Estimation method, estimation model generation method, and model determination model generation method

Abstract

The performance of the coating film can be estimated without actually applying the paint. [Solution] An estimation method that uses a topcoat coating performance estimation model learned from variables including information on paint formulation and / or coating film formation conditions and measurement results of topcoat coating performance, and outputs an estimated value of the measurement results of topcoat coating performance by inputting information on paint formulation and / or coating film formation conditions into the topcoat coating performance estimation model.

Description

Technical Field

[0001] The present invention relates to an estimation method, a method for generating an estimation model, and a method for generating a usage model determination model. This application claims priority based on Japanese Patent Application No. 2024-232769 filed in Japan on December 27, 2024, and incorporates its content herein.

Background Art

[0002] A coating film is formed by applying a paint to an object to be coated and drying it. The coating film has roles such as imparting aesthetics to the object to be coated and protecting it from the external environment, and it is very important to grasp various coating film performances according to the coating purpose. However, it is difficult to predict various performances of the coating film from the formulation information and the state of the paint. Therefore, it is common to grasp various performances of the coating film by measuring the coating film formed by applying the actually prepared paint with a predetermined film thickness and drying it. However, since a large amount of man-hours are required for the preparation of the paint and the coating work, a technique for estimating the performance of the coating film without producing the paint and forming the coating film is demanded.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] An object of the present invention is to provide an estimation method, a method for generating an estimation model, and a method for generating a usage model determination model that can estimate the performance of a topcoat coating film without producing a paint and forming a coating film.

Means for Solving the Problems

[0005] One aspect of the present invention is an estimation method that uses a topcoat coating performance estimation model, which has been learned using variables including information on paint formulation and / or coating film formation conditions and measurement results of topcoat coating performance, to input information on paint formulation and / or coating film formation conditions into the topcoat coating performance estimation model, thereby outputting an estimated value of the measurement results of topcoat coating performance. [Effects of the Invention]

[0006] According to the present invention, the performance of the topcoat coating can be estimated without applying paint. [Brief explanation of the drawing]

[0007] [Figure 1] This figure shows an example of the configuration of the estimation model generation device 10 according to the first embodiment. [Figure 2] This is a flowchart showing the operation of the estimation model generation device 10 according to the first embodiment. [Figure 3] This figure shows an example of the configuration of the estimation device 20 according to the first embodiment. [Figure 4] This is a flowchart showing the operation of the estimation device 20 according to the first embodiment. [Figure 5] This figure shows the configuration of the estimation model generation device 10 according to the second embodiment. [Figure 6] This figure shows the configuration of the estimation device 20 according to the second embodiment. [Figure 7] This is a flowchart showing the operation of the estimation model generation device 10 according to the second embodiment. [Figure 8] This is a flowchart showing the operation of the estimation device 20 according to the second embodiment. [Figure 9] This figure shows the experimental results. [Modes for carrying out the invention]

[0008] Embodiments of the present invention will be described in detail below with reference to the drawings. (First embodiment) Figure 1 shows an example of the configuration of the estimation model generation device 10 according to the first embodiment. The estimation model generation device 10 includes a dataset acquisition unit 110, an estimation model generation unit 120, and an estimation model output unit 130.

[0009] The dataset acquisition unit 110 acquires a dataset. The dataset includes information as variables regarding the paint formulation and / or film formation conditions and the measurement results of the topcoat film performance.

[0010] The performance of a topcoat coating is not limited to the performance of the coating obtained by applying and curing the topcoat paint. Examples include chemical resistance, acid cracking resistance, scratch resistance, yellowing resistance, solvent whitening resistance, stain resistance, flavor, stain removal, self-cleaning properties, washability, bio-adhesion resistance, coating wear resistance, low friction, rust prevention, tactile feel, gloss retention, weathering resistance, smoothness, suitability for recoating, antibacterial / antiviral properties, tactile feel, friction reduction, and fluid resistance. Chemical resistance includes durability against water (rain, etc.), acidic substances (acid rain, etc.), alkaline substances (detergents, etc.), and organic solvents (xylene, etc.), as well as durability against contaminants such as bird droppings, pollen, tree sap, insect carcasses, metal powder, sand and mud, oil, limescale, saltwater, oil fumes, carbon, and tar. Chemical resistance evaluation tests include spot resistance tests and immersion tests. A spot resistance test involves spot-dropping a test liquid onto the coating, allowing it to dry, and then evaluating the appearance of the coating (color, gloss, coating abnormalities, staining, adhesion, hardness, etc.) after washing the area with water. Test liquids can include water, acidic liquids, alkaline liquids, and the aforementioned contaminants. In this specification, the results of a spot resistance test using an acidic liquid may be expressed as acid rain resistance. The estimation model generated by the estimation model generation device 10 according to this embodiment is particularly effective in estimating acid rain resistance.

[0011] In this embodiment, the topcoat coating to be estimated may be a single topcoat film or a multilayer film obtained by applying a topcoat coating on top of another coating film. The estimated model generated by the estimated model generation device 10 according to this embodiment is particularly suitable for estimating the performance of a clear coating obtained by applying a clear coating on top of a base coating film containing a pigment.

[0012] The paint formulation refers to information regarding the amount and proportion of each component that makes up the paint. In this embodiment, the paint formulation used as a variable or input information in the topcoat coating performance estimation model includes information regarding the formulation of the topcoat paint, and includes at least one piece of information selected from the group consisting of: the type of resin contained in the paint, the amount of resin contained in the paint, the chemical structure of the resin contained in the paint, the amount of functional groups in the resin contained in the paint, the type of functional groups in the resin contained in the paint, the type of solvent contained in the paint, the amount of solvent contained in the paint, the type of additive contained in the paint, the amount of additive contained in the paint, the type of thickener contained in the paint, and the amount of thickener contained in the paint. If the topcoat paint contains pigments, it is preferable that the information regarding the formulation of the topcoat paint includes, in addition to the above information, at least one piece of information selected from the group consisting of: the type of glossing agent contained in the paint, the amount of glossing agent contained in the paint, the color of the glossing agent contained in the paint, the particle size of the glossing agent contained in the paint, the thickness of the glossing agent contained in the paint, the type of surface treatment of the glossing agent contained in the paint, the type of coloring pigment contained in the paint, the amount of coloring pigment contained in the paint, the color of the coloring pigment contained in the paint, the chemical structure of the coloring pigment contained in the paint, the particle size of the coloring pigment contained in the paint, the type of dispersant contained in the paint, the amount of dispersant contained in the paint, the chemical structure of the dispersant contained in the paint, the amount of thickener contained in the paint, and the chemical structure of the thickener contained in the paint. In this embodiment, when estimating the performance of a clear coating applied on a base coating, information on the base coating formulation may be used as variables or input information for the clear coating performance estimation model. In this case, the base coating formulation used as variables or input information for the clear coating performance estimation model includes at least one selected from the group consisting of: type of glossing agent contained in the base coating, amount of glossing agent contained in the base coating, color of the glossing agent contained in the base coating, particle size of the glossing agent contained in the base coating, thickness of the glossing agent contained in the base coating, type of surface treatment of the glossing agent contained in the base coating, type of coloring pigment contained in the base coating, amount of coloring pigment contained in the base coating, color of the coloring pigment contained in the base coating, chemical structure of the coloring pigment contained in the base coating, particle size of the coloring pigment contained in the base coating, amount of resin contained in the base coating, chemical structure of the resin contained in the base coating, amount of dispersant contained in the base coating, chemical structure of the dispersant contained in the base coating, amount of thickener contained in the base coating, and chemical structure of the thickener contained in the base coating.

[0013] Furthermore, a glittering material is a flaky or plate-shaped pigment that imparts a sparkling, lustrous appearance or optical interference to a coating film. Examples of glittering materials include flaky aluminum (aluminum flakes), vapor-deposited aluminum, aluminum oxide, oxybismuth chloride, mica, titanium oxide-coated mica, iron oxide-coated mica, mica-like iron oxide, titanium oxide-coated silica, titanium oxide-coated alumina, iron oxide-coated silica, iron oxide-coated alumina, glass flakes, colored glass flakes, vapor-deposited glass flakes, and holographic films.

[0014] Examples of coloring pigments include, for example, white pigments. Examples of white pigments include titanium dioxide and the like. Examples of coloring pigments include, for example, black pigments. Examples of black pigments include carbon black, acetylene black, lamp black, bone black, graphite, iron black, aniline black, and the like. Examples of coloring pigments include, for example, yellow pigments. Examples of yellow pigments include yellow iron oxide, titanium yellow, monoazo yellow, condensed azo yellow, azomethine yellow, bismuth vanadate, benzimidazolone, isoindolinone, isoindoline, quinophthalone, benzidine yellow, permanent yellow, and the like. Examples of coloring pigments include, for example, orange pigments. Examples of orange pigments include permanent orange and the like. Examples of coloring pigments include, for example, red pigments. Examples of red pigments include red iron oxide, naphthol AS-based azo red, anthansolone, anthraquinonyl red, perylene maroon, quinacridone-based red pigments, diketopyrrolopyrrole, watching red, permanent red, and the like. Examples of coloring pigments include, for example, brown pigments. Examples of brown pigments include brown iron oxide and the like. Examples of coloring pigments include, for example, purple pigments. Examples of purple pigments include cobalt violet, quinacridone violet, dioxazine violet, and other purple pigments. Examples of coloring pigments include, for example, blue pigments. Examples of blue pigments include cobalt blue, phthalocyanine blue, threne blue, and the like. Examples of coloring pigments include, for example, green pigments. Examples of green pigments include phthalocyanine green and the like. The coloring pigment may be a combination of these pigments.

[0015] The resin is a binder that forms a coating film. The resin is not particularly limited as long as it is generally used in paint applications, and it may be a solvent-based or water-based liquid or a powder for powder coatings. Examples of resin types include acrylic resin, phenolic resin, alkyd resin, polyester resin, urethane resin, polyether resin, polyolefin resin, epoxy resin, silicone resin, fluororesin, ethylene vinyl acetate copolymer, amino resin, polycarbonate resin, polyvinyl chloride resin, isocyanate compound, melamine resin, and the like.

[0016] The dispersant is an additive for uniformly dispersing a brightening material, a coloring pigment, etc. The dispersant is not particularly limited as long as it is generally used in paint applications, and commercially available products generally sold as dispersants can be used. Examples of dispersant types include nonionic, cationic or anionic functional group-containing compounds.

[0017] The thickener is an additive for adjusting the viscosity of the paint. The thickener is not particularly limited as long as it is generally used in paint applications, and commercially available products generally sold as thickeners can be used. Examples of thickener types include, for example, alkali thickening type, nonionic associative type, cellulose type, water-soluble polymer type, polyamide type, clay type thickeners.

[0018] The film formation conditions of the coating film are mainly information regarding the coating conditions of the topcoat paint. In the present embodiment, the film formation conditions of the coating film used as variables and input information in the topcoat film performance estimation model include at least one selected from the group consisting of coating temperature, coating humidity, drying temperature, drying time, cooling time, film thickness, and coating film composition.

[0019] Formulation information for each topcoat paint can be obtained from, for example, product data for each topcoat paint and experimental level data during paint development. A topcoat film is prepared using each topcoat paint under arbitrary painting conditions, and the coating performance of the prepared topcoat film is measured. This allows for the creation of combinations of the formulation and / or film formation conditions of each paint and the measurement results of the topcoat coating performance, and a dataset can be created that includes information on the formulation and / or film formation conditions of the paint and the measurement results of the topcoat coating performance as variables.

[0020] The estimation model generation unit 120 generates an estimation model based on the dataset. The estimation model is a model that outputs an estimated value of the measurement result of the topcoat coating performance when information on the paint formulation and / or coating film formation conditions is input. The estimation model generation unit 120 generates an estimation model using machine learning methods, with information on the paint formulation and / or coating film formation conditions as explanatory variables and the measurement result of the barrier performance as the objective variable. Examples of machine learning techniques include decision trees, decision trees with gradient boosting, linear regression, logistic regression, simple perceptron, MLP, neural networks, support vector machines, random forests, Gaussian processes, Bayesian networks, k-nearest neighbors, Ridge regression, Lasso regression, graph neural networks, K-means algorithm, naive Bayes, CNN, and other algorithms used in machine learning. These algorithms may be used individually or in combination. In this embodiment, it is preferable to use a decision tree with gradient boosting. An example of a decision tree with gradient boosting is LightGBM.

[0021] The estimated model output unit 130 outputs the generated estimated model.

[0022] Figure 2 is a flowchart illustrating the operation of the estimation model generation device 10 according to the first embodiment. The dataset acquisition unit 110 acquires the dataset (step S101). The estimation model generation unit 120 generates an estimation model based on the dataset (step S102). The estimation model output unit 130 outputs the generated estimation model (step S103).

[0023] Figure 3 shows an example of the configuration of the estimation device 20 according to the first embodiment. The estimation device 20 comprises a paint data acquisition unit 210, a topcoat coating performance measurement result estimation unit 220, a topcoat coating performance measurement result output unit 230, and a storage unit 290. The storage unit 290 stores the estimation model output by the estimation model generation device 10.

[0024] The paint data acquisition unit 210 acquires paint data. The paint data is data indicating the paint formulation and / or film formation conditions of the paint film, which are explanatory variables of the estimation model.

[0025] The topcoat coating performance measurement result estimation unit 220 estimates the measurement result of the topcoat coating performance by inputting paint data into the estimation model and outputting an estimated value of the measurement result of the topcoat coating performance.

[0026] The topcoat coating performance measurement result output unit 230 outputs estimated values ​​of the topcoat coating performance measurement results output by the estimation model. The estimated values ​​of the topcoat coating performance measurement results output from the topcoat coating performance measurement result output unit 230 are recorded in the storage unit 290, for example, along with the corresponding paint data. The estimated values ​​of the topcoat coating performance measurement results output from the topcoat coating performance measurement result output unit 230 are input to and displayed on an external display device, for example.

[0027] Figure 4 is a flowchart showing the operation of the estimation device 20 according to the first embodiment. The paint data acquisition unit 210 acquires paint data (step S201). The topcoat coating performance measurement result estimation unit 220 estimates the measurement result of the topcoat coating performance using an estimation model based on the paint data (step S202). The topcoat coating performance measurement result output unit 230 outputs an estimated value of the measurement result of the topcoat coating performance (step S203).

[0028] As described above, the estimation device 20 can estimate the measurement results of the topcoat coating performance before painting by using an estimation model generated by machine learning to estimate the measurement results of the topcoat coating performance from the paint formulation and / or the film formation conditions of the coating.

[0029] (Second embodiment) The estimation model generation device 10 according to the second embodiment will be described below. For the sake of clarity, the range of values ​​less than a predetermined value will be referred to as range D1, and the range of values ​​greater than or equal to the predetermined value will be referred to as range D2. In the second embodiment, the estimation model generation unit 120 generates a plurality of estimation models based on the dataset, each differing in the range of values ​​indicating the measurement results of the topcoat coating performance. For example, the estimation model generation unit 120 generates estimation models based on the dataset for cases where the measured value of the topcoat coating performance is within range D1 and for cases where it is within range D2. The estimation model generation unit 120 generates estimation models corresponding to range D1 based on the data included in the dataset, specifically the data where the measured value of the topcoat coating performance is within range D1.

[0030] Furthermore, the estimation model generation unit 120 generates an estimation model corresponding to range D2 based on the data in the dataset whose values ​​indicating the measurement results of the topcoat coating performance fall within range D2. However, if the dataset contains only a small number of data where the values ​​indicating the measurement results of the topcoat coating performance fall within range D1 or D2, the estimation model generation unit 120 may generate an estimation model corresponding to range D1 or D2 based on all the data in the dataset.

[0031] For example, if the measurement result for acid rain resistance is a 60-degree specular gloss, the estimation model generation unit 120 generates an estimation model used when the 60-degree specular gloss is less than α, based on the data in the dataset where the 60-degree specular gloss is less than a predetermined α. Alternatively, the estimation model generation unit 120 generates an estimation model used when the 60-degree specular gloss is α or greater, based on all the data in the dataset, regardless of the range of the 60-degree specular gloss. The estimation model generation unit 120 may also generate an estimation model used when the 60-degree specular gloss is α or greater, based on the data in the dataset where the 60-degree specular gloss is α or greater. For example, if the acid rain resistance measurement result is the color difference between the initial paint color and the paint color after the spot resistance test, the estimation model generation unit 120 generates an estimation model used when the color difference is less than β, based on the data in the dataset where the color difference is less than β. β is a predetermined color difference. Alternatively, the estimation model generation unit 120 generates an estimation model used when the color difference is β or greater, based on all the data in the dataset, regardless of the range of the color difference. The estimation model generation unit 120 may also generate an estimation model used when the color difference is β or greater, based on the data in the dataset where the color difference is β or greater.

[0032] Figure 5 shows the configuration of the estimation model generation device 10 according to the second embodiment. The estimation model generation device 10 according to the second embodiment includes, in addition to the estimation model generation device 10 according to the first embodiment, a model determination unit 140 and a model determination unit 150. The Model Determination and Generation Unit 140 generates a model determination model based on the dataset. The dataset used here includes information as variables that include the range of values ​​indicated by the measurement results of the paint formulation and / or coating film formation conditions and the topcoat coating performance. This dataset may be generated by modifying a dataset acquired by the Dataset Acquisition Unit 110, which includes information as variables that include the paint formulation and / or coating film formation conditions and the measurement results of the topcoat coating performance, and determining the range of values ​​indicated by the measurement results from the information on the measurement results. Alternatively, this dataset may be newly acquired by the Dataset Acquisition Unit 110.

[0033] The range in which the measured values ​​of the topcoat coating performance are included corresponds to the range corresponding to the generated estimation model. In this explanation, the information regarding the range in which the measured values ​​of the topcoat coating performance are included is whether the measured values ​​of the topcoat coating performance are included in range D1 or range D2. The model used determination model is a model that determines the estimation model to be used when information on the paint formulation and / or film formation conditions of the paint film is input. When information on the paint formulation and / or film formation conditions of the paint film is input, the model used determination model outputs estimation results for a range that includes the values ​​shown in the measurement results of the topcoat film performance, and determines the estimation model corresponding to the range shown in the estimation results.

[0034] The model used for determining the model to be used does not necessarily have to be generated using machine learning. The model used for determining the model to be used may be determined using a predetermined formula.

[0035] The model determination output unit 150 outputs the generated model determination model.

[0036] In the estimation device 20 according to the second embodiment, the storage unit 290 stores two topcoat coating performance estimation models and a model determination model generated by the estimation model generation device 10. Figure 6 is a diagram showing the configuration of the estimation device 20 according to the second embodiment. The estimation device 20 according to the second embodiment includes a model determination unit 240 in addition to the estimation device 20 according to the first embodiment.

[0037] The model determination unit 240 determines the estimation model used to estimate the topcoat coating performance by inputting paint data into the model determination model stored in the memory unit 290. The topcoat coating performance measurement result estimation unit 220 estimates the measurement result of the topcoat coating performance by inputting paint data into the estimation model determined by the model determination unit 240 and outputting an estimated value of the measurement result of the topcoat coating performance.

[0038] Figure 7 is a flowchart showing the operation of the estimation model generation device 10 according to the second embodiment. The operation of steps S301 to S303 is the same as the operation of steps S101 to S103 shown in Figure 2. The model generation unit 140 modifies the dataset and generates a dataset that includes information on the range of values ​​that include the paint formulation and / or film formation conditions and the measurement results of the topcoat film performance as variables (step S304). Furthermore, the dataset acquisition unit 110 may acquire a dataset at this point that includes, as a new variable, information regarding the range of values ​​that include the paint formulation and / or coating film formation conditions and the measurement results of the topcoat coating performance.

[0039] The model determination model generation unit 140 generates a model determination model based on the dataset (step S305). The model determination model output unit 150 outputs the generated model determination model (step S306). Note that the generation and output of the model determination model may be performed independently of the generation and output of the estimation model, and the model determination model does not necessarily have to be generated after the estimation model has been generated.

[0040] Figure 8 is a flowchart showing the operation of the estimation device 20 according to the second embodiment. The paint data acquisition unit 210 acquires paint data (step S401). The model determination unit 240 determines the estimation model to be used to estimate the topcoat film performance based on the paint data (step S402). The topcoat film performance measurement result estimation unit 220 estimates the topcoat film performance measurement result based on the paint data using the determined estimation model (step S403). The topcoat film performance measurement result output unit 230 outputs the estimated value of the topcoat film performance measurement result (step S404).

[0041] In the above explanation, for the sake of clarity, the values ​​representing the measurement results of the topcoat coating performance were divided into two ranges, and the explanation described the case where one of the two estimation models is used. However, the values ​​representing the measurement results of the topcoat coating performance may be divided into three or more ranges, and one of the three or more estimation models may be used.

[0042] (Example of experiment) The experiment conducted is described below. In this experiment, the spot resistance of a coating obtained with a given paint was estimated based on paint data. First, a dataset was created that included paint data and data on the measurement results of the spot resistance of the coating. As paint data, data indicating the paint formulation and data indicating the film formation conditions of the coating were used. As data indicating the paint formulation, data on the type of resin contained in the topcoat paint, the type of additive contained in the topcoat paint, and the amount of additive contained in the topcoat paint were used. As data indicating the film formation conditions of the coating, data on the coating structure was used. In this experiment, spot resistance was the difference in 60-degree specular gloss from an initial value evaluated by a gloss meter. The estimation model generation device 10 generated a model that, as the model determination model, estimated whether the 60-degree specular gloss difference was less than 1.0 based on the topcoat paint data, and determined the estimation model based on whether the estimated 60-degree specular gloss difference was less than 1.0. In this experiment, the estimation model generation device 10 generated two estimation models: one used when the 60-degree specular gloss difference is less than 1.0 (low 60-degree specular gloss difference model), and another used when the 60-degree specular gloss difference is 1.0 or greater (high 60-degree specular gloss difference model). Here, the low 60-degree specular gloss difference model is generated using data from the dataset where the 60-degree specular gloss difference is less than 1.0. The high 60-degree specular gloss difference model is generated using all the data in the dataset. The estimation accuracy was examined by performing 5-fold cross-validation on the dataset data. The dataset contained 180 data points.

[0043] Figure 9 shows the experimental results. The estimation accuracy of the estimation model was verified using 5-fold cross-validation (5FoldCV). In the graph shown in Figure 9, the vertical axis shows the estimation results by the estimation model, and the horizontal axis shows the measured values. The training data (train) and test data (test) are shown at different points in the graph. The mean squared error (MSE) calculated for the training data and test data for each validation is shown in the graph. In this experiment, the average MSE for the test data was 0.87.

[0044] <Other Embodiments> Although one embodiment of this invention has been described in detail above with reference to the drawings, the specific configuration is not limited to that described above, and various design changes can be made without departing from the spirit of this invention.

[0045] In the experimental example, the case in which the estimation model generation device 10 generates a model for estimating spot resistance and the estimation device 20 estimates spot resistance was described, but it is not limited to this, and the estimation model generation device 10 may also generate a model for estimating topcoat film performance, and the estimation device 20 may also estimate topcoat film performance other than spot resistance. Not only spot resistance, but also chemical resistance, acid crack resistance, scratch resistance, yellowing, solvent whitening resistance, stain resistance, flavor, stain removal, self-cleaning properties, washability, bioadhesion, coating wear, low friction, rust prevention, tactile feel, gloss retention, weathering resistance, smoothness, suitability for recoating, antibacterial / antiviral properties, tactile feel, friction reduction, and fluid resistance, which are included in topcoat film performance, change depending on the paint formulation and / or film formation conditions, and can therefore be estimated in the same way as spot resistance by the embodiments described above. The estimation model generation device 10 may generate multiple estimation models that differ depending on the range of values ​​indicated by the topcoat coating performance, and the estimation device 20 may determine which estimation model to use for estimating the topcoat coating performance based on the paint data and estimate the topcoat coating performance. In addition, the candidate estimation models used for estimating the topcoat coating performance may include estimation models that do not use elements from the paint data that have little influence on the topcoat coating performance. Furthermore, the candidate estimation models used for estimating the topcoat coating performance may include three or more estimation models that differ from several elements from the elements included in the paint data.

[0046] All or part of the functions of the estimation model generation device 10 and estimation device 20 in the above-described embodiment may be implemented by a computer. In that case, the functions may be implemented by recording a program for implementing these functions on a computer-readable recording medium, loading the program recorded on this recording medium into a computer system, and executing it. Here, "computer system" includes the OS and peripheral hardware. Furthermore, "computer-readable recording medium" refers to portable media such as flexible disks, magneto-optical disks, ROMs, CD-ROMs, and recording devices such as hard disks built into a computer system. In addition, "computer-readable recording medium" may include those that dynamically hold programs for a short period of time, such as communication lines used when transmitting programs via networks such as the Internet or communication lines such as telephone lines, and those that hold programs for a certain period of time, such as volatile memory inside a computer system that acts as a server or client in such a case. Furthermore, the above-mentioned program may be for implementing some of the functions described above, and may also be able to implement the above-mentioned functions in combination with a program already recorded in the computer system. Furthermore, all or part of the functions of the estimation model generation device 10 and estimation device 20 may be implemented using a programmable logic device such as an FPGA (Field Programmable Gate Array). [Explanation of symbols]

[0047] 10 Estimation model generation device, 110 Data set acquisition unit, 120 Estimation model generation unit, 130 Estimation model output unit, 140 Model used determination model generation unit, 150 Model used determination model output unit, 20 Estimation device, 210 Paint data acquisition unit, 220 Top coat coating performance measurement result estimation unit, 230 Top coat coating performance measurement result output unit, 240 Model used determination unit, 290 Storage unit

Claims

1. Using a topcoat coating performance estimation model learned from variables including information on paint formulation and / or coating film formation conditions and measurement results of topcoat coating performance, the model outputs an estimated value of the measurement results of topcoat coating performance by inputting information on paint formulation and / or coating film formation conditions into the topcoat coating performance estimation model. Estimation method.

2. An estimation method using a model determination model and a plurality of topcoat coating performance estimation models, wherein the model determination model is a model that, upon input of information regarding the paint formulation and / or coating film formation conditions, determines which of the plurality of topcoat coating performance estimation models will be used for estimation. By inputting information regarding the paint formulation and / or film formation conditions into the aforementioned model determination model, the model to be used for estimation is determined from among the multiple topcoat film performance estimation models. By inputting information regarding the paint formulation and / or film formation conditions into the determined topcoat coating performance estimation model, an estimated value of the measured topcoat coating performance is output. The estimation method according to claim 1.

3. The aforementioned model determination model calculates an estimated value of the measurement result of the topcoat coating performance and, based on the range of the estimated value, determines the topcoat coating performance estimation model to be used for estimation from among the multiple topcoat coating performance estimation models. The estimation method according to claim 2.

4. The aforementioned multiple topcoat coating performance estimation models are topcoat coating performance estimation models that are generated based on data included in the dataset, where the values ​​indicating the measurement results of the topcoat coating performance fall within the corresponding ranges. The estimation method according to claim 2.

5. The aforementioned multiple topcoat coating performance estimation models include a topcoat coating performance estimation model generated based on data in the dataset whose values ​​indicating the measurement results of topcoat coating performance fall within a corresponding range, and a topcoat coating performance estimation model generated based on all data in the dataset. The estimation method according to claim 2.

6. The aforementioned model used for determining the model is a model generated by machine learning. The estimation method according to claim 2.

7. The information relating to the formulation of the aforementioned paint includes information relating to the formulation of the topcoat paint. The information includes at least one piece of information selected from the group consisting of the type of resin contained in the paint, the amount of resin contained in the paint, the chemical structure of the resin contained in the paint, the amount of functional groups in the resin contained in the paint, the type of functional groups in the resin contained in the paint, and the type of solvent contained in the paint. The estimation method according to any one of claims 1 to 6.

8. The aforementioned coating includes a base coat and a clear coat. The estimation method according to any one of claims 1 to 6.

9. The information regarding the formulation of the base paint is as follows: The information includes at least one piece of information selected from the group consisting of: the type of glossing agent contained in the paint, the amount of glossing agent contained in the paint, the color of the glossing agent contained in the paint, the particle size of the glossing agent contained in the paint, the thickness of the glossing agent contained in the paint, the type of surface treatment of the glossing agent contained in the paint, the type of coloring pigment contained in the paint, the amount of coloring pigment contained in the paint, the color of the coloring pigment contained in the paint, the chemical structure of the coloring pigment contained in the paint, the particle size of the coloring pigment contained in the paint, the amount of resin contained in the paint, the chemical structure of the resin contained in the paint, the type of dispersant contained in the paint, the amount of dispersant contained in the paint, the chemical structure of the dispersant contained in the paint, the amount of thickener contained in the paint, and the chemical structure of the thickener contained in the paint. The estimation method according to claim 8.

10. Information regarding the film formation conditions for the aforementioned coating film is as follows: It includes at least one selected from the group consisting of painting temperature, painting humidity, drying temperature, drying time, cooling time, film thickness, and coating structure. The estimation method according to any one of claims 1 to 6.

11. The performance of the topcoat film includes at least one selected from the group consisting of chemical resistance, acid cracking resistance, scratch resistance, yellowing resistance, solvent whitening resistance, flavor, stain removal, self-cleaning properties, washability, bioadhesion resistance, film wear resistance, low friction, rust prevention, tactile feel, gloss retention, weathering resistance, smoothness, suitability for recoating, antibacterial / antiviral properties, tactile feel, friction reduction, and fluid resistance. The estimation method according to any one of claims 1 to 6.

12. By acquiring and training information on paint formulation and / or film formation conditions, as well as measurement results of topcoat film performance, This system generates a machine learning model that takes information on paint formulation and / or film formation conditions as input and outputs an estimated value of the topcoat film performance measurement result. A method for generating a model to estimate the performance of a topcoat coating.

13. By acquiring and training information on paint formulation and / or film formation conditions and measurement results of topcoat film performance, Based on information on the paint formulation and / or film formation conditions, a model determination model is generated to determine the estimation model used to estimate the measurement results of the topcoat film performance. Method for generating a model for determining which model to use.

Images