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

Abstract

The performance of multi-layer coatings can be estimated without applying paint. [Solution] An estimation method that uses a multi-layer coating performance estimation model learned from variables including information on the paint formulation and / or properties of the coating film and the measurement results of the multi-layer coating performance, and outputs an estimated value of the measurement results of the multi-layer coating performance by inputting information on the paint formulation and / or properties of the coating film into the multi-layer 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. 2024-232634 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, and in particular, it is extremely difficult to predict the performance of a multi-layer coating film formed by applying multiple paints in layers. Therefore, it is common to grasp various performances of the coating film by measuring the coating film formed by actually applying the 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 method for estimating the performance of the coating film without producing the paint or 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 multi-layer coating film without producing a paint or forming a coating film.

Means for Solving the Problems

[0005] One aspect of the present invention is an estimation method that uses a multilayer coating performance estimation model learned from variables including information on the formulation of the paint and / or the properties of the coating film and the measurement results of the multilayer coating performance, and outputs an estimated value of the measurement results of the multilayer coating performance by inputting information on the formulation of the paint and / or the properties of the coating film into the multilayer coating performance estimation model. [Effects of the Invention]

[0006] According to the present invention, the performance of a multi-layer 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 properties of the coating film, as well as measurement results of the multi-layer coating performance.

[0010] A multi-layer coating is a coating obtained by sequentially applying and curing two or more different types of paint. For example, in the case of automotive paints, a multi-layer coating is generally formed by sequentially applying a primer (electrodeposition paint), an intermediate coat, a topcoat (base coat), and a clear coat. In the case of industrial paints, for example, a multi-layer coating is formed by sequentially applying an undercoat paint (primer paint) and a topcoat paint. In the case of paints for buildings or steel structures, for example, a sealer, primer (surface preparation agent), intermediate coat, and topcoat are applied in sequence to form a multi-layer coating. In this embodiment, the multi-layer coating may be formed by curing each coat of paint after application before applying the next coat, or the next coat may be applied over the uncured coating (wet-on-wet) and cured together. The curing method for the coating may include room temperature drying, heat drying, or chemical radiation curing, and any of these may be selected. The estimated multilayer coating performance is not limited to the performance of a single multilayer coating obtained by sequentially applying and curing two or more different types of paint. Examples include workability, weather resistance, chipping resistance, adhesion, impact resistance, hardness, cycle crack resistance, water resistance, high-temperature water resistance, color retention, processability, corrosion resistance of processed parts, weathering resistance, gloss retention, rust prevention, weather peel resistance, salt spray test, moisture resistance, salt water resistance, peel resistance, low-temperature impact resistance, low-temperature flexibility, low-temperature brittleness, cold resistance, heat resistance, abrasion resistance, immersion resistance, alkali immersion resistance, acid immersion resistance, fire resistance, flexibility, thermal shock resistance, crack resistance, and abrasion resistance. The estimated model generated by the estimated model generation device 10 according to this embodiment is particularly effective in estimating chipping resistance and adhesion. Chipping resistance refers to the resistance of a coating to damage caused by impacts from obstacles such as pebbles. Adhesion refers to the strength of the bond between the surface of the object to be coated and the coating film, or between different coating films.

[0011] 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 for the multilayer coating performance estimation model includes at least one 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 resin 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.

[0012] 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.

[0013] 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, anthracene, 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.

[0014] The resin is a binder for forming 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.

[0015] 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.

[0016] 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, and clay type thickeners.

[0017] In the present embodiment, it is preferable that the paint for forming a multilayer coating film includes at least two or more selected from the group consisting of an undercoat paint, a middle coat paint, a topcoat paint, and a clear paint.

[0018] The properties of the coating film are information regarding the coating film obtained by various film-forming conditions or various coating film measurements. In the present embodiment, the properties of the coating film used as variables and input information of the multilayer coating film performance estimation model preferably include at least one selected from the group consisting of coating film composition, film thickness, polarity of the coating film, dynamic viscoelasticity of the coating film, internal stress of the coating film, internal stress difference of the coating film, glass transition temperature of the coating film, drying temperature of the coating film, and drying time of the coating film. In this embodiment, it is preferable that the coating film forming the multilayer coating film includes at least two selected from the group consisting of a primer film, an intermediate coating film, a topcoat film, and a clear coating film.

[0019] Formulation information for each paint can be obtained from, for example, product data for each paint and experimental level data during paint development. The properties of the paint film are obtained by measuring each paint film obtained by applying and curing each paint. Subsequently, a multi-layer paint film is created by applying and curing two or more arbitrarily selected paints, and the paint film performance of the created multi-layer paint film is measured. This allows for the creation of combinations of measurement results for paint formulation and / or coating film properties, as well as multi-layer coating performance. By creating multi-layer coatings with several paint combinations and measuring their performance, a dataset can be created that includes information on paint formulation and / or coating film properties, as well as measurement results for multi-layer coating performance, as variables. Alternatively, after creating a multi-layer coating and measuring its performance, the properties of the coatings can be obtained by creating coatings for each individual paint and measuring each one.

[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 multi-layer coating performance when information on the paint formulation and / or properties of the coating film is input. The estimation model generation unit 120 generates an estimation model using machine learning methods, with information on the paint formulation and / or properties of the coating film as explanatory variables and the measurement result of multi-layer coating 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 multi-layer coating performance measurement result estimation unit 220, a multi-layer 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 composition and / or properties of the paint film, which are explanatory variables of the estimation model.

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

[0026] The multi-layer coating performance measurement result output unit 230 outputs estimated values ​​of the multi-layer coating performance measurement results output by the estimation model. The estimated values ​​of the multi-layer coating performance measurement results output from the multi-layer 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 multi-layer coating performance measurement results output from the multi-layer 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 multi-layer coating performance measurement result estimation unit 220 estimates the measurement result of the multi-layer coating performance using an estimation model based on the paint data (step S202). The multi-layer coating performance measurement result output unit 230 outputs an estimated value of the measurement result of the multi-layer coating performance (step S203).

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

[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 multiple estimation models based on the dataset, depending on the range of values ​​indicating the measurement results of the multilayer coating performance. For example, the estimation model generation unit 120 generates an estimation model for when the values ​​indicating the measurement results of the multilayer coating performance are in range D1 and an estimation model for when they are in range D2, based on the dataset. The estimation model generation unit 120 generates an estimation model corresponding to range D1 based on the data included in the dataset where the values ​​indicating the measurement results of the multilayer coating performance are 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 multi-layer 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 multi-layer 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 chipping resistance measurement result is a visual evaluation of the degree of scratching on a multi-layer coating after impact with a small stone using a stone chip tester, the estimation model generation unit 120 generates an estimation model to be used when the visual evaluation result is α or higher, based on the data in the dataset for which the visual evaluation result is α or higher. Alternatively, the estimation model generation unit 120 generates an estimation model to be used when the visual evaluation result is less than α, based on all the data in the dataset, regardless of the range of the visual evaluation result. The estimation model generation unit 120 may also generate an estimation model to be used when the visual evaluation result is less than α, based on the data in the dataset for which the visual evaluation result is less than α.

[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 about the range of values ​​that include the measured values ​​of the paint formulation and / or coating properties and multi-layer coating performance. This dataset may be generated by modifying a dataset acquired by the Dataset Acquisition Unit 110, which includes information as variables about the paint formulation and / or coating properties and multi-layer coating performance, and determining the range of values ​​that include the measured values ​​from the information about the measured results. Alternatively, this dataset may be newly acquired by the Dataset Acquisition Unit 110.

[0033] The range in which the measured values ​​of the multi-layer 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 multi-layer coating performance are included is the information that the measured values ​​of the multi-layer coating performance are included in either 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 the properties of the coating film is input. When information on the paint formulation and / or the properties of the coating 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 multi-layer coating 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 multilayer coating performance estimation models and a model for use 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 for use 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 multi-layer coating performance by inputting paint data into the model determination model stored in the memory unit 290. The multi-layer coating performance measurement result estimation unit 220 estimates the measurement result of the multi-layer 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 multi-layer 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 in which the measured values ​​of the paint formulation and / or coating properties and multi-layer coating performance are included as variables (step S304). At this point, the dataset acquisition unit 110 may acquire a dataset that newly includes information on the range in which the measured values ​​of the paint formulation and / or coating properties and multi-layer coating performance are included as variables.

[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 an estimation model to be used to estimate the multi-layer coating performance based on the paint data (step S402). The multi-layer coating performance measurement result estimation unit 220 estimates the measurement result of the multi-layer coating performance based on the paint data using the determined estimation model (step S403). The multi-layer coating performance measurement result output unit 230 outputs an estimated value of the measurement result of the multi-layer coating performance (step S404).

[0041] In the above explanation, for the sake of clarity, the values ​​representing the measurement results of the multi-layer 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 multi-layer 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 accuracy of estimating the chipping properties of a multi-layer coating obtained using a given paint was verified based on paint data. First, a dataset was created containing paint data and data on the measurement results of the multi-layer coating performance. For the topcoat paint, data on the type and amount of glossing agent, the type and amount of coloring pigment, the type and amount of resin, and the type and amount of dispersant were used as paint data. On the other hand, for the intermediate coat paint, data on the color of the paint was used, and for the clear coat paint, data on the type of clear coat paint was used as paint data. The measurement results of the multi-layer coating performance were evaluated by visual inspection, with chipping properties ranked on a 10-point scale. In this experiment, the estimation model generation device 10 generated a model that, as a model determination model, estimated whether the rank was less than 5 based on the paint data, and determined the estimation model based on whether the estimated rank was less than 5. In this experiment, the estimation model generation device 10 generated two estimation models: an estimation model used when the rank was less than 5 (low-rank model) and an estimation model used when the rank was 5 or higher (high-rank model). Here, the low-rank model is generated using data from the dataset with a chipping rank of less than 5. The high-rank 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 382 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 absolute error (MAE) calculated for the training data and test data for each validation is shown in the graph. In this experiment, the average MAE for the test data was 1.31.

[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 chipping properties and the estimation device 20 estimates chipping properties was described. However, the method is not limited to this, and the estimation model generation device 10 may generate a model for estimating multilayer coating performance, and the estimation device 20 may estimate multilayer coating performance other than chipping properties. In addition to circulation, properties included in the multi-layer coating performance such as workability, weather resistance, adhesion, impact resistance, hardness, resistance to cyclic cracking, water resistance, high-temperature water resistance, color retention, processability, corrosion resistance of processed parts, weathering resistance, gloss retention, rust prevention, weathering peel resistance, salt spray test, moisture resistance, salt water resistance, peel resistance, low-temperature impact resistance, low-temperature flexibility, low-temperature brittleness, cold resistance, heat resistance, abrasion resistance, immersion resistance, alkali immersion resistance, acid immersion resistance, fire resistance, flexibility, thermal shock resistance, crack resistance, and abrasion resistance also change depending on the paint formulation and / or the properties of the coating film, and can therefore be estimated in the same way as chipping performance according to 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 multi-layer coating performance, and the estimation device 20 may determine which estimation model to use for estimating the multi-layer coating performance based on the paint data and estimate the multi-layer coating performance. In addition, the candidate estimation models used for estimating the multi-layer coating performance may include estimation models that do not use elements from the paint data that have little influence on the multi-layer coating performance. Furthermore, the candidate estimation models used for estimating the multi-layer 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 Multilayer coating performance measurement result estimation unit, 230 Multilayer coating performance measurement result output unit, 240 Model used determination unit, 290 Storage unit

Claims

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

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

3. The aforementioned model determination model calculates estimated values ​​of the measurement results for multilayer coating performance and determines the multilayer coating performance estimation model to be used for estimation from among the multiple multilayer coating performance estimation models based on the range of the estimated values. The estimation method according to claim 2.

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

5. The aforementioned multi-layer coating performance estimation models include a multi-layer coating performance estimation model generated based on data in the dataset whose values ​​indicating the measurement results of the multi-layer coating performance fall within a corresponding range, and a multi-layer coating performance estimation model generated based on all the 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 regarding the formulation of the aforementioned paint is, 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 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 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 any one of claims 1 to 6.

8. The information relating to the properties of the coating film is, The coating includes at least one selected from the group consisting of coating film composition, film thickness, coating film polarity, coating film dynamic viscoelasticity, coating film internal stress, coating film internal stress difference, coating film glass transition temperature, coating film drying temperature, and coating film drying time. The estimation method according to any one of claims 1 to 6.

9. The aforementioned paint includes at least two selected from the group consisting of primer, intermediate coat, topcoat, and clear coat. The aforementioned coating film includes at least two selected from the group consisting of a primer film, an intermediate coating film, a topcoat film, and a clear coating film. The estimation method according to any one of claims 1 to 6.

10. The performance of the multi-layer coating includes at least one selected from the group consisting of workability, weather resistance, chipping resistance, adhesion, impact resistance, hardness, resistance to cyclic cracking, water resistance, high-temperature water resistance, color retention, processability, corrosion resistance of processed parts, weathering resistance, gloss retention, rust prevention, weathering peel resistance, salt spray test, moisture resistance, salt water resistance, peel resistance, low-temperature impact resistance, low-temperature flexibility, low-temperature brittleness, cold resistance, heat resistance, abrasion resistance, immersion resistance, alkali immersion resistance, acid immersion resistance, fire resistance, flexibility, thermal shock resistance, crack resistance, and abrasion resistance. The estimation method according to any one of claims 1 to 6.

11. By acquiring and training information on the paint formulation and / or properties of the coating film, as well as the measurement results of the multi-layer coating performance, This system generates a machine learning model that takes information on the paint formulation and / or coating properties as input and outputs an estimated value of the measured performance of a multi-layer coating. A method for generating a multi-layer coating performance estimation model.

12. By acquiring and training information on the paint formulation and / or properties of the coating film and the measurement results of the multi-layer coating performance, Based on information on the paint formulation and / or the properties of the coating film, a model determination model is generated to determine the estimation model used to estimate the measurement results of multi-layer coating performance. Method for generating a model for determining which model to use.

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