System and method for color matching

Abstract

This invention relates to a computer-implemented color matching method using a paint adjustment algorithm running on a processor (110) and a database (220), the database (220) including specific optical data of individual color components, the specific optical data of individual color components being determined based on a known reference paint coating having a known reference color formulation and a known measured reference color, the reference paint coating being applied to a substrate using a reference paint application process, wherein the paint adjustment algorithm is extended by an application adaptation module, the application adaptation module interacting with a color prediction model of the paint adjustment algorithm, and the application adaptation module being configured to receive application adaptation parameters for a specific paint application process as input parameters, and to use the received application adaptation parameters to convert the color predicted by the color prediction model for the reference paint application process into a converted color valid for the specific paint application process. The invention also provides a corresponding system.

Description

Technical Field

[0001] The present invention relates to a computer-implemented color matching method and suitable system for matching a liquid color with a dry color standard. 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, such as the interaction with colorants in a coating layer. Each coating layer has specific light reflection characteristics due to the presence of colorants. Each of these colorants has specific optical properties, represented by corresponding specific optical constants / specific optical data. The physical model can predict the light reflection characteristics (color) of a coating layer / coating layer based on information about the included colorants (individually based on information about the respective formulation) along with the corresponding specific optical properties (individually along with the corresponding specific optical constants).

[0003] The specific optical constants of a colorant describe, for example, the absorption and scattering characteristics (or effect flake orientation) of the colorant in the context of a corresponding physical model, similar to the K / S value in, for example, the well-known "Kubelka / Munk" model. However, the reflective properties of a coating depend not only on the formulation. They also strongly depend on the coating application process, typically how the coating is applied to its substrate. In the context of this invention, a wet coating, specifically a liquid coating, represents a particular coating application process. In this document, the terms "coating application process" or "application" and "state" are used synonymously.

[0004] The specific optical properties of the colorant are determined based on sample data with known formulations and known reflectance data, all of which are applied to the substrate through a common reference coating application process, and all measured in a common reference coating state (liquid or dry). The color prediction and color matching process of the physical model are always related to this reference coating application process.

[0005] Color predictions from physical models used for different target coating application processes are limited by significant systematic errors and are not very accurate. In particular, matching different target coating application processes from scratch can be significantly inaccurate.

[0006] Numerical optimization algorithms can be used to predict the appropriate formulation for a given target color, based on a physical model with existing optical constants of available colorants and reflectance data of the target color as input.

[0007] If the target coating application process is the same as the reference coating application process, the resulting formulation should match the target color as well as possible.

[0008] Color formulations can also be calculated based on existing samples with known formulations, such as in coloring steps that approximate the target color. In this case, we discuss the color adjustment process. Existing samples should be applied and measured during a reference coating application process because the color adjustment algorithm is based on a fundamental assumption: "For formulations close to the sample formulation, model bias is (almost) constant." As long as the sample coating application process is equal to the reference coating application process, and the adjusted formulation for color adjustment is similar to the sample formulation, the model bias is expected to be "similar": the adjustment algorithm interprets the sample offset (the offset between the measured reflectance data and the predicted reflectance data of the sample, respectively) as model bias. This model bias will be automatically considered / compensated within the adjustment algorithm and will result in a modification of the adjusted formulation (example: patent EP2149038B1). If the sample coating application process differs from the reference coating application process, the sample's model bias may be irrelevant to the adjusted formulation. In this case, the sample will include additional non-constant bias caused by the different coating application process. This application process bias will propagate into the adjusted formulation. Depending on the magnitude of the sample's application process bias, the color adjustment results may be significantly inaccurate. In particular, if the reflectivity data from the application of liquid coatings is mixed with the data from the application of dry coatings, the deviation in the application process will be extremely high.

[0009] Currently, tolerances are defined for the target color of dry paint. However, in most cases, paint is a liquid product. The optical properties of dry paint are not the same as those of liquid paint. The color of paint strongly depends on its state: the reasons for the significant color difference between wet and dry paint layers are, for example:

[0010] Differences between instruments / measuring devices used for measuring liquid coatings and dry coatings

[0011] • The effect of measuring elements (cells) (e.g., glass cuvettes or panes) used for liquid paint compared to a clear coating on top of dry paint.

[0012] The difference in refractive index between wet coatings (n≈1.35) and dry coatings (n≈1.5) leads to different optical properties of the pigments embedded in the coatings.

[0013] Different orientations of effect flakes in coatings

[0014] • The “milky white” color in wet paint is transparent, while it is transparent / invisible in dry paint layers.

[0015] Color matching is an iterative process. In practice, a physical model with optical constants for reference coating application processes (dry coating state) is used in the matching process. The matching process begins with either matching from scratch or searching a formula database for a given target color in its dry state.

[0016] The term "de novo matching" encompasses a color matching method that operates in situations where there is no information about existing sample coatings as a first solution. This method is applied, for example, if no formula database is available, or if not enough first solutions are found in the database. In practice, "de novo matching" methods typically begin with a pre-selection step of components expected to be present in the target color. This pre-selection step is not mandatory. The "de novo matching" method / algorithm calculates one or more preliminary matching formulas for the target color as first solutions. These preliminary matching formulas can be sprayed and / or adjusted in the following steps.

[0017] Compared to the “color adjustment method”, sample coatings are available as the first solution to improve the color prediction accuracy of the physical model (e.g., based on approximating model error by analyzing “sample offsets”), while the “matching from scratch” method is generally less accurate.

[0018] The result is the first solution, i.e., the first paint formulation, which is prepared / mixed as a wet paint in a color laboratory. The wet paint is applied / sprayed onto the substrate using a reference paint application process and then dried. Optionally, an additional clear coat is applied on top of the first paint layer / coating. Finally, the reflectance of the dried paint layer / coating is measured. Typically, the color of the first solution, i.e., the first formulation, is not close enough to the target color. In this case, an adjustment process for the first solution begins, where the sample offset between the predicted reflectance used for the first solution and the measured reflectance is taken into account. The sample offset is considered a systematic and constant offset. If the sample offset is not constant in the adjustment steps, for example, if it varies within the paint application process, then the color adjustment result will be significantly inaccurate. The adjusted formulation is a function of the target color and the sample offset. Non-constant sample offsets will propagate into the adjusted formulation as application process deviations.

[0019] The term "non-constant sample offset" refers to an offset of the adjusted formulation (after coating application) that is significantly different from the offset of the sample.

[0020] The spraying and drying process (“coating application process”) is a time-consuming and expensive process. An effective alternative is a color matching and adjustment process based on color measurements of all samples / coloring steps in the liquid state (rather than the dry state) and optical constants for liquid coatings (rather than dry coatings). This method saves on the spraying and drying processes in the color matching and adjustment process. Furthermore, the reflectance data used for the samples will not be affected by deviations caused by variations in the dry coating application process. However, in practice, the target color is always given in the dry state. Comparing the sample color in the liquid state with the color standard in the dry state will result in significant deviations and is unacceptable.

[0021] Therefore, the object of the present invention is to provide a possibility for compensating for application process deviations in a color adjustment method when using both liquid and dry colors. Summary of the Invention

[0022] The objectives mentioned above are achieved by methods and systems having the features of the respective independent claims. Further embodiments are presented in the following description and corresponding dependent claims.

[0023] This disclosure relates to a computer-implemented color matching method using a paint adjustment algorithm running on at least one computer processor, and a database including specific optical data of individual color components, the specific optical data of individual color components being determined based on a known reference paint coating having a known reference color formulation and a known measured reference color, the reference paint coating being applied to a substrate using a reference paint application process, wherein the paint adjustment algorithm is extended by an application adaptation module, the application adaptation module interacting with a color prediction model of the paint adjustment algorithm, and the application adaptation module being configured to receive application adaptation parameters for a specific paint application process 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 paint application process into a converted color valid for the specific paint application process.

[0024] This means that the application adaptation module is configured to convert colors predicted by the color prediction model and appearing when using a reference coating application process into converted colors that appear when using a specific coating application process. Colors predicted for use with the reference coating application process are converted into colors effective for a specific coating application process that differs from the reference coating application process.

[0025] The terms “specific optical data of an individual color component,” “specific optical data of an individual color component,” or “specific optical data of a colorant” are used synonymously herein and include specific optical properties and specific optical constants of the corresponding individual color component (i.e., the colorant). The individual color components used in the color formulation of the corresponding coating are selected from the group consisting of at least the following: colored pigments, i.e., so-called solid pigments, effect pigments, binders, solvents, and additives, such as matting paste.

[0026] The terms “color,” “color data,” “reflectance,” “reflectance data,” and “reflectance characteristics” are used synonymously herein. The terms “coating application process,” “application process,” “application,” and “state” are used synonymously. This means that wet and liquid coatings respectively represent and define a specific coating application process, and dry coatings represent another specific coating application process.

[0027] The terms “coating formulation,” “color formulation,” and “formulation” are used synonymously herein. The terms “coating adjustment algorithm,” “color adjustment algorithm,” and “adjustment algorithm” are used synonymously herein. The terms “coating application process” and “application process” are used synonymously herein. The terms “computer processor” and “processor” are used synonymously herein.

[0028] Known methods for calculating color formulas based on radiative transfer models can be found in the literature, for example, in Georg A. Klein's "Farbenphysik frbeindustrielle Anwendungen (Color Physics for Industrial Applications)".

[0029] The basic idea behind color formulation calculations is to characterize specific optical data—specifically, the optical properties and / or optical constants of all relevant individual color components (e.g., all pigments / colorants)—based on previously calibrated coatings, i.e., on the corresponding measurements of such calibrated coatings. These calibrated coatings correspond to existing letdowns with known formulations and known reflectance data, all applied through a common reference coating application process. Color prediction using a physical model (also referred to herein as a color prediction model) and the color matching process are always related to this reference coating application process.

[0030] An additional application adaptation module extends the physical model used to predict the reflectivity properties of coatings (related to a reference coating application process): the module interacts with the physical model and should adapt predicted reflectivity data from the reference coating application process (e.g., liquid state) to a specific target coating application process (e.g., dry state). Combined with the physical model, it predicts reflectivity data for various coating application processes based on only a set of optical constants.

[0031] Additional modules can be configured by inputting specific target application adaptation parameters. These application adaptation parameters describe the differences (or more precisely, specific transfer functions) between the target coating application process and the corresponding reference coating application process (e.g., the difference between liquid and dried coating states). Examples of application adaptation parameters are:

[0032] • Effect of film orientation adaptation: better / worse film orientation

[0033] (Applies to effect colors; adjusts the brightness / color flipping behavior of the paint layer)

[0034] • Effectiveness of solid colorants: more effective / less effective

[0035] (Adjusting for differences in color intensity of solid colorants, which may be caused, for example, by shear effects or by aggregates)

[0036] ●Effect colorant effectiveness: More effective / More ineffective

[0037] (Adjusting for differences in the reflectivity of effect colorants, which may be caused by overspraying loss, settling, or separation of the effect.)

[0038] • Adaptation for light loss of wet coatings in colorimetric cups or measuring elements

[0039] • Conversion factor for refractive index between wet and dry states

[0040] • Compensation for the “milky white” component, similar to, for example, mixed transparency in mixed wet paint (which disappears / becomes transparent in dry paint).

[0041] If, for example, a formulation for an effect color includes aluminum flakes, and the specific optical constants for that formulation in the liquid state include the scattering coefficient S for the effect pigment, then the overspray loss for the dry state can be compensated within the application adapter module using a simple linear scaling function of the scattering coefficient, S_dry = c * S_wet, where c < 1. If c is 0.95, then it implies a 5% overspray loss for the effect flakes. If, for example, a formulation includes a mixed transparent component that has a “milky” appearance in the wet state (S_wet > 0, K_wet >= 0) but is transparent / invisible in the dry state, then this “vanishing effect” from wet to dry can be modeled within the application adapter module by setting the specific scattering / absorption coefficient (S_dry / K_wet) of that mixed transparent component to S_dry = 0, K_dry = 0. This adapter function implies that the optical properties of the mixed transparent component can be ignored within the color prediction for the dry state (for the wet state).

[0042] Application adaptation parameters can also be implicitly determined based on the analysis of one or more existing samples (e.g., one or more existing coloring steps in a color matching process), which are applied using common, specific paint application processes. A list of existing samples from a database (which relate to specific paint application processes, i.e., specific wet or dry paint applications) can be used to determine the appropriate application adaptation parameters.

[0043] According to one embodiment of the proposed method, numerical methods and color prediction models are used to calculate application adaptation parameters for a specific coating application process. This involves providing measured colors and formulations of multiple sample coatings as input parameters, and optimizing a given cost function starting from a given set of initial application adaptation parameters. The given cost function is chosen as the color distance between the measured color and the predicted color of the sample coating. A physical model is configured to predict the color of the sample coating using specific optical data of the corresponding color formulation of the sample coating and the individual color components used in the color formulation of the sample coating, along with the corresponding initial application adaptation parameters leading to the optimization process, as input parameters. The application adaptation parameters are calculated by comparing the recursive predicted color of the sample coating with the corresponding measured color of the sample coating until the given cost function falls below a given threshold. The initial application adaptation parameters are neutral parameters. This means that using the initial application adaptation parameters produces a color prediction equivalent to that using a reference coating application process. The given threshold can also be determined dynamically, for example, to indicate a specific state where further minimization is not possible.

[0044] As mentioned above, the reference coating application process and the specific coating application process are different from each other. According to a further embodiment, the reference coating application process and the specific coating application process are each selected from the group consisting of: coatings applied in a wet state, coatings applied in a dry state, and coatings simply applied in a wet state.

[0045] According to a further aspect, the proposed method is used to determine a target color formulation for a target coating coating that matches a given target color when applied (on a substrate) using a given target coating application process different from a reference coating application process. The method further includes:

[0046] - Receive the given target color via at least one interface.

[0047] - Receive application adaptation parameters for a given target coating application process via at least one interface.

[0048] - Retrieve specific optical data from the database for the individual color components used in the target color formulation of the target coating.

[0049] - Using a given target color, specific optical data of the retrieved individual color components, and received application adaptation parameters as input parameters for the coating adjustment algorithm, calculate a color formula with optimized concentrations of individual color components, which serves as the target color formula for the target coating when applying the target coating using a given target coating application process (on a substrate).

[0050] The term "coating" in this document can refer to a layer of paint applied to a substrate, as well as the corresponding paint in a wet state, depending on the paint application process used. The terms "coating" and "color coating" are used synonymously in this document.

[0051] A color formulation specifies the individual color components, i.e., colorants, and their corresponding concentrations used in the corresponding paint coating. The terms "color formulation" and "paint formulation" are used synonymously herein.

[0052] This means that the proposed method, namely the proposed color adjustment algorithm, can match the color for a specific target coating application process, which may differ from the reference coating application process.

[0053] Typically, the "true" measured reflectance data (color) of a sample is always (slightly) different from the predicted reflectance data (color) of the physical model ("sample offset"). The reasons for this sample offset between reality and theory are, for example:

[0054] • Model bias: No model is 100% accurate.

[0055] • Statistical error of the instrument: for example, caused by temperature.

[0056] Therefore, sample offset is required. For this purpose, the proposed method further includes the following steps:

[0057] - Receive data on the color formula of a sample paint coating via at least one interface as the first solution for the target color to be matched.

[0058] - Retrieve specific optical data of individual color components used in the color formulation of the sample coating from the database.

[0059] - Receive, via at least one interface, a measured color of a sample coating applied to a substrate using a reference coating application process.

[0060] - Use a color prediction model implemented and running on at least one computer processor to predict the color of the sample paint coating.

[0061] - Calculate the offset of the sample paint coating using at least one computer processor, as the difference between the measured color and the predicted color of the sample paint coating.

[0062] - Consider offset to correct a given target color, that is, incorporate the offset into the calculation of the target color formula.

[0063] The color adjustment algorithm interprets the complete sample offset as a model bias and modifies the adjustment formula in a way that compensates for the corresponding sample offset. Here, the applied process bias is part of the sample offset. If the applied process bias is not constant from one adjustment step to another, it will act as an element of instability. Depending on the proportion of the non-constant sample offset, the color adjustment results may be significantly inaccurate due to error propagation.

[0064] This means there may be application process bias: for example, if reflectance data from a wet coating application process are mixed with data from a dry coating application process.

[0065] In addition to adapting to a given target coating application process (e.g., a specific dry coating application process), this invention also describes how to eliminate application process bias in color adjustment due to sample offset (e.g., wet measurement) when the sample coating application process differs from the reference coating application process. Improved determination of sample offset directly improves the quality / accuracy of the adjusted formulation.

[0066] The basic idea is that the sample offset consists of application process bias and residual error. Residual error includes, for example, model bias. Application process bias is considered to be removed from the sample offset because it is expected to be a non-constant part of the sample offset. The residual error of the sample offset will primarily consist of model bias, which will be properly handled within the adjustment algorithm. This method is based on the assumption that the model bias used for the sample coating application process is similar to the model bias used for the target coating application process.

[0067] The improved adjustment algorithm uses an application adaptation module in conjunction with application adaptation parameters to predict an application-specific reflectance value (predicted color) for a given sample coating application process (i.e., the first solution in the iterative color adjustment process, specifically, the iterative color matching process). The predicted application-specific reflectance value (predicted color) is used to calculate the corrected sample offset in the absence of application process bias.

[0068] As previously mentioned, specific application adaptation parameters for a sample (e.g., existing coloring steps for a color matching process) can be implicitly determined or defined (e.g., through user input) based on analysis of its formulation and reflectance data.

[0069] The terms “reflectance data” and “color” are used synonymously in this document.

[0070] The proposed method results in faster convergence of the color adjustment process, and makes it more robust and reliable.

[0071] Therefore, according to another embodiment of the proposed method, the method is used to determine a target color formulation for a target coating that matches a given target color when applied (on a substrate) using a reference coating application process, and the method further includes:

[0072] - Receive data on the color formula of a sample paint coating via at least one interface as the first solution for the target color to be matched.

[0073] - Retrieve specific optical data from the database for individual color components used in the color formulation of the sample coating or that should be additionally used in the color formulation of the target coating.

[0074] - Receive, via at least one interface, the measured color of a sample coating applied (on a substrate) using a sample coating application process different from that of a reference coating application process.

[0075] - Receive application adaptation parameters for the sample coating application process.

[0076] - A color prediction model and application adaptation module are used to predict the color of a sample coating that is effective for a specific coating application process. The input parameters include data on the color formulation of the sample coating, specific optical data of individual color components used in the color formulation of the sample coating, and application adaptation parameters for the sample coating application process.

[0077] - The offset of the sample paint coating is calculated as the difference between the measured color and the predicted color of the sample paint coating, and

[0078] - Considering the offset, a paint adjustment algorithm is used to correct the given target color, that is, the offset is incorporated into the calculation of the target color formula.

[0079] The phrase "applied coating by means of a coating application process" means that, in cases where the coating application process results in a dry coating, the coating is applied to the substrate using the appropriate coating application process, corresponding to the dry state. Furthermore, if the coating is still wet, the coating application process only provides a wet coating for further use. The phrase "applied to the substrate" is only valid if the corresponding coating application process includes both spraying and drying processes. Therefore, the phrase "on the substrate" is written in parentheses.

[0080] Furthermore, the method can be used to determine a target color formulation for a target coating that matches a given target color when applied (on a substrate) using a target coating application process. The method also includes:

[0081] - Receive data on the color formula of a sample paint coating via at least one interface as the first solution for the target color to be matched.

[0082] - Retrieve specific optical data from the database for individual color components used in the color formulation of the sample coating or that should be additionally used in the color formulation of the target coating.

[0083] - Receive, via at least one interface, the measured color of a sample coating applied (on a substrate) using a sample coating application process.

[0084] - Receive application adaptation parameters for sample coating via at least one interface.

[0085] - A color prediction model and application adaptation module are used to predict the color of a sample coating that is effective for a sample coating application process as a specific coating application process. The input parameters include data on the color formulation of the sample coating, specific optical data of individual color components used in the color formulation of the sample coating, and application adaptation parameters for the sample coating application process.

[0086] - The offset of the sample paint coating is calculated as the difference between the measured color and the predicted color of the sample paint coating, and

[0087] - Taking offset into account, a paint adjustment algorithm is used to correct a given target color, incorporating the offset into the calculation of the target color formula.

[0088] - Receive application adaptation parameters for the target coating application process via at least one interface.

[0089] - Using the target color, the calculated offset, and the received application adaptation parameters for the target coating application process as input parameters for the coating adjustment algorithm, calculate the color formula with the optimized concentration of individual color components, which serves as the target color formula for the target coating when applying the target coating using the target coating application process (on the substrate).

[0090] The present invention also relates to a system comprising at least:

[0091] - A database comprising individual color components, such as pigments and / or pigment classes, and specific optical data associated with the corresponding individual color components. The specific optical data for each individual color component is determined based on a known reference coating with a known reference color formulation and a known measured reference color. The reference coating is applied (on a substrate) using a reference coating application process.

[0092] - At least one computer processor, which is connected in communication with the database and is programmed to perform the methods proposed herein.

[0093] The system may also include an input device configured to receive data input via a suitable interface such as USB. Such an input device may be a computer keyboard, microphone, video camera, data carrier, or any combination thereof. The system may also include an output device configured to output, and specifically display, a corresponding result calculated by performing one of the methods described above. The output device is one of at least the group consisting of: acoustic devices, haptic devices, display devices, and any combination thereof. The output device is communicatively connected to at least one computer processor via a suitable interface.

[0094] Furthermore, the present invention relates to a non-transitory computer-readable medium having a computer program having configured and programmed program code that, when loaded and executed by at least one computer processor, is communicatively connected to a database including individual color components such as pigments and / or pigment classes, and specific optical data associated with the corresponding individual color components, the specific optical data for determining individual color components based on a known reference coating having a known reference color formulation and a known measured reference color, the reference coating being applied (on a substrate) using a reference coating application process to perform the method as presented herein.

[0095] Each of the communication connections between the different components can be either a direct or indirect connection. Each communication connection can be wired or wireless. Suitable communication technologies can be used. The database and at least one computer processor can each include one or more communication interfaces for communicating with each other. Such communication can be performed using wired data transmission protocols such as Fiber Distributed Data Interface (FDDI), Digital Subscriber Line (DSL), Ethernet, Asynchronous Transfer Mode (ATM), or any other wired transmission protocol. Alternatively, communication can be performed wirelessly via 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. The corresponding communication can be a combination of wireless and wired communication.

[0096] 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, for example, semiconductor memory devices such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices; disks such as internal hard disks or removable disks; magneto-optical disks; optical disks, CD-ROMs, DVD+Rs, DVD-Rs, DVD-RAMs, and DVD-ROMs, or combinations thereof. Such memory devices can store a variety of objects or data, including caches, classes, applications, backup data, jobs, web pages, web page templates, database tables, repositories storing dynamic information, and any other suitable information including any parameters, variables, algorithms, instructions, rules, constraints, and / or references to them. Furthermore, memory may include any other suitable data, such as policies, logs, security or access data, report files, and others. Computer processors and memory devices may be supplemented by or incorporated into dedicated logic circuitry.

[0097] Computer program instructions can be computer programs, software applications, modules, software modules, scripts, or code, and can be written in any programming language, including compiled or interpreted languages, or declarative or procedural languages. Instructions can be deployed in any form, including as standalone computer programs or as modules, components, subroutines, or other units suitable for use in a computing environment. In one embodiment, the computer-executable instructions (i.e., program code) of this disclosure are written in HTML, TypeScript (TS), and CSS (Cascading Style Sheets).

[0098] A computer program may, but does not need to, correspond to a file in a corresponding file system. A computer program may be stored as a part of a file that holds other computer programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the computer program in discussion, or in multiple collaborative files (e.g., a file storing portions of one or more modules, subroutines, or code). A computer program may be deployed to execute on a single computer, or on multiple computers located at one location or distributed across multiple locations and interconnected via a communication network. Parts of a computer program may be designed as separate modules implementing various features and functions through various objects, methods, or other processes. Alternatively, a computer program may, as appropriate, include multiple submodules, third-party services, components, libraries, etc. Conversely, the features and functions of various components may be appropriately combined into a single component.

[0099] Systems suitable for performing the methods of this disclosure can be based on general-purpose or special-purpose microprocessors, both, or any other type of CPU. Typically, the CPU receives instructions and data from read-only memory (ROM) or random access memory (RAM), or both. The basic elements of the system are the CPU for executing or performing instructions (i.e., program code) and one or more memory devices (such as a database) for storing instructions (i.e., program code) and data. Typically, the system includes at least one memory device, or is operatively coupled to at least one memory device, and is configured to receive data from or transfer data to at least one memory device for storing data, or both. Such memory devices include, for example, magnetic disks, magneto-optical disks, or optical disks. However, the system does not need to have such a device. Furthermore, the system can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), or a portable storage device, such as a Universal Serial Bus (USB) flash drive, etc.

[0100] Color matching is an iterative process. Starting from scratch and continuing with several adjustment steps until a suitable formulation is found, color matching is time-consuming and expensive. Currently, tolerances are defined for the target color in dry paint. However, paint is typically sold as a liquid product. The optical properties of a dry paint are not the same as those in its liquid (wet) state. This is why each coloring step in today's color matching process must be sprayed to verify that its dry color matches the tolerances for the dry paint. The method described herein allows the adjustment process to be performed entirely in the liquid state, thus removing all paint application used for all coloring steps. This invention describes a method that allows for an adjustment process based on a wet sample coating (i.e., based on a sample coating in a wet state), without further processing through a drying process, and possibly even without a spraying process.

[0101] A primary objective of this invention is to enable the use of measured reflectance data from wet coloring steps / samples in the color matching process for dry color standards (i.e., target colors in a dry state).

[0102] There are two potential use cases for adapting new applications:

[0103] 1. Color matching process based on dry color standards for optical constants used in wet coatings:

[0104] All sample / staining steps were measured under wet conditions.

[0105] Predict reflectance data for all samples / coloring steps without an application adapter for wet conditions.

[0106] The algorithm incorporates the corresponding sample offset for the wet coating state.

[0107] By using appropriate application adaptation parameters, the predicted reflectance data of the formulation to be adjusted is converted from wet state to dry reference coating application process, that is, converted to reference coating application process including spraying and drying processes.

[0108] 2. Color matching process based on dry color standards using optical constants for dry reference applications:

[0109] All sample / staining steps were measured under wet conditions.

[0110] The predicted reflectance data for all sample / coloring steps is converted from a dry reference application process to a wet state by using appropriate application adaptation parameters.

[0111] The coating adjustment algorithm considers the corresponding sample offset for the wet coating state.

[0112] In the absence of application adaptation for dry reference application processes, predict reflectance data for formula adjustment.

[0113] The terms “wet paint,” “wet color,” “paint in a wet state,” and “color in a wet state” are used synonymously in this document. The terms “dry color,” “dry paint,” “paint in a dry state,” and “color in a dry state” are used synonymously in this document.

[0114] The following description is presented and provided in the context of one or more specific embodiments. Various modifications to the disclosed embodiments will be apparent to those skilled in the art, and the general principles defined herein can be applied to other embodiments and applications without departing from the scope of this disclosure.

[0115] The embodiments of the subject matter and functional operation described in this disclosure can be implemented in digital electronic circuits, in tangibly embodied computer software, or in computer hardware, including the structures disclosed in this disclosure and their structural equivalents, or combinations thereof. Embodiments 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 a data processing apparatus or for controlling the operation of a data processing apparatus. Alternatively or additionally, the program instructions can be encoded on artificially generated propagation signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information for transmission to a suitable receiving device for execution by at least one processor.

[0116] Details of one or more embodiments 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. Attached Figure Description

[0117] Figure 1 This schematically illustrates how to determine application adaptation parameters as provided in embodiments of the method according to the invention.

[0118] Figure 2 The process of another embodiment of the method according to the invention is illustrated schematically.

[0119] Figure 3 The process of another embodiment of the method according to the invention is illustrated schematically.

[0120] Figure 4 The process of yet another embodiment of the method according to the invention is illustrated schematically;

[0121] Figure 5 A schematic block diagram illustrating the sample offset used in the color matching process is shown. Detailed Implementation

[0122] The same unit or component is provided with the same reference numerals across all figures.

[0123] Figure 1 An embodiment of the system according to the present invention is shown. The system includes a computer processor 110 and a database 120. The database 120 includes individual color components, colorant 1, colorant 2, colorant 3, ..., colorant n Such as pigments and / or pigment classes, and specific optical data associated with the corresponding individual color components, constant 1, constant 2, constant 3, ..., constant n Specific optical data for individual color components are determined based on a known reference coating with a known reference color formulation and a known measured reference color, the reference coating being applied (to the substrate) using a reference coating application process. A computer processor 110 is communicatively connected to a database 120 and is programmed to perform embodiments of the method according to the invention described herein. In addition to manual input, application adaptation parameters provided or to be provided in the proposed method can also be implicitly determined as follows:

[0124] The application adaptation parameters can be calculated based on the data from the existing coloring steps, namely the data from the existing sample paint coating 101.

[0125] These sample paint coatings 101 are applied using a specific sample paint application process (on a substrate). The corresponding color 103 of the sample paint coating 101 is measured. Data for the corresponding color formulation 102 of the corresponding sample paint coating 101 is provided, wherein the corresponding color formulation 102 specifies all included colorants, colorant 1, colorant 2, colorant 3, ... colorants. n and their corresponding concentrations c1, c2, c3, ..., c n In cases where a specific coating application process includes both spraying and drying, the corresponding sample coating is applied separately to the substrate. Alternatively, using a specific coating application process to apply the sample coating means that the specific coating application process only involves providing the corresponding sample coating separately in a wet state, such as in a cuvette or custom glass element. In the latter case, the sample coating can be sprayed onto the substrate but not yet dried.

[0126] Data on the color formula 102 of the sample paint coating 101 is received via at least one interface 111 of the computer processor 110. Additionally, measured color 103 of the sample paint coating 101 is received via at least one interface 111 of the computer processor 110.

[0127] Numerical method 130 and physical model 140 are provided and implemented on computer processor 110. Numerical method 130 is configured to optimize the application adaptation parameters by minimizing a given cost function starting from a given set of initial application adaptation parameters. The initial application adaptation parameters are neutral parameters. This means that using the initial application adaptation parameters produces a color prediction equal to the color prediction using a reference coating application process. The given cost function is chosen as the color distance between the measured color 103 of the corresponding one of the existing sample coatings 101 and the predicted color of the corresponding sample coating. The physical model is configured to predict the color of the corresponding sample coating by using the color formulation 102 of the corresponding sample coating, specific optical data of the individual color components used in the color formulation 102 of the corresponding sample coating, and the corresponding initial application adaptation parameters leading to the optimization process as input parameters. Specific optical data is retrieved from database 120. The specific optical properties of the colorant are determined based on existing drop / sample data with known formulations and known reflectance data, all of which are applied (on the substrate) by a common reference coating application process that differs from or may differ from the specific coating application process. Therefore, the color prediction of the physical model is related to the application method of the reference coating.

[0128] Using a computer processor 110 and employing a numerical method 130 and a physical model 140 implemented and running on the computer processor 110, the application adaptation parameter 105 is calculated by comparing the recursively predicted color of the corresponding sample paint coating with the measured color 103 of the corresponding sample paint coating until a given cost function falls below a given threshold. The given threshold can also be determined dynamically, for example, to indicate a specific state where further minimization is not possible.

[0129] The optimized application adaptation parameters 105 are then output via interface 112 to an output device such as a display.

[0130] The calculated optimized application adaptation parameters 105 are made available and output via another interface 112. These calculated optimized application adaptation parameters 105 are characteristics for a specific coating application process. The method can be performed for multiple different given specific coating application processes, such as wet and dry, depending on which coating application process the reference coating application process corresponds to. This means that, if the reference coating application process corresponds to a dry state, application adaptation parameters for the wet state are necessary if the sample coating and / or target coating in the wet state involves a corresponding color adjustment process. Furthermore, if the reference coating application process corresponds to a wet state, application adaptation parameters for the dry state are necessary if the sample coating and / or target coating in the dry state involves a color adjustment process. The corresponding application adaptation parameters calculated for the corresponding one of different given specific coating application processes can then be retrievably stored in a repository and assigned to the corresponding one of different given specific coating application processes as process-specific application adaptation parameters. These process-specific application adaptation parameters can then be retrieved from the repository at any time upon request.

[0131] Figure 2 The process of another embodiment of the method according to the invention is illustrated schematically.

[0132] Typically, the matching process begins with matching from scratch or searching for a given target color 201 in a formula database. Target color 201 is in a dry state here, meaning the corresponding target coating appearing as target color 201 is in a dry state, i.e., the corresponding target coating is applied (sprayed) onto the substrate and dried. Therefore, the corresponding paint application process consists of a combination of spraying and drying processes, which is also referred to herein as a dry paint application process. As mentioned above, the terms "paint application process" and "state" are used synonymously herein. Thus, it is stated that a color in a dry state can also be described as a color or paint coating using a dry paint application process. Similarly, a color in a wet state can also be described as a color or paint coating using a wet paint application process that does not include a drying process and may even not include a spraying process.

[0133] The first solution is usually not close enough to the target color 201. Therefore, physical model 140, also referred to herein as the color prediction model, is used in combination with numerical optimization algorithm 130 to obtain an optimized formulation 202 through iteration. The target paint formulation 202 specifies all included colorants, colorant 1, colorant 2, colorant 3, ... colorants. n and their corresponding concentrations c1, c2, c3, ..., c n Physical model 140 uses database 220 as the basis for color prediction. Database 220 includes individual color components, colorant 1, colorant 2, colorant 3, ..., colorant... n Such as pigments and / or pigment classes, and specific optical data associated with the corresponding individual color components, constant 1, constant 2, constant 3, ..., constant n The specific optical properties of the colorant are determined based on existing drop / sample data with known formulations and known reflectance data, all of which are applied through a common reference coating application process, which here corresponds to a wet coating application process, i.e., a wet state. However, when searching for a coating formulation 202 whose color matches the target color 201, where the coating is applied to the substrate using a dry coating application process, and therefore, in addition to the wet coating application process as a reference coating application process, by providing, as Figure 1 The example describes the calculation of the corresponding dry application adaptation parameter 205 for a dry coating application process as a specific coating application process, taking into account the characteristics of the dry coating application process compared with the wet coating application process.

[0134] The target color 201 and target application adaptation parameters 205 are received by the computer processor 110 via interface 111. The physical model 140 and numerical optimization algorithm 130 are implemented and run on the computer processor 110. To determine the formulation 202 for the paint coating, when applied to the substrate using the target paint application process (i.e., the corresponding dry paint application process), the color of the paint coating is matched with the target color 201. The optimized formulation 202 is determined iteratively using the target color 201, the target application adaptation parameters 205, and specific optical constants of available colorants from the database 220. When applied using the target paint application process (i.e., the corresponding dry paint application process), the formulation 202 and its predicted color 206 can be output via interface 112 on the output device. The predicted color 206 consists of the true color of the optimized formulation 202 when applied to the substrate using the dry paint application process and the systematic bias corresponding to the model bias 210 (see [link to detailed explanation]). Figure 5 Since the target application adaptation parameter 205 is included in the calculation, there is no deviation in the coating application process.

[0135] To determine the model bias, i.e., offset 210, the method further includes the following steps:

[0136] - Receive data on the color formula 222 of the sample paint coating 221 via interface 111 as the first solution for the target color 201 to be matched.

[0137] - Retrieve specific optical data of the individual color components used in the color formulation 222 of the sample coating 221 from database 220.

[0138] -Receive the measured color 223 of the sample paint coating 221 applied using the reference paint application process (i.e., the wet paint application process) via interface 111, that is, provide the measured color 223 of the sample paint coating 221 in a wet state.

[0139] - The color prediction model 140, implemented and running on processor 110, is used to predict the color 225 of the sample paint coating 221, which is valid for the reference paint application process, i.e., in its wet state.

[0140] - The processor 110 calculates the offset 210 as the difference between the measured color 223 and the predicted color 225 of the sample paint coating 221.

[0141] Considering offset 210 to correct a given target color 201, offset 210 is provided as a further input parameter via interface 111 for the color adjustment algorithm, which is a combination of numerical optimization algorithm 130 and color prediction model 140. Typically, the physical model, i.e., color prediction model 140, needs to predict the color, i.e., determine the theoretical color of the initial paint formulation during the iterative process of the color adjustment algorithm. All available pigments and their corresponding specific optical data are stored in database 120. Primarily, the first solution 221 and the finally determined target color formulation 202 include the same pigments, but sometimes additional pigments required for the target color formulation 202 may exist. Figure 3 The process of yet another embodiment of the method according to the invention is illustrated schematically.

[0142] The color adjustment process for a given target color 300 begins with sample 301, such as an existing coloring step or a search result from a recipe database, as the first solution. Up to this point, the existing sample 301 must be applied along with a reference coating application process because the color adjustment algorithm is based on the assumption that model bias is constant for all recipes close to the sample recipe. However, as explained above, the real sample or sample coating does not need to be applied with the reference coating application process (here corresponding to a dry coating application process), but rather with the sample coating application process (here corresponding to a wet coating application process). This results in a contribution of systematic bias to the corresponding sample shift. Without considering this contribution of the sample coating application process to the sample shift, the results of the color adjustment process will be significantly inaccurate.

[0143] The first solution 301 is the sample coating applied during the wet coating application process when the target color 300 is in a dry state. In order to predict the color of the optimized coating formulation for the target color 300 in a dry state, an offset 310 must be determined to compensate for such differences, even when using the sample in a wet state; that is, it takes into account such a transition between the wet and dry coating application processes. The first solution 301 is generally not close enough to the target color 300. An adjustment to the first solution 301 is applied, taking into account the offset 310 between the predicted reflectance data 306 and the measured reflectance data 303 for the first solution 301.

[0144] Therefore, the adjusted formulation is a function of the offset 310 between the target color 300 and the predicted reflectance data 306 and measured reflectance data 303 of the first solution 301. If the measured reflectance data 303 of the first solution 301 includes deviations caused by variations during the paint application process, this error will propagate to the following formulations during the iterative color matching process.

[0145] Therefore, it is proposed to avoid such coating application process bias by considering the diversity of coating application processes already in the first iteration step, i.e., when considering the first solution 301.

[0146] An offset 310, independent of the coating application process, is calculated based on the first solution 301. The sample formulation 302 of the first solution 301 is known. The first solution 301 is applied as a coating layer using a sample coating application process, i.e., the corresponding wet coating application process, and its color is measured. The measured color 303 of the first solution 301 is provided. The measured color 303 includes the true color, systematic bias, and 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 therefore related to a reference coating application process, i.e., the corresponding dry coating application process, the sample coating application process is considered by combining the physical model 140 with a sample application adaptation parameter 305, which is determined for a wet coating application process as a specific coating application process. Figure 1 As explained in [the document]. The predicted color 306 of the first solution 301 is now predicted based on the following assumption: the underlying formulation 302 is applied as a paint coating using a sample paint application process. Therefore, both the measured color 303 and the predicted color 306 involve the same paint application process, namely, a wet paint application process. Therefore, the offset 310, which represents the difference between the measured color 303 and the predicted color 306, is independent of the subsequent paint application process, i.e., the wet state. This offset 310 can now be used in an iterative adjustment process, which here is based on a dry state as a reference paint application process.

[0147] Since the first solution 301 is usually not close enough to the target color 300, the physical model 140 is used in combination with the numerical optimization algorithm 130 to obtain an optimized formulation 350 through iteration. The target paint formulation 350 specifies all included colorants, colorant 1, colorant 2, colorant 3, ... colorants. n and their corresponding concentrations c1, c2, c3, ..., c n Physical model 140 uses database 320 as the basis for color prediction. Database 320 includes individual color components, colorant 1, colorant 2, colorant 3, ..., colorant n Such as pigments and / or pigment classes, and specific optical data associated with the corresponding individual color components, constant 1, constant 2, constant 3, ..., constant n The target color 300 is combined with the calculated offset 310 to account for model bias and statistical error, assuming that both are similar for the sample and the paint formulation used for the target color 300.

[0148] The target color 300 and offset 310 are received by the computer processor 110 via interface 111. The physical model 140 and numerical optimization algorithm 130 are implemented and run on the computer processor 110. To determine the formulation 350 for the paint coating, when applied to the substrate using a reference paint application process (i.e., the corresponding dry paint application process), the color of the paint coating is matched to the target color 300, using the target color 300, offset 310, and specific optical constants of available colorants from database 320, and the optimized formulation 350 is determined iteratively. When applied using the reference paint application process, the formulation 350 and its predicted color 351 can be output via interface 112 on the output device. The predicted color 351 consists of the true color of the optimized formulation 350 when applied to the substrate using the reference paint application process and the statistical error of the sample. Since the offset 310 is included to eliminate wet application process bias, wet application process bias is no longer present.

[0149] Figure 4 The process of yet another embodiment of the method according to the invention is illustrated schematically.

[0150] The matching process can begin by matching from scratch or by searching for the target color 400 in the recipe database.

[0151] The term "de novo matching" encompasses color matching methods that operate in the absence of information about existing sample coatings as first solutions. This method is applied, for example, if no formula database is available, or if sufficient first solutions are not found in the database. In practice, "de novo matching" methods typically begin with a pre-selection step of components expected to be present in the target color. This pre-selection step is not mandatory. The "de novo matching" method / algorithm calculates one or more preliminary matching formulas for the target color as first solutions. These preliminary matching formulas can be sprayed and / or adjusted in the following steps.

[0152] Compared to the "color adjustment method," which can use sample coatings as the first solution to improve the color prediction accuracy of the physical model (e.g., based on approximating model error by analyzing "sample offset"), the "matching from scratch" method is generally less accurate.

[0153] The first solution 401 is usually not close enough to the target color 400. An adjustment to the first solution 401 is applied, taking into account the offset 410 between the predicted reflectance data 406 and the measured reflectance data 403 of the first solution 401.

[0154] Therefore, the adjusted formulation is a function of the offset 410 between the predicted reflectance data 406 and the measured reflectance data 403 of the target color 400 and the first solution 401. If the measured reflectance data 403 of the first solution 401 includes deviations caused by variations within the paint application process, this error will propagate to the following formulation during the iterative color matching process. In the case shown here, the sample paint application process is wet, and the reference paint application process is dry.

[0155] Therefore, it is proposed to avoid such coating application process bias by considering the diversity of coating application processes already in the first iteration step, i.e., when considering the first solution 401.

[0156] An offset 410, independent of the paint application process, is calculated based on the first solution 401. The formulation 402 of the first solution 401 is known. The first solution 401 is provided as a paint coating using a wet paint application process, and its color is measured. The 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 formulation 402. Since the physical model 140 uses the database 320 and is therefore related to the corresponding dry paint application process as a reference paint application process, the wet paint application process is considered by combining the physical model 140 with the wet application adaptation parameter 405. The predicted color 406 of the first solution 401 is now predicted based on the assumption that the underlying formulation 402 is applied / provided as a paint coating using a wet paint application process. Therefore, both the measured color 403 and the predicted color 406 involve the same wet paint application process. Therefore, the offset 410, which is the difference between the measured color 403 and the predicted color 406, is independent of the target paint application process below. This offset 410 can now be used in the iterative adjustment process, meaning that offset 410 is used as another input parameter for the paint adjustment algorithm.

[0157] Furthermore, it is desired here to obtain a solution for a target coating formulation whose predicted color matches the target color 400 in the dry state, which differs from the reference coating application process, which is also a dry coating application process. It is possible that the included spraying processes are different. Many factors can vary. Therefore, in addition to the target color 400 and offset 410, a target application adaptation parameter 415 is provided as a further input parameter for the color adjustment algorithm to account for the differences between different dry coating application processes, i.e., the differences between the target coating application process and the reference coating application process. An optimized target coating layer 450 is provided via interface 112, whose predicted color 451 best matches the target color 400 when applied to the substrate using the target coating application process. The target coating formulation 450 specifies all included colorants, colorant 1, colorant 2, colorant 3, ... colorants.n and their corresponding concentrations c1, c2, c3, ..., c n .

[0158] Figure 5 A schematic block diagram illustrating the offset that must be eliminated in the application process deviation, according to an embodiment of the method according to the invention. For a real sample, the measured sample color 500 is always (slightly) different from the predicted sample color that has been predicted using a physical model. The measured sample color 500 can be represented as a combination of the real color 501 and the offset 510. The offset 510, also called the sample offset 510, corresponds to the difference between the measured sample color 500 and the predicted sample color. The reason for this sample offset 510 between reality (measurement result) and theory (physical model) is, for example:

[0159] • Model bias 514: No model is 100% accurate.

[0160] • Application process deviation 515: This refers to how the sample is applied when measuring its color, for example, in a wet or dry state (or using a dry application process instead of the corresponding dry reference application process).

[0161] • Statistical error 512 of instrument 513, for example, caused by temperature

[0162] To date, color adjustment algorithms, i.e., paint color formulation calculation algorithms, interpret the complete sample offset 510 as a model bias and modify the adjusted paint formulation in a way that compensates for the corresponding sample offset 510. This means that the applied process bias 515 is part of the sample offset 510. If the applied process bias 515 is non-constant, then it acts as an element of instability. Depending on the proportion of the sample offset 510, the color adjustment results may be significantly inaccurate due to error propagation.

[0163] The method proposed in this invention is used to eliminate such application process bias 515 in sample offset 510, which is also simply referred to herein as offset 510. Improvement in the accuracy of sample offset 510 directly improves the quality / accuracy of the adjusted paint formulation. Regarding the potential application process bias 515 included in the measured color 500, the sample offset 510 between the measured color 500 and the predicted color is analyzed. As previously mentioned, offset 510 includes systematic bias 511 and statistical bias 512, also referred to as statistical error 512. Statistical bias 512 can be caused by the instrument 513 (with limited accuracy) and is generally small compared to systematic bias, making it negligible. Systematic bias 511 includes model bias 514 (which is expected to be constant) and application process bias 515 (which may be non-constant). The basic idea of ​​the proposed method is to decompose sample offset 510 into application process bias 515 and a residual portion including model bias 514 and statistical bias 512 using the proposed application adaptation module. The application process bias 515 is considered to be removed from the sample offset 510 because it is defined as non-constant. The remaining portion of the sample offset 510 will consist primarily of the model bias 514, which will be correctly handled within the paint adjustment algorithm.

[0164] Reference tag list

[0165] 101 samples

[0166] Formula 102

[0167] 103. Measure the color of the sample.

[0168] 105 Predicted Sample Colors

[0169] 110 Computer Processor

[0170] 111 Input Interface

[0171] 112 Output Interface

[0172] 120 Recipe Database

[0173] 130 physical model, color prediction model

[0174] 140 Numerical Optimization Algorithms

[0175] 201 Dry Target Color

[0176] 202 Sample Formula

[0177] 205 Dry Target Application Adaptation Parameters

[0178] 206 Predictive color for dry coating application process

[0179] 210 computer processor

[0180] 202 Database for Wet Reference Coating Application Process

[0181] 221 samples

[0182] Formula 222

[0183] 223 Wet measurement color

[0184] 225 Wet Predicted Color

[0185] 300 Dry Target Color

[0186] 301 samples

[0187] 302 Formula

[0188] 303 Wet Measurement Sample Color

[0189] 305 Wet Sample Application Adaptation Parameters

[0190] 306 Wet Prediction Sample Color

[0191] 310 Wet Sample Offset

[0192] 320 Database for Dry Reference Applications

[0193] 350 formula

[0194] 351 is used for predicting color in dry reference coating applications.

[0195] 400 target color

[0196] 401 samples

[0197] 402 Formula

[0198] 403 Measuring sample color

[0199] 405 Sample Application Adaptation Parameters

[0200] 406 Predicted Sample Color

[0201] 410 offset

[0202] 415 Target Application Adaptation Parameters

[0203] 450 Formula

[0204] 451 Predicted color for target application

[0205] 500 Measurement Sample Color

[0206] 501 True Color

[0207] 510 offset

[0208] 511 Systematic Bias

[0209] 512 Statistical error, statistical bias

[0210] 513 Instruments

[0211] 514 Model Bias

[0212] 515 Application Process Deviation

Claims

1. A computer-implemented color matching method using a paint adjustment algorithm running on at least one computer processor (110) and a database (120, 220, 320) including specific optical data of individual color components, wherein the specific optical data of the individual color components are determined based on a known reference paint coating having a known reference color formulation and a known measured reference color, the reference paint coating being applied to a substrate using a reference paint application process, wherein, The coating adjustment algorithm is extended by an application adaptation module, which interacts with the color prediction model of the coating adjustment algorithm. The application adaptation module is configured to receive application adaptation parameters for a specific coating application process as input parameters, and to use the received application adaptation parameters to convert the color predicted by the color prediction model for a reference coating application process into a valid converted color for the specific coating application process. The reference coating application process and the specific coating application process are different from each other. The application adaptation parameters (105, 205, 305, 405, 415) for the specific coating application process are calculated using numerical methods and the color prediction model. Measured colors and color formulations of multiple sample coatings are provided as input parameters. A given cost function is optimized starting from a given set of initial application adaptation parameters. The given cost function includes the measured colors (103, 223, 303, 403) and the sample coatings (101, 223, 303, 403). The corresponding color distances between the predicted colors of 21, 301, 401), and the color prediction model (140) being configured to use the specific optical data of the individual color components used in the corresponding color formulations (102, 222, 302, 402) of the sample coatings (101, 221, 301, 401) and the color formulations (102, 222, 302, 402) of the sample coatings (101, 221, 301, 401), and leading to the optimization process. The corresponding preliminary application adaptation parameters are used as input parameters to predict the predicted colors of the sample coatings (101, 221, 301, 401), wherein the application adaptation parameters are calculated by comparing the recursively predicted colors of the sample coatings (101, 221, 301, 401) with the measured colors (103, 223, 303, 403) of the corresponding sample coatings (101, 221, 301, 401) until the given cost function drops below a given threshold.

2. The method according to claim 1, wherein, The reference coating application process and the specific coating application process are each selected from the group consisting of: coatings applied in a wet state and coatings applied in a dry state.

3. The method according to claim 1 or 2, wherein, in order to determine a target color formulation (202, 450) for a target coating, the target color formulation matches a given target color (201, 400) when applied to a substrate using a given target application process different from the reference coating application process, the method further comprises: - Receive the given target color (201, 400) via at least one interface. - Receive application adaptation parameters (205, 415) for the given target application process via the at least one interface. - Retrieve specific optical data from the database (220, 320) for the individual color components to be used in the target color formulation (202, 450) of the target coating (201, 400). - Using the given target color (201, 400), the retrieved specific optical data, and the received application adaptation parameters (205, 415) as input parameters for the coating adjustment algorithm, calculate a color formula with an optimized concentration of individual color components, which serves as the target color formula (202, 450) for the target coating when the target coating is applied to the substrate using the given target coating application process (202, 450).

4. The method according to claim 3, further comprising: - Receive data of the color formula (222) of a sample paint coating (221) as a first solution for a given target color (300) to be matched, via at least one interface (111). - Retrieve specific optical data from the database (220) for the individual color components used in the color formulation (222) of the sample coating (221). - Receive the measured color (223) of the sample coating (221) applied to the substrate using the reference coating application process via the at least one interface (111). - The color of the sample paint coating is predicted using the color prediction model implemented and running on the at least one computer processor (110). - Using the at least one computer processor (110), the offset (210) of the sample paint coating is calculated as the difference between the measured color (223) and the predicted color (225) of the sample paint coating (221). - The offset (210) is incorporated into the calculation of the target color formula (202).

5. The method according to claim 1 or 2, in order to determine a target color formulation (350) for a target coating, said target color formulation (350) matching a given target color (300) when applied to a substrate using the reference coating application process, the method further includes: - Receive data of the color formula (302) of a sample paint coating (301) via at least one interface (111) as a first solution for the given target color (300) to be matched. - Retrieve specific optical data from the database (320) for the individual color components used in the color formulation (302) of the sample coating (301). - Receive the measured color (303) of the sample paint coating (301) applied to the substrate using the sample paint application process via the at least one interface (111). - Receive application adaptation parameters (305) for the application process of the sample coating. - The color (306) of the sample coating (301) valid for the sample coating application process as a specific coating application process is predicted using the color prediction model (140) and the application adaptation module, wherein the data of the color formulation (302) of the sample coating (301), the specific optical data of the individual color components used in the color formulation (302) of the sample coating (301) retrieved, and the application adaptation parameters (305) for the sample coating are used as input parameters. - The offset (310) of the sample coating is calculated as the difference between the measured color (303) and the predicted color (306) of the sample coating (301), and - Using the paint adjustment algorithm, the offset (310) is incorporated into the calculation of the target color formula (350).

6. The method according to claim 1 or 2, in order to determine a target color formulation (450) for a target coating, the target color formulation being matched with a given target color (400) when applied to a substrate using a target coating application process as a specific application process, the method further includes: - Receive data of the color formula (402) of a sample paint coating (401) via at least one interface (111) as a first solution for the given target color (400) to be matched. - Retrieve specific optical data from the database (320) for the individual color components used in the color formulation (402) of the sample coating (401). - Receive the measured color (403) of the sample paint coating (401) applied to the substrate using the sample paint application process via the at least one interface (111). - Receive application adaptation parameters (405) for the sample coating via the at least one interface (111). - The color prediction model (140) and the application adaptation module are used to predict the color (406) of the sample coating (401) that is effective for the sample coating application process as a specific coating application process, wherein the data of the color formula (402) of the sample coating (401), the specific optical data of the individual color components used in the color formula (402) of the sample coating (401) retrieved, and the application adaptation parameters (405) for the sample coating are used as input parameters. - The offset (410) of the sample coating is calculated as the difference between the measured color (403) and the predicted color (406) of the sample coating (401). - Using the paint adjustment algorithm, the offset (410) is incorporated into the calculation of the target color formula (450). - Receive application adaptation parameters (415) for the target coating application process via the at least one interface (111). - Using the given target color (400), the calculated offset (410), and the received application adaptation parameters (415) as input parameters for the paint adjustment algorithm, calculate a color formula with an optimized concentration of individual color components, which serves as the target color formula (450) for the target paint coating when the target paint coating is applied to the substrate using the target paint application process.

7. A system for color matching, comprising at least: - A database (120, 220, 320) comprising individual color components and specific optical data associated with the respective individual color components, the specific optical data of which is determined based on a known reference coating having a known reference color formulation and a known measured reference color, the reference coating being applied to a substrate using a reference coating application process. - At least one computer processor (110) is communicatively connected to the database (220) and is programmed to perform the method according to any one of claims 1-6.

8. The system according to claim 7, wherein, The individual color components include pigments and / or pigment classes.

9. A non-transitory computer-readable medium having a computer program having program code configured and programmed to perform the method according to any one of claims 1 to 6 when the computer program is loaded and executed by at least one computer processor (110), the computer processor (110) being communicatively connected to a database (120, 220, 320) including individual color components and specific optical data associated with the corresponding individual color components, the specific optical data of the individual color components being determined based on a known reference coating having a known reference color formulation and a known measured reference color, the reference coating being applied to a substrate using a reference coating application process.

10. The non-transitory computer-readable medium according to claim 9, wherein, The individual color components include pigments and / or pigment classes.

Images