Hue prediction system, material production method, program, hue prediction method, and hue compensation method

Abstract

SOLUTION: There is provided a hue prediction system including a hue prediction part for predicting, on the basis of composition information for identifying a composition of a material, a hue expressed by a color system of a material having a composition of the composition information, and outputting the hue as a predicted hue. The hue prediction part may predict a hue expressed by a XYZ color system, a xy color system, or Lxy color system. There may be included a learning part for learning a hue prediction model for predicting a hue from the composition information on the basis of learning data including a plurality of pairs of hues expressed by composition information and the color system of the material having the composition information.EFFECT: Predicting a hue on a color system of a material can reduce a variable amount in comparison with a case of predicting an absorption / reflection spectrum so as to reduce use amounts of a computation resource, a memory resource, and the like.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a color prediction system, a material manufacturing method, a program, a color prediction method, and a color compensation method. [Background technology]

[0002] Patent Document 1 describes a color matching method in which at least two types of powder paints having different colors are dry-mixed to match colors, the powder paint containing powder particles, the powder particles containing a thermosetting resin and a thermosetting agent, having a volume average particle diameter of 3 μm or more and 10 μm or less, and a GSDv of 1.3 or less (Claim 1)." [Prior art document] [Patent documents] [Patent Document 1] JP 2016-000794 A Summary of the Invention

[0003] In a first aspect of the present invention, there is provided a color prediction system including a color prediction unit that predicts a color expressed in a color system of a material having a composition of composition information based on composition information specifying the composition of the material, and outputs the predicted color.

[0004] In the above, the color prediction section may predict a color represented in the XYZ color system, the xy color system, or the Lxy color system.

[0005] In the above, the color prediction section may predict a color represented in the XYZ color system.

[0006] In the above, the color prediction system may include a learning unit. The learning unit may learn a color prediction model that predicts a color from composition information based on learning data including a plurality of pairs of composition information and colors expressed in a color system of a material having the composition information. The color prediction unit may predict a color using the color prediction model.

[0007] In the above, the color prediction system may include a property prediction unit and a judgment unit. The property prediction unit may predict physical properties and / or chemical properties other than the color of a material having a composition of the composition information based on the composition information. The judgment unit may judge whether the predicted color output by the color prediction unit and the physical properties and / or chemical properties predicted by the property prediction unit satisfy a predetermined criterion.

[0008] In the above, the determination unit may determine whether the color predicted by the color prediction unit is within a predetermined range in the color system. The determination unit may further determine whether the physical property and / or chemical property predicted by the property prediction unit is within a predetermined numerical range.

[0009] In the above, the physical and / or chemical properties may include the transmittance or reflectance of the material.

[0010] In the above, the physical properties and / or chemical properties may include one or more of the heat resistance, flame retardancy, electrostatic charge, elastic modulus, impact resistance, linear expansion coefficient, chemical resistance, flexibility, anti-glare properties, weather resistance, hydrophilicity, lubricity, and hardness of the material.

[0011] In the above, the material may be a resin material.

[0012] In the above, the color prediction system may further include a recommendation unit that may recommend composition information of a material that the determination unit determines satisfies a predetermined criterion.

[0013] In the above, the recommendation unit may recommend composition information of materials that constitute a set of Pareto solutions in optimizing color and physical properties and / or chemical properties, from among the multiple materials that the judgment unit has determined to satisfy a predetermined criterion.

[0014] In the above, the color prediction system may further include a generating unit that generates composition information. The color prediction unit may output a predicted color from the composition information generated by the generating unit. The characteristic prediction unit may predict physical characteristics and / or chemical characteristics.

[0015] In the above, the color prediction system may further include an input unit and a calculation unit. The input unit may input a product target color, which is a color on a color system that is a target for a product using the material, and a light source color, which is a color on a color system for the light source. The calculation unit may calculate a material target color, which is a target color for the material, from the product target color and the light source color. The judgment unit may judge whether or not the color predicted by the color prediction unit is included within an allowable range from the material target color in the color system.

[0016] In the above, the color prediction system may further include an input unit and a calculation unit. The input unit may input a product target color, which is a color on a color system that is a target for a product using the material, and a light source color, which is a color on a color system of the light source. The calculation unit may calculate a material target color, which is a color on the target color system for the material, from the product target color and the light source color. The color prediction unit may predict a color of the material in the composition information recommended by the recommendation unit and output it as a recommended color. The calculation unit may calculate and output a recommended light source color, which is a color of a light source that compensates for at least a part of a color difference, which is a difference between the recommended color and the material target color.

[0017] In the above, the calculation section may calculate and output the recommended light source color in response to determining that the color difference exceeds the threshold value.

[0018] In a second aspect of the present invention, there is provided a color compensation system including an input unit and a calculation unit. The input unit may input a product target color, which is a color on a color system that is a target for a product using a material, and a light source color, which is a color on a color system of a light source. The calculation unit may calculate a material target color, which is a color on a target color system of the material, from the product target color and the light source color, and calculate and output a recommended light source color, which is a color of a light source that compensates for at least a part of a color difference, which is a difference between a color on the color system of a specified material and a material target color.

[0019] In a third aspect of the present invention, a material manufacturing method is provided, comprising a color prediction step, a characteristic prediction step, a judgment step, and a material manufacturing step. In the color prediction step, a color expressed in a color system of a material having a composition of the composition information may be predicted based on composition information specifying the composition of the material, and output as a predicted color. In the characteristic prediction step, physical characteristics and / or chemical characteristics other than the color of the material having the composition of the composition information may be predicted based on the composition information. In the judgment step, it may be judged whether the color in the color system of the specified material, and the physical characteristics and / or chemical characteristics satisfy a predetermined criterion, based on the predicted color output in the color prediction step and the physical characteristics and / or chemical characteristics predicted in the characteristic prediction step. In the material manufacturing step, a material may be manufactured according to the composition information judged to satisfy the predetermined criterion in the judgment step.

[0020] In a fourth aspect of the present invention, there is provided a program that is executed by a computer and causes the computer to function as a color prediction unit. The color prediction unit may predict a color expressed in a color system of a material having a composition of composition information that specifies a composition of the material, based on the composition information, and output the predicted color.

[0021] In a fifth aspect of the present invention, there is provided a program that is executed by a computer and causes the computer to function as an input unit and a calculation unit. The input unit may input a product target color, which is a color on a color system that is a target for a product using a material, and a light source color, which is a color on a color system of a light source. The calculation unit may calculate a material target color, which is a color on a target color system of a material, from the product target color and the light source color, and calculate and output a recommended light source color, which is a color of a light source that compensates for at least a part of a color difference, which is a difference between a color on the color system of a specified material and a material target color.

[0022] In a sixth aspect of the present invention, there is provided a color prediction method including a color prediction step of predicting a color expressed in a color system of a material having a composition represented by composition information specifying a composition of the material, and outputting the predicted color based on the composition information.

[0023] In a seventh aspect of the present invention, there is provided a color compensation method including an input step and a calculation step. In the input step, a product target color, which is a color on a color system that is a target for a product using a material, and a light source color, which is a color on a color system of a light source, may be input. In the calculation step, a material target color, which is a color on a target color system for the material, may be calculated from the product target color and the light source color, and a recommended light source color, which is a color of a light source that compensates for at least a part of a color difference, which is a difference between a color on the color system of a specified material and a material target color, may be calculated and output.

[0024] The above summary of the invention does not list all of the features of the present invention. In addition, subcombinations of these features may also be inventions. [Brief description of the drawings]

[0025] [Figure 1] 1 shows a configuration of a system 10 according to the present embodiment. [Diagram 2] 2 shows a flow of a material manufacturing method using the system 10 according to the present embodiment. [Diagram 3] 3 shows an example of an input screen 300 for S300 in this embodiment. [Figure 4] 4 shows an example of an output screen 400 of S700 in this embodiment. [Diagram 5] 5 shows an example of an output screen 500 of S700 in this embodiment. [Figure 6] 11 shows another flow of the material manufacturing method using the system 10 according to the present embodiment. [Figure 7] 11 shows yet another flow (first half) of the material manufacturing method using the system 10 according to the present embodiment. [Figure 8] 11 shows still another flow (second half) of the material manufacturing method using the system 10 according to the present embodiment. [Figure 9] 22 illustrates an example computer 2200 in which aspects of the present invention may be embodied, in whole or in part. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0026] The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the scope of the invention according to the claims. Furthermore, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0027] 1 shows the configuration of a system 10 according to the present embodiment. The system 10 functions as a color prediction system that predicts a color based on the composition of materials. The color prediction system 10 may recommend a composition of materials that achieves a target color.

[0028] In addition, the system 10 may function as a color compensation system that outputs a light source that compensates for the difference from a target when the color of a material or the color of a product using the material differs from a target. As shown in FIG. 1, the system 10 includes a color prediction unit 110, a characteristic prediction unit 120, a learning unit 130, a generation unit 140, an input unit 150, a calculation unit 160, a determination unit 170, and a recommendation unit 180.

[0029] The color prediction unit 110 predicts the color expressed in the color system of the material having the composition of the composition information (for example, the color expressed in the XYZ color system) based on the composition information, and outputs it as a predicted color. The composition information may be information that specifies the composition of the material. The color prediction unit 110 may predict the color using a color prediction model.

[0030] The property prediction unit 120 predicts and outputs physical properties and / or chemical properties other than color of a material having the composition of the composition information based on the composition information. The predicted physical properties and / or chemical properties are also referred to as "predicted properties". The physical properties and / or chemical properties may include the transmittance, reflectance, and / or flame retardancy of the material. The property prediction unit 120 may predict the physical properties and / or chemical properties using a property prediction model.

[0031] The learning unit 130 learns the color prediction model and the characteristic prediction model. The learning unit 130 may learn these models based on learning data.

[0032] The generating unit 140 generates composition information. The generating unit 140 provides the composition information to the color predicting unit 110 and the property predicting unit 120. From the composition information generated by the generating unit 140, the color predicting unit 110 may output a predicted color. From the composition information generated by the generating unit 140, the property predicting unit 120 may predict physical properties and / or chemical properties. In addition to / instead of generating the composition information, the generating unit 140 may receive composition information from a user.

[0033] The input unit 150 inputs information required for processing of the system 10 from a user. For example, the input unit 150 may input one or more of a material target color, a product target color, and a light source color. The input unit 150 may further input target properties, which are physical properties and / or chemical properties targeted for the material, from a user.

[0034] The target color of a material is the color on the color system that is the target of the material. The target color of a product is the color on the color system that is the target of the product that uses the material. The light source color is the color on the color system of the light source.

[0035] The calculation unit 160 executes calculations related to color on the color system. For example, the calculation unit 160 calculates a material target color from a product target color and a light source color. For example, the calculation unit 160 calculates a color difference, which is a difference between a predetermined color (for example, a color on the color system of a specified material) and a material target color. For example, the calculation unit 160 calculates a recommended light source color, which is a color of a light source that compensates for at least a part of the difference color. The calculation unit 160 may output the calculation result to the determination unit 170.

[0036] The judgment unit 170 executes judgment regarding the color in the color system. For example, the judgment unit 170 judges whether the predicted color output by the color prediction unit 110 and the physical property and / or chemical property predicted by the property prediction unit 120 (i.e., the predicted property) satisfy a predetermined criterion.

[0037] The predetermined standard of the predicted color may include the material target color or a certain color range including the material target color. For example, the judgment unit 170 may judge whether the color predicted by the color prediction unit 110 is included within an allowable range from the material target color in the color system.

[0038] The predetermined standard of the predicted characteristic may be a target characteristic or a numerical range including the target characteristic. For example, the judgment unit 170 may judge whether the characteristic prediction unit 120 judges whether the predicted characteristic is included within an allowable range from the target characteristic. Note that, in this specification, the term "range" may refer to a single range (e.g., 0≦x≦1 for x) or a concatenation of multiple discontinuous ranges (e.g., 0≦x≦1 and 3≦x≦4 for x).

[0039] The recommendation unit 180 recommends composition information of a material that the determination unit 170 has determined to satisfy a predetermined standard.

[0040] System 10 may be implemented by one device (computer), or may be realized by multiple devices with different roles. When realized by multiple devices, system 10 may be a server / client system, in which case at least a part of the functions of system 10 may be realized by the server device and the rest may be realized by the client device. The client device may only have the function of sending input from a user to the server device and displaying output from the server device, in which case the substantial functions of system 10 are realized only by the server device.

[0041] The device (computer) used in system 10 may be a computer such as a PC (personal computer), a tablet computer, a smartphone, a workstation, a server computer, or a general-purpose computer, or may be a computer system in which multiple computers are connected.

[0042] Alternatively, the device (computer) used in the system 10 may be a dedicated computer designed for each purpose, or may be dedicated hardware realized by a dedicated circuit. Although not specifically described below, the system 10 may be provided with a memory / hard disk, etc., and information required for processing may be appropriately stored, and the system 10 may transmit information between each processing module such as the color prediction unit 110.

[0043] In this way, the system 10 can predict the color of a material in a color system based on the composition information of the material. When adjusting the composition of a material such as a resin, various physical and / or chemical properties as well as color may be required. For example, when a flame retardant is added to the composition to improve flame retardancy, the flame retardant may cause the material to have a yellowish color rather than a desired color. Therefore, a blue agent (also called a "bluing agent") or the like is added to offset the yellowish color, so as to approach the desired color.

[0044] Adjusting the amount of flame retardant and blue agent through trial and error to achieve the required color and characteristics requires a lot of manpower and time. When trying to completely predict the color based on composition information using a computer, it is necessary to handle the absorption / reflection spectra of materials, which have a huge number of variables, and there is a problem of consuming a large amount of computational and memory resources.

[0045] By predicting the color of a material on a color system using the system 10, the number of variables can be reduced compared to predicting the light absorption / reflectance spectrum of the material, and the amount of computational resources and storage resources used can be reduced. Furthermore, the system 10 can suggest materials that are predicted to satisfy both the physical and / or chemical properties and the color on a color system that are predetermined criteria.

[0046] 2 shows a processing flow of the system 10 according to this embodiment. The system 10 recommends composition information of materials having a target color by executing S50 to S800. Some of S50 to S800 may be omitted. Other operations may be executed in addition to S50 to S800. The system 10 may execute a color prediction method by executing S50 and S200 to S400.

[0047] In S50, the learning unit 130 learns a color prediction model that predicts color from composition information based on the learning data. The learning data includes a plurality of pairs of information on explanatory variables and information on target variables. For example, the learning data may include a plurality of pairs of composition information and color expressed in the color system of a material having the composition information. The composition information may include information that identifies all or some of the raw materials contained in the material.

[0048] The material may be any material that can be adjusted in color and has adjustable physical and / or chemical properties, and may be, for example, a resin material. Examples of the resin material include polyethylene terephthalate, polyethylene naphthalate, polyethersulfone, polyimide, nylon, polystyrene, polyvinyl alcohol, ethylene-vinyl alcohol copolymer, fluororesin film, polyvinyl chloride, polyethylene, polypropylene, cyclic polyolefin, cellulose, acetyl cellulose, polyvinylidene chloride, aramid, polyphenylene sulfide, polyurethane, polycarbonate, poly(meth)acrylic resin, phenolic resin, epoxy resin, polyarylate, styrene-isobutylene-styrene block copolymer (SIBS), allyl diglycol carbonate, cycloolefin copolymer, cycloolefin polymer, etc. Alternatively, the material may be ceramics, metal material, paper material, etc.

[0049] For example, the composition information may include, for each of the raw materials contained in the material, the name of the raw material of the material, the abbreviation of the raw material, a chemical formula representing the raw material (compositional formula, structural formula, or SMILES notation, etc.), a symbol corresponding to the raw material, and / or the content of the raw material (e.g., weight, volume, number of moles, weight %, volume %, mole %, weight ratio, volume ratio, molar ratio, or other units, etc.), information about the raw material (molecular weight, molecular weight distribution, polymerization ratio, pH). As an example, the composition information may be a symbol representing the raw material for each of the raw materials, and the content of the raw material (weight %). The composition information may include only information about the solid content of the raw materials, and may not include information about volatile components such as solvents.

[0050] The color shades represented by a color system may be colors in a color system defined by color matching functions. For example, the color shades represented by a color system may be colors in the XYZ color system (CIE1931) or a color system derived from the XYZ color system (CIE1931).

[0051] Specifically, the color shades represented by the color system may be the XYZ color system (CIE1931), the xy color system (i.e., xy chromaticity), the uv color system (i.e., uv chromaticity), the Lxy color system, the Luv color system (CIE1976), or the Lab color system. Also, the color shades represented by the color system may be the RGB color system or the Yellow Index (YI).

[0052] For example, when using a 1 nm-step spectrum, 400-dimensional variable calculations are required to cover visible light (380 to 780 nm), but by using a color system, the amount of variables can be compressed to a few dimensions, which can greatly reduce the computational and storage resources of a computer.

[0053] In particular, by using the XYZ color system, it is possible to achieve prediction accuracy close to that achieved by performing calculations using spectral data. Furthermore, since the XYZ color system is compatible with many colorimeters and can be converted from the XYZ color system to many other color systems, it is particularly preferable to use the XYZ color system.

[0054] The system 10 may use a unified color system throughout the processes of S50 to S800. For example, if the color system used in the color prediction model is the XYZ color system, the system 10 may use the XYZ color system as the color system in the subsequent processes.

[0055] Through the processes of S50 to S800, the system 10 may use a plurality of color systems. For example, the XYZ color system may be used only in S50, and YI or uv chromaticity may be used in other processes (e.g., S200). In this case, the calculation unit 160 may appropriately perform conversion processing between the color systems.

[0056] The learning unit 130 may learn a known regression model as a color prediction model. For example, the learning unit 130 may learn a nonlinear regression model such as support vector regression (SVR), random forest, neural network, Gaussian process regression, etc. Also, for example, the learning unit 130 may learn a linear regression model such as linear regression, ridge regression, partial least squares regression (PLS), etc. The learning unit 130 may learn a composite model that combines multiple models.

[0057] Next, in S100, the learning unit 130 learns a property prediction model that predicts physical properties and / or chemical properties from the composition information based on the learning data. The learning data may include a plurality of pairs of composition information and physical properties and / or chemical properties of a material having the composition information.

[0058] The physical and / or chemical properties may be one or more of the physical and chemical properties of the material. For example, the physical and / or chemical properties may include one or more of the transmittance / reflectance of the material to a predetermined wavelength of light, heat resistance, electrostatic charge, elastic modulus, impact resistance, linear expansion coefficient, chemical resistance, flexibility, anti-glare property, weather resistance (e.g., resistance to heat deterioration and / or resistance to ultraviolet deterioration), hydrophilicity, lubricity, and hardness. Heat resistance may be evaluated by various methods such as thermogravimetric analysis (TGA), differential thermal analysis (DTA), differential scanning calorimetry (DSC), yellowing behavior evaluation, and / or residual monomer amount measurement.

[0059] The learning unit 130 may learn a known regression model as the characteristic prediction model, or may learn a model similar to the one given as the color prediction model. Note that the characteristic prediction model and the color prediction model may be the same type of model or different types of models.

[0060] Next, in S200, the input unit 150 inputs a material target color, which is a color in a color system that is a target for the material, from the user. The material target color may be a representation of the spectral reflectance spectrum and / or the spectral transmittance spectrum of the material in a color system. The input unit 150 may input a color in a color system used in the color prediction model (for example, a stimulus value of the XYZ color system or the Lab color system) as the material target color.

[0061] The input unit 150 may input a color in a color system different from that used in the color prediction model. In this case, the calculation unit 160 may convert the input color into the color system used in the color prediction model.

[0062] The input unit 150 may input a single color (i.e., a color coordinate point in a color system) as the material target color, or alternatively, may input a range of colors (i.e., a color coordinate plane or space in a color system).

[0063] The input unit 150 may input the numerical values ​​or ranges of some variables in the color system as the material target color. For example, the input unit 150 may input only the range of the stimulus value b of the Lab color system.

[0064] The input unit 150 may receive target properties, which are physical properties and / or chemical properties of a material, from a user. For example, the input unit 150 may receive a numerical value or a numerical range of a parameter representing a target property (e.g., a burning rate representing flame retardancy) from a user. The input unit 150 may receive one or more types of target properties.

[0065] 3 shows an example of an input screen 300 in S300 in this embodiment. In S200, the input unit 150 may input a target color and target characteristics of a material via the input screen 300.

[0066] The input screen 300 includes an input field 310 for selecting the type of target property. The input field 310 allows the user to select the target physical property and / or chemical property of the material. In the example of FIG. 3, "flame retardant" is selected.

[0067] The input screen 300 includes an input field 320 for inputting the numerical value of the target characteristic. The input field 310 allows the user to input a single numerical value or a numerical range of the target characteristic. In the example of FIG. 3, the numerical value of "flame retardancy" (burning speed) is "0 to 100 kW / m 2 " is entered.

[0068] The input screen 300 includes an input field 330 for inputting a target color of a material. The user may use the input field 310 to input a color in a color system that is a target for the material. In the example of Fig. 3, "X value", "Y value", and "Z value" that represent the color (i.e., stimulus value) in the "XYZ color system" are input. In response to the user pressing a start button 340, the input unit 150 may proceed to the next process.

[0069] Next, the system 10 repeats the loop process of S300 to S600 to search for a composition that satisfies the color shade and physical / chemical characteristics in the color system.

[0070] In S300, the generating unit 140 generates composition information. The generating unit 140 may generate composition information suitable for search.

[0071] In the first S300, the generating unit 140 may randomly generate composition information. As an example, the generating unit 140 may randomly select ingredients to be included in the material from ingredient options and set the content amount randomly. The generating unit 140 may randomly set the content amount of the ingredient specified by the user.

[0072] In the second or subsequent times of S300, the generating unit 140 may generate composition information that may satisfy the color and physical / chemical characteristics in the color system. For example, the generating unit 140 may generate composition information using an existing search method. For example, the generating unit 140 may perform a search using a genetic algorithm, a local search method, a hill climbing method, a simulated annealing method, or the like. The search may use the processing results of S400 to S600 described below.

[0073] The generating unit 140 may also generate composition information randomly in the second or subsequent S300. The generating unit 140 may also generate composition information based on an experimental design in the second or subsequent S300. In the first and / or second or subsequent S300, the generating unit 140 may use composition information input by the user instead of generating composition information.

[0074] Next, in S400, the color prediction unit 110 predicts the color expressed in the color system of the material having the composition of the composition information based on the composition information generated in S300. The color prediction unit 110 inputs the composition information to the color prediction model trained in S50, and obtains the color output from the color prediction model.

[0075] Next, in S500, the property prediction unit 120 predicts physical properties and / or chemical properties other than color of the material having the composition of the composition information based on the composition information generated in S300. The property prediction unit 120 inputs the composition information to the property prediction model trained in S100, and obtains the physical properties and / or chemical properties (i.e., predicted properties) output from the property prediction model.

[0076] Next, in S600, the determination unit 170 determines whether to end the search of S300 to S600. For example, the determination unit 170 determines whether both the predicted color predicted in S400 and the predicted characteristic predicted in S500 satisfy a predetermined criterion. For example, the determination unit 170 may determine whether the predicted color is included in a predetermined range in the color system, and determine whether the predicted characteristic is included in a predetermined numerical range.

[0077] As an example, the determination unit 170 may determine whether the predicted color matches the material target color input in S200, or whether it is within a predetermined range of the material target color. As an example, the determination unit 170 may determine whether the predicted color is included in the color range of the material target color input in S200. If the material target color and the predicted color have different color systems, the calculation unit 160 may convert one or both of them to match the color systems.

[0078] For example, the determination unit 170 may determine whether the predicted characteristic matches the target characteristic input in S200, or is within a predetermined range of the target characteristic. For example, the determination unit 170 may determine whether the predicted characteristic is included in the range of the target characteristic input in S200.

[0079] When the determination unit 170 determines that the predetermined criterion is satisfied a predetermined number of times (one or more times), it may end the search of S300 to S600 and proceed to S700. When the determination unit 170 performs the processes of S300 to S600 a predetermined number of times, it may end the search and proceed to S700. When the determination unit 170 determines that the search is to be continued, it returns the process to S300.

[0080] The judgment unit 170 may evaluate using an evaluation function that outputs the degree to which the predicted color matches the material target color and / or the degree to which the predicted characteristic matches the target characteristic as an evaluation value. The judgment unit 170 may determine that the predetermined criterion is met when the evaluation value exceeds a threshold in the most recent loop. Alternatively, the judgment unit 170 may determine that the predetermined criterion is met when the number of evaluation values ​​exceeding the threshold reaches a predetermined number.

[0081] In S700, the recommendation unit 180 recommends the composition information of the ingredients that the judgment unit 170 judges to satisfy the predetermined criteria in S600. The recommendation unit 180 may recommend the composition information of the ingredients that the judgment unit 170 judges to satisfy the predetermined criteria in S600, the composition information of the ingredients that has a high evaluation value in a predetermined percentage (e.g., the top 30%).

[0082] The recommendation unit 180 may output the recommended composition information as the recommended composition information. The recommendation unit 180 may output a predicted color and predicted characteristics predicted from the recommended composition information. The predicted color and predicted characteristics predicted in S400 to S500 may be used.

[0083] The recommendation unit 180 may output the predicted color in the XYZ color system. If the color predicted in S400 to S500 is not in the XYZ color system, the calculation unit 160 may convert the predicted color into the XYZ color system, and the recommendation unit 180 may output it.

[0084] 4 shows an example of an output screen 400 of S700 in this embodiment. As shown in the figure, the recommendation unit 180 may output recommended composition information, predicted characteristics (flame retardancy), and predicted color on the output screen 400. The recommendation unit 180 may output the error between the material target color and the predicted color ("Δ error" in the figure), and / or the error between the target characteristic and the predicted characteristic.

[0085] 5 shows an example of an output screen 500 of S700 in this embodiment. The recommendation unit 180 may recommend composition information of materials that constitute a set of Pareto solutions in optimizing color and physical properties and / or chemical properties, from among the multiple materials that the determination unit 170 determined in S600 to satisfy a predetermined criterion.

[0086] The recommendation unit 180 may project and display a collection of multiple composition information consisting of recommended composition information 510, recommended composition information 520, recommended composition information 530, recommended composition information 540, recommended composition information 550, recommended composition information 560, and recommended composition information 570 onto a plane defined by two axes, the difference in predicted color and the difference in predicted characteristics.

[0087] The difference in predicted color may be the difference between the target color of the material and the predicted color, and the smaller this difference is, the better the material is. The difference in predicted characteristics may be the difference between the target characteristics and the predicted characteristics, and the smaller this difference is, the better the material is. The recommendation unit 180 may position the composition information closer to the origin (lower left in the figure) on the output screen 500 as the difference in predicted color and the difference in predicted characteristics are smaller.

[0088] The recommended composition information 510 to 590 may constitute a set of Pareto solutions. On the output screen 500, the closer the composition information is to the origin (the lower and leftward it is located), the more desirable the composition information is.

[0089] In response to a user specifying (e.g., hovering cursor 580 over) a particular composition information being displayed (e.g., recommended composition information 530), the recommendation unit 180 may display the specified composition information, predicted characteristics corresponding to the composition information, and / or predicted color in a predicted information window 590.

[0090] Next, in S800, a material is manufactured based on any of the recommended composition information recommended by the recommendation unit 180. For example, the user may mix ingredients according to the recommended composition information recommended by the recommendation unit 180, and then perform necessary processing (e.g., heating, ultraviolet irradiation, drying, molding, etc.) to manufacture a material. The system 10 may automatically execute material manufacturing on behalf of the user. This allows the material to be manufactured according to the composition information determined in S600 to satisfy the predetermined criteria.

[0091] As described above, according to this embodiment, a material manufacturing method for manufacturing a material having a composition predicted to satisfy a predetermined standard is realized. Also, according to S400 of this embodiment, a color prediction method for predicting a color based on composition information is realized.

[0092] According to this embodiment, by predicting the color of a material on a color system, the number of variables can be reduced compared to the case of predicting the light absorption / reflection spectrum of the material, and the consumed computational resources and storage resources can be reduced. Furthermore, according to this embodiment, it is possible to propose and / or manufacture a material that is predicted to satisfy a predetermined standard in both physical properties and / or chemical properties and color on a color system.

[0093] 6 shows another flow of the material manufacturing method by the system 10 according to the present embodiment. The system 10 recommends material composition information by executing S50 to S800. Some of S50 to S800 may be omitted. Other operations may be executed in addition to S50 to S800. The system 10 may execute a color prediction method by executing S50 and S200 to S400.

[0094] In the flow of this embodiment, steps S220 and S240 in the flow of FIG. 2 may be executed instead of step S200.

[0095] In S220, the input unit 150 receives the product target color and the light source color from the user.

[0096] The target color of a product is a color on a color system that is the target of a product that uses a material. For example, the product may be an optical device that uses a transparent material as an optical member. For example, the product may be a home appliance that uses an opaque material as a part.

[0097] The target color of a product is different from the color expressed in a color system of the spectral reflectance spectrum or the spectral transmittance spectrum of a material, and is the color in a color system of transmitted light and / or reflected light when light is irradiated from a light source. The target color of a product may be a stimulus value such as a numerical value of the XYZ color system. Alternatively, the target color of a product may be a chromaticity such as xy chromaticity.

[0098] The light source color is a color in the color system of the light source. For example, the light source may be a light-emitting element that is a component having light-emitting capability, such as an incandescent light bulb, a fluorescent tube, and / or a light-emitting diode. Alternatively, the light source may be a light source device incorporating a light-emitting element. For example, the light source may be a light source device combining a light-emitting element with optical members such as a diffuser plate, a light-collecting plate, a lens, a reflector, and a light-transmitting plate. The light source may be a standard light source.

[0099] The light source color may be a stimulus value such as a numerical value of the XYZ color system. Alternatively, the light source color may be a chromaticity such as xy chromaticity. The input unit 150 further receives target characteristics input from the user.

[0100] In S240, the calculation unit 160 calculates the material target color from the product target color and the light source color. The color of an object illuminated by a light source (i.e., a product) can be calculated from the color of the light source (i.e., the light source color) and the color of the product material itself.

[0101] For example, the color of a product in the XYZ color system can be calculated by multiplying the color of the light source by the color of the material itself. Conversely, the color of the material itself can be calculated by dividing the color of the product by the color of the light source.

[0102] Therefore, the calculation unit 160 may calculate the material target color by dividing the product target color by the light source color. As an example, the calculation unit 160 may calculate X_opm (material target color in the XYZ color system) by calculating X_LCD (product target color in the XYZ color system) / X_LED0 (light source color in the XYZ color system).

[0103] When the product target color and / or the light source color is a chromaticity such as xy chromaticity, the calculation unit 160 may execute the above calculation after changing the chromaticity to a stimulus value such as a stimulus value of the XYZ color system. In this case, one of the stimulus values ​​(e.g., the Y value of the XYZ values) may be set to a predetermined value or a value input by the user (e.g., Y=100).

[0104] After calculating the material target color tone in S240, the system 10 may execute the flow from S300 onwards in the same manner as in the embodiment of FIG.

[0105] 7 and 8 show the first and second halves of yet another flow of the material manufacturing method by the system 10 according to the present embodiment. The system 10 recommends material composition information by executing S50 to S1000. Some of S50 to S1000 may be omitted. Other operations may be executed in addition to S50 to S1000. The system 10 may execute a color prediction method by executing S50 and S200 to S400.

[0106] In this embodiment, the system 10 may execute S50 to S700 in the same manner as in the embodiment of Fig. 6. In this embodiment, the system 10 may execute S900 after S700.

[0107] In S900, the calculation unit 160 calculates a color difference, which is the difference between the color on the color system of the material having the recommended composition information and the material target color. For example, the color prediction unit 110 first predicts the color on the color system of the material of the recommended composition information recommended by the recommendation unit 180 in S700 and outputs it as a recommended color. Next, the calculation unit 160 calculates a color difference, which is the difference between the recommended color and the material target color.

[0108] The color difference may be the difference between a predicted product color, which is the color of a product using a material predicted from the recommended color, and a product target color. The calculation unit 160 may calculate the color difference as the difference between the product target color and the product obtained by multiplying the recommended color by the light source color.

[0109] The color difference may be a difference in stimulus values ​​such as XYZ values, i.e., a distance in a (stimulus value such as XYZ) space. Alternatively, the color difference may be a difference in chromaticity such as xy chromaticity calculated from stimulus values, i.e., a distance on a (chromaticity value such as xy) plane.

[0110] Next, in S950, the calculation unit 160 judges whether the color difference calculated in S900 exceeds a threshold value. If the calculation unit 160 judges that the color difference exceeds the threshold value, the calculation unit 160 may proceed to S1000, and if not, the calculation unit 160 may end the process. Note that S950 may be omitted, and S1000 may be executed after S900.

[0111] In S1000, the calculation unit 160 calculates and outputs a recommended light source color, which is a color of a light source that compensates for at least a part of the color difference. The calculation unit 160 may calculate, as the recommended light source color, a color of a light source required to realize a product target color when a material having the recommended composition information is used in a product.

[0112] For example, the calculation section 160 may calculate the recommended light source color by dividing the product target color expressed in XYZ stimulus values ​​by the recommended color expressed in XYZ stimulus values. For example, when the product target color is a chromaticity such as xy chromaticity, the calculation section 160 may calculate the recommended light source color by solving a simultaneous equation including the chromaticity value of the product target color and the recommended color.

[0113] For example, if the product target color (xy chromaticity) is x p , y p The stimulation value of the recommended composition is X r , Y r , Z r When expressed as: product target color (XYZ stimulus value) X p , Y p , Z p teeth, x p =X p / (X p +Y p +Z p ), y p =Y p / (X p +Y p +Z p ), 1-(x p +y p )=Z p / (X p +Y p +Z p ) It is expressed as:

[0114] Here, (X p +Y p +Z p ) is the sum of the X, Y, and Z values ​​of the light source color (the color of the current light source) (X c +Y c +Z c )=K, then X p =x p ×K Y p =y p ×K Z p =(1-x p -y p )×K It becomes.

[0115] In addition, X p , Y p , Z p Recommended color tone X r , Y r , Z r By dividing by, the recommended light source color tone X rl , Y rl , Z rl can be calculated. X rl =X p / X r Y rl =Y p / Y r Z rl =Z p / Z r

[0116] The calculation unit 160 calculates the recommended light source color X rl , Y rl , Z rl The calculation unit 160 may output the recommended light source color X rl , Y rl , Z rl may be converted to chromaticity xy and output.

[0117] Recommended part 180 is recommended light source color X rl , Y rl , Zrl A light source element or light source device that realizes the chromaticity (or converted chromaticity xy) may be selected from the light source elements or light source devices included in the existing options and output. For example, the calculation unit 160 may calculate the spatial distance (or distance on a chromaticity plane) between the current light source color and the stimulus value of the recommended light source color, and further calculate the spatial distance (or distance on a chromaticity plane) between the color of the light source element or light source device included in the options and the stimulus value of the recommended light source color. Then, the determination unit 170 may determine whether the distance of any of the light source elements or light source devices in the options is closer than the distance of the current light source color, and if so, the recommendation unit 180 may suggest the light source element or light source device of the closest option.

[0118] Thus, according to this embodiment, in response to determining in S950 that the color difference exceeds the threshold, the calculation unit 160 calculates and outputs the recommended light source color in S1000. According to this embodiment, by using a light source with the recommended light source color (or a light source with a color similar thereto), it is possible to adjust the color of the product to a desired range without changing the material or color of the product.

[0119] In this embodiment, the difference color is calculated based on the color of the recommended composition information recommended in S700 and the material target color, but is not limited to this. For example, the calculation unit 160 may calculate a color difference, which is the difference between the color on the color system of the material of the composition information specified by the user and the material target color, and calculate and output a recommended light source color, which is the color of a light source that compensates for at least a part of the color difference. In this case, the system 10 functions as a color compensation system that realizes a color compensation method.

[0120] Various embodiments of the present invention may be described with reference to flow charts and block diagrams, where the blocks may represent (1) stages of a process in which operations are performed or (2) sections of an apparatus responsible for performing the operations. Particular stages and sections may be implemented by dedicated circuitry, programmable circuitry provided with computer readable instructions stored on a computer readable medium, and / or a processor provided with computer readable instructions stored on a computer readable medium. Dedicated circuitry may include digital and / or analog hardware circuitry and may include integrated circuits (ICs) and / or discrete circuits. Programmable circuitry may include reconfigurable hardware circuitry including logical AND, logical OR, logical XOR, logical NAND, logical NOR, and other logical operations, memory elements such as flip-flops, registers, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), and the like.

[0121] A computer-readable medium may include any tangible device capable of storing instructions that are executed by a suitable device, such that the computer-readable medium having instructions stored thereon comprises an article of manufacture that includes instructions that can be executed to create means for performing the operations specified in the flowcharts or block diagrams. Examples of computer-readable media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, and the like. More specific examples of computer-readable media may include floppy disks, diskettes, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), electrically erasable programmable read-only memories (EEPROMs), static random access memories (SRAMs), compact disk read-only memories (CD-ROMs), digital versatile disks (DVDs), Blu-ray disks, memory sticks, integrated circuit cards, and the like.

[0122] The computer readable instructions may include either assembler instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, JAVA®, C++, etc., and conventional procedural programming languages ​​such as the “C” programming language or similar programming languages.

[0123] Computer readable instructions may be provided to a processor or programmable circuitry of a programmable data processing apparatus, such as a general purpose computer, special purpose computer, or other computer, either locally or over a local area network (LAN), a wide area network (WAN) such as the Internet, etc., to execute the computer readable instructions to create means for performing the operations specified in the flowcharts or block diagrams. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

[0124] 9 illustrates an example of a computer 2200 in which aspects of the present invention may be embodied in whole or in part. Programs installed on the computer 2200 may cause the computer 2200 to function as or perform operations associated with an apparatus or one or more sections of the apparatus according to an embodiment of the present invention, and / or to perform a process or steps of a process according to an embodiment of the present invention. Such programs may be executed by the CPU 2212 to cause the computer 2200 to perform specific operations associated with some or all of the blocks of the flowcharts and block diagrams described herein.

[0125] The computer 2200 according to this embodiment includes a CPU 2212, a RAM 2214, a graphic controller 2216, and a display device 2218, which are connected to each other by a host controller 2210. The computer 2200 also includes input / output units such as a communication interface 2222, a hard disk drive 2224, a DVD-ROM drive 2226, and an IC card drive, which are connected to the host controller 2210 via an input / output controller 2220. The computer also includes legacy input / output units such as a ROM 2230 and a keyboard 2242, which are connected to the input / output controller 2220 via an input / output chip 2240.

[0126] The CPU 2212 operates according to a program stored in the ROM 2230 and the RAM 2214, thereby controlling each unit. The graphic controller 2216 retrieves image data generated by the CPU 2212 into a frame buffer or the like provided in the RAM 2214 or into the graphic controller 2216 itself, and causes the image data to be displayed on the display device 2218.

[0127] The communication interface 2222 communicates with other electronic devices via a network. The hard disk drive 2224 stores programs and data used by the CPU 2212 in the computer 2200. The DVD-ROM drive 2226 reads programs or data from the DVD-ROM 2201 and provides the programs or data to the hard disk drive 2224 via the RAM 2214. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0128] The ROM 2230 stores therein a boot program or the like executed by the computer 2200 upon activation, and / or a program that depends on the hardware of the computer 2200. The input / output chip 2240 may also connect various input / output units to the input / output controller 2220 via a parallel port, a serial port, a keyboard port, a mouse port, and the like.

[0129] The programs are provided by a computer-readable medium such as a DVD-ROM 2201 or an IC card. The programs are read from the computer-readable medium, installed in the hard disk drive 2224, the RAM 2214, or the ROM 2230, which are also examples of computer-readable media, and executed by the CPU 2212. Information processing described in these programs is read by the computer 2200, and brings about cooperation between the programs and the various types of hardware resources described above. An apparatus or method may be constructed by realizing information manipulation or processing according to the use of the computer 2200.

[0130] For example, when communication is performed between the computer 2200 and an external device, the CPU 2212 may execute a communication program loaded in the RAM 2214 and instruct the communication interface 2222 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 2212, the communication interface 2222 reads transmission data stored in a transmission buffer processing area provided in the RAM 2214, the hard disk drive 2224, the DVD-ROM 2201, or a recording medium such as an IC card, and transmits the read transmission data to the network, or writes reception data received from the network to a reception buffer processing area or the like provided on the recording medium.

[0131] The CPU 2212 may cause all or a necessary portion of a file or database stored in an external recording medium such as the hard disk drive 2224, the DVD-ROM drive 2226 (DVD-ROM 2201), an IC card, etc. to be read into the RAM 2214, and perform various types of processing on the data on the RAM 2214. The CPU 2212 then writes back the processed data to the external recording medium.

[0132] Various types of information, such as various types of programs, data, tables, and databases, may be stored in the recording medium and undergo information processing. The CPU 2212 may perform various types of processing on the data read from the RAM 2214, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search / replacement, etc., as described throughout this disclosure and specified by the instruction sequence of the program, and write back the results to the RAM 2214. The CPU 2212 may also search for information in a file, database, etc. in the recording medium. For example, when a plurality of entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored in the recording medium, the CPU 2212 may search for an entry that matches a condition, in which an attribute value of the first attribute is specified, from among the plurality of entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0133] The above-described programs or software modules may be stored in a computer-readable medium on the computer 2200 or in the vicinity of the computer 2200. Also, a recording medium such as a hard disk or a RAM provided in a server system connected to a dedicated communication network or the Internet can be used as a computer-readable medium, thereby providing the programs to the computer 2200 via the network.

[0134] Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It is clear to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the description of the claims that such modifications and improvements can also be included in the technical scope of the present invention.

[0135] It should be noted that the order of execution of each process, such as operations, procedures, steps, and stages, in the apparatus, system, program, and method shown in the claims, specifications, and drawings is not specifically indicated as "before" or "prior to," and that the process may be performed in any order unless the output of a previous process is used in a subsequent process. Even if the operational flow in the claims, specifications, and drawings is described using "first," "next," etc. for convenience, it does not mean that the process must be performed in this order. The expression "A and / or B" may mean "A, B, or A and C." The expression "A, B, and / or C" may mean "any one of A, B, and C, or any combination of two or more of them." [Explanation of symbols]

[0136] 10. System 110 Color Prediction Section 120 Characteristics Prediction Section 130 Learning Department 140 Generation part 150 Input section 160 Calculation Unit 170 Judgment Department 180 Recommendation Section 300 Input screen 310 Input field 320 Input field 330 Input field 340 Start button 400 Output Screen 500 output screens 510 Recommended Composition Information 520 Recommended Composition Information 530 Recommended Composition Information 540 Recommended Composition Information 550 Recommended Composition Information 560 Recommended Composition Information 570 Recommended Composition Information 580 Cursor 590 Forecast Information Window 2200 Computer 2201 DVD-ROM 2210 Host Controller 2212 CPU 2214 RAM 2216 Graphics Controller 2218 Display Device 2220 Input / Output Controller 2222 Communication Interface 2224 Hard Disk Drive 2226 DVD-ROM drive 2230 ROM 2240 Input / Output Chip 2242 Keyboard

Claims

1. A color prediction unit predicts a color represented by a color system of a material having a composition of the composition information based on the composition information specifying the composition of the material, and outputs the predicted color. Color prediction system.

2. The color prediction unit predicts a color represented by an XYZ color system, an xy color system, or an Lxy color system. The color prediction system according to claim 1 .

3. The color prediction unit predicts a color represented by an XYZ color system. The color prediction system according to claim 1 .

4. a learning unit that learns a color prediction model that predicts the color from composition information based on learning data including a plurality of pairs of composition information and colors expressed in a color system of a material having the composition information; The color prediction unit predicts a color using the color prediction model. The color prediction system according to claim 1 .

5. A characteristic prediction unit that predicts physical characteristics and / or chemical characteristics other than color of a material having a composition of the composition information based on the composition information; a judgment unit that judges whether the predicted color output by the color prediction unit and the physical property and / or chemical property predicted by the property prediction unit satisfy a predetermined criterion; The color prediction system according to claim 1 , further comprising:

6. The determination unit is determining whether the color predicted by the color prediction unit is included in a predetermined range in a color system; determining whether the physical property and / or chemical property predicted by the property prediction unit is within a predetermined numerical range; The color prediction system according to claim 5 .

7. The physical and / or chemical properties include the transmittance or reflectance of the material; The color prediction system according to claim 5 .

8. The physical and / or chemical properties include one or more of the heat resistance, flame retardancy, electrostatic charge, elastic modulus, impact resistance, linear expansion coefficient, chemical resistance, flexibility, anti-glare property, weather resistance, hydrophilicity, lubricity, and hardness of the material; The color prediction system according to claim 5 .

9. The material is a resin material. The color prediction system according to claim 1 .

10. The judgment unit further includes a recommendation unit that recommends composition information of a material that the judgment unit judges to satisfy a predetermined standard. The color prediction system according to claim 5 .

11. The recommendation unit is Recommend composition information of materials that constitute a set of Pareto solutions in optimizing color and physical properties and / or chemical properties from among the plurality of materials that the judgment unit judges to satisfy a predetermined criterion. The color prediction system according to claim 10.

12. A generating unit that generates composition information, The color prediction unit outputs a predicted color from the composition information generated by the generation unit, and the characteristic prediction unit predicts physical characteristics and / or chemical characteristics. The color prediction system according to claim 5 .

13. an input unit for inputting a product target color, which is a color on the color system that is a target for a product using the material, and a light source color, which is a color on the color system of a light source; A calculation unit that calculates a material target color, which is a target color of a material, from the product target color and the light source color; Further comprising: The determination unit determines whether or not the color predicted by the color prediction unit is within an allowable range from the material target color in a color system. The color prediction system according to claim 6.

14. an input unit for inputting a product target color, which is a color on the color system that is a target for a product using the material, and a light source color, which is a color on the color system of a light source; a calculation unit that calculates a material target color, which is a target color of a material on the color system, from the product target color and the light source color; Further comprising: The color prediction unit predicts the color of the material of the composition information recommended by the recommendation unit and outputs it as a recommended color, The calculation unit calculates and outputs a recommended light source color, which is a color of a light source that compensates for at least a part of a color difference, which is a difference between the recommended color and the material target color. The color prediction system according to claim 10.

15. The calculation unit calculates and outputs the recommended light source color in response to determining that the color difference exceeds a threshold value. The color prediction system according to claim 14.

16. an input unit for inputting a product target color, which is a color on a color system that is a target for a product using the material, and a light source color, which is a color on the color system of a light source; a calculation unit that calculates a material target color, which is a color on the color system of a target material, from the product target color and the light source color, and calculates and outputs a recommended light source color, which is a color of a light source that compensates for at least a part of a color difference, which is a difference between the color on the color system of a specified material and the material target color; A color compensation system comprising:

17. a color prediction step of predicting a color represented by a color system of a material having a composition of the composition information based on the composition information specifying the composition of the material, and outputting the predicted color; A characteristic prediction step of predicting physical characteristics and / or chemical characteristics other than color of a material having a composition of the composition information based on the composition information; a judgment step of judging whether the color in the color system of the specified material and the physical and / or chemical characteristics satisfy a predetermined criterion based on the predicted color output in the color prediction step and the physical and / or chemical characteristics predicted in the characteristic prediction step; a material production step of producing a material according to the composition information determined in the determination step to satisfy the predetermined standard; A material manufacturing method comprising:

18. The method is executed by a computer, causing the computer to: a color prediction unit that predicts a color represented by a color system of a material having a composition of the composition information based on the composition information specifying the composition of the material, and outputs the predicted color; program.

19. The method is executed by a computer, causing the computer to: an input unit for inputting a product target color, which is a color on a color system that is a target for a product using the material, and a light source color, which is a color on the color system of a light source; a calculation unit that calculates a material target color, which is a color on the color system of a target material, from the product target color and the light source color, and calculates and outputs a recommended light source color, which is a color of a light source that compensates for at least a part of a color difference, which is a difference between the color on the color system of a specified material and the material target color; A program that makes it work.

20. A color prediction step of predicting a color represented by a color system of a material having a composition of the composition information based on the composition information specifying the composition of the material, and outputting the predicted color. A color prediction method including:

21. an input step of inputting a product target color, which is a color on a color system that is a target color of a product using the material, and a light source color, which is a color on the color system of a light source; A calculation step of calculating a material target color, which is a color on the color system of a material target, from the product target color and the light source color, and calculating and outputting a recommended light source color, which is a color of a light source that compensates for at least a part of a color difference, which is a difference between the color on the color system of a specified material and the material target color; A color compensation method including the following:

Images