a represent an experimentally determined constant.

[0035] In some implementations, the strain energy may include a second strain energy (W). neck ), and wherein, setting the feature may include calculating the second strain energy (W) according to the following formula. neck ), and the calculated second strain energy (W) neck Set as a feature,

[0036]

[0037] Where, σ n Represents necking stress, ε n ε represents necking strain. y σ represents the yield strain. br σ represents the fracture stress, d represents the contact diameter of the falling dart, and σ represents the fracture stress. y Let represent the yield stress, t represent the thickness of the measured sample, and b represent an experimentally determined constant.

[0038] In some implementations, the strain energy may include a third strain energy (W). str-hard ), and wherein, setting as a feature may include calculating the third strain energy (W) according to the following formula. str-hard ), and the calculated third strain energy (W)str-hard Set as a feature,

[0039]

[0040] Where, ε br ε represents fracture strain. n σ represents the necking strain. br σ represents the fracture stress. n Let L represent the necking stress, L represent the final tensile propagation length, t represent the thickness of the measured sample, and c represent an experimentally determined constant.

[0041] In some implementations, the strain energy may include a fourth strain energy (W). elast ), and wherein, setting as a feature may include calculating the fourth strain energy (W) according to the following formula. elast ), and the calculated fourth strain energy (W) elast Set as a feature,

[0042]

[0043] Where, σ y ε represents the yield stress. y Let represent the yield strain, d represent the dart contact diameter, D represent the diameter of the fixed disk, L represent the final tensile propagation length, t represent the thickness of the measured sample, and e represent an experimentally determined constant.

[0044] In some implementations, generating design data for the multilayer membrane may include: combining predicted values ​​of at least one of the following characteristics of the multilayer membrane: stiffness, strength, strain, tear strength, high-speed dart impact strength, seal initiation temperature (SIT), haze, moisture permeability, and air permeability, with a predicted value of dart impact strength; generating design data based on the combination when the multilayer membrane designed based on the combination of predicted values ​​meets the design requirements; and redesigning the multilayer membrane by changing the combination to another combination when the multilayer membrane designed based on the combination of predicted values ​​does not meet the design requirements.

[0045] Beneficial effects

[0046] According to the implementation method, when designing a multilayer membrane, by using only information about the single-layer membrane constituting the multilayer membrane to predict the dart impact strength of the multilayer membrane and taking into account the prediction results about other properties, a multilayer membrane that can meet a given design requirement can be designed. Attached Figure Description

[0047] Figure 1 This is a block diagram illustrating an apparatus for designing multilayer films according to an embodiment.

[0048] Figure 2This is a graph used to illustrate stress-strain data for a monolayer membrane according to an embodiment.

[0049] Figures 3 to 4 This is a graph used to illustrate environmental data for measuring the impact intensity of a dart according to an embodiment.

[0050] Figure 5 This is a flowchart illustrating a method for designing a multilayer film according to an embodiment.

[0051] Figure 6 This is a diagram illustrating a computing device according to an embodiment. Detailed Implementation

[0052] In the following description, exemplary embodiments of the present disclosure will be described more fully with reference to the accompanying drawings to facilitate practice by those skilled in the art to which this disclosure pertains. As will be appreciated by those skilled in the art, modifications can be made to the described embodiments in various different ways without departing from the spirit or scope of this disclosure. Furthermore, the drawings and description are to be considered illustrative rather than restrictive in nature, and similar reference numerals denote similar elements throughout the specification.

[0053] Throughout the specification and claims, unless explicitly stated otherwise, the words “comprising” and variations such as “including” or “having” shall be understood to mean including the stated element, but not excluding any other element. Terms including serial numbers such as first, second, etc., may be used to describe various components, but the components are not limited to those terms. The above terms are used only for the purpose of distinguishing one component from others.

[0054] Terms such as “…unit,” “…device,” and “module” used in this specification may refer to a unit capable of performing at least one function or operation described in the specification, which may be implemented as hardware or circuitry, software, or a combination of hardware or circuitry and software. Furthermore, at least some components or functions of the design apparatus and method for multilayer films according to the exemplary embodiments described below may be implemented as programs or software, and such programs or software may be stored on a computer-readable medium.

[0055] Figure 1 This is a block diagram illustrating an apparatus for designing multilayer films according to an embodiment. Figure 2 This is a graph used to illustrate stress-strain data for a monolayer membrane according to an embodiment. Figures 3 to 4 This is a graph used to illustrate environmental data for measuring the impact intensity of a dart according to an embodiment.

[0056] Reference Figure 1According to an embodiment, the apparatus 10 for designing multilayer films can execute program code or instructions loaded on one or more memory devices via one or more processors. For example, the apparatus 10 for designing multilayer films can be implemented as described later. Figure 6 The computing device 50 is described. In this case, one or more processors may correspond to processor 510 of computing device 50, and one or more memory devices may correspond to memory 520 of computing device 50. Program code or instructions may be executed by one or more processors to predict the dart impact strength of the multilayer film using only information about the single-layer films constituting the multilayer film, and to design the multilayer film according to given design requirements. In this specification, the term "module" is used to logically separate these functions executed by program code or instructions.

[0057] The apparatus 10 for designing multilayer membranes according to an embodiment can predict the dart impact strength of the membrane without performing a separate dart impact test. Impact strength can refer to the intensity of a physical impact that a material (in this case, a membrane) can withstand. Impact strength can be used as an indicator to measure, for example, the degree of damage to a membrane when a heavy object is dropped from a predetermined height. The apparatus 10 for designing multilayer membranes can predict the dart impact strength of the multilayer membrane using machine learning based on experimental data measured by methods other than dart impact tests on polymer membranes. For this purpose, the apparatus 10 for designing multilayer membranes may include all or at least some of the following: a multilayer membrane 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 as included within the apparatus 10 for designing multilayer membranes. Alternatively, for example, the learning module 150 may be implemented externally to the apparatus 10 for designing multilayer membranes, and the apparatus 10 for designing multilayer membranes may be provided with a trained machine learning model from the learning module 150.

[0058] The multilayer membrane modeling module 110 can model the laminated structure of the multilayer membrane to be designed. A multilayer membrane can include multiple monolayer membranes. Here, the multiple monolayer membranes can include monolayer membranes of a specific type and different types, and the unique properties of the monolayer membranes can vary depending on their type. Furthermore, the lamination order of the monolayer membranes within the multilayer membrane can also be changed. Regarding the multilayer membrane whose physical properties are to be predicted, the multilayer membrane modeling module 110 can model the multilayer membrane by setting the type of monolayer membrane, the lamination order of the monolayer membranes, etc.

[0059] In some embodiments, the multilayer membrane modeling module 110 can model a multilayer membrane as comprising k single-layer membranes (where k is an integer greater than or equal to 2). For example, the multilayer membrane modeling module 110 can model a multilayer membrane having a first membrane layer, a second membrane layer, ..., a kth membrane layer sequentially laminated from the bottom. For each of the k single-layer membranes forming the multilayer membrane, features set by the feature setting module 130, described later, can be applied.

[0060] In some embodiments, the multilayer membrane modeling module 110 can model a multilayer membrane comprising a single-layer membrane with six layers. For example, the multilayer membrane modeling module 110 can model a multilayer membrane having a first membrane layer, a second membrane layer, a third membrane layer, a fourth membrane layer, a fifth membrane layer, and a sixth membrane layer sequentially laminated from the bottom.

[0061] The data collection module 120 can collect stress-strain data about the single-layer membrane forming the laminated structure modeled by the multilayer membrane modeling module 110. Here, the stress-strain data can represent the mechanical properties of the membrane (i.e., the single-layer membrane forming the multilayer membrane) and, together with reference to... Figure 2 Stress-strain data can be represented in the form of stress-strain curves. Stress-strain curves can show the stress generated when a force is applied to the membrane and how the membrane deforms as a result.

[0062] In some embodiments, the data collection module 120 can collect the results obtained by performing a universal testing machine (UTM) test on a single-layer membrane forming a multilayer membrane as stress-strain data. The UTM test can be designed to perform various mechanical tests on the material, and the load value can be measured when a load due to a force load is generated by applying work to the specimen at specific times, forces, and directions. That is, tests such as compression, tension, and bending can be performed by applying force to the specimen and measuring the load value until the specimen deforms. The data collection module 120 can collect the results obtained by performing a UTM test on a membrane specimen as stress-strain data.

[0063] In some embodiments, the data collection module 120 can collect data on the dart impact strength of the monolayer membrane by means of a test according to Method A of ASTM D1709. Here, the test can be used to evaluate the dart impact strength or toughness of the monolayer membrane by using a free-falling dart. More detailed information about ASTM D1709 can be found in the ASTM standard document, and its description is not included in this specification.

[0064] In some embodiments, the data collection module 120 can acquire stress-strain curves as data based on results obtained from uniaxial tensile tests performed in the machine direction (MD) and transverse direction (TD). Here, MD can be the direction in which the membrane moves between the rollers of the manufacturing machine during the membrane manufacturing process, and TD can be a direction perpendicular to the rollers of the manufacturing machine, perpendicular to MD. Alternatively, the uniaxial tensile test can be performed by measuring the resistance of a monolayer membrane when stretched in a uniaxial direction. In this case, the data collection module 120 can collect data on the stress-strain curves by means of a test according to ASTM D882. More detailed information about ASTM D882 can be found in the ASTM standard document, and its description is not included in this specification.

[0065] In some implementations, stress-strain data may include at least one of yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain. Here, yield stress may represent the stress at the yield point, which is the point where the material exceeds its elastic limit and begins permanent deformation, i.e., 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 may represent the stress at the necking point, which is the point where the cross-section of the material begins to locally narrow, and typically after the necking point, the load-bearing capacity of the material may decrease sharply. Necking strain may represent the strain at the necking point. Fracture stress may represent the stress at the point where the material finally fractures, and at the fracture point, the material may no longer bear a load. Fracture strain may represent the strain corresponding to the fracture stress.

[0066] Refer to together Figure 2 On the stress-strain curve, the example point P corresponding to the yield stress and yield strain can be determined. y Example point P corresponding to necking stress and necking strain n And example point P corresponding to fracture stress and fracture strain. br .

[0067] The feature setting module 130 can perform feature setting according to multiple feature setting modes. The multiple feature setting modes are stored as predetermined values ​​in the processor-accessible storage space of the device 10 for designing multilayer films and managed therein. The device 10 for designing multilayer films can read the values ​​pre-stored in the storage space and determine the feature setting mode for specifying different feature setting methods based on the read values.

[0068] The feature setting module 130 can calculate strain energy based on multiple physical indices selected from stress-strain data according to a feature setting mode, and set the strain energy as a feature. In machine learning, a feature can be an input variable used to predict a target (i.e., the physical properties of a multilayer membrane) and can refer to a single independent variable of the data. Features can provide information about the target variable or dependent variable to be predicted within the dataset and have a significant impact on the performance of the machine learning model 20. The learning module 150 can perform learning of the machine learning model 20 by using features. At this time, features can constitute part of the data input to the machine learning model 20, and the machine learning model 20 can learn patterns and perform predictions based on this data. In particular, feature engineering, such as selecting or transforming 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 significant impact on the high performance of the machine learning model 20.

[0069] In some embodiments, when the feature setting mode includes a first feature setting mode, the feature setting module 130 can set the following as features: at least some of yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain; strain energy calculated based on at least some of yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain; and the thickness of the monolayer membrane. That is, the feature setting module 130 can set the following as features: at least a portion of the stress-strain data collected by the data collection module 120; strain energy calculated based on at least a portion of the stress-strain data collected by the data collection module 120; and the thickness of the monolayer membrane. When the multilayer membrane modeling module 110 models a multilayer membrane comprising k monolayer membranes, the learning module 150 can apply features including: only a portion of the stress-strain data collected by the data collection module 120 regarding the first membrane layer among the k monolayer membranes; strain energy calculated based on at least a portion of the stress-strain data collected by the data collection module 120; and the thickness of the monolayer membrane. Additionally, the learning module 150 can apply features including: only a portion of the stress-strain data collected by the data collection module 120 regarding the second membrane layer among the k monolayer membranes, strain energy calculated based on at least a portion of the stress-strain data collected by the data collection module 120, and the thickness of the monolayer membrane. Furthermore, the learning module 150 can perform machine learning model 20 learning by applying features including: only a portion of the stress-strain data collected by the data collection module 120 regarding the kth membrane layer, strain energy calculated based on at least a portion of the stress-strain data collected by the data collection module 120, and the thickness of the monolayer membrane. In this case, learning can be performed by applying the same type of features to all k monolayer membranes, and alternatively, learning can be performed such that features of a different type than those of other membrane layers can be applied to one of the k monolayer membranes. This embodiment can be employed in environments with limited computational resources for performing the learning and prediction of the machine learning model 20.

[0070] In some embodiments, when the feature setting mode includes a second feature setting mode, the feature setting module 130 can set the following as features: yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain; strain energy calculated based on the yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain; and the thickness of the monolayer membrane. That is, the feature setting module 130 can set the following as features: all stress-strain data collected by the data collection module 120; strain energy calculated based on the stress-strain data collected by the data collection module 120; and the thickness of the monolayer membrane. When the multilayer membrane modeling module 110 models a multilayer membrane comprising k monolayer membranes, the learning module 150 can apply features including: all stress-strain data collected by the data collection module 120 regarding the first membrane layer among the k monolayer membranes; strain energy calculated based on the stress-strain data collected by the data collection module 120; and the thickness of the monolayer membrane. Additionally, the learning module 150 can apply features including: all stress-strain data collected by the data collection module 120 regarding the second membrane layer among the k monolayer membranes, strain energy calculated based on the stress-strain data collected by the data collection module 120, and the thickness of the monolayer membrane. Furthermore, the learning module 150 can perform machine learning model 20 learning by applying features including: all stress-strain data collected by the data collection module 120 regarding the kth membrane layer, strain energy calculated based on the stress-strain data collected by the data collection module 120, and the thickness of the monolayer membrane. In this case, learning can be performed by applying the same type of features to all k monolayer membranes, and alternatively, learning can be performed such that features of a different type than those of other membrane layers can be applied to one of the k monolayer membranes. This embodiment can be employed in an environment with sufficient computational resources for performing the learning and prediction of the machine learning model 20.

[0071] 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 the mechanical properties of the monolayer membrane in a first direction, and the second stress-strain data may represent the mechanical properties of the monolayer membrane 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 parameters selected from at least one of the first stress-strain data and the second stress-strain data as features.

[0072] In some embodiments, when the feature setting mode includes a third feature setting mode, the feature setting module 130 can set the following as features: at least some of the yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain in a first direction; strain energy calculated based on at least some of the yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain in the first direction; at least some of the yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain in a second direction; strain energy calculated based on at least some of the yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain in the second direction; and the thickness of the monolayer film. That is, the feature setting module 130 can set the following as features: at least a portion of the first stress-strain data collected by the data collection module 120; strain energy calculated based on at least a portion of the first stress-strain data collected by the data collection module 120; at least a portion of the second stress-strain data collected by the data collection module 120; strain energy calculated based on at least a portion of the second stress-strain data collected by the data collection module 120; and the thickness of the monolayer film. When the multilayer membrane modeling module 110 models a multilayer membrane comprising k monolayer membranes, the learning module 150 may apply features including: only a portion of the first stress-strain data collected by the data collection module 120 regarding the first membrane layer among the k monolayer membranes; strain energy calculated based on 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 based on at least a portion of the second stress-strain data collected by the data collection module 120; and the thickness of the monolayer membrane. 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 regarding the second membrane layer among the k monolayer membranes; strain energy calculated based on 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 based on at least a portion of the second stress-strain data collected by the data collection module 120; and the thickness of the monolayer membrane. Additionally, the learning module 150 can perform the 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 regarding the k-th membrane layer, strain energy calculated based on 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 based on at least a portion of the second stress-strain data collected by the data collection module 120, and the thickness of the monolayer membrane.At this point, learning can be performed by applying features of the same type to all k monolayer membranes, and alternatively, learning can be performed such that features of a different type than those of other membrane layers can be applied to one of the k monolayer membranes. This implementation can be employed in environments with limited computational resources for performing the learning and prediction of machine learning model 20.

[0073] In some embodiments, when the feature setting mode includes a fourth feature setting mode, the feature setting module 130 can set the following as features: yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain in a first direction; strain energy calculated based on the yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain in the first direction; yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain in a second direction; strain energy calculated based on the yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain in the second direction; and the thickness of the monolayer film. That is, the feature setting module 130 can set the following as features: all first stress-strain data collected by the data collection module 120; strain energy calculated based on the first stress-strain data collected by the data collection module 120; all second stress-strain data collected by the data collection module 120; strain energy calculated based on the second stress-strain data collected by the data collection module 120; and the thickness of the monolayer film. When the multilayer membrane modeling module 110 models a multilayer membrane comprising k monolayer membranes, the learning module 150 can apply features including: all first stress-strain data collected by the data collection module 120 regarding the first membrane layer among the k monolayer membranes, strain energy calculated based on the first stress-strain data collected by the data collection module 120, all second stress-strain data collected by the data collection module 120, strain energy calculated based on the second stress-strain data collected by the data collection module 120, and the thickness of the monolayer membrane. Additionally, the learning module 150 can apply features including: all first stress-strain data collected by the data collection module 120 regarding the second membrane layer among the k monolayer membranes, strain energy calculated based on the first stress-strain data collected by the data collection module 120, all second stress-strain data collected by the data collection module 120, strain energy calculated based on the second stress-strain data collected by the data collection module 120, and the thickness of the monolayer membrane. Furthermore, the learning module 150 can perform the learning of the machine learning model 20 by applying features including: all first stress-strain data collected by the data collection module 120 regarding the k-th membrane layer, strain energy calculated based on the first stress-strain data collected by the data collection module 120, all second stress-strain data collected by the data collection module 120, strain energy calculated based on the second stress-strain data collected by the data collection module 120, and the thickness of the monolayer membrane. In this case, learning can be performed by applying the same type of features to all k monolayer membranes, and alternatively, learning can be performed such that features of a different type than those of other membrane layers can be applied to one of the k monolayer membranes. This embodiment can be employed in an environment with sufficient computational resources for performing the learning and prediction of the machine learning model 20.

[0074] For example, when the multilayer membrane modeling module 110 models a multilayer membrane including a single-layer membrane with 6 layers, the following 21 features can be applied to a membrane layer: all indices of yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain in the first direction; four indices of strain energy calculated based on yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain in the first direction; all indices of yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain in the second direction; four indices of strain energy calculated based on yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain in the second direction; and the thickness of the single-layer membrane. Since there are a total of 6 layers, a total of 126 features can be applied.

[0075] In some implementations, the feature setting module 130 can calculate the first strain energy (W) based on stress-strain data. yield ) and set it as a feature. Specifically, the feature setting module 130 can calculate the first strain energy (W) according to the following formula 1. yield ).

[0076] Formula 1

[0077]

[0078] Here, σ y It can represent the yield stress, ε, in stress-strain data. y It can represent the yield strain in stress-strain data, and σ in stress-strain data br These can represent fracture stress. These variables can correspond to experimental data measured by methods other than the dart impact test described above.

[0079] On the other hand, in order to calculate the strain energy from the stress-strain data, variables related to the environmental settings of the dart impact test according to a specific predetermined standard can be additionally applied to the membrane whose dart impact strength is to be predicted. Here, the standard can refer to the American Society for Testing and Materials (ASTM) standard, and can refer to any other standard customized considering the learning performance of the machine learning model 20. Specifically, as variables related to the environmental settings of the dart impact test, d can represent the dart contact diameter, t can represent the thickness of the measured sample, and a can represent an experimentally determined constant considering the experimental environment or product testing environment. (See also...) Figure 3The engagement diameter d can refer to the diameter of the circular area that engages with the additional membrane when the weight is dropped from a predetermined height. The weight includes a fixed disc with a diameter D, the area represented by l is the region on which the tension propagates, and the area represented by L represents the region (or length) on which the tension ultimately propagates. Variables (d, D, l, L, t, etc.) related to the environmental settings of a separate dart impact test can be appropriately adjusted to improve the accuracy of the final predicted dart impact strength.

[0080] In some implementations, the feature setting module 130 can calculate the second strain energy (W) based on stress-strain data. neck ) and set it as a feature. Specifically, the feature setting module 130 can calculate the second strain energy (W) according to the following formula 2. neck ).

[0081] Formula 2

[0082]

[0083] Here, σ n It can represent the necking stress in stress-strain data, ε n It can represent the necking strain in stress-strain data, ε y It can represent the yield strain in stress-strain data, σ br It can represent the fracture stress in stress-strain data, σ y These variables can represent the yield stress in stress-strain data, and they can correspond to experimental data measured by methods other than the dart impact test described above.

[0084] On the other hand, as a variable related to the environmental settings of the dart impact test, d can be the dart contact diameter. t can represent the thickness of the measured sample, and b can represent an experimentally determined constant considering the experimental environment or product testing environment.

[0085] In some implementations, the feature setting module 130 can calculate the third strain energy (W) based on stress-strain data. str-hard ) and set it as a feature. Specifically, the feature setting module 130 can calculate the third strain energy (W) according to the following formula 3. str-hard ).

[0086] Formula 3

[0087]

[0088] Here, ε br It can represent the fracture strain, ε, in stress-strain data. nIt can represent the necking strain in stress-strain data, σ br It can represent the fracture stress in stress-strain data, and σ n This can represent necking stress in stress-strain data. These variables can correspond to experimental data measured by methods other than the dart impact test described above.

[0089] On the other hand, as variables related to the environmental settings of the dart impact test, L can represent the final tensile propagation length, t can represent the thickness of the measured sample, and c can represent a constant determined by the experiment considering the experimental environment or product test environment.

[0090] In some implementations, the feature setting module 130 can calculate a fourth strain energy (W) based on stress-strain data. elast ) and set it as a feature. Specifically, the feature setting module 130 can calculate the fourth strain energy (W) according to the following formula 4. elast ).

[0091] Formula 4

[0092]

[0093] Here, σ y It can represent the yield stress in stress-strain data, and ε y These can represent the yield strain in stress-strain data. These variables can correspond to experimental data measured by methods other than the dart impact test described above.

[0094] On the other hand, as variables related to the environmental settings of the dart impact test, d can represent the dart contact diameter, D can represent the diameter of the fixed plate, L can represent the final tensile propagation length, t can represent the thickness of the measured sample, and e can represent a predetermined constant.

[0095] In some implementations, the final tensile propagation length L can be calculated based on experimental data measured by methods other than dart impact testing. Specifically, the final tensile propagation length L can be calculated according to the following formula 5.

[0096] Formula 5

[0097]

[0098] Here, σ br It can represent the fracture stress in stress-strain data, σ y It can represent the yield stress in stress-strain data, and d can be the contact diameter of the dart.

[0099] In some implementations, the feature setting module 130 can set the first strain energy (W) yield ), second strain energy (W) neck ), third strain energy (W) str-hard ) and fourth strain energy (W elast These features are set together. The learning module 150 can perform learning of the machine learning model 20 based on the features set in this way. This implementation can be used in an environment with sufficient computing resources for performing learning and prediction of the machine learning model 20.

[0100] Alternatively, in some other embodiments, the feature setting module 130 can assign the first strain energy (W) yield ), second strain energy (W) neck ), third strain energy (W) str-hard ) and fourth strain energy (W elast At least some of the features are set as characteristics. The learning module 150 can perform learning of the machine learning model 20 based on the features set in this way. This implementation can be used in environments with limited computing resources for performing learning and prediction of the machine learning model 20.

[0101] In some embodiments, the feature setting module 130 may additionally set yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain as features. That is, the feature setting module 130 may also set yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain selected from stress-strain data together with strain energy calculated from stress-strain data as features.

[0102] In some embodiments, the feature setting module 130 may additionally set the thickness of a single layer of film forming the multilayer film as a feature. That is, the feature setting module 130 may also set the thickness of the single layer of film together with the strain energy calculated from the stress-strain data as a feature.

[0103] In some embodiments, the feature setting module 130 may set the following as a feature: a first strain energy (W) in a first direction. yield ), second strain energy (W) neck ), third strain energy (W) str-hard ) and fourth strain energy (W elast At least one or all of the following, and the first strain energy (W) in the second direction. yield ), second strain energy (W) neck ), third strain energy (W) str-hard ) and fourth strain energy (W elastAt least one or all of the following. That is, the feature setting module 130 can set the following as features: at least some or all of the strain energy calculated from the first stress-strain data collected by the data collection module 120, at least some or all of the strain energy calculated from the second stress-strain data, selectively as well as yield stress, yield strain, necking stress, necking strain, fracture stress and fracture strain, and selectively as well as the thickness of the monolayer film.

[0104] The model selection module 140 can select a machine learning model for predicting the physical properties (i.e., dart impact strength) of multilayer films. Specifically, the model selection module 140 can select at least one machine learning model from a plurality of supervised learning models capable of performing regression analysis as the machine learning model for predicting the dart impact strength of multilayer films. Supervised learning is a method of training a model using labeled training data, and in supervised learning, the model can learn the relationship between input variables and target variables, thereby enabling the model to predict the value of the target variable with respect to new input data. In some implementations, the model selection module 140 can select a supervised learning model capable of performing regression analysis from linear regression, multinomial regression, ridge regression, lasso regression, logistic regression, decision trees, random forests, gradient boosting, and neural networks. PLS models can be used to model the relationship between various independent variables and various dependent variables (or target variables), and independent variables determined to be important for predicting the dependent variable can be identified. In linear regression, the model can learn a linear relationship between input variables and target variables, and in multinomial regression, the model can learn complex relationships by extending linear regression to include higher-order terms of the input variables. Ridge regression and lasso regression can prevent overfitting and adjust model complexity by adding regularization to linear regression. Logistic regression, a classification algorithm used to solve binary classification problems, can also be used for regression analysis because it predicts results as probabilities. Decision trees, random forests, and gradient boosting learn complex nonlinear relationships and can therefore also be used for regression and classification problems. Neural networks can solve various types of regression problems, and in particular, deep learning can be very effective at learning complex nonlinear relationships. Of course, the supervised learning models capable of performing 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 can be selected considering specific implementation objectives, implementation environments, required performance, etc.

[0105] The learning module 150 can train the machine learning model 20 by using the features set by the feature setting module 130 as independent variables and the dart impact intensity of the multilayer membrane as the target variable. Therefore, the machine learning model 20 can learn the relationship between the features and the target variable to be trained as a model capable of predicting the physical properties of the multilayer membrane with respect to new data (i.e., data on the physical properties of a single-layer membrane). Specifically, the learning module 150 can use different learning methods depending on the model selected by the model selection module 140.

[0106] The prediction module 160 can predict the dart impact strength of the multilayer membrane using the machine learning model 20 trained by the learning module 150. In other words, the prediction module 160 can transmit new data (i.e., physical property data about the single-layer membrane) to the machine learning model 20 and obtain a prediction result about the dart impact strength of the multilayer membrane.

[0107] More specifically, the model selection module 140 can perform model selection based on multiple model selection modes. These multiple model selection modes are stored as predetermined values ​​in processor-accessible storage space of the device 10 for designing multilayer films and managed therein. The device 10 for designing multilayer films can read the values ​​pre-stored in this storage space and determine the model selection mode for specifying different model selection methods based on the read values.

[0108] More specifically, the model selection module 140 can perform model selection based on multiple model selection modes. These multiple model selection modes are stored as predetermined values ​​in processor-accessible storage space of the device 10 for designing multilayer films and managed therein. The device 10 for designing multilayer films can read the values ​​pre-stored in this storage space and determine the model selection mode for specifying different model selection methods based on the read values.

[0109] In some implementations, when the model selection mode is the first model selection mode, the model selection module 140 can select the PLS model as the machine learning model for predicting the dart impact intensity of the multilayer membrane. As described above, the features set by the feature setting module 130 according to the feature setting mode can, for example, be combined in the form of a single vector and used as input to the PLS model. The impact intensity of the multilayer membrane is set as the target variable, and the learning module 150 can train the PLS model in the direction that maximizes the correlation between the input features and the target variable. At this time, the learning module 150 can optimize the "n_components" parameter so that the PLS model correctly interprets the data, and the optimal "n_components" value can be determined through cross-validation. With the PLS model learned in this way, the dart impact intensity of the multilayer membrane can be predicted by inputting features about a new monolayer membrane.

[0110] In some other implementations, when the model selection mode is the second model selection mode, the model selection module 140 can select a lasso regression model as the machine learning model for predicting the dart impact intensity of the multilayer membrane. As described above, the features set by the feature setting module 130 according to the feature setting mode can, for example, be combined in the form of a single vector and used as input to the lasso regression model. The impact intensity of the multilayer membrane is set as the target variable, and the learning module 150 can train the lasso regression model through L1 regularization (a regularization that adds the sum of the absolute values ​​of the regression coefficients as a penalty to the objective function) to guide the model to select only important features. At this time, the learning module 150 can optimize the "alpha" parameter used to adjust the intensity during model learning, and the optimal "alpha" value can be determined through cross-validation. With the lasso regression model learned in this way, the dart impact intensity of the multilayer membrane can be predicted by inputting features about a new monolayer membrane.

[0111] In some other implementations, when the model selection mode is the third model selection mode, the model selection module 140 can select a random forest model as the machine learning model for predicting the dart impact intensity of the multilayer membrane. As described above, the features set by the feature setting module 130 according to the feature setting mode can, for example, be combined in the form of a single vector and used as input to the random forest model. The impact intensity of the multilayer membrane is set as the target variable, and the learning module 150 can be trained by setting "n_estimators", "max_depth", "min_samples_split", etc., as hyperparameters of the random forest model. With the random forest model learned in this way, the dart impact intensity of the multilayer membrane can be predicted by inputting features about a new monolayer membrane.

[0112] In some implementations, a machine learning model selected from PLS models, lasso regression models, and random forest models can be configured to receive vectors generated from at least some of yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain, vectors generated from strain energy calculated from at least some of yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain, and vectors generated from the thickness of the monolayer membrane, and to learn to predict the dart impact strength of the multilayer membrane.

[0113] In some other implementations, a machine learning model selected from the PLS model, lasso regression model, and random forest model can be configured to receive vectors generated from yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain, vectors generated from strain energy calculated from yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain, and vectors generated from the thickness of the monolayer membrane, and to learn to predict the dart impact strength of the multilayer membrane.

[0114] The design data generation module 170 can primarily combine predicted values ​​of at least one of the following characteristics of the multilayer membrane: stiffness, strength, strain, tear strength, high-speed dart impact strength, seal initiation temperature, haze, moisture permeability, and air permeability, along with a predicted value of the dart impact strength. Design requirements for the multilayer membrane to be designed may include requirements to ensure that mechanical, thermal, optical, barrier, and chemical properties achieve the desired performance. When the multilayer membrane designed based on the primary combination of predicted values ​​meets the design requirements, the design data generation module 170 can generate design data based on the primary combination. In this case, the design data may include specific predicted values.

[0115] Alternatively, when the multilayer membrane designed based on the primary combination of predicted values ​​does not meet the design requirements, the design data generation module 170 can redesign the multilayer membrane by changing the primary combination to another combination. That is, the design data generation module 170 can secondarily combine predicted values ​​of at least one of the following characteristics of the multilayer membrane: stiffness, strength, strain, tear strength, high-speed dart impact strength, seal initiation temperature, haze, moisture permeability, and air permeability, with predicted values ​​of dart impact strength, to have a combination different from the primary combination. In this case, the predicted values ​​of dart impact strength in the primary combination and the predicted values ​​of dart impact strength in the secondary combination can be different values.

[0116] When a multilayer membrane designed based on a secondary combination of predicted values ​​meets the design requirements, the design data generation module 170 can generate design data based on the secondary combination. Alternatively, when a multilayer membrane designed based on a secondary combination of predicted values ​​also fails to meet the design requirements, the design data generation module 170 can repeat the process of redesigning the multilayer membrane by changing the secondary combination to yet another combination.

[0117] According to this embodiment, without directly performing a dart impact test on the membrane to measure the dart impact strength, the dart impact strength of the multilayer membrane can be predicted with high accuracy by learning and predicting the strain energy calculated from stress-strain data measured on a single layer of the multilayer membrane using methods other than dart impact testing as a feature. Furthermore, by distinguishing between the number of indicators selected to prioritize performance and the number of indicators selected to save computational resources, flexible design can be performed based on the environment and objectives of the equipment used to design the multilayer membrane. Moreover, considering the prediction results related to other properties of the multilayer membrane and the prediction results of the dart impact strength, a multilayer membrane that meets given design requirements can be designed.

[0118] Figure 5 This is a flowchart illustrating a method for designing a multilayer film of a multilayer material according to an embodiment.

[0119] Reference Figure 5 The method for designing a multilayer membrane according to an embodiment may include: step S501, modeling the laminated structure of the multilayer membrane to be designed; step S502, collecting stress-strain data about the single-layer membrane forming the laminated structure; step S503, reading values ​​pre-stored in a storage space accessible to the device for designing the multilayer membrane, and obtaining a feature setting mode for specifying different feature setting methods based on the read values; step S504, calculating strain energy based on the feature setting mode and multiple physical indices selected from the stress-strain data, and setting the strain energy as a feature; step S505, selecting at least one from multiple supervised learning models capable of regression analysis as a machine learning model; step S506, training the machine learning model with the feature as an independent variable and the dart impact strength of the multilayer membrane as a target variable; step S507, predicting the dart impact strength of the multilayer membrane using the learned machine learning model; and step S508, generating design data for the multilayer membrane by combining the predicted values ​​of other characteristics and the predicted value of the dart impact strength to meet the design requirements of the multilayer membrane.

[0120] Further details of the method for designing multilayer films can be found in the description of the features disclosed in the embodiments described herein, and redundant descriptions are not included herein.

[0121] Figure 6 This is a diagram illustrating a computing device according to an embodiment.

[0122] Reference Figure 6 The apparatus and method for designing multilayer films according to the embodiments can be implemented using the computing device 50. The computing device 50 can be implemented in various forms of electronic devices, servers or similar devices, and its functions can be implemented through a combination of software and hardware.

[0123] 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, which communicate via a bus 520. The computing device 50 may also include a network interface 570 electrically connected to the network 40. The network interface 570 can send or receive signals with other entities via the network 40.

[0124] The processor 510 can be implemented as various types of computing devices, such as a microcontroller unit (MCU), application processor (AP), central processing unit (CPU), graphics processing unit (GPU), neural processing unit (NPU), quantum processing unit (QPU), etc. The processor 510 is a semiconductor device that executes instructions stored in memory 530 or storage device 560 and can play a core role in the system. The program code and data stored in memory 530 or storage device 560 can command the processor 510 to perform specific tasks, thereby realizing the overall operation of the system. Therefore, the processor 510 can be configured to implement a reference... Figures 1 to 5 The various functions and methods described.

[0125] Memory 530 and storage device 560 may include various types of volatile or non-volatile storage media for storing and accessing system data. For example, memory 530 may include read-only memory (ROM) 531 and random access memory (RAM) 532. In some embodiments, memory 530 may be embedded inside processor 510, and in this case, data transfer speeds between memory 530 and processor 510 can be very fast. In some other embodiments, memory 530 may be located outside processor 510, and in this case, memory 530 may be connected to processor 510 via various data buses or interfaces. This connection can be made through various known means, such as a peripheral component rapid interconnect (PCIe) interface for high-speed data transfer or a memory controller.

[0126] In some embodiments, at least some components and features of the apparatus and method for designing multilayer films according to the embodiments may be implemented as programs or software executed by computing device 50, and such programs or software may be stored in a computer-readable medium. Specifically, the computer-readable medium according to the embodiments may record programs for causing a computer, including a processor 510 that executes programs or instructions stored in memory 530 or storage device 560, to execute the steps included in the apparatus and method for designing multilayer films according to the embodiments.

[0127] In some embodiments, at least some components and features of the apparatus and method for designing multilayer films according to the embodiments may be implemented using the hardware or circuitry of the computing device 50, or may be implemented as a separate hardware or circuitry that can be electrically connected to the computing device 50.

[0128] According to the implementation method, without directly performing a dart impact test on the membrane to measure the dart impact strength, the dart impact strength of the multilayer membrane can be predicted with high accuracy by learning and predicting the strain energy calculated from stress-strain data measured on a single layer of the multilayer membrane using methods other than dart impact testing as a feature. Furthermore, by distinguishing between the number of indicators selected to prioritize performance and the number of indicators selected to save computational resources, flexible design can be performed according to the environment and objectives of the equipment and method used to design the multilayer membrane. Moreover, by considering the prediction results related to other properties of the multilayer membrane and the prediction results of the dart impact strength, a multilayer membrane that meets given design requirements can be designed.

[0129] Although the embodiments of this disclosure have been described in detail above, the scope of this disclosure is not limited thereto, but may include various modifications and alterations made by those skilled in the art using the basic concept of this disclosure as defined in the claims.

Claims

1. An apparatus for designing multilayer films, the apparatus designing the multilayer film based on physical properties predicted solely from information about a single-layer film by executing program code loaded on one or more memory devices by one or more processors. in, The program code is configured to perform the following operations when executed: Model the laminated structure of the multilayer membrane to be designed; Collect stress-strain data about the monolayer membrane forming the laminated structure; Read values ​​pre-stored in the device-accessible storage space used for designing multilayer films, and obtain feature setting modes for specifying different feature setting methods based on the read values; Based on the feature setting mode, the strain energy is calculated based on multiple 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 performing regression analysis; The dart impact intensity of the multilayer film is predicted by using the machine learning model that learns by using the features as independent variables and the dart impact intensity of the multilayer film as the target variable. as well as Design data for the multilayer membrane is generated by combining predicted values ​​of other properties with the predicted value of the dart impact strength, in order to meet the design requirements of the multilayer membrane.

2. The device according to claim 1, wherein, When the feature setting mode includes a first feature setting mode, setting the feature includes setting the feature as follows: At least some of the following: yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain; The strain energy is calculated based on at least some of the yield stress, the yield strain, the necking stress, the necking strain, the fracture stress, and the fracture strain; as well as The thickness of the single-layer film.

3. The device according to claim 2, wherein, The selection of the machine learning model includes choosing one of the partial least squares (PLS) model, the lasso regression model, and the 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 is learned by receiving the following information to predict the dart impact intensity of the multilayer film: A vector generated from at least some of the yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain; A vector generated from the strain energy calculated based on at least some of the yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain; and A vector generated from the thickness of the monolayer film.

4. The device according to claim 1, wherein, When the feature setting mode includes a second feature setting mode, setting the feature includes setting the feature as follows: Yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain; The strain energy is calculated based on the yield stress, the yield strain, the necking stress, the necking strain, the fracture stress, and the fracture strain. as well as The thickness of the single-layer film.

5. The device according to claim 4, wherein: The selection of the machine learning model includes choosing one of the partial least squares (PLS) model, the lasso regression model, and the 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 is learned by receiving the following information to predict the dart impact intensity of the multilayer film: A vector generated from the yield stress, the yield strain, the necking stress, the necking strain, the fracture stress, and the fracture strain; A vector generated from the strain energy calculated based on the yield stress, the yield strain, the necking stress, the necking strain, the fracture stress, and the fracture strain; and A vector generated from the thickness of the monolayer film.

6. The device according to claim 2, wherein, The strain energy includes a first strain energy (W) yield ),and The features described include: The first strain energy (W) is calculated according to the following formula. yield ), and the calculated first strain energy (W) yield ) is set as the feature, Where, σ y ε represents the yield stress. y σ represents the yield strain. br Let represent the fracture stress, d represent the contact diameter of the falling dart, t represent the thickness of the measured sample, and a represent an experimentally determined constant.

7. The device according to claim 2, wherein, The strain energy includes a second strain energy (W) neck ),and The features described include: The second strain energy (W) is calculated according to the following formula. neck ), and the calculated second strain energy (W) neck ) is set as the feature, Where, σ n The necking stress, ε n ε represents the necking strain. y σ represents the yield strain. br The fracture stress, σ, represents the stress. y Let d represent the yield stress, d represent the contact diameter of the dart, t represent the thickness of the measured sample, and b represent an experimentally determined constant.

8. The device according to claim 2, wherein, The strain energy includes a third strain energy (W) str-hard ),and The features described include: The third strain energy (W) is calculated according to the following formula. str-hard ), and the calculated third strain energy (W) str-hard ) is set as the feature, Where, ε br ε represents the fracture strain. n The necking strain, σ, represents the necking strain. br The fracture stress, σ, represents the stress. n Let L represent the necking stress, L represent the final tensile propagation length, t represent the thickness of the measured sample, and c represent an experimentally determined constant.

9. The device according to claim 2, wherein, The strain energy includes a fourth strain energy (W) elast ),and The features described include: The fourth strain energy (W) is calculated according to the following formula. elast ), and the calculated fourth strain energy (W) elast ) is set as the feature, Where, σ y ε represents the yield stress. y Let d represent the yield strain, d represent the dart contact diameter, D represent the diameter of the fixed disk, L represent the final tensile propagation length, t represent the thickness of the measured sample, and e represent an experimentally determined constant.

10. The device according to claim 1, wherein, Generating the design data for the multilayer film includes: Combine the predicted values ​​of at least one of the following characteristics of the multilayer film: stiffness, strength, strain, tear strength, high-speed dart impact strength, seal initiation temperature (SIT), haze, moisture permeability and air permeability, and the predicted value of the dart impact strength. When the multilayer membrane designed based on the combination of the predicted values ​​meets the design requirements, the design data is generated based on the combination; and When the multilayer membrane designed based on the combination of the predicted values ​​does not meet the design requirements, the multilayer membrane is redesigned by changing the combination to another combination.

11. A method for designing a multilayer film, the method being performed by a computing device including one or more processors and one or more memory devices, to design the multilayer film based on physical properties of the multilayer film predicted solely from information about a single-layer film, the method comprising: Model the laminated structure of the multilayer membrane to be designed; Collect stress-strain data about the monolayer membrane forming the laminated structure; Read values ​​pre-stored in the storage space accessible by the computing device, and obtain feature setting modes for specifying different feature setting methods based on the read values; Based on the feature setting mode, the strain energy is calculated based on multiple 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 performing regression analysis; The dart impact intensity of the multilayer film is predicted by using the machine learning model that learns by using the features as independent variables and the dart impact intensity of the multilayer film as the target variable. as well as Design data for the multilayer membrane is generated by combining predicted values ​​of other properties with the predicted value of the dart impact strength, in order to meet the design requirements of the multilayer membrane.

12. The method according to claim 11, wherein, When the feature setting mode includes a first feature setting mode, setting the feature includes setting the feature as follows: At least some of the following: yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain; The strain energy is calculated based on at least some of the yield stress, the yield strain, the necking stress, the necking strain, the fracture stress, and the fracture strain; as well as The thickness of the single-layer film.

13. The method according to claim 12, wherein, The selection of the machine learning model includes choosing one of the partial least squares (PLS) model, the lasso regression model, and the 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 is learned by receiving the following information to predict the dart impact intensity of the multilayer film: A vector generated from at least some of the yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain; A vector generated from the strain energy calculated based on at least some of the yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain; and A vector generated from the thickness of the monolayer film.

14. The method according to claim 11, wherein, When the feature setting mode includes a second feature setting mode, setting the feature includes setting the feature as follows: Yield stress, yield strain, necking stress, necking strain, fracture stress, and fracture strain; The strain energy is calculated based on the yield stress, the yield strain, the necking stress, the necking strain, the fracture stress, and the fracture strain. as well as The thickness of the single-layer film.

15. The method according to claim 14, wherein, The selection of the machine learning model includes choosing one of the partial least squares (PLS) model, the lasso regression model, and the 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 is learned by receiving the following information to predict the dart impact intensity of the multilayer film: A vector generated from the yield stress, the yield strain, the necking stress, the necking strain, the fracture stress, and the fracture strain; A vector generated from the strain energy calculated based on the yield stress, the yield strain, the necking stress, the necking strain, the fracture stress, and the fracture strain; and A vector generated from the thickness of the monolayer film.

16. The method according to claim 12, wherein, The strain energy includes a first strain energy (W) yield ),and The features described include: The first strain energy (W) is calculated according to the following formula. yield ), and the calculated first strain energy (W) yield ) is set as the feature, Where, σ y ε represents the yield stress. y σ represents the yield strain. br Let represent the fracture stress, d represent the contact diameter of the falling dart, t represent the thickness of the measured sample, and a represent an experimentally determined constant.

17. The method according to claim 12, wherein, The strain energy includes a second strain energy (W) neck ),and The features described include: The second strain energy (W) is calculated according to the following formula. neck ), and the calculated second strain energy (W) neck ) is set as the feature, Where, σ n The necking stress, ε n ε represents the necking strain. y σ represents the yield strain. br The fracture stress is represented by σ, d represents the contact diameter of the falling dart, and σ represents the fracture stress. y Let represent the yield stress, t represent the thickness of the measured sample, and b represent an experimentally determined constant.

18. The method according to claim 12, wherein, The strain energy includes a third strain energy (W) str-hard ),and The features described include: The third strain energy (W) is calculated according to the following formula. str-hard ), and the calculated third strain energy (W) str-hard ) is set as the feature, Where, ε br ε represents the fracture strain. n The necking strain, σ, represents the necking strain. br The fracture stress, σ, represents the stress. n Let L represent the necking stress, L represent the final tensile propagation length, t represent the thickness of the measured sample, and c represent an experimentally determined constant.

19. The method according to claim 12, wherein, The strain energy includes a fourth strain energy (W) elast ),and The features described include: The fourth strain energy (W) is calculated according to the following formula. elast ), and the calculated fourth strain energy (W) elast ) is set as the feature, Where, σ y ε represents the yield stress. y Let d represent the yield strain, d represent the dart contact diameter, D represent the diameter of the fixed disk, L represent the final tensile propagation length, t represent the thickness of the measured sample, and e represent an experimentally determined constant.

20. The method according to claim 11, wherein, Generating the design data for the multilayer film includes: Combine the predicted values ​​of at least one of the following characteristics of the multilayer film: stiffness, strength, strain, tear strength, high-speed dart impact strength, seal initiation temperature (SIT), haze, moisture permeability and air permeability, and the predicted value of the dart impact strength. When the multilayer membrane designed based on the combination of the predicted values ​​meets the design requirements, the design data is generated based on the combination; and When the multilayer membrane designed based on the combination of the predicted values ​​does not meet the design requirements, the multilayer membrane is redesigned by changing the combination to another combination.

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