Systems and methods for color matching - Patents.com

Abstract

The present invention relates to a computer-implemented color matching method using an application adjustment algorithm running on a processor (110) and a database (220) containing specific optical data for individual color components, the specific optical data for the individual color components being determined based on known reference paint coatings having known reference color formulas and known measured reference colors, each of the reference paint coatings being applied to a substrate using a reference painting method, wherein the paint adjustment algorithm is extended by an application adaptation module that interacts with a color prediction model of the paint adjustment algorithm and is configured to receive application adaptation parameters for the specific painting method as input parameters and to use the received application adaptation parameters to convert a color predicted by the color prediction model for use with the reference painting method into a transformation color valid for use with the specific painting method. The present invention also provides respective systems.

Description

[Technical field]

[0001] The present invention relates to a computer-implemented color matching method and suitable system for matching liquid state colors to dry state color standards. [Background technology]

[0002] Most computer-aided color matching methods are based on physical models that describe the interaction of light with scattering or absorbing media (e.g. colorants in a paint layer). Each paint layer has specific light reflection properties depending on the colorants it contains. These colorants have specific optical properties that are represented by specific optical constants / specific optical data, respectively. The physical model is able to predict the light reflection properties (color) of a paint layer / paint coating, together with the information about the colorants it contains (each based on the information about their respective formulations) and their corresponding specific optical properties (each with their corresponding specific optical constants).

[0003] The optical constants of a colorant describe the absorption and scattering properties (or effect flake orientation) of the colorant in the environment of the respective physical model, for example the K / S value in the well-known "Kubelka / Munk" model. However, the reflectance properties of a paint do not only depend on the formulation. They also strongly depend on the application method, generally how the paint was applied to the substrate. In the context of the present invention, a paint in a wet state (respectively a liquid paint) means one specific application method. In this document, the terms "application method" or "application" and "state" are used synonymously.

[0004] The specific optical properties of a colorant are determined based on sample data, each with a known formula and known reflectance data, that are applied to a substrate by a common reference coating method, and all measured in a common reference coating state (liquid or dry). Physical model color prediction and color matching methods are always relative to this reference coating method.

[0005] Color prediction of physical models for different target coating methods is subject to significant systematic errors and low accuracy, especially when matching from scratch for different target coating methods can be quite inaccurate.

[0006] Using a numerical optimization algorithm, the appropriate formulation for a given target color can be predicted based on a physical model that takes the existing optical constants of the available colorants and the reflectance data of the target color as input.

[0007] The resulting formula should be able to match the target color as closely as possible if the target application process is equivalent to the reference application process.

[0008] It is also possible that the color recipe is calculated based on an existing sample close to the target color with a known recipe, for example based on a tinting step. In this case, we discuss the color adjustment process. The existing sample must have been applied and measured with a reference painting method, since the color adjustment algorithm is based on one basic assumption: "the bias of the model is (approximately) constant for recipes close to the sample recipe". As long as the painting method of the sample is comparable to the reference painting method and the adjusted recipe of the color adjustment is similar to the sample recipe, the model bias is expected to be "similar": that is, the adjustment algorithm interprets the sample offset, i.e. the offset between the measured reflectance data and the predicted reflectance data of the sample, respectively, as a model bias. This model bias is automatically taken into account / corrected within the adjustment algorithm, leading to a correction of the adjusted recipe (e.g. patent EP2149038B1). If the painting method of the sample is different from the reference painting method, the model bias of the sample cannot be related to the adjusted recipe. In this case, the sample contains an additional non-constant bias caused by the different painting method. This bias of the application method propagates to the adjusted recipe. Depending on the magnitude of the application method bias of the sample, the color adjustment results can be significantly inaccurate. In particular, if the reflectance data of the liquid application method is mixed with the data of the dry application method, the application method bias can be very large.

[0009] Nowadays, target color tolerances are defined for dry paints. However, in most cases paints are liquid products. The optical properties of paints in the dry state are not equivalent to their liquid counterparts. The color of the paint depends heavily on the state of the paint. Reasons for significant color differences between wet and dry paint layers include, for example: - Differences between instruments and measuring devices for measuring liquid paint and dry paint - The influence of the measuring cell (e.g. glass cuvette or window glass) on the fluid paint compared to the clear coat on the dry paint - Differences in the refractive index between wet paint (n = approx. 1.35) and dry paint (n = approx. 1.5) leading to different optical properties of the pigments contained in the paint - Different orientation of effect flakes in paint - A "milky" mixed clear in the wet paint that is transparent / invisible in the dry paint layer There is.

[0010] Color matching is an iterative process. In practice, a physical model with the optical constants of a reference painting method (dry paint state) is used in the matching method. The matching method starts with a match from scratch or a search in a recipe database of a given target color in the dry state.

[0011] The term "matching from scratch" includes color matching methods that proceed without information about an existing sample coating as a first solution. This method applies, for example, when a recipe database is not available or when no suitable first solution can be found in the recipe database. In practice, "matching from scratch" methods often start with a pre-selection step of components expected to be present in the target color. This pre-selection step is not mandatory. The "matching from scratch" method / algorithm calculates one or more preliminary matching recipes for the target color as a first solution. This / these preliminary matching recipe(s) can be sprayed and / or adjusted in the next step.

[0012] Compared to "color adjustment methods" which utilize sample coatings as a first solution used to improve the color prediction accuracy of physical models (e.g. based on approximating model errors by analyzing "sample offsets"), the accuracy of "matching from scratch" methods is generally lower.

[0013] The result is a first solution, i.e., first paint formulation, which is prepared / mixed as a wet paint in the color lab. The wet paint is applied / sprayed to a substrate with a reference painting method and dried. Optionally, an additional clear coat is applied on top of the first paint layer / coating. Finally, the reflectance of the dry paint layer / coating is measured. Usually, the first solution, i.e., first blend color is not close enough to the target color. In this case, a first solution adjustment process is started, in which a sample offset between the predicted and measured reflectance of the first solution is taken into account. The sample offset is assumed to be a systematic and constant offset. If the sample offset is not constant over the adjustment steps (e.g., in case of variations in the painting method), the color adjustment result will be significantly inaccurate. The adjusted formula is a function of the target color and the sample offset. The non-constant sample offset is propagated to the adjusted formula as a bias of the application method.

[0014] The term "non-constant sample offset" includes when the offset of the adjusted formula (after application) is significantly different compared to the offset of the sample.

[0015] The spraying and drying process ("painting method") is a time-consuming and expensive process. An efficient alternative would be color measurements of all samples / coloring processes in the liquid state (instead of the dry state) and a color matching and adjustment process based on the optical constants of the liquid paint (instead of the dry paint). This approach would save the spraying and drying process in the color matching and adjustment process. Furthermore, the sample reflectance data is not subject to biases caused by the variations of the dry painting method. However, in practice, the target color is always given in the dry state. Comparing the sample color in the liquid state with the standard color in the dry state leads to a large bias and is not acceptable. [Prior art documents] [Patent documents]

[0016] [Patent Document 1] EP2149038B1 Summary of the Invention [Problem to be solved by the invention]

[0017] It is therefore an object of the present invention to provide the possibility to correct application method biases in color adjustment methods when using both liquid and dry colors. [Means for solving the problem]

[0018] The above mentioned object is solved by a method and a system with the features of the respective independent claims. Further embodiments are given in the following description and in the respective dependent claims.

[0019] The present disclosure relates to a computer-implemented color matching method using an application adjustment algorithm executed on at least one computer processor and a database including specific optical data of individual color components, the specific optical data of the individual color components being determined based on known reference paint coatings having known reference color formulations and known measured reference colors, each of the reference paint coatings being applied onto a substrate using a reference painting method, wherein the paint adjustment algorithm is extended by an application matching module configured to interact with a color prediction model of the paint adjustment algorithm, to receive application matching parameters for the specific painting method as input parameters, and to use the received application matching parameters to convert a color predicted by the color prediction model for use in the reference painting method into a converted color effective for use in the specific painting method.

[0020] That is, the application adaptation module is configured to convert a color predicted by the color prediction model and that will appear when using a reference painting method into a converted color that will appear when using the specific painting method, where the predicted color for use with the reference painting method is converted into a color valid for use with a specific painting method that is different from the reference painting method.

[0021] The terms "specific optical data of individual color components", "specific optical data of the individual color components" or "specific optical data of the colorants" are used synonymously herein and comprise the specific optical properties and specific optical constants of each individual color component, i.e. the colorants. The individual color components used in the color formulation of the respective paint coating are selected from the group comprising at least: color pigments, i.e. so-called solid pigments, effect pigments, binders, solvents and additives, e.g. matt pastes.

[0022] The terms "color," "color data," "reflectance," "reflectance data," and "reflectance properties" are used interchangeably herein. The terms "applying method," "application method," "application," and "state" are used interchangeably, meaning that paint in a wet state, respectively, defines a particular application method, and paint in a dry state defines another particular application method.

[0023] The terms "paint formulation", "color formulation" and "formulation" are used interchangeably herein. The terms "paint balancing algorithm", "color balancing algorithm" and "balancing algorithm" are used interchangeably herein. The terms "painting method" and "application method" are used interchangeably herein. The terms "computer processor" and "processor" are used interchangeably herein.

[0024] Known approaches to color blend calculations based on radiative transfer models can be found in the literature, see for example "Farbenphysik fur industrielle Anwendungen" by Georg A. Klein.

[0025] The basic idea of ​​color formula calculation is to characterize certain optical data, i.e., the optical properties and / or optical constants of all relevant individual color components (e.g., all color pastes / colorants), based on previously calibrated coatings, i.e., based on the respective measurements of such calibration coatings. These calibration coatings correspond to existing let-downs with known formulas and known reflectance data, all of which were applied by a common reference coating method. Color predictions using physical models (also referred to herein as color prediction models) and color matching methods are always associated with this reference coating method.

[0026] The physical model for the prediction of the reflectance properties of paints (related to a reference coating method) is extended by an additional application adaptation module: the additional module is supposed to work in interaction with the physical model and adapt the predicted reflectance data from a reference coating method (e.g. liquid state) to a specific target coating method (e.g. dry state). In combination with the physical model, reflectance data for different coating methods can be predicted based on only one set of optical constants.

[0027] The additional modules are configurable by input of specific target application fit parameters. These application fit parameters describe the differences (or rather specific transfer functions) between the target coating method compared to the respective reference coating method (e.g. the differences between liquid and dry paint conditions). Examples of application fit parameters are: - Effect flake orientation matching: better flake orientation / worse flake orientation (Applicable to effect colors; adjusts the brightness / color flop of the paint layer) - Efficacy of solid colorants: Higher efficacy / lower efficacy (Adjusts the color strength difference of solid colorants that may occur due to shear effects and agglomerates, etc.) - Effect of colorant: Higher effect / lower effect (Adjusts for differences in reflectivity of effect colorants that may result from overspray losses or settling or residual effects) - Matching the light losses of the wet paint in the cuvette or measuring cell - Conversion factor of refractive index between wet and dry - Correction of "milky" components such as clear mixed into wet paint (disappears / becomes transparent in dry paint) It is.

[0028] For example, if a formula for an effect color contains aluminum flakes, and the specific optical constants of this formula in the liquid state include the scattering coefficient S of this effect pigment, then the overspray loss in the dry state is calculated as the scattering coefficient S_dry=c *It can be corrected in the application adaptation module by a simple linear scaling function of S_wet (c<1). A c of 0.95 means that the overspray loss of the effect flake is 55%.

[0029] For example, if a formula contains a mixed clear component that has a "milky" appearance in the wet state (S_wet>0, K_wet>=0) but is transparent / invisible in the dry state, the "vanishing effect" from wet to dry can be modeled in the application fitting module by setting the specific scattering / absorption coefficients (S_dry / K_wet) of this mixed clear component to S_dry=0, K_dry=0. This fitting function means that the optical properties of this mixed clear component (in the wet state) can be ignored in the dry state color prediction.

[0030] The application suitability parameters can also be implicitly determined based on an analysis of existing sample(s) applied with a common specific application method (e.g., one or more existing coloring step(s) of a color matching method). A list of existing samples from a database (associated with a specific application method, e.g., when using a specific wet or dry application) can also be used for the determination of the respective target application suitability parameters.

[0031] According to one embodiment of the proposed method, the application fit parameters for a particular painting method are calculated using a numerical method and a color prediction model, in which the measured colors and the formulas of a number of sample coatings are provided as input parameters, a predefined cost function is optimized starting from a predefined set of initial application fit parameters, the predefined cost function being selected as the color distance between the measured color and the predicted color of each sample coating, and the physical model is configured to predict the color of each sample coating by using as input parameters the respective color formulas of the sample coatings, the specific optical data of the individual color components used in the color formulas of the sample coatings, and the respective preliminary application fit parameters obtained in the course of optimization, and the application fit parameters are calculated by comparing the recursively predicted colors of the sample coatings with the measured colors of each sample coating until the predefined cost function falls below a predefined threshold. The initial application fit parameters are neutral parameters, meaning that the use of the initial application fit parameters results in a color prediction that is equal to that of the use of the reference painting method. The predefined threshold can also be dynamically determined, for example indicating a certain state of minimization where no further improvement is possible.

[0032] As already mentioned above, the reference painting method and the specific painting method are different from each other. According to a further embodiment, the reference painting method and the specific painting method are each selected from the group consisting of a paint coating applied in a wet state, a paint coating applied in a dry state and just a paint in a wet state.

[0033] According to a further aspect, the proposed method is used to determine a target color formulation of a target paint coating that matches a predetermined target color when applied (on a substrate) using a predetermined target painting method different from a reference painting method as a specific painting method, the method further comprising: - receiving, via at least one interface, a predetermined target color; - receiving application adaptation parameters for a given target coating recipe via at least one interface; - retrieving from a database specific optical data for each color component used in the target color formulation of the target paint coating; - using the predetermined target color, the specific optical data of the retrieved individual color components and the application adaptation parameters received as input parameters of the paint adjustment algorithm, calculating a color formula having an optimized concentration of the individual color components as a target color formula for the target color formula of the target paint coating when the target paint coating is applied (on the substrate) using the predetermined target painting method; Includes.

[0034] "Paint coating" as used herein can refer to a paint layer applied onto a substrate, and the respective paint in its wet state, depending on the application method used. The terms "paint coating" and "color coating" are used interchangeably herein.

[0035] A color formula defines the individual color components, i.e., colorants, and their respective concentrations used in each paint coating. In this specification, the terms "color formula" and "paint formula" are used interchangeably.

[0036] That is, the proposed method, i.e. the proposed color adjustment algorithm, is capable of matching color to a specific target painting method, which may differ from the reference painting method.

[0037] In general, the "real" measured reflectance data (color) of a sample always (slightly) differs from the predicted reflectance data (color) of a physical model ("sample offset"). Reasons for this sample offset between reality and theory include, for example: - Model bias: no model is 100% accurate - Statistical errors of the instrument: e.g. caused by temperature There is.

[0038] It is therefore necessary to provide a sample offset. To this end, the proposed method further comprises the following steps: - receiving, via at least one interface, data of a color formulation of a sample paint coating as a first solution of a target color to be matched; - retrieving from a database specific optical data of the individual color components used in the color formulation of the sample paint coating; - receiving, via at least one interface, a measured color of a sample paint coating applied to a substrate using a reference painting method; - predicting the color of the sample paint coating using a color prediction model implemented and executed on at least one computer processor; - calculating, with at least one computer processor, an offset of the sample paint coating as a difference between the measured color and the predicted color of the reference paint coating; - correcting the given target taking into account the offset, i.e. introducing the offset into the target color recipe; Includes.

[0039] The color adjustment algorithm interprets the complete sample offset as a model bias and corrects the adjusted paint formula as each sample offset is corrected, where the application method bias is part of the sample offset. If the application method bias is not constant from one adjustment step to another, it acts as an instability factor. Depending on the magnitude of the non-constant sample offset, the error will propagate and the color adjustment results may be significantly inaccurate.

[0040] This means that there may be application method bias: for example, reflectance data for wet application methods may be mixed with data for dry application methods.

[0041] In addition to application matching to a given target application method (e.g., a specific dry application method), the present invention also describes a method to remove application method bias in the sample offset (e.g., wet measurements) in the color adjustment when the sample application method differs from the reference application method. Improved determination of sample offset directly improves the quality / accuracy of the adjusted formula.

[0042] The basic idea is that the sample offset consists of application method bias and residual errors. The residual errors include model biases, etc. Application method biases are expected to be a non-constant part of the sample offset and should therefore be removed from the sample offset. The residual errors of the sample offset consist mainly of model biases, which are correctly handled within the adjustment algorithm. This approach is based on the assumption that the model biases of the sample coating method are similar to the model biases of the target coating method.

[0043] The improved tuning algorithm uses the coating match module in combination with the coating match parameters to predict coating method specific reflectance values ​​(predicted colors) of a given sample coating method for a sample coating (i.e., each of the first solutions in an iterative color tuning process, i.e., an iterative color matching method). The predicted coating method specific reflectance values ​​(predicted colors) are used to calculate a corrected sample offset that is free of coating method bias.

[0044] As already mentioned above, specific application match parameters for a sample (e.g., an existing coloring step of a color matching method) can be defined or implicitly determined (e.g., by user input) based on analysis of its formulation and reflectance data.

[0045] As used herein, the terms "reflectance data" and "color" are used synonymously.

[0046] The proposed method makes the color adjustment process converge faster and more robust and reliable.

[0047] Thus, according to yet another embodiment of the proposed method, the method is used to determine a target color formulation of a target paint coating that matches a given target color applied (on a substrate) using a reference painting method, the method further comprising: - receiving, via at least one interface, color formula data for a sample paint coating as a first solution for a target color to be matched; - retrieving from the database specific optical data of the individual color components that have been used in the color formulation of the sample paint coating or that are to be additionally used in the color formulation of the target paint coating; - receiving, via at least one interface, measured colors of sample paint coatings applied (on a substrate) using a sample application method different from the reference application method; - receiving application fit parameters for a sample coating method; - predicting a color of a sample paint coating effective for use in a sample painting method as a specific painting method using a color prediction model and an application match module, where data of the color formula of the sample paint coating, retrieved specific optical data of individual color components used in the color formula of the sample paint coating, and application match parameters of the sample painting method are used as input parameters; - calculating an offset for the sample paint coating as the difference between the measured color and the predicted color of the sample paint coating; - correcting the given target color using a paint adjustment algorithm, taking into account the offset, i.e. introducing an offset into the calculation of the target color formula; Includes.

[0048] The expression "paint coating applied using a coating method" means that the paint coating is applied onto a substrate using the respective coating method if the coating method results in a paint coating in a dry state corresponding to the respective dry state, and means that the coating method only provides a paint coating in a wet state for further use if the paint coating is still in a wet state. The expression "applied onto a substrate" is only valid if the respective coating method includes the steps of spraying and drying. Therefore, the expression "onto a substrate" is written in parentheses.

[0049] Additionally, the method may be used to determine a target color formula for a target paint coating that matches a given target color when applied (on a substrate) using a target application method as a particular application method, the method further comprising: - receiving, via at least one interface, data of a color formulation of a sample paint coating as a first solution of a target color to be matched; - retrieving from the database specific optical data of each color component used in the color formulation of the sample paint coating or additionally used in the color formulation of the target paint coating; - receiving, via at least one interface, a measured color of a sample paint coating applied (on a substrate) using the sample application method; - receiving application fit parameters for a sample paint coating via at least one interface; - predicting the color of a sample paint coating effective for use in a sample painting method as a specific painting method using a color prediction model and an application matching module, where the color formula data of the sample paint coating, the retrieved specific optical data of the individual color components used in the color formula of the sample paint coating, and application matching parameters for the sample painting method are used as input parameters; - calculating an offset for the sample paint coating as the difference between the measured color and the predicted color of the sample paint coating; - correcting the given target color using a paint adjustment algorithm, taking into account an offset, i.e. introducing an offset into the calculation of the target color formula; - receiving application adaptation parameters for a target coating method via at least one interface; - calculating a color formula having an optimized concentration of individual color components as a target color formula of the target applied coating when the target paint coating is applied (on the substrate) using the target application method, using the target color, the calculated offset, and the received application match parameters for the target application method as input parameters of a paint adjustment algorithm; Includes.

[0050] The present invention also relates to a system, the system comprising at least: - a database comprising individual color components, such as pigments and / or pigment classes, and specific optical data associated with each individual color component, the specific optical data of the individual color components being determined on the basis of known reference paint coatings having a known reference color formula and a known measured reference color, each reference paint coating being applied (on a substrate) using a reference painting method; - at least one computer processor, communicatively connected to the database and programmed to execute the method proposed herein; Includes.

[0051] The system may further comprise an input device configured to receive data input via a suitable interface, such as a USB. Such an input device may be a computer keyboard, a microphone, a video camera, a data carrier, or any combination thereof. The system may further comprise an output device configured to output, and in particular display, a respective result calculated by executing one of the above methods. The output device is one of the group including at least an acoustic device, a tactile device, a display device, and any combination thereof. The output device is communicatively connected to the at least one computer processor via a respective interface.

[0052] Furthermore, the present invention relates to a non-transitory computer readable medium comprising a computer program, the computer program having a program code configured and programmed to perform the method proposed herein when loaded and executed by at least one computer processor communicatively connected to a database comprising individual color components, such as pigments and / or pigment classes, and specific optical data associated with each individual color component, wherein the specific optical data of the individual color components are determined based on known reference paint coatings having known reference color formulations and known measured reference colors, and the reference paint coatings are each applied (on a substrate) using a reference painting method.

[0053] Each communication connection between the different components may be a direct or indirect connection. Each communication connection may be a wired or wireless connection. Any suitable communication technology may be used. The database and the at least one computer processor may each include one or more communication interfaces for communicating with each other. Such communication may be performed using a wired data transmission protocol, such as Fiber Distributed Data Interface (FDDI), Digital Subscriber Line (DSL), Ethernet, Asynchronous Transfer Mode (ATM) or other wired transmission protocol. Alternatively, the communication may be performed wirelessly over a wireless communication network using any of a variety of protocols, such as General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), Code Division Multiple Access (CDMA), Long Term Evolution (LTE), Wireless Universal Serial Bus (USB), and / or any other wireless protocol. Each communication may be a combination of wireless and wired communication.

[0054] Computer-readable media suitable for storing computer program instructions (i.e., program code) and data include all forms of non-volatile memory and memory devices, including, by way of example, semiconductor memory devices such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM) and flash memory devices; magnetic disks such as internal hard disks or removable disks; magneto-optical disks; optical disks; CD-ROM, DVD+R, DVD-R, DVD-RAM, and DVD-ROM disks, or one or more combinations thereof. Such memory devices may store a variety of objects or data, including caches, classes, applications, backup data, jobs, web pages, web page templates, database tables, repositories for storing dynamic information, and other suitable information, including any parameters, variables, algorithms, instructions, rules, constraints, and / or references thereto. Additionally, memory devices may include any other suitable data, such as policies, logs, security or access data, report files, and the like. Computer processors and memories may be supplemented by, or incorporated in, special purpose logic circuitry.

[0055] The computer program instructions may be a computer program, a software application, a module, a software module, a script, or code, and may be written in any type of programming language, including compiled or interpreted languages, or declarative or procedural languages. The instructions may be deployed in any type, such as as a stand-alone computer program, or as a module, component, subroutine, or other unit suitable for use in a computing environment. In one embodiment, the computer executable instructions (i.e., program code) of the present disclosure are written in HTML, TypeScript (TS), and Cascading Style Sheets (CSS).

[0056] A computer program may correspond to a file in a respective file system, but need not. A computer program may be stored as part of a file that holds other computer programs or data, e.g., in one or more scripts stored in a markup language document, in a single file dedicated to the computer program, or in multiple coordination files, e.g., files that store one or more modules, subprograms, or code portions. A computer program may be deployed to run on one computer, or on multiple computers located at one site, or distributed across multiple sites and interconnected by a communication network. Some computer programs may be designed as individual modules that implement various features and functions through various objects, methods, or other processes. Alternatively, a computer program may instead include multiple submodules, third-party services, components, libraries, etc., as appropriate. Conversely, features and functions of various components may be combined into a single component as appropriate.

[0057] A system suitable for carrying out the disclosed method can be based on a general-purpose or special-purpose microprocessor, or both, or any other type of CPU. In general, the CPU will receive instructions and data from a read-only memory (ROM) or a random access memory (RAM), or both. The essential elements of the system are a CPU for executing or executing instructions (i.e., program code) and one or more memory devices (such as databases) for storing instructions (i.e., program code) and data. In general, the system includes at least one memory device, or is operatively coupled to at least one memory device, and configured to receive data from the at least one memory device for storing data, or to transfer data to the at least one memory device, or both. Such memory devices include, for example, magnetic, magneto-optical, or optical disks. However, the system need not have such devices. Furthermore, the system can be incorporated into other devices, such as a mobile phone, a personal digital assistant (PDA), or a portable storage device, such as a universal serial bus (USB) flash drive, to name just a few examples.

[0058] Color matching is an iterative process. Color matching methods are time-consuming and costly because one starts with a match from scratch and continues with several adjustment steps until a suitable formula is found. Today, the tolerance of the target color is defined for the dry paint. However, paints are generally sold as liquid products. The optical properties of the paint in the dry state are not equal to those of the paint in the liquid (wet) state. Therefore, today, each coloring step of the color matching method needs to be sprayed to check whether its dry color matches the tolerance of the paint in the dry state. The method described herein allows the adjustment process to be performed entirely in the liquid state in order to remove all the paint in all the coloring steps. The present invention describes a method that allows an adjustment process based on a wet sample coating, i.e. a sample coating in the wet state, that is not further processed by a drying procedure and possibly not even by a spraying procedure.

[0059] One of the main objectives of the present invention was to enable the use of measured reflectance data of a wet sample / stain step in a dry reference, i.e. dry target color, color matching method.

[0060] The newly developed application adaptation method has two use cases: 1. Color matching method based on the optical constants of wet paint and dry color standard: All samples / staining steps are measured wet Reflectance data for all samples / staining steps are predicted without coating adaptation to wet conditions The corresponding sample offset of the wet paint condition is taken into account in the adjustment algorithm · The predicted reflectance data of the adjusted formulation is converted to the wet to dry reference application method, i.e., the reference application method including spraying and drying procedures, by using appropriate application match parameters. 2. Dry color standard color matching method based on the optical constants of the dry standard coating method: All samples / staining steps are measured wet Predicted reflectance data for all samples / staining steps are converted from the dry reference application method to the wet state by using appropriate application fitting parameters The corresponding sample offset of the wet paint condition is taken into account in the adjustment algorithm Reflectance data for adjusted formulations is predicted without coating adaptation to the dry reference coating method The terms "wet paint", "wet color", "paint in a wet state" and "wet color" are used interchangeably herein. The terms "dry color", "dry paint", "dry paint" and "dry color" are used interchangeably herein.

[0061] The following description is illustrated and provided in the context of one or more specific implementations. Various modifications to the disclosed implementations will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other implementations and applications without departing from the scope of the disclosure.

[0062] Implementations of the subject matter and functional operations described in this disclosure can be implemented in digital electronic circuitry, in tangibly embodied computer software, in computer hardware including the structures disclosed in this disclosure and their structural equivalents, or in one or more combinations thereof. Implementations of the subject matter described in this disclosure can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory computer storage medium for execution by or for controlling the operation of a data processing device. Alternatively or additionally, the program instructions can be encoded on an artificially generated propagated signal, such as a machine-generated electrical, optical, or electromagnetic signal generated to encode information for transmission to a suitable receiving device executed by at least one computer processor.

[0063] The details of one or more implementations of the subject matter of this specification are set forth in the accompanying drawings and description. Other features, aspects, and advantages of the subject matter will become apparent from the description, drawings, and claims. [Brief description of the drawings]

[0064] [Figure 1] FIG. 2 shows a schematic diagram of a method for determining application suitability parameters provided by an embodiment of the method according to the present invention. [Diagram 2] 5A-5C show schematic steps of a further embodiment of the method according to the invention. [Diagram 3] 5A-5C show schematic diagrams of steps of another embodiment of the method according to the invention. [Figure 4] 5A-5C show schematic diagrams of steps of yet another embodiment of the method according to the invention. [Diagram 5] 1 shows a schematic block diagram illustrating sample offsets of a color matching method. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0065] Detailed Description of the Drawings Identical units or components are given the same reference numbers throughout all the figures.

[0066] 1 illustrates one embodiment of a system according to the present invention. The system includes a computer processor 110 and a database 120. The database 120 stores information about individual color components, colorants, such as pigments and / or pigment classes. 1 , colorant 2 , colorant 3 , ..., colorant n , and specific optical data, constants associated with each individual color component. 1 ,constant 2 ,constant 3 ,...,constant n and the specific optical data of the individual color components are determined based on known reference paint coatings having known reference color formulations and known measured reference colors, each of which has been applied (on a substrate) using a reference painting method. The computer processor 110 is communicatively connected to the database 120 and is programmed to execute the embodiments of the method according to the invention described herein. The application suitability parameters provided or provided in the proposed method, in addition to manual input, can also be implicitly determined as follows: The application suitability parameters may be calculated based on data for each existing tint step of the existing sample paint coating 101 .

[0067] These sample paint coatings 101 are applied (on a substrate) using a particular application method. The color 103 of each of the sample paint coatings 101 is measured. Data is provided for each color formula 102 of each sample paint coating 101. Each color formula 102 includes all the colorants, 1 , colorant 2 , colorant 3 , ... coloring agent n and their respective concentrations, c 1 , c 2 , c 3 , , c nEach of the sample coatings is applied onto a substrate if the particular application method includes a spray and dry procedure. Alternatively, the sample paint coatings are applied using the particular application method means where the particular application method only includes providing each of the sample paint coatings wet, for example in a cuvette or custom glass cell, respectively. In the latter case, the sample paint coatings may be sprayed onto the substrate but not yet dried.

[0068] Data for a color recipe 102 of a sample paint coating 101 is received via at least one interface 111 of a computer processor 110. Additionally, a measured color 103 of the sample paint coating 101 is received via at least one interface 111 of the computer processor 110.

[0069] A numerical method 130 and a physical model 140 are provided and implemented on the computer processor 110. The numerical method 130 is configured to optimize the application fit parameters by minimizing a predetermined cost function starting from a set of predetermined initial application fit parameters. The initial application fit parameters are neutral parameters, meaning that the use of the initial application fit parameters results in a color prediction equal to that of using a reference painting method. The predetermined cost function is selected as the color distance between the measured color 103 of each one of the existing sample paint coatings 101 and the predicted color of the respective sample paint coating. The physical model is configured to predict the color of each sample paint coating by using as input parameters the color formula 102 of each sample paint coating, the specific optical data of the individual color components used in the color formula 102 of each sample paint coating, and the respective preliminary application fit parameters resulting in the course of optimization. The specific optical data are retrieved from the database 120. The specific optical properties of the colorants are determined based on data of existing letdowns / test pieces with known formulas and known reflectance data, all applied (on a substrate) by a common reference painting method that may or may not be different from the specific painting method. Therefore, the color predictions of the physical model are related to this reference painting method.

[0070] Using a computer processor 110, and using a numerical method 130 and a physical model 140 implemented and executed on the computer processor 110, the application fit parameters 105 are calculated by comparing the recursively predicted color of each of the sample paint coatings with the measured color 103 of each of the sample paint coatings until a predefined cost function falls below a predefined threshold. The predefined threshold may also be determined dynamically and may, for example, indicate a certain state of minimization where no further improvement can be made.

[0071] The coating compatibility parameters 105, which are therefore considered to be optimized, are output via an interface 112 to an output device such as a display.

[0072] The calculated optimized application adaptation parameters 105 are made available and output via a further interface 112. These calculated optimized application adaptation parameters 105 are characteristic for one specific painting method. The method can be performed for a number of different predefined specific painting methods, such as wet and dry, depending on which painting method the reference painting method corresponds to. That is, if the reference painting method corresponds to the dry state, then application adaptation parameters for the wet state are required if the sample coating and / or the target coating in the wet state are included in the respective color adjustment process. Furthermore, if the reference painting method corresponds to the wet state, then application adaptation parameters for the dry state are required if the sample coating and / or the target coating in the dry state are included in the color adjustment process. The respective application adaptation parameters calculated for each one of the different predefined specific painting methods can then be searchably stored in a repository and assigned to each one of the different predefined specific painting methods as method-specific application adaptation parameters. These method-specific application adaptation parameters can then be called up from the repository at any time on demand.

[0073] FIG. 2 shows diagrammatically the steps of a further embodiment of the method according to the invention.

[0074] Generally, the matching method starts with a zero-match or a search in a recipe database for a given target color 201. The target color 201 is here in the dry state, i.e. the respective target coating appears as target color 201 in the dry state, i.e. the respective target coating is applied (sprayed) onto a substrate and dried. The respective painting method therefore consists of a combination of spraying and drying steps, also referred to herein as dry painting method. As already mentioned above, the terms "painting method" and "state" are used synonymously in this document. A color in the dry state can therefore also be represented as a color or paint coating using a dry painting method. By analogy, a color in the wet state can also be represented as a color or paint coating using a wet painting method that does not include a drying step, and possibly not even a spraying step.

[0075] The first solution is typically not close enough to the target color 201. Therefore, a physical model 140 (also referred to herein as a color prediction model) is used in combination with a numerical optimization algorithm 130 to obtain an iteratively optimized formula 202. The target paint formula 202 is a formula that is optimized by taking into account all included colorants, colorants, and 1 , colorant 2 , colorant 3 , ... coloring agent n and their respective concentrations, c 1 , c 2 , c 3 , , c n The physical model 140 uses a database 220 as the basis for color prediction. The database 220 defines individual color components, colorants, such as pigments and / or pigment classes. 1 , colorant 2 , colorant 3 , ..., colorant n , and specific optical data, constants associated with each individual color component. 1 ,constant 2 ,constant 3 ,...,constant nThe specific optical properties of the colorants are determined here based on existing letdown / test specimen data with known formulas and known reflectance data, all applied by a wet application method, a common reference application method, respectively, in wet form. However, when searching for a paint coating formula 202 whose color matches the target color 201, and the paint coating is applied onto the substrate using a dry application method (and thus other than the wet application method as the reference application method), the properties of the dry application method compared to the wet application method are taken into account by providing respective dry application match parameters 205 for the dry application method as the specific application method, which are calculated as exemplarily described in FIG.

[0076] The target color 201 and the target application fit parameters 205 are received via the interface 111 by the computer processor 110, in which the physical model 140 and the numerical optimization algorithm 130 are implemented and executed. The target color 201, the target application fit parameters 205 and the specific optical constants of the available colorants from the database 220 are used to determine a formula 202 for a paint coating whose color matches the target color 201 when applied on a substrate using the target painting method, i.e., the respective dry painting method, and an optimized formula 202 is iteratively determined. This formula 202 and the predicted color 206 when applied using the target painting method, i.e., the respective dry painting method, can be output on an output device via the interface 112. The predicted color 206 is composed of the true color of the optimized formula 202 when applied on a substrate using the dry painting method and a systematic bias corresponding to the model bias 210 (see FIG. 5 for a detailed description). There is no bias of the painting method because the target application fit parameters 205 are included in the calculation.

[0077] To determine the model bias, or offset 210, the method further includes the following steps.

[0078] - receiving, via the interface 111, data of a color formula 222 of a sample paint coating 221 as a first solution of a target color 201 to be matched; - retrieving from a database 220 specific optical data for the individual color components used in the color formula 222 of the sample paint coating 221; - receiving, via the interface 111, a measured color 223 of a sample paint coating 221 applied using a reference painting method, i.e. a wet painting method (i.e. the measured color 223 of the sample paint coating 221 is provided in a wet state); - predicting the color 225 of the sample paint coating 221 valid for a reference painting method, i.e. here for wet conditions, using a color prediction model 140 implemented and executed on the processor 110; - calculating, with the processor 110, an offset 210 as the difference between the measured color 223 and the predicted color 225 of the sample paint coating 221; - correcting the given target color 201 by taking into account the offset 210 as a further input parameter via the interface 111 of the color adjustment algorithm consisting of a combination of a numerical optimization algorithm 130 and a color prediction model 140, respectively providing an offset 210. Generally, the physical model, i.e. the color prediction model 140, requires all available pigments and their respective specific optical data stored in the database 120 in order to predict the color, i.e. to determine the theoretical color of the preliminary paint formulation during the iterative process of the color adjustment algorithm. In most cases, the first solution 221 and the finally determined target color formulation 202 contain the same pigments, but there may be additional pigments required for the target color formulation 202. Figure 3 shows the process of another embodiment of the method according to the invention in a schematic manner.

[0079] The color adjustment process for a given target color 300 starts with a sample 301, e.g. an existing coloring step or a search result in a recipe database, as a first solution. So far, the existing sample 301 has had to be applied with a reference painting method, since the color adjustment algorithm is based on the assumption that the model bias is constant for all recipes close to the sample recipe. However, as already explained, the actual sample or sample coating does not have to be applied with a reference painting method corresponding here to a dry painting method, but can be applied with a sample painting method corresponding here to a wet painting method, thereby contributing to a systematic bias of the respective sample offset. If the contribution of the sample painting method to this sample offset is not taken into account, the result of the color adjustment process will be significantly inaccurate.

[0080] The first solution 301 is a sample coating where the target color 300 is in the dry state, but now applied using a wet application method. In order to be able to make a color prediction of the paint formulation optimized for the target color 300 in the dry state even when the sample is used in the wet state, an offset 310 needs to be determined that corrects for such differences, i.e. takes into account such a conversion between the wet and dry application methods. The first solution 301 is typically not close enough to the target color 300. An adjustment of the first solution 301 is applied where the offset 310 between the predicted reflectance data 306 and the measured reflectance data 303 for the first solution 301 is taken into account.

[0081] The adjusted formula is therefore a function of the target color 300 and the offset 310 between the predicted reflectance data 306 and the measured reflectance data 303 of the first solution 301. If the measured reflectance data 303 of the first solution 301 contains a bias caused by variations in the painting process, this error will propagate to the next formula in the iterative color matching method.

[0082] It is therefore proposed to avoid such painting method bias by already taking into account the diversity of painting methods when considering the first iterative step, i.e. the first solution 301.

[0083] A coating method independent offset 310 is calculated based on a first solution 301. A sample formulation 302 of the first solution 301 is known. The first solution 301 is applied as a paint coating using the sample coating method, i.e. using the respective wet coating method, and its color is measured. A measured color 303 of the first solution 301 is provided. The measured color 303 comprises the true color, a systematic bias and a statistical error. Furthermore, a physical model 140 is used to predict the color of the first solution 301 based on the known formulation 302. Since the physical model 140 uses the database 120 and is thus related to the reference coating method, i.e. the respective dry coating method, the sample coating method is taken into account by combining the physical model 140 with the sample application fit parameters 205 determined for the wet coating method as a specific coating method as explained in FIG. 1. A predicted color 306 of the first solution 301 is now predicted under the assumption that the underlying formulation 302 is applied as a paint coating using the sample coating method. Thus, both measured color 303 and predicted color 306 refer to the same painting method, i.e., the wet painting method. Therefore, the offset 310 as the difference between measured color 303 and predicted color 306 is independent of the underlying painting method, i.e., the wet condition. This offset 310 can now be used in an iterative adjustment process based on the dry condition as the reference painting method.

[0084] Because the first solution 301 is typically not close enough to the target color 300, a physical model 140 is used in combination with a numerical optimization algorithm 130 to iteratively obtain a target paint formula 350. The target paint formula 350 is a combination of all the colorants, colorants, 1 , colorant 2, colorant 3 , ... coloring agent n and their respective concentrations, c 1 , c 2 , c 3 , , c n The physical model 140 uses a database 320 as a basis for color prediction. The database 320 includes individual color components, colorants, such as pigments and / or pigment classes, and 1 , colorant 2 , colorant 3 , ..., colorant n , and specific optical data, constants associated with each individual color component. 1 ,constant 2 ,constant 3 ,...,constant n The target color 300 is combined with a calculated offset 310 to account for model bias and statistical errors, both of which are assumed to be similar in terms of paint formulations of the sample and target color 300.

[0085] The target color 300 and the offset 310 are received via an interface 111 by a computer processor 110 in which the physical model 140 and the numerical optimization algorithm 130 are implemented and executed. The target color 300, the offset 310 and the specific optical constants of the available colorants from the database 320 are used to determine a formula 350 for a paint coating whose color matches the target color 300 when applied on a substrate using a reference painting method, i.e., the respective dry painting method, and an optimized formula 350 is iteratively determined. This formula 350 and a predicted color 351 when applied using the reference painting method can be output to an output device via the interface 112. The predicted color 351 is composed of the true color of the optimized formula 350 when applied on a substrate using the reference painting method and the statistical error of the samples. Due to the inclusion of the offset 310 with the wet paint process bias removed, the wet paint process bias is no longer present.

[0086] FIG. 4 shows a schematic diagram of the steps of yet another embodiment of the method according to the invention.

[0087] The matching method starts with a zero-to-zero match against a target color 400, or a search in a formula database.

[0088] The term "matching from scratch" includes color matching methods that operate without information about existing sample paint coatings as a first solution. This method applies, for example, when a formula database is not available or when no suitable first solution can be found in the formula database. In practice, "matching from scratch" methods often start with a pre-selection step of ingredients that are expected to result in the target color. This pre-selection step is not mandatory. "Matching from scratch" methods / algorithms calculate one or more preliminary matching formulas for the target color as a first solution. This / these preliminary matching formula(s) can be sprayed and / or adjusted in a next step.

[0089] Compared to “color adjustment methods” (e.g. based on approximating model errors by analyzing “sample offsets”), where sample coatings are available as a first solution used to improve the color prediction accuracy of physical models, the accuracy of “matching from scratch” methods is generally lower.

[0090] The first solution 401 is typically not close enough to the target color 400. An adjustment to the first solution 401 is applied where an offset 410 between the predicted reflectance data 406 and the measured reflectance data 403 for the first solution 401 is taken into account.

[0091] That is, the adjusted formula is a function of the target color 400 and the offset 410 between the predicted reflectance data 406 and the measured reflectance data 403 for the first solution 401. If the measured reflectance data 403 of the first solution 401 contains bias caused by coating method variations, this error will be propagated to the next formula in the iterative color matching method. In the illustrated case, the sample coating methods are wet, respectively, while the reference coating methods are dry, respectively,.

[0092] It is therefore proposed to avoid such painting method bias by already taking into account the diversity of painting methods when considering the first iterative step, i.e. the first solution 401.

[0093] A painting method independent offset 410 is calculated based on a first solution 401. A formula 402 of the first solution 401 is known. The first solution 401 is provided as a paint coating using a wet painting method and its color is measured. A measured color 403 of the first solution 401 is provided. Furthermore, a physical model 140 is used to predict the color of the first solution 401 based on the known formula 402. Since the physical model 140 uses the database 320 and is thus related to the respective dry painting method as a reference painting method, the wet painting method is taken into account by combining the physical model 140 with the target application fit parameters 405. A predicted color 406 of the first solution 401 is now predicted by the assumption that the underlying formula 402 is applied / provided as a paint coating using a wet painting method. Thus, both the measured color 403 and the predicted color 406 refer to the same wet painting method. Thus, the offset 410, as the difference between the measured color 403 and the predicted color 406, is independent of the underlying wet painting method. This offset 410 can now be used in the iterative adjustment process, i.e., the offset 410 is used as a further input parameter to the paint adjustment algorithm.

[0094] Moreover, it is now desired to obtain a solution for a target paint formulation whose predicted color matches a target color 400 in a dry state different from the reference painting method (which is also a dry painting method). The spray procedures involved may also be different. There are a number of factors that may vary. Therefore, in addition to the target color 400 and the offset 410, a target application compatibility parameter 415 is provided as a further input parameter for the color adjustment algorithm to take into account the differences between the different dry painting methods, i.e. between the target painting method and the reference painting method. An optimized target paint coating 450 is provided via the interface 112 whose predicted color 451, when applied on a substrate with the target painting method, best matches the target color 400. The target paint formulation 450 is a combination of all the included colorants, colorants,1 , colorant 2 , colorant 3 , ... coloring agent n and their respective concentrations, c 1 , c 2 , c 3 , , c n This section stipulates:

[0095] 5 is a schematic block diagram showing the offset that must be cleared for application method bias by an embodiment of the method according to the invention. In real samples, the measured sample color 500 always differs (slightly) from the predicted sample color predicted using a physical model. The measured sample color 500 can be represented as a combination of a true color 501 and an offset 510. The offset 510, also called sample offset 510, corresponds to the difference between the measured sample color 500 and the predicted sample color. Reasons for this sample offset 510 between reality (measurement) and theory (physical model) can include, for example: - Model Bias514: No model is 100% accurate - Application Method Bias 515: i.e. how the sample was applied when the sample color was measured, e.g. wet or dry (or using a dry application method other than the respective dry reference coating method) - statistical errors 512 of the instrument 513: e.g. caused by temperature There is.

[0096] Up to now, the color adjustment algorithm, i.e., the paint color formula calculation algorithm, interprets the complete sample offset 510 as a model bias and modifies the adjusted paint formula in such a way that each sample offset 510 is compensated for. This means that the application method bias 515 is part of the sample offset 510. If the application method bias 515 is not constant, it acts as an instability factor. Depending on the magnitude of the sample offset 510, the color adjustment result may become significantly inaccurate due to error propagation.

[0097] The proposed method of the present invention helps to eliminate such application method biases 515 in the sample offset 510 (also referred to herein simply as offset 510). Improving the accuracy of the sample offset 510 directly improves the quality / accuracy of the adjusted paint formula. The sample offset 510 between the measured color 500 and the predicted color is analyzed with respect to the application method biases 515 potentially contained in the measured color 500. As mentioned before, the offset 510 includes a systematic bias 511 and a statistical bias 512, also referred to as a statistical error 512. The statistical bias 512 may be caused by the instrument 513 (limited accuracy) and is usually small compared to the systematic bias and can be ignored. The systematic bias 511 includes a model bias 514 (expected to be constant) and an application method bias 515 (may not be constant). The basic idea of ​​the proposed method is to decompose the sample offset 510 by the proposed application adaptation module into an application method bias 515 and a residual part including the model bias 514 and the statistical bias 512. Application method bias 515 is assumed to be removed from sample offset 510 because it is defined as not being constant. The remaining portion of sample offset 510 consists primarily of model bias 514, which is properly handled within the application tuning algorithm. [Explanation of symbols]

[0098] 101 Samples 102 combination 103 Measured sample colors 105 predicted sample colors 110 Computer Processors 111 Input Interface 112 Output Interface 120 Formulation Database 130 Physical model, color prediction model 140 Numerical Optimization Algorithms 201 Dry target color 202 Sample composition 205 Dry Target Application Compatible Parameters 206 Predicted Color for Dry Target Paint Method 210 Computer Processors 220 Wet Standard Painting Method Database 221 Sample 222 Compounding 223 Wet Measured Color 225 Wet Predicted Color 300 Dry target color 301 Sample 302 combination 303 Wet measurement sample color 305 Wet Sample Application Compatible Parameters 306 Wet Prediction Sample Color 310 Wet Sample Offset 320 Dry Reference Application Database 350 combination 351 Predicted color for dry reference painting method 400 target color 401 Sample 402 combination 403 Measurement sample color 405 Sample application compatibility parameters 406 Predicted sample colors 410 Offset 415 Target application compatibility parameters 450 combination 451 Predicted color of target application 500 measured sample colors 501 True Colors 510 Offset 511 Systemic Bias 512 Statistical Error, Statistical Bias 513 Equipment 514 Model Bias 515 Coating Method Bias

Claims

1. 1. A computer-implemented color matching method using a coating adjustment algorithm executed on at least one computer processor (110) and a database (120, 220, 320) containing specific optical data of individual color components, the specific optical data of the individual color components being determined based on known reference paint coatings having known reference color formulations and known measured reference colors, each of the reference paint coatings having been applied onto a substrate using a reference painting method, the application adjustment algorithm is extended by an application adaptation module configured to interact with a color prediction model of the application adjustment algorithm, to receive application adaptation parameters for a particular painting method as input parameters, and to convert a color predicted by the color prediction model for use in the reference painting method into a conversion color valid for use in the particular painting method using the received application adaptation parameters; The application suitability parameters (105, 205, 305, 405, 415) for said particular coating method are calculated using a numerical method and said color prediction model, wherein measured colors and color formulations of a plurality of sample coatings are provided as input parameters, and a predefined cost function is optimized starting from a set of predefined initial application suitability parameters, said predefined cost function comprising a color distance between the measured color (103, 223, 303, 403) and the predicted color of each of said sample coatings (101, 221, 301, 301, 401), and wherein the color prediction model (140) comprises as input parameters the respective color formulations (102, 222, 302, 402) of said sample coatings (101, 221, 301, 301, 401) and the respective color formulations (102, 222, 302, 402) of said sample coatings (101, 221, 301, 301, 401) and the respective color formulations (102, 222, 302, 402) of said sample coatings (101, 221, 301, 301, 401) of said respective ... configured to predict the color of each of said sample coatings (101, 221, 301, 301, 401) by using specific optical data of the individual color components used in the color formula (102, 222, 302, 402) of said sample coatings (101, 221, 301, 301, 401) and respective preliminary application suitability parameters obtained in the course of optimization, said application suitability parameters being calculated by comparing a recursively predicted color (103, 223, 303, 403) of said sample coatings (101, 221, 301, 301, 401) with the predicted color of said respective sample coatings (101, 221, 301, 301, 401) until said predetermined cost function falls below a predetermined threshold, The method, wherein the reference coating method and the specific coating method are different from each other and are selected from the group consisting of wet paint coating and dry paint coating, respectively.

2. 1. A method for determining a target color formulation (202, 450) of a target paint coating that matches a predetermined target color (201, 400) when applied onto a substrate using a predetermined target painting method different from the reference painting method as a specific painting method, the method comprising: receiving, via at least one interface, a predetermined target color (201, 400); - receiving, via said at least one interface, application adaptation parameters (205, 415) for said predetermined target coating method; - retrieving from said database (220, 320) specific optical data of the individual color components used in the target color formula (202, 450) of said target paint coating (201, 400); - using said predetermined target color (201, 400), the retrieved specific optical data of the individual color components and said received application adaptation parameters (205, 415) as input parameters of said application adjustment algorithm, calculating a color formula together with optimized concentrations of the individual color components of a target color formula (202, 450) of a target paint coating when said target paint coating is applied onto a substrate using said predetermined target painting method; The method of claim 1 , comprising:

3. moreover: receiving, via at least one interface (111), data of a color formula (222) of a sample paint coating (221) as a first solution of a target color (300) to be matched; - retrieving from said database (220) specific optical data of the individual color components used in the color formula (222) of said sample paint coating (221); - receiving, via said at least one interface (111), a measured color (223) of said sample paint coating (221) applied onto a substrate using said reference painting method; - predicting the color of said sample paint coating using said color prediction model implemented and executed on at least one computer processor (110); - calculating, with said at least one computer processor (110), the offset (210) of said paint coating as the difference between the measured color (223) and the predicted color (225) of said sample paint coating; - introducing said offset (210) into the calculation of said target color recipe (202); The method of claim 2 , comprising:

4. 1. A method for determining a target color formulation (350) of a target paint coating that matches a given target color (300) when applied to a substrate using said reference painting method, said method further comprising: receiving, via at least one interface (111), data of a color formula (302) of a sample paint coating (301) as a first solution of said target color (300) to be matched; - retrieving from said database (320) specific optical data of the individual color components used in the color formula (302) of said sample paint coating (301); - receiving, via said at least one interface (111), a measured color (303) of said sample paint coating (301) applied onto a substrate using a sample painting method; - receiving application adaptation parameters (305) of said sample coating method; - predicting, using the color prediction model (140) and the application adaptation module, the color (306) of the sample paint coating (301) valid for use in the sample painting method as a specific painting method, where data of the color formula (302) of the sample paint coating (301), the retrieved specific optical data of the individual color components used in the color formula (302) of the sample paint coating (301), and application adaptation parameters (305) of the sample painting method are used as input parameters; - calculating the offset (310) of said sample paint coating as the difference between the measured color (303) and the predicted color (306) of said sample paint coating (301); - introducing said offset (310) into the calculation of said target color recipe (350) using said application adjustment algorithm; The method of claim 1 , comprising:

5. 1. A method for determining a target color formulation (450) of a target paint coating that matches a given target color (400) when applied to a substrate using a target painting method as a specific painting method, the method further comprising: receiving, via at least one interface (111), data (402) of a color formulation of a sample paint coating (401) as a first solution of said target color (400) to be matched; - retrieving from said database (320) specific optical data of the individual color components used in the color formula (402) of said sample paint coating (401); - receiving, via said at least one interface (111), a measured color (403) of said sample paint coating (401) applied onto a substrate using a sample application method; receiving, via said at least one interface (111), application suitability parameters (405) of said sample paint coating; - predicting a color (406) of said sample paint coating (401) valid for use in said sample painting method as a specific painting method using said color prediction model (140) and said application match module, where data of the color formula (402) of said sample paint coating (401), retrieved specific optical data of the individual color components used in the color formula (402) of said sample paint coating (401) and application match parameters (405) of said sample paint coating are used as input parameters; - calculating the offset (410) of said sample paint coating as the difference between the measured color (403) and the predicted color (406) of said sample paint coating (401); - introducing said offset (410) into the calculation of said target color recipe (450) using said application adjustment algorithm; receiving, via said at least one interface (111), said application adaptation parameters (415) of said target coating method; - using said target color (400), said calculated offset (410) and said received application adaptation parameters (415) as input parameters of said application adjustment algorithm, calculating a color formula together with optimized concentrations of the individual color components of a target color formula (450) of said target paint coating when said target paint coating is applied onto a substrate using said target application method; The method of claim 1 , comprising:

6. at least: a database (120, 220, 320) comprising individual color components, such as pigments and / or pigment classes, and specific optical data associated with each individual color component, said specific optical data of said individual color components being determined on the basis of known reference paint coatings having a known reference color formulation and a known measured reference color, each of said reference paint coatings being applied onto a substrate using a reference painting method; at least one computer processor (110), communicatively connected to said database (220) and programmed to execute the method according to any one of claims 1 to 5; Including, the system.

7. 6. A non-transitory computer readable medium comprising a computer program having program code configured and programmed to perform the method of any one of claims 1 to 5, when the computer program is loaded and executed on at least one computer processor (110) communicatively coupled to a database (120, 220, 320) comprising individual color components, such as pigments and / or pigment classes, and specific optical data associated with each individual color component, the specific optical data of each individual color component being determined based on known reference paint coatings having known reference color formulas and known measured reference colors, each of the reference paint coatings being applied onto a substrate using a reference painting method.

Images