Methods for Creating Color Laser Markings

Abstract

A method (1) for preparing a laser marking system (100) for creating a colored laser marking on a test piece, comprising the following steps: a) providing a laser marking system (100) and a test piece (105) comprising a surface layer (105a), wherein the laser marking system comprises a preset number of laser parameters (12); b) exploring a first color gamut (2) specified by the laser marking system (100) and the test piece (105) comprising the surface layer (105a), comprising the following steps: aa) creating (3) a design space (10) having a preset number of design points (11), wherein each design point (11) represents a combination of the preset number of laser parameters (12); bb) marking a sample on the test piece (105) for each design point (11); cc) measuring (5) the sample using at least one detection device (106), and Determining a performance point (14) for each design point, wherein the measured performance points (14) define a performance space (13); dd) evaluating (6) the performance space (13) according to a preset performance criterion using an evaluation device (107), wherein a Pareto front including a subset of performance points is determined; ee) generating (7) a descendant design space (10a) having descendant design points (11a); ff) creating (8) a first color gamut (2) using the subset of performance points that form the Pareto front; wherein steps bb) to dd) are iterated (9) a preset number of iterations, wherein in each iteration (9) the descendant design space (10a) of the previous iteration is used in step bb), wherein in each iteration the measured performance space is combined (15) with the performance space of the previous iteration (9) such that in step dd) the combined performance space (13a) is used.

Description

Technical Field

[0001] The present invention relates to a method for preparing a laser marking system to create a color laser marking on a test piece, and a method for creating a color laser marking on a test piece comprising a surface. Background Art

[0002] Creating visible patterns on surfaces using laser irradiation is a rapidly developing technology with many applications in object identification, customization, and authentication [Liu et al., 2019]. Laser marking is an environmentally friendly, low-maintenance process without consumables, dyes, or pigments. While most are monochromatic methods, some materials exhibit a range of colors when treated with lasers due to complex physicochemical phenomena. Among these materials, stainless steel and titanium are some of the most important industrial metals. Due to its challenging nature, industrial applications of color laser marking are almost non-existent despite its huge potential. In the absence of such properties, the relationship between the design space of the device (laser parameters) and the performance space (e.g., marking color) is unknown. This relationship is too complex to be captured using physics-based methods [Nánai et al., 1997]. Instead, current practice finds design parameters that produce "interesting colors" through trial-and-error measurements. These primary colors are then used to mark simple motives and logos. This brute-force color gamut exploration accounts for a low percentage of the high-dimensional design space for laser marking, resulting in the neglect of some design parameters.

[0003] Coloring of different substrates using laser irradiation is an active research area with a long history [Birnbaum, 1965]. There are many color formation mechanisms using different laser sources and different materials; see [Liu et al., 2019] for a recent review. Surface oxidation is one of the mechanisms, where heat (generated by the laser) promotes the reaction of the material with oxygen. Oxidation-induced colors are thought to originate from a multi-level, inhomogeneous mixture of structural colors (based on thin film interference) on the one hand [Del Pino et al., 2004] and from traditional pigment-based colors of oxides on the other hand [Langlade et al., 1998]. Despite some initial efforts [Veiko et al., 2013], predicting the structure and composition of the oxide layer is extremely difficult due to the complex thermodynamics of the laser marking process [Nánai et al., 1997]. Even for known material compositions, predicting surface color requires a challenging light-matter interaction model, most likely based on electromagnetic simulations [Auzinger et al., 2018].

[0004] For some popular metals, such as stainless steel and titanium, oxidation-based color laser marking has been extensively studied. This covers a range of laser-marked metal behaviors, from electromechanical [Lawrence et al., 2013] to corrosion resistance [ et al., 2016]. Further related to the present invention is a class of studies that focus on the effect of various process parameters on the marking color [Laakso et al., 2009]. Most of these works [Adams et al., 2014; Antończak et al., 2013, 2014] depend on uniformly sampling and marking the process parameter space. Since laser marking is time-consuming and material-intensive, these methods cannot handle the size of the design space and ultimately ignore most of the process parameters. It is worth noting that some empirical color discovery methods [Veiko et al., 2016] try to find different laser parameters that lead to the same color. But this color needs to be known in advance. Moreover, these methods are limited to interference-based colors within a limited range of laser energies and a limited number of parameters.

[0005] Formulating design problems based on multi-objective optimization and solving them by computing the Pareto front is known in the field of computer graphics. Notable examples are simplifying procedural shaders [SitthiAmorn et al., 2011] or minimizing power consumption in real-time rendering [Wang et al., 2016]. In computational manufacturing, exploring the performance space of processes has attracted recent attention. With the advent of 3D additive technologies, these efforts have focused on exploring the mechanical properties of 3D printed microstructures. For example, Schumacher et al.

[2015] precomputed the performance space of microstructures defined by a family of mechanical metamaterials to accelerate their heterogeneous topology optimization. They first filled the performance space by perturbing the initial design (in the design space) and then filled the unfilled regions of the performance space by interpolation or inverse optimization. For a similar purpose, Zhu et al.

[2017] combined discrete random perturbations of designs near the gamut boundary with continuous optimization to further expand the gamut by improving existing designs. In a more general approach, Schulz et al.

[2018] further emphasize the importance of exploring the hypersurface (or Pareto front) of the performance gamut rather than the hypervolume. The Pareto front captures the set of solutions in the performance space that compromise between different, potentially conflicting objectives. Although these approaches are important sources of inspiration, it is not possible to rely on any of them because they depend on closed-form, smooth characteristic functions that connect the design and performance spaces. For example, the approach of Schulz et al.

[2018] requires smooth (twice differentiable) positive characteristics of the process and is applicable only to continuous design parameters. Summary of the invention

[0006] The object of the present invention is to provide a method for preparing a laser marking system to create a color laser marking on a test piece, and a method for creating a color laser marking on a test piece comprising a surface, so that the color laser marking has a high degree of versatility.

[0007] This problem is solved by a method according to claim 1 and by a method according to claim 10. Other dependent claims provide preferred embodiments.

[0008] According to the present invention, a method for preparing a laser marking system to reproduce a color image of a laser mark on a test piece comprises the following steps:

[0009] a) providing a laser marking system and a test piece including a surface layer, wherein the laser marking system includes a preset number of laser parameters;

[0010] b) exploring a first color gamut specified by the laser marking system and the test piece including the surface layer, comprising the following steps:

[0011] aa) creating a design space having a preset number of design points, wherein each design point includes a combination of a preset number of laser parameters;

[0012] bb) Mark the sample on the test piece for each design point;

[0013] cc) measuring the sample using at least one detection device and determining a performance point for each design point, wherein the measured performance points define a performance space;

[0014] dd) evaluating the performance space according to a preset performance criterion using an evaluation device, wherein a Pareto front comprising a subset of performance points is determined;

[0015] ee) generating a descendant design space having descendant design points;

[0016] ff) creating a first color gamut using said subset of performance points forming said Pareto front;

[0017] wherein steps bb) to dd) are iterated for a preset number of iterations, wherein in each iteration, the descendant design space of the previous iteration is used in step bb), wherein in each iteration, the performance space and the measured performance space are combined so that the combined performance space is used in step dd). Preferably, in each iteration, the design space is combined with the design space of the previous iteration so that the combined design space is used in steps dd), ee) and ff). Preferably, the design space is preliminarily populated with randomly selected design points. Preferably, the population size is in the range of 50 to 500, more preferably in the range of 75 to 250. Preferably, the evaluation device is integrated in a control unit of the device controlling the laser marking system. Preferably, the method is fully or partially performed by the control unit. Preferably, the evaluation device and / or the control unit are computers, processor units or similar devices.

[0018] The method provides device characteristics that are a prerequisite for any color reproduction system including laser marking. The data-driven method according to the present invention is performed without an analytical function that maps laser marking parameters to marking colors. The method provides a black box model of the process, excluding physics-based predictions of oxide laser-induced components. The present invention introduces the first system color discovery algorithm for laser marking systems. The method is a non-exhaustive performance space exploration of laser marking systems. Preferably, the surface layer is a metallic surface layer. However, the method can also be applied to surface layers or specimens made of non-metallic materials. The method can be applied to any type of laser system and any type of specimen.

[0019] Preferably, step dd) involves multi-objective optimization. Unlike typical optimization, multi-objective optimization problems are evaluated based on multiple criteria. These criteria often conflict. For example, in this case, some marking colors may be saturated, but will leave thicker marks and reduce resolution. Therefore, there is no single optimal solution, but a set of optimal solutions, called Pareto optimal solutions or Pareto sets. The Pareto set projected into the performance space is called the Pareto front. The Pareto front captures a set of solutions in the performance space that compromise different, potentially conflicting objectives. In all performance criteria, members of the Pareto front are not dominated by any other point in the performance space. In other words, in at least one criterion, it performs better than all other points. Preferably, a dense Pareto optimal solution set for the color laser marking problem with the above objectives is revealed.

[0020] Preferably, step dd) employs a non-dominated sorting genetic algorithm (NSGAII), which is a sorting algorithm based on the presence of performance points in a multi-level Pareto front.

[0021] According to a preferred embodiment, the laser system comprises at least one pulsed laser and at least one scanning device. Preferably, the laser spot is movable relative to the specimen by means of the scanning device. Alternatively or additionally, the specimen is movable relative to the laser spot by means of the scanning device. The laser spot is preferably the area on the specimen where the laser beam impinges. In other words, it is conceivable that the specimen is in a fixed position and the laser beam moves relative to the specimen. The scanning device may preferably be a galvanometer scanner, a movable mirror or the like, by means of which the direction of the reflected laser beam can be controlled. Alternatively, it is conceivable that the laser beam does not move, but the specimen moves relative to the laser beam. Thus, the scanning device may preferably be an xy platform or an xyz platform. It is also conceivable that both the laser beam and the specimen can be moved by means of the scanning device. Further, the laser and the scanning device are preferably controlled by a control unit. The control unit may also advantageously be connected to at least one detection device to receive the measured data, preferably, which are then used by the evaluation device. It is also conceivable that more than one laser beam is used for the color laser marking. The type of pulsed laser may be selected according to the method used to create the color laser marking. Therefore, the pulsed laser may preferably be a nanosecond laser, a picosecond laser or a femtosecond laser.

[0022] According to a preferred embodiment, the design point comprises at least one laser parameter, selected from the group consisting of: laser pulse frequency, laser pulse power, laser pulse width, speed of the laser beam along a vector relative to the specimen during marking, number of lines defining the number of lines in a cluster representing a marked sample, distance between lines in a cluster representing a marked sample, number of marking vectors. Preferably, the dimensionality of the design space is set by the number of laser parameters represented by the design point. Thus, if all seven of the above laser parameters are included in the design space, the design space may be 7-dimensional. However, the design space may be adjusted according to specific needs. Thus, it may be any number and any combination of the above laser parameters. It is conceivable that the design space comprises other parameters that may affect the color formation. The other parameters depend on the method actually used for creating the color on the specimen. Preferably, the design point comprises the parameters focal length of the laser beam, type of dielectric gas, ambient temperature. The dielectric gas is the ambient gas surrounding the specimen during the laser marking process. For example, the dielectric gas may be air. A preferred method for color marking is laser-induced oxidation of the surface layer of the specimen. In this method, the type of ambient dielectric gas is important in view of the presence of oxygen and the amount of oxygen present. Therefore, preferably, the design space may include relevant parameters that may affect the color produced in the laser marking process.

[0023] According to another preferred embodiment, the performance criteria in step dd) include at least one of chromaticity, hue extension, resolution, performance space diversity, design space diversity, color repeatability, color uniformity. Preferably, the performance criteria in step dd) include all of the above criteria. According to another preferred embodiment, the trimming criteria color repeatability, color uniformity. It is conceivable that other performance criteria need to be considered. Preferably, the type, number and combination of performance criteria used are adjusted according to the test piece used, the laser system and other influencing factors.

[0024] Preferably, the chromaticity can be described by:

[0025]

[0026] where a* and b* are color coordinates in the CIELAB color space [Wyszecki and Stiles, 1982]. Marker colors with large chromaticities produce more saturated color images. Preferably, the hue extension (f HS ) ensures the presence of high chromaticity colors at all hue angles. Preferably, the resolution can be assessed by measuring the thickness of a line marked by a given set of laser parameters. Preferably, the resolution (f R ) can be described by:

[0027]

[0028] where t is the line thickness. The standard is preferred because it preferably uses a line-based halftoning method. Preferably, the performance space diversity (f PSD ) is measured for each point in the performance space as the inverse of the distance to its two nearest neighbors and is given by:

[0029]

[0030] i≠j,0≤i,j<|P|

[0031] Where p is the point to be scored and P is the corresponding population in the performance space. In addition to the design space, the design space diversity (f DSD ) is preferably measured similarly to performance space diversity.

[0032] According to a preferred embodiment, the performance criteria in step dd) include at least one of chromaticity, resolution, performance space diversity, design space diversity. Preferably, the performance criteria in step dd) include all of the above criteria. Preferably, hue extension is not a performance criterion. Preferably, wherein the performance point is projected into the CIECH space. Advantageously, the CIECH space is divided into a first number of circular sectors. Preferably, the circular sectors form a color wheel as a whole. Advantageously, the performance points in each circular sector of the color wheel are evaluated according to the performance criteria. Advantageously, the evaluation is iterated for a preset number of iterations, wherein in each iteration, the number of sectors forming the color wheel is changed. Therefore, for each iteration, the area of ​​the sector is different. Preferably, in each circular sector, the performance point is evaluated using a non-dominated sorting algorithm based on all of the performance criteria except hue extension. Advantageously, the evaluation is repeated each time using a randomly selected number of sectors and a random angle offset. After each iteration, each performance point is assigned to a potential different Pareto front. At the end of a preset number of iterations, each individual performance point is characterized by its frontier frequency vector, which indicates the frequency of its presence in the first frontier, the second frontier, etc. Preferably, each performance point is characterized by a frequency vector, which indicates the presence in a particular Pareto front. By the described process, a color gamut with balanced hue extension is achieved. Therefore, the performance criterion hue extension can be effectively evaluated by evaluating all remaining performance criteria multiple times.

[0033] According to another preferred embodiment, an additional evaluation of the achromatic performance according to the performance points is performed by performing step b) using the performance criteria of step dd) brightness, resolution, performance space diversity, design space diversity. Thus, in the resulting first color gamut, both the chromatic and achromatic axes are covered.

[0034] According to another preferred embodiment, the method further comprises the step of selecting a set of primary colors from the first color gamut, wherein the selected primary colors form the second color gamut. The extraction of the primary colors involves selecting a set of colors that produces the largest color gamut by using a preferred marking method, such as a halftoning method. Unlike a printer, although the number of primary colors is not strictly limited, the fewer the number of primary colors, the longer the marking time because they cause less switching delays in the laser system. Not all colors in the explored color gamut can be considered for primary color extraction. Therefore, before primary color extraction, the color gamut is preferably pruned by excluding the following colors: 1) does not meet the specified resolution requirements, 2) shows low repeatability, and 3) exhibits non-uniformity.

[0035] According to another preferred embodiment, data related to the design space and the performance space of the first color gamut are stored in a database. This has the advantage that, for a given laser system and a specific type of test piece, the method for preparing the laser marking system preferably has to be performed once. For the same type of test piece, the relevant data on the first color gamut can be retrieved from the database before further steps regarding marking are performed.

[0036] The object of the invention is also solved by a method for creating a colored laser marking on a specimen comprising a surface.

[0037] The method for creating a colored laser mark on a test piece including a surface layer may include a single feature or a combination of features of the above-mentioned method for preparing a laser marking system to create a colored laser, and vice versa. Further, the same advantages as the above-mentioned method for preparing a laser marking system to create a colored laser can be applied to the method for creating a colored laser mark on a test piece including a surface layer, and vice versa.

[0038] A method for reproducing a laser-marked color image on a test piece including a surface layer comprises the following steps:

[0039] a) a verification database, wherein the relevant data of the database are related to the first color gamut and / or the second color gamut, the first color gamut and / or the second color gamut being related to the type of the laser marking system and the test piece, wherein the data are obtained by the method for preparing a laser marking system according to one of the above embodiments;

[0040] b) retrieving data related to the first color gamut and / or the second color gamut from a database, or performing a method for preparing a laser marking system according to one of the above-described embodiments;

[0041] c) providing an input image to be reproduced as a laser mark on the test piece;

[0042] d) executing a color management workflow by which control data for a laser marking system derived from an input image is created;

[0043] e) Marking according to control data.

[0044] In step a), if there is data of a specific type of specimen related to the first color gamut and / or the second color gamut, the database is searched. The data is obtained by performing a method for preparing a laser marking system according to one of the above-described embodiments. Preferably, this verification is performed by a control unit. The user provides the control unit with specifications of the specimen, in particular specifications of a surface layer of the specimen. Preferably, the surface layer is a metallic surface layer. However, the method can also be applied to specimens or surface layers made of non-metallic materials. The method can be applied to any type of laser system and any type of specimen.

[0045] According to a preferred embodiment, the laser system comprises at least one pulsed laser and at least one scanning device. Preferably, the laser spot is movable relative to the specimen by means of the scanning device. Alternatively or additionally, the specimen is movable relative to the laser spot by means of the scanning device. The laser spot is preferably the area on the specimen where the laser beam impinges. In other words, it is conceivable that the specimen is in a fixed position and the laser beam moves relative to the specimen. The scanning device may preferably be a galvanometer scanner, a movable mirror or the like, by means of which the direction of the reflected laser beam can be controlled. Alternatively, it is conceivable that the laser beam does not move, but the specimen moves relative to the laser beam. Thus, the scanning device may preferably be an xy platform or an xyz platform. It is also conceivable that both the laser beam and the specimen can be moved by means of the scanning device. Further, the laser and the scanning device are preferably controlled by a control unit. The control unit may also advantageously be connected to at least one detection device to receive the measured data, preferably, which are then used by the evaluation device. It is also conceivable that more than one laser beam is used for the color laser marking. The type of pulsed laser may be selected according to the method used to create the color laser marking. Therefore, the pulsed laser may preferably be a nanosecond laser, a picosecond laser or a femtosecond laser.

[0046] According to another preferred embodiment, the color management workflow is a halftone workflow. By using multicolor halftones through a color reproduction workflow, the reproduction of arbitrary images can be achieved, not just uniform colors. Further, the image is previewed before enabling the marking. Therefore, for laser marking, halftone technology is implemented. Using halftone technology, the visual impression of a continuous tone image is reproduced by exploiting the low-pass filtering characteristics of the human visual system. The halftone method aims to create a two-layer image that conveys the optical illusion of a continuous tone image. Color and white pixel groups are printed in a specific ratio and structure so that when viewed by the eye, it gives the impression of a continuous color. In color halftoning, a given number of color layers are halftoned separately. The final color halftone is the result of mixing the colors of different halftone layers overlaid on each other. For example, existing color halftone methods for printers are cluster dots and blue noise dithering. Thereby, a halftone layer is created separately for each color. The final color halftone image is formed by the superposition of all layers, where the dot layers partially overlap. In laser marking, the overlap of colors and the superposition of layers are not preferred. Preferably, the color management workflow is a parallel halftone workflow. Therefore, for laser marking, a side-by-side half-toning technique is preferably implemented. The preferred side-by-side half-toning technique advantageously relies on discrete line geometry, which provides sub-pixel accuracy for creating discrete thick lines. Preferably, the continuous tone color image is converted into a set of binary images, each binary image corresponding to a primary color. The binary images are synthesized in the form of lines, which are adjacent to each other without overlapping.

[0047] According to another preferred embodiment, the color management workflow includes the following steps:

[0048] aa) applying a forward color prediction model to construct a third color gamut with respect to the second color gamut, and the use of parallel halftones;

[0049] bb) mapping the input image to a third color gamut;

[0050] cc) performing color separation such that for each mapped color, the corresponding area coverage of each primary color is determined;

[0051] dd) binarizing the area coverage using a side-by-side halftone method and creating a raster halftone image;

[0052] ee) converting the raster halftone image into vector data, wherein the control data comprises said vector data. Preferably, said control data is sent to a laser.

[0053] In step aa), a third color gamut is created from the second color gamut and / or the first color gamut, preferably using a halftone method. Preferably, the third color gamut is generated by halftoning a set of primary colors. The forward color prediction model predicts the colors of thousands of halftones across the space of the relative areas of the primary colors in each halftone, called area coverage. The third color gamut surface is then fitted to the volume point cloud and subsequently used for gamut mapping in step bb). Preferably, the Yule-Nielsen (YN) prediction model is used to predict multi-color, side-by-side halftones of laser primary colors. In gamut mapping, the color space of the input image is transformed into the color space of the third color gamut. Typically, the color space is displayed as a volume of achievable colors. In step cc), color separation is built on the forward prediction model to calculate the area coverage of a specific primary color and its reproduction of a given color (within the gamut).

[0054] The method for creating a colored laser marking on a test piece comprising a metal surface may include single features or a combination of features of the above-described method for preparing a laser marking system to create a colored laser, or vice versa.

[0055] According to another preferred embodiment, the laser marking is based on laser-induced oxidation of a surface layer of the specimen. Preferably, this applies to a method for creating a colored laser mark on a specimen comprising a metal surface and / or a method for preparing a laser marking system to create a colored laser. In this method, the laser induces heating, resulting in the formation of a transparent or translucent oxide film on the surface of the specimen. A with illumination can be reflected from the top and bottom surfaces of the oxide film. The constructive interference of the reflected light beams gives the surface a specific color, which depends on the film thickness, the refractive index of the oxide and the interference order [Liu et al., 2019].

[0056] Preferably, the laser marking is based on laser induced structuring of a layer on the surface of the specimen. Preferably, this applies to a method for creating a colored laser marking on a specimen including a metal surface and / or a method for preparing a laser marking system to create colored lasers. In this method, the laser system generates laser induced periodic surface structures (LIPSSs) on the surface of the specimen. The LIPSSs act as gratings and produce rainbow colors due to optical diffraction effects. These colors are not caused by pigments, but originate from the micro / nanostructure of the material surface, i.e. structural colors.

[0057] Preferably, the laser marking is based on the generation of laser-induced micro / nanoparticles on the surface layer of the specimen. Preferably, this is applicable to a method for creating a colored laser marking on a specimen comprising a metal surface and / or a method for preparing a laser marking system to create a colored laser. In this method, the surface structure is induced by a laser system. The surface structure that excites the surface color is randomly distributed, has no regularity, and the color does not change with the viewing angle. The surface plasmon resonance (SPR) effect generated by metal nanostructures and nanoparticles is the main reason for this type of coloring.

[0058] Preferably, laser marking is based on laser-induced plasma color on metals. Metal nanoparticles exhibit scattering properties due to the excitation of plasmas, which depend on their shape, size, composition and substrate medium. There are various known techniques to render plasma colors, including laser interference lithography. With plasma colors, precious metals such as gold and silver as well as metals such as copper and aluminum can be marked.

[0059] However, the method for creating a colored laser mark on a test piece including a surface layer and / or the method for preparing a laser marking system to create a colored laser mark can also be applied in combination with other laser-induced color marking methods. Other mechanisms can achieve marking on a wide range of metals and even non-metals. Since the actual marking process is preferably treated as a black box and the method is a data-driven method, it is applicable to various other laser-induced color marking methods.

[0060] Preferably, the test piece comprises at least a top surface layer made of metal, on which the laser marking is performed. It is also conceivable that the test piece is made entirely of metal. Advantageously, the surface layer and / or the entire test piece is made of stainless steel, titanium or a similar metal. Preferably, this applies to the method for creating a colored laser marking on a test piece comprising a metal surface and / or to the method for preparing a laser marking system to create a colored laser. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Further advantages, objects and properties of the present invention will be described by means of the accompanying drawings and the following description.

[0062] In the attached picture:

[0063] Figure 1 A schematic diagram of the color response of the laser is shown;

[0064] Figure 2 A method for preparing a laser marking system (100) for creating a colored laser mark on a specimen is shown;

[0065] Figure 3 Examples of different color wheel configurations used with the described method are shown;

[0066] Figure 4 A method for creating colored laser markings on a test piece is shown;

[0067] Figure 5 A schematic color reproduction workflow is shown;

[0068] Figure 6 A laser marking system is shown;

[0069] Figure 7 Visualization of laser parameters is shown;

[0070] Figure 8 The color gamut evolution for a comprehensive exploration of AISI 304 stainless steel is shown;

[0071] Fig. 9 The color gamut evolution for a random exploration of AISI 304 stainless steel is shown;

[0072] Fig.10 Shows the explored color gamut in different configurations on AISI 304 stainless steel;

[0073] Fig.11 a shows the difference between the values ​​with and without f R The average thickness of the iterations;

[0074] Fig.11 b shows the average reproduction error of the Yule-Nielsen model for different n values;

[0075] Fig.11 c shows the extraction of chromaticity primaries;

[0076] Fig.12 Several examples of original, gamut-mapped, and labeled images are shown;

[0077] Fig.13 shows a comparison of the marking parameters of Antończak et al. and the marking of the present laser marking system;

[0078] Fig.14 Images of markings on AISI 304 and AISI 430 are shown;

[0079] Fig.15 The color gamut evolution for a comprehensive exploration of AISI 430 stainless steel is shown;

[0080] Fig.16 shows a painting of Maria de' Medici by Alessandro Allori, marked on AISI 304;

[0081] Fig.17 An image of a laser mark on stainless steel using a method according to one embodiment of the present invention is shown. DETAILED DESCRIPTION

[0082] The present invention provides a device to equip color laser marking with the same level of versatility as a color printer. Assuming a black box model of the device characteristics, a measurement-based, data-driven exploration of the performance space is designed. Different performance criteria, including color gamut and marking resolution, are explored through continuous marking and measurement. To this end, a multi-objective optimization problem is formulated and solved using an evolutionary approach enhanced with Monte-Carlo methods, revealing the Pareto frontier of the process. The optimization explores the hidden corners of the 7-dimensional design space to search for useful parameters, resulting in a dense set of a diverse, high-resolution set of colors. The present invention goes far beyond the state of the art color image marking by introducing a complete color management workflow that brings the input image and the laser mark on the metal surface to the closest approximation. The color reproduction workflow uses the principle of halftone-based color printing to extract many primary colors from the explored color gamut and reproduce the input color by juxtaposing the extracted primary colors to each other in a controlled manner. The manufactured color image has high resolution, does not introduce noticeable artifacts, and shows accurate color reproduction. Thus, the present invention provides a discovery method that automatically finds the desired design parameters of a black box manufacturing system and a first color image reproduction workflow for laser marking on metal.

[0083] Device characterization is a prerequisite for any color reproduction system, including laser marking. In the absence of analytical functions that map laser marking parameters to marked colors, one must rely on data-driven approaches. In a first attempt, one can sample the design space, mark and measure the sampled design points, and construct a lookup table. Given the relatively large number of parameters involved in color laser marking, this exhaustive strategy suffers from the curse of dimensionality. The process is further slowed down by the fact that functional evaluation requires actual marking and measurement. In addition, the non-smooth color response to the laser parameters makes interpolation schemes ineffective. As Figure 1 As shown in Figure 1, the color coordinates of a small marking patch may change abruptly with the marking parameters. This is in contrast to the smooth response of a typical printer to its control parameters. Figure 1The upper left graph shows the color response of a laser versus a typical printer. CIE L*, a* and b* values ​​are plotted in red, green and blue, respectively. The laser marked colors (on stainless steel AISI 304) were repeated three times. Their average values ​​(solid lines) and standard deviations (shaded areas) are shown. The non-smooth behavior of the laser marked colors is evident.

[0084] Figure 2 A method 1 for preparing a laser marking system 100 to reproduce a laser marked color image on a test piece is shown, comprising the following steps:

[0085] a) providing a laser marking system 100 and a test piece 105 including a metal surface layer 105a, wherein the laser marking system includes a preset number of laser parameters 12;

[0086] b) exploring a first color gamut 2 specified by the laser marking system 100 and the test piece 105 including the metal surface layer 105a, comprising the following steps:

[0087] aa) creating 3 a design space 10 having a predetermined number of design points 11, wherein each design point 11 represents a combination of a predetermined number of laser parameters 12;

[0088] bb) performing 4 sample markings on the test piece 105 for each design point 11;

[0089] cc) measuring 5 the sample using at least one detection device 106 and determining a performance point 14 for each design point, wherein the measured performance points 14 define a performance space 13;

[0090] dd) evaluating 6 the performance space 13 according to a preset performance criterion using an evaluation device, wherein a Pareto front comprising a subset of performance points is determined;

[0091] ee) generating 7 a descendant design space 10a having descendant design points 11a;

[0092] ff) creating 8 a first color gamut 2 using a subset of the performance points forming a Pareto front;

[0093] wherein steps bb) to dd) are iterated 9 for a preset number of iterations, wherein in each iteration 9, the descendant design space 10a of the previous iteration is used in step bb), wherein in each iteration, the measured performance space is combined 15 with the performance space of the previous iteration 9, so that the combined performance space 13a is used in step dd). Preferably, in each iteration, the design space 10, 10a is combined with the design space 10, 10a of the previous iteration 9, so that the combined design space 10b is used in steps dd), ee) and ff). Preferably, the design space is initially filled with randomly selected design points. Preferably, the overall size is in the range of 50 to 500, more preferably in the range of 75 to 250. Preferably, the evaluation device is a computer, a processor unit or a similar device. Preferably, method 1 is fully or partially performed by a control unit. The control unit may be a processor, a computer or a similar device.

[0094] Laser system 100 Figure 6 As shown, it includes a pulsed laser 101 and scanning devices 103, 104. According to one embodiment, the scanning devices 103, 104 move the laser spot relative to the specimen 105 or the surface 105a of the specimen 105. Therefore, the specimen 105 is in a fixed position. The scanning device 103 can preferably be a galvanometer scanner, a movable mirror or a similar device, by which the direction of the reflected laser beam can be controlled. It is also conceivable that the specimen 105 is movable relative to the laser spot. Therefore, the scanning device can preferably be an xy platform or an xyz platform. It is also conceivable that both the laser beam and the specimen can be moved by the scanning device. Further, the laser 101 and the scanning devices 103, 104 are controlled by a control unit 108. The control unit 108 can also be connected to at least one detection device 106 to receive the measured data, which is then used by the evaluation device 107.

[0095] The design points 11, 11a include at least one laser parameter 12, which is selected from: laser pulse frequency, laser pulse power, laser pulse width, speed of the laser beam along the vector relative to the specimen during marking, number of lines defining the number of lines in the cluster representing the marked sample, distance between lines in the cluster representing the marked sample, number of marking vectors. The design points (11, 11a) may also include parameters of the focal length of the laser beam, type of dielectric gas (in this case air), ambient temperature. In general, the design space may include relevant parameters that affect color formation on a specific specimen. The performance criteria in step dd) include at least one of the following: chromaticity, hue extension, resolution, performance space diversity, design space diversity, color repeatability, color uniformity. However, in this case, the standard color repeatability and color uniformity are trimmed.

[0096] The method 1 for preparing a laser marking system 100 provides a non-exhaustive performance space 13 exploration of the laser marking system 100. Qualitatively, the performance criteria favor diverse, saturated, and high-resolution colors: basic requirements for color images. To address this problem, a multi-objective optimization method is proposed. Unlike typical optimization, multi-objective optimization problems are evaluated based on multiple criteria. These criteria often conflict. For example, in this case, some marking colors may be saturated but leave thick marks and reduce resolution. Therefore, there is no single optimal solution, but rather a set of optimal solutions, called Pareto optimal solutions or Pareto sets. The projection of the Pareto set into the performance space is called the Pareto front. A member of the Pareto front is not dominated by any other point in the performance space in all criteria. In other words, it performs better than all other points in at least one criterion. The goal is to reveal a dense set of Pareto optimal solutions for the color laser marking problem with the above objectives. To this end, a multi-objective evolutionary method is used, which is a successful tool for finding Pareto optimal solutions [Fonseca et al., 1993]. The method described is called the Non-dominated Sorting Genetic Algorithm (NSGAII) [Deb et al., 2002] and is well suited to our model-free characteristic functions with both discrete and continuous parameters. The core of the method described is an evaluation or ranking method based on the presence of members on a multi-level Pareto front. Due to our tonal diversity purposes, the NSGA-II non-dominated sorting is not sufficient for the specific problem at hand. Therefore, a preferred Monte Carlo method based on the non-dominated evaluation is considered and a new evaluation method based on the frequency of the frontier is introduced. This method is called the Monte Carlo, Multi-Objective, Genetic Algorithm or MCMOGA for short.

[0097] In order to optimally employ halftones for color reproduction, a set of primaries covering both the achromatic and chromatic axes is advantageous. In a divide-and-conquer strategy, chromatic and achromatic (black and white) explorations may be separated, with the former explained first. High chromatic performance requires corresponding Figure 3 Saturated colors with larger radius in the CIECH color space shown [Schanda, 2007]. Further, it is desirable to span a range of colors with different hues. Such colors mixed with black and white (via halftoning) produce a highly filled color gamut (2) that can be used for color image marking. By using the method 1 for preparing the laser marking system 100 using multi-objective optimization, the performance criteria are maximized:

[0098] (1) Chroma, because a marker color with a large chroma will produce a more saturated color image.

[0099]

[0100] where a* and b* are the color coordinates in the CIELAB color space [Wyszecki and Stiles, 1982].

[0101] (2) Tone extension (f HS ) ensures that high chroma colors are present at all hue angles.

[0102] (3) Resolution; since we use line-based halftones, this standard is evaluated by measuring the thickness of a line marked by a given set of laser parameters.

[0103]

[0104] where t is the wire thickness.

[0105] (4) Performance Space Diversity (PSD); for each performance point (14) in the performance space (13), it is measured as the inverse of the distance to its two nearest neighbors

[0106]

[0107] i≠j,0≤i,j<|P|

[0108] Where p is the point to be scored and P is the corresponding population in the performance space.

[0109] (5) Design space diversity (f DSD ); except in design space, measured similarly to performance space diversity.

[0110] Performance criteria (1) to (4) are measured in the performance space, while performance criterion (5) is measured in the design space. Performance criteria (1) to (3) are qualities sought directly from the laser-marked images. Performance criterion (4) improves the convergence speed, while criterion (5) helps avoid local extrema by promoting separate points in the performance space 14.

[0111] Method 1 navigates the laser design space 10, 10a, 10b in the direction of a dense Pareto set, i.e., a set of designs (laser parameters) that improve the above performance criteria. It starts with a random population in the design space 10, labels and measures its performance, and then iteratively evolves it into a larger population with as many Pareto optimal solutions as possible. In each iteration, as Figure 2 As shown, the overall improvement of the Pareto set to be passed to the next iteration 9 using the genetic algorithm. Iterations are stopped when no significant improvement of the Pareto front is observed anymore.

[0112] As with almost any genetic method, a fitness measure is assigned to each member of the population. The more fit solutions are selected and used to create the next generation. Given the difficulty of assigning a single fitness value to a multi-criteria objective, the non-dominated evaluation method [Deb et al., 2002] evaluates the population members according to their membership in a multi-level Pareto front. First, the first non-dominated front is found, that is, all solutions or performance points 14 in the population that belong to the Pareto front. This is achieved by comparing the 14 performance objectives of each performance point one by one with the other performance points 14 in the population. If a performance point 14 performs better than all other performance points 14 in at least one criterion, it is labeled as the first frontier performance point 14. The second non-dominated front is calculated by temporarily discarding the first frontier and repeating the above process. This process continues until all members of the population are labeled with their respective frontiers. This results in many disjoint subsets that make up the entire population, each with its frontier label. Note that in the spirit of the Pareto concept, members within the same frontier are not sorted.

[0113] Figure 2 One iteration of method 1 is shown. The method takes the starting population at iteration i (Pi) and generates offspring Qi using a genetic approach. Labeling and measuring the design space (DS) produces corresponding performance points 14 in the performance space 13 (PS). Pi and Qi are combined into Ri and evaluated using the proposed method. Ri is added to the first gamut 2 and its fitted half Pi+1 is passed as the starting generation to the next iteration 9.

[0114] The described method is disadvantageous considering the purpose of hue expansion. A hue expansion standard helps the gamut to grow in all angular directions in a balanced manner. Without a hue expansion standard, the described method may explore some specific hue angles more than other methods, resulting in an uneven growth of the chromaticity gamut. In order to achieve a gamut 2 with balanced hue expansion, a single solution cannot be evaluated but combined with other solutions. This can quickly lead to non-trivial computations: for 10 angular samples out of a population of 200, An assessment.

[0115] This combinatorial explosion can be avoided by adopting a Monte Carlo method. The performance criteria in step dd) include at least one of chromaticity, resolution, performance space diversity, and design space diversity. Preferably, all of the performance criteria are used. The performance points 14 are projected into the CIECH space, wherein the CIECH space is divided into a first number of circular sectors 15 to form a color wheel 16. Therefore, the color wheel 16 divides the CIECH space into a random number of circular sectors 15 ( Figure 3). Within each sector 15, the performance points 14 are evaluated using the described non-dominated sorting algorithm based on all performance criteria except hue extension. The evaluation is iterated a preset number of iterations, wherein in each iteration the number of sectors 15 forming the color wheel 16 is changed. Thus, the process is repeated each time with a randomly selected number of sectors 15 and a random angular offset. After each revolution or iteration of the color wheel 16, each individual performance point 14 is assigned to a potentially different leading edge. At the end of this cycle, each individual performance point 14 is characterized by its leading edge frequency vector, which represents the frequency of its presence in the first leading edge, the second leading edge, and so on. The iteration stops when the change in leading edge frequency is below a certain threshold. The population of the performance space 13 is sorted based on the frequency of its "top" leading edge, where a single first leading edge is worth more than any number of second leading edges.

[0116] Figure 3 The process is schematically shown, where multiple turns of the color wheel 16, with different numbers of sectors 15 and angular offsets, ensure that all performance points are evaluated in different configurations and sequenced in an appropriate manner. Figure 2 In , it is easy to see that some points may not be ordered correctly using a single color wheel configuration. Figure 3 , examples of different color wheel 16 configurations used in the method (left). First, the points within each circular sector of each color wheel 16 are assigned to a leading edge label (circled number next to each point) using the traditional NDS method. Next, all leading edge labels for each point are counted to form a leading edge frequency (right). For example, the point indicated by an asterisk has been assigned twice to the first leading edge and twice to the third leading edge. Note that for the same point, only the first two color wheels 16 are not enough, because it will only be assigned to the third leading edge, despite the high potential for improving the color gamut 2.

[0117] Further, an additional evaluation of the achromatic performance of the performance point 14 is performed by performing step b) using the performance criteria in step dd) brightness, resolution, performance space diversity, design space diversity. Therefore, in the chromaticity exploration, the brightness value (CIE L*) is discarded. Two separate explorations are performed for black and white on the brightness axis. For black, the minimum chromaticity criterion is minimized, thereby encouraging low chromaticity colors. In addition, the hue extension performance criterion is replaced by brightness minimization. The exploration of white is the same as black, but the brightness performance criterion is maximized.

[0118] After performing the method 1 for preparing the laser marking system 100, the performance space 13 of the laser marking system 100 is explored. The performance space 13 can then be used for color image reproduction. This is done in the present embodiment by adopting the principles of halftone-based color printing for color laser marking. To this end, a number of primary colors are found that meet the resolution requirements and produce the largest second color gamut. A color management workflow is then established that uses the selected primary colors to make the closest approximation of the input color image and the marking.

[0119] Therefore, method 1 may further include the step of selecting a set of primary colors from the first color gamut 2. The selected primary colors then form a second color gamut. Extraction of the primary colors involves selecting a set of colors that produces the largest color gamut through halftoning. Although unlike a printer, the number of primary colors is not strictly limited, the fewer the number of primary colors, the longer the marking time because they reduce the switching delay of the laser. Not all colors in the explored first color gamut 2 can be considered for primary color extraction. Therefore, before primary color extraction, the first color gamut 2 is pruned by excluding the following colors: 1) do not meet the specified resolution requirements, 2) show low repeatability, and 3) exhibit non-uniformity.

[0120] Similar to the above gamut exploration, the primaries of achromaticity and chromaticity are extracted respectively. First, the explored chromaticity gamut of the laser marking system consisting of a discrete set of colors is explored. The convex hull of the set is found in the CIExy chromaticity space, where x = X / (X+Y+Z), y = Y / (X+Y+Z) [Wyszecki and Stiles, 1982]. The reason for applying the convex hull in CIExy is that, unlike CIEa*b* or CIECH, it is a linear space under halftones. The colors inside the convex hull can be reproduced by halftones with high precision. The colors in the convex set give the largest area and therefore also the largest chromaticity gamut. In order to reduce the number of primary colors, those members of the convex set that do not contribute significantly to the gamut area can be further excluded. Achromatic primary extraction selects the darkest and brightest colors with negligible chromaticity from the black and white exploration gamuts, respectively.

[0121] Data related to the design space 10, 10a, 10b and the performance space 13, 13a of the first color gamut 2 and / or data related to the second color gamut are stored in a database 109. The database 109 may be connected to the control unit 108 and / or the evaluation unit 107.

[0122] The present invention also includes a method 20 for creating a colored laser marking on a test piece 105 including a metal surface 105a, the method comprising the following steps:

[0123] a) verifying 21 a database 109, wherein the relevant data of the database 109 are related to the first color gamut 2 and / or the second color gamut, wherein the first color gamut 2 and / or the second color gamut are related to the type of the laser marking system 100 and the test piece 2, wherein the data are obtained by the method 1 for preparing a laser marking system according to the above-mentioned embodiment;

[0124] b) retrieving 22 data related to the first color gamut 2 and / or the second color gamut from a database 109, or performing 23 the method 1 for preparing a laser marking system 100 according to one of the above-described embodiments;

[0125] c) providing 24 an input image 27 to be reproduced as a laser marking on the test piece 105;

[0126] d) executing 25 a color management workflow 28 by which control data for the laser marking system originating from the input image 27 is created;

[0127] e) Marking is performed according to the control data.

[0128] In step a), if there are data of a specific type of test piece related to the first color gamut 2 and / or the second color gamut, the database 109 is searched. The data is obtained by performing the method 1 for preparing the laser marking system 100 according to one of the above-described embodiments. Preferably, this verification is performed by the control unit 108. The user provides the control unit 108 with the specifications of the test piece 105, in particular the specifications of the surface layer 105a of the test piece 105. The retrieved data includes data about the first and / or second color gamut, which is matched to the laser system used and the specific test piece 105. The method is as follows Figure 4 As shown. Further, preferably, step d) is performed by the control unit 108. In step e), the control unit 109 controls the laser 101 and the scanning devices 103, 104 accordingly.

[0129] Preferably, the color management workflow 18 is a parallel halftone workflow. Therefore, the method 20, wherein the color management workflow 28a) comprises the following steps:

[0130] aa) applying 29 a forward color prediction model to construct a third color gamut with respect to the second color gamut, and the use of parallel halftones;

[0131] bb) mapping 30 the input image 27 to a third color gamut;

[0132] cc) performing 31 color separation such that for each mapped color, the corresponding area coverage of each primary color is determined;

[0133] dd) binarizing 32 the area coverage using a side-by-side halftone method and creating 33 a raster halftone image;

[0134] ee) converting 34 the raster halftone image into vector data, wherein the control data comprises said vector data.

[0135] The control data is then sent to the laser and step e) 28 can be performed.

[0136] The color management workflow ensures color reproducibility between different imaging devices. The real advantage of the current approach is the reproduction of arbitrary images, not just uniform colors, using multicolor halftones through the color reproduction workflow. It also allows previewing the image before marking. A typical example is printing, where, for example, the input image from a camera is printed as accurately as possible. Figure 5 Schematic diagram of the color reproduction workflow for color laser marking. Given an input color in a given color space (e.g., sRGB), its reproducibility is ensured by mapping it to the color gamut of the laser marking. Color separation computes the coverage of different laser primaries that reproduce the input color when placed next to each other through halftones. The color reproduction workflow of a typical printer produces different colors through spatial mixing and superposition of multiple inks. If such a workflow is to be mimicked in the laser marking process, it is necessary to ensure that both the laser primaries and their superpositions are optimal. It is much more difficult to explore the design space for such unlikely combinations. Instead, the different primaries are strictly placed next to each other. This leads to a rather simple exploration, where only a set of suitable primaries (not their superpositions) is searched. To build the color management workflow, juxtaposed halftones of the extracted primaries are synthesized. The overall color of the multi-primary halftone is predicted. The prediction model is digitally inverted in order to map the input color to the primary halftone.

[0137] exist Figure 5 , a color reproduction workflow 28, 28a is depicted. The input image 27 is mapped to a second color gamut of the laser marking system 100. In a color separation step 31, for each mapped color, the corresponding area coverage of each primary color is calculated (the creation of the color gamut and the color separation is based on a color prediction model). Successive area coverages are binarized 32 and placed next to each other using a side-by-side halftone method 33. The raster halftone image is converted into a vector 34.

[0138] Color halftoning converts a continuous-tone color image into a set of binary images, one for each printer's ink. Discrete-line juxtaposed halftoning [Babaei and Hersch, 2012] synthesizes these binary images in the form of lines and places them next to each other without overlapping. In the original approach designed for bitmap printers, the use of digital lines [Reveilles, 1995] allows sub-pixel thickness, low computational complexity, and, more importantly for us, continuity. The continuous laser path ensures less switching delay, thus resulting in faster marking and lower granularity at both ends of each marking vector. Since the original juxtaposed halftoning was designed for raster devices, the resulting raster image needs to be converted into a vector representation suitable for our laser device. To do this, an initial line (a discrete line with unit thickness) is used as a mask and slid onto each halftone layer corresponding to each laser primary color ( Figure 5 ). This produces a vector list of different primary colors that span the image plane and are sent to the laser device for marking.

[0139] The color prediction model has two roles in the color management workflow. First, it constructs a third color gamut produced by halftoning a set of primary colors. It predicts the colors of thousands of halftones spanning the space of the relative areas of the primary colors in each halftone, called the area coverage. The gamut surface is then fit to the volumetric point cloud and subsequently used for gamut mapping 30. Second, the forward model is used in the color separation step 31, which calculates the area coverage of the primary colors for any input color to be reproduced.

[0140] The Yule-Nielsen (YN) prediction model is used to predict multi-color, side-by-side halftones of laser primaries. The Yule-Nielsen equation [Yule and Nielsen, 1951] predicts the CIEX color coordinates (X t )for:

[0141]

[0142] Among them, X i is the CIEX value of the ith primary color, a i is its area coverage. The same equation applies to predicting CIEX and CIEY color coordinates. The exponent n, called the Yule-Nielsen n value, is a tuning parameter.

[0143] Color separation is built on a forward prediction model to calculate the specific primaries and their area coverage that reproduce a given color (in the third color gamut). Since the YN model is analytically irreversible, color separation is performed by optimization:

[0144]

[0145] ||a|| 1 =1,a∈[0,1] q

[0146] Where c is the target color in the CIELAB color space and a is the optimization variable, i.e., the vector of area coverages of the q primaries. Since the CIEDE2000 color difference formula [Sharma et al., 2005] is used for distance measurement, colors modeled using the YN model (YN(a)) should be converted from CIEXYZ to CIELAB (represented by the function Lab in the above equation). The equation searches for the area coverage vector that, after labeling, results in the minimum distance to the target color. Since the different primaries are in parallel, the sum of their relative area coverages should be 1 and non-negative.

[0147] The laser marking is based on laser-induced oxidation of the surface layer (105a) of the specimen (105), or laser-induced structuring of the surface layer (105a) of the specimen (105), or laser-induced generation of micro / nanoparticles on the surface layer (105a) of the specimen (105).

[0148] In the following, different analyses and evaluations are performed on the color gamut exploration and image reproduction. Figure 6 As shown. In a preferred experimental setup, the hardware described below can be used. The laser marking device 101 includes main components in the form of an ytterbium fiber laser system (IPG Photonics YLPM-1-4x200-20-20) and a galvanometer scanner (Scanlab IntelliScanIII 10). The laser system (20W, 1064nm) generates a laser beam, which is redirected to any desired laser spot on the specimen by the Galvo Mirror system of the scanning device 103. The scanning device 103 is equipped with an infrared F-Theta lens (f=163mm) capable of imaging a 116×116mm flat field. The air filtration system prevents small particles from expanding in the room. In most experiments, 1mm thick 1.4301V2A type stainless steel (AISI 304) was used as the specimen 105. Color laser marking can also be performed on titanium.

[0149] Figure 7 Seven laser marking parameters are described, including:

[0150] (1) Frequency: limit the number of laser emissions per second (1.6-1000kHz, 100Hz step),

[0151] (2) Power: adjust the output power of each transmission (0-100%, 256 steps),

[0152] (3) Pulse width: defines the duration of a single shot (4, 8, 14, 20, 30, 50, 100, 200 nanoseconds) and the scan parameters that form a line cluster with the following properties:

[0153] (4) Speed: The speed of movement along the vector when limiting the mark (0-2000mm / s, 1mm / s step),

[0154] (5) Number of lines: limit the number of lines in the cluster (1-20 lines, 1 line step),

[0155] (6) Halo line: defines the distance between lines within a cluster (1-15 μm, 1 μm step)

[0156] (7) Channel count: indicates the number of times the vector is marked (1-10 times, 1 step).

[0157] Figure 7 Visualizations of the laser (left) and scanning (right) parameters are shown. The multi-channel, line clusters in the schematic on the right form the final color. It is worth noting that due to the technical limitations of the laser source, the laser parameters cannot be used in any combination. Furthermore, it is not possible to change the laser parameters in a hurry. For example, the switching frequency and power require 0.6ms and 3ms respectively; changing the pulse width takes about 2 seconds because it requires re-establishing the connection between the controller board and the laser. Therefore, in our path planning, we allow for switching delays after changing these parameters to ensure that the laser source can properly adapt to the new parameters.

[0158] For each point in the design space 10, 10a, 10b, its performance point 14 is measured in order to decide how to use the point in our exploration framework. Except for the design space diversity, all performance criteria can be evaluated by measuring the thickness of the clusters of marker lines and the color of the marker patches. This is done in two stages. First, to measure the thickness of the clusters, a first detection device 106 in the form of a handheld digital microscope (Reflecta DigiMicroscope USB 200) is used. In the second step, by juxtaposing multiple clusters in the desired area, the thickness of a given cluster is used to mark its corresponding patch. The pillars of the performance space 13 exploration, hue and chroma, are both calculated from the perceptual color space CIELAB. Therefore, a color calibration for measuring the color of the marker patches is performed [Hong et al., 2001]. The color calibration connects the camera RGB signals to CIEXYZ coordinates through regression form. Then, the CIEXYZ values ​​can be converted to CIELAB coordinates using a set of known analytical transformations [Wyszecki and Stiles, 1982]. To train the regression, 121 printed color patches were used, known spectra were measured using an X-Rite i7 spectrophotometer, and ground-truth CIEXYZ values ​​assuming D65 illumination were obtained.

[0159] The same printed patch is captured with a second detection device 106 in the form of a Nikon D750 digital SLR camera (with a macro lens Tamron SP 90mm F / 2.8Di), obtaining a raw RGB signal that has been corrected for spatial and temporal light fluctuations. It should be noted that this setup is one possible experimental setup. As already noted, at least one detection device 106 can be connected to and controlled by a control unit 108. The above-mentioned measurements can then be performed automatically.

[0160] Color calibration shows high accuracy on a test set of 16 printed patches, with an average ΔE 00 =2.26, with a maximum of 5.00. Therefore, the calibration was used to estimate the CIELAB color of the marked patch. The structural nature of oxide colors leads to significant changes in their appearance, depending on the viewing and illumination geometry. It was observed that the color of the laser mark is most saturated in specular and near-specular geometries. Therefore, inspired by previous work on metal printing [Pjanic and Hersch, 2013], the color reproduction was limited to non-scattering geometries. To this end, the stainless steel substrate was illuminated with a large area of ​​scattered light, which was tilted about 45° to the substrate normal and photographed with a camera at a similar angle.

[0161] To evaluate the proposed color gamut exploration algorithm, multiple runs are performed while dropping different targets during different runs to show the impact of the target on the exploration behavior. For fair comparison, it always starts from the same randomly generated initial population. All generations have the same population size of 100. For the color wheel, the random number of circular cross-sections is restricted between 4 and 72, while the random angle offset α is between 0 and 2π. The stopping threshold of MCMOS is set to 0.001%.

[0162] Figure 8 Shown is the color gamut evolution for a comprehensive exploration on AISI 304 stainless steel (using f C 、f HS 、f R 、f PSD 、f DSD ). It shows the evolution of the chromaticity gamut in the hue-chromaticity polar plot when optimizing all performance criteria (called full exploration). Overall, a good evolution of colors with symmetrical, dense gamuts can be seen. Interestingly, the purple to red region is filled with a considerable delay, suggesting that some colors are harder to find than others.

[0163] Fig. 9 Shows the color gamut evolution for random exploration on AISI 304 stainless steel. Figure 8 ), random labeling does not lead to a sufficient increase in the gamut. Since random exploration does not include resolution goals, the comparison Fig. 9 d and Fig.10 e is more illustrative because they all have the same number of samples and none of them include resolution purposes. The stagnant behavior of random markers over time ( Fig. 9 ) and the lack of systematic resolution enhancement indicate that a very large number of samples would be required to match the full color gamut produced by the method of the present invention.

[0164] Fig.10 The explored color gamuts for different configurations on AISI 304 stainless steel are shown in Figure 2. To evaluate the effectiveness of the Monte Carlo color wheel approach, two similar explorations were run, where the only difference was the hue expansion objective f HS In one ( Fig.10 a), and in the second ( Fig.10 d). It is observed that the MC method promotes hue diversity, leading to a symmetrical color gamut. Ignoring the Monte Carlo method leads to a bias towards high chromaticity regions. For example, in Fig.10 d, due to the initial population (such as Fig. 9 a) has a large number of chromatic yellow members, so this area was emphasized during exploration.

[0165] Labeling a high-quality image requires a diverse set of saturated colors placed next to each other at high spatial resolution. The criteria are given by f R Define, where the design parameters are encouraged to label clusters of thin lines. Two explorations with equal number of iterations are compared, one with ( Fig.10 b) and the other time does not have ( Fig.10 e) Minimize thickness. The results show that this objective slows gamut growth and overall gamut area by disadvantaging saturated but thicker colors. Crucially, however, it produces denser gamuts at lower thicknesses, as can be seen when comparing gamuts that include only colors with less thickness ( Fig.10 c and 10f). Dense color gamuts are very important during primary color pruning (Section 4.1). Furthermore, Fig.11 a shows the average thickness of the entire population at each iteration. The continuous line depicts the thickness of the R The average thickness of the iterations. The dashed line indicates no f R Average thickness of iterations (considering only t < 80 μm). Unlike the exploration without thickness objective, the full exploration shows a steady decrease in the thickness of the marker line.

[0166] The proposed color reproduction pipeline is evaluated through quantitative analysis as well as various fully color labeled images. After the pruning step, a total of 6 primaries are obtained, including black and white primaries. Fig.11 c shows four primary colors. Their parameters are shown in the following table:

[0167]

[0168] In the pruning phase, spatial and temporal repeatability was checked by marking candidate primaries at four different locations on the substrate and comparing their colors pairwise. Across all comparisons, the average ΔE 00 Primaries with a thickness greater than 4 are discarded. Progressive primaries are discarded as well, reducing the number of primaries until the gamut area drops by more than 10%. For resolution clipping, colors with a thickness of 40 ± 5 μm are retained. In addition, colors with a thickness of about 20 μm are considered by juxtaposing two of them together to the target resolution.

[0169] To test the accuracy of the Yule-Nielsen model, the primary color and the 92 test patches were labeled with different area coverage of the primary color. The average ΔE obtained 00 The error is 2.25 (Std = 0.96, Min = 0.50, Max = 4.26), proving the high accuracy of the forward model. Fig.11In b, the average reproduction error of the Yule–Nielsen model for different values ​​of n is plotted. It is shown that a value of n equal to 1 works very well for the described configuration, simplifying the current model to the well-known Neugebauer model [Rolleston and Balasubramanian, 1993]. There are several physical and empirical interpretations of the Yule–Nielsen n value in the literature [Lewandowski et al., 2006]. In classical ink printing on paper, it explains the optical spot enlargement due to the lateral propagation of light inside the substrate [Hébert, 2014]. Babaei and Hersch

[2015] showed that the parameter is responsible for shading and masking in metallic ink halftones. As can be inferred from the optimal value of n we set, subsurface scattering in metals is very negligible, as expected. Furthermore, the primary colors of the mark are very flat on the surface and do not cause shading or masking.

[0170] Fig.12 Different results generated by the proposed image reproduction pipeline using the selected primaries are shown. Comparing the marked images with their gamut-mapped counterparts, it can be observed that the colors are reproduced faithfully. Furthermore, no noticeable artifacts are introduced in the laser marked images. Fig.12 As shown in the images in the second row, the color gamut of the primaries allows for the tagging of diverse, vivid, and relatively saturated colors. Due to the high resolution of the primaries, high spatial frequencies are preserved. This allows for the tagging of images with a lot of detail, such as Fig.12 Shown in the lower right row.

[0171] In the following, the question of whether labeling parameters are transferable when using different labeling settings or substrates is investigated. First, a set of parameters reported in the literature [Antończak et al., 2013] are labeled and Fig.13 The results are shown in .

[0172] exist Fig.13 The labeled parameters of Antończak et al.

[2013] resulted in the colors shown in the middle row (as described in the original paper). The same parameters labeled on the same material (AISI 304) using the present apparatus (bottom) resulted in a significant color difference (mean ΔE 00 =15.3) and huge thickness variations. At the top, the colors in the current gamut are closest to the colors reported in the middle. Despite using highly similar hardware and materials, the reported colors are not reproducible on the setup. In addition, the color thickness variations are significant, making them unsuitable for halftones and therefore image marking.

[0173] In the table below, the color difference is reported when a common set of parameters for different situations is marked on the current settings:

[0174] Base A AISI 304 AISI 304 AISI 304 Base B AISI 304 2mm thick AISI 304 AISI 43 universal set 5.42(5.63) 9.30(6.27) 16.37(7.50) primary color 1.96(2.06) 6.46(4.15) 12.33(7.25)

[0175] The table is expressed in ΔE 00 The repeatability errors of color laser marking are shown in the form of means (and standard deviations). A generic set of 89 design points was chosen to represent the different colors in the explored color gamut. Pure repeatability tests were performed on an AISI 304 alloy, with acceptable but unsatisfactory accuracy obtained using the same marking settings. Repeatability deteriorated when the marking settings were changed by using a thicker substrate (2 mm) and thus leaving the focal plane. Finally, using a different stainless steel (AISI430) alloy resulted in the worst repeatability.

[0176] For comparison, in Table 1, the results of the same experiment performed with 6 extracted primaries are shown. Significantly higher accuracy is observed in the pure repeatability experiment, since the primaries have been pruned in this case. Interestingly, the primaries show acceptable repeatability when labeled out of focus, indicating that the extracted primaries are robust to some perturbations. However, the larger deviations when using a new substrate indicate that the primaries cannot be used to accurately label images on a new substrate. Fig.14 In this paper, images on a new substrate (stainless steel AISI 430) were marked using the primary colors explored and extracted on the default substrate (AISI 304). Although the image on the new substrate retains spatial details, there is a clear color shift compared to the image marked on the default substrate. In order to show that the method is generalizable, a full color gamut exploration was performed, primary color extraction and color reproduction were performed on the new substrate. The results are shown in Figure 2. Fig.14 (bottom row). Fig.14 A labeled image on AISI 304 (upper left) and AISI 430 (upper right) is shown, with the same primaries explored and extracted on AISI 304 showing clear color shifts. A newly explored and extracted set of primaries on AISI 430 shows good agreement between the gamut mapping of the same image labeled on AISI 430 (lower left) and the photograph (lower right). Further, Fig.15 Comprehensive exploration on the new substrate shown led to Figure 8 Different color gamuts are obtained on the default substrate shown. Fig.15 shows the color gamut evolution for a comprehensive exploration on AISI 430 stainless steel (using f C 、f HS 、f R 、f PSD 、f DSD ).

[0177] An iteration of the gamut exploration with a population size of 100 takes about 30 minutes. This involves marking individual clusters, measuring their thickness with a handheld microscope, marking the corresponding patches with the appropriate distance of the cluster and finally photographing them with a colorimetric camera. The manual measurement of the cluster thickness is the bottleneck as it takes about 20 minutes. In Matlab, computing a new generation using MCMOGA takes only a few seconds. The marking time of an image is a function of the number of vectors (after halftone vectorization) and the marking speed of the different primary colors. A larger number of vectors results in more switching delays, making the marking time highly dependent on the image content in addition to its size. For example, Fig.12 The two labeled images on the top and bottom rows on the right, despite having similar image sizes (7 by 11 cm), required approximately 18 million and 30 million vectors, and approximately 3 hours and 5.5 hours of labeling time, respectively.

[0178] In this paper, we propose a computational framework that enables a new application of laser marking: color image reproduction. The method first characterizes the device using an evolutionary exploration of its performance space, and then uses this space to mark high-resolution, color images. An obvious limitation of this approach is that, e.g. Fig.16 The dramatic change in appearance from diffuse to non-diffusing configuration is shown in Figure 1, which depicts Alessandro Allori's painting of Maria de' Medici, marked on AISI 304, photographed in non-diffusing (left) and diffuse (right) modes.

[0179] exist Fig.17 In FIG. 1 , an image of a laser marking on stainless steel (plate 13×13 cm) using the method according to the invention is shown.

[0180] Protection is claimed for all features disclosed in the application documents which are essential to the invention if they are novel over the prior art either individually or in combination.

[0181] Reference numerals list

[0182] 1 Optical components

[0183] 2 First color gamut

[0184] 3. Step aa) of the method for preparing a laser marking system

[0185] 4. Step bb) of the method for preparing a laser marking system

[0186] 5. Step cc) of the method for preparing a laser marking system

[0187] 6. Step dd) of the method for preparing a laser marking system

[0188] 7. Step ee) of the method for preparing a laser marking system

[0189] 8 Step ff) of the method for preparing a laser marking system

[0190] 9 Iterations

[0191] 10 Design Space

[0192] 10a Descendant Design Space

[0193] 10b Combined design space

[0194] 11 Design Points

[0195] 11a Descendant Design Point

[0196] 12 Laser parameters

[0197] 13 Performance Space

[0198] 13a Combined performance space

[0199] 14 Performance Points

[0200] 15 Circular sectors

[0201] 16 Color Wheel

[0202] 20 Methods for creating colored laser markings on test pieces

[0203] 21. Step a) of a method for creating a colored laser marking on a test piece

[0204] 22. Step b) of the method for creating a colored laser marking on a test piece

[0205] 23. Step b) of the method for creating a colored laser marking on a test piece

[0206] 24. Step c) of the method for creating a colored laser marking on a test piece

[0207] 25. Step d) of the method for creating a colored laser marking on a test piece

[0208] 26. Step e) of the method for creating a colored laser marking on a test piece

[0209] 27 Input Image

[0210] 28 Color Management Workflow

[0211] 28a Side-by-side halftone workflow

[0212] 29 Steps in the Color Management Workflow aa)

[0213] 30 Steps in the Color Management Workflow

[0214] 31 Steps in the Color Management Workflowcc)

[0215] 32 Steps in the Color Management Workflow

[0216] 33 Steps in the Color Management Workflow

[0217] 34 Steps in the Color Management Workflow

[0218] 100 Laser Marking System

[0219] 101 Laser

[0220] 102 Laser Beam

[0221] 103 Scanning device

[0222] 104 Scanning Device

[0223] 105 Specimens

[0224] 105a Surface layer

[0225] 106 Detection device

[0226] 107 Evaluation Device

[0227] 108 Control Unit

[0228] 109 Database

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Claims

1. A method (1) for preparing a laser marking system (100) for reproducing a laser marked color image on a specimen, comprising the following steps: a) providing a laser marking system (100) and a test piece (105) comprising a surface layer (105a), wherein the laser marking system comprises a preset number of laser parameters (12); b) exploring a first color gamut (2) specified by a laser marking system (100) and a test piece (105) comprising a surface layer (105a), comprising the following steps: aa) creating (3) a design space (10) having a preset number of design points (11), wherein each design point comprises a combination of a preset number of laser parameters (12), the design space being a space having design points; bb) performing (4) sample marking on the test piece (105) for each design point (11); cc) measuring (5) the sample using at least one detection device (106) and determining a performance point (14) for each design point, wherein the measured performance points (14) define a performance space (13); dd) evaluating (6) the performance space (13) according to a preset performance criterion using an evaluation device (107), wherein a Pareto front comprising a subset of performance points is determined, the Pareto front being a Pareto set projected into the performance space; ee) generating (7) a descendant design space (10a) having descendant design points (11a); ff) creating (8) a first color space (2) using the subset of performance points forming the Pareto front; Wherein steps bb) to dd) are iterated (9) a preset number of iterations, wherein in each iteration (9) the offspring design space (10a) of the previous iteration is used in step bb), wherein in each iteration, the measured performance space is combined (15) with the performance space of the previous iteration (9) so that in step dd) the combined performance space (13a) is used.

2. The method (1) according to claim 1, wherein The laser system (100) comprises at least one pulsed laser (101) and at least one scanning device (103, 104), wherein: The laser spot is movable relative to the test piece (105) by means of the scanning device (103, 104), or the test piece is movable relative to the laser spot by means of the scanning device (103, 104).

3. The method according to claim 2, wherein The design point (11, 11a) includes at least one laser parameter (12), which is selected from: laser pulse frequency, laser pulse power, laser pulse width, speed of the laser beam along the vector relative to the specimen during marking, the number of lines that defines the number of lines in the cluster representing the marked sample, the distance between the lines in the cluster representing the marked sample, and the number of marking vectors, wherein the design point (11, 11a) also includes parameters of the laser beam focal length, the type of medium gas, and the ambient temperature.

4. The method (1) according to any one of claims 1 to 3, wherein The performance criteria in step dd) include at least one of chromaticity, tone extension, resolution, performance space diversity, design space diversity, color repeatability, and color uniformity.

5. The method (1) according to any one of claims 1 to 3, wherein The performance criteria in step dd) include at least one of chromaticity, resolution, performance space diversity, design space diversity, wherein the performance points (14) are projected into a CIECH space, wherein the CIECH space is divided into a first number of circular sectors (15) forming a color wheel (16), wherein The performance points (14) within each sector (15) of the color wheel (16) are evaluated according to the performance criterion, wherein the evaluation is iterated a preset number of iterations, wherein in each iteration the number of sectors (15) forming the color wheel (16) is changed, wherein each performance point (14) is characterized by a frequency vector indicating presence in a specific Pareto front.

6. The method (1) according to any one of claims 1 to 3, wherein An additional evaluation of the achromatic performance according to the performance point (14) is performed by performing step b) using the performance criteria in step dd) brightness, resolution, performance space diversity, design space diversity.

7. The method (1) according to any one of claims 1 to 3, further comprising the following steps: A set of primary colors is selected from said first color gamut (2), wherein the selected primary colors form a second color gamut.

8. The method (1) according to any one of claims 1 to 3, wherein Data related to the design space (10, 10a, 10b) and the performance space (13, 13a) of the first color gamut (2) and / or data related to the second color gamut are stored in a database (109).

9. A method (20) for reproducing a laser-marked color image on a test piece (105) comprising a surface layer (105a), comprising the following steps: a) verifying (21) a database (109) of data relating to a first color gamut (2) and / or a second color gamut, the first color gamut (2) and / or the second color gamut being related to the type of laser marking system (100) and the test piece, wherein the data were obtained by the method (1) for preparing a laser marking system (100) according to one of claims 1 to 8; b) retrieving (22) data related to the first color gamut (2) and / or the second color gamut from a database (109), or performing (23) a method (1) for preparing a laser marking system (100) according to one of claims 1 to 8; c) providing (24) an input image (27) to be reproduced as a laser mark on the test piece (105); d) executing (25) a color management workflow (28, 28a) by which control data for a laser marking system is created from an input image (27); e) Marking is performed (26) according to the control data.

10. The method (20) according to claim 9, wherein The color management workflow (28) is a parallel halftone workflow (28a).

11. The method (20) according to claim 9 or 10, wherein The color management workflow (28) includes the following steps: aa) applying the forward color prediction model of (29) to construct a third color gamut with respect to the second color gamut, and the use of parallel halftones; bb) mapping (30) the input image (27) into a third color domain; cc) performing (31) color separation such that for each mapped color, the corresponding area coverage of each primary color is determined; dd) binarizing the area coverage using a side-by-side halftone method (32) and creating (33) a raster halftone image; ee) converting (34) the raster halftone image into vector data, wherein the control data comprises said vector data.

12. The method (20) according to claim 9 or 10, wherein The specimen (105) has a metal surface layer (105a), wherein the laser marking is based on laser-induced oxidation of the surface layer (105a) of the specimen (105), or laser-induced structuring of the surface layer (105a) of the specimen (105), or laser-induced generation of micro / nanoparticles on the surface layer (105a) of the specimen (105).

Images