Paint manufacturing method

Abstract

To provide a method for producing paint capable of preventing a blending amount for adjustment from becoming excessive when adjusting paint properties of the paint.SOLUTION: A method includes: setting a gap target value of paint properties; or using conditions that are limited to candidates for solutions generated from a finite numerical range; or setting gap formulation compositions. Prediction of formulation is performed by a computer using a theoretical calculation and / or variation response curve data.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a method for producing a paint. [Background technology]

[0002] Adjustment of paint properties (one or more of color, gloss, and viscosity, hereinafter collectively referred to as color, etc.) of paint mainly includes adjustment of paint for automobile repair on-site and adjustment of industrial paint mass-produced in paint factories. As automobiles age while being driven outdoors, they acquire unique colors, etc. for their bodies. Therefore, when carrying out repairs, it is necessary to calculate a paint mix suitable for such unique colors, etc. Furthermore, strictly speaking, since there are differences between lots, it is difficult to adjust to the desired color, etc. in one go, and adjustments are made multiple times to match the target color, etc. (such as the color of the normal parts of the incoming car).

[0003] On the other hand, industrial paints that are mass-produced in paint factories are adjusted to match each lot to a reference plate. Unlike automotive repair paints that blur the boundary with normal parts, for example, when adjusting the paint properties of coil coating paint, painted steel plates painted with different paint lots are aligned when assembled, resulting in a linear boundary between the paint, making slight differences in the paint properties visible. For this reason, extremely high adjustment precision is required. In addition, since there are lot differences in the raw materials used to adjust the paint properties, it is difficult to adjust the paint properties to the desired properties in one go, and adjustments are made multiple times to match the target paint properties (paint properties of the reference plate).

[0004] For example, one example of a lot difference in raw materials for adjusting paint properties is the lot difference in coloring power of a colorant. Since the coloring power of a lot when the primary data or the fluctuation response curve is created is different from the coloring power of a lot when the paint is actually manufactured, it is very difficult to match the color in one go as calculated for paints that require very high adjustment accuracy. As a countermeasure, a method of managing the coloring power of each colorant lot and taking the lot difference into account in the calculation can be considered. However, since the coloring power fluctuates slightly even during the time from the production of the colorant to the production of the paint, it is necessary to measure the coloring power immediately before production rather than for each lot, which results in huge management costs. For this reason, the conventional method was to adjust the color multiple times, repeating shortfalls and excesses with respect to the target paint properties, gradually reducing the fluctuation range, and ending when it is within the target range.

[0005] However, in both the preparation of automotive repair paints and the preparation of industrial paints mass-produced in paint factories, if too much of the preparation compound is added, it is necessary to add more compound to restore the paint properties. For example, in the case of color, a colorant of the opposite hue (for example, if too much black colorant is added, a white colorant) is added to restore the color, and in the case of gloss, if the gloss is too low, the original paint that does not contain a matting agent is added, and in the case of viscosity, if the viscosity becomes too low, the original high-viscosity paint is added, etc. In such cases, the amount of paint produced increases, often resulting in waste. In particular, in the production of industrial paints mass-produced in paint factories, several hundred kg to several tons (tons) may be produced per run, resulting in large waste of materials and resources. In addition, there were also disadvantages such as the need to transfer the paint to a larger manufacturing container to prevent it from overflowing, which required a lot of additional labor, so adjustments were made by relying on the skills of experienced workers.However, adjustments by experienced workers had the disadvantages of being costly and requiring a lot of labor, as the accuracy of adjustments varies depending on the level of skill, making it difficult to maintain quality. In addition, a particular colorant with a maximum blend amount is often blended close to the target value when the paint is blended before adjustment, and even a small amount of a colorant with a minimum blend amount can result in a large excess, resulting in a noticeable problem of overproduction, where an amount far exceeding the planned amount is produced in order to return to the original amount. For example, if the correct blend amount is 99 kg of white colorant and 1 kg of black colorant, even if each colorant is blended in the correct amount, if the coloring strength of the black colorant is 10% higher, 9.9 kg of white colorant must be added to return to the original amount, resulting in at least 9.9 kg of overproduction.

[0006] To address such an overproduction problem, Patent Document 1 proposes applying a gap target value to a method using machine learning. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Patent No. 7116266 Summary of the Invention [Problem to be solved by the invention]

[0008] However, machine learning techniques have the problem that they require a large amount of training data. In addition, if there is insufficient training data for the gap target value, the prediction accuracy decreases, and there is a risk that the problem of overproduction will not be resolved, such as by mistakenly predicting the need to add an excessive amount of adjustment mix.

[0009] Therefore, an object of the present invention is to provide a method for producing a paint that can prevent the amount of an additive for adjustment from becoming excessive when adjusting the paint properties of the paint. [Means for solving the problem]

[0010] The gist and configuration of the present invention are as follows. (1) A method for producing a paint having a desired paint property by adding a raw material for adjusting the paint property to a paint before adjustment, The paint property is color, A pre-adjustment paint property information acquisition process for acquiring information on the paint property before adjustment; a gap target value setting process for setting a gap target value of a paint property; A gap target value corresponding blend composition prediction step of predicting a blend composition of raw materials for adjusting the paint properties suitable for obtaining the paint properties of the gap target value by theoretical calculation using a computer, In the gap target value setting step, an absolute value of a difference between the acquired pre-adjustment paint property and the gap target value is smaller than an absolute value of a difference between the acquired pre-adjustment paint property and a target paint property, A method for manufacturing a paint, characterized in that the gap target value setting step and the gap target value corresponding blend composition predicting step are repeated until a difference between the paint properties obtained by adding the predicted blend composition and the target paint properties becomes equal to or less than a predetermined reference value.

[0011] (2) A method for producing a paint having a desired paint property by adding a raw material for adjusting the paint property to a paint before adjustment, The paint properties are at least one of color, gloss, and viscosity, A pre-adjustment paint property information acquisition process for acquiring information on the paint property before adjustment; a fluctuation response curve data acquisition step for obtaining information on the paint properties when only one of the parameters of the addition ratio of one or more colorants, the addition ratio of one or more gloss modifiers, and the addition ratio of one or more viscosity modifiers is varied based on the pre-variation paint composition, and obtaining fluctuation response curve data showing the relationship between the amount of variation of each of the parameters and the amount of variation of the paint property information; a gap target value setting process for setting a gap target value of a paint property; A gap target value corresponding blend composition prediction step, which predicts a suitable amount of fluctuation in the addition ratio of the raw material for adjusting the paint properties by using the gap target value and the fluctuation amount response curve data by a computer, In the gap target value setting step, an absolute value of a difference between the acquired pre-adjustment paint property and the gap target value is smaller than an absolute value of a difference between the acquired pre-adjustment paint property and a target paint property, A method for manufacturing a paint, characterized in that the gap target value setting step and the gap target value corresponding blend composition predicting step are repeated until a difference between the paint properties obtained by adding the predicted blend composition and the target paint properties becomes equal to or less than a predetermined reference value.

[0012] (3) A manufacturing method described in (1) or (2) above, in which, in the gap target value setting process, the gap target value is set so that the smaller the absolute value of the difference between the acquired paint properties before adjustment and the target paint properties, the closer the gap target value is to the target paint properties, either continuously or stepwise.

[0013] (4) The paint properties consist of a plurality of properties, The manufacturing method according to (1) or (2), wherein in the step of setting the target gap value, the target gap value is set independently for each of the properties.

[0014] (5) A method for producing a paint having a desired paint property by adding a raw material for adjusting the paint property to a paint before adjustment, The paint property is color, A pre-adjustment paint property information acquisition process for acquiring information on the paint property before adjustment; and a step of predicting a restricted paint property adjusting composition by theoretical calculation under a condition that each blend amount in the blend composition of the raw material for adjusting the paint property is individually limited to a solution candidate generated from a finite numerical range, The candidate solutions include a case where the blend amount is 0, A method for producing a paint, comprising repeating the step of predicting a blend composition for adjusting limited paint properties until a difference between the paint properties obtained by adding the predicted blend composition and the target paint properties falls below a predetermined reference value.

[0015] (6) The manufacturing method according to (5) above, further comprising, when the composition of the raw materials for adjusting the paint properties cannot be calculated by theoretical calculation in the restricted paint property adjustment composition prediction step, releasing some or all of the conditions and re-predicting the composition of the raw materials for adjusting the paint properties by theoretical calculation.

[0016] (7) In the gap target value corresponding mixture composition prediction step, the mixture composition of the raw material for adjusting the paint properties is predicted by theoretical calculation under the condition that each mixture amount in the mixture composition of the raw material for adjusting the paint properties is individually limited to a candidate solution generated from a finite numerical range; The manufacturing method according to (1) above, wherein the candidate solutions include a case where the blending amount is 0.

[0017] (8) The manufacturing method according to (7) above, further comprising, in the gap target value corresponding composition prediction step, when a composition of the raw materials for adjusting the paint properties suitable for obtaining the paint properties of the gap target value cannot be calculated by theoretical calculation, releasing a part or all of the conditions and re-predicting the composition of the raw materials for adjusting the paint properties by theoretical calculation.

[0018] (9) In the gap target value corresponding mixture composition prediction step, the mixture composition of the raw material for adjusting the paint properties is predicted by calculation based on the fluctuation response curve data under conditions in which the mixture amount in the mixture composition of the raw material for adjusting the paint properties is individually limited to a candidate solution generated from a finite numerical range, The manufacturing method according to (2) above, wherein the candidate solutions include a case where the blending amount is 0.

[0019] (10) The manufacturing method according to (9) above, further comprising, in the gap target value corresponding composition prediction step, when a composition of the raw materials for adjusting the paint properties suitable for obtaining the paint properties of the gap target value is not calculated based on the fluctuation response curve data, releasing a part or all of the conditions and re-predicting the composition of the raw materials for adjusting the paint properties by calculation based on the fluctuation response curve data.

[0020] (11) The method further includes a main colorant specifying step of specifying a main colorant that is contained in the colorant composition of the paint before the preparation, The manufacturing method according to (7) or (9), wherein in the gap target value corresponding blend composition prediction process, a prediction is made for at least the main colorant using conditions limited to solution candidates generated from the finite numerical range.

[0021] (12) A method for producing a paint having a desired paint property by adding a raw material for adjusting the paint property to a paint before adjustment, The paint property is color, A pre-adjustment paint property information acquisition process for acquiring information on the paint property before adjustment; A target value corresponding blend composition prediction step of predicting a blend composition of raw materials for adjusting the paint properties suitable for obtaining the target paint properties by theoretical calculation using a computer; A gap composition setting step of setting a gap composition of the raw material for adjusting the paint properties, In the gap composition setting step, the set gap composition is smaller than the predicted target value corresponding composition, A method for producing a paint, characterized in that the target value corresponding composition prediction step and the gap composition prediction step are repeated until a difference between the paint properties obtained by adding the predicted gap composition and the target paint properties becomes equal to or less than a predetermined reference value.

[0022] (13) A method for producing a paint having a desired paint property by adding a raw material for adjusting the paint property to a paint before adjustment, The paint properties are at least one of color, gloss, and viscosity, a fluctuation response curve data acquisition step for obtaining information on the paint properties when only one of the parameters of the addition ratio of one or more colorants, the addition ratio of one or more gloss modifiers, and the addition ratio of one or more viscosity modifiers is varied based on the pre-variation paint composition, and obtaining fluctuation response curve data showing the relationship between the amount of variation of each of the parameters and the amount of variation of the paint property information; A target value corresponding blend composition prediction step of predicting a suitable amount of fluctuation in the addition ratio of the raw material for adjusting the paint properties by using the target paint properties and the fluctuation amount response curve data by a computer; A gap composition setting step of setting a gap composition of the raw material for adjusting the paint properties, In the gap composition setting step, the set gap composition is smaller than the predicted target value corresponding composition, A method for producing a paint, characterized in that the target value corresponding composition prediction step and the gap composition calculation step are repeated until a difference between the paint properties obtained by adding the gap composition and the target paint properties becomes equal to or less than a predetermined reference value.

[0023] (14) Prior to the gap blend composition calculation step, information on the paint properties before adjustment is acquired, and a pre-adjustment paint property information acquisition step is further included; The manufacturing method according to (12) or (13), wherein in the gap composition calculation step, the gap composition is calculated so that the smaller the absolute value of the difference between the acquired paint properties before adjustment and the target paint properties is, the closer the gap composition becomes to the predicted composition continuously or stepwise.

[0024] (15) The manufacturing method according to (12) above, wherein in the gap composition calculation step, the gap composition is calculated independently for each colorant.

[0025] (16) The manufacturing method according to (1) above, wherein in the gap target value corresponding blend composition prediction step, a brute force search method, a mathematical optimization method, or a random search method is used to calculate a blend composition of raw materials for adjusting the paint properties suitable for obtaining the paint properties of the gap target value.

[0026] (17) The manufacturing method according to (2) above, wherein in the gap target value corresponding blend composition prediction step, when predicting the suitable amount of variation in the addition ratio of raw materials for adjusting the paint properties and / or the suitable amount of variation in the manufacturing conditions, any one of a brute force search method, a mathematical optimization method, and a random search method is used.

[0027] (18) The manufacturing method according to (12) above, wherein in the target value corresponding blend composition prediction step, when predicting a blend composition of raw materials for adjusting the paint properties suitable for obtaining the target value of the paint properties, any one of a brute force search method, a mathematical optimization method, and a random search method is used.

[0028] (19) The manufacturing method according to (13) above, wherein in the target value corresponding blend composition prediction step, when predicting the suitable amount of variation in the addition ratio of raw materials for adjusting the paint properties and / or the suitable amount of variation in the manufacturing conditions, any one of a brute force search method, a mathematical optimization method, and a random search method is used. Effect of the Invention

[0029] According to the present invention, a method for producing a paint can be provided that makes it possible to prevent the amount of an adjusting agent from becoming excessive when adjusting the paint properties of the paint. [Brief description of the drawings]

[0030] [Figure 1] 1 is a flowchart of a method for producing a paint according to a first embodiment of the present invention. [Diagram 2] 5 is a flowchart of a method for producing a paint according to a modified example of the first embodiment of the present invention. [Diagram 3] FIG. 13 is a diagram for explaining a fluctuation response curve data acquisition step. [Figure 4] FIG. 1 shows the names of fluctuation response curves. [Diagram 5] FIG. 13 is a diagram illustrating an example of a predictive calculation. [Figure 6] FIG. 13 is a diagram illustrating an example of a predictive calculation. [Figure 7] 5 is a flowchart of a method for producing a paint according to a second embodiment of the present invention. [Figure 8] 10 is a flowchart of a method for producing a paint according to a modified example of the second embodiment of the present invention. [Figure 9] 5 is a flowchart of a method for producing a paint according to a third embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0031] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0032] <First embodiment> The paint manufacturing method according to the first embodiment of the present invention is a method for manufacturing paint having target paint properties by adding raw materials for adjusting paint properties to unadjusted paint.

[0033] Here, the term "paint before adjustment" refers to a paint in a state in which, among the materials constituting the target paint composition, raw materials having the same function as the raw materials for adjusting the paint properties are blended in a predetermined amount of 0 to 100%, and raw materials other than those having the same function as the raw materials for adjusting the paint properties are blended in a predetermined amount of 100%, and the properties are approximately similar to the target properties. The paint may or may not contain raw materials for adjusting the paint properties. In addition, the blending amount of each material may be unknown. Here, the paint is, for example, a colorant prepared by dispersing a resin, a solvent, and one type of coloring pigment with an SG mill or the like, and a plurality of colorants of various colors are prepared, and in order to adjust to an arbitrary color, a resin, a solvent, and an additive are added to one or more colorants and mixed. The paint is not particularly limited, but the form can be, for example, a water-based paint, a solvent-based paint, a powder paint, a solvent-free paint, or the like. Furthermore, examples of uses include coil paints, general industrial paints, automotive paints, automotive repair paints, architectural paints, heavy-duty anticorrosion paints, and marine paints; coating methods include spray coating, 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 bake drying, forced drying, natural drying, and ultraviolet curing; and formulations include resin raw materials, pigments, design raw materials, solvents (including water), and additives. Examples of resin raw materials include acrylic resin, polyester resin, epoxy resin, alkyd resin, fluororesin, urethane resin, aminomelamine resin, isocyanate resin, blocked isocyanate resin, urethane-modified polyester resin, and other modified resins of these resins. Examples of pigments include inorganic pigments, organic pigments, coloring pigments, and extender pigments. Examples of design raw materials include lustrous materials such as metallic and pearl, aggregate, silica, resin beads, wax, and the like. Examples of additives include viscosity adjusters, silicone-based additives, rust inhibitors, catalysts, and defoamers.

[0034] In addition, the term "raw material for adjusting paint properties" refers to a general term for, for example, colorants, gloss regulators, viscosity regulators, and design raw materials used for adjusting paint properties. The raw material for adjusting paint properties includes at least one of a plurality of types of colorants, one or more types of gloss regulators, and one or more types of viscosity regulators. It is preferable that the raw material for adjusting paint properties includes at least a plurality of types of colorants. The colorant includes a color pigment, and examples of the color pigment include black pigment, white pigment, yellow pigment, green pigment, red pigment, blue pigment, titanium oxide, and red iron oxide. Examples of the gloss regulator include aggregate (sand, etc.), silica, reflective material, and alumina as components. Examples of the viscosity regulator include, for example, synthetic resin-based viscosity regulators, natural product-based viscosity regulators, and inorganic viscosity regulators. Furthermore, examples of the synthetic resin-based viscosity regulator include, for example, polymer-type viscosity regulators and association-type viscosity regulators. Solvents such as water and organic solvents can also be used as viscosity regulators. The coloring agent, gloss modifier, viscosity modifier, and design raw material may be used as the raw material itself, or may be dispersed in a solvent such as water or an organic solvent using a dispersant or the like.

[0035] Also, "color" is, for example, L * a * b * L in color space * Value, a * Value, b * Value (JIS Z 8781-4 (2013)), XYZ color system, RGB color system, Yxy color system, Hunter Lab color system, L * C * h * The color can be measured using a known color measurement method, for example, a spectrophotometer CM-M6 (Konica Minolta) is used to measure the L value measured by irradiating a light source at angles of 25°, 45°, and 75° with a light receiving part perpendicular to the coating film formed on a test plate as 0°. * Value, a * Value, b *The value can be measured. Alternatively, it can be measured using a spectrophotometer X-Rite MA68II (manufactured by X-Rite). The measurement angle can be appropriately adjusted according to the purpose or the equipment used. Any other index can be used. Furthermore, for example, any index can be used, such as reflection spectrum data, which is an index in which the reflection spectrum intensity at every 5 nm from 380 nm to 780 nm is used as the color. The color may be directly evaluated from the obtained coating composition. As an example, the coating composition can be measured in a state where it is placed in a quartz glass cell or a plastic cell using a spectrophotometer SE7700 (manufactured by Nippon Denshoku Industries Co., Ltd.). Here, adjusting any of the properties of color, gloss, and viscosity may affect the other properties.

[0036] FIG. 1 is a flow chart of a paint manufacturing method according to a first embodiment of the present invention. The example of FIG. 1 involves theoretical calculation by a computer. In the example of FIG. 1, the paint property to be adjusted is color. As shown in FIG. 1, in the first embodiment, first, primary data is acquired (step S101). The primary data is actual measurement data of the spectral reflectance at wavelengths of 400 to 700 nm for each paint composition for calculation. By acquiring such data, the relationship between the formulation composition of the paint composition for calculation in an arbitrary paint formulation and the change in color can be obtained, and the formulation composition of raw materials for adjusting the paint property suitable for obtaining the paint property of the gap target value described later can be used to predict by theoretical calculation by a computer.

[0037] Next, in the first embodiment, the paint before adjustment is charged (step S102). At this time, the main colorant, which is the colorant with the largest blending amount, may be multiplied by a rate of 100% so that the other colorants are mainly adjusted.

[0038] Next, information on the paint properties (color in this example) before adjustment is obtained (step S103: before-adjustment paint property information acquisition step). As an example, the before-adjustment paint prepared in step S102 is applied to a substrate, such as art paper or a metal plate, to prepare a sample plate, and a color difference meter or the like is used to measure the color difference between a standard plate (which has the target paint properties and is prepared in advance) and the sample plate.

[0039] Next, a gap target value of the paint property (color in this example) is set (step S104: gap target value setting process). Here, the difference in absolute value between the paint property (color) before adjustment acquired in step S103 and the gap target value is smaller than the difference in absolute value between the paint property (color) before adjustment acquired and the target paint property. For example, if the paint property (color) before adjustment is 25 and the target paint property is 30, the absolute value of the difference between the paint property (color) before adjustment and the target paint property is 5 (=30-25), and the value multiplied by a rate of 0.5 to 0.9 (hereinafter also referred to as approach rate) is 3.5 (=5×0.7) if the approach rate is 0.7. In this case, the gap target value can be 28.5 (=25+3.5). In this case, the absolute value of the difference between the paint properties (color) before adjustment and the gap target value is 1.5 (= 30 - 28.5), which is smaller than the absolute value of the difference between the paint properties (color) before adjustment and the target paint properties, which is 5 (= 30 - 25). When managing the same thing using color difference, if the difference (color difference) between the paint properties before adjustment and the target paint properties is -5 and the approach rate is 0.7, the initial gap target value can be a color difference of -1.5. In this case, the absolute value of the color difference of the gap target value is "1.5", which is smaller than the absolute value of the color difference before multiplying by the approach rate, which was "5".

[0040] Next, a mixture composition of raw materials for adjusting the paint properties suitable for obtaining the paint properties of the target gap value is predicted by theoretical calculation using a computer (step S105: gap target value corresponding mixture composition prediction step). As described above, the theoretical calculation can be performed by a known method such as the Kubelka-Munk equation or mathematical optimization using the primary data acquired in step S101.

[0041] Next, from the predicted formulations, one formulation is selected in which the difference between the color difference of the gap target value and the calculated color difference is equal to or less than a predetermined reference value (step S106). When multiple candidates are predicted, the one in which the difference between the color difference of the gap target value and the calculated color difference is the smallest can be selected, although this is not particularly limited. In a calculation method in which only one formulation can be selected, the one in which the difference between the color difference of the gap target value and the calculated color difference is the smallest is calculated and used as the formulation of the raw material for adjusting the paint properties. In the gap target value corresponding blend composition prediction process (step S105), it is preferable to use any one of the methods of brute force search method, mathematical optimization method, and random search when calculating the blend composition of raw materials for adjusting the paint properties suitable for obtaining the paint properties of the gap target value.

[0042] Next, the paint prepared by adding the selected compounding composition is applied to a plate to prepare a sample plate, and the color difference between the standard plate and the sample plate is measured using a color difference meter or the like (step S107).

[0043] Next, it is determined whether the color difference measured in step S107 is equal to or less than a predetermined reference value (step S108). If the measured color difference exceeds the predetermined reference value, the process returns to step S104, and steps S104 to S108 are repeated. However, the target gap value set in step S104 is set based on the color difference obtained in step S107. For example, in the first step S104, when the paint property at the time of preparation is 25, the target paint property is 30, and the approach rate is 0.7, the initial target gap value is 28.5 as described above. As a result, if the measurement result in the first step S107 shows that the paint properties of the paint adjusted by adding the blend composition selected in step S106 are 28.4, then in the second step S104, based on the current value of "28.4", the absolute value of the difference between the current paint properties and the target paint properties is 1.6 (=30-28.4), and this can be multiplied by, for example, an approach rate of 0.9 to set the gap target value to 29.84 (=28.4+1.6×0.9). In the case of a method of managing using color difference, for example, when the difference (color difference) between the paint properties before adjustment and the target paint properties is -5 and the approach rate is 0.7, the first gap target value is color difference -1.5 as described above. As a result, if the result of the measurement in the first step S107 shows that the difference in the paint adjusted by adding the blending composition selected in step S106 is -1.6, which is 0.1 lower than the gap target value (0.1 larger in absolute value) due to the influence of the lot difference of the raw materials, in the second step S104, this can be multiplied by, for example, an approach rate of 0.9 to set the gap target value to color difference -0.16 (=-1.6+1.6×0.9). On the other hand, if the measured color difference is equal to or less than the predetermined reference value, the color matching is completed (step S109). The effects of the coating material production method according to the first embodiment will be described below.

[0044] In the paint manufacturing method according to the first embodiment of the present invention, information on the paint properties (color in this example) before adjustment is acquired (step S103: pre-adjustment paint property information acquisition step), and a gap target value of the paint properties (color in this example) is set (step S104: gap target value setting step). In the gap target value setting step, the absolute value of the difference between the acquired pre-adjustment paint properties (color in this example) and the gap target value is smaller than the absolute value of the difference between the acquired pre-adjustment paint properties and the target paint properties. In the first embodiment, a blending composition of raw materials for adjusting the paint properties suitable for obtaining the paint properties of the gap target value is predicted by theoretical calculation using a computer (step S105: gap target value corresponding blending composition prediction step). In this way, in the first embodiment, a gap target value that is lower than the original target value (small difference from the current paint properties) is set, and the composition of the raw materials for adjusting the paint properties that corresponds to that gap target value is predicted. From the predicted compositions, one composition is selected such that the difference between the color difference of the gap target value and the calculated color difference is equal to or less than a predetermined reference value (in this example, the color difference is 0.1 or less) to adjust the paint properties (step S106), thereby preventing the amount of material used for adjustment from becoming excessive when adjusting the paint properties of the paint. Then, the paint adjusted by adding the selected compounding composition is applied to a plate to prepare a sample plate, and the color difference between the standard plate and the sample plate is measured using a color difference meter or the like (step S107). Next, it is determined whether the color difference is below a predetermined reference value (step S108). As a result, by repeating steps S104 to S108 as necessary, a paint having the final target paint properties can be produced. Here, the relationship between the amount of color and the change in color is not linear. * , a * , b * In contrast, in the first embodiment, the gap target value is set by multiplying the approach rate based on the paint properties (color), so that it is possible to more accurately prevent the blending amount for adjustment from becoming excessive, compared to, for example, a method of multiplying a predicted blending amount by the approach rate. For example, the amount of colorant added and the amount of color change are not in a linear relationship, and the more colorant added, the smaller the color change becomes. In other words, if the predicted blend amount is multiplied by 90% of the approach rate, the color difference will be closer than if the color difference was multiplied by 90% of the approach rate. For example, if white colorant is added to black paint, the color difference will be closer than if the color difference was multiplied by 90% of the approach rate. * When the value is adjusted from -0.5 to -0.05 at 90% of the approach rate of the color difference, the color difference is too close at -0.5 to -0.02 when the approach rate of the amount of addition is 90%. If there is no difference in the lot of the colorant, the number of times of adjustment may be reduced because the color difference is closer, but if there is a difference in the lot of the colorant and its coloring power is 20% stronger, the color difference is only slightly over at 90% of the approach rate of the color difference, from -0.5 to 0.04 (=-0.5+0.5×0.9×1.2), and the adjustment is completed if the control width is ±0.05. However, if the approach rate of the amount of addition is 90%, the color difference will exceed the opposite side, from -0.5 to 0.076 (=-0.5+(0.5-0.02)×1.2), and more black colorant must be added to bring the color back, which leads to the overproduction mentioned above. It may be possible to anticipate this and lower the approach rate of the amount of addition, but this would require checking the fluctuation curve of the amount of addition and the color, which would be cumbersome and prone to errors. On the other hand, the color difference approach rate is efficient and easy to set because it is faithful to the intended approach to the target property.

[0045] <Modification of the first embodiment> Next, a modified example using a fluctuation response curve will be described. In this modified example, one or more of color, gloss, and viscosity are targeted for adjustment. Here, the color is as described above, and the gloss is not particularly limited, but gloss can be used as an index. The gloss can be measured using a known gloss measurement method, and as an example, the 60° gloss of a coating film formed on a test plate can be measured using a specular gloss meter (VG 7000 gloss meter (manufactured by Nippon Denshoku Industries Co., Ltd.)) in accordance with 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). The "pre-variation coating composition" may consist of only a resin and a solvent, or may contain one or more coating property adjusting materials (such as colorants, gloss adjusters, and viscosity adjusters) in addition to the resin and solvent.

[0046] Fig. 2 is a flow chart of a method for producing a paint according to a modification of the first embodiment of the present invention. As shown in Fig. 2, in this modification, first, based on the pre-variation paint composition, information on paint properties when only one of the parameters of the addition ratio of one or more types of colorants, the addition ratio of one or more types of gloss modifiers, and the addition ratio of one or more types of viscosity modifiers is variously changed is obtained in advance for each parameter, and variation response curve data showing the relationship between the variation amount of each parameter and the variation amount of the paint property information is obtained (Step S201: Variation response curve data acquisition step).

[0047] In the example shown in Figure 3, the amount of gloss regulator added was changed from 0 to 0.03% in change 1, and the amount of color change (L * Amount of change in value (ML1), a * Amount of change in value (Ma1), b * Data on the relationship between the amount of variation in parameters and the amount of variation in paint property information, which is similarly obtained for the other variations 2 to 6, is obtained as well as data on the relationship between the amount of variation in parameters and the amount of variation in paint property information, which is obtained for the other variations 2 to 6. Although it is possible to prepare one pre-variation paint composition, in order to accommodate various adjustments, it is preferable to prepare multiple pre-variation paint compositions and obtain data on the relationship between the amount of variation in parameters and the amount of variation in paint property information for the properties of each pre-variation paint composition. For example, multiple pre-variation paint compositions are prepared based on the color (L * Value, a * Value, b * It is preferable to create the value in increments of 5, and more preferably in increments of 1.

[0048] In the method of this modification, fluctuation-response curve data is obtained from the data obtained as described above. FIG. 4 shows an example of the names of the fluctuation-response curves. The term "obtain" as used herein includes not only the case of obtaining by calculation using a calculation unit (processor) of a computer, but also the case of receiving the above-mentioned fluctuation response curve data using a communication unit of a computer, or reading the above-mentioned fluctuation response curve data recorded in a memory using a reading unit. Even when the addition rate of the gloss regulator is changed as only one parameter, for example, if the target paint properties include not only the target gloss but also the target color and target viscosity, it is preferable to obtain the fluctuation amount of not only the gloss but also the color and viscosity. This is because a change in the addition rate of the gloss regulator can affect not only the gloss but also the color and viscosity. Furthermore, when adjusting gloss, not only the gloss modifier but also the colorant, viscosity modifier, and manufacturing conditions can affect the gloss, so fine adjustments may be required. As shown in Figure 4, it is also possible to obtain a variation response curve when the colorant and viscosity modifier are varied.

[0049] Next, in this modified example, the unadjusted paint is prepared (step S202).

[0050] Next, information on the paint properties before adjustment (in this example, any one or more of color, gloss, and viscosity) is obtained (step S203: paint property information before adjustment acquisition process). As an example, the paint before adjustment prepared in step S202 is applied to a substrate, such as art paper or a metal plate, to prepare a sample plate, and a gloss meter or the like is used to measure the difference in gloss between a standard plate (which has the target paint properties and is prepared in advance) and the sample plate. In the case of viscosity, the paint is directly subjected to liquid measurement using a viscometer or the like to obtain the difference in viscosity from a standard product. In the case of color, the color difference between the standard plate and the sample plate can be measured using a color difference meter or the like, as in step S103.

[0051] Next, a gap target value of the paint property (in this example, one or more of color, gloss, and viscosity) is set (step S204: gap target value setting step). Here, the absolute value of the difference between the paint property before adjustment acquired in step S203 and the gap target value is smaller than the absolute value of the difference between the paint property before adjustment acquired and the target paint property. For example, if the paint property before adjustment is 25 and the target paint property is 30, the difference between the paint property before adjustment and the target paint property is 5 (=30-25), and the value obtained by multiplying this by an approach rate of, for example, 0.5 to 0.9 is 3.5 (=5×0.7) if the approach rate is 0.7. In this case, the gap target value can be 28.5 (=25+3.5). Also, similarly to the explanation regarding step S104, it is possible to manage the paint based on differences in paint properties such as color differences.

[0052] Next, the computer uses the target gap value and the fluctuation response curve data to predict the fluctuation amount of the addition ratio of the raw material for adjusting the suitable paint properties (step S205: target gap value corresponding blend composition prediction process). When two types of additives are added simultaneously, the amount of change in paint properties when the first additive is added is calculated using a change response curve, and when the paint changes, for example, from "paint properties before adjustment" to "paint properties 1," the amount of change when the second additive is added is calculated as the amount of change from "paint properties before adjustment," which can be calculated as "change amount 2." In this case of simultaneous addition, the amount of change after addition can be calculated as "paint properties before adjustment" + "change amount 1" + "change amount 2." 5 and 6 are diagrams for explaining the prediction calculation. A specific calculation method in the second method will be described. FIG. 5 shows the calculation results obtained by substituting the fluctuation amount of each coating film property into the fluctuation amount response curve when gloss modifier is added at 0.01%, 0.02%, and 0.03%. Similarly, FIG. 6 shows the calculation results in the case of a viscosity modifier. The fluctuation amount of the g value when gloss modifier is added at 0.01% and viscosity modifier is added at 0.02% is Mg(0.01) and Sg(0.02), respectively. In addition, the fluctuation amount when both are added simultaneously can be calculated by adding up the fluctuation amount of the g value when each colorant is added alone [Mg(0.01)+Sg(0.02)]. Although not shown in particular, when the addition ratio of the colorant is variously changed, the same method as shown in FIG. 5 and FIG. 6 can be used. It is also preferable to create a fluctuation response curve as an approximation curve (e.g., by the least squares method) based on the fluctuation response curve data, and in the predictive calculation, use the fluctuation response curve to calculate the amount of fluctuation in one or more of the color, gloss, and viscosity when at least one of the addition ratios of multiple types of colorants, the addition ratios of one or more types of gloss adjusters, and the addition ratios of one or more types of viscosity adjusters is varied.

[0053] Next, from among the predicted formulations, one formulation is selected in which the difference between the property of the gap target value and the calculated property is equal to or less than a predetermined reference value (step S206). When multiple candidates are predicted, the one in which the difference between the property of the gap target value and the calculated property is the smallest may be selected, although this is not particularly limited. In a calculation method in which only one formulation can be selected, the one in which the difference between the color difference of the gap target value and the calculated color difference is the smallest is calculated and used as the formulation of the raw material for adjusting the paint properties. In the gap target value corresponding blend composition prediction process (step S205), it is preferable to use any one of the methods of brute force search method, mathematical optimization method, and random search when calculating the blend composition of raw materials for adjusting the paint properties suitable for obtaining the paint properties of the gap target value.

[0054] Next, the paint adjusted by adding the selected compounding composition is applied to a plate to prepare a sample plate, and the difference in gloss between the standard plate and the sample plate is measured using a gloss meter or the like (step S207). In the case of viscosity, liquid measurement can be performed as described above. In the case of color, the color difference between the standard plate and the sample plate can be measured using a color difference meter or the like as described above. Color can also be liquid measured.

[0055] Next, it is determined whether the property difference measured in step S207 is equal to or less than a predetermined reference value (step S208). If the property difference measured exceeds the predetermined reference value, the process returns to step S204, and steps S204 to S208 are repeated. However, the target gap value set in step S204 is set based on the property difference obtained in step S207. For example, in the first step S204, when the paint property at the time of preparation is 25, the target paint property is 30, and the approach rate is 0.7, the initial target gap value is 28.5 as described above. As a result, if the measurement result in the first step S207 indicates that the paint properties of the paint adjusted by adding the blend composition selected in step S206 are 28.4, then in the second step S204, based on the current value of "28.4", the absolute value of the difference between the current paint properties and the target paint properties is 1.6 (=30-28.4), and this can be multiplied by, for example, an approach rate of 0.9 to set the gap target value to 29.84 (=28.4+1.6×0.9). On the other hand, if the measured property difference is equal to or smaller than the predetermined reference value, the adjustment is completed (step S109). The effects of the coating material production method according to the modified example of the first embodiment will be described below.

[0056] In the paint manufacturing method according to the modified example of the first embodiment of the present invention, information on the paint properties when only one of the parameters of the addition ratio of one or more colorants, the addition ratio of one or more gloss adjusters, and the addition ratio of one or more viscosity adjusters is changed based on the paint composition before the change, and the information on the paint properties is obtained in advance for each parameter, and the variation response curve data showing the relationship between the variation of each parameter and the variation of the paint property information is obtained (step S201: variation response curve data acquisition step), and the gap target value of the paint properties is set (step S204: gap target value setting step). In the gap target value setting step, the absolute value of the difference between the acquired paint properties before the adjustment and the gap target value is smaller than the absolute value of the difference between the acquired paint properties before the adjustment and the target paint properties. In this modified example, the computer predicts the variation of the addition ratio of the raw material for adjusting the paint properties (step S205: gap target value corresponding blend composition prediction step) using the gap target value and the variation response curve data. In this manner, in this modified example, a gap target value lower than the original target value (small difference from the current paint properties) is set, the composition of the raw materials for adjusting the paint properties corresponding to that gap target value is predicted, and from the predicted compositions, one composition is selected such that the difference between the properties of the gap target value and the calculated properties is equal to or less than a predetermined reference value (step S206), and the paint properties are adjusted. Therefore, when adjusting the paint properties of the paint, it is possible to prevent the amount of material used for adjustment from becoming excessive. For example, in the case of adjusting gloss, a paint adjusted by adding a selected compounding composition is applied to a plate to prepare a sample plate, and the difference in gloss between the standard plate and the sample plate is measured using a gloss meter or the like (step S207). Next, it is determined whether the difference in gloss is equal to or less than a predetermined reference value (step S208). As a result, steps S204 to S208 are repeated as necessary to manufacture a paint having the final target paint properties. In the case of viscosity, liquid measurement can be performed as described above. In the case of color, the color difference between the standard plate and the sample plate can be measured using a color difference meter or the like. Color can also be liquid measured.

[0057] The color adjustment shown in Fig. 1 and the gloss and / or viscosity adjustment shown in Fig. 2 to Fig. 6 can be performed simultaneously. In other words, the color can be adjusted by the method illustrated and explained in Fig. 1, and the gloss and / or viscosity can be adjusted by the method illustrated and explained in Fig. 2 to Fig. 6, and these can be performed simultaneously to adjust the color, gloss and / or viscosity. That is, as a preliminary preparation for prediction, steps S101 and S201 are performed, then the paint before adjustment is prepared (steps S102, S202), and then each paint property (color, gloss and / or viscosity) before adjustment is measured and obtained (steps S103, S203). Next, the gap target value of each paint property (color, gloss and / or viscosity) is set (steps S104, S204). Then, for gloss and / or viscosity, the amount of variation in the appropriate ratio of raw materials for adjusting the paint properties is predicted by calculation using the above-mentioned variation response curve data, and the color variation when the raw materials for adjusting the paint properties necessary for adjusting the gloss and / or viscosity are added is also calculated (step S205), and for color, the color variation due to the adjustment of the gloss and / or viscosity is taken into account and an appropriate blending composition is predicted by the above-mentioned theoretical calculation (step S105). Then, each property is measured, and depending on whether it is equal to or less than a predetermined reference value, the process is repeated or the adjustment is terminated (steps S104 to S109, steps S204 to S209).

[0058] Here, in the first embodiment and its modified examples, in the gap target value setting process (steps S104, S204), it is preferable to set the gap target value so that the smaller the absolute value of the difference between the acquired pre-adjustment paint properties and the target paint properties, the closer the gap target value becomes to the target paint properties, either continuously or stepwise. As described above, the first embodiment and its variations include repeating steps S104 to S108 (steps S204 to S208), and as the paint properties of the paint gradually approach the target paint properties, the amount of compounding composition added for adjustment gradually decreases, and therefore the excess amount in the event of adding too much of the compounding composition also becomes smaller, so the risk gradually decreases. Therefore, by repeating steps S104 to S108 (steps S204 to S208), the smaller the absolute value of the difference between the acquired pre-adjustment paint properties and the target paint properties, the closer the gap target value is to the target paint properties, either continuously or stepwise (for example, by adopting an approach rate closer to "1"), thereby reducing the number of repetitions required to produce paint as much as possible, and improving production efficiency without compromising the effect of suppressing overproduction.

[0059] In the first embodiment and its modified examples, the paint properties are composed of a plurality of properties, and in the gap target value setting step (step S104, step S204), it is preferable to set a gap target value independently for each property. Specifically, the paint properties include color, gloss and / or viscosity, and the color is L * , a * , b * Using a common gap target value can lead to problems with overproduction if any property exceeds its target value due to overfilling. Also, setting the common gap target value too low compared to the target paint property can reduce productivity. In contrast, setting an independent gap target value for each property can further reduce overproduction while maintaining manufacturing efficiency.

[0060] For example, ΔL * If is greater than 1, the approach rate is 50% and ΔL * When ΔL is between 0.5 and 1.0, the approach rate is set to 70%. * If Δa is 0.5 or less, the approach rate can be set to 100%. * , Δb *, Δg, Δv) can also be applied independently. In the case of gloss, for example, when Δg is more than 1 in the case of a gloss control width of ±0.3, the approach rate can be set to 50%, and when Δg is 1 or less, the approach rate can be set to 100%. In the case of viscosity, for example, when Δv is 30 seconds or more, the approach rate can be set to 50%, and when Δv is less than 30 seconds, the approach rate can be set to 100%.

[0061] Here, in the gap target value corresponding blend composition prediction step (step S104) in the first embodiment, when calculating the blend composition of raw materials for adjusting the paint properties suitable for obtaining the paint properties of the gap target value, it is preferable to use any one of the methods of brute force search method, mathematical optimization method, and random search. Similarly, in the step of predicting the gap target value corresponding blend composition (step S204) in the modified example of the first embodiment, when predicting the suitable variation in the addition ratio of raw materials for adjusting the paint properties and / or the suitable variation in the manufacturing conditions, it is preferable to use any one of the methods of brute force search method, mathematical optimization method, random search, and Bayesian optimization.

[0062] The brute force search method is a method of specifying a range of parameters and a numerical interval or an arbitrary numerical value, and calculating the variation values ​​of color, gloss, and viscosity by theoretical calculation and / or calculation using the variation response curve data for all patterns of the extracted variation (for example, 100,000 to 1 billion), thereby finding various variation amounts when the difference from the target value is within a certain level. Here, it is synonymous with grid search. The mathematical optimization method is a technique for finding x1, x2, x3, and x4 as an optimization problem of a function of the amount of variation x1 in the addition ratio of colorant 1, the amount of variation x2 in the addition ratio of colorant 2, the amount of variation x3 in the addition ratio of gloss modifier, and the amount of variation x4 in the addition ratio of viscosity modifier. As the mathematical optimization method, it is preferable to use the simplex method, the interior point method, the gradient descent method, the quadratic programming method, the continuous linear programming method, the sequential quadratic programming method, the Lagrange undetermined multiplier method, the barrier function method, or the like. Random search is a method of determining the amount of variation of various colors, gloss, and viscosity when the difference from the target value is below a certain level by specifying the number of trials and calculating the variation values ​​of color, gloss, and viscosity through theoretical calculations and / or calculations using variation response curve data for a pattern of variation amount randomly extracted from within the specified value range of a parameter.

[0063] <Second embodiment> Next, a description will be given of a method for producing a paint according to a second embodiment of the present invention. The method for producing a paint having a desired paint property is also performed by adding raw materials for adjusting the paint property to the paint before adjustment.

[0064] FIG. 7 is a flow chart of a paint manufacturing method according to a second embodiment of the present invention. The example of FIG. 7 involves theoretical calculations by computer (as in the example of FIG. 1). In the example of FIG. 7, the paint property is color (as in the example of FIG. 1). As shown in FIG. 7, in the second embodiment, first, primary data is acquired (step S301). Next, the paint before adjustment is prepared (step S302), and then information on the paint property before adjustment (color in this example) is acquired (step S303: paint property information acquisition process before adjustment). These steps are the same as those in the embodiment shown in FIG. 1, so a detailed explanation will be omitted.

[0065] Next, in the example of FIG. 7, a compounding composition of raw materials for adjusting paint properties suitable for obtaining paint properties of the target value is predicted by theoretical calculation by computer (step S304: target value corresponding compounding composition prediction process). For the theoretical calculation, the primary data acquired in step S301 can be used. Next, in the example of FIG. 7, one compounding composition is selected from the predicted compounding compositions such that the difference between the color difference of the target value and the calculated color difference is equal to or less than a predetermined reference value (in this example, the color difference is 0.1 or less) (step S305). When multiple candidates are predicted, the one with the smallest difference between the color difference of the target value and the calculated color difference can be selected, although this is not particularly limited. In a calculation method in which only one compounding composition can be selected, the one with the smallest difference between the color difference of the gap target value and the calculated color difference is calculated and used as the compounding composition of raw materials for adjusting paint properties. In the target value corresponding blend composition prediction process (step S304), it is preferable to use any one of the following methods to calculate the blend composition of raw materials for adjusting the paint properties suitable for obtaining the target paint properties: brute force search method, mathematical optimization method, and random search method.

[0066] Next, in the example of Fig. 7, the gap composition of the raw material for adjusting the paint properties is set (step S306: gap composition setting process). Here, in the gap composition setting process (step S306), the set gap composition is smaller than the predicted target value corresponding composition. When the predicted target value corresponding composition is 1.5 kg of red colorant, 0.2 kg of black colorant, and 0.6 kg of gloss adjuster, for example, by multiplying by an approach rate of 0.7, the gap composition can be set to 1.05 (= 1.5 x 0.7) kg of red colorant, 0.14 (= 0.2 x 0.7) kg of black colorant, and 0.42 (= 0.6 x 0.7) kg of gloss adjuster.

[0067] Next, the paint prepared by adding the set compounding composition is applied to a plate to prepare a sample plate, and the color difference between the standard plate and the sample plate is measured using a color difference meter or the like (step S307).

[0068] Next, it is determined whether the color difference measured in step S307 is equal to or less than a predetermined reference value (step S308). If the measured color difference is greater than the predetermined reference value, the process returns to step S304, and steps S304 to S308 are repeated. However, in step S304, a prediction is made based on the difference (color difference) between the paint property (color) of the paint adjusted by adding the set blending composition and the target paint property (color). For example, in the first step S304, a prediction is made based on the paint property at the time of preparation being 25 and the target paint property being 30, and as a result of the measurement in step S307, if the paint property of the paint adjusted by adding the gap blending composition set in step S306 is 28.4, in the second step S304, a prediction is made based on the current "28.4" and the target "30". On the other hand, if the measured color difference is equal to or less than the predetermined reference value, the color matching is completed (step S309). The effects of the coating material production method according to the second embodiment will be described below.

[0069] In the paint manufacturing method according to the second embodiment of the present invention, information on the paint properties before adjustment (color in this example) is acquired (step S301: paint property information before adjustment acquisition step), the mixture composition of the raw materials for adjusting the paint properties suitable for obtaining the paint properties of the target value is predicted by theoretical calculation by a computer (step S304: target value corresponding mixture prediction step), and the gap mixture composition of the raw materials for adjusting the paint properties is set (step S306: gap mixture setting step). Then, in the gap mixture setting step (step S306), the set gap mixture composition is smaller than the predicted target value corresponding mixture composition. In this way, in the second embodiment, the paint properties are adjusted by setting a gap composition smaller than the predicted composition, so that when adjusting the paint properties of the paint, it is possible to prevent the amount of the paint used for adjustment from becoming excessive. Then, the paint adjusted by adding the set gap composition is applied to a plate to prepare a sample plate, and the color difference between the standard plate and the sample plate is measured using a color difference meter or the like (step S307). Next, it is determined whether the color difference is below a predetermined reference value (step S308). As a result, by repeating steps S304 to S308 as necessary, a paint having the final target paint properties can be produced.

[0070] <Modification of the second embodiment> Next, a modified example of the second embodiment using a fluctuation response curve will be described. In this modified example, one or more of color, gloss, and viscosity are targeted for adjustment. Fig. 8 is a flow chart of a method for producing a paint according to a modified example of the second embodiment of the present invention. As shown in Fig. 8, in this modified example, first, based on the pre-variation paint composition, information on the paint properties when only one of the parameters of the addition ratio of one or more colorants, the addition ratio of one or more gloss modifiers, and the addition ratio of one or more viscosity modifiers is variously changed is obtained in advance for each parameter, and fluctuation response curve data showing the relationship between the fluctuation amount of each parameter and the fluctuation amount of the paint property information is obtained (step S401: fluctuation response curve data acquisition step). This step is the same as step S201 in the example of Fig. 2, which is a modified example of the first embodiment, so a detailed description will be omitted again.

[0071] Next, in this modified example, the paint before adjustment is prepared (step S402). Next, information on the paint properties before adjustment (in this example, one or more of color, gloss, and viscosity) is obtained (step S403: paint property information before adjustment acquisition step). These steps are similar to steps S202 and S203 in the example of FIG. 2, which is a modified example of the first embodiment, and therefore will not be described in detail again.

[0072] Next, the computer uses the target value and the fluctuation response curve data to predict the amount of fluctuation in the addition ratio of the raw material for adjusting the appropriate paint properties (step S404: target value corresponding mixture composition prediction step). Next, from the predicted mixture compositions, one mixture composition is selected in which the difference between the target value property and the calculated property is equal to or less than a predetermined reference value (step S405). These steps can be performed in the same way by replacing the "gap target value" in steps S205 and S206 of the example in FIG. 2, which is a modification of the first embodiment, with the "original target value" (without the step of multiplying the approach rate), and therefore a detailed explanation will be omitted.

[0073] Next, as shown in Fig. 8, the gap composition of the raw material for adjusting the paint properties is set (step S406: gap composition setting process). In the gap composition setting process, the set gap composition is smaller than the predicted target value corresponding composition. When the predicted target value corresponding composition is 1.5 kg of red colorant, 0.2 kg of black colorant, and 0.6 kg of gloss adjuster, the gap composition can be set, for example, by multiplying the approach rate of 0.7 to 1.05 (= 1.5 x 0.7) kg of red colorant, 0.14 (= 0.2 x 0.7) kg of black colorant, and 0.42 (= 0.6 x 0.7) kg of gloss adjuster.

[0074] Next, the paint prepared by adding the set composition is applied to a plate to prepare a sample plate, and the difference in properties between the standard plate and the sample plate is measured (step S407).

[0075] Next, it is determined whether the property difference measured in step S407 is equal to or less than a predetermined reference value (step S408). If the property difference measured exceeds the predetermined reference value, the process returns to step S404, and steps S404 to S408 are repeated. However, in step S404, prediction is made based on the difference between the paint property of the paint adjusted by adding the set blending composition and the target paint property. For example, in the first step S404, a prediction is made based on the case where the paint property at the time of preparation is 25 and the target paint property is 30, and as a result of the measurement in step S407, the paint property of the paint adjusted by adding the gap blending composition set in step S406 is 28.4, in the second step S404, a prediction is made based on the current "28.4" and the target "30". On the other hand, if the measured property difference is equal to or smaller than the predetermined reference value, the adjustment is completed (step S309). The effects of the coating material production method according to the modified example of the second embodiment will be described below.

[0076] In the modified example of the second embodiment, the fluctuation response curve data is acquired as described above (step S401: fluctuation response curve data acquisition step), and the computer predicts the fluctuation of the addition ratio of the raw material for adjusting the paint properties using the target paint properties and the fluctuation response curve data (step S404: target value corresponding composition prediction step), and sets the gap composition of the raw material for adjusting the paint properties (step S406: gap composition setting step). In the gap composition setting step, the set gap composition is smaller than the predicted target value corresponding composition. In this way, in the modified example of the second embodiment, the paint properties are adjusted by setting a gap composition smaller than the predicted composition, so that when adjusting the paint properties of the paint, it is possible to prevent the amount of the paint used for adjustment from becoming excessive. Then, the paint adjusted by adding the set gap composition is applied to a plate to prepare a sample plate, and the property difference between the standard plate and the sample plate is measured (step S407). Next, it is determined whether the property difference is equal to or less than a predetermined reference value (step S408). As a result, by repeating steps S404 to S408 as necessary, a paint having the final target paint properties can be produced.

[0077] The color adjustment shown in Fig. 7 and the gloss and / or viscosity adjustment shown in Fig. 8 can be performed simultaneously. In other words, the color can be adjusted by the method illustrated and explained in Fig. 7, and the gloss and / or viscosity can be adjusted by the method illustrated and explained in Fig. 8, and these can be performed simultaneously to adjust the color, gloss and / or viscosity. That is, as a preliminary preparation for prediction, steps S301 and S401 are performed, then the paint before adjustment is prepared (steps S302 and S402), and then each paint property before adjustment (color, gloss and / or viscosity) is measured and obtained (steps S303 and S403). Next, for color, a suitable blending composition is predicted by the theoretical calculation, and for gloss and / or viscosity, the color fluctuation when the raw material for adjusting the paint properties necessary for adjusting the gloss and / or viscosity is added is calculated by calculation using the fluctuation response curve data (steps S404, 405). For color, the suitable blending composition is predicted by the theoretical calculation after taking into account the color fluctuation due to the adjustment of the gloss and / or viscosity (steps S304, 305). Next, the gap blending composition for each paint property (color, gloss and / or viscosity) is set (steps S306, S406). Then, each property is measured, and depending on whether it is below a predetermined reference value, it is repeated or the adjustment is terminated (steps S304 to S309, S404 to S409).

[0078] Here, in the second embodiment and its modified example, a pre-adjustment paint property information acquisition process is further included, which acquires information on the paint properties before adjustment, prior to the gap composition calculation process (steps S306 and S406). In the gap composition calculation process (steps S306 and S406), it is preferable to calculate the gap composition continuously or stepwise so that the smaller the absolute value of the difference between the acquired pre-adjustment paint properties and the target paint properties, the closer the gap composition is to the predicted composition. As described above, the second embodiment and its variations include repeating steps S304 to S308 (steps S404 to S408), and as the paint properties of the paint gradually approach the target paint properties, the amount of compounding composition added for adjustment gradually decreases, and therefore the excess amount in the event of adding too much of the compounding composition also becomes smaller, so the risk gradually decreases. Therefore, as steps S304 to S308 (steps S404 to S408) are repeated, the smaller the absolute value of the difference between the acquired pre-adjustment paint properties and the target paint properties, the closer the gap composition is to the target paint properties, either continuously or stepwise (for example, by adopting an approach rate closer to "1"), and by setting the gap composition, the number of repetitions required to produce paint can be reduced as much as possible, thereby improving production efficiency without compromising the effect of suppressing overproduction.

[0079] In the second embodiment, in the gap composition calculation step (step S306), it is preferable to calculate the gap composition for each colorant independently. * , a * , b * Further, the gap composition is set independently for each colorant, which can further suppress overproduction while maintaining manufacturing efficiency.

[0080] For example, ΔL * If is greater than 1, the approach rate is 50% and ΔL * When ΔL is between 0.5 and 1.0, the approach rate is set to 70%. * is 0.5 or less, the approach rate can be 100%, etc. This can then be applied to each colorant independently.

[0081] Here, in the target value corresponding blend composition prediction step (step S304) in the second embodiment, when predicting the blend composition of raw materials for adjusting the paint properties suitable for obtaining the target paint properties, it is preferable to use any one of the methods of brute force search method, mathematical optimization method, and random search. Similarly, in the target value corresponding blend composition prediction step (step S404) in the modified example of the second embodiment, when predicting the suitable variation in the addition ratio of raw materials for adjusting the paint properties and / or the suitable variation in the manufacturing conditions, it is preferable to use any one of the methods of brute force search method, mathematical optimization method, and random search.

[0082] <Third embodiment> The paint manufacturing method according to the third embodiment of the present invention is a method of manufacturing paint having a target paint property by adding raw materials for adjusting the paint property to the paint before adjustment. FIG. 9 is a flowchart of the paint manufacturing method according to the third embodiment of the present invention. The example of FIG. 9 involves theoretical calculation by a computer. In the example of FIG. 9, the paint property is color. As shown in FIG. 9, in the third embodiment, first, primary data is acquired (step S501), then the primary color of the paint before adjustment is prepared (step S502), and then information on the paint property before adjustment (color in this example) is acquired (step S503: paint property information acquisition process before adjustment). These steps S501 to S503 are the same as steps S101 to S103 in the example of FIG. 1, so a detailed description will be omitted.

[0083] Next, in the third embodiment, the main colorant of the paint before adjustment is identified (step S504). Here, the "main colorant" is the colorant that is mixed in the paint in the largest amount before adjustment. Although not particularly limited, the main colorant may be identified by visually inspecting the paint before adjustment, or the main colorant may be identified by estimation based on the paint properties acquired in step S503.

[0084] Next, a condition is set in which each blend amount in the blend composition of the raw material for adjusting the paint properties of the colorant is individually limited to a candidate solution (including the case where the blend amount is 0) generated from a finite numerical range. In this example, a condition is set in which the blend composition of the main colorant is fixed to 0 (step S505). That is, a set of solutions in which the main colorant is not added is set as a precondition. Note that the target for limiting the candidate solution is the main colorant in this example, but it is not limited to the main colorant as long as it is a colorant, and multiple colorants may be targeted. Also, in this example, the blend amount of the main colorant is fixed to a single solution of 0, but it is sufficient to limit it to a finite numerical range, and it is preferable to limit it to a numerical range close to 0 including the case of 0, and such a limitation can be performed by multiplying a normal numerical range by a rate close to 0, etc.

[0085] Next, a gap target value of the paint property (color in this example) is set (step S506: gap target value setting step). Here, the difference between the paint property (color) before adjustment acquired in step S503 and the gap target value is smaller than the difference between the paint property (color) before adjustment acquired and the target paint property. Step S503 is similar to step S103 in FIG. 1, so a detailed description will be omitted.

[0086] Next, a suitable mixture composition is predicted by theoretical calculation (step S507: limited paint property adjustment mixture composition prediction step). That is, a limited paint property adjustment mixture composition, which is a mixture composition of raw materials for adjusting paint properties, is predicted by theoretical calculation under conditions in which the amounts of each ingredient in the mixture composition for adjusting paint properties of the colorant are individually limited to solution candidates generated from a finite numerical range. In this example, a mixture composition suitable for obtaining a target gap value is predicted under conditions limited to a set of solutions in which the amount of the main colorant is 0.

[0087] Next, among the predicted compositions, it is determined whether there is a composition in which the difference between the color difference of the gap target value and the calculated color difference is equal to or less than a predetermined reference value (step S508). In the limited paint property adjustment composition prediction process (step S507), if the composition of the raw material for adjusting the paint property is not calculated by theoretical calculation, some or all of the conditions are released, and the composition of the raw material for adjusting the paint property is predicted again by theoretical calculation (step S509). On the other hand, in the limited paint property adjustment composition prediction process (step S507), if the composition of the raw material for adjusting the paint property is calculated by theoretical calculation, one composition in which the difference between the color difference of the gap target value and the calculated color difference is equal to or less than a predetermined reference value (in this example, the color difference is 0.1 or less) is selected from the predicted compositions (if multiple candidates are predicted, the one with the smallest difference between the color difference of the gap target value and the calculated color difference can be selected, although this is not particularly limited). In addition, in the case of a calculation method in which only one compounding composition can be selected, the compounding composition for the raw material for adjusting the paint properties is calculated to have the smallest difference between the color difference of the gap target value and the calculated color difference. In the step of predicting a blend composition for adjusting paint properties under restrictions (step S507), it is preferable to use any one of the following methods: brute force search method, mathematical optimization method, and random search method when calculating a blend composition of raw materials for adjusting paint properties suitable for obtaining paint properties with a gap target value under the above-mentioned limited conditions.

[0088] Next, the paint adjusted by adding the selected compounding composition is applied to a plate to prepare a sample plate, and the color difference between the standard plate and the sample plate is measured using a color difference meter or the like (step S510). Next, it is determined whether the color difference measured in step S510 is equal to or less than a predetermined reference value (step S511). If the measured color difference exceeds the predetermined reference value, the process returns to step S505, and steps S505 to S511 are repeated. However, the gap target value set in step S506 is set based on the color difference obtained in step S510. On the other hand, if the measured color difference is equal to or less than the predetermined reference value, the color matching is completed (step S512). The effects of the coating material production method according to the third embodiment will be described below.

[0089] As mentioned above, it is often the case that a particular colorant with a maximum blend amount is blended close to the target value when the paint is blended before adjustment. For example, if the correct blend is 99 kg of white colorant and 1 kg of black colorant, even if each colorant is blended in the correct amount, if the black colorant has a coloring strength that is 10% higher than the original blend, 9.9 kg of white colorant must be added to return the blend to its original state, resulting in overproduction of at least 9.9 kg. In such cases, the problem of overproduction was evident. In contrast, in the third embodiment, under conditions in which the amounts of each component in the composition of raw materials for adjusting the paint properties of the colorant are individually limited to solution candidates generated from a finite numerical range, a restricted composition for adjusting the paint properties, which is the composition of raw materials for adjusting the paint properties, is predicted by theoretical calculation (step S507: restricted composition prediction process for adjusting the paint properties). The solution candidates include cases in which the amount is 0, and the restricted composition prediction process for adjusting the paint properties is repeated until the difference between the paint properties obtained by adding the predicted composition and the target paint properties becomes equal to or less than a predetermined reference value. According to the third embodiment, for example, it is possible to obtain a solution under the precondition that the blend composition that is originally close to the target value as described above is not excessively added (when the blend amount is set to 0 as a solution candidate, it is not added), so that it is possible to suppress overproduction, particularly in a situation where a specific colorant having a particularly maximum blend amount as described above is blended close to the target value at the time of intended charging. Furthermore, in the third embodiment, if no solution exists, prediction is performed again with some or all of the conditions removed, so that there is no risk of being unable to calculate a predicted value due to limiting the conditions. Furthermore, in the third embodiment, a gap target value that is lower than the original target value (the difference from the current paint properties is small) is set, and the composition of the raw materials for adjusting the paint properties that corresponds to that gap target value is predicted. From the predicted compositions, one composition is selected in which the difference between the color difference of the gap target value and the calculated color difference is equal to or less than a predetermined reference value, and the paint properties are adjusted. Therefore, when adjusting the paint properties of the paint, it is possible to prevent the amount of material used for adjustment from becoming excessive. Then, the paint adjusted by adding the selected compounding composition is applied to a plate to prepare a sample plate, and the color difference between the standard plate and the sample plate is measured using a color difference meter or the like. It is then determined whether the color difference is below a predetermined reference value. As a result, steps S505 to S511 are repeated as necessary to ultimately produce a paint having the desired paint properties. Here, the relationship between the amount of color and the change in color is not linear. * , a * , b * In contrast, in the third embodiment, the gap target value is set by multiplying the approach rate based on the paint properties (color), so that it is possible to more accurately prevent the blending amount for adjustment from becoming excessive, compared to, for example, a method of multiplying a predicted blending amount by the approach rate.

[0090] In the example shown in FIG. 9, in the gap target value corresponding composition prediction process (step S105) in the example of FIG. 1, the composition of the raw materials for adjusting the paint properties of the colorant is predicted by theoretical calculation under conditions in which each amount in the composition of the raw materials for adjusting the paint properties of the colorant is individually limited to solution candidates generated from a finite numerical range, and the solution candidates include cases in which the amount is 0. On the other hand, the method of Fig. 9 can also be applied to the method of Fig. 2, and in the gap target value corresponding mixture composition prediction step (step S205) in the example of Fig. 2, the mixture composition of the raw material for adjusting the paint properties of the colorant is predicted by calculation based on the fluctuation response curve data under conditions in which the amount of the raw material for adjusting the paint properties of the colorant in the mixture composition is individually limited to solution candidates generated from a finite numerical range, and the solution candidates can also include cases in which the amount of the raw material is 0. In this case, too, it is possible to obtain the effect of suppressing overproduction in a situation in which a specific colorant having a particularly maximum amount as described above is added close to the target value at the time of intended preparation.

[0091] Here, the computer can include a calculation unit and an acquisition unit. The calculation unit performs prediction in the gap target value corresponding mixture composition prediction process (step S105, step S205), prediction in the target value corresponding mixture composition prediction process (step S304, step S404), and prediction in the limited paint property adjustment mixture composition prediction process (step S507). The calculation unit can be a processor or the like. The acquisition unit can be configured to acquire fluctuation response curve data. The acquisition unit can be a processor (in this case, the calculation unit may have such a function, or may be a processor separate from the calculation unit), or may be, for example, a communication unit capable of receiving the above-mentioned fluctuation response curve data, or may be a reading unit having a function of reading the above-mentioned fluctuation response curve data recorded in a memory. It should be noted that the color adjustment shown in Fig. 9 and the gloss and / or viscosity adjustment shown in Fig. 2 or 8 can be performed simultaneously. In other words, the color can be adjusted by the method illustrated and explained in Fig. 9, and the gloss and / or viscosity can be adjusted by the method illustrated and explained in Fig. 2 or 8, and these can be performed simultaneously to adjust the color, gloss and / or viscosity. EXAMPLES

[0092] The present invention will be described in more detail with reference to the following examples, but the present invention is not limited thereto.

[0093] The production rates (average of 20 runs) when paints were produced by the methods according to Examples 1 to 4 and Comparative Examples 1 and 2 were compared.

[0094] First, the methods for preparing the colorants used in Examples 1 to 4 and Comparative Examples 1 and 2 will be described. <Preparation example of white colorant> 20 parts by mass of acrylic resin as resin, 35 parts by mass of isophorone as organic solvent, and 46 parts by mass of titanium oxide as white pigment were mixed, and dispersed using a sand mill (dispersion medium: glass beads) until the maximum particle diameter of the pigment coarse particles became 10 μm or less, to prepare white colorant-10. <Adjustment of colorants to reproduce lot variation in coloring strength> In order to reproduce the lot variation in coloring strength, the white colorant-8 was prepared in the same manner as white colorant-10, except that the pigment coarse particles were dispersed until the maximum particle diameter was 8 μm or less. Similarly, a white colorant-12 was prepared in the same manner as in white colorant-10, except that the pigment coarse particles were dispersed until the maximum particle size was 12 μm or less. <Preparation examples of other colorants> Except for changing the type and amount of each material as shown in Table 1 below, black colorant 1-10, black colorant 2-10, yellow colorant-10, red colorant-10, black colorant 1-8, black colorant 2-8, yellow colorant-8, red colorant-8, black colorant 1-12, black colorant 2-12, yellow colorant-12 and red colorant-12 were prepared in the same manner as in the preparation example of the white colorant. The composition of each colorant is shown in Table 1. A random number table was used to randomly select which of the different color strength lots of colorant-8, -10, and -12 would be used. Below, the same procedure was followed unless a different color strength lot was specified. For example, if "white colorant" was specified, it was randomly selected whether white colorant-8, white colorant-10, or white colorant-12 would be used, which is intended to reproduce the fact that the color strength of the colorant lot used in the actual paint manufacturing process at the factory is unknown.

[0095] [Table 1]

[0096] <Gloss adjuster> The gloss adjusting agent used was commercially available silica (silicon dioxide) used as a matting agent. <Viscosity modifier> The viscosity modifier used was isophorone, which is also used as a solvent.

[0097] <Preparation example of calculation coating composition W> 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 were added, and the resins were uniformly dissolved using a disper. Then, 100 parts by mass of the white colorant was further added, and the mixture was uniformly mixed using a disper to prepare a calculation coating composition W (solid content concentration: 48% by mass). <Preparation examples of other paint compositions for calculation> Except for changing the material type and amount as shown in Table 2 below, 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. The formulation of each calculation coating composition is shown in Table 2.

[0098] [Table 2]

[0099] <Preparation example of paint 1 before change> 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 were added and the resins were uniformly dissolved using a disper, after which 25 parts by mass of white colorant, 2 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 adjuster 1 were added and mixed uniformly using a disper to prepare paint 1 before dispersion (solids concentration: 47% by mass).

[0100] <Preparation examples of paints 2 to 4 before change> Except for changing the type and amount of each material as shown in Table 3 below, paints 2 to 4 before the change were prepared in the same manner as in the preparation example of paint 1 before the change. The formulation of each paint before the change is shown in Table 3.

[0101] [Table 3]

[0102] Details of the materials used in preparing the coating compositions are as follows: Acrylic resin: Paraloid B44 (manufactured by Rohm & Haas), solid content: 100% by weight Fluorine resin: KYNAR500 (made by ARKEMA), solid content: 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: Silica 435 (silicon dioxide, manufactured by Fuji Silysia Chemical) Organic solvent: Isophorone (ARKEMA) Organic solvent: Cyclohexanone (manufactured by Ube Industries)

[0103] <How to prepare the coating> The surface of the material (zinc-aluminum alloy plated steel sheet: 1,800 x 300 x 0.35 mm) was coated with Fine Tough G Primer (epoxy resin primer: Nippon Paint Industrial Coatings) as an undercoat paint using a roll coater (standard film thickness: 5 μm), and then baked for 60 seconds under conditions where the maximum temperature reached by the material was 210°C, forming an undercoat paint film. Next, a paint was applied onto the undercoat film using a roll coater (standard film thickness: 15 μm), and then baked for 60 seconds under conditions that resulted in a maximum material temperature of 250°C, and then immediately cooled to obtain a coating film of the paint composition.

[0104] Next, predictions for each of the Examples and Comparative Examples will be described. First, the creation of a fluctuation response curve will be described.

[0105] <Example of acquiring paint properties> The paint properties required in each of the Examples and Comparative Examples were measured by the following methods. 1) Measurement of color data The color (L) of each coating film obtained by the coating film preparation method described above was measured using a spectrophotometer LabScan XE (manufactured by HunterLab). * , a * , b * value or the L with the target standard plate * , a * , b * The difference between the values ​​was measured. 2) Measurement of gloss data For each of the coating films obtained by the above-mentioned coating film manufacturing method, the 60° gloss was measured using a gloss meter VG7000 (manufactured by Nippon Denshoku Industries Co., Ltd.). 3) Measurement of viscosity data For each of the coating compositions obtained in the above coating composition preparation examples, the number of seconds measured at 25°C using a Ford Cup No. 4 was taken as the viscosity value.

[0106] <Example of creating a fluctuation response curve> A coating film was prepared using the coating composition 1 before the change, and data on the color, gloss, and viscosity of the coating composition before the change were obtained. Next, a coating film was prepared in the same manner by adding 0.03% of a white colorant, and the color L of the coating composition after the change was obtained. * Similarly, the white colorant was added at 0.1%, 0.3%, 1%, 3%, and 10%, respectively, and the color L value of paint properties 2 to 7 after the change was obtained. * The fluctuation response curve WL(x)-1 was obtained as an approximation curve by the least squares method from the pre-fluctuation paint properties and the post-fluctuation paint properties 1 to 7. Using the same method as above, other paint property data was obtained for black colorant 1, black colorant 2, yellow colorant, red colorant, gloss modifier 1, gloss modifier 2, and viscosity modifier, and fluctuation response curves were obtained for each. The results are shown in Table 4.

[0107] The pre-adjustment coating composition 2 to 4 (pre-adjustment coating properties; L * Value: 29-90, a * Value: -1 to 13, b * The fluctuation response curves were obtained for each of the following items (value: 2 to 36, gloss: 7 to 29, viscosity: 230 to 260 seconds) in the same manner as above. The results are shown in Table 4.

[0108] [Table 4]

[0109] <Example of creating primary data> <Example 1 of obtaining absorption coefficient and scattering coefficient> The three types of coating compositions were applied, namely, the calculation coating composition W, the calculation coating composition K1, and a coating composition obtained by mixing the calculation coating composition W and the calculation coating composition K1 in an 80:20 ratio. The spectral reflectance of each was measured, and the absorption coefficient K of the calculation coating composition W and the absorption coefficient K and scattering coefficient S of the calculation coating composition K1 were calculated.

[0110] <Example 2 of obtaining absorption coefficient and scattering coefficient> Except for changing the calculation coating composition K1 to the calculation coating compositions K2, Y, and R, the absorption coefficient K and scattering coefficient S of the calculation coating composition K2, the calculation coating composition Y, and the calculation coating composition R were calculated in the same manner as in Example 1 for obtaining the absorption coefficient and scattering coefficient.

[0111] <Example of obtaining the theoretical formula for hue fluctuation> The theoretical calculation formula for hue variation was obtained using these absorption coefficients and scattering coefficients, the refractive indexes of pigments and coatings, and the Kubelka-Munk theory, Duncan theory, and Sanderson correction formula.

[0112] <Paint preparation pass / fail criteria> The criteria for passing the test were to satisfy all three of the following conditions. * Value, a * Value, b * The difference between the values ​​is ΔL * , Δa * , Δb * It was decided. 1) ΔE = √(ΔL *2 +Δa *2 +Δb *2 ) is 0.05 or less, and ΔL * , Δa * , Δb * Each of the above is less than or equal to 0.05. 2) The difference in gloss between the target value and the actual measured value must be 0.3 or less from the standard plate when the gloss value is less than 10, 0.5 or less when it is 10 or more but less than 20, and 2.0 or less when it is 20 or more. 3) The difference between the target and measured viscosity values ​​is within 5 seconds <Criteria for pass / fail number of adjustments> Of the 20 adjustments, an average number of adjustments less than 5 was considered a pass, and 5 or more was considered a fail. <Production rate pass criteria> In the 20 adjustments, a production rate of less than 105% was considered to be pass, and a rate of 105% or more was considered to be fail.

[0113] <Example 1> In Example 1, as shown in Fig. 2, a pre-adjustment paint property information acquisition step for acquiring information on the paint property before adjustment, a gap target value setting step for setting a gap target value of the paint property, and a gap target value corresponding mixture composition prediction step for predicting a mixture composition of raw materials for adjusting the paint property suitable for obtaining the paint property of the gap target value using a fluctuation response curve were performed, and the gap target value setting step and the gap target value corresponding mixture composition prediction step were repeated until the difference between the paint property obtained by adding the predicted mixture composition and the target paint property became equal to or less than a predetermined reference value. In the gap target value setting step, the difference between the acquired paint property before adjustment and the gap target value was made smaller than the difference between the acquired paint property before adjustment and the target paint property. <Pre-adjustment paint property information acquisition process> Raw paint 1 was prepared. Colorant lots were selected randomly. When the coating film properties of Paint 1 before adjustment were obtained, ΔL * Value=4.5, Δa * Value=0.3, Δb * The value was -1.4. In the same manner, the coating film properties of paints 2 to 4 before adjustment were obtained, and information on the properties of the paint before adjustment was obtained. The preparation of the paint before adjustment is summarized in Table 5 below. <Setting the gap target value for paint properties> The gap target value for paint 1 before adjustment is an approach rate of 0.7, and ΔL * Value=1.35, Δa * Value = 0.09, Δb * Value = -0.42. In the same manner, the gap target values ​​of paints 2 to 4 before adjustment were set. <Prediction process of blend composition of raw materials for adjusting paint properties> -Data entry for corrective mix prediction The lower and upper limits of the correction blend variation range for each colorant and gloss modifier, which are raw materials used to adjust the paint properties, were set to any amount in the range of 0 to 2% of the total amount of paint before adjustment, and the viscosity modifier was similarly set to a range of 0 to 10%.Based on the set range of variation for the correction blend, 10 million candidate data sets were created for combinations of the correction blend composition of the raw materials used to adjust the paint properties (combinations of the amounts of each colorant, gloss modifier, and viscosity modifier added).

[0114] [Table 5]

[0115] <Mixture prediction using variation response curves and brute force search> The 10 million candidate data of the amount of added white colorant in the first candidate formulation of 10 million possible combinations were substituted into the fluctuation response curves WL(x)-1 to Sg(x)-1 shown in Table 4 to obtain predicted values ​​of the paint properties of the adjusted paint composition 1. Specifically, the L * The fluctuation of the value and the L when black colorant 1, yellow colorant, red colorant, gloss modifier 1, viscosity modifier, nip pressure, and maximum temperature are substituted for KL(x)-1, YL(x)-1, RL(x)-1, ML(x)-1, and SL(x)-1, respectively. * The sum of all the changes in the values ​​is the L of the first candidate combination. * Similarly, the a of each candidate is * , b * The fluctuations in value, gloss, and viscosity were obtained. This was repeated 10 million times. L of the gap target value of approach rate 0.7 of paint composition 1 after adjustment * , a * , b * From the candidate data for corrected blend composition in which the difference between the predicted E value and the fluctuations in value, gloss, and viscosity was within 0.05, the difference in gloss was within 0.3, and the difference in viscosity fluctuation was within 5 seconds, one with the smallest total amount of materials added for adjusting the paint properties was extracted, and predicted values ​​for the corrected addition amount of raw materials for adjusting the paint properties and the corrected fluctuation amount of the painting conditions were obtained.

[0116] <Acquisition of paint properties of adjusted paint composition and acquisition of difference from target properties> The predicted paint property adjustment raw material correction mixture was added to the paint 1 before adjustment, and the paint composition 1 after adjustment was adjusted to obtain the paint properties. The difference from the target properties was obtained. ΔL * Value=1.1, Δa * Value = 0.09, Δb * The value was -0.4.

[0117] <Re-adjustment of the paint composition after adjustment> The paint properties of the prepared paint composition 1 did not satisfy the pass criteria for the target values ​​of the paint properties, and therefore it was judged to be unacceptable. The paint composition before the readjustment and its properties before the readjustment were the paint composition after the previous adjustment that had failed and its properties after the adjustment. As in the previous study, 10 million candidate data sets for the corrected blend composition of raw materials for adjusting the paint properties were created, and the 10 million candidate data sets for the corrected blend composition of raw materials for adjusting the paint properties and painting conditions were substituted into the fluctuation response curves WL(x)-1, KL(x)-1, YL(x)-1, RL(x)-1, ML(x)-1, and SL(x)-1 to obtain predicted paint properties for the adjusted paint composition. In the same manner as for the unadjusted paint 1, the predicted corrective blend of paint property adjusting materials was added to the unadjusted paint to adjust the adjusted paint composition and obtain the paint properties. This readjustment procedure was repeated, and it was confirmed that the paint properties of the adjusted paint composition met the target pass criteria, and the paint adjustment was completed. (Adjustment times: 2) Repeat operation 1 The process from preparation of Post-Prepared Paint Composition 1 through to the end of paint preparation, including the step of randomly selecting a colorant lot, was repeated five times. Repeat operation 2 The same operation was carried out for pre-prepared paint compositions 2, 3, and 4. Including pre-prepared paint 1, the four types of paint were prepared 5 times each for a total of 20 preparations. The average number of preparations was 3.8, and the production rate was 101%, which was acceptable.

[0118] <Example 2> In Example 2, as shown in Figures 1 and 2, a pre-adjustment paint property information acquisition step of acquiring information on the paint property before adjustment, a gap target value setting step of setting a gap target value of the paint property, and a gap target value corresponding mixture composition prediction step of predicting a mixture composition of raw materials for adjusting the paint property suitable for obtaining the paint property of the gap target value, the hue being calculated theoretically by a computer, and the gloss and viscosity being calculated by a fluctuation response curve, were performed, and the gap target value setting step and the gap target value corresponding mixture composition prediction step were repeated until the difference between the paint property obtained by adding the predicted mixture composition and the target paint property became equal to or less than a predetermined reference value. In the gap target value setting step, the difference between the acquired paint property before adjustment and the gap target value was made smaller than the difference between the acquired paint property before adjustment and the target paint property. <Pre-adjustment paint property information acquisition process> The same procedure as in Example 1 was carried out. <Setting the gap target value for paint properties> The same procedure as in Example 1 was carried out. <Prediction process of blend composition of raw materials for adjusting paint properties> - Acquire candidate data for corrective composition combinations As in Example 1, 10 million candidate data sets were created for combination candidates of the corrected blend composition of raw materials for adjusting paint properties (combinations of the amounts of each colorant, gloss modifier, and viscosity modifier added).

[0119] <Prediction of gloss and viscosity modifier blending using fluctuation response curve and brute force search> The 10 million candidate data of the amount of addition was substituted into the fluctuation response curves ML(x)-1 to Sg(x)-1 shown in Table 4, and the predicted values ​​of the paint properties of the adjusted paint composition 1 were obtained. Specifically, the L * The change in value and the L when the viscosity modifier is substituted into SL(x)-1 * The sum of the fluctuations in the values ​​is the L of the first candidate combination. * Similarly, the a of each candidate is * , b *The fluctuations in value, gloss, and viscosity were obtained. This was repeated 10 million times. From the candidate data for the corrected formulation composition in which the difference between the gloss of the target gap value of the adjusted paint composition 1 and the predicted gloss was within 0.3 and the difference in viscosity was within 5 seconds, the one with the smallest difference was extracted, and predicted values ​​for the corrected addition amount and corrected variation amount of the gloss adjuster and viscosity adjuster were obtained.

[0120] <Colorant correction blend composition prediction by theoretical calculation and mathematical optimization> A theoretical formula was obtained to calculate color variation from the corrected blend composition. The amount of colorant blended for the gap target value of the adjusted paint composition 1 varies depending on the predicted values ​​of the corrected amounts of gloss modifier 1 and viscosity modifier obtained from the fluctuation response curve. * , a * , b * Taking into account the amount of fluctuation in the values, the combination of the corrected colorant composition that gives the smallest gap target value and predicted value was calculated by mathematical optimization using a theoretical formula. Note that it was assumed that the colorant has no effect on gloss and viscosity.

[0121] <Acquisition of paint properties of adjusted paint composition> The same procedure as in Example 1 was carried out.

[0122] <Re-adjustment of the paint composition after adjustment> The same procedure as in Example 1 was carried out. The average number of times the coating composition was adjusted was 3.6, and the production rate was 101%, which was acceptable.

[0123] <Example 3> In Example 3, as shown in Figures 7 and 8, the following steps were carried out: a pre-adjustment paint property information acquisition step for acquiring information on the paint property before adjustment; a target value corresponding composition prediction step for predicting the composition of the raw material for adjusting the paint property suitable for obtaining the paint property of the target value, with the hue predicted by theoretical calculation by computer and the gloss and viscosity predicted by the fluctuation response curve; and a gap composition setting step for setting the gap composition of the raw material for adjusting the paint property. The target value corresponding composition prediction step and the gap composition prediction step were repeated until the difference between the paint property obtained by adding the predicted gap composition and the target paint property became equal to or less than a predetermined reference value. In the gap composition setting step, the set gap composition was made smaller than the predicted target value corresponding composition. Except for using a gap blending composition instead of setting a target gap value, the same procedure as in Example 2 was carried out. The average number of times the coating composition was adjusted was 4.1, and the production rate was 102%, which was acceptable.

[0124] <Example 4> In Example 4, as shown in Fig. 9, a pre-adjustment paint property information acquisition step for acquiring information on the paint property before adjustment, and a restricted paint property adjustment composition prediction step for predicting a restricted paint property adjustment composition, which is a composition of raw materials for adjusting the paint property, by theoretical calculation under conditions in which each blend amount in the blend composition of the raw materials for adjusting the paint property of the primary color is individually limited to a solution candidate generated from a finite numerical range, are performed, and the restricted paint property adjustment composition prediction step is repeated until the difference between the paint property obtained by adding the predicted blend composition and the target paint property becomes equal to or less than a predetermined reference value. The solution candidates include the case where the blend amount is 0. In <Colorant-corrected blend composition prediction by theoretical calculation and mathematical optimization>, the same procedure as in Example 2 was carried out, except that the blend correction amount of the main colorant was fixed at zero and no target gap value was set.

[0125] <Comparative Example 1> In Comparative Example 1, the composition of raw materials for adjusting the paint properties suitable for obtaining the target paint properties (the original, not the gap target value) was predicted by theoretical calculation using a computer, and the above prediction was repeated until the difference between the paint properties obtained by adding the predicted composition and the target paint properties fell below a predetermined standard value. The same procedure as in Example 2 was carried out, except that no target gap value was set and the blending composition of raw materials for adjusting the paint properties was predicted to obtain the target paint properties. The average number of times the coating composition was adjusted was 3.5, which was acceptable, but the production rate was 118%, which was unacceptable.

[0126] <Comparative Example 2> In Comparative Example 2, 27 types of paint (paint properties before adjustment: L * Value 24~94, a * Value -3 to 10, b * The experiment was carried out in the same manner as in Example 1 using the gap target value, except that the fluctuations in color, gloss, and viscosity (value -2 to 37, gloss 3 to 55, viscosity 130 to 280 seconds) were learned using a neural network from the results of approximately 1,000 variations in paint property adjustment materials, painting conditions, and paint property variations, an artificial intelligence for color prediction was created, and the predicted value of the correction fluctuation amount of the correction addition amount was obtained using the brute force search method. The average number of times the paint composition was adjusted was 5.7, and the production rate was 109%, which did not meet the pass standard.

[0127] In this disclosure, each term has the following meaning. "Number of adjustments" refers to the average number of adjustments required to pass a test when a total of 20 paint compositions, five for each of the four paint colors, were prepared and adjusted. A smaller number of adjustments means higher prediction accuracy. "Production rate" refers to the proportion of the adjusted production rate when the planned production rate of the target paint composition is taken as 1. Here, the fact that the production rate does not greatly exceed 100% means that the paint property prediction system has high prediction accuracy and the method for calculating the corrected blend composition is highly accurate, and that it is an efficient system that does not produce more paint composition than necessary, while allowing for raw materials and errors. The evaluation results are shown in Table 6 below.

[0128] [Table 6]

[0129] As shown in Table 6, according to Examples 1 to 4, the production rate was lower than that of Comparative Examples 1 and 2, and it was possible to prevent the amount of the additive for adjustment from becoming excessive when adjusting the paint properties of the paint, and it was found that the number of repeated adjustments did not increase significantly. Also, a comparison between Example 2 and Example 4 shows that Example 4, in which the paint property adjustment composition prediction step with restrictions was performed, had an even lower production rate than Example 2, in which this step was not performed. Also, a comparison between Examples 1 and 2 and Example 3 shows that the production rate was particularly low in the method in which the gap target value setting step was performed.

Claims

1. A method for producing a paint having a desired paint property by adding a raw material for adjusting the paint property to a paint before adjustment, The paint property is color, A pre-adjustment paint property information acquisition process for acquiring information on the paint property before adjustment; a gap target value setting process for setting a gap target value of a paint property; a gap target value corresponding blend composition prediction step, which is carried out after the gap target value setting step, and predicts a blend composition of raw materials for adjusting the paint properties suitable for obtaining the paint properties of the gap target value by theoretical calculation using a computer, In the gap target value setting step, an absolute value of a difference between the acquired pre-adjustment paint property and the gap target value is smaller than an absolute value of a difference between the acquired pre-adjustment paint property and a target paint property, A method for manufacturing a paint, characterized in that the gap target value setting step and the gap target value corresponding blend composition predicting step are repeated until a difference between the paint properties obtained by adding the predicted blend composition and the target paint properties becomes equal to or less than a predetermined reference value.

2. A method for producing a paint having a desired paint property by adding a raw material for adjusting the paint property to a paint before adjustment, The paint properties are one or more of color, gloss, and viscosity, A pre-adjustment paint property information acquisition process for acquiring information on the paint property before adjustment; a fluctuation response curve data acquisition step for obtaining information on the paint properties when only one of the parameters of the addition ratio of one or more colorants, the addition ratio of one or more gloss modifiers, and the addition ratio of one or more viscosity modifiers is varied based on the pre-variation paint composition, and obtaining fluctuation response curve data showing the relationship between the amount of variation of each of the parameters and the amount of variation of the paint property information; a gap target value setting process for setting a gap target value of a paint property; A gap target value corresponding blend composition prediction step, which is performed after the gap target value setting step, predicts a suitable amount of fluctuation in the addition ratio of the raw material for adjusting the paint properties by using the gap target value and the fluctuation amount response curve data by a computer, In the gap target value setting step, an absolute value of a difference between the acquired pre-adjustment paint property and the gap target value is smaller than an absolute value of a difference between the acquired pre-adjustment paint property and a target paint property, A method for producing a paint, characterized in that the gap target value setting step and the gap target value corresponding blend composition predicting step are repeated until a difference between the paint properties obtained by adding the predicted blend composition and the target paint properties becomes equal to or less than a predetermined reference value.

3. 3. The manufacturing method according to claim 1 or 2, wherein in the gap target value setting process, the gap target value is set so that the smaller the absolute value of the difference between the acquired paint properties before adjustment and the target paint properties, the closer the gap target value becomes to the target paint properties, either continuously or stepwise.

4. The paint properties include a plurality of properties, 3. The manufacturing method according to claim 1, wherein in the step of setting the target gap value, the target gap value is set independently for each of the properties.

5. In the gap target value corresponding blend composition prediction step, the blend composition of the raw material for adjusting the paint properties is predicted by theoretical calculation under conditions in which each blend amount in the blend composition of the raw material for adjusting the paint properties is individually limited to a solution candidate generated from a finite numerical range, The manufacturing method according to claim 1 , wherein the solution candidates include a case where the blending amount is 0.

6. 6. The manufacturing method according to claim 5, further comprising a step of canceling a part or all of the conditions and re-predicting the composition of the raw material for adjusting the paint properties by theoretical calculation when the composition of the raw material for adjusting the paint properties suitable for obtaining the paint properties of the target gap value is not calculated by theoretical calculation in the gap target value corresponding composition prediction step.

7. In the gap target value corresponding mixture composition prediction step, the mixture composition of the raw material for adjusting the paint properties is predicted by calculation based on the fluctuation response curve data under conditions in which the mixture amount in the mixture composition of the raw material for adjusting the paint properties is individually limited to a candidate solution generated from a finite numerical range, The manufacturing method according to claim 2 , wherein the solution candidates include a case where the blending amount is 0.

8. 8. The manufacturing method according to claim 7, further comprising a step of canceling a part or all of the conditions and re-predicting the composition of the raw material for adjusting the paint properties by calculation based on the fluctuation response curve data, when the composition of the raw material for adjusting the paint properties suitable for obtaining the paint properties of the target gap value is not calculated based on the fluctuation response curve data, in the gap target value corresponding composition prediction step.

9. The method further includes a main colorant specifying step of specifying a main colorant that is blended in the largest amount in the colorant blend composition of the paint before the adjustment, 8. The manufacturing method according to claim 5, wherein in the gap target value corresponding blend composition prediction step, a prediction is made for at least the main colorant using conditions limited to solution candidates generated from the finite numerical range.

10. A method for producing a paint having a desired paint property by adding a raw material for adjusting the paint property to a paint before adjustment, The paint property is color, A pre-adjustment paint property information acquisition process for acquiring information on the paint property before adjustment; A target value corresponding blend composition prediction step of predicting a blend composition of raw materials for adjusting the paint properties suitable for obtaining the target paint properties by theoretical calculation using a computer; A gap composition setting step of independently setting the gap composition of the raw material for adjusting the paint properties for each colorant, In the gap composition setting step, the set gap composition is smaller than the predicted target value corresponding composition, A method for producing a paint, characterized in that the target value corresponding composition prediction step and the gap composition setting step are repeated until a difference between the paint properties obtained by adding the predicted gap composition and the target paint properties becomes equal to or less than a predetermined reference value.

11. A method for producing a paint having a desired paint property by adding a raw material for adjusting the paint property to a paint before adjustment, The paint properties are at least one of gloss and viscosity, a fluctuation response curve data acquisition step for obtaining information on the paint properties when only one of the parameters of the addition ratio of one or more colorants, the addition ratio of one or more gloss modifiers, and the addition ratio of one or more viscosity modifiers is varied based on the pre-variation paint composition, and obtaining fluctuation response curve data showing the relationship between the amount of variation of each of the parameters and the amount of variation of the paint property information; A target value corresponding blend composition prediction step of predicting a suitable amount of fluctuation in the addition ratio of the raw material for adjusting the paint properties by using the target paint properties and the fluctuation amount response curve data by a computer; A gap composition setting step of setting a gap composition of the raw material for adjusting the paint properties, In the gap composition setting step, the set gap composition is smaller than the predicted target value corresponding composition, A method for producing a paint, characterized in that the target value corresponding composition prediction step and the gap composition setting step are repeated until a difference between the paint properties obtained by adding the gap composition and the target paint properties becomes equal to or less than a predetermined reference value.

12. Prior to the gap blending composition setting step, information on the paint properties before adjustment is acquired, and a pre-adjustment paint property information acquisition step is further included; 12. The manufacturing method according to claim 10 or 11, wherein in the gap composition setting step, the gap composition is calculated so that the smaller the absolute value of the difference between the acquired paint properties before adjustment and the target paint properties, the closer the gap composition is to the predicted composition continuously or stepwise.

13. 2. The manufacturing method according to claim 1, wherein in the gap target value corresponding blend composition prediction step, when calculating a blend composition of raw materials for adjusting the paint properties suitable for obtaining the paint properties of the gap target value, any one of a brute force search method, a mathematical optimization method, and a random search method is used.

14. 3. The manufacturing method according to claim 2, wherein in the gap target value corresponding blend composition prediction step, when predicting the suitable amount of variation in the addition ratio of the raw material for adjusting the paint properties, any one of a brute force search method, a mathematical optimization method, and a random search method is used.

15. The manufacturing method according to claim 10, wherein in the target value corresponding blend composition prediction step, when predicting a blend composition of raw materials for adjusting the paint properties suitable for obtaining the paint properties of the target value, any one of a brute force search method, a mathematical optimization method, and a random search method is used.

16. 12. The manufacturing method according to claim 11, wherein in the target value corresponding blend composition prediction step, when predicting the suitable amount of fluctuation in the addition ratio of the raw material for adjusting the paint properties, any one of a brute force search method, a mathematical optimization method, and a random search method is used.

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