Method and system for color matching process with compensation for application process bias

Abstract

The invention relates to a computer-implemented method for a color matching process, wherein an offset that eliminates application process bias is determined (310). The invention also provides a corresponding system.

Description

Technical Field

[0001] This invention relates to a method and system for a color matching process that compensates for process deviations in applications. 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 properties (or effect flake orientation) of the colorant in the context of a corresponding physical model, similar to the K / S value in the well-known "Kubelka / Munk" model, for example. However, the reflective properties of a coating layer depend not only on the formulation. It also depends strongly on the coating application process, typically how the coating is applied to its substrate.

[0004] The specific optical properties of the colorant are determined based on sample data from existing letdown / samples with known formulations and known reflectance data, all of which are applied to the substrate using a common reference coating application process. Color predictions from the physical model and the color matching process are always correlated with this reference coating application process. The specific optical constants of the colorant include the effect of the reference coating application process on the reflectance properties of the final coating layer, and consequently, on the reflectance properties of the final coating coating.

[0005] Color predictions from physical models used for different target coating application processes are limited by significant systematic errors and are not very accurate.

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

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

[0008] Color adjustments can be calculated based on samples, such as existing coloring steps or search results in a formulation database. Existing samples must be applied with a reference paint application process because color adjustment algorithms are based on the fundamental assumption that "for all formulations close to the sample formulation, the corresponding model bias is constant." As long as the adjusted formulation for color adjustment is similar to the sample formulation, similar model biases are expected. Typically, color adjustment algorithms interpret sample offsets (the offset between the measured reflectance data and the predicted reflectance data of the sample, respectively) as model biases. This model bias is automatically considered / compensated within the color adjustment algorithm and results in modifications to the adjusted formulation (e.g., patent EP2149038B1). However, in addition to model bias, real samples can also include systematic but non-constant biases caused by (minor) differences in the corresponding paint application process, also known as application process bias. This application process bias will propagate to the adjusted formulation. Depending on the scale of the application process bias of the sample, the color adjustment results may be significantly inaccurate.

[0009] Several methods exist for applying coatings to a substrate; some examples of coating application processes are:

[0010] • Automatic or manual spraying process,

[0011] • Different types or configurations of spray guns in the laboratory or body shop.

[0012] • Different coating lines or drying processes at OEM (Original Equipment Manufacturer) customer sites.

[0013] • Drawdown methods in the Color Development Lab.

[0014] Even when the same paint (using the same raw materials) is applied in different ways, the resulting color of the paint layer (coating coating) can be strongly influenced by the respective paint application process. The reasons for this color variation are:

[0015] • Different orientations of effect flakes in the coating layer

[0016] • Effect thin films, especially the excessive spraying loss of large effect thin films,

[0017] • Settling of effect flakes in the coating layer

[0018] • Variation in film thickness of non-hidden coating layers

[0019] • Changes in color intensity (shear effect or aggregates).

[0020] This is why it is important to use a common reference coating application process to prepare training data (descent) for calculating the optical constants of the colorant and the specific optical data of the colorant, respectively.

[0021] For manual coating applications, such as those using spray guns, the resulting color depends not only on the type and configuration of the spraying equipment but also on the individual spraying characteristics (“fingerprint”) of the sprayer. Sprayer-related variations within a single coating application process are typically significantly greater for manual applications than for automated applications. The spray profile in a manual coating application process depends on the spraying equipment (e.g., the type of spray gun), the conditions within the spray gun chamber (e.g., air temperature or humidity), the configuration of the spraying equipment, the individual spraying characteristics (“fingerprint”) of the sprayer, and the drying process. These variations include systemic components (“fingerprint”) that are nearly constant for a single sprayer / spray profile, as well as statistical components.

[0022] Color matching is an iterative process. The matching process can begin by matching from scratch or by searching for a given target color in a recipe database.

[0023] The term "de novo matching" encompasses a color matching method that manages the application of a first solution when no information is available about an existing sample coating. This method is applied, for example, if no recipe 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 the components expected to be used in the target color. This pre-selection step is not mandatory. The "de novo matching" method / algorithm calculates one or more preliminary matching recipes for the target color as first solutions. These preliminary matching recipes can be sprayed and / or adjusted in the following steps.

[0024] 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 generally has lower accuracy.

[0025] The first solution is often not close enough to the target color. An adjustment to the first solution is applied, taking into account the sample offset between the predicted and measured colors. The sample offset includes a systematic component (the sprayer's "fingerprint") and a statistical component. If the systematic component of the sample offset is not constant—for example, if the adjusted formulation will be sprayed by a different sprayer than the first solution (e.g., if the adjustment is calculated based on search results from a formulation database containing solutions from a large number of different sprayers with individual spray profiles)—then the color adjustment results may be significantly inaccurate. The adjusted formulation is a function of the target color and the sample offset. Non-constant sample offsets will propagate to the adjusted formulation.

[0026] 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.

[0027] Therefore, the object of the present invention is to provide a possibility for compensating for application process deviations in a color adjustment method. Summary of the Invention

[0028] 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.

[0029] This disclosure relates to a computer-implemented method for a color matching process, wherein an offset, also referred to herein as a sample offset, is determined to eliminate application process bias, and wherein the color matching process uses a color adjustment algorithm comprising a color prediction model (also simply referred to as a physical model) implemented and running on at least one computer processor and a database comprising specific optical data of individual color components, the specific optical data of 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, the method comprising:

[0030] A. Receives color formula data of a sample paint coating as a first solution for matching the target color, via at least one interface.

[0031] B. Retrieve specific optical data from the database for the individual color components used in the color formulation of the sample coating.

[0032] C. Receive, via at least one interface, the measured color of a sample paint coating applied to a substrate using a sample paint application process.

[0033] D. Using at least one computer processor and a numerical method implemented and run on at least one computer processor, to compute application adaptation parameters for a sample coating application process by minimizing a given cost function starting from a given set of initial application adaptation parameters, and

[0034] This allows the calculated application adaptation parameters to be used as input parameters for the application adaptation module.

[0035] E. Using data from a color prediction model and the color formulation of a sample coating, along with specific optical data of individual color components used in the color formulation of the sample coating, as model input parameters, and through an application adaptation module that interacts with the color prediction model, including calculated application adaptation parameters, the color of the sample coating for a given sample coating application process is predicted.

[0036] F. The offset is calculated as the difference between the measured color and the predicted color of the sample paint coating.

[0037] The measured color of the sample coating can be retrieved from a database containing data on the measured colors of multiple coatings, including the sample coating, which has been previously applied to the substrate using the sample coating application process. Alternatively, when performing the proposed method, i.e., as another step in the proposed method, i.e. during the implementation of the proposed method, the sample coating can be applied to the substrate using the sample coating application process.

[0038] Within the scope of this invention, the sample coating application process defines a specific spray profile that specifies the spraying equipment, spraying conditions, configuration of the spraying equipment, individual spraying characteristics (“fingerprints”) of individual sprayers, and / or drying process.

[0039] 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 respective individual color component (i.e., the colorant). Individual color components used in the color formulation of the respective 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 pastes.

[0040] The terms “color,” “color data,” “reflectance,” “reflectance data,” and “reflectance characteristics” are used synonymously herein. The terms “color formulation,” “coating formulation,” and “formulation” are used synonymously herein. The terms “processor” and “computer processor” are used synonymously herein.

[0041] 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 für industrielle Anwendungen (Color Physics for Industrial Applications)".

[0042] The basic idea behind color formulation calculations is to characterize specific optical data based on previously calibrated coatings—that is, based on the corresponding measurements of such calibrated coatings—specifically, to characterize the optical properties and / or optical constants of all relevant individual color components (e.g., all pigments / colorants). These calibrated coatings correspond to existing reductions with known formulations and known reflectance data, all applied through a common reference coating application process. Using a physical model (also referred to herein as a color prediction model), color prediction and color matching processes are always related to this reference coating application process. The specific optical constants of the colorants include the influence of the reference coating application process on the predicted reflectance properties of the corresponding final coating / layer.

[0043] According to the present invention, the physical model used to predict the reflectance properties of a sample coating (related to a reference coating application process), i.e., to predict the color of the sample coating, is extended with an additional application adaptation module. This additional application adaptation module interacts with the physical model and is configured to adapt the predicted reflectance data to a specific target coating application process. The additional module can be configured by inputting calculated application adaptation parameters. These application adaptation parameters describe the differences between the sample coating application process and the corresponding reference coating application process, or more precisely, a specific transfer function. Examples of application adaptation parameters are:

[0044] • Coating thickness adaptation: thicker / thinner

[0045] (Applicable to non-concealed coating layers; adjusts the concealment effect of the coating layer)

[0046] • Effect of film orientation adaptation: better / worse film orientation

[0047] (Applies to effect colors; adjusts the brightness / color flipping behavior of the paint layer)

[0048] • Effectiveness of solid colorants: more effective / less effective

[0049] (Adjusting for differences in color intensity of solid colorants, which may be caused, for example, by shear effects or by aggregates)

[0050] • Effectiveness of effect colorants: more effective / less effective

[0051] (Adjusting for differences in the reflectivity of effect colorants, which may be caused by overspraying, loss of effect, settling, or separation.)

[0052] Application adaptation parameters for the sample paint application process can be implicitly determined based on the analysis of one or more existing sample coatings (e.g., one or more existing coloring steps in a color matching process) applied using the sample paint application process. Furthermore, a list of one or more existing sample coatings from a database (which relates to the sample paint application process, particularly to individual manual sprayers) can be used to determine the application adaptation parameters.

[0053] Typically, sample coating application adaptation parameters are determined based on sample data from search results in a formulation database. Alternatively, sample coating application adaptation parameters can be loaded from a database that stores coating application adaptation parameters (“spray profiles”) associated with individual sprayers. The latter option is possible if information about the individual sprayer for the corresponding sample is available / given.

[0054] It's possible that the search result sample was sprayed by a first sprayer, and the adjusted formula will be sprayed by a different second sprayer. The spray profiles of the first and second sprayers are significantly different. This leads to systematic errors in the color adjustment algorithm that should be compensated for. The application adaptation module "learns" the individual spray profile of the first sprayer from the search result sample and compensates for the relevant application process deviations in the calculation of the sample offset, which is used as basic information within the color adjustment algorithm.

[0055] Alternatively, the paint application adaptation parameters of the first sprayer (which describe its spray profile) can also be loaded from a database containing paint application adaptation parameters corresponding to individual sprayers.

[0056] As mentioned above, numerical methods implemented and run on at least one computer processor are used to calculate application adaptation parameters for the sample coating application process. The numerical methods are configured to minimize a given cost function. The numerical methods and the color prediction model form part of the color adjustment algorithm.

[0057] According to one embodiment of the proposed method, a given cost function is selected as the color distance between the measured color and the predicted color of a second sample coating applied to the substrate using a sample coating application process. The color prediction model is used to predict the color of the second sample coating by using the color formulation of the second sample coating and specific optical data of individual color components used in the color formulation of the second sample coating and retrieved from a database as input parameters, along with corresponding initial application adaptation parameters that lead to the minimization process. Starting with a given set of initial application adaptation parameters, the application adaptation parameters are calculated by comparing the recursively predicted color of the second sample coating with the measured color of the second 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 equal to the color prediction 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. It is possible that the application adaptation parameters are calculated using multiple second sample coatings, all described by corresponding color formulations and measured colors, and processed as previously described. Because of the large amount of data used, reliable application adaptation parameters can be obtained.

[0058] In a further embodiment, the sample coating and the second coating are identical. Alternatively, the sample coating and the second coating may be different from each other, but applied using the same sample application adaptation process.

[0059] According to another embodiment of the proposed method, the application adaptation module can be configured by directly inputting specific application adaptation parameters, for example, through human input.

[0060] According to another embodiment of the proposed method, the application adaptation module can be configured by loading specific application adaptation parameters from a database that stores application adaptation parameters related to the spray profile ID (e.g., related to an individual sprayer).

[0061] Each application adaptation parameter is assigned to an adaptation metric of multiple different adaptation metrics, which include at least one of the following: layer thickness adaptation, effect flake orientation distribution adaptation, solid color component effectiveness adaptation, and effect color component effectiveness adaptation.

[0062] According to a second aspect of the invention, the method further includes the following steps:

[0063] - Provides a color formulation calculation algorithm, also known as a color adjustment algorithm, implemented and run on at least one computer processor, for determining the target color formulation of a target coating that matches the target color when applied to a substrate using a reference coating application process.

[0064] - Using the target color and the calculated offset (from the sample paint coating) as input parameters for the color formulation calculation algorithm, calculate the optimized concentration of the color formulation with individual color components, which serves as the target color formulation for the target paint coating when the target paint coating is applied to the substrate using the reference paint application process.

[0065] According to an embodiment of the proposed method, a color recipe calculation algorithm is implemented using a numerical method and a color prediction model. The numerical method is configured to optimize the concentration of individual color components of a preliminary color recipe relative to a target color by minimizing a given cost function, starting from a given initial color recipe. This given cost function is specifically chosen as the color distance between the predicted color of the preliminary color recipe and the received target color. The color prediction model is configured to predict the color of the preliminary color recipe using calculated offsets of corresponding sample paint coatings, the concentration of individual color components used in the preliminary color recipe, and specific optical data of the individual color components used in the preliminary color recipe and retrieved from a database as input parameters. The optimized concentration of the color components is calculated by comparing the recursively predicted color of the preliminary color recipe with the target color until the given cost function falls below a given threshold. Each numerical method includes multiple successive approximation steps. In each approximation step, a preliminary color recipe is assumed / provided and fed into a physical model to predict its corresponding reflectance data, i.e., its color, and then compared with the target color using the cost function. The given threshold can also be determined dynamically, for example, to indicate a specific state where further minimization is not possible.

[0066] As previously indicated, for real-world samples, the measured reflectance data (measured color) will always (slightly) differ from the predicted reflectance data (predicted color) of the physical model (“sample offset”). The reasons for this sample offset between reality and theory are, for example:

[0067] • Model bias: Since no model is 100% accurate.

[0068] • Application process deviations: such as the unique characteristics of manual sprayers (“fingerprints”).

[0069] • Statistical error of the instrument: for example, caused by temperature.

[0070] So far, color adjustment algorithms interpret the complete sample offset as model bias and modify the adjusted paint formulation in a way that compensates for the corresponding sample offset. Here, application process bias is part of the sample offset and is expected to be constant. If the application process bias is non-constant, then it acts as an element of instability in the color adjustment algorithm. If, for example, the measured color of a sample is too bright due to application process bias, then the color adjustment algorithm will calculate an adjusted formulation that, if applied together with a reference paint application process, will be too dark. Depending on the proportion of the sample offset, the color adjustment result can be significantly inaccurate due to error propagation.

[0071] The method proposed in this invention is used to eliminate such application process bias in sample offset, which is also referred to herein as offset. Improved accuracy of sample offset directly improves the quality / accuracy of the adjusted paint formulation. Regarding the potential application process bias included in the measured color, the sample offset between the measured color and the predicted color is analyzed. The basic idea is to decompose the sample offset into an application process component and a residual component using an application adaptation model. The residual component includes, for example, model bias and statistical error. The application process component is considered to be removed from the sample offset because it is defined as non-constant and systematic: it represents the systematic application difference between an individual sprayer and a reference paint application process. The residual component of the sample offset will consist primarily of model bias, which will be correctly handled within the color adjustment algorithm (also referred to herein as the color formula calculation algorithm).

[0072] As mentioned above, it's possible that the search result sample was sprayed by a first sprayer, and the adjusted formula will be sprayed by a different second sprayer. The spray profiles of the first and second sprayers are significantly different. This leads to systematic errors in the color adjustment algorithm that should be compensated for. The application adaptation module "learns" the individual spray profile of the first sprayer from the search result sample and compensates for the relevant application process deviations in the calculation of sample offsets, which are used as basic information within the color adjustment algorithm.

[0073] Alternatively, the paint application adaptation parameters of the first sprayer (which describe its spray profile) can also be loaded from a database containing paint application adaptation parameters corresponding to individual sprayers.

[0074] The present invention also relates to a system comprising at least:

[0075] - 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 to the substrate using a reference coating application process.

[0076] - At least one computer processor, which is communicatively connected to a database and is programmed to perform the proposed methods of the present invention as described herein.

[0077] 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 embodiment 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.

[0078] 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 comprising individual color components such as pigments and / or pigment classes and specific optical data associated with the respective individual color components. The specific optical data for the individual color components are 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 to perform the proposed method of the invention as described herein.

[0079] 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 wireless via 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. The corresponding communication can be a combination of wireless and wired communication.

[0080] Computer-readable media suitable for storing computer program instructions (i.e., program code) and data include all forms of non-volatile memory, media, 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.

[0081] Computer program instructions can be program software, software applications, modules, software modules, scripts, or code, and can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and can be deployed in any form, including as a standalone 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 this disclosure are written in HTML, TS (TypeScript), and CSS (Cascading Style Sheets).

[0082] Computer programs may, but do not need to, correspond to files in a specific file system. A computer program may be stored as a part of a file that holds other 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 question, 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 characteristics and functions of various components may be appropriately combined into a single component.

[0083] 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. The at least one memory device may include, for example, a magnetic disk, magneto-optical disk, or optical disk. However, the system itself does not need to have such a memory 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.

[0084] 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.

[0085] 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-readable medium for execution by a data processing apparatus or for controlling the operation of a data processing apparatus. Alternatively or additionally, the computer program instructions can be encoded on artificially generated propagation signals, such as machine-generated electrical, optical, or electromagnetic signals, generated to encode information for transmission to a suitable receiving device for execution by at least one computer processor. The computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination thereof.

[0086] 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

[0087] Figure 1A schematic block diagram illustrating an embodiment of a method according to the present invention for providing sample offsets for a color matching process is shown;

[0088] Figure 2 A schematic block diagram illustrating another embodiment of the method for compensating for application process deviations according to the present invention is shown;

[0089] Figure 3 A schematic block diagram illustrating another embodiment of the method for color matching according to the present invention is shown. Detailed Implementation

[0090] The same unit or component is provided with the same reference numerals across all figures.

[0091] Figure 1 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 100 is always (slightly) different from the predicted sample color that has been predicted using a physical model. The measured sample color 100 can be represented as a combination of the real color 101 and the offset 110. The offset 110, also called the sample offset 110, corresponds to the difference between the measured sample color 100 and the predicted sample color. The reason for this sample offset 110 between reality (measurement result) and theory (physical model) is, for example:

[0092] • Model bias 114: No model is 100% accurate.

[0093] • Application process deviation 115: This refers to how the sample is applied to the substrate when measuring its color.

[0094] For example, the special characteristics of manual sprayers ("fingerprints").

[0095] • Statistical error 112 of instrument 113, i.e., the applied instrument, such as a spray gun: for example, caused by temperature.

[0096] To date, color adjustment algorithms, i.e., paint color formulation calculation algorithms, interpret the complete sample offset 110 as a model bias and modify the adjusted paint formulation in a way that compensates for the corresponding sample offset 110. This means that the application process bias 115 is part of the sample offset 110. If the application process bias 115 is non-constant, for example, in the case of different manual sprayers, then it acts as an element of instability. If, for example, the measured color 100 of the sample is too bright due to the application process bias 115, then the color adjustment algorithm will calculate an adjusted formulation that, if applied together with the corresponding reference paint application process, will be too dark. Depending on the proportion of the sample offset 110, the color adjustment results may be significantly inaccurate due to error propagation.

[0097] The method proposed in this invention is used to eliminate such application process bias 115 in sample offset 110, also referred to herein as offset 110. Improvement in the accuracy of sample offset 110 directly improves the quality / accuracy of the adjusted paint formulation. Regarding the potential application process bias 115 included in the measured color 100, the sample offset 110 between the measured color 100 and the predicted color is analyzed. As previously mentioned, offset 110 includes systematic bias 111 and statistical bias 112, also referred to as statistical error 112. Statistical bias 112 is caused by the instrument 113, such as a spray gun. Systematic bias 111 includes model bias 114 (which is expected to be constant) and application process bias 115 (which may be non-constant). The basic idea of ​​the proposed method is to decompose sample offset 110 into application process bias 115 and a residual portion, which includes model bias 114 and statistical bias 112, using an application adaptation module. Application process bias 115 is considered to be removed from sample offset 110 because it is defined as non-constant. The remaining portion of sample offset 110 will consist primarily of model bias 114, which will be correctly handled within the color adjustment algorithm.

[0098] Figure 2 An embodiment of a system 200 according to the present invention is shown. The system includes a computer processor 210 and a database 220. The 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 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 common reference coating application process. A computer processor 210 is communicatively connected to a database 220 and is programmed to perform embodiments of the methods described herein.

[0099] To eliminate sample offset 110 from the application process bias 115 assigned to the sample coating application process, it is proposed to determine the difference between the measured color of the corresponding sample coating applied to the substrate using the sample coating application process and the predicted color of the corresponding sample coating determined using the physical model 240. Therefore, it is necessary to consider the sample coating application process when making predictions using the physical model, since the actual physical model 240 is based on the assumption of using a reference coating application process and uses a database 220, which includes data on the specific optical properties of colorants determined based on existing drop / sample data with known formulations and known reflectance data, all applied to the substrate using a common reference coating application process. The consideration of the sample coating application process is achieved by determining sample application adaptation parameters as further input parameters to the physical model 240. These sample application adaptation parameters are calculated based on data from existing coloring steps of existing sample coatings, respectively.

[0100] These sample paint coatings are applied to the substrate using the sample paint application process. The corresponding sample color 203 of the sample paint coating is measured. Data for the corresponding color formulation 202 of the corresponding sample paint coating is provided. The corresponding color formulation 202 specifies all included colorants: colorant 1, colorant 2, colorant 3, ... colorant. n and their corresponding concentrations c1, c2, c3, ..., c n .

[0101] Data on the color formula 202 of the sample paint coating is received via at least one interface 211 of the computer processor 210. Additionally, the measured color 203 of the sample paint coating is received via at least one interface 211 of the computer processor 210.

[0102] Numerical method 230 and physical model 240 are provided and implemented on computer processor 210. Numerical method 230 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 the use of the initial application adaptation parameters produces a color prediction equal to that produced using a reference coating application process. The given cost function is chosen as the color distance between the measured color 203 of the corresponding one of the existing sample coatings and the predicted color of the corresponding sample coating. Physical model 240 is configured to predict the color of the corresponding sample coating by using the color formulation 202 of the corresponding sample coating, specific optical data of the individual color components used in the color formulation 202 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 220. The specific optical properties of the colorant are determined based on data from existing substrates / samples with known formulations and known reflectance data, all of which are applied to the substrate using a common reference coating application process. Therefore, the color prediction of physical model 240 is related to this reference coating application method. The specific optical constants / data of the colorant include the effect of the reference coating application process on the reflective properties of the final coating layer.

[0103] Using a computer processor 210 and employing a numerical method 230 and a physical model 240 implemented and running on the computer processor 210, the application adaptation parameter 205 is calculated by comparing the recursively predicted color of the corresponding sample paint coating with the measured color 203 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.

[0104] The calculated optimized application adaptation parameters 205 are made available and optionally output via another interface 212 on an output device such as a display. These calculated optimized application adaptation parameters 205 are characteristics used in the sample coating application process. Figure 3 In the color adjustment method shown, when the sample offset is determined as the difference between the measured sample color and the predicted sample color for the first solution, adaptation parameter 205 is applied as an input parameter for the physical model 240. By using adaptation parameter 205, both the measured sample color and the predicted sample color are related to the same sample paint application process. Therefore, the difference between the measured sample color and the predicted sample color eliminates the influence of the sample paint application process.

[0105] 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 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, real samples or sample coatings are typically not applied with a reference coating application process, but rather with a sample coating application process that contributes to the systematic bias that causes the sample offset. If this contribution of the sample coating application process to the sample offset is not considered, the results of the color adjustment process will be significantly inaccurate.

[0106] The first solution 301 is usually 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 of the first solution 301.

[0107] Therefore, the adjusted formulation is a function of the offset 310 between the predicted reflectance data 306 and the measured reflectance data 303 of the target color 300 and 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.

[0108] Therefore, it is proposed to avoid such coating application process deviations 115 by considering the diversity of coating application processes already in the first iteration step, i.e., when considering the first solution 301.

[0109] An offset 310, independent of the coating application process, is calculated based on the first solution 301. A sample formulation 302 for the first solution 301 is known. The first solution 301 is applied as a coating onto a substrate using the sample 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 304, systematic bias 305, and statistical error 306. Furthermore, a physical model 240 is used to predict the color of the first solution 301 based on the known formulation 302. Since the physical model 240 uses a database 220 and is therefore related to a reference coating application process, the physical model 240 is compared with... Figure 2The sample application adaptation parameters 205, as explained in the text, are combined to consider the sample coating application process. The predicted color 307 of the first solution 301 is now predicted based on the following assumption: the underlying formulation 302 is applied as a coating layer to the substrate using the sample coating application process. Therefore, both the measured color 303 and the predicted color 307 involve the same sample coating application process. Therefore, the offset 310, which represents the difference between the measured color 303 and the predicted color 307, is independent of the subsequent sample coating application process. This offset 310 can now be used for the iterative color adjustment process.

[0110] Since the first solution 301 is usually not close enough to the target color 300, the physical model 240 is used in combination with the numerical optimization algorithm 230 to obtain an optimized formula 350 through iteration. The optimized formula 350 specifies all included colorants: colorant 1, colorant 2, colorant 3, ... colorants. n and their corresponding concentrations c1, c2, c3, ..., c n The physical model 240 again uses the database 220 as the basis for color prediction. The target color 300 and the calculated offset 310 are combined to account for model bias and statistical error. It is assumed that the formulations for both the sample and the target color 300 are similar.

[0111] The target color 300 and offset 310 are received by the computer processor 210 via interface 211. The physical model 240 and numerical optimization algorithm 230 are implemented and run on the computer processor 210. To determine the formulation 350 for the coating, when applied to the substrate using a reference coating application process, its color is matched to the target color 300. Specific optical constants of the target color 300, offset 310, and available colorants from database 220 are used, and an optimized formulation 350 is determined iteratively. When applied using the reference coating application process, this formulation 350 and its predicted color 351 can be output via interface 212 on the output device. The predicted color 351 consists of the statistical error between the true color 352 of the optimized formulation 350 when applied to the substrate using the reference coating application process and the sample 353. Since the offset 310 eliminates the sample application bias 115, the sample coating application process bias 354 is no longer present.

[0112] Reference tag list

[0113] 100 Measurement sample color

[0114] 101 True Colors

[0115] 110 offset

[0116] 111 Systematic Bias

[0117] 112 Statistical error, statistical bias

[0118] 113 Instruments

[0119] 114 Model Bias

[0120] 115 Application Process Deviation

[0121] 200 system

[0122] 202 Color Formula

[0123] 203 Measuring Color

[0124] 205 Sample Application Adaptation Parameters

[0125] 210 Computer Processor

[0126] 211 (Input) Interface

[0127] 212 (output) interface

[0128] 220 Database

[0129] 230 Numerical Optimization Algorithm

[0130] 240 Physical Models

[0131] 300 target color

[0132] Sample 301, sample coating

[0133] 302 Sample Formulation

[0134] 303 Measurement of sample color

[0135] 304 True Color

[0136] 305 Systematic Bias

[0137] 306 Statistical Error

[0138] 307 Predicts sample color (for sample coating application processes)

[0139] 310 Sample Application Adaptation Parameters

[0140] 350 Optimized Formula

[0141] 351 is used for predicting colors in reference coating application processes.

[0142] 352 True Colors

[0143] Statistical error of 353 samples

[0144] Application process bias of 354 samples

Claims

1. A computer-implemented method for a color matching process, wherein, Determine the offset (310) to eliminate the application process deviation (115, 354) for the sample coating (301), wherein the color matching process uses a color prediction model (240) implemented and running on at least one computer processor (210) and a 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 coating with 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, the method comprising: A. Receive data of the color formula (302) of the sample paint coating (301) as a first solution for matching the target color (300) via at least one interface (211) of the at least one computer processor. B. Retrieve specific optical data of the individual color components used in the color formulation (302) of the sample coating (301) from the database (220). C. Receive, via the at least one interface (211), the measured color (303) of the sample paint coating (301) applied to the substrate using the sample paint application process. D. Using the at least one computer processor (210) and a numerical method (230) implemented and run on the at least one computer processor (210), an application adaptation parameter (205) is calculated for the sample coating application process by minimizing a given cost function starting from a given set of initial application adaptation parameters, and the calculated application adaptation parameter (205) is made available as an input parameter to the application adaptation module. E. Using data from the color formulation (302) of the sample coating (301) and specific optical data of the individual color components used in the color formulation (302) of the sample coating (301) as input parameters to the color prediction model (240), and by means of the application adaptation module that interacts with the color prediction model (240) to include the calculated application adaptation parameters (205), the color (307) of the sample coating (301) for the application process of the sample coating is predicted. F. The offset (310) of the sample coating is calculated as the difference between the measured color (303) and the predicted color (307) of the sample coating (301).

2. The method according to claim 1, wherein, The given cost function is selected as the color distance between the measured color (203) of the second sample coating applied to the substrate using the sample coating application process and the predicted color of the second sample coating, wherein the color prediction model (240) is used to predict the color of the second sample coating by using the color formula (202) of the second sample coating, specific optical data of the individual color components used in the color formula (202) of the second sample coating and retrieved from the database (220), and corresponding preliminary application adaptation parameters leading to the minimization process as input parameters, the minimization process starting from the given set of initial application adaptation parameters, wherein the application adaptation parameters are calculated by comparing the recursively predicted color of the second sample coating with the measured color (203) of the second sample coating until the given cost function drops below a given threshold.

3. The method according to claim 2, wherein, The coating of the first sample paint is the same as that of the second sample paint.

4. The method according to any one of the preceding claims, wherein, The application adaptation module can be configured by inputting specific application adaptation parameters.

5. The method according to any one of the preceding claims, wherein, Each application adaptation parameter is assigned to an adaptation metric among a plurality of different adaptation metrics, which include at least one of the following: layer thickness adaptation, effect flake orientation distribution adaptation, solid color component effectiveness adaptation, effect color component effectiveness adaptation, and individual characteristics of manual sprayer adaptation.

6. The method according to any one of the preceding claims, further comprising the step of: - Provides a color formulation calculation algorithm implemented and running on the at least one computer processor (210) for determining a target color formulation (350) for a target coating that matches the target color (300) when applied to a substrate using the reference coating application process. - Using the target color (300) and the calculated offset (310) as input parameters for the color formula calculation algorithm, calculate a color formula (350) with an optimized concentration of individual color components, as the target color formula (350) for the target coating when the target coating is applied to the substrate using the reference coating application process.

7. The method according to claim 6, wherein, The color formula calculation algorithm is implemented by a numerical method (230) and the color prediction model (240), wherein the numerical method (230) is configured to optimize the concentration of individual color components of the initial color formula relative to the target color (300) by minimizing a given cost function starting from a given initial color formula, the given cost function being selected as the color distance between the received target color (300) and the predicted color of the initial color formula, and the color prediction model (240) is configured to predict the color of the initial color formula by using the calculated offset (310) of the sample coating (301), the concentration of the individual color component used in the initial color formula, and specific optical data of the individual color component used in the initial color formula and retrieved from the database (220) as input parameters, and wherein the optimized concentration of the color component is calculated by comparing the recursively predicted color of the initial color formula with the target color (300) until the given cost function drops below a given threshold.

8. A system comprising at least: - A database (220) 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 (210) is communicatively connected to the database (220) and is programmed to perform the method according to any one of the preceding claims.

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

10. 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 7 when the computer program is loaded and executed by at least one computer processor (210), the computer processor (210) being communicatively connected to a database (220) including individual color components and specific optical data associated with the respective 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.

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

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