WO1997031247A1 - Procede et appareil de colorimetrie par ordinateur - Google Patents

Procede et appareil de colorimetrie par ordinateur Download PDF

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Publication number
WO1997031247A1
WO1997031247A1 PCT/JP1996/000738 JP9600738W WO9731247A1 WO 1997031247 A1 WO1997031247 A1 WO 1997031247A1 JP 9600738 W JP9600738 W JP 9600738W WO 9731247 A1 WO9731247 A1 WO 9731247A1
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Prior art keywords
color
colorant
mixture
value
computer
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PCT/JP1996/000738
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English (en)
Japanese (ja)
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WO1997031247A6 (fr
Inventor
Hiroshi Kumamoto
Hideharu Imoto
Hiroshi Tamae
Nobuo Adachi
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Toto Ltd
Hiroshi Kumamoto
Hideharu Imoto
Hiroshi Tamae
Nobuo Adachi
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Application filed by Toto Ltd, Hiroshi Kumamoto, Hideharu Imoto, Hiroshi Tamae, Nobuo Adachi filed Critical Toto Ltd
Priority to JP52996597A priority Critical patent/JP3870421B2/ja
Publication of WO1997031247A6 publication Critical patent/WO1997031247A6/fr
Publication of WO1997031247A1 publication Critical patent/WO1997031247A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/46—Measurement of colour; Colour measuring devices, e.g. colorimeters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/46—Measurement of colour; Colour measuring devices, e.g. colorimeters
    • G01J3/463—Colour matching

Definitions

  • the present invention relates to a method and apparatus for predicting the mixing ratio of a colorant or the color of a mixture by computer color matching.
  • the tristimulus values X, ⁇ , and ⁇ of the mixture are calculated. Therefore, the color of the mixture can be calculated.
  • the absorption coefficient ( ⁇ ) and scattering coefficient S ⁇ ( ⁇ ) of the material to be colored and various colorants must be determined in advance.
  • the absolute method is a method for calculating the absolute values of the absorption coefficient ⁇ ⁇ ( ⁇ ) and the scattering coefficient (;.) Of each substance.
  • the relative method a primary pigment (usually white pigment) assuming scattering coefficient s w 1 and the absorption coefficient of each substance
  • Equation 2 This is a method of calculating the relative value of ( ⁇ ) and the scattering coefficient Si (A).
  • the spectral reflectance R) of the mixture is given by the ratio of the absorption coefficient K M and the scattering coefficient S M of the mixture. Therefore, even if the absolute values of the absorption coefficient Ki (;.) And the scattering coefficient Si ( ⁇ ) of each substance are unknown, if the relative values of the absorption coefficient ( ⁇ ) and the scattering coefficient Si ( ⁇ ) of each substance are known, From Equations 1 and 2, the correct spectral reflectance R ( ⁇ ) can be determined. Since the preparation of samples for the absolute method is quite difficult and requires complicated work, the relative method is usually used.
  • the absorption coefficient K w ( ⁇ ) of the reference white pigment is calculated as follows. to determine the scattering coefficient S w. First, a mixture is prepared by mixing only a white pigment with an object to be colored, and its spectral reflectance R ( ⁇ ) is measured. In the conventional relative method, the object to be colored is regarded as colorless and transparent, and it is assumed that both the absorption coefficient and the scattering coefficient of the object to be colored are zero.
  • the absorption coefficient given by Equation 1 above kappa Micromax (lambda) and the scattering coefficient S “(lambda) is the scattering coefficient of the white pigment S w and absorption coefficient K w ( ⁇ ) equal to, respectively.
  • an object to be colored is regarded as colorless and transparent, and its absorption coefficient and scattering coefficient are assumed to be zero.
  • the objects to be colored Glaze layers in the case of ceramics
  • the blending ratio of the white pigment is large, the error caused by assuming that the object to be colored is colorless and transparent is small, but when the blending ratio of the white pigment is small, the error becomes so large that it cannot be ignored. In order to avoid such errors, it is necessary to take into account the absorption coefficient and scattering coefficient of the colorless and non-transparent object in Equation 1 above.
  • a first object of the present invention is to perform computer color matching in consideration of an absorption coefficient and a scattering coefficient of an object to be colored that is not colorless and transparent.
  • a second object of the present invention is to reduce a prediction error in computer color matching.
  • a third object of the present invention is to make effective use of the prepared colorant by using computer color matching and to simplify re-formulation.
  • the present invention relates to a method for predicting a mixing ratio of a colorant or a color of a mixture by computer color matching.
  • This computer color matching method is
  • the basis of the scattering coefficient S w ' a step of determining a format which depends the turbulent coefficient dispersion and absorption coefficient K p of the colored colorant not white S p in Formulation ratio C p of the colored colorant, a desired color Mixing ratio of the colorant to adjust the mixture having The color of the mixture produced in formulation ratio, the absorption coefficient K w ', p, and the scattering coefficient S w', a step of determining by performing computer color matching using S p,
  • the scattering coefficient s w of the second mixture obtained by mixing the white colorant with the non-colorless and transparent coloring object is expressed by a function f (C w ), and this scattering coefficient s w , is used as a reference.
  • the scattering coefficient S vom′ of the plurality of the second mixtures is represented by a function f (C w ) of the mixing ratio C w of the white colorant, and the coefficient f (C w ) Temporarily determining the value of
  • the coefficients of the function f (C w ) can be obtained so that the prediction accuracy by computer color matching is improved.
  • a computer color matching method includes: (a) mixing a plurality of colorants to prepare a plurality of samples having different mixing ratios; and (b) the plurality of samples. Measuring the spectral reflectances of the plurality of samples, and obtaining actual measured values of coordinate values of a predetermined color system representing respective colors of the plurality of samples from the measured values of the spectral reflectances; Calculating, for each of the samples, a prediction error of the coordinate values of the color system; and Analyzing the relationship between the coordinate values of the color system with respect to the sample and the prediction error by a predetermined error correction method; and (e) calculating a target value or a prediction value of computer color matching using the error correction method. Predicting the blending ratio of the colorant in the new mixture or predicting the color of the mixture by computer color matching while making corrections.
  • the coordinate values of the predetermined color system and the prediction errors thereof for a plurality of samples are analyzed by a predetermined error correction method, and the prediction is performed while correcting the target value or the predicted value of the computer color matching by the error correction method. it is possible to reduce the prediction error without correcting the absorption coefficient Ki and scattering coefficient number S Alpha of each component.
  • the step (d) includes a step of causing a dual neural network to learn a relationship between the coordinate values of the color system and the prediction error for the plurality of samples, and the step (e). ) Shows a process of performing prediction by computer color matching using a trained neural network.
  • the neural network has a three-layer hierarchical structure including an input layer including three neurons, an intermediate layer including a plurality of neurons, and an output layer including three neurons. It is preferred to have.
  • a computer color matching method is a method for predicting a mixing ratio of a colorant in a target mixture having a desired color
  • the target colorimetric coordinate of the target mixture is corrected by the calculation error of the coordinate value of the colorimetric system of the adjacent color sample, and computer color matching is performed using the corrected target value.
  • Computer color matching can be performed under conditions that reduce value calculation errors.
  • a color difference from the target mixture is minimized from a database including a mixing ratio of a coloring agent and an actual measurement value of coordinates of the color system.
  • the step of searching for the adjacent color sample by selecting the sample to be performed is provided.
  • a computer color matching method includes: adjusting a compounding ratio of a plurality of colorants so that the mixture obtained by mixing the plurality of colorants exhibits a color similar to a desired target color. Is the method of seeking
  • the amount of change in the calculated color evaluation value at that time is determined. Then, based on the amount of change of the color evaluation value calculation amount, the amount of increase correction for each colorant is calculated, and the difference between the actually measured value of the color evaluation value of the preparation sample and that of the primary preparation is determined within a predetermined range. To match. For this reason, if each colorant is actually added to the primary formulation by the calculated halo correction amount, the primary formulation will be added to the target color of the sample of the formulation or a formulation exhibiting a color similar to the target color. Can be reconstituted.
  • the computer color matching method of the present invention there is no need to obtain a negative correction amount meaning removal of the colorant, so that it is not necessary to dispose of the preparation in which the colorant has been prepared (primary preparation).
  • the existing formulation can be used effectively. In this case, even if the primary formulation is newly prepared on a trial basis instead of the existing formulation, the technician need only be involved once in the formulation, and at that time, Since no intuition or experience is required, re-mixing of the composition can be simplified.
  • the color evaluation value is calculated based on the assumption that a small amount of the coloring agent is corrected to be added to the primary formulation compared to the blending amount of the coloring agent in the secondary formulation. Calculating the amount of change in the calculated value.
  • a temporary increase correction of the colorant which is performed in order to obtain a change amount of the calculated value of the color evaluation value, is performed in a minute unit, and a change amount in a minute unit can be obtained.
  • the step (e) includes a step of calculating the minimum amount of increase correction for each of the colorants by a linear measurement surface method using a cost function representing a derivation cost associated with the increase in the amount of the colorant. It is preferable to settle.
  • the present invention is also directed to an apparatus for predicting a mixing ratio of a colorant or a color of a mixture by computer color matching.
  • This device has a scattering coefficient S w _ of a second mixture in which a white colorant is mixed with a colorless and transparent object to be colored.
  • the absorption coefficient K P and the scattering coefficient s P of the non-white colored colorant are determined in a form depending on the mixing ratio c p of the colored colorant, and a desired color is obtained.
  • the blending ratio of a young agent for preparing a mixture having the following formulas, or the color of the mixture produced at a predetermined blending ratio, is calculated using the absorption coefficients K w ′, K P and the scattering coefficients s w ′, S P Means to obtain by performing the computer color matching used;
  • a computer color matching device comprises: means for measuring spectral reflectances of a plurality of samples having different mixing ratios formed by mixing a plurality of colorants;
  • a computer color matching apparatus includes: an actual measurement value of coordinates of a predetermined color system for a proximity color sample having a colorant mixing ratio known and having a color close to the desired color; Means to determine
  • a computer color matching device calculates a blending ratio of a plurality of coloring agents so that the formulation obtained by blending the plurality of coloring agents exhibits a color similar to a desired target color. Things.
  • Each of the colorants based on the amount of change in the calculated value of the color evaluation value so that the difference between the measured value of the color evaluation value of the preparation sample and that of the primary preparation matches within a predetermined range.
  • Means for calculating the increase correction amount Means for calculating the increase correction amount.
  • the invention is further directed to a method for determining the absorption and scattering coefficients of a colorant used in computer color matching. This method
  • the scattering coefficient s w ′ of the second mixture is given by the following relationship involving the mixing ratio c w of the white colorant, a constant 3 l and a coefficient S B :
  • the scattering coefficient S w 'of the second mixture, the mixing ratio C w of the white colorant and the following relations a, a, b, d, e, C WU Given by:
  • the present invention is also directed to mixed glazes made using computer color matching. This mixed glaze
  • the relative values of the absorption coefficient K p and the scattering coefficient S p of the non-white colored colorant are determined, and the absorption coefficient K w , , K P and the scattering coefficient s w,, by performing Konbiyu Takara over matching using S P, formulation ratio of the colorant to adjust the mixed glaze having a desired color, or, at a predetermined compounding ratio Predict the color of the resulting mixed glaze,
  • the present invention is further directed to ceramics manufactured using a mixed glaze made using computer color matching. This ceramic is
  • the relative values of the absorption coefficient ⁇ ⁇ and the scattering coefficient S p of the non-white colored colorant are determined, and the absorption coefficient K w , , K p and the scattering coefficient s w, by performing Konbiyu Takara over pine quenching with S p, formulation ratio of the colorant to adjust the mixed glaze having a desired color or a predetermined compounding ratio Predict the color of the mixed glaze generated by
  • a method for determining an absorption coefficient and a scattering coefficient of a colorant used in computer color matching comprises:
  • an apparatus for determining an absorption coefficient and a scattering coefficient of a colorant used in computer color matching comprises:
  • the present invention is also directed to a toilet bowl manufactured using mixed glaze made using computer color matching.
  • FIG. 1 is a flowchart showing the overall processing procedure in the first embodiment.
  • Fig. 2 is a graph showing the scattering coefficient S w 'given by Eq.
  • Fig. 3 is a flowchart showing the detailed procedure of step S1.
  • Fig. 4 is an explanatory diagram showing the mixing ratio of a sample to determine the physical properties of (base glaze + white pigment).
  • Figure 5 is a conceptual diagram showing the spectral reflectance R (;.) Of the sample.
  • FIG. 6 is a flowchart showing the detailed procedure of step S14.
  • Fig. 7 is an explanatory diagram showing the mixing ratio of the pigment physical property value determination sample.
  • FIG. 8 is an explanatory diagram showing the mixing ratio of the verification sample used in the first embodiment.
  • Figure 9 is a graph showing an example of the dependence of the absorption coefficient ⁇ ⁇ ( ⁇ ) on the blending rate C p .
  • FIG. 10 is an explanatory diagram showing a prediction result of a mixing ratio in the first embodiment.
  • FIG. 11 is a flowchart illustrating an overall procedure of a process according to the second embodiment.
  • Figure 12 is an explanatory diagram showing the configuration of a neural network.
  • FIG. 13 is a flowchart showing the detailed procedure of step S31.
  • Fig. 14 is a conceptual diagram showing the distribution of tristimulus values in the prediction target range PA and multiple samples M1 to M7 in computer color matching.
  • FIG. 15 is a conceptual diagram showing prediction errors of seven samples M1 to M7 in the first embodiment.
  • Figure 16 is an explanatory diagram showing the target tristimulus values (the values determined in step S41) and the prediction error AMi ( ⁇ , ⁇ , ⁇ ) for each sample.
  • Figure 17 is an explanatory diagram showing the results of the neural network learning in the second embodiment.
  • Fig. 18 is an explanatory diagram showing the verification results of the prediction of computer color matching in the second embodiment.
  • Fig. 19 is an X-Y chromaticity diagram showing the variation between the designed colors (standard colors) and the colors of the ceramics actually manufactured.
  • FIG. 20 is a ⁇ -chart showing the overall procedure of the process in the third embodiment.
  • FIG. 21 is a flowchart showing the detailed procedure of computer color matching in step S57.
  • FIG. 22 is a table showing the tristimulus values of the reference color sample and the tristimulus values of the overflow limit sample used in the third embodiment.
  • Fig. 23 is a table showing the results of the prediction of tristimulus values of the light limit sample, comparative examples, and the actual mixing ratio according to the third embodiment.
  • Figure 24 is a block diagram showing the equipment for implementing the computer color matching method of each embodiment.
  • FIG. 25 is a flowchart showing the overall procedure of the process in the computer color matching method of the fourth embodiment.
  • FIG. 26 is a flowchart showing the detailed processing of step S76 in FIG.
  • Figure 27 shows the comparison between the extreme U value (color value) for the target color cast obtained in step S72 and the value of the tristimulus for the first prototype glaze obtained in step S73, and Table showing the mixing ratio (mixing ratio) of each pigment in the target color sample glaze and the first prototype glaze.
  • Fig. 28 is a table showing the rate of change (derivative coefficient) of tristimulus values when each pigment is added to the first trial glaze shown in Fig. 27 by a small amount of each pigment.
  • FIG. 29 is a table showing the result of the computer color matching method according to the fourth embodiment.
  • FIG. 30 is a table showing another result according to the fourth embodiment.
  • FIG. 1 is a flowchart showing the overall procedure of the process in the embodiment.
  • the mixture of interest in the first embodiment is a glaze for covering the surface of the ceramic body.
  • the base glaze (base glaze) containing no pigment is the object to be colored, and the glaze obtained by adding a pigment to this base glaze is a mixture to be subjected to computer color matching.
  • an emulsifier is effective as a white pigment.
  • Emulsifiers include zirconium compounds such as zircon and phosphorus compounds such as calcium phosphate.
  • step S ⁇ the absorption coefficient K w _ 'and the scattering coefficient S w ' of the mixture of the object to be colored (base glaze) and the white pigment are determined.
  • the difference from the conventional relative method is that the material to be colored is not assumed to be colorless and transparent, and the absorption coefficient of the mixture is determined without calculating the absorption coefficient or scattering coefficient of the material to be colored or the white pigment alone.
  • the point is to find K w 'and the scattering coefficient s w '.
  • the scattering coefficient S w ' is a function depending on the compounding ratio C w of the white pigment f (C w
  • the absorption coefficient ⁇ ses' is determined in a form that depends on the white pigment blending ratio c w .
  • step S 2 pigment other than white pigment (hereinafter, “colored pigments” and hump) absorption coefficient K p and scattering coefficient S p of is also determined (step S 2).
  • the absorption and scattering coefficients of the chromatic color pigment is determined Me in a format depending on the compounding ratio c p.
  • step S3 computer color matching is performed using the above-described equations 1 and 2 based on the absorption coefficient and the scattering coefficient obtained in steps S1 and S2, and the color prediction of the mixture and the prediction of the blending ratio are performed. Do.
  • Equation 1 Equation 3
  • K w,, S w ' is the absorption coefficient and scattering coefficient of the mixture
  • K w, S w is the absorption coefficient and scattering coefficient of the white pigment alone
  • K B, S B is the scattering and absorption coefficient of the base glaze alone coefficient
  • C w is the mixing ratio of the white pigment
  • c B is the mixing ratio of the base glaze. It should be noted that the mixing ratios c w and c B should be expressed accurately by volume ratio, but even if expressed by weight ratio, the error is usually negligible.
  • Equation 4 the scattering coefficient S w 'of the mixture is given by Equation 4 below. , _ Cw S W + Les RSR. ⁇ ,
  • the scattering coefficient s w is independent of the mixing ratio c w of the white pigment.
  • the scattering coefficient S w for the mixture of (base glaze + white pigment) is determined. Then, if the scattering coefficient S w , is obtained, the absorption coefficient K w ′ can be obtained from Equation 2 using the measured value of the spectral reflectance R (A).
  • FIG. 3 is a flowchart showing a detailed procedure of step S1 in FIG.
  • step S 1 ⁇ a mixture (first mixture) was prepared in which only the base glaze and the white pigment were mixed.
  • FIG. 4 shows the mixing ratios of 13 samples W] 2 to W0 prepared in the first embodiment. As can be seen from FIG. 4, 1 3 samples Wl 2 ⁇ W0 are those created by the compounding ratio C w of the white pigment is changed by 1% in the range of 1 2% 0%.
  • the sample in the first example was prepared by baking a normal ceramic body with a blended glaze. In addition, other samples described later were prepared under the same conditions.
  • step S12 the spectral reflectance R '(A) of each sample W2 to W0 was measured with a spectrophotometer.
  • step S13 the measured values of spectral reflectance R,
  • Equation 6 Based on ( ⁇ ), the coefficients a and b in Equation 6 were experimentally determined as follows.
  • the coefficient, k 2 is a value that depends on the optical properties of the object to be colored (base glaze).
  • the coefficients, k 2 can be determined from the refractive index n of the object to be colored according to the following equation 10:
  • the base glaze used in the first embodiment has a refractive index ⁇ of about 1.4.
  • (KZS) wi is obtained from the above-described equation (2).
  • Equation 9 The calculation to determine the ideal state spectral reflectance R (1) from the spectral reflectance measured value R 'U) according to Equation 9 is performed when calculating (K / S) from the spectral reflectance of the sample in another process described later. Is similarly performed.
  • the base glaze mixed with a high concentration of white pigment exhibits almost constant reflectance over the entire visible wavelength range (about 400 nm to about 700 nm).
  • the scattering coefficient S wi ′ can be obtained from the value of the spectral reflectance R ( ⁇ ) using Expressions 2 and 8.
  • the scattering coefficient S has the same value in the entire wavelength range of visible light.
  • Expression 6 was rewritten into Expression 11 below.
  • step S14 of FIG. 3 the values of the coefficients d and e of Equation 7 on the low concentration side are determined.
  • the blending ratio C w of the white pigment is relatively small, the influence of the ground becomes large, so that the measured value of the spectroscopic reflectance R ( ⁇ ) is not information of only the true glaze layer. Therefore, it is not easy to determine the coefficients d and e based on the measured values of the spectral reflectance R ( ⁇ ). Therefore, the values of the coefficients d and e are determined according to the procedure shown in Fig. 6.
  • FIG. 7 is an explanatory diagram showing the mixing ratio of the pigment determination sample.
  • the sample for determining the physical properties of the pigment was prepared by mixing a base sleeve, a white pigment and another colored pigment.
  • the total compounding ratio of the white pigment and colored pigment (Pigment Volume Concentration, P VC) 1 2% - a constant, 1 formulatory ratio C p of colored pigment in the range of 1% to 1 2% 12 samples M] to M12 were prepared.
  • P VC ment Volume Concentration
  • sample Ml2 is a mixture containing no white pigment, and corresponds to the second mixture of the present invention.
  • Samples M1 to M11 correspond to the third mixture of the present invention. Hit.
  • step S22 a verification sample is created.
  • 8 are explanatory diagrams showing the mixing ratios of the verification samples used in the first example.
  • the verification samples D ⁇ to D4 correspond to the fourth mixture in the present invention.
  • step S23 temporary values are assigned to the coefficients d and e.
  • the coefficient d is also a rough value. It is possible to decide.
  • the value of the scattering coefficient S wi 'does not depend on the wavelength, but the value of the absorption coefficient K w ,' depends on the wavelength.
  • the scattering coefficient s wi ′ is used as a reference value that does not depend on the wavelength, and the relative values of other physical properties are obtained. Since the value of the spectral reflectance RU) depends of course on the wavelength, the value of (KZS) wi obtained according to Equation 2 also depends on the wavelength. Therefore, the absorption coefficient K wi 'obtained according to Equation 12 also depends on the wavelength. In other words, the absorption coefficient K wi 'is obtained in a form that depends on the blending ratio C w and the wavelength of the white pigment.
  • step S25 the absorption coefficient ⁇ ⁇ ( ⁇ ) and the scattering coefficient S p ( ⁇ ) of each colored pigment are determined using the pigment property value determination sample.
  • Step S25 corresponds to step S2 in FIG. (.)
  • SP (s) are calculated by the following procedure. First, for a pigment physical property value determination sample, the following expression 13 is obtained from expression 1 described above.
  • Equation 16 (KZS) p is the ratio between the absorption coefficient and the scattering coefficient of the colored pigment alone, (K / S) is the ratio between the absorption coefficient and the scattering coefficient of the pigment physical property determination sample, and (KZ S) is ( This is the ratio between the absorption coefficient and the scattering coefficient of the sample (base glaze + white pigment).
  • C w is the sum of the blending ratios of the base glaze and the white pigment, and s is the scattering coefficient of the mixture of (base glaze + white pigment), which is given by Equations 6 and 7.
  • Equation 16 The terms on the right side of Equation 16 can be obtained as follows.
  • K of (K / S) p can be obtained by the above equation 2 from the spectral reflectance R ( ⁇ ) of the pigment physical property determination sample (sample Ml 2 in FIG. 7) which does not contain white pigment.
  • the value of (KZS) in Equation 16 is a value for a colored pigment that does not include the effect of the base glaze.
  • (KZS) ⁇ obtained by actual measurement as described above is for the sample of (base glaze + colored pigment). The influence of the glaze is impaired.
  • the mixing ratio of the pigment of 17 samples M12 is 12%, which is a ⁇ ⁇ value, the contribution of the base glaze to (KZS) p is extremely small. Therefore, as the value of (K / S) p in Equation 16, even if the value obtained from Equation 2 from the spectral reflectance R ( ⁇ ) of sample M 12 is used, the error is negligible. is there.
  • (KZS) value of Micromax is the spectral reflectance of the white pigment and Complex both colored pigments unpigmented physical properties determined for samples (samples of Figure 7 ⁇ 1 ⁇ VI 1 1! R (;.) From the formula 2 Can be calculated according to Therefore, (KZS) ⁇ is determined in a form depending on the mixing ratio C p of the colored pigment.
  • FIG. 9 is a graph showing an example of the dependence of the absorption coefficient ⁇ ⁇ ( ⁇ ) on the mixing ratio C p .
  • the scattering coefficient S p (; J shows a similar dependence. Note that FIG. 9 shows only a graph of typical wavelengths, but in actuality, the actual wavelength range of visible light (about 400 nm ⁇ about 7 00 nm) absorption coefficient for each ⁇ 0 nm in ⁇ ⁇ ( ⁇ ) is obtained.
  • the absorption coefficient K w , (s) and the scattering coefficient S w ′ of the mixture of (base glaze + white pigment) and the absorption coefficient K p of other colored pigments and the scattering coefficient S p (; J When is obtained, in step S26 in FIG. 6, a simulation is performed on the verification sample (FIG. 8) by computer color matching.
  • Equation 18 the spectral reflectance R of the mixture is given by Equation 18 below.
  • Equation 19 the spectral reflectance ( ⁇ ) of the mixture can also be determined from Equation 18. Since this spectroscopic reflectance R ( ⁇ ) is an ideal state spectral reflectance, the spectral reflectance ( ⁇ ) that can be measured by a spectrophotometer is calculated according to the following Equation 19, which is a modification of Equation 9 (Sanderson's equation). Ask.
  • the tristimulus values X, ⁇ , ⁇ of the mixture are determined by the following equation 20.
  • S is the spectral distribution of the standard light
  • X (e), y ( ⁇ ), ⁇ ( ⁇ ) are color matching functions.
  • the tristimulus value is calculated by the above-described procedure assuming the blending ratio of the mixture, and the desired value is calculated by a successive approximation method such as the Newton-Raphson method. Find a blending ratio that matches the color of the product within a specified error.
  • the compounding ratio of each of the verification samples D1 to D4 shown in FIG. 8 is predicted by computer color matching. Since the mixing ratio of the verification sample is known, the degree of coincidence between the predicted mixing ratio and the actual mixing ratio can be easily calculated. For example, the root mean square error given by the following equation 21 is used as an index of the degree of coincidence of the mixing ratio.
  • Equation 7 coefficients in Equation 7 as cut with accurately predicting the formulation ratio of the verification samples in the first embodiment d, since the determined e, Formulation ratio C w of the A color pigment is relatively low area
  • the scattering coefficient S w 'and the absorption coefficient K w ' ( ⁇ ) of the mixture of (base glaze + white pigment) could be determined with high accuracy.
  • FIG. 10 is an explanatory diagram showing a prediction result of the mixing ratio in the first embodiment.
  • the prediction result of the first embodiment in FIG. 10 is a result obtained when Expressions 11 and 22 are used.
  • the blending ratio that minimizes the color difference was predicted using the L * a * b * color system.
  • the prediction result of the Example] has a significantly smaller root mean square error of the mixing ratio than that of the comparative example, indicating that the mixing ratio can be predicted with higher accuracy.
  • the mixing ratio of the white pigment is small as in the samples P2 and P3, the prediction accuracy of the comparative example is considerably deteriorated, whereas the prediction accuracy of the first embodiment is good.
  • the computer is used. High-precision prediction can be performed by color matching. In addition, it is only necessary to prepare a sample as shown in Figs. 4, 7 and 8 on which a mixture is applied on a normal substrate, so that it is easy to prepare a sample.
  • step S23 it is not necessary to find the coefficients by the successive approximation method, and it is possible to assume various coefficient values and select the coefficient that optimizes the simulation result in step S26. ,.
  • Equation 5 when using Equation 5 as it is, set several values of S s in the range of about 0.001 to about 0.010, and in Step S26, the value closest to the actual mixing rate is set. the compounding ratio may be so that to determine the value of S B predictable.
  • the function for expressing the scattering coefficient S is not limited to Equations 5 to 7.
  • the scattering coefficient S w ′ may be expressed by a function f (C w ) that depends on the mixing ratio C w .
  • Equation 5 can be expressed by the following Equation 23 including the constant 3l .
  • the second embodiment described below corrects the absorption coefficient Ki and the scattering coefficient S i of each component.
  • the aim is to reduce the prediction error without any change.
  • FIG. 1 is a flowchart illustrating an overall procedure of a process according to a second embodiment.
  • the mixture targeted in the second embodiment is a glaze for covering the surface of the ceramic body.
  • the base glaze (base glaze) without pigment is the object to be colored, and the glaze obtained by adding pigment to this base glaze is the mixture to be subjected to computer color matching.
  • step S31 learning of a neural network for correcting the prediction result (tristimulus value) of computer color matching is performed.
  • steps S32 to S35 the target value of computer color matching is corrected using the trained neural network to obtain an accurate prediction result.
  • FIG. 12 is an explanatory diagram showing the configuration of the neural network.
  • This dual-purpose network has a three-layer hierarchical structure composed of an input layer 10, a middle shoulder 20 and an output layer 30.
  • the human stratum I0 is composed of three neurons N11 to N13
  • the middle layer 20 is composed of five neurons N21 to N25
  • the output shoulder 30 is composed of three neurons N31 to N33. Have been.
  • Two: stimulus values X, ,, and Z are input to the three neurons N11 to N13 of the input layer 10 respectively.
  • the signal transmitted from the neuron Nij of the input layer 10 to the neuron Nk of the hidden layer 20 is obtained by multiplying each input signal by weights W and k .
  • i is a number indicating the debris of interest
  • j is a number indicating the order of neurons in the layer of interest
  • k is a number indicating the order of neurons in the next layer.
  • the signal transmitted from the first neuron N il of the input ⁇ 10 to the first neuron N 21 of the middle layer 20 is W u .
  • the first neuron N il of the input dust 10 is signal transmitted to the second neuron N 22 of pressurized et intermediate layer 2 0 is W u, 2 X.
  • ⁇ th neuron N (i-l) j in the (i ⁇ 1) th hierarchy that is, the input layer 10
  • k is the weight applied to the signal transmitted from the j-th neuron N (i-1) j in the (i-1) th hierarchy to the neuron Nij of interest.
  • t is a threshold, some other being assigned.
  • the information transfer function f (u, in Equation 24 is called a sigmoid function.
  • Equation 24 when Equation 24 is applied to the input / output relationship of the second neuron N21 of the middle layer 20, the following Equation 25 is obtained.
  • u 2 i W n , iX + W, 2jY + W 131 Z-.. (25b)
  • the input / output relationship of each neuron of the output layer 30 is also given by the above equation (24).
  • the output of the three neurons N31 to N33 in the output layer 30 is
  • the to Q 13 predicts errors ⁇ tristimulus values by computer color one matching (CCM), ⁇ , and Derutazeta.
  • Neural network learning gives a number of relationships between the inputs (X, Y, Z) to the input debris 10 and the outputs ( ⁇ , ⁇ , ⁇ ) from the output layer 30 to give the correct input / output relationships. This is the task of determining the values of the weights W and k .
  • FIG. 13 is a flowchart showing the detailed procedure of step S31 in FIG.
  • step S41 a plurality of tristimulus values (Xit. Yit, Zit) that cover the color range of the mixture to be predicted in computer color matching
  • the blending ratio of a plurality of samples having the plurality of tristimulus values is determined by computer color matching.
  • FIG. 14 is a conceptual diagram illustrating a color prediction target range PA in computer color matching and a distribution of a plurality of tristimulus values covering the prediction target range PA.
  • colors are expressed in the CIE-XYZ color system
  • the color prediction target range PA can be obtained as a three-dimensional range in the XYZ coordinate system.
  • the prediction target range PA indicates the range of colors that the mixture to be predicted can take, and is a range that can be set arbitrarily.
  • step S41 the compounding ratio of the seven types of samples M1 to M7 having these seven sets of tristimulus values (Xit, Yit, Zit) was further predicted by computer color matching.
  • Equation 26 the spectral reflectance R (s) of the mixture is given by Equation 26 below.
  • the ratio (KZS) ⁇ between the absorption coefficient and the scattering coefficient of the M mixture can be calculated from the absorption coefficient Ki (1) and the scattering coefficient S ⁇ ) of each component and the mixing ratio Ci according to Equation 1 described in the first embodiment.
  • the spectral reflectance R ( ⁇ ) of the mixture can be obtained from the above equation 26. Since this spectral reflectance R ( ⁇ ) is the spectral reflectance in an ideal state (when the thickness of the object to be colored is infinite), the spectral reflectance R '(;-) that can be measured by a spectrophotometer is expressed by the following equation. 2 7 (Sanderson's equation)
  • R ' k 1 -Kl -ki) (l -k 2 ) l ..- (27) where the coefficient, k 2 is a value that depends on the optical properties of the object to be colored (base glaze).
  • the coefficients, k 2 can be determined from the refractive index n of the object to be colored according to the following equation 28.
  • the base glaze used in the second example had a refractive index n of about 1.4.
  • the spectral reflectance R ') is determined by Equation 27
  • the tristimulus values X, Y, and Z of the mixture are determined by Equation 29 below.
  • S ( ⁇ ) is the spectral distribution of the standard light
  • X (/), y ( ⁇ ), and ⁇ ( ⁇ ) are color matching functions.
  • the tristimulus values X, ⁇ , ⁇ Is calculated. Since the tristimulus values represent the color of the mixture, the color of any mixture can be predicted.
  • the tristimulus value is calculated by the above-described procedure assuming the blending ratio of the mixture, and the desired value is calculated by a successive approximation method such as the Newton-Raphson method.
  • Formula 1 is determined so that the color matches the color within a specified error.
  • step S42 of FIG. 13 a plurality of samples having the compounding ratio C i predicted in step S4 ⁇ are created.
  • seven samples M1 to M7 corresponding to the seven sets of tristimulus values (Xit, Yit, Zit) in FIG. 14 were created.
  • step S43 the spectral reflectance of each sample Mi is measured with a spectrophotometer, and its tristimulus values (Xim Yim, Zin) are obtained according to the above equation (29).
  • step S44 for each sample Mi, the difference between the measured value (Xim, Yim, Zim) obtained in step S43 and the target value (Xit, Yit, Zit) determined in step S41 is calculated.
  • FIG. 15 is a conceptual diagram illustrating prediction errors of seven samples M1 to M7 obtained in the second example.
  • FIG. 16 is a description “ ⁇ 1” showing the target value of the tristimulus value (the value determined in step S41) of each sample and the prediction error ⁇ ( ⁇ , ⁇ , ⁇ ).
  • the predicted values (Xic, Yic, Zic) of the tristimulus ⁇ of each sample were obtained from the mixture ratio C i obtained in step S41 by computer color matching, and the measured values (Xim, Yim, Zim) were obtained.
  • the difference (Xim—Xic, Yim-Yic, Zim—Zic) between the prediction error and the prediction ⁇ (Xic, Yic, Zic) may be defined as the prediction error AMi.
  • the mixing ratio C i is determined so that the difference between (Xit, Yit, Zit) and the predicted value (Xic, Yic, Zic) is equal to or less than a predetermined allowable error
  • the predicted values (Xic, Yic, Zic) have substantially the same value. Therefore, even if the difference (X im-Xic, Yim—Yic, Zim—Zic) between the measured value (Xim, Yim, Zim) and the prediction ⁇ (Xic, Yic, Zic) is defined as the prediction error ⁇ , Measured value
  • step S45 in FIG. 13 learning of the neural network is performed using the target values (Xit, Yit, Zit) and the prediction error ⁇ ⁇ ⁇ ⁇ ⁇ of the tristimulus values of each sample Mi shown in FIG. Determine the weights Wij, k at.
  • a learning method of the neural network for example, a back error propagation learning method is used.
  • FIG. 17 is an explanatory diagram showing the results of the verification of the neural network learning in the second embodiment.
  • another set of tristimulus values included in the prediction target range PA shown in Figs. 14 and 5 is set as the target value for computer color matching.
  • An eighth sample M8 having a standard value was prepared. Then, the sagittal depression of this sample M8 was actually measured.
  • CCM target value means the target value used for the computer color matching of the eighth sample M8.
  • error (true planting)” is the difference between the target value and the measured value of the tristimulus value.
  • Neuro prediction error is the prediction error obtained when the CCM target value is input to a trained neural network (Fig. 12). From the results in Fig. 7, it can be seen that the trained bi-ural network can accurately predict the error of tristimulus values.
  • steps S32 to S35 in FIG. 11 are executed to predict the mixing ratio of a color sample whose mixing ratio is unknown.
  • step S32 the spectral reflectance of the color sample whose mixing ratio is unknown is measured, and its tristimulus value (Xs, Ys, Zs) is obtained.
  • step S33 the tristimulus values (Xs, Ys, Zs) of the color samples are input to the neural network shown in Fig. 12, and the prediction error ⁇ (AXs, AYs, ⁇ s) is obtained. .
  • step S34 the tristimulus values (Xs, Ys, Zs) are corrected by the prediction error, and the target of the tristimulus values ⁇ (Xs— ⁇ s, Y s - ⁇ s, Zs—AZs).
  • step S35 computer color matching is performed using the corrected target value to predict the mixing ratio of the color sample.
  • FIG. 18 is an explanatory diagram showing the results of an experiment performed to verify the prediction accuracy of computer color matching in the second embodiment.
  • we aimed to verify the prediction accuracy so we measured the tristimulus values for a color sample with a known blending ratio, and calculated the blending ratio that realized the measured values (Xs, Ys, Zs) using a computer. Predicted by color matching. From the results in Fig. 17, it can be seen that the true value of the blending ratio and the predicted value by computer color matching are in very good agreement.
  • the learned neural network shown in FIG. 12 it is possible to accurately predict the tristimulus values of the mixture with a known mixing ratio. That is, tristimulus values are obtained from the mixture ratio C i of the mixture to be predicted using the above formulas, 26 to 29, and the tristimulus values are input to the neural network shown in FIG. Ask. Then, if the tristimulus value obtained by Equation 29 is corrected by the prediction error, it will be extremely close to the actual value. Very close: Stimulus value is obtained.
  • the theory of color mixing may not be established due to firing conditions, chemical reaction of raw materials during melting, and the like.
  • the method of this embodiment in which the neural network learns the relationship between the tristimulus value of the mixture and the prediction error due to CCM is effective.
  • the target values of the tristimulus values in the computer color matching were input to the neural network.
  • the human power of the neural network the measured tristimulus values (Xim, Yim, Zim) may be used, or predicted values (Xic, Yic-, Zic) by computer color matching may be used.
  • the neural network may be made to learn the relationship between the tristimulus values (coordinate values in the XYZ color system) of a plurality of samples and their prediction errors.
  • the tristimulus values are corrected using a neural network, but may be corrected using other error correction methods such as regression analysis or neuro-fuzzy technology. is there.
  • Industrial porcelain such as sanitary ware
  • the glaze corresponds to the mixture in computer color matching.
  • the designer first determines the color by coloring the paper or the like or selecting a sample having a desired color from pre-baked samples. Then, the spectral reflectance of the color is measured with a spectrophotometer, and the mixing ratio of a coloring agent such as a pigment or a dye is predicted from the measured value of the spectral reflectance using computer matching.
  • a coloring agent such as a pigment or a dye
  • Figure 9 is an X-Y chromaticity diagram showing the variation between the designed colors and the colors of the pottery actually manufactured.
  • the color indicated by the double circle is the designed color (standard color) L0
  • the small white circle is the color distribution of the ceramics actually manufactured. Pottery with a color that is significantly different from the standard color L0 must be recognized as defective. Therefore, the shade limit colors L1 and L2, which have the colors indicated by the black circles in Fig. 19, are set to indicate the shade limits of non-defective products.
  • the chromaticity coordinate values of the standard color L0 are determined by the designer, the chromaticity coordinate values of the standard color L0 are taken into consideration by considering the manufacturing error and the like, and the two 'shade limit colors L1, L2' Determine the chromaticity coordinate value of.
  • a pottery having the standard color L0 is created as a standard color sample, and two potteries having shading limit colors L I and L2 are created as shading limit samples. Then, in the inspection process, the color of the standard color sample and the color limit sample is compared with the color of the manufactured ceramic, and only the ceramics in the color range of the color limit sample are regarded as good products.
  • the third embodiment described below aims at accurately estimating the blending ratio of a mixture having a desired color.
  • FIG. 20 is a flowchart showing the overall procedure of the process in the third embodiment.
  • the mixture targeted in the third embodiment is a glaze for covering the surface of a ceramic body.
  • the base glaze without pigment (base glaze) is the object to be colored, and the glaze obtained by adding pigment to this base glaze is the mixture to be subjected to computer color matching.
  • step S51 first, a standard color sample is created. Therefore, the mixing ratio of the colorant of the standard color sample is known.
  • step S52 the spectral reflectance R 'of the created standard color sample is measured with a spectrophotometer, and the tristimulus value X is calculated from the spectral reflectance R' according to the following equation (30). , ⁇ . , ⁇ Calculate () .
  • S ( ⁇ ) is the standard-light spectral distribution
  • X ( ⁇ ), y) and ⁇ ( ⁇ (with a bar in the formula) are color matching functions.
  • step S53 the measured value X of the tristimulus value of the standard color obtained in step S52. , ⁇ . , Z.
  • the designer Set the tristimulus values for the shade limit colors LI, L2 of.
  • a blending ratio for realizing the first shade limit L ⁇ is predicted.
  • the blending ratio can be accurately predicted by the same processing.
  • step S54 the standard color sample is obtained from the mixing ratio Ci (i indicates the number of the colorant), the scattering coefficient Si, and the absorption coefficient Ki of each colorant of the standard color sample created in step S51.
  • the calculated values X M , Y M , and Z N of the tristimulus values are calculated by the following procedure.
  • the spectral reflectance R ( ⁇ ) of the mixture is given by the following Equation 31.
  • the ratio (KZS) ⁇ between the absorption coefficient and the scattering coefficient of the mixture can be calculated from the absorption coefficient ( ⁇ ), the scattering coefficient Si (), and the blending ratio Ci of each colorant according to Equation 1 described in the first embodiment.
  • the spectral reflectance R of the mixture (lambda) from the ratio (KZS) Micromax can be determined according to equation 3 1.
  • the thickness of the spectral reflectance R (/) is an ideal state (the colorings infinite )
  • the spectral reflectance R '( ⁇ ) that can be measured with a spectrophotometer is calculated according to the following equation 32 (Sanderson's equation).
  • R ' k 1 + (lk 1 ) (lk 2 ) 1 ... (32)
  • k 2 is a value that depends on the optical properties of the object to be colored (base glaze).
  • the refractive index n of the base glaze is, for example, about 1.4.
  • the tristimulus values X-, Y M and Z H can be calculated according to the same formula as the above-described formula 30.
  • the absorption coefficient and the scattering coefficient S i of each colorant include an error, and the equations 32 to 33 are empirical rather than theoretical, so the tristimulus values obtained in step S 54 are calculated.
  • the values X M , Y M , and Z M have errors.
  • This calculation error is the measured value X obtained in step S52. , Y. , ⁇ . Is the difference from
  • the prediction of the blending ratio by computer color matching also includes the step of calculating the tristimulus values of the mixture according to the formulas 1, 2, 30 to 33. Therefore, when predicting the mixing ratio of the shading limit sample by computer color matching, if the calculation error of the tristimulus value in the standard color sample is considered, the prediction accuracy can be improved.
  • step S55 of FIG. 20 an actual measurement ⁇ X of the tristimulus value of the standard color obtained in step S52. , Upsilon 0, and Zeta 0, Step S 5 4 out was calculated values ⁇ ⁇ , ⁇ ⁇ , difference ⁇ with ⁇ ⁇ , ⁇ , seek ⁇ following Equation 3 4.
  • step S56 the tristimulus values X ⁇ , ⁇ .,., ⁇ ⁇ set in step S53 are corrected by the differences ⁇ , ⁇ , ⁇ of the above ti. target value X c tristimulus values in computer color matching, Y c, Ru seek Z c. That is, the target values X c , Y c , and Z c of the tristimulus values for the lysis limit sample are given by the following Expression 35.
  • step S57 the mixing ratio of the density limit sample is predicted by computer color matching.
  • the mixing ratio is determined so that the target values Xc , ⁇ c, and Zc given by Expression 35 are obtained.
  • FIG. 21 is a flowchart showing the detailed procedure of the computer color matching in step S57. The procedure in Fig. 21 applies computer color matching using the Newton-Raphson method.
  • step S6 the change in tristimulus value when the mixing ratio Ci of each colorant (pigment) is slightly changed is calculated in the following procedure.
  • the differences AX Ci , AY ci , and AZ Ci from the calculated values ⁇ , ⁇ ,, ⁇ ⁇ of the tristimulus values of the standard color obtained in step S 52 are calculated according to the following equation 36. .
  • Equation 36 the change rate of the tristimulus value when only the mixing ratio Ci of each colorant is slightly changed is given by the following Equation 37. )
  • Equation 38 assumes that there are four types of colorants. Note that a fixed value is substituted for the total value ⁇ CV of the change amount of the mixing ratio of each colorant. For example, if the sum ⁇ Ci of the mixing ratios Ci of the four colorants is kept constant,? Is 0.
  • step S63 using him obtained in step S62 , the mixing ratio C iT of each colorant of the shading limit sample is calculated according to the following Expression 39 .
  • step S 64 from the blending ratio C iT obtained in step S 63, the number mentioned above Equation 1, 2, calculated values of tristimulus values according to. 30 to 3 3, Upsilon chi, seek Z 2.
  • scan Tetsupu S 65 thus resulting tristimulus values, Upsilon:, Z: the target value X c tristimulus values obtained in scan Tetsupu S 5 6 of FIG. 20, Y c, the color difference between Z c It is determined whether or not the predetermined tolerance is less than or equal to ⁇ 5.
  • the color difference is given by the following equation 40 using the Lab color system.
  • step S65 if the color difference ⁇ ⁇ ⁇ is smaller than the allowable error S, The value of the mixing ratio CiT obtained in step S63 is adopted as the predicted value, and the computer color matching is terminated.
  • the color difference E is equal to or larger than the allowable error ⁇
  • the calculated values ⁇ 1 , ⁇ 1 , ⁇ , of the tristimulus values obtained in step S 64 are replaced with ⁇ ⁇ , ⁇ ,, ⁇ in step S 66.
  • steps S62 to S65 is repeated.
  • FIG. 22 is a table showing tristimulus values of a standard color sample and tristimulus values of a shading limit sample used in the third embodiment of the present invention.
  • Samples T l, ⁇ 2, ⁇ 3 are standard color samples having different colors.
  • Fig. 22 shows the tristimulus values X actually measured in step S52 in Fig. 20 for each standard color sample. , Upsilon 0, and Zeta ,,, Step S 5 4 tristimulus values chi Micromax calculated in, Upsilon Micromax, are shown and Zeta Micromax is also shading boundary samples set in Sutetsu flop S 5 3
  • the set values of the tristimulus values ⁇ ⁇ , ⁇ ⁇ , and ⁇ ⁇ are also shown.
  • the target values X c , Y c , .Z c of the tristimulus values of the gray scale limit sample in the computer color matching are calculated from these values according to the above-mentioned equations 34
  • FIG. 23 shows the prediction results of the tristimulus values of the density limit sample in the third embodiment, the prediction results of the comparative example, and the actual blending ratio.
  • the blending ratio of the comparative example is a predicted value obtained when the set values ⁇ ⁇ , ⁇ ⁇ , ⁇ ⁇ of the tristimulus values of the gray scale limit sample are directly used as the target values of the computer color matching.
  • compounding ratio of the third embodiment is the predicted value obtained using the target value X c were corrected in the calculation error of the standard color swatch, Y c, the Z c.
  • a gray scale limit sample was actually manufactured using the mixing ratio shown on the right end of FIG. 23, and the measured tristimulus values were compared with those in FIG. Are used as the set values ⁇ ⁇ , ⁇ ⁇ , ⁇ ⁇ shown in Fig.
  • the third example was able to predict the mixing ratio with higher accuracy than the comparative example.
  • the prediction accuracy of the third embodiment is higher than that of the comparative example in all cases.
  • the calculation error of tristimulation was calculated for a standard color sample having a tristimulus value close to the filtering limit sample, and this was used to calculate the tristimulus value of the shading limit sample. The target value has been corrected. As a result, it is possible to improve the prediction accuracy when predicting the mixing ratio of the shading limit sample.
  • the standard-color sample is used as a sample having a tristimulus value close to the density limit sample (near-color sample), and the calculation error of the tristimulus value for the standard color sample is used.
  • the target value of the tristimulus value of the light-limit sample was corrected.
  • a sample having a color close to the shading limit sample may be selected as a nearby color sample.
  • This database should preferably include at least the formula C i of each colorant and the measured tristimulus value (or the reflectance R ( ⁇ )).
  • CCM computer color matching
  • the object to be colored is a fiber, and if the color of the dyed (dyed) fiber is not the desired one, the dyed fiber is additionally dyed to change the color to the desired one. I do.
  • the color is determined by the mixing ratio (mixing ratio) of the pigment mixed with the glaze, and the color is obtained through firing of the glaze (hereinafter referred to as glaze) in which the pigment is mixed.
  • glaze the glaze in which the pigment is mixed.
  • the above-mentioned conventional CCM cannot be applied to an object to be colored that cannot be additionally dyed (colored), such as pottery and tiles.
  • the colorant (dye for fiber, glaze for pottery and tile) is used for fiber, pottery, etc.
  • the mixing ratio of the colorant is maintained, but if the mixing process fluctuates, for example, if there is a deviation in the temperature, the timing of the mixing, or the like, the color obtained from the colorant previously mixed or the color of the color sample is changed. There is a case that cannot be redone. Especially in ceramics and tiles, the reliability of color reproduction is somewhat lacking due to the use of natural pigments.
  • the color can be corrected by additional dyeing using the above-mentioned CCM.
  • the colorant that has been prepared must be used in combination with the already prepared colorant. It is complicated. In order to avoid this complication, a colorant that can obtain a color sample color may be newly prepared and dyed with only this colorant. However, the already prepared colorant is discarded as unnecessary or discarded. It is necessary to add a new colorant to the prepared colorant and re-formulate. Also, for pottery and tiles, it is not possible to add additional color, so the prepared colorant must be discarded or remixed. But However, disposing of the prepared colorant is useless, while re-formulation of the colorant is also cumbersome because the colorant is gradually added based on the intuition and experience of engineers.
  • the fourth embodiment described below aims at simplifying re-mixing while effectively utilizing the mixed colorant.
  • FIG. 24 is a block diagram illustrating an apparatus for performing the computer color matching method according to the embodiment.
  • This device can also be used as a device that implements the first to third embodiments described above.
  • the arithmetic unit 40 is a general-purpose computer, and realizes each step and each means of computer color matching according to the present invention by causing a CPU (not shown) to execute a software program.
  • FIG. 25 is a flowchart showing the entire procedure of the process in the fourth embodiment.
  • the composition targeted in the fourth embodiment is a glaze for covering the surface of a ceramic body.
  • the base glaze without pigment (base glaze) is the material to be colored, and the glaze with the pigment added to this base glaze is the target for computer color matching.
  • the color system is the XYZ color system, it goes without saying that other color systems, for example, the L * a * b * color system may be adopted.
  • an arithmetic unit 40 for executing a rare operation which will be described later, relating to the CCM
  • an input device 42 such as a keyboard and a mouse for inputting data
  • a display device 44 for displaying the results of pass / fail judgment described later
  • a storage device 46 for storing the results of the pass / fail judgment and various arithmetic expressions, etc., and acquiring a spectral reflectance as data necessary for CCM.
  • a spectrophotometer 48 is used. Then, in the arithmetic unit 40, the following blending process is performed.
  • step S71 a glaze (sample glaze) exhibiting a target color is prepared. Since the H standard color in this case is the color presented by the prepared glaze, the blending ratio of the pigment in this glaze (sample glaze) is known.
  • step S72 the ⁇ standard color sample glaze is measured with a spectrophotometer 48, and tristimulus values (actually measured values) ⁇ ⁇ , , ⁇ , ⁇ ⁇ which are color evaluation values in the XYZ color system Ask for.
  • the tristimulus value is calculated from the spectral reflectance R ′ (-) of the target color sample glaze obtained by colorimetry with a spectrophotometer 48 according to the following equation 42. Is done.
  • the calculated tristimulus value is displayed on the display device 44 together with the target sample color, and is stored in the storage device 46 for use in processing described later.
  • the calculation results of the tristimulus values and the like described below are stored in the storage device 46 each time.
  • each pigment is added at a blending ratio assumed to exhibit a color similar to the target color exhibited by the sample glaze, and the second trial glaze is blended.
  • the color is measured in the same manner as in S72.
  • the tristimulus values (actually measured values),, and Z for the first prototype glaze are obtained.
  • the pigments are blended at the above blending ratios by a technician, but the values are arbitrary values that are known and need to be repeatedly blended by trial and error as in the past. No special experience or intuition is required to determine the mix ratio.
  • the tristimulus value and the pigment preparation amount (preparation ratio) for the first prototype glaze are also displayed on the display device 44 and stored in the storage device 46.
  • the amount of the mixture at this time is input from the input device 42.
  • the color difference ⁇ * (JIS Z 8 "730) between the color of the target color sample glaze created in step S71 and the color of the first prototype glaze in step S73 is set within a predetermined range.
  • the allowable value of the color difference ⁇ * is determined by the difference between the color of the target color sample and the color of the first prototype glaze. Value that cannot be distinguished by The value is set in advance from the manpower device 42.
  • the allowable value of the color difference ⁇ * can be set to a value other than 0.3 to 0.5.
  • step S74 If it is judged that the color difference ⁇ * is within 0.3 to 0.5 in step S74, the color of the target color sample can be re-used with the glaze of the first prototype in step S73. It is determined that no further compounding processing is necessary, and all the processing ends. In other words, the new glaze blended at the blending ratio at the time of the first trial production has almost the same color as the target color sample glaze.
  • step S74 if the color difference ⁇ * does not fall within the range of 0.3 to 0.5 in step S74, the rejection is determined, and in step S75, the target color obtained in step S72 is determined.
  • the difference between the tristimulus values according to the following equation 43 is ⁇ , ⁇ , ⁇ .
  • the differences ⁇ , ⁇ , ⁇ of the tristimulus values reflect the color difference ⁇ * between the color presented by the target swatch and the color exhibited by the first prototype glaze.
  • step S76 the rate of change in tristimulus value ( ⁇ coefficient) when a small amount of each pigment (mixing ratio is known) is added to each pigment (mixing ratio is known) at the time of the first trial glaze blending performed in step S73 Is calculated using the CCM method according to the following procedure. It should be noted that a slight increase in the amount of each additional pigment to be prepared is also input from the input device 42.
  • the tristimulus value of the color exhibited by the first prototype glaze was calculated for each pigment at the time of the first prototype. Calculate using the CCM method from the known blending ratio.
  • the tristimulus values (calculated values) X 1 / E , Y 1 / E , and Z 1 / E in this case are represented by the following Equation 44.
  • the absorption coefficient ⁇ ?.) And the scattering coefficient Si (.) Of the coloring matter and the coloring agent are expressed by the following Duncan's equation represented by the following equation 45 and Kubelka-munk represented by the equation 46. -Munk), the spectral reflectance RU) of any formulation can be determined by CCM based on these equations.
  • the ratio between the absorption coefficient and the scattering coefficient (KZS) of the preparation is represented by the absorption coefficient Ki (A), the scattering coefficient Si (;.) And the mixing ratio Ci of each pigment, as shown in Equation 47. Is defined by Therefore, the spectral reflectance R ( ⁇ ) can be calculated and calculated from this ratio (KZS).
  • K w and S w are the absorption coefficient and scattering coefficient of the white component (white pigment), and C w is the blending ratio of the white component.
  • step S92 for each pigment (pigments 1, 2, and 3), the tristimulus value (calculation) for the color of the glaze, in which the proportion of the first prototype glaze was separately increased by a small amount, was calculated.
  • Value) X1 / 1 / E , Y1 / 1 / E , Z1 / 1 / E were calculated from the known blending ratio of each pigment at the time of the first trial production and the known blending ratio of the increased pigment. Calculate using the method described above. To explain in more detail, first, add a small amount of pigment 1 (0.1 * C,) to the first trial glaze, and add other pigments 23 and white pigment to the glaze with the same blending ratio as the first trial glaze. The tristimulus value for the color presented by is calculated. Even in this case, the above Equation 44 Equation 47 is used, and in Equation 47, +0.1
  • Pigment 2 is added in only a small amount (0.1 * C 2 ), and the other pigments are tristimulus values X 1/2 / E Y 1/2 / E Z for the color of the glaze of the same mixing ratio. 1/2 / E , and pigment 3 with only a small amount (0.) * C 3 ), and the other pigments have the same stimulus tristimulus value for the color of the glaze X 1/3 / E Calculate Y 1/3 / E Z 1/3 / E.
  • the tristimulus value X 1 / w / E Y 1 / w / E z 1 / w / E is similarly calculated for the white pigment.
  • Pigment 1 Minor increase formulation: d6fcvJ
  • step S77 it is necessary to correct the differences ⁇ ⁇ , ⁇ , ⁇ (actual measurement values) of the tristimulus values obtained in step S75 through colorimetry of the target color sample glaze and the first prototype glaze.
  • the required additional compounding amount of each pigment is calculated using the following Equations 49 and 50.
  • Equation 4-9 the difference between the tristimulus values ⁇ , ⁇ , provided for correcting ⁇ (difference measured value), additional bulking formulation amount of each pigment (ACi, AC 2, AC 3 , AC W) Is a variable.
  • Equation 50 is a cost function when each pigment is varied, and the amount of addition of each pigment ( ⁇ ( ⁇ , AC 2 , m C 3 , AC W ) is also a variable.
  • equation 4 9, 5 0 these variables by solving the four above variables, i.e. additional increase formulation amount of each pigment ( ⁇ ( ⁇ , AC 2, AC 3 , AC W) is determined.
  • equation 5 0 is an expense required to a unit variation amount of the pigment i.
  • step S 7 in this case, in step S 7 7, ⁇ satisfies ⁇ 0, AC W satisfies AC W ⁇ 0, and, in equation 5 0 the AC W a cost function F to delta ⁇ sequence that minimizes represented solved using linear programming. Accordingly, and never AC W is calculated as a negative value.
  • the blending ratio of each pigment is calculated by taking into account the determined amount of added bulk compounding, and the physical property values of the additionally blended glaze are calculated and updated. I do. More specifically, the spectral reflectance R (;-) is obtained from the formulas 45 to 47 using the blending ratio, the absorption coefficient and the scattering coefficient of each pigment, and the like. Then, in the subsequent step S79, the calculated spectral reflectance R ( ⁇ ) is substituted into Equation 44, and the tristimulus value (calculated value) for the color of the glaze that is obtained by adding and increasing each face bridge is added.
  • step S80 the tristimulus values X END / E , Y END / E , Z END / E. And the tristimulus values ⁇ ⁇ , ⁇ ⁇ ⁇ ⁇ for the target swatches are used.
  • JIS Z 8730 the pass / fail judgment is again made as to whether or not the color difference ⁇ ⁇ * falls within a predetermined range. If it is judged that the color difference ⁇ * is within 0.30.5 in this step S80, if the glaze prepared with the correction amount obtained in the previous steps reproduces the color of the target color sample You can do it. Therefore, in this case, the process is terminated assuming that the blending process is completed.
  • the blending ratio that takes into account the correction amount (additional blending amount) obtained for each pigment is used as the final blending ratio when the glaze that has been blended is reblended. More specifically, the blended glaze whose color has been modulated due to variations in the blending process, etc., corresponds to the first prototype glaze in the fourth embodiment because the blending ratio is known, If each pigment is added to the blended glaze whose color has been modulated and added at the blending ratio according to the correction amount described above, the glaze exhibiting the target color can be blended again.
  • the tristimulus values for differential coefficient calculation and correction amount calculation are set to the previous values. And change the slight increase of each pigment. More specifically, the tristimulus value X BND / E Y BND / E , which is obtained in step S79 , is shifted to the tristimulus values, ⁇ ,, and Z X in Expression 43. From the tristimulus values (X / E , YEND / E , Z / E ) after this shift, the difference (mmX ⁇ , ⁇ ) between the tristimulus and the target color sample is newly obtained in step S75. Is calculated.
  • step S76 adds the microaddition amount considered in step S76 to the previous formulation amount (in this case, pigment 1 is Ci + 0.1 ⁇ ). Change to a small increase (( ⁇ + 0.1) * 0.1) multiplied by 1. This allows each pigment to be 0. 0 each less than if it were rejected in step S80. Since the amount is increased by a small amount, in step S76, the amount of each pigment is ⁇ tft, based on the proportion of the fine ftif that has been reduced. It is required according to.
  • this new tristimulus value due to the slight increase of each pigment is used in place of the tristimulus values X 1/1 / E , ⁇ 1/1 / ⁇ , Z 1/1 / E, etc. in Equation 48 .
  • the derivative is calculated again. After that, as described above, the amount of additional compounding (correction amount) of each pigment is determined, and the above-described processing is repeated until a pass is determined in step S80.
  • the glaze target color sample glaze
  • the glaze that has existed up to that point has a blending ratio such that the color presents a color that is somewhat similar.
  • Ask the amount of negative compounding that means the removal of pigment is not determined by CCM. Therefore, according to the computer color matching method of the fourth embodiment, it is not necessary to dispose of the glaze (colorant) in which the pigment has been prepared, so that the existing glaze can be effectively used.
  • the process involving the engineer is the one-time trial production of the glaze in step S73. At this time, since the engineer's many years of intuition and experience are not required, the glaze is used. Can be simplified.
  • the correction amount (AC ⁇ and AC W ) of each pigment is calculated by Equation 50.
  • the cost was calculated by the linear programming method using the expressed cost function F so that the cost required to correct each pigment was minimized. For this reason, according to the computer color matching method of the fourth embodiment, in addition to simplifying the above-mentioned effective use and re-mixing of the existing sleeves, the cost can be reduced.
  • Figure 27 shows tristimulus values (color values) for the target color sample glaze obtained in step S72. And the tristimulus values of the first trial glaze obtained in step S73 and the blending ratio (mixing ratio) of each pigment in the target color sample glaze and the first trial glaze.
  • ⁇ ⁇ * in the figure is a color difference between the target color sample glaze and the first prototype glaze, and a pass / fail judgment is made in step S74 based on this value.
  • Figure 28 shows the rate of change (differential coefficient) of tristimulus values when each pigment was added to the first prototype glaze shown in Figure 27 in a very small amount. It is calculated from At this time, the calculated tristimulus values X 1/1 / E , Y 1/1 / E , and Z 1/1 / E are used to calculate the differential coefficient when the amount of pigment 1 (red pigment) is slightly increased.
  • pigment 2 yellow pigment
  • tristimulus values X 1/2 / E, Y 1/2 / E, Z 1/2 / E power the pigment 3 (blue pigment) the tristimulus values X 1/3 / E, Y 3 / E , z 1/3 / E force;
  • tristimulus values X 1 / w / E , Y 1 / H / E , ⁇ 1 ⁇ / ⁇ are used.
  • FIG. 29 shows the result of the computer color matching method according to the fourth embodiment.
  • the glaze prepared at the final blending ratio determined for each pigment and the target color sample glaze are shown in FIG.
  • the stimulus values are shown in comparison.
  • the color difference ⁇ * for both glazes was 0.47, indicating that the pass was judged in step S80.
  • Equation 51 a utility function F represented by the following Equation 51 may be used.
  • the minimum value of the additional additional compounding amount of each pigment is defined in advance as the minimum additional additional compounding amount ⁇ C step-.
  • each pigment is additionally increased by an integral multiple of this ⁇ C step.
  • Each pigment is additionally added in this way, and the additional amount when the color difference ⁇ ⁇ * from the target color sample glaze becomes the smallest is used as a correction amount for calculating the differential coefficient, and this correction amount is taken into account.
  • the blending ratio determined may be used as the final blending ratio when re-mixing the blended glaze.
  • the minimum additional compounding amount AC The step is defined as the minimum increment of the minimum unit in which the color changes slightly when each pigment is added.
  • the maximum allowable amount A Craax of the additional amount of each pigment may be determined in advance, and the total amount of the additional amount of each pigment may be defined by this A Cmax.
  • the maximum allowable amount A Cmax is defined as an additional increase in the color that the color changes when the pigment is added, and it is considered that the original color cannot be returned with the additional amount of the other pigment.
  • the present invention is not limited to the linear programming using the above-mentioned function F, and other methods can be adopted.
  • the pass / fail judgment is made based on the color difference ⁇ *
  • a certain width is allowed for the pass / fail judgment. Therefore, a so-called fuzzy linear programming method that introduces a certain degree of ambiguity in this pass / fail decision can be adopted.
  • the result of the computer color matching method using the method of Fujii «Keisei ⁇ is shown in Figure 30 below. As shown in Fig. 30, the glaze blended at the final blending ratio determined for each pigment and the target color sample glaze have a color difference ⁇ * of 0.20, and both colors are well It turns out that they match.
  • the glaze for coloring pottery and tiles has been described as an example, but it is needless to say that the present invention can be applied to a dye for dyeing fibers.
  • a computer color matching method and apparatus include a method for predicting a mixing ratio of a colorant to be mixed with a glaze for coloring pottery tiles and a mixture thereof.

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  • Physics & Mathematics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • General Physics & Mathematics (AREA)
  • Spectrometry And Color Measurement (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)
  • Coloring (AREA)

Abstract

On exprime un coefficient de dispersion (SW') associé à un mélange d'un objet colorant et d'un pigment blanc par une fonction du contenu en pigment blanc (CW). On effectue une comparaison de teintes par ordinateur pour un échantillon de vérification et l'on définit une valeur qui donne une grande précision de prédiction sous forme de coefficients (a, b, c et d) (ou SE) contenus dans la fonction représentant le coefficient de dispersion (SW'). On évalue un coefficient d'absorption de l'ensemble (objet + pigment blanc), des coefficients d'absorption d'autres pigments colorants et des coefficients de dispersion en utilisant le coefficient de dispersion (SW') obtenu par la fonction ainsi établie. On procède à la comparaison des teintes par ordinateur en utilisant les constantes physiques de chacun des composants obtenus de cette manière, et l'on prédit la couleur ainsi que la proportion correspondante du mélange. En colorimétrie automatique, on peut réduire une erreur de prédiction en utilisant un réseau neuronal.
PCT/JP1996/000738 1996-02-22 1996-03-21 Procede et appareil de colorimetrie par ordinateur WO1997031247A1 (fr)

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JP2011059059A (ja) * 2009-09-14 2011-03-24 Seiren Co Ltd コンピュータカラーマッチング方法およびコンピュータカラーマッチングプログラムを記録したコンピュータ読み取り可能な記録媒体
KR101993752B1 (ko) * 2018-02-27 2019-06-27 연세대학교 산학협력단 신경망을 이용한 영상 컬러 일치 방법 및 장치
WO2019218549A1 (fr) * 2018-05-18 2019-11-21 Zhang Ye Système et procédé intelligents d'identification de couleur et de coloration
EP3605202A1 (fr) * 2018-07-31 2020-02-05 Essilor International Procédé et système permettant de déterminer une lentille de couleur personnalisable
US11062479B2 (en) 2017-12-06 2021-07-13 Axalta Coating Systems Ip Co., Llc Systems and methods for matching color and appearance of target coatings

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CA2350411C (fr) 2000-06-16 2006-06-27 Dainichiseika Color & Chemicals Mfg. Co., Ltd. Systeme de calcul ccm, methode de calcul ccm et support d'enregistrement
US7158672B2 (en) * 2003-06-11 2007-01-02 E. I. Du Pont De Nemours And Company Recipe calculation method for matt color shades
JP6326955B2 (ja) * 2014-05-15 2018-05-23 凸版印刷株式会社 色予測システムおよび色予測方法
CN104309300B (zh) * 2014-11-01 2017-05-24 广东理想彩色印务有限公司 一种彩印油墨计算机配色方法
JP6623679B2 (ja) * 2015-10-26 2019-12-25 凸版印刷株式会社 色予測システムおよび色予測方法
CA3143802A1 (fr) * 2019-08-06 2021-02-11 Guido BISCHOFF Procede et systeme de mise en correspondance et d'ajustement de la pigmentation d'un revetement d'echantillon sur un revetement cible
CN112936553B (zh) * 2021-03-30 2022-08-02 清远市简一陶瓷有限公司 一种计算多设计通体瓷砖各颜色用料比例的方法
KR20230073433A (ko) * 2021-11-18 2023-05-26 삼성디스플레이 주식회사 영상 보정 장치 및 영상 보정 방법

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JP2011059059A (ja) * 2009-09-14 2011-03-24 Seiren Co Ltd コンピュータカラーマッチング方法およびコンピュータカラーマッチングプログラムを記録したコンピュータ読み取り可能な記録媒体
US11062479B2 (en) 2017-12-06 2021-07-13 Axalta Coating Systems Ip Co., Llc Systems and methods for matching color and appearance of target coatings
US11568570B2 (en) 2017-12-06 2023-01-31 Axalta Coating Systems Ip Co., Llc Systems and methods for matching color and appearance of target coatings
US11692878B2 (en) 2017-12-06 2023-07-04 Axalta Coating Systems Ip Co., Llc Matching color and appearance of target coatings based on image entropy
KR101993752B1 (ko) * 2018-02-27 2019-06-27 연세대학교 산학협력단 신경망을 이용한 영상 컬러 일치 방법 및 장치
WO2019218549A1 (fr) * 2018-05-18 2019-11-21 Zhang Ye Système et procédé intelligents d'identification de couleur et de coloration
EP3605202A1 (fr) * 2018-07-31 2020-02-05 Essilor International Procédé et système permettant de déterminer une lentille de couleur personnalisable
WO2020025595A1 (fr) * 2018-07-31 2020-02-06 Essilor International Procédé et système de détermination d'une lentille de couleur personnalisée
CN112534342A (zh) * 2018-07-31 2021-03-19 依视路国际公司 用于确定定制颜色的镜片的方法和系统
CN112534342B (zh) * 2018-07-31 2022-11-01 依视路国际公司 用于确定定制颜色的镜片的方法和系统

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CN1207808A (zh) 1999-02-10

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