Method for predicting change in coating film, system for predicting change in coating film, and application software

Abstract

An objective is to provide a method for predicting a change in a coating film from information on the coating film and / or information on a coating material used in forming the coating film by using an artificial intelligence model that has machine-learned the relationship between the information on the coating film and / or the information on the coating material constituting the coating film and a weather resistance test result of the coating film. Provided as a solution is a method for predicting a change in a coating film from information on the coating film and / or information on a coating material used in forming the coating film by using an artificial intelligence model that has machine-learned the relationship between the information on the coating film and / or the information on the coating material constituting the coating film and a weather resistance test result of the coating film.

Description

Method for predicting changes in coatings, system for predicting changes in coatings, and application software.

[0001] The present invention relates to a method for predicting changes in a coating film, a coating film change prediction system for predicting changes in a coating film, and application software.

[0002] In paint development, understanding how the paint film formed by applying the paint changes in the usage environment, particularly how it deteriorates, is one of the important technical considerations. Until now, paint development has been carried out by exposure tests in the usage environment and harsh environments, where painted panels were placed, and the results were taken into consideration. However, obtaining the results of exposure tests takes years, which has been one of the obstacles to rapid paint development. For this reason, accelerated weathering tests are being conducted using accelerated weathering testers to change the paint film in a short time, but there is often a discrepancy between the results of actual exposure tests and tests using accelerated weathering testers, and improvement has been needed.

[0003] Furthermore, exposure tests require the creation of a large number of painted panels for each of the various types of paints, and there is also the task of transporting the panels to the exposure testing facility and placing them there. Similarly, accelerated weathering tests using accelerated weathering testing machines also require the creation of a large number of painted panels for each of the various types of paints, which is a significant burden in terms of cost and workload, and improvements were desired.

[0004] Studies have been conducted to date on predicting the deterioration of coatings. For example, Patent Documents 1 to 7 disclose methods or systems for predicting the deterioration of coatings.

[0005] Japanese Patent Publication No. 2005-17132, Japanese Patent Publication No. 2008-196887, Japanese Patent Publication No. 2005-156309, Japanese Patent Publication No. 2022-124933, Japanese Patent Publication No. 2015-206603, Japanese Patent Publication No. 57-197447, Chinese Patent Application Publication No. 116976196 Specification

[0006] The methods or systems disclosed in Patent Documents 1 to 7 for predicting the deterioration of coating films do not use an artificial intelligence model that has been trained on the relationship between information about the coating film and / or information about the paint constituting the coating film and the results of weathering tests of the coating film, and do not predict changes in the coating film from information about the coating film and / or information about the paint used to form the coating film. Therefore, the accuracy of predicting changes in the coating film was not satisfactory.

[0007] One of the problems that the present invention aims to solve is to provide a method for predicting changes in a coating film from information about the coating film and / or information about the paint used to form the coating film, using an artificial intelligence model that has been trained on the relationship between information about the coating film and / or information about the paint constituting the coating film and the results of weathering tests on the coating film. Another problem that the present invention aims to solve is to provide a coating film change prediction system that predicts changes in a coating film from information about the coating film and / or information about the paint used to form the coating film, and includes an artificial intelligence model that has been trained on the relationship between information about the coating film and / or information about the paint constituting the coating film and the results of weathering tests on the coating film. Another problem that the present invention aims to solve is to provide a method for predicting changes in a coating film and a coating film change prediction system that can predict the degree of change in a coating film over time with high accuracy. Another problem that the present invention aims to solve is to provide application software for causing an information processing device to execute the method for predicting changes in a coating film or application software for controlling and operating the coating film change prediction system.

[0008] As a result of diligent research to solve the above problems, the inventors have found that the above problems can be solved by (i) a method for predicting changes in a coating film using a specific artificial intelligence model, (ii) a coating film change prediction system equipped with a specific artificial intelligence for predicting changes in a coating film, (iii) application software for causing an information processing device to execute the method for predicting changes in a coating film, and (iv) application software for controlling and operating the system for predicting changes in a coating film, and have completed the present invention. That is, the present invention is as shown in the following sections 1 to 13.

[0009] [Item 1] A method for predicting changes in a coating film from information about the coating film and / or information about the paint used to form the coating film, using an artificial intelligence model that has been trained on the relationship between information about the coating film and / or information about the paint used to form the coating film and the results of a weathering test of the coating film. [Item 2] The method according to Item 1, wherein the changes in the coating film include one or more changes selected from the group consisting of color tone, water resistance, acid resistance, chemical resistance, surface roughness, gloss, shine, smoothness, adhesion, water contact angle, oil contact angle, scratch resistance, and appearance of the coating film. [Item 3] The method according to Item 1 or 2, wherein the information about the coating film includes one or more selected from the group consisting of the composition of the coating film, information about the material to which the coating film is applied, conditions for the formation of the coating film, conditions for measuring the coating film, and conditions for testing the coating film. [Item 4] The method according to any one of Items 1 to 3, wherein the information about the coating film includes the composition of the coating film, and the composition of the coating film includes the layer composition of the coating film and the thickness of each layer. [Clause 5] The method according to any one of Clauses 1 to 4, wherein the information of the coating film includes measurement conditions for the coating film, and the measurement conditions for the coating film include, when measuring the coating film, whether or not the surface of the coating film is polished and / or whether or not the surface of the coating film is washed with water. [Clause 6] The method according to any one of Clauses 1 to 5, wherein the information of the paint includes one or more selected from the group consisting of the type and amount of coating film-forming resin, the type and amount of pigment, the type of glossing agent, the color tone and amount thereof, the type and amount of additive, the type and amount of pigment paste, and the type and amount of solvent. [Clause 7] The method according to any one of Clauses 1 to 6, wherein the information of the paint includes the type and amount of pigment paste. [Clause 8] The method according to any one of Clauses 1 to 7, wherein the coating film is a multilayer coating film including a color base coating film and a clear coating film. [Clause 9] The method according to any one of Clauses 1 to 8, wherein the machine learning uses one or more prediction algorithms such as decision trees, linear regression, partial least squares regression, Lasso regression, Ridge regression, polynomial regression, Gaussian process regression, support vector machines, random forests, gradient boosting decision trees, K-nearest neighbors, neural networks, graph neural networks, Bayesian estimation, or ensemble learning thereof. [Clause 10] The method according to any one of Clauses 1 to 9, including the prediction of the weather resistance of automobile paint.[Item 11] A coating change prediction system that predicts changes in a coating film from information about the coating film and / or information about the paint used to form the coating film, comprising an artificial intelligence model trained on the relationship between information about the coating film and / or information about the paint used to form the coating film and the results of a weathering test of the coating film. [Item 12] Application software for causing an information processing device to execute the method described in any one of Items 1 to 10. [Item 13] Application software for controlling and operating the coating change prediction system described in Item 11.

[0010] The present invention provides a method for predicting changes in a coating film from information about the coating film and / or information about the paint used to form the coating film, using an artificial intelligence model that has been trained on the relationship between information about the coating film and / or information about the paint constituting the coating film and the results of weathering tests on the coating film. The present invention also provides a coating film change prediction system that predicts changes in a coating film from information about the coating film and / or information about the paint used to form the coating film, comprising an artificial intelligence model that has been trained on the relationship between information about the coating film and / or information about the paint constituting the coating film and the results of weathering tests on the coating film. The present invention also provides a method for predicting changes in a coating film and a coating film change prediction system that can predict the degree of change in a coating film over time with high accuracy. The present invention also provides application software for causing an information processing device to execute the method for predicting changes in a coating film or application software for controlling and operating the coating film change prediction system.

[0011] A flowchart illustrating a method for predicting changes in a coating film according to one embodiment of the present invention. A diagram showing an example configuration of a coating film change prediction system according to one embodiment of the present invention.

[0012] Embodiments of the present invention will be described in detail below with reference to the drawings. It should be understood that the present invention is not limited to the following embodiments, but also includes various modifications that do not alter the essence of the invention.

[0013] [Method for predicting changes in coating film] The present invention provides a method for predicting changes in coating film using an artificial intelligence model that has been trained on the relationship between information about the coating film and / or information about the paint used to form the coating film and the results of weathering tests of the coating film. By predicting changes in the coating film, it is possible to provide information on the deterioration and aging of the coating film, and the aging changes and evaluation of the appearance of painted articles and physical properties caused by the coating. The present invention provides a method for predicting changes in coating film that is preferably carried out using the coating film change prediction system described later.

[0014] <Coating Film> In the method for predicting changes in a coating film of the present invention, the "coating film" is not particularly limited as long as it is a coating film formed by applying paint to a material to be coated. The coating film may be a single-layer coating film consisting of only one layer, or a multi-layer coating film formed by applying two or more types of paint in layers. Examples of multi-layer coating films include those having two or more coating films, such as an electrodeposited coating film, an intermediate coating film, a color base coating film (color base coat layer), a glossy coating film, a colored coating film, and a clear coating film (clear coat layer). In the method for predicting changes in a coating film of the present invention, the coating film is preferably a multi-layer coating film (for example, (a) a multi-layer coating film including a color base coating film and a clear coating film, (b) a multi-layer coating film including an electrodeposited coating film, a color base coating film and a clear coating film, (c) a multi-layer coating film including an intermediate coating film, a color base coating film and a clear coating film, (d) a multi-layer coating film including an electrodeposited coating film, an intermediate coating film, a color base coating film and a clear coating film, etc.). When forming a coating on top of a coating, the coating may be formed on a wet coating, or on a coating that has been set and / or preheated and / or cured. When forming the coating, each coating may be heated and cured as it is formed, or multiple cured coatings may be formed simultaneously by heating any multiple uncured coatings at the same time. In the present invention, multiple uncured coatings can be formed to create an uncured multilayer coating, which is then heated and cured, thereby simultaneously heating and curing multiple uncured coatings.

[0015] <Changes in the coating film> In the method for predicting changes in the coating film of the present invention, the "changes in the coating film" to be predicted are changes in the coating film after any time has elapsed from immediately after the coating film is made, and include at least changes in the appearance of the coating film and / or changes in the physical properties of the coating film.

[0016] Changes in the coating film include, for example, one or more changes selected from the group consisting of color tone, water resistance, acid resistance, chemical resistance, surface roughness, gloss, shine, smoothness, adhesion, contact angle, scratch resistance, electrical resistance, film thickness, stain resistance, solar reflectance, ultraviolet transmittance, haze, light transmittance, light reflectance, weather resistance, flip-flop properties, micro-glossiness, and coating film appearance. Of these, it is preferable that one or more changes selected from the group consisting of color tone, water resistance, acid resistance, chemical resistance, surface roughness, gloss, shine, smoothness, adhesion, water contact angle, oil contact angle, scratch resistance, and coating film appearance are included.

[0017] Changes in color tone include, for example, fading and whitening, L ＊ a ＊ b ＊ Color system, XYZ color system, Munsell color system, L ＊ u ＊ v ＊ Color system, RGB color system, Yxy color system, Hunter-Lab color system, L ＊ C ＊ The values ​​can be expressed as changes in the values ​​of each color system, such as the h color system, between the values ​​immediately after coating film preparation and the values ​​of the coating film after a specified period of time. Alternatively, the changes may be based on spectral reflectance measurement results or the evaluator's visual perception.

[0018] Changes in water resistance, acid resistance, chemical resistance, surface roughness, gloss, shine, smoothness, adhesion, contact angle, scratch resistance, electrical resistance, film thickness, stain resistance, solar reflectance, ultraviolet transmittance, light reflectance, weather resistance, flip-flop properties, and micro-glossiness can be expressed, for example, by using a physical property measuring device to show the change between the value immediately after coating application and the value of the coating after a specified period of time. Changes based on the evaluator's visual and tactile perception may also be used.

[0019] Changes in the appearance of the paint film include, for example, changes due to the occurrence of paint film cracks, blistering, peeling, scratches, contamination, shrinkage, and blistering, as well as changes in paint film gloss and sheen. The degree of these changes in the appearance of the paint film can be confirmed by visual inspection by an evaluator, and can also be quantified by means of instrumental measurement and image diagnosis.

[0020] <Information about the coating film> In the method for predicting changes in a coating film according to the present invention, "information about the coating film" includes one or more of the following: the composition of the coating film, information about the material to which the coating film is applied, the conditions for the formation of the coating film, the conditions for measuring the coating film, and the conditions for testing the coating film.

[0021] The composition of the coating film includes information about the coating film itself, such as one or more of the following: the layer structure of the coating film (single layer, multi-layer, number of layers, layering order, function of each layer, etc.), the coating film thickness (color base coating film thickness, clear coating film thickness), the color tone of the coating film, the physical properties of the coating film (reflectance, light transmittance, water absorption rate, contact angle, gloss, surface roughness, micro-glossiness, flip-flop properties, water repellency, water-slip properties, smoothness, haze, opacity, etc.), the shape of the coating film, the application of the coating film, and the product code (product number, coating code, etc.). In particular, from the viewpoint of improving prediction accuracy, it is preferable that the composition of the coating film includes the layer structure of the coating film and the coating film thickness.

[0022] Information about the material to be coated includes, for example, one or more of the following: the material of the material to be coated, the physical properties of the material, the surface condition of the material, the surface treatment of the material, the shape of the material, and the use of the material (use of the painted object (part name, location, etc.)).

[0023] Examples of conditions for the formation of a coating include one or more of the following: the painting method, painting work conditions (orientation of the painted surface on the object to be painted (vertical surface, inclined surface, flat surface, etc.), temperature during painting, humidity during painting, painting location, person who applies the coating, etc.), the painting equipment used, the operating conditions of the painting equipment, and drying conditions (baking temperature, maximum temperature reached, heating time, heating rate, cooling rate, cooling time).

[0024] Measurement conditions for the coating film include, for example, one or more of the following: measuring equipment, measurement method, measurement conditions, measurer, and pretreatment during measurement (such as whether the coating surface is polished or washed with water when measuring the exposed plate after exposure). In particular, from the viewpoint of improving prediction accuracy, it is preferable to include information on the pretreatment of the coating film during measurement, especially information on whether the coating surface is polished and / or whether the coating surface is washed with water.

[0025] Examples of test conditions for the coating film include one or more test conditions in an exposure test (exposure location, exposure period, exposure timing, etc.) and test conditions in an accelerated test (type of accelerated test, accelerated test equipment, accelerated test conditions, etc.).

[0026] <Information on paint used for paint film formation> In the method for predicting changes in a paint film according to the present invention, "information on paint used for paint film formation" includes, for example, one or more of the following: information on the paint itself, information on the manufacturing conditions of the paint, information on each component that makes up the paint / information on each component used in the manufacture of the paint.

[0027] Information about the paint itself may include, for example, the paint's composition, color, physical properties (gloss, viscosity, storage modulus, loss tangent, electrical resistance, contact angle, solar reflectance, UV transmittance, weather resistance, viscoelasticity, stringiness, haze, light transmittance, foaming properties, surface tension, solids content, pH, storage stability, repellency, circulation properties, etc.), paint product number / product name, and paint supplier.

[0028] Information related to the manufacturing conditions of the paint may include one or more of the following: the paint manufacturer, the time of manufacture, the place of manufacture, and the manufacturing conditions of the paint (dispersion conditions, temperature conditions, equipment used, etc.).

[0029] Information on each component that makes up the paint / information on each component used in the manufacture of the paint includes, for example, information on each component contained in the paint and each component used in the manufacture of the paint. For example, this includes one or more information on the type, shape (e.g., form, aspect ratio, thickness, primary particle diameter, secondary particle diameter, particle diameter distribution, etc.), properties (e.g., curability, thermoplasticity, solubility, dispersibility, dispersed particle diameter, molecular weight, molecular weight distribution, heat resistance temperature, glass transition temperature, softening point, reactivity, etc.) and their blending amount, the type, shape, properties (e.g., hardness, surface properties, presence or absence of surface modification, wettability, dispersibility, pH, zeta potential, etc.), color tone and its blending amount, the type, shape, properties, color tone and its blending amount of the glossing agent, the type, shape, properties and its blending amount of the dispersant, the type, properties and its blending amount of the solvent, the type, shape, properties and its blending amount of the additives, the type, properties, composition and its blending amount of the premix such as pigment paste, the manufacturing method, manufacturing date, manufacturer, seller, product number, lot number, etc. In particular, from the viewpoint of improving prediction accuracy, it is preferable to include information on the premix, especially information on the type of pigment paste and / or its amount.

[0030] <Results of Weathering Test of Coating Film> In the method for predicting changes in a coating film according to the present invention, "results of weathering test of coating film" refers to data obtained from weathering tests of the coating film, such as outdoor exposure, indoor exposure, and weathering test results using accelerated weathering testers (e.g., xenon weather meter, sunshine weather meter, ultraviolet fluorescent lamp weather meter, ultraviolet fade meter). The data may include measured values ​​from measuring instruments, evaluation results from evaluators such as panelists, etc. The types of data obtained from the weathering test of coating film include one or more data related to the changes in the coating film mentioned above, and may also include data related to the period of the weathering test.

[0031] <Machine-learned artificial intelligence model> In the method for predicting changes in a coating film according to the present invention, the algorithm used for machine learning when creating the "machine-learned artificial intelligence model" is not particularly limited as long as it is a machine learning algorithm capable of creating an artificial intelligence model. For example, one or more prediction algorithms such as decision trees, linear regression, partial least squares regression, Lasso regression, Ridge regression, polynomial regression, Gaussian process regression, support vector machines, random forests, gradient boosting decision trees, K-nearest neighbors, neural networks, graph neural networks, Bayesian estimation, or ensemble learning of these can be used. In the present invention, it is preferable to use one or more of random forests and neural networks as the machine learning algorithm, and it is more preferable to use an algorithm based on random forests.

[0032] When performing machine learning on artificial intelligence models, appropriate machine learning frameworks and libraries can be used depending on the data scale, execution environment, etc. These frameworks and libraries may be developed by the user, or one or more of the available ones may be used. Examples of machine learning frameworks and libraries include TensorFlow, PyTorch, Keras, Caffe, Microsoft Cognitive Toolkit, MXNet, Chainer, Scikit-learn, OpenCV, and spaCy.

[0033] Machine learning may be either supervised or unsupervised. In this invention, it is preferable to use supervised learning, which involves associating information about the coating film and / or information about the paint used to form the coating film with the results of weather resistance tests of the coating film.

[0034] For machine learning, information on the coating film and / or information on the paint used to form the coating film, as well as the results of weather resistance tests of the coating film, can be input by transmitting data via wired, wireless, or a combination thereof, or via a recording medium. Examples of input using communication means include one or more combinations of various communication networks such as LAN (Local Area Network), WAN (Wide Area Network), the Internet, and telephone networks. Input via a recording medium can be performed by reading data from a recording medium such as a magnetic recording medium, optical recording medium, or paper recording medium using an appropriate reading means.

[0035] In machine learning, the reliability of information on coating films and / or paints used to form them, as well as the results of weathering tests on coating films, can be enhanced by improving consistency (consistency of labeling criteria), reproducibility (highly reproducible labeling for the same data), and transparency (clarity of the data creation process and the basis for labeling).

[0036] In machine learning, it is preferable to preprocess raw data related to information on coating films and / or paints used to form the coating films, as well as the results of weather resistance tests of the coating films, to create training data. Examples of preprocessing include processing such as imputing missing data, filtering such as removing outliers, data normalization / standardization, and arithmetic processing (logarithmic scaling, power scaling, etc.). This makes the data applicable to various machine learning algorithms, and also allows for data specialized for specific machine learning algorithms.

[0037] <Prediction of Coating Film Changes> FIG. 1 is a flowchart of a method for predicting changes in a coating film according to an embodiment of the present invention. A method for predicting changes in a coating film according to an embodiment of the present invention includes at least the following steps S01 to S05: - Step S01: Obtaining information on the coating film and / or information on the paint used for forming the coating film and the result of the weather resistance test of the coating film - Step S02: Machine learning of the relationship between the information on the coating film and / or information on the paint used for forming the coating film and the result of the weather resistance test of the coating film - Step S03: Creating an artificial intelligence model - Step S04: Inputting information on the coating film and / or information on the paint used for forming the coating film - Step S05: Using the artificial intelligence model to predict changes in the coating film.

[0038] (Step S01) Step S01 is a step of obtaining information on the coating film and / or information on the paint used for forming the coating film and the result of the weather resistance test of the coating film. Details of the information on the coating film, the information on the paint used for forming the coating film, and the result of the weather resistance test of the coating film are as described in <Information on the Coating Film>, <Information on the Paint Used for Forming the Coating Film>, and <Result of the Weather Resistance Test of the Coating Film> respectively. In step S01, the information on the coating film and / or the information on the paint used for forming the coating film and the corresponding result of the weather resistance test of the coating film are obtained in an associated form with each other.

[0039] The information on the coating film and / or the information on the paint used for forming the coating film and the result of the weather resistance test of the coating film may be obtained by importing them into a database in a system for predicting changes in the coating film, or may be obtained by accessing an external database via a communication line.

[0040] The information on the coating film, the information on the paint used for forming the coating film, and the result of the weather resistance test of the coating film can be easily obtained in an associated form with each other by forming the coating film, performing the weather resistance test of the coating film, recording the result of the weather resistance test, and accumulating this as data to create a database.

[0041] (Step S02) Step S02 is a step of performing machine learning on the relationship between information on the coating film and / or information on the paint used for forming the coating film and the results of the weather resistance test of the coating film. The algorithm in the machine learning is as described in the <Machine-Learned Artificial Intelligence Model>. By performing machine learning while associating the information on the coating film and / or the information on the paint used for forming the coating film with the results of the weather resistance test of the coating film, supervised machine learning can be performed.

[0042] Machine learning can be started by inputting information on the coating film and / or information on the paint used for forming the coating film and the results of the weather resistance test of the coating film. At the time of input, the information on the coating film and / or the information on the paint used for forming the coating film and the results of the weather resistance test of the coating film are input by an appropriate data input means. These inputs may be performed by data transfer from a database and data capture (download).

[0043] (Step S03) Step S03 is a step of creating an artificial intelligence model. In Step S02, by performing machine learning on the relationship between information on the coating film and / or information on the paint used for forming the coating film and the results of the weather resistance test of the coating film, an artificial intelligence model can be created that takes as input the information on the coating film and / or the information on the paint used for forming the coating film and outputs a prediction of the change in the coating film.

[0044] (Step S04) Step S04 is a step of inputting information on the coating film and / or information on the paint used for forming the coating film into the artificial intelligence model. The information on the coating film and / or the information on the paint used for forming the coating film to be input may be different from that used during machine learning or may be the same information.

[0045] By inputting information on the coating film and / or the information on the paint used for forming the coating film that is different from that used during machine learning, it is possible to predict the change in the coating film at an arbitrary point in time for a desired coating film or a coating film obtained from a desired paint. Thereby, it is possible to provide information regarding deterioration and change over time of the coating film, changes over time and evaluation of the appearance of the painted article and physical properties resulting from the painting.

[0046] By inputting information on the same coating film used in machine learning and / or information on the paint used for forming the coating film, it is possible to verify and evaluate the performance and versatility of the artificial intelligence model created by machine learning. Further, it is possible to predict changes in future coating films and to predict changes in coating films at a time point when specific measurements have not been performed.

[0047] The method of inputting information on the coating film and / or information on the paint used for forming the coating film into the artificial intelligence model is not particularly limited. Similar to the input of information during machine learning in step S02, information can be input by appropriate data input means. The input of information may be performed by extracting, transferring, and importing (downloading) information from a database. The input of information can also be performed using a program.

[0048] (Step S05) Step S05 is a step of predicting changes in the coating film by using the artificial intelligence model and calculating prediction data on changes in the coating film at an arbitrary time point based on the information on the coating film input in step S04 and / or the information on the paint used for forming the coating film. When calculating prediction data on changes in the coating film at an arbitrary time point, only one prediction data can be calculated, or a plurality of prediction data can be calculated as a candidate group. When a plurality of prediction data are calculated as a candidate group, the optimal prediction data can be selected from the candidate group based on a predetermined criterion. As the predetermined criterion, for example, it can be selected so that the characteristic value falls within a predetermined range by focusing on a specific physical property, or it can be a numerical value calculated from a predetermined calculation formula or the like.

[0049] When predicting changes in the coating film, it is possible to perform highly accurate prediction considering mutual influences by simultaneously predicting various physical properties and the like involved in the changes in the coating film. For example, not only changes in the appearance of the coating film but also changes in the physical properties of the coating film can be simultaneously predicted, enabling highly accurate prediction considering these mutual influences. In the present invention, it is possible to simultaneously predict two or more of the changes in the appearance of the coating film, and it is possible to simultaneously predict two or more of the physical properties of the coating film. For example, ΔE and L ＊ a ＊ b ＊It is possible to predict each of these values ​​simultaneously, and it is also possible to predict any one of them individually. When predicting changes in the coating, the accuracy of the prediction can be improved by using the predicted data output from the artificial intelligence model as the amount of variation, thereby offsetting factors such as variability and measurement errors.

[0050] When predicting changes in the coating film, by programming steps S01 to S05, especially steps S04 to S05, it is possible to predict changes in the coating film as a group, even for coating films where various components and conditions included in the coating film information and various components and conditions included in the coating-forming paint information are varied within a certain range. Furthermore, it is possible to verify whether the input is appropriate and to verify the suitability of system updates.

[0051] (Other Steps) In a method for predicting changes in a coating film according to one embodiment of the present invention, one or more other steps may be included in addition to steps S01 to S05 described above. Examples of other steps include a data preprocessing step, which is a step prior to step S01, in which raw data relating to information on the coating film and / or information on the paint used to form the coating film and the results of weathering tests of the coating film are preprocessed; an artificial intelligence model verification and evaluation step, which is the next step after step S03, in which the created artificial intelligence model is verified and evaluated; and a prediction data selection step, which is the next step after step S05, in which the optimal prediction data is selected when multiple candidate data have been calculated in step S05.

[0052] <Applications of the Method for Predicting Changes in Paint Films> The method for predicting changes in paint films according to the present invention can predict the state of the paint film at a specific point in the future, and therefore can be suitably used in applications where it is necessary to predict changes in the paint film on painted objects, particularly in the application of paints. For example, it can be used to predict the durability of paint on products, predict changes in the paint film of buildings and structures in the civil engineering and construction industries (prediction of maintenance such as repainting timing, etc.), and promote the commercial value of paints. In particular, it can predict the weather resistance of paint on products such as automobiles, and is useful in the development and proposal of paints.

[0053] [Coating film change prediction system] The coating film change prediction system of the present invention is a system that predicts changes in a coating film from information on the coating film and / or information on the paint used to form the coating film, and includes an artificial intelligence model that has been trained on the relationship between information on the coating film and / or information on the paint used to form the coating film and the results of weather resistance tests of the coating film. The coating film change prediction system according to the present invention is suitably used in the method for predicting changes in a coating film according to the present invention.

[0054] Figure 2 shows an example of the configuration of a coating change prediction system 1 according to one embodiment of the present invention. The coating change prediction system 1 shown in Figure 2 includes a computer 10, and the coating change prediction system 1 shown in Figure 2 is configured within the computer 10. The coating change prediction system 1 includes an artificial intelligence model 101, a coating change prediction unit 102, an (internal) database 103a, a reception unit 104, a display unit 105, and a recording unit 106. The coating change prediction system 1 is connectable to user terminals 20a to 20c of users 2a to 2c via a communication line N, and is also connectable to an (external) database 103b via the communication line N. Furthermore, the display unit 105 is connectable to a virtual space M, and is configured to display images of coating changes in the virtual space M.

[0055] Computer 10 is under the control of the operator or provider of the coating change prediction system 1. The operator or provider of the coating change prediction system 1 is the provider of the method for predicting changes in the coating of the present invention, or a person commissioned or entrusted by the provider. Users 2a to 2c are not particularly limited as long as they are people who use the coating change prediction system 1. For example, one or more may be paint users, paint manufacturers, painting contractors, painted product manufacturers, etc. User terminals 2a to 2c are not particularly limited as long as they are terminals that can connect to the coating change prediction system 1 via a communication line N. For example, one or more may be laptop computers, mobile phones (smartphones), personal digital assistants, etc. In Figure 2, only users 2a to 2c are shown, but the number of users is not particularly limited. The operator or provider of the coating change prediction system 1 may also charge at least some users for the use of the system or for some operations during system use.

[0056] The coating change prediction system 1 obtains information on coatings and / or paints used to form the coatings, as well as the results of weather resistance tests of the coatings, from information sources such as the (internal) database 103a, the (external) database 103b, and external measuring instruments (not shown), sometimes via a communication line N, and uses this information to train the artificial intelligence model 101. When obtaining information on coatings and / or paints used to form the coatings, as well as the results of weather resistance tests of the coatings, from sources other than the (internal) database 103a, such as the (external) database 103b or external measuring instruments, and performing machine learning on the artificial intelligence model 101, the system can use the reception unit 104. In this case, data verification and preprocessing can be performed at the reception unit 104.

[0057] After the machine learning of the artificial intelligence model 101 is completed, information about the coating film and / or information about the paint used to form the coating film is input via an input means (not shown) on the computer 10, or via one or more input means from user terminals 20a to 20c via the communication line N. Based on the input information, the coating film change prediction system 1 uses the artificial intelligence model 101 to predict the changes in the coating film in the coating film change prediction unit 102 and calculates the result.

[0058] The calculated prediction of changes in the coating can be displayed in the display unit 105 and / or in the virtual space M connected to the display unit 105. Furthermore, the calculated prediction of changes in the coating can be displayed on user terminals 20a to 20c via the communication line N, and users can participate in the virtual space M via the user terminals 20a to 20c. The calculated prediction of changes in the coating can be stored in the recording unit 106, and the prediction accuracy of the coating change prediction system 1 can be improved by comparing it with measured values ​​of changes in the coating as needed.

[0059] [Application Software] The application software of the present invention is application software that causes an information processing device to execute a method for predicting changes in the coating film, and functions to execute the method for predicting changes in the coating film of the present invention. The application software of the present invention is application software that controls and operates the coating film change prediction system, and functions to control and operate the coating film change prediction system of the present invention.

[0060] The application software of the present invention may be pre-stored in a recording device such as an HDD (Hard Disk Drive) or flash memory located in an information processing terminal that performs the method for predicting changes in the coating film of the present invention, the coating film change prediction system of the present invention, or equipment that performs each of the means constituting the information processing terminal or system. Alternatively, it may be installed in equipment using wireless or wired communication means, removable recording media such as DVDs, CD-ROMs, or USB memory.

[0061] 1 Coating film change prediction system 10 Computer 101 Artificial intelligence model 102 Coating film change prediction unit 103a (Internal) database 103b (External) database 104 Reception unit 105 Display unit 106 Recording unit N Communication line M Virtual space 2a-2c Users 20a-20c User terminals

Claims

1. A method for predicting changes in a coating film using an artificial intelligence model that has been trained on the relationship between information about the coating film and / or information about the paint used to form the coating film and the results of a weathering test of the coating film, wherein the information about the coating film includes one or more selected from the group consisting of the composition of the coating film, information about the material to which the coating film is applied, the conditions for the formation of the coating film, the conditions for measuring the coating film, and the conditions for testing the coating film.

2. A method for predicting changes in a coating film using an artificial intelligence model trained on the relationship between information about the coating film and / or information about the paint used to form the coating film and the results of a weathering test of the coating film, wherein the information about the paint includes one or more selected from the group consisting of the type and amount of coating film forming resin, the type and amount of pigment, the type of glossing agent, the color tone and amount thereof, the type and amount of additive, the type and amount of pigment paste, and the type and amount of solvent.

3. The method according to claim 1 or 2, wherein the change in the coating film includes one or more changes selected from the group consisting of color tone, water resistance, acid resistance, chemical resistance, surface roughness, gloss, shine, smoothness, adhesion, water contact angle, oil contact angle, scratch resistance, and appearance of the coating film.

4. The method according to claim 2, wherein the information on the coating film includes one or more selected from the group consisting of the composition of the coating film, information on the material to which the coating film is applied, the conditions for the formation of the coating film, the conditions for measuring the coating film, and the conditions for testing the coating film.

5. The method according to claim 1 or 2, wherein the information of the coating film includes the structure of the coating film, and the structure of the coating film includes the layer structure of the coating film and the thickness of each layer.

6. The method according to claim 1 or 2, wherein the information of the coating film includes measurement conditions for the coating film, and the measurement conditions for the coating film include, when measuring the coating film, whether or not the surface of the coating film is polished and / or whether or not the surface of the coating film is washed with water.

7. The method according to claim 1, wherein the information of the paint includes one or more selected from the group consisting of the type and amount of film-forming resin, the type and amount of pigment, the type of glossing agent, the color tone and amount thereof, the type and amount of additive, the type and amount of pigment paste, and the type and amount of solvent.

8. The method according to claim 1 or 2, wherein the information of the paint includes the type of pigment paste and the amount thereof.

9. The method according to claim 1 or 2, wherein the coating film is a multilayer coating film including a color base coating film and a clear coating film.

10. The method according to claim 1 or 2, wherein the machine learning method uses one or more of the following prediction algorithms: decision trees, linear regression, partial least squares regression, Lasso regression, ridge regression, polynomial regression, Gaussian process regression, support vector machines, random forests, gradient boosting decision trees, K-nearest neighbors, neural networks, graph neural networks, Bayesian estimation, or ensemble learning thereof.

11. The method according to claim 1 or 2, comprising predicting the weather resistance of an automobile paint.

12. A coating change prediction system that predicts changes in a coating film from information about a coating film and / or information about paint used to form a coating film, comprising an artificial intelligence model trained on the relationship between information about the coating film and / or information about paint used to form the coating film and the results of a weathering test of the coating film, wherein the information about the coating film includes one or more selected from the group consisting of the composition of the coating film, information about the material to which the coating film is applied, the conditions for the formation of the coating film, the conditions for measuring the coating film and the conditions for testing the coating film.

13. A coating change prediction system that predicts changes in a coating film from information about a coating film and / or information about paint used to form a coating film, comprising an artificial intelligence model trained on the relationship between information about the coating film and / or information about paint used to form the coating film and the results of a weathering test of the coating film, wherein the information about the paint includes one or more selected from the group consisting of the type and amount of coating film forming resin, the type and amount of pigment, the type of glossing agent, the color tone and amount thereof, the type and amount of additive, the type and amount of pigment paste, and the type and amount of solvent.

14. Application software for causing an information processing device to execute the method described in claim 1 or 2.

15. Application software for controlling and operating the coating change prediction system according to claim 12 or 13.

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