Coating film characteristic estimation method and coating film characteristic estimation model generation method

Abstract

To provide a coating film characteristic estimation method and a coating film characteristic estimation model generation method that can estimate coating film characteristics before coating is performed.SOLUTION: A coating film characteristic estimation method uses a machine learning model in which information related to a coating condition, a coating property, coating composition, a coating film characteristic measurement condition, and a coating film characteristic measurement result is learned as variables, and outputs, by inputting information related to the coating condition, the coating property, the coating composition, and the coating film characteristic measurement condition into the machine learning model, an estimated value of the coating film characteristic measurement result.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a paint film characteristic estimation method and a paint film characteristic estimation model generation method. [Background technology]

[0002] When painting automobiles and their parts, a multi-layer coating is usually formed by sequentially applying electrodeposition coating, primer coating, base coating, and clear coating to the object to be coated. As a result of painting, the painted object develops coating properties such as smoothness and gloss. Understanding coating properties is very important because they are closely related to the appearance and function of the coating. However, it is difficult to estimate the properties of a coating film before painting, because the properties change in a complex manner depending on the combination of paints used, the type of substrate, the painting conditions, etc. Therefore, in order to know the properties of a coating film, it is necessary to actually prepare the paint and paint it, which poses the problem of taking a huge amount of work time. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-140420 Summary of the Invention [Problem to be solved by the invention]

[0004] An object of the present invention is to provide a coating film characteristic estimation method and a coating film characteristic estimation model generation method that can estimate coating film characteristics before coating is performed. [Means for solving the problem]

[0005] One aspect of the present invention is a coating film property estimation method that uses a machine learning model in which information regarding coating conditions, paint properties, paint formulation, coating film property measurement conditions, and coating film property measurement results is learned as variables, and outputs an estimated value of the coating film property measurement results by inputting information regarding the coating conditions, paint properties, paint formulation, and coating film property measurement conditions into the machine learning model. [Effects of the Invention]

[0006] According to the present invention, it is possible to estimate the coating film characteristics before coating is performed. [Brief explanation of the drawings]

[0007] [Figure 1] 1 is a diagram showing an example of the configuration of a coating film characteristic estimation model generating device 10 according to a first embodiment. [Figure 2] 3 is a flowchart showing the operation of the coating film characteristic estimation model generating device 10 according to the first embodiment. [Figure 3] 1 is a diagram showing an example of the configuration of a coating film property estimation device 20 according to a first embodiment. [Figure 4] 3 is a flowchart showing the operation of the coating film property estimation device 20 according to the first embodiment. [Figure 5] FIG. 10 is a diagram showing data sets in the second to fourth embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. (First embodiment) FIG. 1 is a diagram showing an example of the configuration of a coating film characteristic estimation model generating device 10 according to the first embodiment. The coating film property estimation model generating device 10 includes a data set acquisition unit 110, a machine learning model generating unit 120, and a machine learning model output unit 130.

[0009] The dataset acquisition unit 110 acquires a dataset. The dataset includes, as variables, information on painting conditions, paint properties, paint formulation, coating film property measurement conditions, and coating film property measurement results. Hereinafter, the painting conditions and paint properties may be collectively referred to as test conditions.

[0010] The coating properties to be estimated preferably include at least one of smoothness, gloss, and color. Smoothness can be measured using a Wave-scan, Wave-scan dual (both manufactured by BYK Gardner), etc. Smoothness is expressed as the amplitude of a specific wavelength range, and when measured using a Wave-scan, it is expressed in six levels: Dullness / Wa / Wb / Wc / Wd / We, or in two levels: SW / LW. Gloss, also known as sheen or gloss, can be measured using a micro-tri-gloss (manufactured by BYK Gardner) or the like. When measuring using a micro-tri-gloss, gloss can be measured at three angles of 20°, 60°, and 85°, and in this embodiment, it is preferable to include information on 20° and 60°. Color is measured using a multi-angle spectrophotometer, such as a BYK-Mac i (manufactured by BYK) or an MA-68II (manufactured by X-Rite). Color-related data includes data representing lightness, saturation, and hue, or data that allows color to be specified by calculation. For example, data based on a color system such as the XYZ color system (X, Y, Z values), the RGB color system, the L*a*b* color system (L*, a*, b* values), the Hunter Lab color system (L, a, b values), the L*C*h color system (L*, C*, h values) specified by CIE (1994), or the Munsell color system (H, V, C values) is preferred. Among these, data based on the L*a*b* color system or the L*C*h color system is preferred. When measuring using BYK-Mac i, color can be measured at six angles: -15°, 15°, 25°, 45°, 75°, and 110°, and when measuring using MA-68II, color can be measured at five angles: 15°, 25°, 45°, 75°, and 110°.In this embodiment, it is preferable to include color information from three angles: 25°, 45°, and 75°. The coating film properties to be estimated are preferably the properties of a multi-layer coating film formed by sequentially applying an undercoat coat, a base coat, and a clear coat.

[0011] The coating conditions are conditions related to the coating work. In this embodiment, the information on the coating conditions preferably includes at least one of the following groups, which differ depending on the coating process: paint type, coating film structure, temperature and humidity in the coating booth, discharge rate, gun speed, film thickness, and baking conditions. The painting process includes, for example, at least one of an intermediate paint painting process, a base paint painting process, and a clear paint painting process. The paint type includes information about the number of paint types used in each painting process. The coating composition includes information regarding the number of times each paint is applied (number of layers) in each coating process. The paint booth temperature and humidity are the temperature and humidity inside the booth used for painting in each painting process, and may include the amount of water vapor inside the booth calculated from the temperature and humidity as a variable. The discharge rate is the amount of paint discharged per unit time from the paint sprayer used for painting in each painting process. The gun speed is the speed at which the sprayer used for painting moves during each painting process. The film thickness is the thickness of the coating film after the paint is applied and dried in each coating process. Baking conditions refer to whether or not baking is performed after the paint is applied in each coating process, and / or the heating conditions when baking and drying to form a coating film. Baking conditions typically include variables such as the setting time after application before placing the coating in the oven, the temperature during setting, the time the coating is placed in the oven, the oven temperature, and the orientation of the coating film surface (horizontal or vertical) during setting and drying. It is preferable to include at least one variable that is the orientation of the coating film surface (horizontal or vertical) during application, setting, and drying, more preferably to include at least one variable that is the orientation of the coating film surface during setting and drying, and it is particularly preferable to include both variables that are the orientation of the coating film surface during setting and drying. The above-mentioned painting conditions are often different for each coating film to be applied, and must be set for each painting process. Specifically, for example, the painting conditions for the undercoat painting process, the base coating process, and the clear coating process are each input as variables.

[0012] The paint properties are specific values related to the paint, and preferably include at least one of viscosity and solid content concentration. The viscosity can be measured without limitation by any conventionally known method, but for the primer paint and base paint, it is preferable to use a viscosity measured with a B-type rotational viscometer, and for the clear paint, it is preferable to use a viscosity measured with a Ford cup. The viscosity measured using a Brookfield type rotational viscometer is defined as the viscosity one minute after the start of measurement under certain conditions. Specifically, the viscosity is defined as the viscosity one minute after the start of measurement using a Brookfield type viscometer at 6 rpm at a temperature of 20°C (also referred to as the "B6 value" in this specification). It may also be defined as the viscosity one minute after the start of measurement using a Brookfield type viscometer at 6 rpm at a temperature of 20°C (also referred to as the "B60 value" in this specification). The viscometer used here is the LVDV-I (trade name, Brookfield, Brookfield type viscometer). Viscosity may also include the "Ti value (thixotropic index)" as a variable. The "Ti value (thixotropic index)" is described in JIS K 5101-6-2 (2004), Pigment Testing Method, Section 2, Rotational Viscosity Method, and can be determined by measuring the viscosity (mPa·s) at 20°C and rotation speeds of 6 rpm and 60 rpm using a B-type viscometer, and calculating the viscosity measurement value at "6 rpm / 60 rpm." The solids concentration refers to the concentration of the residue after removing the volatile components, and the residue may be either solid or liquid at room temperature. For example, it can be calculated by measuring the amount of remaining components after heating a sample at 105°C for 3 hours to remove the volatile components. In most cases, the paint properties described above differ for each paint, so in the case of a multi-layer process, they must be set for each painting process. Specifically, for example, the paint properties of the primer paint, the paint properties of the base paint, and the paint properties of the clear paint are each input as variables.

[0013] The formulation of a paint refers to the components that make up the paint and their amounts, such as resins, pigments, solvents, and additives. In particular, the information regarding the paint formulation preferably includes information regarding the type of pigment and information related to its amount, and in particular, it preferably includes information related to at least one of the size of the luster pigment, the color of the luster pigment, the color of the color pigment, and the chemical structure of the color pigment. Since the formulation of the above-mentioned paints differs for each paint, it is necessary to set it for each painting process in the case of a multi-layer process. Specifically, for example, the information on the paint formulation includes information on the formulation of the primer paint, information on the formulation of the base paint, and information on the formulation of the clear paint. The primer paint process, base paint process, and clear paint process may involve applying multiple layers of two or more paints in one process, in which case it is preferable to input information for each paint. In this embodiment, from the perspective of improving prediction accuracy, it is preferable that the information on the paint formulation includes information on the formulation of the base paint.

[0014] The information on the coating film property measurement conditions is information on the conditions for measuring the coating film properties. If the coating film property to be estimated is smoothness, the equipment used, the wavelength to be measured, etc. are input. If the coating film property to be estimated is gloss or color, the equipment used, the measurement angle, etc. are input. The coating film characteristic measurement results are the results of measuring the coating film characteristics of a coating film applied under the above-mentioned coating conditions, paint properties, and paint blend, and the measurement results corresponding to the above-mentioned coating film characteristic measurement conditions are input.

[0015] The information contained in the data set may be processed by a computer or the like.

[0016] The machine learning model generation unit 120 generates a machine learning model based on the dataset. The machine learning model is a model that outputs an estimated value of the coating film property measurement result when information on the coating conditions, paint properties, paint formulation, and coating film property measurement conditions is input. The machine learning model generation unit 120 generates the machine learning model using the information on the coating conditions, paint properties, paint formulation, and coating film property measurement conditions as explanatory variables and the coating film property measurement result as a target variable. Examples of machine learning model algorithms include decision trees, decision trees using gradient boosting, linear regression, logistic regression, simple perceptron, MLP, neural networks, support vector machines, random forests, Gaussian processes, Bayesian networks, k-nearest neighbor methods, Ridge regression, Lasso regression, graph neural networks, K-means methods, naive Bayes, CNNs, and other algorithms used in machine learning, and these algorithms may be used alone or in combination. Note that in the present invention, it is preferable to use a decision tree using gradient boosting.

[0017] The machine learning model output unit 130 outputs the generated machine learning model.

[0018] 2 is a flowchart showing the operation of the coating film property estimation model generating device 10 according to the first embodiment. The dataset acquisition unit 110 acquires a dataset (step S101). The machine learning model generation unit 120 generates a machine learning model based on the dataset (step S102). The machine learning model output unit 130 outputs the generated machine learning model (step S103).

[0019] 3 is a diagram showing an example of the configuration of the paint film property estimation device 20 according to the first embodiment. The paint film property estimation device 20 includes a paint data acquisition unit 210, a paint film property measurement result estimation unit 220, a paint film property measurement result output unit 230, and a memory unit 290. The memory unit 290 stores the machine learning model output by the paint film property estimation model generation device 10.

[0020] The painting data acquisition unit 210 acquires painting data, which are explanatory variables of the machine learning model, such as painting conditions, paint properties, paint formulations, and coating film property measurement conditions.

[0021] The coating film characteristic measurement result estimation unit 220 inputs painting data into a machine learning model and outputs an estimated value of the coating film characteristic measurement result, thereby estimating the coating film characteristic measurement result.

[0022] The coating film property measurement result output unit 230 outputs estimated values of the coating film property measurement results output by the machine learning model. The estimated values of the coating film property measurement results output from the coating film property measurement result output unit 230 are recorded in the memory unit 290 together with the corresponding painting data, for example. The estimated values of the coating film property measurement results output from the coating film property measurement result output unit 230 are input to and displayed on, for example, an external display device.

[0023] 4 is a flowchart showing the operation of the paint film property estimation device 20 according to the first embodiment. The paint data acquisition unit 210 acquires paint data (step S201). The paint film property measurement result estimation unit 220 estimates paint film property measurement results using a machine learning model based on the paint data (step S202). The paint film property measurement result output unit 230 outputs estimated values of the paint film property measurement results (step S203).

[0024] As described above, the coating film characteristic estimation device 20 can estimate coating film characteristic measurement conditions from painting conditions, paint properties, and paint formulation using a machine learning model, thereby estimating coating film characteristic measurement conditions before painting.

[0025] (Second embodiment) The following describes a coating film property estimation model generation device 10 according to a second embodiment. In the second embodiment, the dataset acquired by the dataset acquisition unit 110 includes, in addition to the dataset in the first embodiment, differences in test conditions and differences in coating film property measurement results corresponding to the differences in test conditions when the paint formulation and coating film property measurement conditions are the same. Figure 5 is a diagram showing the dataset in the second embodiment.

[0026] The data set includes test conditions B1 and coating film property measurement results F1, and test conditions B2 and coating film property measurement results F2, when the paint formulation is C1 and the coating film property measurement conditions are E1. The data set also includes the difference in test conditions ΔB and the difference in coating film property measurement results ΔF, where ΔB and ΔF are the differences between B1 and B2 and between F1 and F2, respectively.

[0027] The machine learning model generation unit 120 according to the second embodiment generates a machine learning model that outputs a second coating film characteristic measurement result corresponding to the second test condition when a paint formulation, coating film characteristic measurement conditions, first test conditions, a difference between the first test conditions and the second test conditions, and a first coating film characteristic measurement result corresponding to the first test condition are input. The first test condition and the second test condition are different test conditions. To explain this using the dataset shown in Figure 5, the machine learning model generation unit 120 according to the second embodiment generates a machine learning model that outputs F2 when C1, E1, B1, ΔB, and F1 are input.

[0028] The machine learning model generation unit 120 according to the second embodiment may generate a machine learning model that outputs the difference between the second coating film characteristic measurement result and the first coating film characteristic measurement result when a paint formulation, coating film characteristic measurement conditions, first test conditions, the difference between the first test conditions and the second test conditions, and the first coating film characteristic measurement result corresponding to the first test conditions are input. To explain using the dataset shown in Figure 5, the machine learning model generation unit 120 according to the second embodiment may generate a machine learning model that outputs ΔF when C1, E1, B1, ΔB, and F1 are input.

[0029] The following describes a paint film property estimation device 20 according to the second embodiment. The paint data acquisition unit 210 according to the second embodiment acquires paint formulations, paint film property measurement conditions, known paint film property measurement results, known test conditions, and differences between known test conditions and unknown test conditions. Here, the known paint film property measurement results are paint film property measurement results acquired by the paint data acquisition unit 210 under the paint formulations, paint film property measurement conditions, and known test conditions. The known paint film property measurement results are acquired in advance, similar to the data set. The unknown test conditions are test conditions under which the coating film property measurement results are unknown, including the unknown test conditions, the paint formulation, and the coating film property measurement conditions acquired by the coating data acquisition unit 210. Hereinafter, the paint formulation, the coating film property measurement conditions, and the coating film property measurement results acquired by the coating data acquisition unit 210 under the unknown test conditions will be referred to as unknown coating film property measurement results.

[0030] The coating film characteristic measurement result estimation unit 220 of the second embodiment inputs the paint formulation, coating film characteristic measurement conditions, known test conditions, the difference between the known test conditions and the unknown test conditions, and the known coating film characteristic measurement results into the machine learning model generated by the machine learning model generation unit of the second embodiment, and outputs an estimated value of the unknown coating film characteristic measurement results.

[0031] The coating film characteristic measurement result estimation unit 220 of the second embodiment may input the paint formulation, coating film characteristic measurement conditions, known test conditions, the difference between the known test conditions and the unknown test conditions, and the known coating film characteristic measurement results into the machine learning model generated by the machine learning model generation unit of the second embodiment, and output an estimated value of the difference between the known coating film characteristic measurement results and the estimated value of the unknown coating film characteristic measurement results.

[0032] (Third embodiment) The following describes a paint film property estimation model generation device 10 according to a third embodiment. In the third embodiment, the data set acquired by the data set acquisition unit 110 includes, in addition to the data set in the first embodiment, differences in test conditions and differences in paint film property measurement results corresponding to the differences in test conditions when the paint formulation and paint film property measurement conditions are the same. Figure 5 is a diagram showing a data set in the third embodiment. Explanation of symbols in Figure 5 will be omitted as they are the same as those in the second embodiment.

[0033] The machine learning model generation unit 120 according to the third embodiment generates a machine learning model that outputs a second coating film characteristic measurement result corresponding to the second test condition when a paint formulation, coating film characteristic measurement conditions, second test conditions, a difference between the second test conditions and the first test conditions, and a first coating film characteristic measurement result corresponding to the first test condition are input. The first test condition and the second test condition are different test conditions. To explain this using the dataset shown in Figure 5, the machine learning model generation unit 120 according to the second embodiment generates a machine learning model that outputs F2 when C1, E1, B2, ΔB, and F1 are input.

[0034] The machine learning model generation unit 120 according to the third embodiment may generate a machine learning model that outputs the difference between the second coating film characteristic measurement result and the first coating film characteristic measurement result when the paint formulation, coating film characteristic measurement conditions, second test conditions, the difference between the second test conditions and the first test conditions, and the first coating film characteristic measurement result are input. To explain using the dataset shown in Figure 5, the machine learning model generation unit 120 according to the third embodiment may generate a machine learning model that outputs ΔF when C1, E1, B2, ΔB, and F1 are input.

[0035] The coating film property estimation device 20 according to the third embodiment will be described below. The coating data acquisition unit 210 according to the third embodiment acquires paint formulation, coating film property measurement conditions, unknown test conditions, differences between the unknown test conditions and known test conditions, and known coating film property measurement results.

[0036] The coating film characteristic measurement result estimation unit 220 of the third embodiment inputs the paint formulation, coating film characteristic measurement conditions, unknown test conditions, the difference between the unknown test conditions and the known test conditions, and the known coating film characteristic measurement results into the machine learning model generated by the machine learning model generation unit of the third embodiment, and outputs an estimated value of the unknown coating film characteristic measurement results.

[0037] The coating film characteristic measurement result estimation unit 220 of the third embodiment may input the paint formulation, coating film characteristic measurement conditions, unknown test conditions, the difference between the unknown test conditions and the known test conditions, and the known coating film characteristic measurement results into the machine learning model generated by the machine learning model generation unit of the third embodiment, and output the difference between the known coating film characteristic measurement results and the estimated value of the unknown coating film characteristic measurement results.

[0038] (Fourth embodiment) The following describes a coating film property estimation device 20 according to the fourth embodiment. The coating film property estimation device 20 according to the fourth embodiment calculates an estimated value of the second coating film property measurement result using both the machine learning model according to the second embodiment and the machine learning model according to the third embodiment.

[0039] The coating film characteristic measurement result estimation unit 220 of the fourth embodiment inputs the paint formulation, coating film characteristic measurement conditions, known test conditions, the difference between the known test conditions and the unknown test conditions, and the known coating film characteristic measurement results into the machine learning model of the second embodiment, and outputs an estimated value of the unknown coating film characteristic measurement result (hereinafter referred to as the second estimated value).

[0040] In addition, the coating film characteristic measurement result estimation unit 220 of the fourth embodiment inputs the paint formulation, coating film characteristic measurement conditions, unknown test conditions, the difference between the unknown test conditions and the known test conditions, and the known coating film characteristic measurement results into the machine learning model of the third embodiment, and outputs an estimated value of the unknown coating film characteristic measurement results (hereinafter referred to as the third estimated value).

[0041] The coating film property measurement result estimating unit 220 according to the fourth embodiment calculates an estimate of the unknown coating film property measurement result based on the second and third estimates. For example, the coating film property measurement result estimating unit 220 according to the fourth embodiment calculates the average value of the second and third estimates as the estimate of the unknown coating film property measurement result.

[0042] The coating film characteristic measurement result estimation unit 220 of the fourth embodiment may input the paint formulation, coating film characteristic measurement conditions, known test conditions, the difference between the known test conditions and the unknown test conditions, and the known coating film characteristic measurement results into the machine learning model of the second embodiment, and output the difference between the known coating film characteristic measurement results and the estimated value of the unknown coating film characteristic measurement results (hereinafter referred to as the second difference estimated value).

[0043] In addition, the coating film characteristic measurement result estimation unit 220 of the fourth embodiment may input the paint formulation, coating film characteristic measurement conditions, unknown test conditions, the difference between the unknown test conditions and the known test conditions, and the known coating film characteristic measurement results into the machine learning model of the third embodiment, and output the difference between the known coating film characteristic measurement results and the estimated value of the unknown coating film characteristic measurement results (hereinafter referred to as the third difference estimated value).

[0044] The coating film property measurement result estimating unit 220 according to the fourth embodiment calculates an estimate of the difference between the known coating film property measurement result and the unknown coating film property measurement result based on the second difference estimate and the third difference estimate. For example, the coating film property measurement result estimating unit 220 according to the fourth embodiment may calculate the average value of the second difference estimate and the third difference estimate as the estimate of the difference between the known coating film property measurement result and the unknown coating film property measurement result.

[0045] In the second to fourth embodiments, the coating film characteristic measurement result estimation unit 220 may calculate a correction value for the estimated value of the unknown coating film characteristic measurement result or a correction value for the difference between the known coating film characteristic measurement result and the estimated value of the unknown coating film characteristic measurement result, and correct the difference between the known coating film characteristic measurement result and the estimated value of the unknown coating film characteristic measurement result or the estimated value of the unknown coating film characteristic measurement result.

[0046] In the second to fourth embodiments, the coating film property measurement result estimation unit 220 inputs the paint formulation, coating film property measurement conditions, known test conditions, and known coating film property measurement results into a machine learning model, thereby outputting an estimate of the known coating film property measurement results. At this time, the coating film property measurement result estimation unit 220 inputs 0 as the difference in test conditions.

[0047] In the second to fourth embodiments, the coating film property measurement result estimating unit 220 calculates a correction value based on the known coating film property measurement result and the estimated value of the known coating film property measurement result. For example, the coating film property measurement result estimating unit 220 calculates the correction value as the difference between the known coating film property measurement result and the estimated value of the known coating film property measurement result.

[0048] Other Embodiments One embodiment of the present invention has been described in detail above with reference to the drawings, but the specific configuration is not limited to that described above, and various design changes and the like are possible within the scope that does not deviate from the gist of the present invention.

[0049] All or part of the functions of the coating film property estimation model generating device 10 and the coating film property estimation device 20 in the above-described embodiments may be implemented by a computer. In this case, a program for implementing these functions may be recorded on a computer-readable recording medium, and the program may be loaded into a computer system and executed. Note that the term "computer system" as used herein includes an operating system and peripheral hardware. Furthermore, the term "computer-readable recording medium" refers to portable media such as flexible disks, optical magnetic disks, ROMs, and CD-ROMs, as well as storage devices such as hard disks built into a computer system. Furthermore, the term "computer-readable recording medium" may also include media that dynamically store programs for a short period of time, such as communication lines used when transmitting programs via networks such as the Internet or telephone lines, or media that store programs for a fixed period of time, such as volatile memory within a computer system serving as a server or client. Furthermore, the program may be a program that implements part of the above-described functions, or may be a program that can implement the above-described functions in combination with a program already stored in the computer system. Furthermore, all or part of the functions of the coating film property estimation model generating device 10 and the coating film property estimation device 20 may be realized using a programmable logic device such as an FPGA (Field Programmable Gate Array). [Explanation of symbols]

[0050] 10 Coating film property estimation model generation device, 110 Data set acquisition unit, 120 Machine learning model generation unit, 130 Machine learning model output unit, 20 Coating film property estimation device, 210 Painting data acquisition unit, 220 Coating film property measurement result estimation unit, 230 Coating film property measurement result output unit, 290 Memory unit

Claims

1. a machine learning model trained with variables including information on coating conditions, paint properties, paint formulation, coating film property measurement conditions, and coating film property measurement results, and an estimated value of the coating film property measurement results is output by inputting information on the coating conditions, paint properties, paint formulation, and coating film property measurement conditions into the machine learning model; Paint film property estimation method.

2. The variables include information regarding differences in test conditions, including coating conditions and paint properties, when the paint formulation and coating film property measurement conditions are the same, and differences in coating film property measurement results corresponding to the differences in the test conditions, By inputting information into the machine learning model regarding a first test condition, a difference between the first test condition and a second test condition, where the second test condition is different from the first test condition, the difference, a paint formulation, coating film characteristic measurement conditions, and a first coating film characteristic measurement result corresponding to the first test condition, the machine learning model outputs an estimated value of a second coating film characteristic measurement result corresponding to the second test condition and / or a difference between the first coating film characteristic measurement result and the second coating film characteristic measurement result. The coating film characteristic estimation method according to claim 1 .

3. The variables include information regarding differences in test conditions, including coating conditions and paint properties, when the paint formulation and coating film property measurement conditions are the same, and differences in coating film property measurement results corresponding to the differences in the test conditions, By inputting information about second test conditions, a difference between the second test conditions and the first test conditions, where the first test conditions are different from the second test conditions, the difference, the paint formulation, the coating film characteristic measurement conditions, and the first coating film characteristic measurement results corresponding to the first test conditions into the machine learning model, the machine learning model outputs an estimated value of the second coating film characteristic measurement results corresponding to the second test conditions and / or a difference between the estimated value of the second coating film characteristic measurement results and the first coating film characteristic measurement results. The coating film characteristic estimation method according to claim 1 .

4. inputting information about the second test conditions, the difference between the first test conditions and the second test conditions, the paint formulation, the coating film property measurement conditions, and the first coating film property measurement results into the machine learning model, thereby outputting an estimated value of the second coating film property measurement results and / or a difference between the first coating film property measurement results and the estimated value of the second coating film property measurement results; calculating the estimated value of the second coating film characteristic measurement result and / or the difference between the estimated value of the first coating film characteristic measurement result and the estimated value of the second coating film characteristic measurement result, which is output to the machine learning model by inputting the first test conditions, and based on the estimated value of the second coating film characteristic measurement result and / or the difference between the estimated value of the first coating film characteristic measurement result and the estimated value of the second coating film characteristic measurement result, which is output to the machine learning model by inputting the second test conditions, and based on the estimated value of the second coating film characteristic measurement result and / or the difference between the estimated value of the first coating film characteristic measurement result and the estimated value of the second coating film characteristic measurement result, which is output to the machine learning model by inputting the second test conditions; The coating film characteristic estimation method according to claim 2.

5. inputting information about the first test conditions, the paint formulation, the coating film property measurement conditions, and the first coating film property measurement results into the machine learning model, thereby outputting an estimated value of the first coating film property measurement results corresponding to the first test conditions; calculating a correction value based on the first coating film characteristic measurement result and an estimated value of the first coating film characteristic measurement result; correcting the estimated value of the second coating film characteristic measurement result and / or the difference between the first coating film characteristic measurement result and the estimated value of the second coating film characteristic measurement result using the correction value; The coating film characteristic estimation method according to any one of claims 2 to 4.

6. The coating film characteristic estimating method according to any one of claims 1 to 4, wherein the coating film characteristics include at least one of smoothness, gloss, and color.

7. The coating film characteristic estimating method according to any one of claims 1 to 4, wherein the coating conditions include at least one of a group of conditions that differ depending on the coating process, the group being temperature and humidity in the coating booth, discharge amount, gun speed, film thickness, and baking conditions.

8. The coating film characteristic estimating method according to any one of claims 1 to 4, wherein the paint properties include at least one of viscosity and solid content concentration.

9. The coating film characteristic estimating method according to any one of claims 1 to 4, wherein the formulation of the paint includes at least one of the size of the luster material, the color of the luster material, the color of the color pigment, and the chemical structure of the color pigment.

10. As variables, information on painting conditions, paint properties, paint formulation, coating film property measurement conditions, and coating film property measurement results is acquired, Using the variables, a machine learning model is generated that outputs an estimated value of the coating film property measurement result when information on the coating conditions, paint properties, paint formulation, and coating film property measurement conditions is input. A method for generating a model to estimate coating properties.

11. The variables include information regarding differences in test conditions, including coating conditions and paint properties, and differences in coating film property measurement results corresponding to the differences in test conditions, when the paint formulation and coating film property measurement conditions are the same, By inputting information on a first test condition, a difference between the first test condition and a second test condition, the second test condition being different from the first test condition, the difference, the paint formulation, the coating film characteristic measurement conditions, and the first coating film characteristic measurement result corresponding to the first test condition, a machine learning model is generated that outputs an estimated value of a second coating film characteristic measurement result corresponding to the second test condition and / or a difference between the estimated value of the first coating film characteristic measurement result and the second coating film characteristic measurement result. The coating film characteristic estimation model generating method according to claim 10.

12. The variables include information regarding differences in test conditions, including coating conditions and paint properties, when the paint formulation and coating film property measurement conditions are the same, and differences in coating film property measurement results corresponding to the differences in the test conditions, By inputting information on second test conditions, a difference between the second test conditions and the first test conditions, where the first test conditions are different from the second test conditions, the difference, the paint formulation, the coating film characteristic measurement conditions, and the first coating film characteristic measurement results corresponding to the first test conditions, a machine learning model is generated that outputs an estimated value of the second coating film characteristic measurement results corresponding to the second test conditions and / or a difference between the estimated value of the second coating film characteristic measurement results and the first coating film characteristic measurement results. The coating film characteristic estimation model generating method according to claim 10.

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