WO2021132654A1 - 塗料の製造方法、色彩データを予測する方法及びコンピュータ調色システム - Google Patents

塗料の製造方法、色彩データを予測する方法及びコンピュータ調色システム Download PDF

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Publication number
WO2021132654A1
WO2021132654A1 PCT/JP2020/048972 JP2020048972W WO2021132654A1 WO 2021132654 A1 WO2021132654 A1 WO 2021132654A1 JP 2020048972 W JP2020048972 W JP 2020048972W WO 2021132654 A1 WO2021132654 A1 WO 2021132654A1
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Prior art keywords
data
color
color data
artificial intelligence
intelligence model
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Ceased
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PCT/JP2020/048972
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English (en)
French (fr)
Japanese (ja)
Inventor
博 清水
智章 東谷
準治 赤羽
千尋 長野
睦 永見
山長　伸
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Kansai Paint Co Ltd
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Kansai Paint Co Ltd
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Priority claimed from JP2019238855A external-priority patent/JP6703639B1/ja
Application filed by Kansai Paint Co Ltd filed Critical Kansai Paint Co Ltd
Priority to CN202080089917.7A priority Critical patent/CN115244149B/zh
Priority to JP2021567719A priority patent/JPWO2021132654A1/ja
Publication of WO2021132654A1 publication Critical patent/WO2021132654A1/ja
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • C—CHEMISTRY; METALLURGY
    • C09—DYES; PAINTS; POLISHES; NATURAL RESINS; ADHESIVES; COMPOSITIONS NOT OTHERWISE PROVIDED FOR; APPLICATIONS OF MATERIALS NOT OTHERWISE PROVIDED FOR
    • C09D—COATING COMPOSITIONS, e.g. PAINTS, VARNISHES OR LACQUERS; FILLING PASTES; CHEMICAL PAINT OR INK REMOVERS; INKS; CORRECTING FLUIDS; WOODSTAINS; PASTES OR SOLIDS FOR COLOURING OR PRINTING; USE OF MATERIALS THEREFOR
    • C09D201/00—Coating compositions based on unspecified macromolecular compounds
    • C—CHEMISTRY; METALLURGY
    • C09—DYES; PAINTS; POLISHES; NATURAL RESINS; ADHESIVES; COMPOSITIONS NOT OTHERWISE PROVIDED FOR; APPLICATIONS OF MATERIALS NOT OTHERWISE PROVIDED FOR
    • C09D—COATING COMPOSITIONS, e.g. PAINTS, VARNISHES OR LACQUERS; FILLING PASTES; CHEMICAL PAINT OR INK REMOVERS; INKS; CORRECTING FLUIDS; WOODSTAINS; PASTES OR SOLIDS FOR COLOURING OR PRINTING; USE OF MATERIALS THEREFOR
    • C09D7/00—Features of coating compositions, not provided for in group C09D5/00; Processes for incorporating ingredients in coating compositions
    • C09D7/80—Processes for incorporating ingredients
    • 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
    • G01J3/50—Measurement of colour; Colour measuring devices, e.g. colorimeters using electric radiation detectors
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Definitions

  • the present invention relates to a method for producing a coating material and a method for predicting color data. More specifically, the present invention relates to a method for producing a paint based on computer toning and a system for predicting color data of a coating film. The present invention also relates to a computer toning system, a system for predicting color data of a coating film, and application software for controlling and operating these systems.
  • Patent Document 1 in a color matching system using a computer, the total spectral reflectance of an unknown color panel is determined by a spectrophotometer, and this reflectance data is sent to the computer, and the computer sends the K value (light absorption coefficient) of the pigment. ) And the pre-recorded data representing the S value (light scattering coefficient) are mathematically processed to perform logical color matching.
  • Patent Document 2 describes a colorimeter, a micro-brilliance measuring device, a plurality of paint formulations, color data and micro-brilliance data corresponding to each paint formulation, color characteristic data and micro-brilliance of a plurality of primary color paints.
  • Characteristic data is registered, and a computer in which a color matching calculation logic operates, a computer toning device composed of the same, and a toning method using the computer are disclosed.
  • Patent Document 3 describes a colorimeter, a micro-brilliance sample color tag, a plurality of paint formulations, color data and micro-brilliance data corresponding to each paint formulation, color characteristic data of a plurality of primary color paints, and micro-brilliance.
  • Sensitive characteristic data is registered, and a color matching method using a computer toning device composed of a computer on which the color matching calculation logic operates and a computer toning device composed of the same are disclosed.
  • Patent Document 4 as a toning method of a metallic coating color useful as a final fine toning of a repair paint, a toning combination of a metallic paint is based on a difference when a front color and a scab color are visually compared. It is stated that when changing the color, the characteristics based on the scale of the front color and the scab color of the paint color and the compounding conversion index of each metallic primary color paint in which the brightness of the front color does not change are used.
  • Patent Document 5 describes determining or predicting the visual texture parameters of a paint by an artificial neural network based on the color components used in the paint distribution composition. However, optical properties such as spectroscopic reflection properties are determined or predicted by a physical model for a known paint distribution composition.
  • Patent Document 6 in order to determine a coating color having a desired texture or a coating color belonging to a desired color category in a brilliant coating film or the like, data such as the spectral reflectance of the coating color and the micro-brilliance feeling are used.
  • a method of creating a paint color database and a method of searching for a paint color using the database are described, including a step of training a neural network.
  • no specific operation or the like when preparing a repair paint for a brilliant color whose optical characteristics are difficult to predict is not disclosed.
  • a first object of the present invention is a method for producing a coating material for obtaining a coating color of a wide variety of colors including a brilliant color whose optical characteristics are difficult to predict, regardless of the skill level of an operator. It is to provide a method of manufacturing a paint based on computer toning which can finish toning with a small number of trial productions.
  • a second object of the present invention is to provide a method for predicting the color data of a coating film capable of predicting the color data of the coating film of a coating film having a wide variety of compositions including a bright pigment and the like with high accuracy. ..
  • a third object of the present invention is a computer toning system for preparing a paint for obtaining a wide variety of paint colors, including bright colors whose optical characteristics are difficult to predict, and is used by an operator. It is to provide a computer toning system that can finish toning with a small number of trial productions regardless of the skill level.
  • a fourth object of the present invention is to provide a system for predicting the color data of a coating film capable of predicting the color data of the coating film of a coating material having a wide variety of compositions including a bright pigment and the like with high accuracy. ..
  • the present inventors have conducted diligent studies to solve the above-mentioned problems, and have found that the above-mentioned problems can be solved by adopting the following configuration, and have completed the present invention. Specifically, it is as follows.
  • Item 1 A database in which each compounding composition data Y1 to Yn of one or more kinds of compositions C1 to Cn (n is an integer of 2 or more) and color data X1 to Xn corresponding to each compounding composition data are registered, and the said.
  • a computer that operates the color matching calculation logic using the data registered in the database, It is a method of manufacturing a paint based on computer toning, which uses a device equipped with.
  • the method for producing a coating material which comprises the following steps S101 to S111. (S101) A process of inputting learning data to the computer using the data registered in the database.
  • Step 101 A step of generating a learned artificial intelligence model including at least one type of artificial intelligence model that estimates compounding composition data Y from color data X by machine learning using the learning data (S103).
  • the prediction combination composition data Ya1 predicted from the color data Xp is used as a composition composition containing one or more of the compositions C1 to Cn as components.
  • Step of obtaining as data (S106) Using a prediction formula other than the learned artificial intelligence model and / or the artificial intelligence model, the prediction color data Xa1 predicted from the prediction combination composition data Ya1 is obtained, and the pass / fail is determined by comparing with the color data Xp.
  • Step (S107) If the result is not passed in the step S106, the predicted blending composition data different from the predicted blending composition data predicted from the color data Xp using the predicted formula other than the learned artificial intelligence model and / or the artificial intelligence model.
  • the predicted compounding composition data Yai is used by using a prediction formula other than the learned artificial intelligence model and / or artificial intelligence model.
  • a step of obtaining the predicted color data Xai predicted from the above and repeating the pass / fail judgment by comparison with the color data Xp until it passes (S108).
  • Item 2 A database in which at least the compounding composition data Y and the corresponding color data X of one or more kinds of compositions are registered, and a computer in which the color matching calculation logic using the data registered in the database is operated are provided.
  • a method for producing a paint based on computer toning using an apparatus, the method for producing the paint which comprises the following steps S201 to S211.
  • S201 A step of inputting training data to the computer using the data registered in the database
  • S202 An artificial intelligence model in which the training data is machine-learned and color data X is estimated from the compounding composition data Y.
  • the learned to produce an artificial intelligence model (S203) to obtain the target color data X t colors that the target (S204) the target color data X t comprising at least one step of inputting to the computer (S205) the search using a computer, together with obtaining the target color data X t approximation to find the color data X n1 and search color approximation formulation composition data Y n1 corresponding to the data X n1, the target color data X t and the search color Step of comparing with data X n1 and determining pass / fail (S206) After obtaining candidate compounding composition data Y ni which is predicted to give target color data X t using a computer when the process of S205 is not passed, the target color data X t is obtained.
  • Item 3 A database in which at least the compounding composition data Y of one or more kinds of compositions and the corresponding color data X are registered, and a computer on which the color matching calculation logic using the data registered in the database is operated are provided.
  • a method for predicting color data of a coating film using an apparatus which comprises the following steps S301 to S309. (S301) A step of inputting training data to the computer using the data registered in the database (S302) An artificial intelligence model in which the training data is machine-learned and color data X is estimated from the compounding composition data Y.
  • step (S303) to obtain a formulation composition data Y CM paint CM t for predicting color data of coating the blend composition data Y CM containing at least one, said computer optionally step (S305) to be input to, the search using the computer, search color data X corresponding search step of acquiring color data X n1 (S306) the S305 process corresponding to the blended composition data Y CM If n1 is not found, or, in the case of not performed the S305 step, from the blend composition data Y CM, wherein the at least one learned artificial intelligence model, or, wherein the at least one learned artificial intelligence Step of obtaining predicted color data X m1 using a model and a prediction formula other than the artificial intelligence model (S307) If necessary, the measured color data X CM of the coated plate coated with the paint CM t is acquired. Step of comparing with the predicted color data X m1
  • the S105 step and / or the S107 step comprises the predicted compounding composition data Ya1 and / or Yai predicted from the color data Xp using the multi-label classification, and one or more of the compositions C1 to Cn.
  • Item 2. The method according to Item 1, which comprises a step of obtaining the compounding composition data.
  • Item 2 The method according to Item 1 or 4, wherein the composition containing a metallic pigment is 5 or less, and the composition containing a pearl pigment is 5 or less.
  • Item 6 When the step S211 does not pass, the steps S206 to S211 are repeated after inputting the difference ⁇ between the predicted color data X ni and the measured color data X Ci as a correction coefficient ⁇ into the computer.
  • the method described in. Item 7 The compounding composition data Y and the corresponding color data X of one or more kinds of compositions registered in the database include actual measurement data or data calculated based on the actual measurement data and the actual measurement data. The method according to any one of 6 to 6.
  • the step of generating the learned artificial intelligence model in the step S102 or the step S202 is (I) A step of learning an artificial intelligence model by using each compounding composition data Y and each color data X as learning data relating to one or more kinds of compositions containing no glitter pigment. (Ii) A step of learning an artificial intelligence model by using each compounding composition data Y and each color data X as learning data relating to one or more kinds of compositions containing a brilliant pigment.
  • Item 1 The method according to any one of Items 1, 2, 4 to 7.
  • the step of generating the learned artificial intelligence model in the step S102 or the step S202 is Content of light-reflecting pigment in composition, content of photo-interfering pigment, content of orientation control agent, content of light-reflecting pigment in composition for each hue, each hue of photo-interfering pigment One or more data selected from different contents, the content of each hue of the colorant, and the sum of two or more of those contents, and / or Shape data of the coloring material contained in the composition, Item 1.
  • Item 14 A database in which each compounding composition data Y1 to Yn of one or more kinds of compositions C1 to Cn (n is an integer of 2 or more) and color data X1 to Xn corresponding to each compounding composition data are registered. , And a computer on which the color matching calculation logic using the data registered in the database operates.
  • a computer toning system comprising the following means S401 to S411.
  • S401 Means for inputting learning data into the computer using the data registered in the database
  • S402 A means for machine learning the learning data to generate a learned artificial intelligence model including at least one type of artificial intelligence model that estimates compounding composition data Y from color data X (S403).
  • Means for obtaining color data Xp of a target color whose compounding composition Yp is unknown Means for inputting the color data Xp into the computer (S405) Using a prediction formula other than the learned artificial intelligence model and / or the artificial intelligence model, the prediction combination composition data Ya1 predicted from the color data Xp is used as a composition composition containing one or more of the compositions C1 to Cn as components. Means to obtain as data (S406) Using a prediction formula other than the learned artificial intelligence model and / or the artificial intelligence model, the prediction color data Xa1 predicted from the prediction combination composition data Ya1 is obtained, and the pass / fail is determined by comparing with the color data Xp.
  • the predicted compounding composition data Yai is used by using a prediction formula other than the learned artificial intelligence model and / or artificial intelligence model.
  • Means for obtaining pass compounding composition data Yap1 when passing by either of the means S406 or S407 Means for preparing an actual candidate paint CMap1 based on the accepted compounding composition data Yap1, obtaining a coated plate of the actual candidate paint CMap1, and acquiring actual measurement color data Xap1 (S410).
  • Item 15 A database in which at least one or more composition composition data Y and corresponding color data X are registered, and a computer on which a color matching calculation logic using the data registered in the database is operated are provided.
  • the system which is a computer toning system and includes the following means S501 to S511.
  • S501 Means for inputting training data to the computer using the data registered in the database
  • S502 An artificial intelligence model in which the training data is machine-learned and color data X is estimated from the compounding composition data Y.
  • an artificial intelligence model trained comprising at least one (S503) color to target the target color data X t get means (S504) the target color data X t, input to the computer means (S505) the search using a computer, together with obtaining the target color data X t approximation to find the color data X n1 and search color approximation formulation composition data Y n1 corresponding to the data X n1, the target color data X t and the search color Means for determining pass / fail by comparing with data X n1 (S506) After obtaining candidate compounding composition data Y ni which is predicted to give target color data X t using a computer when the means S505 does not pass, using said at least one learned artificial intelligence model and / or prediction expression other than the artificial intelligence model, along with obtaining a predicted color data X ni predicted from the candidate blend composition data Y ni, and the color data X t Means for determining pass / fail by comparing with
  • the color Means for comparing the data X t with the predicted color data X ni and repeating the means for determining pass / fail until it passes (S508) Passed compounding composition data Y C1 when any of the means S505 to S507 passed.
  • S509 Means for preparing the actual candidate paint CM Ci based on the accepted compounding composition data Y C1 , obtaining the coated plate of the actual candidate paint CM Ci , and acquiring the measured color data X Ci (S510).
  • Means for determining pass / fail by comparing the color data X t with the measured color data X Ci and / or comparing the target color with the color of the coating plate of the actual candidate paint CM Ci (S511). Means for repeating the means S506 to S510 when the means S510 does not pass.
  • Item 16 A database in which at least the compounding composition data Y of one or more kinds of compositions and the corresponding color data X are registered, and a computer on which the color matching calculation logic using the data registered in the database is operated are provided.
  • a system for predicting color data of a coating film using an apparatus including the following means S601 to S609. (S601) Means for inputting training data to the computer using the data registered in the database (S602) An artificial intelligence model in which the training data is machine-learned and color data X is estimated from compounding composition data Y.
  • paint CM means for obtaining blending composition data Y CM of t (S604) the blending composition data Y CM for predicting color data of the learned means for generating an artificial intelligence model (S603) a coating film containing at least one, said computer optionally means for inputting (S605) to a find using a computer, corresponding search color data X by means (S606) said means S605 of obtaining search color data X n1 corresponding to the blending composition data Y CM If n1 is not found, or, in the case of not performed the means S605, from the blend composition data Y CM, wherein the at least one learned artificial intelligence model, or, wherein the at least one learned artificial intelligence Means for obtaining predicted color data X m1 using a model and a prediction formula other than the artificial intelligence model (S607) If necessary, the measured color data X CM of the painted plate coated with the paint CM t is obtained. Means for comparison with the predicted color data X m1
  • Item 17 The system according to any one of Items 14 to 16, wherein the system includes an automatic blending means for automatically blending and toning blending based on the obtained blending composition data.
  • Item 18 Application software for controlling and operating the system according to any one of Items 14 to 17.
  • the present invention is a method for manufacturing a paint for obtaining a wide variety of paint colors including bright colors whose optical characteristics are difficult to predict, and the number of trial manufactures is small regardless of the skill level of the operator.
  • a method for producing a paint based on computer toning which can be completed with.
  • a method for predicting the color data of a coating film capable of predicting the color data of the coating film of a coating film having a wide variety of compositions including a bright pigment and the like with high accuracy.
  • the present invention is a computer toning system for preparing a paint for obtaining a wide variety of paint colors, including bright colors whose optical characteristics are difficult to predict, and it depends on the skill level of the operator. Regardless, a computer toning system that can finish toning with a small number of trial productions is provided. According to the present invention, there is provided a system for predicting the color data of a coating film capable of predicting the color data of the coating film of a coating film having a wide variety of compositions including a bright pigment and the like with high accuracy.
  • the burden on workers is reduced by reducing the work time and man-hours such as the number of tonings, the reduction of waste and energy saving by reducing the number of paints to be produced, and stable toning regardless of the skill of the workers. It is extremely useful in industry because it is possible to obtain effects such as improvement of workability by being able to perform color tone prediction.
  • Schematic configuration diagram showing an embodiment of an apparatus used in the method according to the present invention Schematic block diagram showing another embodiment of the apparatus used in the method according to the present invention.
  • Schematic configuration diagram of a neural network in the artificial intelligence model of the present invention Schematic configuration diagram showing an embodiment of angle-changing color measurement by the multi-angle spectrophotometer of the present invention.
  • a flowchart showing an embodiment of a method for producing a paint based on computer toning according to a second embodiment of the present invention A flowchart showing an embodiment of a method for predicting color data of a coating film according to a third embodiment of the present invention.
  • the present invention (I) A method for producing a coating material according to the first embodiment, which comprises a step of generating a learned artificial intelligence model including at least one type of artificial intelligence model for estimating compounding composition data Y from color data X. (Ii) A method for producing a coating material according to a second embodiment, which comprises a step of generating a learned artificial intelligence model including at least one type of artificial intelligence model for estimating color data X from compounding composition data Y. (Iii) A method for predicting color data of a coating film according to a third embodiment, which comprises a step of generating a learned artificial intelligence model including at least one type of artificial intelligence model for estimating color data X from compounding composition data Y.
  • a computer toning system comprising means for generating a learned artificial intelligence model that includes at least one type of artificial intelligence model that estimates compounding composition data Y from color data X.
  • V A computer toning system according to a fifth embodiment, comprising means for generating a learned artificial intelligence model including at least one type of artificial intelligence model that estimates color data X from compounding composition data Y.
  • Vi A system for predicting color data of a coating film according to a sixth embodiment, which comprises means for generating a learned artificial intelligence model including at least one type of artificial intelligence model for estimating color data X from compounding composition data Y. ,as well as, (Vii) Application software for controlling and operating the 4th to 6th systems, Including.
  • a database in which at least the color data X and the compounding composition data Y of the composition are registered and the database are registered.
  • a device is used that includes a computer that operates a color matching calculation logic that uses data.
  • the color data X of the composition includes color data X1 to Xn corresponding to the respective compounding composition data Y1 to Yn of one or more kinds of compositions C1 to Cn (n is an integer of 2 or more).
  • the compounding composition data Y of the composition includes the respective compounding composition data Y1 to Yn of the composition C1 to Cn (n is an integer of 2 or more) of more than one kind.
  • the computer toning system of the present invention and the system for predicting the color data of the coating film include a device including the database and a computer on which the color matching calculation logic operates.
  • FIGS. 1 and 2 are schematic configuration diagrams showing embodiments of an apparatus used in a method for producing a paint based on the computer toning of the present invention and a method for predicting color data of a coating film.
  • This device is also used as a device included in the computer toning system of the present invention and the system for predicting the color data of the coating film.
  • the apparatus D includes a database 1 and a computer 2.
  • the apparatus D may include two or more databases 1 and may include two or more computers 2. Further, the database 1 and the computer 2 may be integrated.
  • the device D used in the present invention includes an input device 31 (32), an output device 41 (42), a display device 51 (52), a colorimeter 61 (62), and an imaging device 71 (72), if necessary. ), (Automatic) compounding machine 81 (82) and the like, it may be provided with one or more devices having one or more functions. Further, one or more of these devices may be integrated with a database and / or a computer.
  • a known recording device, server, or the like can be used.
  • the computer 2 a commercially available personal computer, a mobile terminal, a smartphone or the like can be used.
  • the input device 31 (32) is a known input device such as a keyboard, a touch panel, and a reading device
  • the output device 41 (42) is a known output device such as a printing device or a data writing device.
  • a known display device such as a display can be used.
  • the colorimeter 61 (62) is a known colorimeter such as a (multi-angle) spectrophotometer, a colorimeter, and a color difference meter
  • the imaging device 71 (72) is a CCD camera, a solid-state imaging device, etc.
  • a known imaging device such as an infrared spectroscopic imaging device can be used, and as the (automatic) compounding machine 81 (82), a known (automatic) compounding machine including an electronic balance device or the like can be used.
  • the database, the computer and the device are connected so as to be able to send and receive data to and from each other by a communication means of wire, wireless or a combination thereof, or a means via a recording medium. ..
  • Examples of the communication means include one or more combinations of various communication networks such as LAN (local area network), WAN (wide area network), the Internet, and a telephone network.
  • FIG. 1 is an example of a device in which one database 1 and one computer 2 are connected so as to be able to send and receive data to and from each other.
  • One or more of the input device 31, the output device 41, the display device 51, the colorimeter 61, the imaging device 71, and the (automatic) compounding machine 81 may be connected to the database 1, and the computer 2 may be connected to the computer 2.
  • one or more of the input device 32, the output device 42, the display device 52, the colorimeter 62, the imaging device 72, and the (automatic) compounding machine 82 may be connected.
  • the colorimeters 61, 62, the imaging devices 71, 72, and the (automatic) compounding machines 81, 82 connected to the database 1 or the computer 2 are measured and compounded according to a command from the database 1 or the computer 2. And so on. Further, the measured data or the like can be transmitted to the database 1 or the computer 2, and finally the data can be registered in the database.
  • the database 1 may be formed in a recording device in the computer 2. In such a case, the device alone does not communicate with the database 1 and is an independent computer. It is possible to perform toning and prediction of color data of the coating film.
  • the database can be maintained by updating (updating) the data registered in the database at an appropriate timing as needed, so that the worker can perform the work based on the latest data. Can be done.
  • FIG. 2 is an example of a device in which two or more computers 21 to 2X are connected to one database 1.
  • FIG. 2 shows an example in which four computers 21 to 24 are connected.
  • the database 1 may be one in which two or more databases 1 are communicably connected. By increasing the number of databases 1, it is possible to increase the number of computers that can be connected in a communicable manner.
  • an input device 31, an output device 41, and a display device 51 may be connected to the database 1, and any one or more of a colorimeter, an imaging device, and an (automatic) compounding machine (not shown). Equipment may be connected.
  • FIG. 1 shows an example in which four computers 21 to 24 are connected.
  • the database 1 may be one in which two or more databases 1 are communicably connected. By increasing the number of databases 1, it is possible to increase the number of computers that can be connected in a communicable manner.
  • an input device 31, an output device 41, and a display device 51 may be connected to the database 1, and any one or more of a colorimeter,
  • computers 21 to 24 are connected to one or more of an input device 32, an output device 42, a display device 52, a colorimeter 62, an imaging device 72, and an (automatic) compounding machine 82. You may.
  • the device of FIG. 2 corresponds to a device in which a plurality of computers 2 are connected by using the database 1 as a server.
  • a computer 2 of a worker can be connected to a database 1 managed by a paint company or the like through a communication line (for example, an internet line, a telephone line, etc.) so that data communication can be performed.
  • FIG. 3 shows a neural network 9 (programming the function of nerve cells in the brain) in an artificial intelligence model that estimates the composition data Y from the color data X or an artificial intelligence model that estimates the color data X from the composition data Y. It is a schematic block diagram which shows (reproduced thing).
  • the artificial intelligence model is generated by inputting the learning data to the computer and machine learning the learning data using the data registered in the database.
  • the neural network 9 is configured to include three processing layers (three neuron layers) of an input layer 91, a hidden layer 92, and an output layer 93.
  • the input layer 91 contains at least 1 to i processing elements called input nodes 911 to 91i and is coupled to hidden nodes 921 to 92j in the hidden layer of the network.
  • Neural network in an artificial intelligence model that estimates compounding composition data Y from color data X used in the method for producing a paint according to the first embodiment of the present invention and the computer toning system according to the fourth embodiment of the present invention.
  • Each unit of the input layer 91 of 9 corresponds to each one or more types of feature quantities related to the color data X.
  • a method for producing a paint according to a second embodiment of the present invention, a method for predicting color data of a coating film according to a third embodiment of the present invention, a computer toning system according to a fourth embodiment of the present invention, and a computer toning system according to a fourth embodiment of the present invention are provided.
  • Each unit of the input layer 91 of the neural network 9 in the artificial intelligence model for estimating the color data X from the compounding composition data Y used in the system for predicting the color data of the coating film according to the sixth embodiment of the present invention. It corresponds to one or more kinds of feature amounts related to the compounding composition data Y.
  • the hidden layer 92 has at least 1 to j processing elements called hidden nodes 921, and is coupled to the output node 931 of the output layer 93 of the network.
  • Each hidden layer 92 (hidden nodes 921 to 92j) exists between the input layer 91 (input nodes 911 to 91i) and the output layer 93 (output nodes 931 to 93k).
  • the number of hidden nodes 921-92j can be varied by increasing the number of hidden nodes added to the network function to model the complexity of the I / O relationship.
  • the output layer 93 is organized to have at least 1 to k processing elements called output nodes 931 to 93k.
  • the processing elements or nodes are interconnected so that the relationship between the formulation data and the color data can be calculated during network execution.
  • a neural network in an artificial intelligence model that estimates compounding composition data Y from color data X which is used in the method for producing a paint according to the first embodiment of the present invention and the computer toning system according to the fourth embodiment of the present invention.
  • Each unit of the output layer 93 of 9 corresponds to each one or more types of feature amounts related to the compounding composition data Y.
  • a method for producing a paint according to a second embodiment of the present invention, a method for predicting color data of a coating film according to a third embodiment of the present invention, a computer toning system according to a fourth embodiment of the present invention, and a computer toning system according to a fourth embodiment of the present invention are identical to the output layer 93 of the neural network 9 in the artificial intelligence model for estimating the color data X from the compounding composition data Y used in the system for predicting the color data of the coating film according to the sixth embodiment of the present invention.
  • data flows in only one direction, and each node only transmits a signal to one or more nodes and does not receive feedback.
  • Input in the artificial intelligence model for estimating the compounding composition data Y from the color data X used in the method for producing the paint according to the first embodiment of the present invention and the computer toning system according to the fourth embodiment of the present invention correspond to one input variable (input element; parameter) in each color data by one input node.
  • the output nodes 931 to 93k in the output layer 93 correspond to one output node (output element; parameter) in each compounding composition data.
  • a method for producing a paint according to a second embodiment of the present invention, a method for predicting color data of a coating film according to a third embodiment of the present invention, a computer toning system according to a fourth embodiment of the present invention, and a computer toning system according to a fourth embodiment of the present invention are included in the artificial intelligence model for estimating the color data X from the compounding composition data Y used in the system for predicting the color data of the coating film according to the sixth embodiment of the present invention.
  • the input nodes 911 to 91i in the input layer 91 are One input node corresponds to one input variable (input element; parameter) in each compounding composition data.
  • the output nodes 931 to 93k in the output layer 93 correspond to one output node (output element; parameter) in each color data.
  • the number of hidden nodes 921 to 92j in the hidden layer 92 can be increased or decreased according to the complexity of the input / output relationship.
  • Each connection between each input node of the input layer 91, between an input node and a hidden node, between a hidden node and an output node, and between each output node of the output layer 93 has a connection weight associated therewith, and further.
  • Each of the hidden node 92 and the output node 93 may have one or more additional threshold weights.
  • the neural network 9 can be generated on the server computer side that constitutes the database in the device as shown in FIG. 2, for example. As a result, the connected worker (user) can receive high-quality data at any time regardless of geographical requirements and the like. Further, by improving the security of the server computer, it is possible to prevent data modification, neural network destruction, etc. due to unauthorized access.
  • At least one type of artificial intelligence model that estimates the compounding composition data Y from the color data X at least one type of artificial intelligence model that estimates the color data X from the compounding composition data Y may be provided.
  • the neural network in this artificial intelligence model is configured in the same manner as that according to FIG. 3, and each unit of the input layer 91 has an output layer for each one or more types of feature amounts related to the compounding composition data Y.
  • Each of the 93 units corresponds to each of one or more types of feature quantities related to the color data X.
  • the database in the present invention is configured by registering (recording) color data and compounding composition data in association with each other.
  • various data related to color or composition can be registered in the database of the present invention in association with each other.
  • the data registered in the database is preferably a very large number of data (so-called big data). Specifically, it is 5,000 or more, 10,000 or more, and more preferably 20,000 or more. These data can be added, changed, and deleted at will.
  • the compounding composition data Y and the corresponding color data X of one or more kinds of compositions registered in the database include actual measurement data or data calculated based on the actual measurement data and the actual measurement data. Examples of the data calculated based on the measured data include various parameter values calculated using the measured data and using a predetermined mathematical formula, and predicted values that can be calculated based on the measured data.
  • the database may be a single database, or may be a plurality of databases associated with each other by at least one common information element or a plurality of databases not associated with each other.
  • the database can be installed on a server that can communicate with a computer and can be operated remotely.
  • at least a part of the database is provided in a recording unit (memory, etc.) of a computer, a colorimeter, a micro-brilliance measuring device, an automatic compounding machine, and other devices that acquire or use data registered in the database. be able to.
  • each database may be connected by wire or wirelessly.
  • the database may be connected by wire or wirelessly to one or more of a computer, a colorimeter, a micro-brightness measuring device, an automatic compounding machine, and other devices that acquire or use data registered in the database. ..
  • the composition in the present invention may contain one or more kinds of coloring materials in an arbitrary amount ratio.
  • the coloring material contained in the composition is, for example, a material having a function of developing a color of the composition, such as a coloring pigment, a dye, and a bright pigment (light-reflecting pigment, light-interfering pigment, etc.).
  • the "primary color paint" used for repair may be used.
  • the paint may be a paint obtained by mixing raw materials including one or more primary color paints and color-matching them to a desired color.
  • the composition of the present invention may contain a coloring pigment paste, a bright pigment paste, an orientation control agent, a gloss control agent, and various additives used in the field of paints and the like.
  • the color data registered in the database includes data related to color and data related to appearance characteristics such as texture, brilliance, and luster. These data can be obtained by measuring the coating film obtained from the composition using a device such as a colorimeter or an imaging device. Further, the coating film may be obtained by analyzing, converting, correcting or the like one or more image data acquired by using an apparatus or the image data as necessary. Further, at least a part of various color data obtained by the measurement obtained by processing the image data may be calculated by arithmetic processing. The data obtained by measuring with the device may be corrected for errors due to measurement fluctuations or the like between the measuring devices, if necessary.
  • the K value (light absorption coefficient) and the S value (light scattering coefficient) of the composition itself may be used as color data.
  • the K value and the S value can be obtained, for example, by numerically processing the composition and the color measurement data of the diluted color of the composition.
  • the data related to color and / or the data related to appearance characteristics include, for example, a colorimeter, a multi-angle spectrophotometer, a laser metallic sensation measuring device, a variable angle spectrophotometer, a gloss meter, an imaging device, a micro brilliance measuring device, and the like. It is either directly acquired by the measurement using the measuring device of No. 1 or calculated from the data acquired by the measurement.
  • the data related to color and / or the data related to appearance characteristics can include data such as one or a plurality of illumination angles, one or a plurality of observation angles, or an image related to a combination thereof.
  • an instrument for obtaining color data if it is an instrument capable of measuring the color of a bright coating film (metallic coating film, pearl color coating film, etc.), solid color coating film, etc. and acquiring color data, it is measured. There are no particular restrictions on the principle, the method of calculating color data using measured values, and the like, and conventionally known ones can be used.
  • a colorimeter such as a single-angle spectrophotometer, a multi-angle spectrophotometer, a colorimeter, a color difference meter, a variable-angle spectrophotometer, an imaging device, and a micro-luminance, which are equipped with a light source that illuminates the surface to be measured.
  • a measuring instrument and an instrument such as a color sample plate can be used.
  • a data processing device that processes various color data obtained from those instruments can be arbitrarily used.
  • data related to color includes data representing lightness, saturation, and hue, or data in which color can be specified by calculation.
  • XYZ color system (X, Y, Z value)
  • RGB color system L * a * b * color system
  • L *, a *, b * value Hunter Lab color system
  • L * C * h color system L *, C *, h value
  • CIE (1994) Mansell color system
  • H, V, C value Mansell color system
  • H, V, C value Mansell color system
  • the data related to color can be data based on any one or more color systems.
  • the data is based on the L * a * b * color system or the L * C * h color system, which is widely used in various fields including the repair coating field.
  • the data related to the appearance characteristics is, for example, the texture felt when observing the surface to be measured such as a coating film containing a bright pigment, which is a macroscopic observation. Macro-brightness, which is the perceived texture, micro-brightness, which is the texture perceived by microscopic observation, depth (depth), sharpness, etc. can be mentioned.
  • the macroscopic brilliance examples include multi-angle spectral reflectance obtained by irradiating the surface to be measured with uniform light, receiving the reflected light at each angle, and measuring the color.
  • the FF value flip flop value
  • the IV value intensity value value
  • the SV value scatter indicating the brightness of the front of the highlight side.
  • the macroscopic brilliance can be directly obtained by using, for example, a multi-angle spectrophotometer, a laser metallic sensation measuring device, a variable-angle spectrophotometer, a gloss meter, or the like, and can be calculated from these.
  • a multi-angle spectrophotometer BYK-Mac i (trade name, manufactured by BYK), MA-68II (trade name, manufactured by X-Rite) and the like can be used.
  • the FF value, IV value and SV value can also be obtained by using the laser type metallic feeling measuring device Alcorp (registered trademark) LMR-200 (trade name, manufactured by Kansai Paint Co., Ltd.).
  • the multi-angle spectral reflectance is a spectral reflectance measured by a spectrophotometer capable of multi-angle colorimetric measurement, and is represented by R (x, ⁇ ).
  • R is the spectral reflectance (Reflectance), and is represented by the spectral reflectance% calibrated by the calibration plate attached to the measuring instrument.
  • x is the light receiving angle and is represented by the declination from the specularly reflected light.
  • ⁇ is a wavelength, and the visible light range of 400 to 700 nm is measured at 10 nm intervals (number of wavelengths: 31).
  • the angle of incidence is -45 degrees, which is generally standard.
  • the light receiving angle x is shaded from the highlight (25 degrees, 15 degrees, -15 degrees) and face (45 degrees) when the incident angle is 45 degrees. ) (75 degrees, 110 degrees), any one angle or more, preferably three angles or more.
  • the light receiving angle can be 6 angles of -15 degrees, 15 degrees, 25 degrees, 45 degrees, 75 degrees and 110 degrees.
  • the light receiving angles can be set to 5 angles of 15 degrees, 25 degrees, 45 degrees, 75 degrees and 110 degrees.
  • the FF value indicates the degree of change in the L value (brightness) depending on the observation angle (light receiving angle).
  • the flip-flop corresponds to the difference in brightness between the highlight direction (the direction closer to the specular reflection direction of light) and the shade direction (the direction away from the specular reflection direction of light).
  • the larger the FF value the larger the change in the L value (brightness) depending on the observation angle (light receiving angle), indicating that the flip-flop property is excellent.
  • the IV value is a Y value in the XYZ color system obtained from the spectral reflectance measured from the direction in which the declination is 15 degrees.
  • the SV value is a Y value in the XYZ color system obtained from the spectral reflectance measured from the direction in which the declination is 45 degrees.
  • the FF value is the XYZ color obtained from the spectral reflectance measured from the direction of the declination of 45 degrees, where the Y value in the XYZ color system obtained from the spectral reflectance measured from the direction of the declination of 15 degrees is Y15.
  • the Y value in the system is Y45, and it can be obtained by the following formula.
  • cFF value 2 x (Y15-Y45) / (Y15 + Y45)
  • the cFF value is the spectral reflection measured from the direction of the declination of 45 degrees, where the c * value in the L * c * h color system obtained from the spectral reflectance measured from the direction of the declination of 15 degrees is c * 15. It can be calculated by the following formula, where the c * value in the L * c * h color system obtained from the rate is c * 45.
  • cFF value 2 ⁇ (c * 15-c * 45) / (c * 15 + c * 45)
  • the metal feeling index can be calculated by the following formula, where Y15 is the Y value in the XYZ color system obtained from the spectral reflectance measured from the direction in which the declination is 15 degrees, and FF is the FF value.
  • Metal feeling index Y15 x FF 2
  • the depth sensation index represents the depth sensation given by the brilliant pigment, and the L * and c * values in the L * c * h color system obtained from the spectral reflectance at a representative angle are L * R and c, respectively. * R, and using these, it can be calculated by the following formula.
  • Depth index c * R / L * R
  • c * 75 which is a c * value at a light receiving angle of 75 degrees
  • L * 75 which is an L * value at a light receiving angle of 75 degrees
  • micro-brilliance sensation is a sensibility regarding two-dimensional brightness non-uniformity such as glitter and particle sensation, which is expressed by a brilliant pigment in the surface to be measured when the color-measured surface is observed from a short distance.
  • the micro brilliance can be obtained using a micro brilliance measuring instrument.
  • the micro-luminance measuring device include a light irradiation device that irradiates a bright coating surface with light, a CCD camera that forms an image by photographing the light-irradiated coating surface at an angle at which the irradiation light does not enter.
  • Examples thereof include a micro-brightness measuring device which is connected to a CCD camera and equipped with an image analysis device for analyzing the image.
  • the surface to be measured is irradiated with pseudo (artificial) sunlight.
  • the angle of light irradiation on the surface to be measured is usually in the range of 5 to 60 degrees, preferably 10 to 20 degrees based on the vertical line of the surface to be measured, and in particular, about 15 degrees with respect to the vertical line. Is preferable.
  • the shape of the light irradiation region is not particularly limited, but it is usually circular, and the irradiation area on the surface to be measured is usually within the range of 1 to 10,000 mm 2 of the surface to be measured. Suitable, but not limited to this range.
  • the illuminance of the irradiation light is usually preferably in the range of 100 to 2,000 lux.
  • the surface to be measured is irradiated with light, and of the reflected light based on the light, the surface to be measured to be irradiated with light is photographed with a CCD (Charge Couple Device) camera at an angle at which the specular reflected light is not incident.
  • the imaging angle may be any angle at which the specularly reflected light does not enter, but the vertical direction with respect to the surface to be measured is particularly preferable. Further, the angle between the shooting direction of the CCD camera and the specularly reflected light is preferably in the range of 10 to 60 degrees.
  • the measurement range of the light-irradiated surface to be measured by the CCD camera is not particularly limited as long as it is uniformly irradiated with light, but usually includes the central portion of the irradiated portion and the measurement area. Is in the range of 1 to 10,000 mm 2 , preferably 10 to 600 mm 2 .
  • the image taken by the CCD camera is a two-dimensional image, which is divided into a large number (usually 10,000 to 1,000,000) sections (pixels, pixels), and the brightness in each section is measured.
  • the "brightness” means "a digital gradation indicating a shade value for each section of a two-dimensional image obtained by taking a picture with a CCD camera, and a digital amount corresponding to the brightness of the subject". ..
  • the digital gradation which means the brightness of each section output from the 8-bit resolution CCD camera, shows a value of 0 to 255.
  • the section corresponding to the portion where the reflected light of the brilliant pigment is strong has a strong glittering feeling, so that the brightness is high, and the section corresponding to the portion not corresponding to the bright pigment has a low brightness. Further, even in the section corresponding to the portion where the reflected light of the bright pigment is strong, the brightness changes depending on the size, shape, angle, material and the like of the bright pigment. That is, the brightness can be displayed for each section, and in the present invention, the brightness distribution of the two-dimensional image taken by the CCD camera can be displayed three-dimensionally based on the brightness in each section. This three-dimensional distribution map of brightness is divided into mountains, valleys, and flat areas.
  • the height and size of the mountains indicate the degree of brilliance caused by the brilliant pigment, and the higher the hills, the more pronounced the brilliance. Shown, valleys and flat areas show no or little brilliance and show reflection of light primarily by colored pigments or substrates.
  • the analysis of the image taken by the CCD camera can be performed by an image analysis device connected to the CCD camera.
  • image analysis software used in this image analysis apparatus, for example, WinLoof (trade name: manufactured by Mitani Corporation) is suitable.
  • WinLoof trade name: manufactured by Mitani Corporation
  • “glitter” perception of irregular and fine brilliance caused by the light directly reflected from the bright pigment in the surface to be measured
  • "graininess” as much as possible glitter is expressed.
  • the perception generated from the irregular / non-directional pattern random pattern
  • a two-dimensional image obtained by photographing the light-irradiated surface to be measured with a CCD camera is divided into a large number of sections, and the brightness of each section is summed over all of the sections to obtain a total value.
  • the value is divided by the total number of sections to obtain the average brightness x, and the threshold value ⁇ is set to a value equal to or higher than the average brightness x. It is usually appropriate that the threshold value ⁇ is the sum of the average luminance x and y (y is a number of 24 to 40, preferably 28 to 36, more preferably 32).
  • a threshold value ⁇ which is equal to or more than the average brightness x and equal to or less than the threshold value ⁇ is set.
  • the threshold value ⁇ is equal to or less than the threshold value ⁇ , and it is usually appropriate that the average brightness x and z (z is a number of 16 to 32, preferably 20 to 28, more preferably 24).
  • the value of the threshold value ⁇ is subtracted from each luminance of the above-mentioned section, and the subtracted values whose subtracted values are positive values are summed up to obtain the total volume W which is the total sum.
  • the total area A which is the total number of compartments having brightness equal to or higher than the threshold value ⁇ (the total number of compartments having the threshold value ⁇ or higher obtained by binarizing with the threshold value ⁇ ), is obtained.
  • the average particle area of the optical particles can be obtained from the total area A at the threshold value ⁇ and the number C of the optical particles exhibiting the brightness equal to or higher than the threshold value ⁇ .
  • the "optical particle” means "an independent continuum whose brightness is equal to or higher than a threshold value on a two-dimensional image”. Assuming that the shape of the optical particles is a circle, the diameter D of the circle having the same area as the average particle area is calculated by the following formula.
  • the "glittering feeling" of the bright coating film can be quantitatively measured by the brilliance value BV obtained as described above, and the brilliance value BV and the "glittering feeling” by visual observation can be measured.
  • the correlation with the sensory evaluation result is high even when the difference in the density and the difference in brightness of the bright material in the coating film is large.
  • the graininess is represented by the MGR value.
  • the MGR value is one of the measures of microscopic brilliance, which is the texture when observed microscopically, and is a parameter representing the graininess in highlights (observing a multi-layer coating film from the vicinity of specular reflection with respect to incident light). Is.
  • the MGR value is obtained by imaging a multi-layer coating film with a CCD camera at an incident angle of 15 degrees and a light receiving angle of 0 degrees, and performing two-dimensional Fourier transform processing on the obtained digital image data, that is, two-dimensional brightness distribution data. From the obtained power spectrum image, only the spatial frequency region corresponding to the grain feeling was extracted, and the calculated measurement parameters were further taken from 0 to 100, and a linear relationship with the grain feeling was maintained. It is a measured value obtained by converting so as to be obtained. The one without a grainy feeling is set to 0, and the one with the most grainy feeling is almost 100.
  • a light-irradiated bright coating surface is photographed with a CCD camera to obtain a two-dimensional image, and the two-dimensional image is obtained in 2D.
  • the power of the low spatial frequency component is integrated and the two-dimensional power spectrum integrated value obtained by normalizing with the DC component is obtained, and the particle feeling of the coating film is obtained from this two-dimensional power spectrum integrated value.
  • the extraction region of the low spatial frequency component extracted from is a region in which the line density representing the resolution is any value in the range of the lower limit value of 0 lines / mm to the upper limit value of 2 to 13.4 lines / mm, preferably 0.
  • the range of this / mm to 4.4 lines / mm is suitable from the viewpoint of increasing the correlation with the sensory evaluation result of "grain feeling" by visual observation. The larger the two-dimensional power spectrum integral value, the greater the graininess.
  • the two-dimensional power spectrum integral value (hereinafter, may be abbreviated as "IPSL”) can be obtained by the following equation.
  • MBV (BV-50) / 2 It is also possible to evaluate the "glittering feeling" by the value of MBV calculated by.
  • the value of MBV is 0 for those without a feeling of glitter, and a value of about 100 for the one with the most glittering feeling, and the larger the value is, the larger the value is.
  • the MBV value is sometimes referred to as the HB value (Hi-light Brilliant value).
  • the value of MGR is a value in which the one having no grain feeling of the bright material is 0 and the one having the most grain feeling of the bright material is almost 100, and the one having "grain feeling" shows a larger value.
  • the value of MGR is sometimes referred to as the HG value (Hi-light Graininess value).
  • micro-brightness index (MGR + ⁇ ⁇ MBV) / (1 + ⁇ )
  • MGR + ⁇ ⁇ MBV 1 + ⁇
  • ⁇ is preferably 1.80 to 1.40, more preferably 1.63, which matches well with the visual brilliant sensation. You can get the result.
  • the micro-brilliance index is 0 when there is no brilliance (no brilliance or graininess), and the value with the most brilliance (the most brilliance and particle sensation) is approximately 100.
  • the compounding composition data of the composition is registered in the database.
  • the compounding composition data includes data relating to each compounding component such as one or more kinds of coloring materials, binders, additives and the like contained in the composition, and the respective compounding amounts.
  • the product name (product number) itself can be used as the compounding composition data
  • the compounding amount composition of each product can be used as the compounding composition data. For example, it is effective when a commercially available product whose compounding composition data is unknown or when the composition is managed by a product number.
  • the shape, chemical properties, and the like of each component such as one or more kinds of coloring materials, binders, and additives contained in the composition can be registered as compounding composition data.
  • the shape include the shape of a coloring material (spherical, scaly, fibrous, etc.), average primary particle size, average secondary particle size, average dispersed particle size, particle size distribution, aspect ratio, thickness, and the like.
  • Chemical properties include molecular weight, molecular weight distribution, discoloration temperature, reactivity and the like.
  • the composition when the composition contains a bright pigment such as a light-reflecting pigment or a light-interfering pigment, the content of the light-reflecting pigment, the content of the light-interfering pigment, and the orientation of the bright pigment are controlled.
  • the content of the orientation control agent to be used is also registered in the database as compounding composition data.
  • the content of the colorant for each hue, the content of the light-reflecting pigment for each hue, and the content of the light-interfering pigment for each hue, which are contained in the composition are also stored in the database as compounding composition data. To be registered in.
  • each hue is the L * C * h table devised based on the L * a * b * color system, which was defined by the International Commission on Illumination in 1976 and adopted in JIS Z 8729. It can be done in a color system.
  • the L * C * h color system chromaticity diagram calculated based on the spectral reflectance when light emitted from 45 degrees to the coating film is received at 45 degrees with respect to the positively reflected light, red.
  • the system color is defined as a color in which the hue angle h is within the range of ⁇ 45 degrees or more and less than 45 degrees when the a * red direction is 0 degrees.
  • an orange color is a color in which the hue angle h is within the range of 45 degrees or more and less than 67.5 degrees when a * red direction is 0 degrees
  • a yellow color is a hue angle h.
  • Paint condition data K may be further registered in the database.
  • the painting condition data K is all data related to painting. For example, information on painting equipment used for painting (type of painting equipment, manufacturer of painting equipment, model number, etc.), painting condition information (painting temperature, humidity at the time of painting, dry film, etc.) Thickness, paint solid content, painting distance, painting speed, etc.), painter information (name, painting skill, painting tendency, habit, etc.), drying condition information (drying temperature, drying humidity, drying equipment manufacturer, drying equipment model number), etc. can give.
  • the computer included in the device used in the present invention means an electronic device having a calculation function and an information processing function such as a supercomputer (supercomputer), a desktop computer, a laptop computer, a personal computer such as a mobile computer, a tablet terminal, and a smartphone. doing.
  • the computer may be installed at any place including the work site, or may be carried by an operator or the like.
  • the computer includes a calculation unit and a control unit, and may further include an input / output unit, a communication unit, a recording unit, and the like. Further, in the present invention, by incorporating it into a colorimeter or the like provided with recording, calculation, control, input / output functions, etc., it can be realized integrally with the colorimeter or the like. It is also possible to record color data and compounding composition data in a computer recording unit and provide a database in the recording unit. If the database is located outside the computer, the computer is connected to the database by wire or wirelessly.
  • the computer further inputs various devices for measuring data related to color and / or data related to appearance characteristics, an automatic compounding machine, other arithmetic devices, a keyboard, a mouse, a bar code reader, a touch panel, an image recognition device, and the like. It may be connected to an device, a monitor screen, an output device such as a printing device, or the like by wire or wirelessly. If necessary, application software (program) for executing the method of the present invention or for controlling and operating the system of the present invention is installed in the computer so that necessary control and operation can be performed. You may be.
  • the device may include an automatic compounding machine that automatically mixes and adjusts each compounding component based on the compounding composition data.
  • the automatic compounding machine can be connected to the computer or database by wire or wirelessly.
  • the automatic compounding machine has at least an electronic balance that automatically weighs the weight or capacity of each compounding component such as a coloring material, and an injector that injects each weighed compounding component into the compounding machine.
  • high-precision weighing can be performed automatically, human error in compounding can be reduced, compounding can be performed quickly, and any amount of toning can be adjusted. Finished paint can be easily prepared.
  • production control can be facilitated by recording the compounding work.
  • the automatic compounding machine may be one that automates all the operations related to compounding, or may be such that the operator can perform a part of fine color adjustment and the like.
  • FIG. 6 is a flowchart for executing a method for producing a paint based on computer toning according to the first embodiment of the present invention.
  • the flow shown in FIG. 6 is only one embodiment of the present invention.
  • the method for producing a paint based on computer toning according to the first embodiment of the present invention includes each of the compounding composition data Y1 to Yn of one or more kinds of compositions C1 to Cn (n is an integer of 2 or more), and each of them.
  • the step S101 is a step of inputting learning data into the computer using the data registered in the database.
  • learning data using compounding composition data and color data of one or more kinds of compositions but not containing a brilliant pigment, one or more kinds of coloring materials, and one or more kinds of brilliance is preferable to separately create the compounding composition data of the composition containing the pigment and the learning data using the color data, and input them separately.
  • the present inventors have found that by creating separate learning data based on the presence or absence of a bright pigment and inputting them separately, the matching rate in computer toning is significantly improved.
  • the compounding composition data of a composition containing one or more kinds of coloring materials and one or more kinds of bright pigments is used as learning data
  • the content of the light-reflecting pigment and the light-interfering pigment are used. It is preferable to use one or more types of data selected from the content, the content of the orientation control agent, and the sum of one or more of them as learning data.
  • the compounding composition data of the composition containing one or more kinds of coloring materials and one or more kinds of bright pigments is used as learning data
  • the data of the content of the bright pigments for each hue is learned. It is preferable to use the data for use.
  • one or more types of data selected from the content of each hue of the light-reflecting pigment in the composition, the content of each hue of the light-interfering pigment, and the content of each hue of the colorant. is preferably used as training data.
  • the definition of the color of the glitter pigment is the L * C * h table devised based on the L * a * b * color system, which was defined by the International Commission on Illumination in 1976 and adopted in JIS Z 8729. It can be done in a color system.
  • the L * C * h color system chromaticity diagram calculated based on the spectral reflectance when light emitted from 45 degrees to the coating film is received at 45 degrees with respect to the positively reflected light, red.
  • the system color is defined as a color in which the hue angle h is within the range of ⁇ 45 degrees or more and less than 45 degrees when the a * red direction is 0 degrees.
  • an orange color is a color in which the hue angle h is within the range of 45 degrees or more and less than 67.5 degrees when a * red direction is 0 degrees
  • a yellow color is a hue angle h.
  • the compounding composition data of one or more kinds of compositions when used as learning data, it is preferable to use the shape data of the coloring material contained in the composition as learning data.
  • the shape of the coloring material such as a coloring pigment or a bright pigment (spherical, scaly, fibrous, etc.), the average primary particle size of the coloring material, the average secondary particle size, the average dispersed particle size, and the particle size distribution.
  • Aspect ratio, thickness and other shape data are preferably used as learning data.
  • Input to the computer can be performed by transmitting data by wire, wireless or a combination of these communication means or means via a recording medium.
  • Examples of the input using the communication means include one or more combinations of various communication networks such as LAN (local area network), WAN (wide area network), the Internet, and a telephone network.
  • Input by means via a recording medium can be performed by reading data from a recording medium such as a magnetic recording medium, an optical recording medium, or a paper recording medium by using an appropriate reading means.
  • the step S102 is a step of generating a learned artificial intelligence model including at least one type of artificial intelligence model that estimates the compounding composition data Y from the color data X by machine learning using the learning data.
  • the artificial intelligence model in the present invention include a determination tree using gradient boosting, linear regression, logistic regression, simple perceptron, MLP, neural network, support vector machine, random forest, Gaussian process, Bayesian network, and k-nearest neighbor method. , Others It can be composed of one or more types selected from the group consisting of models used in machine learning.
  • the artificial intelligence model uses one or more selected types.
  • the learned artificial intelligence model generated in the step S102 is an artificial intelligence model that estimates color data X from composition data Y in addition to at least one type of artificial intelligence model that estimates compounding composition data Y from color data X. It may include at least one type of intelligence model.
  • at least one type of artificial intelligence model that estimates the color data X from the composition data Y can be generated by machine learning using the learning data input in the step S101. Even in such a case, a neural network can be constructed and trained.
  • Learning of the artificial intelligence model is performed using the learning data input to the computer in the S101 process.
  • the learning data at least the color data X and the composition compounding data Y related to one or more kinds of compositions are used.
  • an algorithm of the neural network a known backpropagation method, which is one of the supervised learning methods, can be used.
  • a neural network by setting the learning rate (real value between 0 and 1), which is a parameter representing the learning speed, and the margin of error (real value between 0 and 1), which is the permissible value of the error of the output value in learning. To learn.
  • one or more kinds of feature amounts related to the blended composition data Y can be associated with one or more kinds of feature amounts related to the color data X of the coating film of the paint film based on the blended composition data Y.
  • the learned network can make these predictions without performing labor-intensive experimental confirmations such as cost and time.
  • each compounding composition data Y and each color data X relating to one or more kinds of compositions not containing a glitter pigment are used as learning data.
  • each compounding composition data Y and each color data X related to one or more kinds of compositions containing a brilliant pigment are used as learning data to train an artificial intelligence model. It is preferable to include steps. At that time, the step (ii) is performed on one or more types selected from the content of the light-reflecting pigment, the content of the light-interfering pigment, the content of the orientation control agent, and the sum of one or more of them in the composition.
  • step of training the artificial intelligence model it is preferable to include a step of using the data as training data and training an artificial intelligence model.
  • the order of the steps (i) and the step (ii) is not particularly limited, and the step (ii) may be performed after the step (i), or after the step (ii). Step (i) may be performed.
  • learning data is created as separate data from the viewpoint of whether or not the composition contains a bright pigment, and by training this, an artificial intelligence model capable of making more accurate predictions can be performed. Can be generated.
  • the step of generating the learned artificial intelligence model is the content of the light-reflecting pigment, the content of the photo-interfering pigment, the content of the orientation control agent such as amorphous silica, and the like. It is preferable to include a step of learning an artificial intelligence model by using one or more kinds of data selected from one or more totals of the above as learning data. As a result, it is possible to obtain compounding composition data that can be accurately adapted to the color data of the surface to be measured, which particularly contains a bright pigment.
  • the step of generating the learned artificial intelligence model is the content of the light-reflecting pigment in the composition for each hue, the content of the photocoherent pigment for each hue, and each hue of the colorant. It is preferable to include a step of learning an artificial intelligence model by using one or more kinds of data selected from different contents as learning data. As a result, it is possible to obtain compounding composition data that can be more accurately adapted to the color data of the surface to be measured, which particularly contains a bright pigment.
  • the step of generating the learned artificial intelligence model includes a step of learning the artificial intelligence model by using the shape data of the coloring material contained in the composition as learning data.
  • the coloring material includes not only ordinary colorants such as inorganic coloring pigments and organic coloring pigments, but also bright pigments such as particulate or flake-shaped (scaly) glass, metal, silica, and alumina, and flakes. It contains light-coherent pigments such as glass having a shape and an interfering property (for example, silica-coated glass flakes), silica, and alumina.
  • the shape data includes data such as shape appearance such as spherical shape, flake shape, fiber shape, particle size, particle size distribution, thickness, aspect ratio, fiber length, fiber diameter and the like.
  • the step S103 is a step of obtaining color data Xp (hereinafter, may be referred to as “target color data Xp”) of a target color whose compounding composition Yp is unknown.
  • the target color data Xp include color data for all colors possessed by painted products, molded products, natural structures, and the like. In particular, it is preferable to use the color data of the painted object.
  • INDUSTRIAL APPLICABILITY According to the present invention, it is possible to perform color adjustment with high accuracy even if the color data of the coating film containing the bright pigment, which has been difficult to adjust by computer, is used as the target color data Xp. Therefore, the target color data Xp in the S103 step is preferably the color data of the coating film containing the bright pigment.
  • the target color data Xp in the S103 step may be the color data of the coating film that does not contain the bright pigment.
  • the elements constituting the target color data Xp can be the same as the elements constituting the color data registered in the database.
  • it can be color data measured by an instrument or color data calculated from the color data.
  • an instrument for obtaining color data if it is an instrument capable of measuring the color of a bright coating film (metallic coating film, pearl color coating film, etc.), solid color coating film, etc. and acquiring color data, it is measured.
  • a bright coating film metallic coating film, pearl color coating film, etc.
  • solid color coating film etc.
  • a colorimeter such as a single-angle spectrophotometer, a multi-angle spectrophotometer, a colorimeter, a color difference meter, a variable-angle spectrophotometer, an image pickup device, and a micro-luminance, which are equipped with a light source that illuminates the surface to be measured.
  • One or more measuring instruments such as measuring instruments and instruments such as color swatches can be used.
  • a data processing device that processes various color data obtained from those instruments can be arbitrarily used.
  • the target color data Xp can be obtained by directly measuring the object to be measured by the operator using various instruments.
  • various instruments may be automatically acquired based on a program or the like. Further, it may be calculated based on these color measurement data.
  • the target color data Xp when the target color data Xp cannot be obtained by directly measuring the object to be measured, the color data obtained from the product name of the object to be measured can be used as the target color data Xp.
  • the target color data Xp when the target color data Xp is color data related to an automobile, the target color data Xp can be set based on the paint data obtained from the product name, model number, year, serial number, etc. of the automobile. ..
  • the step S104 is a step of inputting the target color data Xp into the computer.
  • Input to the computer is performed by transmitting data from various devices that measure and / or calculate color data Xp by wire, wireless, or a combination of these communication means, or by means via a recording medium, and having the computer receive the data. be able to.
  • Examples of the input using the communication means include one or more combinations of various communication networks such as LAN (local area network), WAN (wide area network), the Internet, and a telephone network.
  • Input by means via a recording medium can be performed by reading data from a recording medium such as a magnetic recording medium, an optical recording medium, or a paper recording medium by using an appropriate reading means. Further, input can be performed using a keyboard, a mouse, a bar code reader, a touch panel, a voice input device, an image recognition device, or other input means connected to the computer or provided by the computer.
  • the prediction compounding composition data Ya1 predicted from the color data Xp is composed of one or more of the compositions C1 to Cn. This is a step of obtaining the compounding composition data.
  • the learned artificial intelligence model used in the step S105 is the learned artificial intelligence model generated in the step S102, and is at least one type of the artificial intelligence model that estimates the compounding composition data Y from the color data X.
  • the predicted compounding composition data Ya1 predicted from the color data Xp as the compounding composition data containing one or more of the compositions C1 to Cn as components by using the learned artificial intelligence model it has been learned.
  • the feature amount of the color data Xp may be input to each unit of the input layer in the neural network of the artificial intelligence model.
  • the color data Xp input to the input layer is transmitted while being weighted between each node and each layer, and is output as the predicted compounding composition data Ya1 from each unit of the output layer.
  • the step S105 can include a step of obtaining the predicted compounding composition data Ya1 predicted from the color data Xp as compounding composition data containing one or more of the compositions C1 to Cn as components by using the multi-label classification. ..
  • the predicted compounding composition data Ya1 referred to here is obtained as two or more kinds of compositions of the compositions C1 to Cn (for example, as a composition based on a composition unit such as an amount of C1, an amount of C2 ... an amount of Cn, etc. It is also possible to obtain it as the amount of each component (pigment, etc.) constituting the compositions C1 to Cn (for example, the amount of red pigment A, the amount of red pigment B, etc.).
  • the multi-label classification is a setting in which two or more answers (labels) exist at the same time for a specific object, or two or more answers (classes) can be sorted at the same time.
  • the predicted compounding composition data Ya1 predicted from the color data Xp can be obtained more efficiently.
  • the compounding composition data satisfying the green color data Xg is obtained by using the multi-label classification
  • the compounding composition data can be used as the composition of the green compositions Cg1 and Cg2, and further solutions can be obtained.
  • the compounding composition data can be solved.
  • the multi-label classification by using the multi-label classification, it is also possible to probabilistically represent the abundance of each component that gives the predicted compounding composition data, thereby each component composition (composition) in the predicted compounding composition data. Both the unit-based composition and the composition based on each component) can be predicted with high probability.
  • CCM computer color matching
  • the step S105 may be a step corresponding to computer color search (CCS).
  • CCS computer color search
  • a color data similar to the target color data Xp is searched from a large number of color data registered in the database, acquired as the search color data Xn1, and then the compounding composition data corresponding to the search color data Xn1 is predicted. It can be used as compounding composition data.
  • the color data registered in the database is, for example, the color data of a known color sample book, the color data of a coated plate produced in the past, and the like, all of which are associated with the color data and the corresponding compounding composition data. .. Therefore, by obtaining the search color data Xn1, the corresponding compounding composition data can be easily obtained as the predicted compounding composition data.
  • the search color data Xn1 compares each of one or more of the elements constituting the color data (for example, each value in the L * a * b * color system, etc.) with the corresponding elements constituting the target color data Xp. , Value difference, degree of agreement, error rate, etc. can be searched and obtained within a certain range.
  • the fixed range may be set by the operator based on experience or the like, or may be set by a computer.
  • step S105 when a pass / fail judgment is made by comparing the target color data Xp with the search color data Xn1, one or more of the elements constituting the search color data Xn1 and the elements constituting the target color data Xp. This can be done by focusing on one or more of the above and comparing each corresponding component.
  • a threshold value may be set for the difference, the degree of agreement, the error rate, etc. in each component, and the pass / fail judgment may be made by the device or the operator with reference to this. At that time, weighting may be performed among the components, reflecting the viewpoint of a skilled worker and the like.
  • the predicted compounding composition data Ya1 obtained in the step S105 can be used as data as one or more kinds of the compositions C1 to Cn. Further, it may be used as data as a compounding amount ratio of each component such as a resin, a coloring material, and a solvent. Preferably, it is the data as one or more kinds of the compositions C1 to Cn in consideration of workability and the like when actually blending.
  • the types of the compositions C1 to Cn are not particularly limited, but 15 types or less, preferably 12 types or less, in consideration of workability and the like when actually compounding. More preferably, the number of types can be 10 or less.
  • the composition containing the metallic pigment is 5 types or less, preferably 3 types or less
  • the composition containing the pearl pigment is 5 types or less, preferably 3 types or less.
  • the step S105 is a predicted blending composition predicted from the color data Xp using at least one kind of the learned artificial intelligence model, which is an artificial intelligence model that estimates the blending composition data Y from the color data X. It is preferable that the data Ya1 is obtained as compounding composition data containing one or more of the compositions C1 to Cn as components.
  • step S106 the predicted color data Xa1 predicted from the predicted compounding composition data Ya1 is obtained by using a prediction formula other than the learned artificial intelligence model and / or the artificial intelligence model, and compared with the color data Xp. This is a step of determining pass / fail.
  • the learned artificial intelligence model used in the method of obtaining the predicted color data Xa1 predicted from the predicted combination composition data Ya1 is the learned artificial intelligence model generated in the step S102, and the color data is obtained from the composition data Y.
  • the feature amount of the predicted compounding composition data Ya1 may be input to each unit of the input layer in the neural network of the learned artificial intelligence model.
  • the predicted compounding composition data Ya1 input to the input layer is transmitted while being weighted between each node and each layer, and is output as predicted color data Xa1 from each unit of the output layer.
  • CCM computer color matching
  • the calculation based on the color matching calculation logic using a computer is, for example, comparing the target color data Xp with the various color data based on the various color data registered in the database and the composition composition data corresponding thereto, and making a difference.
  • one or more compounding compositions considered to be the most rational are determined as the predicted compounding composition data Ya1. It can be done by using various functions that compose the calculation logic and modifying an arbitrary composition or an approximate composition in a small iterative process. At that time, a logical command in the form of a rule can be created to assist the calculation speed and the accuracy of the adjustment algorithm.
  • the error ⁇ L * on the L * axis is a positive value
  • a component having characteristic information acting in the direction of decreasing the value of L * 2 is searched, and the error ⁇ L * on the L * axis is negative. If the value is, the component having the characteristic information that increases the value of L * 2 is searched.
  • the error ⁇ a * on the a * axis is a positive value
  • the component having the characteristic information (green) that reduces the value of a * 2 is the component, and the error ⁇ a * on the a * axis is a negative value.
  • the component has characteristic information (red) that increases the value of a * 2
  • a component having (blue) is searched, and a component having characteristic information (yellow) that increases the value of b * 2 when the error ⁇ b * on the b * axis is a negative value is searched.
  • each coordinate axis of the color system constituting the color space is made to act in the direction of reducing the error, that is, by adding a component for giving predetermined characteristic information, the target is achieved. It is possible to obtain the predicted compounding composition data Ya1 that is close to the color. If a component having characteristic information that acts in the direction of reducing the error is not searched, it is possible to obtain candidate compounding composition data after obtaining a more appropriate new approximate compounding composition based on the target color data Xp. it can.
  • the predicted compounding composition data Ya1 refers to the compounding composition data obtained by CCM with reference to corrections by the operator (for example, color data of a known color sample book, color data of a coated plate produced in the past, own experience, etc.). It can also be obtained by making corrections by the operator), making corrections using the computer, making corrections using an artificial intelligence model, and the like. Furthermore, when the color materials or compositions that can be used at a work site such as an automobile repair shop are limited, the predicted compounding composition data Ya1 is based only on the color materials or compositions that can be used at the work site. You can also get.
  • the predicted compounding composition data Ya1 can be output by a display means, a printing means, or the like. Further, it can be transmitted from the computer to a device or the like that carries out the next process without outputting.
  • various prediction formulas known in the field of toning by CCM can be used. Examples of such a prediction formula include a method using the optical density formula of Kubelka Munk, a prediction formula of the two-constant method based on Duncan's color mixture theory formula, a method using fuzzy inference, and other color data or compounding composition data. Examples include a method of comparing with a computer and indexing the degree of matching of each.
  • the method using Kubelka Munch's optical density formula and Duncan's color mixing theory formula is as follows.
  • the light scattering coefficient and light absorption coefficient of each color material contained in one or more kinds of compositions and the compounding ratio of each color material were obtained, and from the optical density formula of Kubelka Munk, "light absorption coefficient after color mixing / light after color mixing".
  • the "scattering coefficient” can be calculated, and this value can be used to obtain the spectral reflectance using Duncan's color mixture theory formula.
  • the "light absorption coefficient / light scattering coefficient” can be calculated from the spectral reflectance of the target color, and the blending ratio of each primary color paint such as a color material or composition required to match the color can be obtained. .. By performing this calculation for each wavelength of the visible spectrum, it is possible to determine the pigment compounding ratio for achieving the target color.
  • optical density formula of Kubelka Munch is as follows.
  • the optical density formula of Kubelka Munk is obtained by calculating the ratio of the light absorption coefficient and the light scattering coefficient from the spectral reflectance.
  • the light absorption coefficient and the light scattering coefficient It is necessary to obtain each of the light scattering coefficients.
  • a known method can be used, and for example, a relative method or an absolute method can be used.
  • the color mixture calculation can be performed.
  • iterative calculation by the Newton-Rapson method can be used as a method of adjusting the blending ratio of the colorant in order to match the blending of the colorant with the target color, and the target reflectance and the predicted reflectance are color-matched.
  • the convergence calculation is performed by the Newton-Rapson method while evaluating the difference between the target value and the predicted value.
  • an isometric method can be used in which the convergence calculation is performed while evaluating the sum of squares of the difference between the target reflectance and the predicted reflectance.
  • Fuzzy inference is a method of defining ambiguity by using the membership function in fuzzy set theory. Many proposals have been made so far as a specific method of fuzzy inference, and any method may be used in the present invention. For example, a fuzzy reasoning method devised by Mandani can be used.
  • the step S106 can be a step of obtaining the predicted color data Xa1 by using only one kind of artificial intelligence model. Further, in the second and subsequent steps S106, the step of obtaining the predicted color data Xa1 can be performed without using the artificial intelligence model. Examples of the predicted color data Xa1 that can be obtained in the step S106 include a wide variety of color data recorded in the database.
  • the predicted color data Xa1 preferably includes multi-angle spectral reflectance and / or brilliance parameters. Since the predicted color data Xa1 includes a multi-angle spectral reflectance and / or a brilliant feeling parameter, it is possible to perform more accurate toning even for a brilliant color whose optical characteristics are difficult to predict.
  • the predicted color data Xa1 and the color data Xp are equivalent.
  • a criterion for judging that they are equivalent for example, each element constituting the color data Xp and each element constituting the predicted color data Xa1 are individually compared, and whether the difference is within a predetermined range. It can be done by judging whether or not. For example, when the color data Xp and the predicted color data Xa1 include an element using the L * a * b * color system and an element obtained from the element, in addition to each of L *, a * and b *, the color difference ⁇ E is also compared.
  • a pass / fail judgment can be made.
  • a threshold value for the difference, the degree of agreement, the error rate, etc. in each component is set, and various correction formulas are used, and the worker, the computer or the device, preferably the computer or the device, refers to these.
  • a pass / fail judgment may be performed.
  • the predicted color data Xa1 is color data related to brilliance
  • the operator may be notified of improvements and the like for making the predicted color data Xa1 close to the target color data Xp regardless of pass / fail.
  • step S107 does not pass in the step S106, it is different from the conventional predicted compounding composition data predicted from the color data Xp using a prediction formula other than the learned artificial intelligence model and / or the artificial intelligence model.
  • Prediction compounding composition data Yai is obtained as compounding composition data containing one or more of the compositions C1 to Cn as components, and then predicted using a prediction formula other than the learned artificial intelligence model and / or artificial intelligence model. This is a step of obtaining the predicted color data Xai predicted from the compounding composition data Yai and repeating the pass / fail judgment by comparison with the color data Xp until it passes.
  • the prediction combination composition data Yai which is predicted from the color data Xp and is different from the prediction combination composition data so far, is obtained.
  • the method for obtaining the compounding composition data containing one or more of the compositions C1 to Cn as components include the following methods (1) to (5). (1) By using the multi-label classification, among a plurality of compounding composition data satisfying the color data Xp, the compounding composition data not selected in S105 is used as the new predicted compounding composition data Yai, whereby the composition Obtained as compounding composition data containing one or more types of C1 to Cn as components.
  • new predicted compounding composition data Yai can be obtained. It is obtained as compounding composition data containing one or more of the compositions C1 to Cn as components.
  • the predicted compounding composition data Yai which is predicted from the color data Xp and is different from the predicted compounding composition data so far, is obtained from the compositions C1 to Cn. Obtained as compounding composition data containing one or more types of components.
  • the composition is obtained by using the prediction composition data Yai, which is different from the prediction composition data so far, which is predicted from the color data Xp. Obtained as compounding composition data containing one or more types of C1 to Cn as components.
  • Prediction compounding composition data Yai by using a prediction formula other than the learned artificial intelligence model and / or the artificial intelligence model in consideration of the difference obtained by comparing Xp and Xa1 performed in the step S106. Is obtained as compounding composition data containing one or more of the compositions C1 to Cn as components.
  • the prediction combination composition data Ya1 predicted from the color data Xp contains one or more of the compositions C1 to Cn as components.
  • the specific method obtained as the compounding composition data can be substantially the same as the method in the S105 step.
  • the predicted blending composition data Ya1 predicted from the color data Xp is obtained by using the multi-label classification, and the blending composition containing one or more of the compositions C1 to Cn as components. The process of obtaining as data can be included.
  • the predicted color data Xai predicted from the predicted compounding composition data Yai is obtained by using a prediction formula other than the learned artificial intelligence model and / or the artificial intelligence model, and is compared with the color data Xp.
  • the predicted color data Xa1 predicted from the predicted compounding composition data Ya1 is obtained by using a prediction formula other than the learned artificial intelligence model and / or the artificial intelligence model in the step S106, and the above-mentioned It is the same as the method of determining pass / fail by comparing with the color data Xp.
  • the step S107 may be a step of repeatedly making minor corrections so that the predicted color data Xa1 and the color data Xp are equivalent.
  • a step (means) for switching may be provided so as to use only one of the learned artificial intelligence model and the prediction formula other than the artificial intelligence model.
  • the prediction formula can be switched manually, or can be set to be automatically performed when a predetermined condition is satisfied.
  • the predicted compounding composition data Yai obtained in the step S107 can be used as data as one or more kinds of the compositions C1 to Cn. Further, it may be used as data as a compounding amount ratio of each component such as a resin, a coloring material, and a solvent. Preferably, it is the data as one or more kinds of the compositions C1 to Cn in consideration of workability and the like when actually blending.
  • the predicted compounding composition data Yai obtained in the step S107 can be used as data as one or more kinds of the compositions C1 to Cn. Further, it may be used as data as a compounding amount ratio of each component such as a resin, a coloring material, and a solvent. Preferably, it is the data as one or more kinds of the compositions C1 to Cn in consideration of workability and the like when actually blending.
  • the types of the compositions C1 to Cn are not particularly limited, but 15 types or less, preferably 12 types or less, in consideration of workability and the like when actually compounding. More preferably, the number of types can be 10 or less.
  • the composition containing the metallic pigment is 5 types or less, preferably 3 types or less
  • the composition containing the pearl pigment is 5 types or less, preferably 3 types or less.
  • the step S108 is a step of obtaining the pass compounding composition data Yap1 when passing any of the steps S106 or S107.
  • the accepted compounding composition data Yap1 may be output, or the data may be transmitted without being output.
  • the accepted compounding composition data Yap1 can include the compounding composition data of the coating composition obtained by the toning method. For example, data such as a blending ratio of a plurality of commercially available toning paints, a blending ratio of a color material component such as a pigment and a toning paint, and a blending ratio of one or more kinds of coloring materials can be given.
  • the accepted compounding composition data Yap1 can include data on the components and / or the compounding amount thereof necessary for eliminating the difference between the accepted compounding composition and the predicted compounding composition.
  • one or more types of data such as a difference between one or more compounding components when the accepted compounding composition and the predicted compounding composition are compared can be mentioned.
  • These difference data correspond to the fine color adjustment composition data used when performing fine color adjustment from a specific compound composition, and are useful for simplifying the color adjustment work.
  • any output device capable of displaying or outputting information or an image based on a monitor, a display, a mobile terminal device, a mobile phone such as a smartphone, or a signal can be used. ..
  • an output device such as a printing device capable of displaying information or an image on an appropriate medium such as paper or plastic based on a signal can also be used.
  • the output of the pass compounding composition data Yap1 may be an output inside the computer.
  • the pass compounding composition data Yap1 output inside the computer is an automatic compounding device, a terminal device, a data recording device, and a data recording medium. Etc. are sent through communication means.
  • the pass compounding composition data Yap1 may be transmitted to an automatic compounding device, a terminal device, a data recording device, a data recording medium, or the like through a communication means or the like without being output.
  • the step S109 is a step of preparing the actual candidate paint CMap1 based on the accepted compounding composition data Yap1, obtaining the coated plate of the actual candidate paint CMap1, and acquiring the measured color data Xap1.
  • the method for preparing the actual candidate paint CMap1 is not particularly limited, and it can be carried out by a known method for preparing the paint.
  • each component constituting the actual candidate paint CMap1 can be placed in a blending container and mixed with a stirrer, a dispersion device or the like as necessary to prepare.
  • one or more kinds of compositions commercially available as primary color paints can be mixed and adjusted.
  • the actual candidate paint CMap1 may be prepared by transmitting the accepted composition data Yap1 calculated by a computer to an automatic compounding machine equipped with an electronic balance or the like via a wired or wireless network. .. Thereby, even if the worker is not an expert, the actual candidate paint CMap1 can be easily prepared.
  • the method for obtaining the coated plate of the actual candidate paint CMap1 is also not particularly limited, and can be performed by a known method when preparing the coated plate.
  • one or more layers of toning paint are formed on the base material so as to have a hiding film thickness or more, and a clear paint film is formed on the uppermost layer so as to have a dry film thickness of, for example, 10 to 100 ⁇ m.
  • a method of forming a coated plate in this way can be mentioned.
  • each coating film it may be dried and cured by heating if necessary. When drying / curing by heating, it may be carried out collectively after all the coating films have been formed, or it may be carried out each time the coating film is formed.
  • the coating plate of the actual candidate coating material CMap1 may be produced fully automatically by using an automatic coating apparatus using a robot or the like, or may be produced by performing a part of the steps by an operator.
  • the base material used in the method of the present invention is not particularly limited, and the base material used for producing a test coating plate for toning can be used.
  • a metal plate, paper, a plastic film and the like can be mentioned.
  • the size of the base material is not particularly limited as long as the color can be measured and the color tone can be visually confirmed.
  • the length of one side is generally about 5 to 20 cm. Is the target.
  • the process of measuring the color of the coated plate of the actual candidate paint CMap1 and acquiring the measured color data Xap1 is a colorimeter, a multi-angle spectrophotometer, a laser metallic sensation measuring device, a variable-angle spectrophotometer, a gloss meter, and a micro-luminance. It can be acquired directly by measurement using a measuring device such as a measuring instrument, or by calculation using the data acquired by measurement.
  • the step S110 is a step of determining pass / fail by comparing the color data Xp with the measured color data Xap1 and / or comparing the target color with the color of the coating plate of the actual candidate paint CMap1. ..
  • the pass / fail judgment is made by an operator, a computer or a device.
  • the pass / fail judgment can be made by the operator visually comparing the color of the article (painted plate) having the target color data Xp and the color of the coated plate of the actual candidate paint CMap1. Further, for example, the target color data Xp or each element constituting the target color data Xp or the color data obtained by measuring the coating plate of the actual candidate paint CMap1 with a colorimeter or the like or each element constituting the same can be obtained. It may be compared individually and performed by the operator, computer or device. At that time, as in the case of the pass / fail judgment in the steps S106 and S107, threshold values for the difference, the degree of agreement, the error rate, etc. in each component are set, and various correction formulas are used, and the work is performed with reference to these. The pass / fail judgment may be made by a person, a computer, or a device.
  • machine learning when the pass / fail determination is performed by a computer, for example, machine learning can be used. For example, from decision trees using gradient boosting, linear regression, logistic regression, simple perceptrons, MLPs, neural networks, support vector machines, random forests, Gaussian processes, Bayesian networks, k-nearest neighbors, and other models used in machine learning.
  • One or more types selected from the group can be used. In the present invention, it is preferable to use one or more selected from the group consisting of the neural network, the decision tree using gradient boosting, and the Gaussian process, and it is selected from the group consisting of the neural network and the decision tree using gradient boosting. It is more preferable to use one or more of the above types.
  • SOM Self-Organizing Map
  • SOM Self-Organizing Map
  • the pass / fail judgment is performed by combining the pass / fail judgment visually by the operator and the pass / fail judgment performed by the worker, the computer, or the device based on the color data or each element constituting the same. May be good.
  • the operator may be notified of improvements in the blending composition of the actual candidate paint CMap1 regardless of pass / fail.
  • the paint can be prepared based on the compounding composition of the actual candidate paint CMap1. Further, if necessary, it is possible to add a step of finely adjusting the color by the operator without using a computer to bring the color closer to the target color.
  • the S111 step is a step of repeating the steps S105 to S110 or S107 to S110 until the steps are passed when the step is not passed in the step S110.
  • the S105 to S110 steps or the S107 to S110 steps are repeated until the pass / fail judgment in the S110 step passes. It can be carried out.
  • the predicted color data Xa1 is used in the same manner as the previous time, using a prediction formula other than the learned artificial intelligence model and / or the artificial intelligence model.
  • Xai can be obtained.
  • a step (means) for switching may be provided so as to use only one of the learned artificial intelligence model and the prediction formula other than the artificial intelligence model.
  • the predicted color data Xa1 or Xai obtained by using at least one type of learned artificial intelligence model does not pass, it is preferable to switch to using a prediction formula other than the artificial intelligence model next. ..
  • the learned artificial intelligence model and / or the artificial intelligence model of the prediction formulas other than the above it is possible to switch to use the learned artificial intelligence model and / or the prediction formula that was not used last time.
  • the switching can be performed manually, or can be set to be performed automatically when a predetermined condition is satisfied.
  • the process does not pass in the S111 step, it is preferable to repeat the steps S105 to S110 or S107 to S110 after inputting the difference ⁇ between the predicted color data Xa1 or Xai and the actually measured color data into the computer as the correction coefficient ⁇ .
  • the actual candidate paint can be prepared within 5 times, preferably within 3 times, more preferably within 2 times, and a paint capable of obtaining the desired color can be prepared.
  • FIG. 7 is a flowchart for executing a method for producing a paint based on computer toning according to the second embodiment of the present invention.
  • the flow shown in FIG. 7 is only one embodiment of the present invention.
  • the method for producing a paint based on computer toning according to the second embodiment of the present invention includes a database in which at least color data X and compounding composition data Y of one or more kinds of compositions are registered, and registered in the database.
  • This is a method including the following steps S201 to S211 using a computer toning device including a computer on which a color matching calculation logic using data is operated.
  • the steps S201 to S211 will be described in detail.
  • the step S201 is a step of inputting learning data into the computer using the data registered in the database.
  • the learning data used in the S201 step can be the same as the learning data used in the S101 step in the method for producing a paint based on computer toning according to the first embodiment of the present invention.
  • the input means to the computer in the step S201 can be the same as the input means to the computer in the step S101 in the method for producing a paint based on the computer toning according to the first embodiment of the present invention.
  • the step S202 is a step of machine learning the learning data to generate a learned artificial intelligence model including at least one type of artificial intelligence model that estimates color data X from composition data Y.
  • the artificial intelligence model in the present invention include a determination tree using gradient boosting, linear regression, logistic regression, simple perceptron, MLP, neural network, support vector machine, random forest, Gaussian process, Bayesian network, and k-nearest neighbor method. , Others It can be composed of one or more types selected from the group consisting of models used in machine learning.
  • the artificial intelligence model uses one or more selected types.
  • a neural network is composed of one or more types selected from a group consisting of a neural network, a decision tree using gradient boosting, and a Gaussian process, and the neural network is trained using the training data input in the S201 process. Therefore, it is possible to generate a trained artificial intelligence model including at least one kind of trained artificial intelligence models that estimate the color data X from the composition data Y.
  • Learning of the artificial intelligence model is performed using the learning data input to the computer in the S201 process.
  • the learning data at least the color data X and the composition compounding data Y related to one or more kinds of compositions are used.
  • an algorithm of the neural network a known backpropagation method, which is one of the supervised learning methods, can be used.
  • a neural network by setting the learning rate (real value between 0 and 1), which is a parameter representing the learning speed, and the margin of error (real value between 0 and 1), which is the permissible value of the error of the output value in learning. To learn.
  • one or more kinds of feature amounts related to the blended composition data Y can be associated with one or more kinds of feature amounts related to the color data X of the coating film of the paint film based on the blended composition data Y.
  • the learned network can make these predictions without performing labor-intensive experimental confirmations such as cost and time.
  • the step of generating at least one of the learned artificial intelligence models is at least the learned artificial intelligence model of step S102 in the method for producing a paint based on computer toning according to the first embodiment of the present invention. It can be the same as the step of producing one type.
  • S203 is a step of obtaining a target color data X t colors a target.
  • the target color data Xt include color data for all colors possessed by painted products, molded products, natural structures, and the like. In particular, it is preferable to use the color data of the painted object. The present invention has thus far computer toning was difficult, as the target color data X t color data of the coating film containing the bright pigment can be performed accurately toned.
  • S203 is the target color data X t in step, it is preferable that the color data of the coating film containing the bright pigment.
  • the target color data X t at S203 step may be color data of the coating film not containing the bright pigment.
  • Elements of the target color data X t may be the same as the elements constituting the color data registered in the database.
  • it can be color data measured by an instrument or color data calculated from the color data.
  • an instrument for obtaining color data if it is an instrument capable of measuring the color of a bright coating film (metallic coating film, pearl color coating film, etc.), solid color coating film, etc. and acquiring color data, it is measured.
  • a bright coating film metallic coating film, pearl color coating film, etc.
  • solid color coating film etc.
  • a colorimeter such as a single-angle spectrophotometer, a multi-angle spectrophotometer, a colorimeter, a color difference meter, a variable-angle spectrophotometer, an image pickup device, and a micro-luminance, which are equipped with a light source that illuminates the surface to be measured.
  • One or more measuring instruments such as measuring instruments and instruments such as color swatches can be used.
  • a data processing device that processes various color data obtained from those instruments can be arbitrarily used.
  • Target color data X t is the worker by using the various instruments, can be obtained by measuring the detected material directly.
  • various instruments may be automatically acquired based on a program or the like. Further, it may be calculated based on these color measurement data.
  • the target color data X t cannot be obtained by directly measuring the color object to be measured, the color data obtained from the product name of the color object to be measured can be used as the target color data X t. it can.
  • the target color data Xt is color data related to an automobile, the target color data can be set based on the paint data obtained from the product name, model number, year, serial number, etc. of the automobile. ..
  • the step S204 is a step of inputting the target color data Xt into the computer.
  • the input means to the computer in the step S204 can be the same as the input means to the computer in the step S104 in the method for producing a paint based on the computer toning according to the first embodiment of the present invention.
  • S205 step the search using a computer, together with obtaining the target color data X t approximation to find the color data X n1 and search color approximation formulation composition data Y n1 corresponding to the data X n1, the target color data X t
  • S205 step may be a step corresponding to the computer color search (CCS), among many color data registered in the database, retrieves the color data which approximates the target color data X t, search Color It is acquired as data X n1.
  • the color data registered in the database is, for example, the color data of a known color sample book, the color data of a coated plate produced in the past, and the like, all of which are associated with the color data and the corresponding compounding composition data. .. Therefore, by obtaining the search color data X n1 , it is possible to easily obtain the approximate compounding composition data Y n1 which is the corresponding compounding composition data.
  • the search color data X n1 is a combination of one or more elements constituting the color data (for example, each value in the L * a * b * color system, etc.) and the corresponding elements constituting the target color data X t.
  • the fixed range may be set by the operator based on experience or the like, or may be set by a computer.
  • step S205 when the target color data X t and the search color data X n1 are compared and a pass / fail judgment is made, one or more of the elements constituting the search color data X n1 and the target color data X t1 are used. This can be done by focusing on one or more of the constituent elements and comparing each corresponding constituent element.
  • a threshold value may be set for the difference, the degree of agreement, the error rate, etc. in each component, and the pass / fail judgment may be made by the device or the operator with reference to this. At that time, weighting may be performed among the components, reflecting the viewpoint of a skilled worker and the like.
  • step S206 after obtaining the candidate compounding composition data Y ni which is predicted to give the target color data X t by using a computer when the step S205 is not passed, the at least one type of learned artificial intelligence model and the above-mentioned artificial intelligence model and / or by using a prediction expression other than the artificial intelligence model, along with obtaining a predicted color data X ni predicted from the candidate blend composition data Y ni, comparing the predicted color data X ni and the color data X t, This is a step of determining pass / fail.
  • a method of obtaining the candidate compounding composition data Y ni which is predicted to give the target color data X t using a computer for example, a method known as computer color matching (CCM), which is a color using a computer.
  • CCM computer color matching
  • Calculations based on combined calculation logic and calculations by mathematical optimization can be mentioned.
  • Calculated based on the color matching calculation logic using computer for example, on the basis of various color data and composition formulation data corresponding to that registered in the database, and comparing the target color data X t various color data, the difference ,
  • One or more compounding compositions considered to be the most rational are determined as candidate compounding composition data Y ni by calculating so that the degree of agreement and the like are within a certain range.
  • the error ⁇ L * on the L * axis is a positive value
  • a component having characteristic information acting in the direction of decreasing the value of L * 2 is searched, and the error ⁇ L * on the L * axis is negative. If the value is, the component having the characteristic information that increases the value of L * 2 is searched.
  • the error ⁇ a * on the a * axis is a positive value
  • the component having the characteristic information (green) that reduces the value of a * 2 is the component, and the error ⁇ a * on the a * axis is a negative value.
  • the component has characteristic information (red) that increases the value of a * 2
  • a component having (blue) is searched, and a component having characteristic information (yellow) that increases the value of b * 2 when the error ⁇ b * on the b * axis is a negative value is searched.
  • each coordinate axis of the color system constituting the color space is made to act in the direction of reducing the error, that is, by adding a component for giving predetermined characteristic information, the target is achieved.
  • Candidate compounding composition data Y ni that is close to the color can be obtained.
  • the candidate compounding composition data Y ni refers to the compounding composition data obtained by CCM with reference to corrections by the operator (for example, color data of a known color sample book, color data of a coated plate produced in the past, own experience, etc.). It can also be obtained by making corrections by the operator), making corrections using the computer, making corrections using an artificial intelligence model, and the like. Furthermore, when the color materials or compositions that can be used at a work site such as an automobile repair shop are limited, the candidate compounding composition data Y is based only on the color materials or compositions that can be used at the work site. You can also get ni.
  • the candidate compounding composition data Y ni can be output by a display means, a printing means, or the like. In addition, it can be transmitted from the computer to a device or the like that carries out the next process without outputting.
  • the S206 step can be a step of obtaining the predicted color data X ni by using only one kind of artificial intelligence model. Further, in the second and subsequent steps S206, it is possible to obtain the predicted color data X ni without using the artificial intelligence model. Examples of the predicted color data X ni that can be obtained in the step S206 include a wide variety of color data recorded in the database.
  • the predicted color data X ni preferably includes multi-angle spectral reflectance and / or brilliance parameters.
  • the multi-angle spectral reflectance and / or the brilliance parameter in the predicted color data X ni, it is possible to perform more accurate toning even for a brilliant color whose optical characteristics are difficult to predict.
  • each unit of the input layer in the neural network of the trained artificial intelligence model has a feature amount of the candidate compounding composition data Y ni. Just enter.
  • the candidate compounding composition data Y ni input to the input layer is transmitted while being weighted between each node and each layer, and is output as color data from each unit of the output layer.
  • step S206 as a method of obtaining the predicted color data X ni using a prediction formula other than the learned artificial intelligence model, various prediction formulas known in the field of toning by CCM can be used. Examples of such a prediction formula include a method using the optical density formula of Kubelka Munk, a prediction formula of the two-constant method based on Duncan's color mixture theory formula, a method using fuzzy inference, and other color data or compounding composition data. Examples include a method of comparing with a computer and indexing the degree of matching of each.
  • step S106 of the method for producing a paint based on computer toning according to the first embodiment of the present invention can be the same as the method using the optical density formula and Duncan's color mixing theory formula.
  • the method using fuzzy inference can be the same as the method using fuzzy inference described in step S106 in the method for producing a paint based on computer toning according to the first embodiment of the present invention.
  • the pass / fail determination can be performed, for example, by individually comparing each element constituting the color data X t and each element constituting the predicted color data X ni. For example, when the color data X t and the predicted color data X ni include an element using the L * a * b * color system and an element obtained from the element, in addition to each of L *, a * and b *, the color difference ⁇ E Can be compared to make a pass / fail judgment. At that time, a threshold value for the difference, the degree of agreement, the error rate, etc. in each component may be set, and the pass / fail judgment may be performed with reference to this.
  • the predicted color data X ni is color data related to brilliance
  • pass / fail if necessary, the operator may be notified of improvements and the like for making the predicted color data X ni closer to the target color data X t regardless of pass / fail.
  • step S207 after obtaining the candidate compounding composition data Y ni which is predicted to give the target color data X t by using a computer when the step S206 does not pass, at least one of the learned artificial intelligence models and the above-mentioned artificial intelligence model / or by using a prediction expression other than the artificial intelligence model, along with obtaining a predicted color data X ni predicted from the candidate blend composition data Y ni, comparing the predicted color data X ni and the color data X t, This is a process of repeating the process of determining pass / fail until it passes.
  • step the method of obtaining the expected candidate blending composition data Y ni to give the target color data X t using a computer, the candidate formulation is expected to provide the target color data X t using computer in S206 step It is the same as the method for obtaining the composition data Y ni. Further, in the step S207, the method of determining pass / fail is the same as the method of determining pass / fail in step S206.
  • step S207 when the predicted color data X ni predicted from the candidate compounding composition data Y ni is obtained, the same at least one type of learned artificial intelligence model and / or artificial intelligence model used in the step S206 is used. Prediction formula can be used. Further, a step (means) for switching may be provided so as to use only one of at least one type of learned artificial intelligence model or a prediction formula other than the artificial intelligence model. In the present invention, it is preferable to use a prediction formula different from the prediction formula used when the test is rejected. For example, if the predicted color data X ni obtained by using at least one type of learned artificial intelligence model in step S206 does not pass, the next step S207 switches to using a prediction formula other than the artificial intelligence model.
  • the prediction formula can be switched manually, or can be set to be automatically performed when a predetermined condition is satisfied. In the present invention, it is preferable to use a prediction formula other than the artificial intelligence model for at least one of the plurality of S207 steps.
  • the step S208 is a step of obtaining pass compounding composition data Y C1 that has passed in any of the steps S205 to S207.
  • the accepted compounding composition data Y C1 may be output, or the data may be transmitted without being output.
  • the accepted compounding composition data Y C1 can include compounding composition data of the coating composition obtained by the toning method. For example, data such as a blending ratio of a plurality of commercially available toning paints, a blending ratio of a color material component such as a pigment and a toning paint, and a blending ratio of one or more kinds of coloring materials can be given.
  • the accepted compounding composition data Y C1 can include data on the components and / or the compounding amount thereof necessary for eliminating the difference between the accepted compounding composition and the approximate compounding composition and / or the candidate compounding composition.
  • the difference of one or more compounding components when the acceptable compounding composition is compared with the approximate compounding composition or the candidate compounding composition the difference of one or more compounding components when comparing the approximate compounding composition and the candidate compounding composition, etc.
  • One or more types of data can be mentioned. These difference data correspond to the fine color adjustment composition data used when performing fine color adjustment from a specific compound composition, and are useful for simplifying the color adjustment work.
  • the pass composition composition data Y C1 When outputting the pass composition composition data Y C1 , it is possible to use a monitor, a display, a mobile terminal device, a mobile phone such as a smartphone, or an arbitrary output device capable of displaying or outputting information or an image based on a signal. it can. Further, an output device such as a printing device capable of displaying information or an image on an appropriate medium such as paper or plastic based on a signal can also be used.
  • the output of the pass blending composition data Y C1 may be the output of the internal computer, in this case, pass blending composition data Y C1 outputted by the internal computer, automatic compounding device, the terminal device, data recording device, data It is sent to a recording medium or the like via a communication means or the like. Further, the accepted compounding composition data Y C1 may be transmitted to an automatic compounding device, a terminal device, a data recording device, a data recording medium, or the like through a communication means or the like without being output.
  • the step S209 is a step of preparing the actual candidate paint CM Ci based on the accepted compounding composition data Y C1 , obtaining the coated plate of the actual candidate paint CM Ci , and acquiring the measured color data X Ci .
  • the method for preparing the actual candidate paint CM Ci is not particularly limited, and it can be carried out by a known method when preparing the paint.
  • each component constituting the actual candidate paint CM Ci can be placed in a blending container and mixed with a stirrer, a dispersion device or the like as necessary to prepare.
  • the actual candidate paint CM Ci may be prepared by transmitting the data of the accepted compounding composition calculated by the computer to an automatic compounding machine equipped with an electronic balance or the like via a wired or wireless network. .. Thereby, even if the worker is not an expert, the actual candidate paint CM Ci can be easily prepared.
  • the method for obtaining the coated plate of the actual candidate paint CM Ci is also not particularly limited, and can be carried out by a known method when preparing the coated plate.
  • one or more layers of toning paint are formed on the base material so as to have a hiding film thickness or more, and a clear paint film is formed on the uppermost layer so as to have a dry film thickness of, for example, 10 to 100 ⁇ m.
  • a method of forming a coated plate in this way can be mentioned.
  • each coating film it may be dried and cured by heating if necessary. When drying / curing by heating, it may be carried out collectively after all the coating films have been formed, or it may be carried out each time the coating film is formed.
  • the coating plate of the actual candidate coating material CM Ci may be produced fully automatically by using an automatic coating apparatus using a robot or the like, or may be produced by performing a part of the steps by an operator.
  • the base material used in the method of the present invention is not particularly limited, and the base material used for producing a test coating plate for toning can be used.
  • a metal plate, paper, a plastic film and the like can be mentioned.
  • the size of the base material is not particularly limited as long as the color can be measured and the color tone can be visually confirmed.
  • the length of one side is generally about 5 to 20 cm. Is the target.
  • the process of measuring the color of the coated plate of the actual candidate paint CM Ci and acquiring the measured color data X Ci is a colorimeter, a multi-angle spectrophotometer, a laser metallic sensation measuring device, a variable-angle spectrophotometer, a gloss meter, and a micro. It can be acquired directly by measurement using a measuring device such as a brilliance measuring device, or by calculation using the data acquired by the measurement.
  • step S210 pass / fail is determined by comparing the color data X t with the measured color data X Ci and / or comparing the target color with the color of the coating plate of the actual candidate paint CM Ci. It is a process. The pass / fail judgment is made by an operator, a computer or a device.
  • Determination of Compliance is a color painting plate of the the color X t that the target actual candidate paint CM Ci, it can be performed by comparing visually the operator. Further, for example, the target color data Xt or each element constituting the target color data Xt or the color data obtained by measuring the coating plate of the actual candidate paint CM Ci with a colorimeter or the like or each element constituting the same can be obtained. , Each may be individually contrasted and performed by a worker, computer or device. At that time, as in the case of the pass / fail judgment in the steps S206 and S207, threshold values for the difference, the degree of agreement, the error rate, etc. in each component are set, and various correction formulas are used, and the work is performed with reference to these. The pass / fail judgment may be made by a person, a computer, or a device.
  • the pass / fail judgment when the pass / fail judgment is performed by a computer, it may be the same as the pass / fail judgment described in the S110 step in the method for producing a paint based on the computer toning according to the first embodiment of the present invention. it can.
  • the pass / fail judgment in order to reduce the burden on the operator, it is preferable to perform the pass / fail judgment by a computer or a device.
  • the pass / fail judgment is performed by combining the pass / fail judgment visually by the operator and the pass / fail judgment performed by the worker, the computer, or the device based on the color data or each element constituting the same. May be good.
  • the operator may be notified of improvements in the composition of the actual candidate paint CMCi regardless of pass / fail.
  • the paint can be prepared based on the compounding composition of the actual candidate paint CMCi. Further, if necessary, it is possible to add a step of finely adjusting the color by the operator without using a computer to bring the color closer to the target color.
  • the step S211 is a step of repeating the steps S206 to S210 when the pass / fail determination in the step S210 does not pass. In the present invention, even if the pass / fail judgment in the S210 step fails twice or more, the steps S206 to S210 can be repeated until the pass / fail judgment in the S210 step passes.
  • the predicted color data X ni is used in the same manner as the previous time, using at least one type of learned artificial intelligence model and / or a prediction formula other than the artificial intelligence model. Can be obtained.
  • a step (means) for switching may be provided so as to use only one of at least one type of learned artificial intelligence model or a prediction formula other than the artificial intelligence model.
  • a prediction formula other than the artificial intelligence model when the predicted color data X ni obtained by using at least one type of learned artificial intelligence model does not pass, it is preferable to switch to using a prediction formula other than the artificial intelligence model next.
  • step S207 When repeating steps S206 to S210 without passing in step S211 and obtaining predicted color data X ni in steps S206 and / or step S207, at least one type of learned artificial intelligence model and / or other than the artificial intelligence model Of the prediction formulas, it is possible to switch to using the prediction formula that was not used last time.
  • the prediction formula can be switched manually, or can be set to be automatically performed when a predetermined condition is satisfied.
  • the process does not pass in the step S211, it is preferable to repeat the steps S205 to S211 after inputting the difference ⁇ between the predicted color data X ni and the measured color data X Ci as the correction coefficient ⁇ into the computer.
  • the actual candidate coating material CMCi can be prepared within 5 times, preferably within 3 times, more preferably within 2 times in the S210 step, and a coating material capable of obtaining the desired color can be prepared.
  • any of the methods for producing a paint based on computer toning according to the first and second embodiments of the present invention can be used when preparing a paint in order to obtain a desired color. Further, it can be used for identification in the compounding composition of the paint and for modifying the compounding composition.
  • the method for producing a paint based on computer toning according to the first and second embodiments of the present invention includes colored articles, for example, vehicles such as automobiles and motorcycles or parts thereof, trucks, buses, trains, and monorails. It can be used for the preparation of repair paints to be applied to large vehicles such as the above, parts thereof, and other industrial products.
  • the colored article may in particular have a single-layer or multi-layer coating.
  • the effect of the present invention can be maximized in the case of a multi-layer coating film in which a clear coating film is provided on a coating film containing a bright pigment such as a metallic coating color or a pearl glossy color.
  • FIG. 8 is a flowchart for executing the method of predicting the color data of the coating film according to the third embodiment of the present invention.
  • the flow shown in FIG. 7 is only one embodiment of the present invention.
  • the method for predicting the color data of the coating film according to the third embodiment of the present invention is a database in which at least the color data X and the compounding composition data Y of one or more kinds of compositions are registered, and registered in the database.
  • This is a method including the following steps S301 to S307 using a computer toning device including a computer on which a color matching calculation logic using data is operated.
  • the S301 step and the S302 step are the same as the S101 step and the S102 step, respectively.
  • the steps S303 to S307 will be described in detail.
  • the step S303 is a step of obtaining the compounding composition data Y CM of the paint CM t that predicts the color data of the coating film.
  • the paint CM t for predicting the color data of the coating film can be a paint whose color or color data is desired to be acquired without actually preparing the paint. As a result, for example, when a large number of colors are prototyped at one time, the color or color data can be easily obtained without performing the steps of preparing the paint, preparing the coating film by painting, and measuring the color data of the coating film. Can be obtained.
  • Blending composition data Y CM is data relating to each type (trade name, product number, etc.) and blending amount of binders, coloring pigments, additive components, etc. contained in the paint. Specifically, it can be the same as the data related to the composition and the blending amount, which constitutes the blending composition data registered in the database.
  • S304 step the compounded composition data Y CM, a step of inputting to the computer.
  • the data can be input by using the same means as the means for inputting the target color data Xt into the computer in the step S104.
  • the step S305 is a step of acquiring the search color data X n1 corresponding to the compounding composition data Y CM by a search using a computer, if necessary.
  • S305 step may be a step similar to computer color search (CCS), among many blending composition data registered in the database, retrieves the blending composition data approximating said blend composition data Y CM , The color data corresponding to the obtained compounding composition data is acquired as the search color data X n1.
  • the compounding composition data registered in the database is, for example, the compounding composition data of a commercially available paint, the compounding composition data of a paint produced in the past, etc., and the compounding composition data and the corresponding color data are all associated with each other. There is. Therefore, to obtain a formulation composition data that approximates the blend composition data Y CM, it can be easily obtained the corresponding color data as search color data X n1.
  • the formulation composition data for approximating the blend composition data Y CM when searching from a number of formulation composition data, one or more elements constituting the blending composition data (e.g., content of the pigment of a specific color, etc.) For each, it can be obtained by comparing with the corresponding element and searching for the value difference, the degree of agreement, the error rate, etc. within a certain range.
  • the fixed range may be set by the operator based on experience or the like, or may be set by a computer.
  • the S305 step is performed as needed, and there is no problem even if it is not performed.
  • ⁇ S306 process> S306 process, if the search color data X n1 addressed in the S305 step is not found, or, in the case of not performed the S305 step, from the blend composition data Y CM, and wherein the at least one learning artificial This is a step of obtaining predicted color data X m1 by using an intelligent model or at least one learned artificial intelligence model and a prediction formula other than the artificial intelligence model.
  • the predicted color data X m1 can be obtained from the compounding composition data Y CM by using at least one type of artificial intelligence model. Further, the predicted color data X m1 can be obtained by using at least one type of learned artificial intelligence model and a prediction formula other than the artificial intelligence model in combination.
  • the prediction formula other than the artificial intelligence model and the method using the artificial intelligence model, such as the method using the artificial intelligence model can be the same as those described in the step S106.
  • the step S307 is a step of acquiring the actually measured color data X CM of the coated plate coated with the paint CM t and comparing it with the predicted color data X m1 as needed. By carrying out the step S307 and feeding back the results, it is possible to predict the color data of the coating film with higher accuracy. For example, if there is a large discrepancy between the measured color data X CM and the predicted color data X m1 , only the prediction formula other than the artificial intelligence model is used to obtain the predicted color data again and compare it with the measured color data X CM. , Can give feedback. Further, after inputting the difference ⁇ between the predicted color data X m1 and the actually measured color data X CM into the computer as the correction coefficient ⁇ , the steps S305 to S307 can be repeated.
  • the method of predicting the color data of the coating film of the present invention can be used to predict the color tone of the coating film when preparing a coating material used for painting such as vehicle painting.
  • a coating material used for painting such as vehicle painting.
  • the method of predicting the color data of the coating film of the present invention it is possible to accurately predict the color for a large number of specified paint distribution compositions without having to generate a specific paint for each paint distribution composition. Become.
  • the paint distribution composition having the smallest deviation from the designated color can be easily selected from a large number of paint distribution composition candidates. This makes it possible to prepare paints for each of a large number of paint distribution compositions, and then obtain the corresponding color data without actually applying the paint to the material to be coated to prepare a coated plate and then performing measurement.
  • each of the compounding composition data Y1 to Yn of one or more kinds of compositions C1 to Cn (n is an integer of 2 or more) and each compounding composition data are used.
  • a computer toning system including a database in which corresponding color data X1 to Xn are registered and a computer in which a color matching calculation logic using the data registered in the database is operated, wherein the following means S401 to S411 Is included.
  • (S401) Means for inputting learning data into the computer using the data registered in the database (S402)
  • Means for obtaining color data Xp of a target color whose compounding composition Yp is unknown Means for inputting the color data Xp into the computer (S405) Using a prediction formula other than the learned artificial intelligence model and / or the artificial intelligence model, the prediction combination composition data Ya1 predicted from the color data Xp is used as a composition composition containing one or more of the compositions C1 to Cn as components.
  • a means of obtaining the predicted color data Xai predicted from the above and repeating the pass / fail judgment by comparison with the color data Xp until it passes (S408).
  • Means for obtaining pass compounding composition data Yap1 when passing by either of the means S406 or S407 (S409)
  • Means for preparing an actual candidate paint CMap1 based on the accepted compounding composition data Yap1, obtaining a coated plate of the actual candidate paint CMap1, and acquiring actual measurement color data Xap1 S410.
  • the “database in which the data X1 to Xn are registered” and the “computer in which the color matching calculation logic using the data registered in the database is operated” are based on the computer toning according to the first embodiment of the present invention. It can be substantially the same as the “one or more compositions", “database” and “computer” in the equipment used in the method of making paints.
  • the means S401 to S411 correspond to the means for carrying out the steps S101 to S111 in the method for producing a paint based on the computer toning according to the first embodiment of the present invention, they are substantially the same.
  • the means can be the same as the means described for the steps S101 to S111.
  • the automatic blending means can be the same as the automatic blending means by the automatic blending machine used in the method for producing a paint based on the computer toning according to the first embodiment of the present invention of the present invention.
  • the computer toning system has a database in which color data X and compounding composition data Y of one or more kinds of compositions are registered, and color matching using the data registered in the database.
  • a computer toning system including a computer on which calculation logic is operated, which includes the following means S501 to S511.
  • (S501) Means for inputting training data to the computer using the data registered in the database (S502)
  • the color Means for comparing the data X t with the predicted color data X ni and repeating the means for determining pass / fail until it passes (S508) Passed compounding composition data Y C1 when any of the means S505 to S507 passed.
  • S509 Means for preparing the actual candidate paint CM Ci based on the accepted compounding composition data Y C1 , obtaining the coated plate of the actual candidate paint CM Ci , and acquiring the measured color data X Ci (S510).
  • Means for determining pass / fail by comparing the color data X t with the measured color data X Ci and / or comparing the target color with the color of the coating plate of the actual candidate paint CM Ci (S511). Means for repeating the means S506 to S510 when the means S510 does not pass.
  • compositions a database in which color data X and compound composition data Y of one or more kinds of compositions are registered
  • color matching calculation logic using the data registered in the database can be substantially the same as the “database” and “computer” in the apparatus used in the method for producing paint based on computer toning according to the second embodiment of the present invention. ..
  • the means S501 to S511 correspond to the means for carrying out the steps S201 to S211 in the method for producing a paint based on the computer toning according to the second embodiment of the present invention, they are substantially the same.
  • the means can be the same as the means described for the steps S201 to S212.
  • the automatic blending means can be the same as the automatic blending means by the automatic blending machine used in the method for producing a paint based on computer toning according to the second embodiment of the present invention.
  • the system for predicting the color data of the coating film according to the sixth embodiment of the present invention is a database in which color data X and compounding composition data Y of one or more kinds of compositions are registered, and data registered in the database. It is a system for predicting the color data of the coating film including a computer on which the color matching calculation logic using the above is operated, and includes the following means S601 to S607.
  • (S601) Means for inputting training data to the computer using the data registered in the database (S602)
  • paint CM means for obtaining blending composition data Y CM of t (S604) the blending composition data Y CM for predicting color data of the learned means for generating an artificial intelligence model (S603) a coating film containing at least one, said computer optionally means for inputting (S605) to a find using a computer, corresponding search color data X by means (S606) said means S605 of obtaining search color data X n1 corresponding to the blending composition data Y CM If n1 is not found, or, in the case of not performed the means S605, from the blend composition data Y CM, wherein the at least one learned artificial intelligence model, or, wherein the at least one learned artificial intelligence Means for obtaining predicted color data X m1 using a model and a prediction
  • compositions a database in which color data X and compound composition data Y of one or more kinds of compositions are registered
  • color matching calculation logic using the data registered in the database The "computer on which the computer operates” can be substantially the same as the “database” and “computer” in the apparatus used in the method of predicting the color data of the coating film according to the third embodiment of the present invention.
  • the means S601 to S607 correspond to the means for carrying out the steps S301 to S307 in the method for predicting the color data of the coating film according to the third embodiment of the present invention, they are substantially the same.
  • the means can be the same as the means described for the steps S301 to S309.
  • the automatic blending means can be the same as the automatic blending means by the automatic blending machine used in the method for predicting the color data of the coating film according to the third embodiment of the present invention.
  • the present invention controls and operates a computer toning system according to the fourth and fifth embodiments of the present invention and / or a system for predicting color data of a coating film according to the sixth embodiment of the present invention. Also related to application software for.
  • the application software according to the seventh embodiment of the present invention uses the computer toning system according to the fourth and fifth embodiments of the present invention and / or the color data of the coating film according to the sixth embodiment of the present invention. It functions to control and operate the predictive system, thereby performing the methods of the invention.
  • the application software according to the seventh embodiment of the present invention uses the computer toning system according to the fourth and fifth embodiments of the present invention and / or the color data of the coating film according to the sixth embodiment of the present invention. It may be stored in advance in a recording device such as an HDD (Hard Disk Drive) or a flash memory possessed by a system for predicting or a device for executing each means constituting the system. Further, it may be installed in a device or the like by using a wireless or wired communication means, a detachable recording medium such as a DVD, a CD-ROM, or a USB memory.
  • a recording device such as an HDD (Hard Disk Drive) or a flash memory possessed by a
  • Example 1 A total of 86 types of primary colors, metallic primary colors and pearl primary colors were selected from the Retan PG80, Retan PG Hybrid Eco, Retan WB Eco EV and Retan Eco Fleet (all trade names manufactured by Kansai Paint Co., Ltd.) series.
  • the compounding composition data and color data were acquired and registered in the database.
  • the training data created using the data registered in the database was input to a computer and machine-learned by a neural network, and training including at least one type of artificial intelligence model for estimating the compounding composition data Y from the color data X was trained. Generated an artificial intelligence model.
  • the reduction effect is shown by the ratio of the man-hours (time) required to obtain the final pass toning composition when Comparative Example 1 is set to 100.
  • the results are shown in Table 1.
  • the toning load during the toning work was 40 in Example 1 when Comparative Example 1 was 100, which was a 60% reduction effect from the conventional toning man-hours.
  • Example 1 A conventional computer color matching device (Kansai) that acquires a total of 86 types of compounding composition data and color data of the same color primary colors, metallic primary colors, and pearl primary colors used in Example 1 and does not have an artificial intelligence model. Registered with Paint).
  • the candidate compounding composition was obtained using the above computer color matching device, and the color matching work was performed until a passing compounding was obtained by a skilled person for 5 years or more. This was repeated and the toning load was evaluated in the same manner as in Example 1. The results are shown in Table 1.
  • Example 2 A total of 86 types of primary colors and pearls were selected from the Retan PG80, Retan PG Hybrid Eco, Retan WB Eco EV, and Retan Eco Fleet (all trade names manufactured by Kansai Paint Co., Ltd.) series.
  • the compounding composition data and color data were acquired and registered in the database. There are 155 types of color data at 400 to 700 nm at 5 angles measured with a multi-angle spectrophotometer (incident angle 45 degrees, light receiving angle highlights 15 degrees, 25 degrees, face 45 degrees, shade 75 degrees, 110 degrees). Reflection spectrum data was used.
  • the training data created using the data registered in the database was input to a computer and machine-learned by a neural network to generate an artificial intelligence model that estimates color data X from compounding composition data Y.
  • Example 2 A conventional computer color matching device (manufactured by Kansai Paint Co., Ltd.) that acquires a total of 86 types of compounding composition data and color data of the same color primary colors and pearl primary colors used in Example 2 and is not equipped with an artificial intelligence model. Registered in. For the same 100 coated plates used in Example 2, the candidate compounding composition was obtained using the above computer color matching device, and the toning work was performed until a passing compounding was obtained by a skilled person for 5 years or more. This was repeated and the toning load was evaluated in the same manner as in Example 2. The results are shown in Table 1.
  • Example 3 In the same manner as in Example 2, the machine learning using the training data was changed from the machine learning by the neural network to the machine learning by the decision tree using the gradient boosting. We generated an artificial intelligence model to estimate. In the same manner as in Example 2, the color matching work was performed using the artificial intelligence model, and the color matching load during the color matching work was evaluated by the color matching accuracy and the reduction effect. The results are shown in Table 1.

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