JP2023181928A - Automatic adjustment method and automatic adjustment system - Google Patents

Abstract

To provide an automatic adjustment method and an automatic adjustment system which can adjust a coating material composition accurately with a simple method.SOLUTION: Variation response curve data showing a relation between variations of each parameter and variations of information on a property of a coating material is obtained based on a coating material composition which is not varied. A calculating part calculates suitable variations of an addition ratio of materials for adjusting coating material properties and/or suitable variations of a manufacturing condition, using target data and the variation response curve data. The suitable variations of an addition ratio of materials for adjusting coating material properties and/or the suitable variations of a manufacturing condition are calculated using a brute-force-search method or a mathematical optimization technique.SELECTED DRAWING: Figure 1

Description

The present invention relates to an automatic adjustment method and an automatic adjustment system, and particularly to an automatic adjustment method and an automatic adjustment system for a coating composition.

Conventionally, it has been proposed to use theoretical formulas to adjust the viscosity of a coating composition and the color, gloss, etc. of a coating film obtained from the coating composition. For example, in Patent Document 1, the Kubelka-Munk optical density formula is used to predict the spectral reflectance when coloring materials are mixed in a predetermined proportion, and the predicted reflectance of the adjusted color is compared with the reflectance of the sample color. A method has been proposed for calculating the colorant formulation by

It has also been proposed to use machine learning techniques to automatically adjust the viscosity of a paint composition and the color, gloss, etc. of a coating film obtained from the paint composition. For example, Patent Document 2 proposes creating an artificial intelligence model that uses composition as an explanatory variable and color as an objective variable to predict the color when colored with a predetermined composition.

Japanese Patent Application Publication No. 2008-111116 Japanese Patent Application Publication No. 2021-107781

However, in recent years, there has been an increasing demand for accuracy in adjusting the color, gloss, viscosity, etc. of paint compositions, and calculations using theoretical formulas such as those in Patent Document 1 require more accurate adjustment. There was a particular issue. In addition, although methods using machine learning such as Patent Document 2 can perform highly accurate automatic adjustments, they require a huge amount of learning data to perform machine learning. There are problems in that the method cannot be used if the data cannot be collected, and that it takes a long time to collect a huge amount of learning data.

SUMMARY OF THE INVENTION Therefore, an object of the present invention is to provide an automatic adjustment method and an automatic adjustment system that can accurately adjust a coating composition using a simple method.

The gist of the present invention is as follows.
(1) When adjusting a paint composition under predetermined manufacturing conditions, the amount of variation in the addition ratio of the paint property adjusting material to the paint composition before adjustment suitable for obtaining the target paint properties and/or An automatic adjustment method for a paint composition, in which the amount of variation in manufacturing conditions is calculated by a calculation section of a computer, the method comprising:
The paint property adjusting material includes at least one of a plurality of types of colorants, one or more types of gloss modifiers, and one or more types of viscosity modifiers, and the target paint property is determined by the target color, the target color, and the target paint property. and a target viscosity,
Based on the coating composition before variation, the addition ratio of the one or more colorants, the addition ratio of the one or more gloss modifiers, the addition ratio of the one or more viscosity modifiers, and each of the manufacturing conditions. Information on the paint properties when only one of the parameters is varied in various ways is obtained for each parameter in advance, and the relationship between the amount of variation in each parameter and the amount of variation in the information on the paint properties is determined. a fluctuation amount response curve data acquisition step of acquiring fluctuation amount response curve data indicating the
Target data including at least one of the color of the target, the gloss of the target, and the viscosity of the target, and the variation response curve obtained in the variation response curve data acquisition step by the calculation unit of the computer. a calculation step of calculating the amount of variation in the addition ratio of the paint property adjusting material to the paint composition before adjustment and/or the amount of variation in suitable manufacturing conditions using the data;
In the calculation step, the amount of variation in the addition ratio of the paint property adjusting material to the suitable pre-adjustment paint composition and/or the amount of variation in suitable manufacturing conditions is calculated using a brute force search method. Features an automatic adjustment method.

(2) When adjusting the paint composition under predetermined manufacturing conditions, the amount of variation in the addition ratio of the material for adjusting paint properties to the paint composition before adjustment suitable for obtaining the target paint properties and/or An automatic adjustment method for a paint composition, in which the amount of variation in manufacturing conditions is calculated by a calculation section of a computer, the method comprising:
The paint property adjusting material includes at least one of a plurality of types of colorants, one or more types of gloss modifiers, and one or more types of viscosity modifiers, and the target paint property is determined by the target color, the target color, and the target paint property. and a target viscosity,
Based on the coating composition before variation, the addition ratio of the one or more colorants, the addition ratio of the one or more gloss modifiers, the addition ratio of the one or more viscosity modifiers, and each of the manufacturing conditions. Information on the paint properties when only one of the parameters is varied in various ways is obtained for each parameter in advance, and the relationship between the amount of variation in each parameter and the amount of variation in the information on the paint properties is determined. a fluctuation amount response curve data acquisition step of acquiring fluctuation amount response curve data indicating the
Target data including at least one of the color of the target, the gloss of the target, and the viscosity of the target, and the variation response curve obtained in the variation response curve data acquisition step by the calculation unit of the computer. a calculation step of calculating the amount of variation in the addition ratio of the paint property adjusting material to the paint composition before adjustment and/or the amount of variation in suitable manufacturing conditions using the data;
In the calculation step, the amount of variation in the addition ratio of the paint property adjusting material to the suitable pre-adjustment paint composition and/or the amount of variation in suitable manufacturing conditions is calculated using a mathematical optimization method. Automatic adjustment method.

(3) In the calculation step,
Using the variation response curve data, determine the addition ratio of the plurality of types of colorants, the addition ratio of the one or more types of gloss modifier, the addition ratio of the one or more types of viscosity modifier, and the manufacturing conditions. Calculating the amount of change in at least one of color, gloss, and viscosity when at least one of them is changed,
At least one of the color, the gloss, and the viscosity after a change that has been changed by the calculated amount of change in at least one of the color, the gloss, and the viscosity, the target color, the target gloss, and the addition ratio of the plurality of types of colorants, the addition ratio of the one or more types of gloss modifiers, and the addition of the one or more types of viscosity modifiers such that the difference from at least one of the target viscosity is small. The method according to (1) or (2) above, wherein the amount of variation in at least one of the ratio and the manufacturing conditions is calculated.

(4) Prior to the calculation step, the addition ratio of the plurality of types of colorants, the addition ratio of the one or more types of gloss modifiers, and the viscosity adjustment of the one or more types are adjusted using the variation response curve data. Obtaining in advance data on the amount of variation in at least one of color, gloss, and viscosity when varying at least one of the addition ratio of the agent and the manufacturing conditions,
In the calculation step, at least one of the color, the gloss, and the viscosity after a change is changed by the amount of change in at least one of the color, the gloss, and the viscosity obtained in advance, and the target color. , an addition ratio of the plurality of types of colorants such that the difference from at least one of the target gloss and the target viscosity is small, an addition ratio of the one or more types of gloss modifiers, and the one or more types of gloss modifiers. The method according to (1) or (2) above, wherein the amount of change in at least one of the addition ratio of the viscosity modifier and the manufacturing conditions is calculated.

(5) The calculation step is
Using the data of the target gloss and/or the target viscosity and the fluctuation amount response curve data obtained in the fluctuation amount response curve data acquisition step, the suitability of the gloss modifier and/or the viscosity modifier is determined. a first calculation sub-step of calculating the amount of variation in the addition ratio;
The first calculation sub-step using the variation amount of the optimal addition ratio of the gloss modifier and/or the viscosity modifier obtained in the first calculation sub-step and the variation response curve data. a second calculation sub-step of calculating the amount of variation in color caused by adding the gloss modifier and/or the viscosity modifier by the amount of variation in the appropriate addition ratio obtained in step 1;
A third calculation sub-step of calculating the amount of change in the appropriate addition ratio of the colorant using the amount of variation in the color obtained in the second calculation sub-step and the variation response curve data. The method according to any one of (1) to (4) above, comprising:

(6) setting a numerical value generation range for the amount of variation in at least one of the addition ratio of the colorant, the addition ratio of the gloss modifier, the addition ratio of the viscosity modifier, and the manufacturing conditions;
Combining the amount of variation in at least any of the addition ratio of the colorant, the addition ratio of the gloss modifier, the addition ratio of the viscosity modifier, and the manufacturing conditions that are generated within the set numerical value generation range. further comprising the step of preparing a data group of the amount of variation in the addition ratio of the paint property adjusting material to the various pre-adjustment coating compositions and/or the amount of variation in the manufacturing conditions,
In the calculation step,
Using the variation response curve data, the variation amount of the addition ratio of the paint property adjusting material to the prepared various pre-adjustment coating compositions and/or the variation amount of the production conditions is calculated by the numerical value of the data group. Calculating the amount of change in at least one of color, gloss, and viscosity when changing the addition ratio of the paint property adjusting material to the pre-adjustment paint composition and/or the manufacturing conditions,
At least one of the color, the gloss, and the viscosity after a change that has been changed by the calculated amount of change in at least one of the color, the gloss, and the viscosity, the target color, the target gloss, and the addition ratio of the plurality of types of colorants, the addition ratio of the one or more types of gloss modifiers, and the addition of the one or more types of viscosity modifiers such that the difference from at least one of the target viscosity is small. The method according to (3) above, wherein the amount of variation in at least one of the ratio and the manufacturing conditions is calculated.

(7) The method according to (2) above, wherein the mathematical optimization method uses a convex optimization method.

(8) The method according to (2) above, wherein the mathematical optimization method uses quadratic programming.

(9) The method according to (2) above, wherein the mathematical optimization method uses a gradient descent method.

(10) The pre-adjustment coating composition comprises (A1) the one or more coloring agents, (A2) the one or more gloss modifiers, and (A3) the one or more viscosity modifiers. ) to (A3), the method according to any one of (1) to (9) above.

(11) The manufacturing conditions are:
(a) one or more of the viscosity and heating residue of the paint;
(b) any one or more of roll circumferential speed, spray discharge amount, electrodeposition coating voltage, coating pressure, and paint flow rate in the process of applying the coating composition to the object to be coated;
(c) any one or more of the maximum temperature of the object to be coated, baking temperature, and baking time in the baking process, and (d) any one or more of the environmental temperature and humidity of the production line, and the temperature of the paint;
The method according to any one of (1) to (10) above, comprising any one or more of (a) to (d).

(12) The paint property adjusting material includes the plurality of types of colorants,
The target paint properties include the target color,
The pre-adjustment coating composition includes the one or more coloring agents,
In the fluctuation amount response curve data acquisition step, the color is obtained in advance by variously changing the addition ratio of one type of colorant, and the fluctuation amount of the addition ratio of the one type of colorant and the fluctuation amount of the color are obtained. and obtaining the fluctuation amount response curve data indicating the relationship between
The calculation step includes calculating an appropriate amount of variation in the addition ratio of the colorant using the target color and the variation response curve data obtained in the variation response curve data acquisition step. The method according to any one of (1) to (11) above.

(13) When adjusting a paint composition under predetermined manufacturing conditions, the amount of variation in the addition ratio of the paint property adjusting material to the paint composition before adjustment suitable for obtaining the target paint properties and/or An automatic adjustment system for a paint composition that calculates the amount of variation in manufacturing conditions by a calculation section of a computer,
The paint property adjusting material includes at least one of a plurality of types of colorants, one or more types of gloss modifiers, and one or more types of viscosity modifiers, and the target paint property is determined by the target color, the target color, and the target paint property. and a target viscosity,
Based on the coating composition before variation, the addition ratio of the one or more colorants, the addition ratio of the one or more gloss modifiers, the addition ratio of the one or more viscosity modifiers, and each of the manufacturing conditions. Information on the paint properties when only one of the parameters is varied in various ways is obtained for each parameter in advance, and the relationship between the amount of variation in each parameter and the amount of variation in the information on the paint properties is determined. further comprising an acquisition unit that acquires fluctuation amount response curve data indicating the
The calculation unit calculates the appropriate pre-adjustment paint using target data including at least one of the target color, the target gloss, and the target viscosity, and the obtained variation response curve data. configured to calculate the amount of variation in the addition ratio of the paint property adjusting material to the composition and/or the amount of variation in suitable manufacturing conditions,
The calculation unit calculates the amount of variation in the addition ratio of the paint property adjusting material to the suitable pre-adjustment coating composition and/or the amount of variation in suitable manufacturing conditions using a brute force search method. Features an automatic adjustment system.

(14) When adjusting the paint composition under predetermined manufacturing conditions, the amount of variation in the addition ratio of the paint property adjusting material to the paint composition before adjustment suitable for obtaining the target paint properties and/or An automatic adjustment system for a paint composition that calculates the amount of variation in manufacturing conditions by a calculation section of a computer,
The paint property adjusting material includes at least one of a plurality of types of colorants, one or more types of gloss modifiers, and one or more types of viscosity modifiers, and the target paint property is determined by the target color, the target color, and the target paint property. and a target viscosity,
Based on the coating composition before variation, the addition ratio of the one or more colorants, the addition ratio of the one or more gloss modifiers, the addition ratio of the one or more viscosity modifiers, and each of the manufacturing conditions. Information on the paint properties when only one of the parameters is varied in various ways is obtained for each parameter in advance, and the relationship between the amount of variation in each parameter and the amount of variation in the information on the paint properties is determined. further comprising an acquisition unit that acquires fluctuation amount response curve data indicating the
The calculation unit calculates the appropriate pre-adjustment paint using target data including at least one of the target color, the target gloss, and the target viscosity, and the obtained variation response curve data. configured to calculate the amount of variation in the addition ratio of the paint property adjusting material to the composition and/or the amount of variation in suitable manufacturing conditions,
The calculation unit is characterized in that the amount of variation in the addition ratio of the paint property adjusting material to the suitable pre-adjustment paint composition and/or the amount of variation in suitable manufacturing conditions is calculated using a mathematical optimization method. do.

According to the present invention, it is possible to provide an automatic adjustment method and an automatic adjustment system that can accurately adjust a coating composition using a simple method.

3 is a flowchart of an automatic adjustment method according to an embodiment of the present invention. FIG. 3 is a diagram for explaining the middle step of the fluctuation amount response curve data acquisition step. FIG. 3 is a diagram showing names of variation response curves. FIG. 3 is a diagram for explaining a first stage process of a calculation process. FIG. 3 is a diagram for explaining a first stage process of a calculation process. FIG. 1 is a block diagram of an automatic adjustment system according to an embodiment of the present invention. It is a figure which shows an example of a variation amount response curve.

Hereinafter, embodiments of the present invention will be illustrated in detail with reference to the drawings.
In addition, in this disclosure, each term means the following content.
"Materials for adjusting paint properties" is a general term for, for example, colorants, gloss modifiers, viscosity modifiers, etc. used for adjusting paint properties. "Pre-adjustment paint composition" refers to a paint in which about 85 to 100% of the predetermined amount of the material having the same function as the paint property adjusting material is blended among the materials constituting the target paint composition. A coating composition whose properties are approximately similar to the target properties. It may or may not contain a material for adjusting paint properties. Moreover, the blending amount of each material may be unknown. The term "calculation paint composition" refers to a paint composition made of one type of colorant, which is created in order to theoretically calculate the absolute value of a color. In addition to materials for controlling paint properties, including colorants, it also includes resins, solvents, curing agents, etc. Note that this material is not necessary if the amount of color variation is not obtained by predictive calculation using the Kubelka-Munk optical density equation, which will be described later.

<Automatic adjustment method>
An automatic adjustment method according to an embodiment of the present invention includes applying a paint property adjusting material to a pre-adjustment paint composition suitable for obtaining target paint properties when a paint composition is adjusted under predetermined manufacturing conditions. This is an automatic adjustment method for a coating composition in which the amount of variation in the addition ratio and/or the amount of variation in suitable manufacturing conditions is calculated by a calculation section of a computer. Note that the automatic adjustment method of this embodiment can be executed using, for example, an automatic adjustment system according to an embodiment of the present invention, which will be described later.

Here, the coating composition is prepared by, for example, dispersing a resin, a solvent, and one type of coloring pigment in an SG mill, etc., preparing a plurality of coloring pigments of various colors, and adjusting the color to any desired color. In order to do this, one or more colorants are mixed with a resin, a solvent, and an additive. The coating composition is not particularly limited, but may be in the form of, for example, a water-based coating, a solvent-based coating, a powder coating, a solvent-free coating, or the like. Applications include, for example, coil paints, general industrial paints, automotive paints, automotive repair paints, architectural paints, heavy-duty anticorrosion paints, and marine paints, and the coating methods include spray painting. , roller coating, brush coating, roll coating (including natural and reverse rotation), curtain flow coating, die coating, electrodeposition coating, powder coating, and electrostatic coating.Drying methods include baking drying and forced drying. , natural drying, and ultraviolet curing. Examples of the composition include resin raw materials, pigments, design raw materials, solvents (including water), additives, and the like. As resin raw materials, mutually modified resins such as acrylic resin, polyester resin, epoxy resin, alkyd resin, fluororesin, urethane resin, aminomelamine resin, isocyanate resin, block isocyanate resin, and urethane-modified polyester resin can be used. Pigments include inorganic pigments, organic pigments, coloring pigments, and extender pigments; design raw materials include glittering materials such as metallics and pearls; aggregates; silica; resin beads; and waxes; additives include viscosity modifiers. , silicone additives, rust preventives, catalysts, antifoaming agents, etc.

In this embodiment, the paint property adjusting material includes at least one of a plurality of types of colorants, one or more types of gloss modifiers, and one or more types of viscosity modifiers. It is preferable that the paint property adjusting material contains at least a plurality of types of colorants. The coloring agent includes a colored pigment, and examples of the colored pigment include a black pigment, a white pigment, a yellow pigment, a green pigment, a red pigment, a blue pigment, titanium oxide, red iron oxide, and the like. Examples of gloss modifiers include aggregates (sand, etc.), silica, reflective materials, alumina, and the like. Examples of the viscosity modifier include synthetic resin viscosity modifiers, natural product viscosity modifiers, and inorganic viscosity modifiers. Furthermore, examples of the synthetic resin viscosity modifier include polymer type viscosity modifiers, associative viscosity modifiers, and the like. Additionally, solvents such as water and organic solvents can also be used as viscosity modifiers.

Further, the target paint properties include at least one of a target color, a target gloss, and a target viscosity. In addition to color, gloss, and viscosity, paint properties include, for example, film thickness, hiding rate, flip-flop properties, smoothness, visual appearance, electrical resistance, contact angle, staining properties, solar reflectance, ultraviolet transmittance, Weather resistance, viscoelasticity, processability, coating film abnormality, stringability, flash point, foaming property, etc. can also be included.

Colors include, for example, L ^* value, a ^* value, b ^* value in L ^* a ^* b ^* color space (JIS Z 8781-4 (2013)), XYZ color system, RGB color system, Yxy color system , Hunter Lab color system, L ^* C ^* h ^* color system, Munsell color system, and other color systems can be used. Color can be measured using a known color measurement method; for example, using a spectrophotometer CM-M6 (manufactured by Konica Minolta), the light receiving area is measured perpendicular to the coating film formed on the test plate. When 0° is assumed, the L ^* value, a ^* value, and b ^* value can be measured by irradiating the light source from angles of 25°, 45°, and 75°. Alternatively, it can be measured using a spectrophotometer X-Rite MA68II (manufactured by X-Rite). The measurement angle can be adjusted as appropriate depending on the purpose or the equipment used. Any other index can be used. Furthermore, for example, any index can be used, such as an index that is reflection spectrum data and uses a color of reflection spectrum intensity every 5 nm from 380 nm to 780 nm. Although gloss is not particularly limited, gloss can be used as an index. Gloss can be measured using a known gloss measurement method. For example, the 60° gloss of a coating film formed on a test plate is measured using a specular gloss meter (Gloss Meter VG 7000 (manufactured by Nippon Denshoku Kogyo Co., Ltd.)). )) according to JIS K 5600-4-7 (specular gloss). The viscosity can be measured using a known viscosity measuring method, for example, in accordance with JIS K 5600-2-2 (flow cup method). As for smoothness, it is preferable to use a wave scan value as an index. The wave scan value can be measured using, for example, a wave scan (manufactured by BYK Gardner), and includes du (wavelength 0.1 mm or less), Wa (wavelength 0.1 to 0.3 mm), Wb (wavelength 0. .3 to 1.0 mm), Wc (wavelength 1.0 to 3.0 mm), Wd (wavelength 3.0 to 10.0 mm), We (wavelength 10.0 to 30.0 mm), Lw (wavelength 1.2 12 mm), and Sw (wavelength 0.3 to 1.2 mm). Note that the smaller the wave scan value, the less unevenness of the wavelength on the surface and the better the appearance quality of the coating film. The flip-flop property can be measured using, for example, a multi-angle colorimeter BYK-mac i (manufactured by BYK Gardner). Specifically, paint film abnormalities include unevenness, blisters, cracks, sagging, pinholes, picture frames, and the like. These can also be measured using known methods.
Moreover, the color may be evaluated directly on the obtained coating composition. As an example, the measurement can be performed using a spectrophotometer CM-M6 (manufactured by Konica Minolta) with the coating composition placed in a quartz glass cell.
Here, adjusting any of the properties of color, gloss, and viscosity may affect the other properties as well.

Manufacturing conditions include base data, coating condition data, measurement condition data, and the like. The base data includes the type of base, the color of the base, surface tension, surface roughness, etc., and a simple product name or substance name can be used as data. For example, in the case of roll coating, the coating condition data includes (i) line speed, roll peripheral speed, nip pressure, number of rolls used, roll rotation direction, coating pressure, paint flow rate, applicator roll condition, material, Conditions related to equipment and devices such as hardness, type of pickup roll, and coating line configuration; (ii) Conditions related to coating composition such as viscosity and amount of coating; (iii) Baking time in the baking process and temperature rise of the object to be coated. Conditions related to temperature and humidity such as curve, maximum temperature, baking furnace temperature, hot air velocity, environmental temperature and humidity, (iv) conditions related to instruments such as color difference meter and gloss meter, (v) other conditions, Examples include physical property data such as the type and color of the undercoat film, gloss, roughness, surface tension, and surface SP value, and chemical property data. Further, in the case of spray painting, the viscosity of the coating composition, discharge pressure, discharge amount, gun type, baking temperature in the baking process, setting time, baking time, environmental temperature, humidity, etc. can be mentioned. Furthermore, in the case of electrodeposition coating, the coating voltage, liquid temperature, current application time, coating direction (horizontal or vertical plane), line speed, etc. may be mentioned. The measurement condition data includes the model of the color difference meter, the temperature measured by the model of the gloss meter, the time since the device was energized, and the name of the individual measuring device.

The manufacturing conditions include (a) one or more of the viscosity of the paint and the heating residue, (b) the peripheral speed of the roll, the amount of spray discharge, the electrodeposition voltage in the process of applying the paint composition to the object to be coated, (c) any one or more of the maximum temperature of the object to be coated in the baking process, the baking temperature, and the baking time; and (d) the environmental temperature of the production line. It is preferable to include any one or more of (a) to (d), including humidity and paint temperature.

FIG. 1 is a flowchart of an automatic adjustment method according to an embodiment of the present invention.
In this embodiment, first, a pre-variation paint composition is prepared (step S101: the first step of the variation response curve data acquisition process). The pre-change paint composition may contain only a resin and a solvent, or may contain one or more types of paint property adjusting materials (colorant, gloss modifier, viscosity modifier, etc.) in addition to the resin and solvent. Information on the paint properties of the paint composition before variation (also referred to as "paint properties before variation") is obtained in advance.

Next, in this embodiment, based on the pre-change coating composition (prepared in step S101), the addition ratio of one or more colorants, the addition ratio of one or more gloss modifiers, and one or more viscosity adjustments are determined. For each parameter, information on the paint properties when only one parameter of the agent addition ratio and each manufacturing condition is varied is obtained in advance (Step S102: Acquisition of variation response curve data middle stage of the process). The addition ratio can be expressed, for example, as % by mass when the mass of the prepared coating composition before variation is taken as 100%.

In step S102, at least one of color, gloss, and viscosity is obtained by measurement as information on paint properties. Color, gloss, and viscosity can be measured by measuring the coating composition or the coating film obtained from the coating composition. The methods for measuring color, gloss, and viscosity are, for example, as described above. The only thing that can be changed is the addition ratio of only one type of colorant, only the addition ratio of only one type of gloss modifier, only the addition ratio of only one type of viscosity modifier, or only one of the various manufacturing conditions. be. Regarding color, for example, using the Kubelka-Munk optical density formula, etc., and using the spectral reflectance when colorants are mixed in a predetermined formulation, the amount of color variation can be predicted from the difference before and after adding the paint composition. You can also calculate it and get it.

FIG. 2 is a diagram for explaining the middle step of the fluctuation amount response curve data acquisition step, and is an example in which the parameter to be changed is the addition ratio of the white colorant. Specifically, the addition ratio of the white paint composition is changed from 0% to 0.03%, 0.1%, 0.3%, 1%, 3%, based on the total mass of the paint composition before change. It is changed to 10%. In order to make fine adjustments with high precision, it is preferable that the addition ratio is varied in increments of about 0.01 to 1% when the variation is large, 1% or less. The number of variations can be, for example, from 2 to 20 per parameter. As shown in FIG. 2, information is obtained by measuring, for example, the color, gloss, and viscosity when only one parameter is varied. The same thing is done for each parameter (other colorants, gloss modifiers, viscosity modifiers, and manufacturing conditions). As for the manufacturing conditions, it is preferable that the fluctuations be made in small enough increments to make fine adjustments with high accuracy, taking into consideration the adjustment range to be actually adjusted under each manufacturing condition.

Next, in this embodiment, variation response curve data indicating the relationship between the variation of each parameter and the variation of paint property information is acquired (step S103: subsequent step of the variation response curve data acquisition step). In the example shown in FIG. 2, in variation 1, the amount of variation in which the addition ratio of white colorant was varied from 0 to 0.03%, and the amount of variation in color at that time (L ^* amount of variation in value (WL1 ), a ^* value variation amount (Wa1), b ^* value variation amount (Wb1)), gloss variation amount (Wg1), viscosity variation amount (Wv1), and other variation 2 Similarly, data on the relationship between the amount of variation in parameters and the amount of variation in paint property information obtained in 6 to 6 is obtained. One pre-variation paint composition may be prepared, but in order to accommodate various adjustments, pre-variation pre-adjustment paint compositions are prepared and the parameters of the color of each pre-variation paint composition are adjusted. It is preferable to obtain data on the relationship between the amount of variation and the amount of variation in paint property information. For example, the plurality of pre-variation paint compositions are preferably created in 5 increments, and more preferably in 1 increments, with respect to color (L ^* value, a ^* value, b ^* value). More efficiently, it is preferable to prepare each type of coloring agent, and it is possible to prepare them by classifying them by color type, such as having two or more of them.
In the method of this embodiment, fluctuation amount response curve data consisting of the data obtained as described above is obtained. FIG. 3 shows examples of names of variation response curves.
Acquisition here refers not only to the case of calculation and acquisition by the calculation unit (processor) of the computer, but also to the case of receiving the above-mentioned variation response curve data by the communication unit of the computer, or by the reading unit. This also includes the case of reading the above-mentioned fluctuation amount response curve data recorded in the memory. For example, even when changing the addition ratio of colorant as only one parameter, if the target paint properties include not only the target color but also the target gloss and target viscosity, it is necessary to change the color only. First, it is preferable to also obtain variations in gloss and viscosity. This is because a change in the addition ratio of the colorant can affect not only the color but also the gloss and viscosity.

Next, in this embodiment, the calculation unit of the computer generates target data including at least one of target color, target gloss, and target viscosity, and a variation response curve obtained in the variation response curve data acquisition step. Using the data, the amount of variation in the addition ratio of the paint property adjusting material and/or the amount of variation in suitable manufacturing conditions with respect to the appropriate pre-adjustment coating composition is calculated (calculation step). The calculation process will be explained in more detail below.

In the present embodiment, in the calculation step, using the variation response curve data, the addition ratio of a plurality of types of colorants, the addition ratio of one or more types of gloss modifier, the addition ratio of one or more types of viscosity modifier, and the amount of variation in at least one of color, gloss, and viscosity when at least one of the manufacturing conditions is varied (step S104: the first step of the calculation step).
In addition, the calculation when changing the addition ratio of two or more types of additives is explained using an example of two types of colorants.The first method is to add the first type of colorant. Calculate the amount of change in the paint properties at the time using the change amount response curve. For example, when the paint properties change from "paint properties before adjustment" to "paint properties 1", when a second type of colorant is added, The variation amount from "paint property 1" can be calculated using a variation response curve. If two types of colorants are added at the same time, calculate the amount of variation from "paint properties 0" for each of the two types of colorants, and set the variation amounts to 1 and 2, respectively.The paint properties after the change will be the "paint properties before adjustment." + “variation amount 1” + “variation amount 2”.
In addition, as a second method, especially when the amount of change in paint properties due to the addition of the first type of colorant is small, the amount of change due to the addition of the second type of colorant can be calculated by calculating the amount of change from the "paint properties before adjustment". It can be calculated as a "variation amount 2'". In the case of simultaneous addition in this case, the amount of variation after addition can be set to "paint properties before adjustment" + "amount of variation 1" + "amount of variation 2'". By doing so, the variation response curve can be made common for the two types of colorants, so the amount of data that needs to be prepared can be significantly reduced.
4A and 4B are diagrams for explaining the first step of the calculation step.
A specific calculation method in the second method will be described. Figure 4A shows the calculation results obtained by substituting the amount of change in each coating film property into the amount of change response curve when adding 0.01%, 0.02%, and 0.03% of white colorant. There is. Similarly, FIG. 4B shows the calculation results for the red colorant. When 0.01% of white coloring agent and 0.02% of red coloring agent are added alone, the amount of variation in L ^* value is WL (0.01) and RL (0.02), respectively. . Also, the amount of variation when both are added at the same time can be calculated by adding up the amount of variation in L ^* value when each colorant is added individually [WL (0.01) + RL (0.02)] .
In addition, based on the variation response curve data, a variation response curve is created as an approximate curve (for example, by the least squares method), and in the calculation process, the variation response curve is used to calculate multiple types of colorants. color, gloss, and/or viscosity when at least one of the following is changed: It is also preferable to calculate the amount of variation.

Next, in the present embodiment, the target color, the target gloss, and at least one of the changed color, gloss, and viscosity that has been changed by the amount of change in at least one of the calculated color, gloss, and viscosity are used. Addition ratios of multiple types of colorants, addition ratios of one or more types of gloss modifiers, addition ratios of one or more types of viscosity modifiers, and manufacturing conditions such that the difference from at least one of the target viscosity is small. (Step S105: subsequent step of the calculation step).
Although not particularly limited, as an indicator, the ratio of addition of multiple types of colorants such that the difference between the target value and the calculated value becomes small (for example, below a predetermined threshold, or becomes the minimum), 1 The amount of variation in at least one of the addition ratio of one or more types of gloss modifiers, the addition ratio of one or more types of viscosity modifier, and manufacturing conditions is calculated. More specifically, when g is gloss, v is viscosity, G is the gloss tolerance range, V is the viscosity tolerance range, and Δ is the difference from the target, |Δg|≦G and |Δv|≦V. Under these conditions, calculate the value that minimizes ΔE. Here, ΔE represents color difference, and is determined by ΔE=(ΔL ^*2 +Δa ^*2 +Δb ^*2 ) ^1/2 .

Another example is the addition ratio of multiple types of colorants, the addition ratio of one or more types of gloss modifier, the addition ratio of one or more types of viscosity modifier using variation response curve data prior to the calculation process. Data on the amount of change in at least one of color, gloss, and viscosity when changing at least one of the addition ratio and manufacturing conditions is obtained in advance, and in the calculation process, the data on the amount of change in at least one of color, gloss, and viscosity is obtained in advance. such that the difference between at least one of the color, gloss, and viscosity after the change that has changed by the amount of change of at least one of the above and at least one of the target color, target gloss, and target viscosity becomes small; The amount of variation in at least any one of the addition ratio of a plurality of types of colorants, the addition ratio of one or more types of gloss modifier, the addition ratio of one or more types of viscosity modifier, and manufacturing conditions may be calculated.

Here, it is preferable that the paint property adjusting material includes a plurality of types of colorants, and the target paint property includes a target color. In this case, the pre-adjustment coating composition contains one or more types of colorants, and the variation response curve data acquisition step involves obtaining colors in advance by variously changing the addition ratio of one type of colorant. Preferably, the method includes obtaining variation response curve data indicating the relationship between the variation in the addition ratio of each type of colorant and the variation in color. The calculation step may include calculating the amount of variation in the appropriate colorant addition ratio using the target color and the variation response curve data obtained in the variation response curve data acquisition step. preferable. Although not particularly limited, this is because colors are often adjusted when adjusting paint.

When the paint property adjusting material includes multiple types of colorants, one or more types of gloss modifiers and/or one or more types of viscosity modifiers, the calculation process is preferably performed as follows. .
First, using the target gloss and/or target viscosity data and the variation response curve data obtained in the variation response curve data acquisition step, determine the appropriate addition ratio of the gloss modifier and/or viscosity modifier. Calculate the amount of variation (first calculation sub-step).
Next, using the amount of variation in the optimum addition ratio of the gloss modifier and/or viscosity modifier obtained in the first calculation sub-step and the variation response curve data, calculate the amount of variation obtained in the first calculation sub-step. In addition, the amount of variation in color caused by adding the gloss modifier and/or viscosity modifier corresponding to the amount of variation in the appropriate addition ratio is calculated (second calculation sub-step).
Next, using the color variation (and target color data) and variation response curve data obtained in the second calculation sub-step, the variation in the appropriate addition ratio of the colorant is calculated. (Third calculation sub-step).
According to this, it becomes possible to calculate the optimum amount of variation in the addition ratio of the colorant, taking into consideration the influence of the gloss modifier and/or viscosity modifier on the color.

Here, it is preferable to perform the following preparations prior to the calculation step. A step of setting each numerical value generation range for the amount of variation in at least one of the colorant addition ratio, gloss modifier addition ratio, viscosity modifier addition ratio, and manufacturing conditions, and within the set numerical value generation range. Various pre-adjustment methods, which are obtained by combining (for example, randomly) the amount of variation in at least one of the colorant addition ratio, gloss modifier addition ratio, viscosity modifier addition ratio, and manufacturing conditions produced in Prior to the calculation step, a step of preparing a data group of the amount of variation in the addition ratio of the paint property adjusting material to the paint composition and/or the amount of variation in manufacturing conditions is performed. Here, although the data group is not particularly limited, it is preferable that the data group consists of, for example, 100,000 to 10 million combinations. In the calculation process, the fluctuation amount response curve data is used to calculate the numerical value of the data group of the amount of fluctuation in the addition ratio of the paint property adjusting material and/or the amount of fluctuation in manufacturing conditions to the various prepared paint compositions before adjustment. Calculate the amount of change in at least one of color, gloss, and viscosity when the addition ratio and/or manufacturing conditions of the paint composition are changed by A plurality of types of coloring such that the difference between at least one of the color, gloss, and viscosity after variation that has changed by the amount of variation and at least one of the target color, target gloss, and target viscosity is small. The amount of change in at least one of the addition ratio of the agent, the addition ratio of one or more types of gloss modifier, the addition ratio of one or more types of viscosity modifier, and manufacturing conditions is calculated. This enables efficient calculation. The numerical value generation range can be determined based on a predetermined range from the standard value or actual value of the adjusted paint composition. This allows calculation results with small deviations from standard values and actual values to be obtained. Alternatively, the numerical value generation range can be determined based on at least one of the passing range of the weather resistance test of the coating film and the passing range of the metamerism test.

Here, in the present embodiment, in the calculation step, the amount of variation in the addition ratio of the paint property adjusting material to the paint composition before suitable adjustment and/or the amount of variation in suitable manufacturing conditions is calculated using the brute force search method or Calculate using mathematical optimization method.

In the brute force search method, for example, when 100,000 to 10 million patterns of variation are prepared in the process of preparing a data group of the variation amount that can be performed prior to the calculation process, This method calculates the variation values of color, gloss, and viscosity through calculations using variation response curve data for all of them, and thoroughly determines the various variation amounts when the difference from the target value becomes small.

The mathematical optimization method is an optimization problem of a function of the amount of variation in the addition ratio of colorant x1, the amount of variation in the addition ratio of gloss modifier x2, the amount of variation in the addition ratio of viscosity modifier x3, and the amount of variation in manufacturing conditions x4. This method calculates x1, x2, x3, and x4 as follows.
As the mathematical optimization method, it is preferable to use a convex optimization method. Alternatively, it is also preferable to use quadratic programming as the mathematical optimization method. Alternatively, it is also preferable to use gradient descent as the mathematical optimization method.
The effects of the automatic adjustment method of this embodiment will be described below.

In the automatic adjustment method of the present embodiment, in the calculation process, when calculating the amount of change in the addition ratio of the paint property adjusting material to the paint composition before suitable adjustment and/or the amount of change in the suitable manufacturing conditions, the amount of change is responded to. The fluctuation amount response curve data obtained in the curve data acquisition step is used. The variation response curve data is obtained using the coating composition before adjustment. For this reason, (as shown in Examples below), the target paint is theoretically calculated from the absolute value of the color of the pre-adjustment paint composition whose colorant mixing ratio is known and the absolute value of the target color. Compared to the method of calculating the amount of colorant to be added to the paint composition before adjustment so as to compensate for the difference in the mixing ratio of the colorant in the composition, paint properties can be automatically adjusted with higher precision. Additionally, the amount of data required is smaller than in the case of machine learning. In addition, the variation response curve data may be any one of the addition ratio of one or more colorants, the addition ratio of one or more gloss modifiers, the addition ratio of one or more viscosity modifiers, and each manufacturing condition. Since information on the paint properties when only one parameter is varied in various ways is obtained for each parameter in advance, the influence of variations in only one parameter can be obtained with high accuracy. , it is possible to improve the accuracy of calculation results in subsequent calculation steps. In addition, in general, errors may occur in the measured values due to, for example, the measuring device, but in the method of this embodiment, the variation response curve data is based on the variation amount rather than the absolute value of the paint properties. , it is possible to cancel out such errors and perform calculations with higher accuracy.
In the calculation process, the amount of variation in the addition ratio of the paint property adjusting material to the paint composition before suitable adjustment and/or the amount of variation in suitable manufacturing conditions is calculated using the brute force search method or mathematical optimization method. are doing. According to these methods, it is possible to accurately determine the amount of variation in the addition ratio of the material for adjusting paint properties and/or the amount of variation in suitable manufacturing conditions for the paint composition before adjustment from the obtained variation response curve. can.
As described above, according to the automatic adjustment method of this embodiment, the coating composition can be adjusted with high accuracy using a simple method.
In particular, it has been found that it is difficult to adjust color, gloss, and viscosity at the same time because they can influence each other. When used as a calculation target, it has become possible to perform these simultaneous adjustments with high precision.

In the brute force search method, when searching for only one solution, if there are many types and numbers of parameters, the number of candidates increases explosively. Even when applied to the paint field, if there is only one solution, there is only one parameter.For example, even if the amount of colorant to be added is 0 to 10% by mass, the amount to be added is set in 0.1% increments. Then, if there are 2 types of colorants, there are 100 x 100 = 10,000 combinations, and even if the material is divided into 100 parts and the coating conditions are divided into 10 parts, the total number of combinations is 10 to the 20th power for a total of 10 parameters, which is an astronomical number of combinations. It ends up. However, in the case of the paint field, 1) there is a range of standards (there is a range of solutions), and 2) there are multiple combinations that give the same solution (for example, when adjusting a paint composition to black, only a black colorant is required). (or if a red colorant, blue colorant, and yellow colorant are mixed, it is possible to adjust the color to black with either of them), 3) The amount that can be added is limited depending on the coating performance. Since there is also a restriction that the amount of addition must be within the limited range, it is actually easy to limit the range to 20 divisions in the range of 0 to 2%. Because of these unique reasons, you can use random combinations (the number of divisions for one parameter is the same, or the combinations are random) without calculating all combinations, such as 10 to the 6th power, which do not cause any practical problems. It is possible to obtain a solution even if the number of combinations is reduced. For these reasons, the brute force search method can be particularly effectively utilized in the present disclosure for calculating paint properties.
Furthermore, in producing a coating composition, it is not necessarily the case that the closer the color, gloss, or viscosity is to the target, the better. For example, when the standard for color difference from the target is within ±0.1, the combination predicted to be closest to the target color is one using five materials with a color difference of 0.01 from the target, and the second If a combination predicted to be close to the target color uses two materials and has a color difference of 0.02 from the target, then the second combination, which is 0.01 inferior to the target but has a smaller number of added materials, is selected. It can be determined that the combination closest to , satisfies the standards and has the highest productivity. In addition, "suitable" should be selected based on various factors, such as avoiding as much as possible the use of expensive materials or materials that may adversely affect coating performance. It is theoretically possible to incorporate such coefficients into the formula and have them calculated, but when handling color, productivity, price, coating film performance, etc. in one formula, it is necessary to set these coefficients appropriately. This also complicates maintenance when adding factors later. In addition, if the formula becomes complex, it may not be possible to calculate the solution, so it is difficult to achieve the overall "optimum" of productivity and other factors using only mathematical optimization when using a large number of materials, such as in the paint field. It was difficult. On the other hand, the brute force search method calculates all solutions and then selects the appropriate combination. '' etc., it is easy to change the program to suit the user's request after calculating the solution, and it is also advantageous that there is no possibility that the solution cannot be calculated.
On the other hand, although it takes time to perform a huge number of calculations, it is possible to limit the numerical generation range and calculate in advance 10 to 10 billion variations within the possible range and with the variation response curve that may be used. , etc. Even with a commercially available personal computer, calculations can be made within 10 seconds, and the impact on work efficiency can be minimized.
Furthermore, the brute force search method can always calculate the optimal solution no matter how complex the conditions and functions (other than convex functions) are.

Since the mathematical optimization method is a method for finding a solution as an optimization problem, it is suitable for adjusting paint properties which requires high accuracy. Furthermore, calculations can be made more efficiently and in a shorter time than searching exhaustively, and one mathematically optimal combination can be obtained. For example, calculation can be easily performed by using convex optimization of a convex function. In addition, especially by using quadratic programming, calculations can be performed more easily and quickly than convex optimization.

Here, the pre-adjustment coating composition comprises (A1) to (A3) of (A1) one or more colorants, (A2) one or more gloss modifiers, and (A3) one or more viscosity modifiers. It is preferable to include two or more of them. As mentioned above, each of the colorants, gloss modifiers, and viscosity modifiers can affect both color, gloss, and viscosity. Therefore, for example, even when changing the addition ratio of a colorant as only one parameter, since gloss modifiers and viscosity modifiers are included in the paint composition before adjustment, changes can be made that take these effects into account. A dose-response curve can be obtained, and calculation results with even higher accuracy can be obtained in the subsequent calculation process.

<Automatic adjustment system>
FIG. 4 is a block diagram of an automatic adjustment system according to an embodiment of the present invention. This system deals with the amount of variation and/or the amount of change in the addition ratio of paint property adjusting materials to the paint composition before adjustment that is suitable for obtaining target paint properties when adjusting a paint composition under predetermined manufacturing conditions. This is an automatic adjustment system for coating compositions in which the amount of variation in manufacturing conditions is calculated by a computer calculation section.

The paint property adjusting material includes at least one of a plurality of types of colorants, one or more types of gloss modifiers, and one or more types of viscosity modifiers, and the target paint properties include a target color, a target gloss , and at least one of a target viscosity. These details are the same as those in the embodiment of the automatic adjustment method, and therefore will not be explained again.

As shown in FIG. 4, the system 10 includes a computer 11. As shown in FIG. The computer 11 includes an acquisition section 12 and a calculation section 13.

The acquisition unit 12 acquires one or more colorants based on the pre-variation paint composition containing at least one of one or more colorants, one or more gloss modifiers, and one or more viscosity modifiers. Information on paint properties when only one parameter among the addition ratio of , addition ratio of one or more types of gloss modifier, addition ratio of one or more types of viscosity modifier, and each manufacturing condition is varied. is obtained in advance for each parameter, and variation response curve data indicating the relationship between the variation of each parameter and the variation of paint property information is obtained. The details of this function are the same as those in the embodiment of the automatic adjustment method (fluctuation amount response curve data acquisition step), so a repeated explanation will be omitted. As mentioned above, the acquisition unit 12 may be, for example, a calculation unit (processor) of a computer (in this case, the calculation unit 13 described later may have such a function, or it may be a processor separate from the calculation unit 13). Alternatively, it may be a communication unit capable of receiving the above variation response curve data, or has a function of reading the above variation response curve data recorded in the memory. It may also be used as a reading section.

The calculation unit 13 uses target data including at least one of target color, target gloss, and target viscosity, and the obtained variation response curve data to calculate paint properties for a suitable pre-adjustment paint composition. It is configured to calculate a variation in the addition proportion of the conditioning material and/or a variation in suitable manufacturing conditions. The calculation unit may be a processor or the like. Note that the details of the calculation are the same as those in the embodiment (calculation process) of the automatic adjustment method, and therefore will not be explained again. The calculation unit calculates the amount of variation in the addition ratio of the paint property adjusting material to the paint composition before suitable adjustment and/or the amount of variation in suitable manufacturing conditions using the brute force search method or mathematical optimization method. do. The details of these methods are also the same as those of the embodiment (calculation process) of the automatic adjustment method, and therefore will not be explained again.

The present invention will be explained in more detail with reference to the following examples, but the present invention is not limited thereto.

First, a method for preparing the colorants used in Examples 1 to 3 and Comparative Examples 1 to 2 will be explained.
<Example of preparation of white colorant>
20 parts by mass of acrylic resin as a resin, 35 parts by mass of isophorone as an organic solvent, and 46 parts by mass of titanium oxide as a white pigment were mixed, and using a sand mill (dispersion medium: glass beads), the maximum particle size of coarse pigment particles was 10 μm or less. A white coloring agent was prepared.
<Examples of preparation of other colorants>
Black colorant 1, black colorant 2, yellow colorant, and red colorant were prepared in the same manner as in the preparation example of the white colorant, except that the types and amounts of each material were changed as shown in Table 1 below. did. The formulation of each colorant is shown in Table 1.

<Gloss adjuster>
As the gloss modifier, commercially available silica (silicon dioxide) used as a matting agent was used.
<Viscosity modifier>
Isophorone, which is used as a solvent, was used as the viscosity modifier.

<Example of preparation of calculation coating composition W>
After adding 5 parts by mass of acrylic resin, 25 parts by mass of fluororesin, 35 parts by mass of isophorone, and 35 parts by mass of cyclohexanone and uniformly dissolving the resin using a disper, 100 parts by mass of the white colorant was added and were mixed uniformly to prepare a calculation coating composition W (solid content concentration: 48% by mass).
<Preparation examples of other calculation paint compositions>
The calculation coating compositions K1, K2, Y, and R were prepared in the same manner as in the preparation example of the calculation coating composition W except that the types and amounts of each material were changed as shown in Table 2 below. Ta. Table 2 shows the formulation of each calculation coating composition.

<Preparation example of pre-adjustment coating composition 1>
After adding 5 parts by mass of acrylic resin, 25 parts by mass of fluororesin, 35 parts by mass of isophorone, and 35 parts by mass of cyclohexanone, and uniformly dissolving the resin using a disper, 25 parts by mass of white colorant, and 12 parts by mass of black colorant. 1, 69 parts by mass of yellow colorant, 4 parts by mass of red colorant, and 2 parts by mass of gloss modifier 1 were added and mixed uniformly using a disperser to obtain pre-adjustment coating composition 1 (solid content concentration: 47% by mass). ) was prepared.

<Preparation examples of pre-adjustment coating compositions 2 to 4>
Pre-adjustment coating compositions 2 to 4 were prepared in the same manner as in the preparation example of the pre-adjustment coating composition 1, except that the types and amounts of each material were changed as shown in Table 3 below. Table 3 shows the formulation of each coating composition before adjustment.

Details of the materials used to prepare the coating composition are as follows.
・Acrylic resin: Paraloid B44 (manufactured by Rohm & Haas), solid content concentration: 100% by mass
・Fluororesin: KYNAR500 (manufactured by ARKEMA), solid content concentration: 100% by mass
・White pigment: TI-PURE R-706 (titanium oxide, manufactured by DuPont)
・Black pigment 1: Mitsubishi Carbon Black MA-100 (carbon black, manufactured by Mitsubishi Chemical Corporation)
・Black pigment 2: SUNBLACK X15 (carbon black, manufactured by Shiraishi Calcium Co., Ltd.)
・Yellow pigment: TAROX synthetic iron oxide LL-XLO (yellow iron oxide, manufactured by Titan Kogyo Co., Ltd.)
・Red pigment: TODA COLOR 140ED (iron oxide, manufactured by Toda Kogyo Co., Ltd.)
・Gloss adjuster 1: GASIL HP395 (synthetic silica, manufactured by INEOS SILICAS)
・Gloss adjuster 2: Silicia 435 (silicon dioxide, manufactured by Fuji Silicia Chemical Co., Ltd.)
・Organic solvent: Isophorone (manufactured by ARKEMA)
・Organic solvent: Cyclohexanone (manufactured by Ube Industries)

<Method for preparing coating film of pre-conditioned coating composition>
The surface of the material (zinc-aluminum alloy plated steel plate: 1,800 x 300 x 0.35 mm) is coated with Fine Tough G primer (epoxy resin primer: manufactured by Nippon Paint Industrial Coatings) as an undercoat using a roll coater. (Reference film thickness: 5 μm) After that, baking was performed for 60 seconds under conditions such that the maximum temperature reached by the material was 210° C. to form an undercoat film.
Next, the pre-adjustment paint composition 1 was applied on the undercoat film using a roll coater (standard film thickness: 15 μm), baked for 60 seconds under conditions such that the maximum temperature of the material reached 250°C, and then immediately By cooling, a coating film of the coating composition was obtained.

Next, predictions for each example and comparative example will be explained. First, the creation of a variation response curve will be explained.

<Example 1 of creating a variation response curve>
A coating film of the coating composition 1 before variation was created, and data on color, gloss, and viscosity of the coating properties before variation were obtained. Next, a coating film was prepared in the same manner by adding 0.03% of a white colorant, and data on the color L ^* value of paint property 1 after variation was obtained. Similarly, 0.1%, 0.3%, 1%, 3%, and 10% of white colorant were added, respectively, to obtain color L ^* value data for paint properties 2 to 7 after variation. From these paint properties before variation and paint properties 1 to 7 after variation, a variation response curve WL(x)-1 was obtained as an approximate curve using the least squares method or the like.
In the same manner as above, other paint property data of black colorant 1, yellow colorant, red colorant, gloss modifier 1, and viscosity modifier were obtained, and variation response curves were obtained for each. The results are shown in Table 4.
In addition, in the same manner as above, the nip pressure of the roll coater, which is one of the manufacturing conditions, was varied to -100 Kgf, -50 Kgf, +50 Kgf, and +100 Kgf, respectively, coating films were created in the same way, and the variation response curve PL ( x)-1 was obtained.
Furthermore, similarly to the above, the maximum temperature of the material, which is one of the manufacturing conditions, was varied to 10°C, 20°C, -10°C, and -20°C, respectively, to obtain a variation response curve D(x)-1.

Paint compositions 2 to 4 before adjustment (paint properties before adjustment; L ^* value: 29 to 90, a ^* value: -1 to 13, b ^* value: 2 to 36, gloss: 7 to 29, viscosity: 230 to 260 seconds), variation response curves were obtained in the same manner as above. The results are shown in Table 4.

<Example 2 of creating a variation response curve>
<Example 1 of acquiring absorption coefficient and scattering coefficient>
For calculation coating composition W, calculation coating composition K1, and coating composition in which calculation coating composition W and calculation coating composition K1 were mixed at a ratio of 80:20, the three types of coating compositions described above were applied respectively. , the spectral reflectance of each was measured, and the absorption coefficient K and scattering coefficient S of Black Pigment 1 were calculated.

<Example 2 of acquiring absorption coefficient and scattering coefficient>
Absorption coefficients of black pigment 2, yellow pigment, and red pigment were obtained in the same manner as in Example 1 for obtaining absorption coefficients and scattering coefficients, except that calculation coating composition K1 was changed to calculation coating compositions K2, Y, and R. K and scattering coefficient S were calculated.

Using these absorption coefficients, scattering coefficients, pigments, refractive indexes of paint films, Kubelka-Munk theory, Duncan's theory, and Sanderson correction formula, 0.03% of white colorant was added to paint composition 1 before variation. Data on paint properties 1c after changes in the paint film were calculated. Similarly, white colorants were added in an amount of 0.1%, 0.3%, 1%, 3%, and 10%, respectively, to obtain coating properties 2c to 7c after variation. A variation response curve WL(x)-1c was obtained as an approximate curve using the least squares method or the like from these paint properties before variation and paint properties 1 to 7c after variation. Similarly, variation response curves were created for each paint property, each parameter, and each pre-adjustment paint composition. The results are shown in Table 5.

<Paint adjustment acceptance criteria>
The criteria for passing the test was to meet all three conditions below. Note that the differences between the L value, a value, and b value between the target value and the actual measurement value were defined as ΔL ^* , Δa ^* , and Δb ^*, respectively.
1) The value of ΔE=√(ΔL ^*2 +Δa ^*2 +Δb ^*2 ) is 0.05 or less, and each of ΔL ^* , Δa ^* , and Δb ^* is 0.05 or less.
2) If the difference in gloss between the target value and the measured value is less than 10, the difference from the standard plate is 0.3 or less, if it is 10 or more and less than 20, it is 0.5 or less, and if it is 20 or more. must be 2.0 or less.
3) The difference in viscosity between the target value and the actual value is 5 seconds or less.

<Criteria for passing the number of adjustments>
Regarding the four types of paints, those whose average number of adjustments was less than three times were considered to be passed, and those whose average number of adjustments were three or more times were judged to be failed.

<Example 1>
<Paint adjustment>
・Data input for corrected blend prediction
The lower and upper limits of the corrected blending variation range of each colorant and gloss modifier, which are raw materials for adjusting paint properties, can be set to an arbitrary amount within the range of 0 to 2% based on the total amount of the paint before adjustment, and the viscosity modifier can also be set to 0 to 2%. The nip pressure is set in the range of 10%, the nip pressure is set in the range of -100Kgf to +100Kgf, and the high temperature is set in the range of -20℃ to 20℃, and the raw material for adjusting the paint properties is corrected based on the set variation range of the correction mixture. We created candidate data for 10 million possible combinations of formulation composition and coating conditions (combinations of addition amounts of each colorant, gloss modifier, and viscosity modifier and coating conditions).

<Blend prediction using variation response curve and brute force search>
By substituting candidate data for 10 million addition amounts into the variation response curve WL(x)-1 to Tg(x)-1 shown in Table 4, the predicted value of the paint properties of the adjusted paint composition 1 was calculated. Obtained. Specifically, the amount of variation in the L value when the amount of white colorant added in the first candidate combination of 10 million combinations is substituted for WL(x)-1, black colorant 1, and yellow colorant. , red colorant, gloss modifier 1, viscosity modifier, nip pressure, and maximum temperature, KL(x)-1, YL(x)-1, RL(x)-1, ML(x)-1, respectively. , SL(x)-1, PL(x)-1, and TL(x)-1, the sum of all the variations in the L value is the variation in the L ^* value of the first candidate combination. amount. Similarly, the amount of variation in a ^* , b ^* value, gloss, and viscosity of each candidate was obtained. This was repeated 10 million times.
The difference between the target L ^* , a ^* , b ^* values, gloss, and viscosity variation of adjusted coating composition 1 and the predicted E value is within 0.05, the difference in gloss is within 0.3, and the viscosity Among candidate data for corrected formulation compositions in which the difference in amount of variation is within 5 seconds, extract the one with the smallest total amount of addition of materials for adjusting paint properties, and calculate the corrected addition amount of raw materials for adjusting paint properties and A predicted value of the correction variation amount of coating conditions was obtained.

<Obtaining paint properties of the adjusted paint composition>
A corrected blend of the predicted raw materials for adjusting the paint properties was added to the pre-adjustment paint composition 1, and the post-adjustment paint composition 1 was adjusted to obtain the paint properties.

<Readjustment of paint composition after adjustment>
The paint properties of the adjusted paint composition 1 did not meet the pass criteria for the target value of paint properties, so it was judged as failed.
The pre-adjusted coating composition and its properties of the pre-adjusted coating composition at the time of readjustment were taken as the adjusted coating composition and the properties of the adjusted coating composition that failed in the previous adjustment.
Similar to last time, we created 10 million candidate data for corrected compositions of raw materials for adjusting paint properties, and created variation response curves WL(x)-1, KL(x)-1, YL(x). -1, RL(x)-1, ML(x)-1, SL(x)-1, PL(x)-1, TL(x)-1, correction of raw materials and coating conditions for paint property adjustment By substituting 10 million candidate data for formulation compositions, predicted values of paint properties of the adjusted paint composition were obtained. (The number of adjustments is 2 times)
In the same manner as in Pre-Adjustment Paint Composition 1, the predicted corrected formulation of the material for adjusting paint properties was added to the Pre-Adjustment Paint Composition, and the post-adjustment paint composition was adjusted to obtain paint properties. This readjustment operation was repeated, and it was confirmed that the paint properties of the adjusted paint composition met the acceptance criterion of the target value, and the paint adjustment was completed. (Adjustment number of times: 2 times)
・Repeat operation 1
Similar operations were carried out for two types of pre-adjustment coating compositions of different primary color lots.
・Repeat operation 2
Similar operations were carried out for pre-adjusted paint compositions 2, 3, and 4, and for 4 types of paints, including pre-adjusted paint composition 1, and 2 types of primary color lots each, a total of 8 types of paint compositions were prepared. The average number of adjustments was 1.5 and the test was passed.

<Example 2>
・Property prediction using variation response curve
Except for changing the variation response curves WL(x)-1 to TL(x)-1 shown in Table 4 to the variation response curves WL(x)-1c to TL(x)-1c shown in Table 5, respectively. It was carried out in the same manner as in Example 1. The average number of adjustments for the eight types of paint was 2.8, which passed the test.

<Example 3>
Same as Example 1, except that the brute force search method was changed to a mathematical optimization method, and the one with the smallest difference in E value, gloss, and viscosity was obtained as the predicted value of the corrected addition amount and the corrected variation amount of coating conditions. It was carried out in The average number of adjustments for the eight types of paints was 2.3, which passed the test.

<Comparative example 1>
Using the basic spectral reflectance (primary data) when colorants are mixed in a predetermined composition, we can calculate absorption coefficients and scattering coefficients of pigments, refractive indices of pigments and coatings, Kubelka-Munk theory, and Duncan theory. , the Sanderson correction formula is used to predict and calculate the color of an arbitrary mixing ratio of colorants, and calculate the absolute value of the color of a pre-mixing paint composition with a known mixing ratio of colorants and the absolute value of the target color. The predicted value of the corrected amount of colorant to be added to the paint composition before toning was calculated and adjusted to compensate for the difference between the target colorant mixing ratio of the paint composition and the theoretically calculated value. , even after 5 adjustments, it did not reach the passing standard.
Furthermore, gloss and viscosity as paint properties, and gloss modifier and viscosity modifier as parameters, could not be calculated because theoretical formulas had not been established in the first place.

<Comparative example 2>
Colors ^{, gloss ,} A neural network was used to learn about viscosity changes based on the results of approximately 6,000 changes in paint property adjustment materials, coating conditions, and paint property changes, and artificial intelligence for color prediction was created using the brute force search method. It was carried out in the same manner as in Example 1, except that the corrected addition amount and the corrected variation amount of coating conditions were obtained as predicted values. The average number of adjustments for the eight types of paint was 5.2, which did not meet the passing standard.

<Evaluation method>
Each coating property determined in each Example and Comparative Example was measured by the following method.
1) Measurement of color data For the various coating films obtained by the above coating film manufacturing method, the color (L ^* , a ^* , b ^* value or target value) was measured using a spectrophotometer LabScan The differences in L ^* , a ^* , b ^* values from the standard plate were measured.
2) Measurement of Gloss Data The 60° glossiness of the various coating films obtained by the above coating film manufacturing method was measured using a gloss meter VG7000 (manufactured by Nippon Denshoku Kogyo Co., Ltd.).
3) Measurement of viscosity data Regarding the various coating compositions obtained in the above coating composition preparation examples, Ford Cup No. 4, and the number of seconds measured at 25°C was taken as the value of viscosity.

In addition, in this disclosure, each term means the following content.
"Property adjustment possible" means whether each of the paint properties (color, gloss, and viscosity) reaches the acceptance criteria for the paint adjustment, that is, whether each property can be adjusted. If adjustment is possible, it is ``possible,'' and if adjustment is not possible, it is ``impossible.''
"Number of adjustments" means that when a total of 8 types of paint compositions of 4 types of paint colors are prepared, each with 2 types of primary color lots, and each paint composition is adjusted, the pass criteria for the paint adjustment is met. Shows the average value until reaching the target value. Here, a small number of adjustments means that the prediction accuracy is high.
"Limit adjustment color difference" means the limit value that can bring the color closer to the target color. For example, a limit adjustment color difference of 0.03 means that automatic toning is possible until the color difference from the target color is 0.03. It means that.
The "manufacturing rate" refers to the ratio of the adjusted manufacturing amount when the planned manufacturing amount of the target coating composition is set to 1. Here, the fact that the production rate does not greatly exceed 100% means that the prediction accuracy of the paint property prediction system and the calculation method of the corrected composition are high, and even if some errors are allowed, the amount of paint that exceeds the required amount is high. This means that it is an efficient system that does not produce compositions.
The “necessary amount of data” means the amount of data that needs to be acquired in advance to acquire a variation response curve or to acquire artificial intelligence by machine learning.
The "necessary number of coatings" indicates the number of times coating and measurement are performed in order to secure the required number of data. Note that even when creating variation response curve data by calculation, painting and measurement are required when applying the calculation coating composition and measuring the spectral reflectance.

As shown in Table 6, according to Examples 1 to 3, very highly accurate color toning with a limit adjustment color difference of 0.05 was possible. Furthermore, in Examples 1 to 3, the number of adjustments was less than three, making it possible to make adjustments with fewer man-hours. Furthermore, in Examples 1 to 3, a large amount of data was not required, and the number of man-hours could be significantly reduced.
On the other hand, Comparative Example 1 is an example using the theoretical formula calculation method, but the limit adjustment color difference was 0.3, and highly accurate color matching was not possible. In addition, a theoretical method for adjusting gloss and viscosity has not been established for coating compositions, and the acceptance criteria could not be achieved. In addition, Comparative Example 2 is an example using artificial intelligence using machine learning method, and the limit adjustment color difference is 0.1, which allows for a certain degree of highly accurate color matching, but the number of adjustments is 5.2. Since a large number of times and 6,000 pieces of data are required, the number of man-hours becomes extremely large.

10; automatic adjustment system;
11: Computer,
12: Acquisition Department;
13: Calculation part

Claims (14)

When adjusting a paint composition under predetermined manufacturing conditions, the amount of variation in the addition ratio of the paint property adjusting material to the unadjusted paint composition suitable for obtaining the target paint properties and/or the adjustment of suitable manufacturing conditions. An automatic adjustment method for a paint composition, in which the amount of variation is calculated by a calculation unit of a computer,
The paint property adjusting material includes at least one of a plurality of types of colorants, one or more types of gloss modifiers, and one or more types of viscosity modifiers, and the target paint property is determined by the target color, the target color, and the target paint property. and a target viscosity,
Based on the coating composition before variation, the addition ratio of the one or more colorants, the addition ratio of the one or more gloss modifiers, the addition ratio of the one or more viscosity modifiers, and each of the manufacturing conditions. Information on the paint properties when only one of the parameters is varied in various ways is obtained for each parameter in advance, and the relationship between the amount of variation in each parameter and the amount of variation in the information on the paint properties is determined. a fluctuation amount response curve data acquisition step of acquiring fluctuation amount response curve data indicating the
Target data including at least one of the color of the target, the gloss of the target, and the viscosity of the target, and the variation response curve obtained in the variation response curve data acquisition step by the calculation unit of the computer. a calculation step of calculating the amount of variation in the addition ratio of the paint property adjusting material to the paint composition before adjustment and/or the amount of variation in suitable manufacturing conditions using the data;
In the calculation step, the amount of variation in the addition ratio of the paint property adjusting material to the suitable pre-adjustment paint composition and/or the amount of variation in suitable manufacturing conditions is calculated using a brute force search method. Features an automatic adjustment method. When adjusting a paint composition under predetermined manufacturing conditions, the amount of variation in the addition ratio of the paint property adjusting material to the unadjusted paint composition suitable for obtaining the target paint properties and/or the adjustment of suitable manufacturing conditions. An automatic adjustment method for a paint composition, in which the amount of variation is calculated by a calculation unit of a computer,
The paint property adjusting material includes at least one of a plurality of types of colorants, one or more types of gloss modifiers, and one or more types of viscosity modifiers, and the target paint property is determined by the target color, the target color, and the target paint property. and a target viscosity,
Based on the coating composition before variation, the addition ratio of the one or more colorants, the addition ratio of the one or more gloss modifiers, the addition ratio of the one or more viscosity modifiers, and each of the manufacturing conditions. Information on the paint properties when only one of the parameters is varied in various ways is obtained for each parameter in advance, and the relationship between the amount of variation in each parameter and the amount of variation in the information on the paint properties is determined. a fluctuation amount response curve data acquisition step of acquiring fluctuation amount response curve data indicating the
Target data including at least one of the color of the target, the gloss of the target, and the viscosity of the target, and the variation response curve obtained in the variation response curve data acquisition step by the calculation unit of the computer. a calculation step of calculating the amount of variation in the addition ratio of the paint property adjusting material to the paint composition before adjustment and/or the amount of variation in suitable manufacturing conditions using the data;
In the calculation step, the amount of variation in the addition ratio of the paint property adjusting material to the suitable pre-adjustment paint composition and/or the amount of variation in suitable manufacturing conditions is calculated using a mathematical optimization method. Automatic adjustment method. In the calculation step,
Using the variation response curve data, determine the addition ratio of the plurality of types of colorants, the addition ratio of the one or more types of gloss modifier, the addition ratio of the one or more types of viscosity modifier, and the manufacturing conditions. Calculating the amount of change in at least one of color, gloss, and viscosity when at least one of them is changed,
At least one of the color, the gloss, and the viscosity after a change that has been changed by the calculated amount of change in at least one of the color, the gloss, and the viscosity, the target color, the target gloss, and the addition ratio of the plurality of types of colorants, the addition ratio of the one or more types of gloss modifiers, and the addition of the one or more types of viscosity modifiers such that the difference from at least one of the target viscosity is small. The method according to claim 1 or 2, wherein the amount of variation in at least one of the ratio and the manufacturing conditions is calculated. Prior to the calculation step, the addition ratio of the plurality of types of colorants, the addition ratio of the one or more types of gloss modifier, and the addition of the one or more types of viscosity modifier using the fluctuation amount response curve data. Obtaining in advance data on the amount of variation in at least one of color, gloss, and viscosity when varying at least one of the ratio and the manufacturing conditions,
In the calculation step, at least one of the color, the gloss, and the viscosity after a change is changed by the amount of change in at least one of the color, the gloss, and the viscosity obtained in advance, and the target color. , an addition ratio of the plurality of types of colorants such that the difference from at least one of the target gloss and the target viscosity is small, an addition ratio of the one or more types of gloss modifiers, and the one or more types of gloss modifiers. The method according to claim 1 or 2, wherein the amount of change in at least one of the addition ratio of the viscosity modifier and the manufacturing conditions is calculated. The calculation step is
Using the data of the target gloss and/or the target viscosity and the fluctuation amount response curve data obtained in the fluctuation amount response curve data acquisition step, the suitability of the gloss modifier and/or the viscosity modifier is determined. a first calculation sub-step of calculating the amount of variation in the addition ratio;
The first calculation sub-step using the variation amount of the optimal addition ratio of the gloss modifier and/or the viscosity modifier obtained in the first calculation sub-step and the variation response curve data. a second calculation sub-step of calculating the amount of variation in color caused by adding the gloss modifier and/or the viscosity modifier by the amount of variation in the appropriate addition ratio obtained in step 1;
A third calculation sub-step of calculating the amount of change in the appropriate addition ratio of the colorant using the amount of variation in the color obtained in the second calculation sub-step and the variation response curve data. The method according to claim 1 or 2, comprising: setting a numerical value generation range for the amount of variation in at least one of the addition ratio of the colorant, the addition ratio of the gloss modifier, the addition ratio of the viscosity modifier, and the manufacturing conditions;
Combining the amount of variation in at least any of the addition ratio of the colorant, the addition ratio of the gloss modifier, the addition ratio of the viscosity modifier, and the manufacturing conditions that are generated within the set numerical value generation range. further comprising the step of preparing a data group of the amount of variation in the addition ratio of the paint property adjusting material to the various pre-adjustment coating compositions and/or the amount of variation in the manufacturing conditions,
In the calculation step,
Using the variation response curve data, the variation amount of the addition ratio of the paint property adjusting material to the prepared various pre-adjustment coating compositions and/or the variation amount of the production conditions is calculated by the numerical value of the data group. Calculating the amount of change in at least one of color, gloss, and viscosity when changing the addition ratio of the paint property adjusting material to the pre-adjustment paint composition and/or the manufacturing conditions,
At least one of the color, the gloss, and the viscosity after a change that has been changed by the calculated amount of change in at least one of the color, the gloss, and the viscosity, the target color, the target gloss, and the addition ratio of the plurality of types of colorants, the addition ratio of the one or more types of gloss modifiers, and the addition of the one or more types of viscosity modifiers such that the difference from at least one of the target viscosity is small. 4. The method according to claim 3, wherein the amount of variation in at least one of the ratio and the manufacturing conditions is calculated. 3. The method according to claim 2, wherein the mathematical optimization method uses a convex optimization method. 3. The method according to claim 2, wherein the mathematical optimization method uses quadratic programming. 3. The method according to claim 2, wherein the mathematical optimization method uses a gradient descent method. The pre-adjustment coating composition comprises (A1) the one or more coloring agents, (A2) the one or more gloss modifiers, and (A3) the one or more viscosity modifiers. A method according to claim 1 or 2, comprising two or more of A3). The manufacturing conditions are:
(a) one or more of the viscosity and heating residue of the paint;
(b) any one or more of roll circumferential speed, spray discharge amount, electrodeposition coating voltage, coating pressure, and paint flow rate in the process of applying the coating composition to the object to be coated;
(c) any one or more of the maximum temperature of the object to be coated, baking temperature, and baking time in the baking process, and (d) any one or more of the environmental temperature and humidity of the production line, and the temperature of the paint;
The method according to claim 1 or 2, comprising any one or more of (a) to (d). The paint property adjusting material includes the plurality of types of colorants,
The target paint properties include the target color,
The pre-adjustment coating composition includes the one or more coloring agents,
In the fluctuation amount response curve data acquisition step, the color is obtained in advance by variously changing the addition ratio of one type of colorant, and the fluctuation amount of the addition ratio of the one type of colorant and the fluctuation amount of the color are obtained. and obtaining the fluctuation amount response curve data indicating the relationship between
The calculation step includes calculating an appropriate amount of variation in the addition ratio of the colorant using the target color and the variation response curve data obtained in the variation response curve data acquisition step. 3. The method according to claim 1 or 2, comprising: When adjusting a paint composition under predetermined manufacturing conditions, the amount of variation in the addition ratio of the paint property adjusting material to the unadjusted paint composition suitable for obtaining the target paint properties and/or the adjustment of suitable manufacturing conditions. An automatic adjustment system for a paint composition in which the amount of variation is calculated by a calculation unit of a computer,
The paint property adjusting material includes at least one of a plurality of types of colorants, one or more types of gloss modifiers, and one or more types of viscosity modifiers, and the target paint property is determined by the target color, the target color, and the target paint property. and a target viscosity,
Based on the coating composition before variation, the addition ratio of the one or more colorants, the addition ratio of the one or more gloss modifiers, the addition ratio of the one or more viscosity modifiers, and each of the manufacturing conditions. Information on the paint properties when only one of the parameters is varied in various ways is obtained for each parameter in advance, and the relationship between the amount of variation in each parameter and the amount of variation in the information on the paint properties is determined. further comprising an acquisition unit that acquires fluctuation amount response curve data indicating the
The calculation unit calculates the appropriate pre-adjustment paint using target data including at least one of the target color, the target gloss, and the target viscosity, and the obtained variation response curve data. configured to calculate the amount of variation in the addition ratio of the paint property adjusting material to the composition and/or the amount of variation in suitable manufacturing conditions,
The calculation unit calculates the amount of variation in the addition ratio of the paint property adjusting material to the suitable pre-adjustment coating composition and/or the amount of variation in suitable manufacturing conditions using a brute force search method. Features an automatic adjustment system. When adjusting a paint composition under predetermined manufacturing conditions, the amount of variation in the addition ratio of the paint property adjusting material to the unadjusted paint composition suitable for obtaining the target paint properties and/or the adjustment of suitable manufacturing conditions. An automatic adjustment system for a paint composition in which the amount of variation is calculated by a calculation unit of a computer,
The paint property adjusting material includes at least one of a plurality of types of colorants, one or more types of gloss modifiers, and one or more types of viscosity modifiers, and the target paint property is determined by the target color, the target color, and the target paint property. and a target viscosity,
Based on the coating composition before variation, the addition ratio of the one or more colorants, the addition ratio of the one or more gloss modifiers, the addition ratio of the one or more viscosity modifiers, and each of the manufacturing conditions. Information on the paint properties when only one of the parameters is varied in various ways is obtained for each parameter in advance, and the relationship between the amount of variation in each parameter and the amount of variation in the information on the paint properties is determined. further comprising an acquisition unit that acquires fluctuation amount response curve data indicating the
The calculation unit calculates the appropriate pre-adjustment paint using target data including at least one of the target color, the target gloss, and the target viscosity, and the obtained variation response curve data. configured to calculate the amount of variation in the addition ratio of the paint property adjusting material to the composition and/or the amount of variation in suitable manufacturing conditions,
The calculation unit is characterized in that the amount of variation in the addition ratio of the paint property adjusting material to the suitable pre-adjustment paint composition and/or the amount of variation in suitable manufacturing conditions is calculated using a mathematical optimization method. do.

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