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Characterization of Print Quality in Terms of Colorimetric Aspects
20 Characterization of Print Quality in Terms of Colorimetric Aspects 1
Michael Dattner 1 and Daniel Bohn 2 Innovation Management, BST eltromat International GmbH, Bielefeld, Germany 2 Bergische Universita¨t Wuppertal, Wuppertal, Germany
O U T L I N E 20.1 Colorimetric Aspects 20.1.1 Color Perception 20.1.2 Color Measurement 20.1.3 Spectral Data to CIE Lab Data 20.1.4 Color Deviation Formulas
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20.2 Characterization of Print Quality 20.2.1 Homogeneity of Unprinted Substrate
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20.2.2 Influence of Ink Transfer 20.2.3 Different Materials, Different Perception 20.2.4 Inline Spectral Measurements for Continuous Quality Control in Terms of Color References
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20.1 Colorimetric Aspects
20.1.1 Color Perception
Colorimetric aspects originated with the possibility to describe color in a metrical way; however, a formal definition of color (DIN 5033, part 1) only deals with the perception of color by a human being. It is also defined to ensure that only colorrelated aspects are taken into account for color matching (DIN, 1992). According to DIN 5033, for example, gloss and texture have to be excluded from a sufficient and reproducible color description. Figure 20.1 shows two adjacent color areas with a non-textured surface in a homogeneous surrounding area for color matching, as is suggested in this definition. If differences are still visible, even if the matching is done in the suggested way, these variances are actually related solely to color issues (Richter, 1980). This is the focus of the following sections. Normative and biological fundamentals are introduced especially for describing the perception of color. Measurement geometries and color measurement standards are presented to establish the basis for defining a color gamut with color deviation formulas.
Standardized frameworks for color measurement are based on the introduced specifications of color matching. Science defines light as the band of electromagnetic radiation that is perceptible by the human eye. The relevant wavelength range is l ¼ 380e780 nm (1 nm ¼ 109 m). This range is responsible for the color stimulus and is located between the short-wavelength ultraviolet radiation and the infrared radiation, which is on the other side of the visible spectrum (cf. Figure 20.2). Color perception or in fact the color stimulus occurs because of the absorption of radiation power of the visible light in the cones of the eye. Cones are a type of photoreceptor cells that are responsible for the human color vision. From a physical point of view, the radiation can be described by its spectral composition in the visible wavelength range. This composition is in the context of colorimetry the spectral radiation distribution which defines the color stimulus function 4(l) (Bergmann, Schaefer, & Lang, 1993). In the case of a self-luminous object, 4(l) is equal to its relative spectral power distribution S(l). In the
Printing on Polymers. http://dx.doi.org/10.1016/B978-0-323-37468-2.00020-8 Copyright © 2016 Elsevier Inc. All rights reserved.
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Figure 20.1 Nontextured surface and adjacent color areas for color matching.
Figure 20.2 Electromagnetic spectrum.
case of noneself-illuminant objects (in particular, printed products), the color stimulus is equal to the product of the relative spectral power distribution of the used illumination S(l) and the radiance factor b(l) of the surface (Bergmann, et al., 1993; Richter, 1980; Urban, 2005). Therefore, 4(l) of a so-called nonluminous color is defined by the color stimulus function (cf. Eq. (20.1)): 4ðlÞ :¼ SðlÞ$bðlÞ c l ˛ ½380 nm; :::; 780 nm: (20.1) A closer look at the retina shows that the cones are mainly located inside of the fovea and within a ring of rods (“R”) (cf. Figure 20.3). This is important in the context of the “CIE standard observer” introduced in following section. Furthermore, rods are mainly responsible for the twilight vision, when the light has not enough radiation energy to activate the cones (mesopic vision/scotopic vision).
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Figure 20.3 Distribution of cones and rods in the eye.
There are three types of cones: short-wavelength receptors (“S” with an absorption maximum at 420 nm), medium-wavelength receptors (“M” with an absorption maximum at 534 nm), and long-wavelength receptors (“L” with an absorption maximum at 563 nm) (cf. Figure 20.4; Bowmaker, 1981). The normalized absorption curves of the cones describe how the individual cone types respond if these are stimulated by light with a specific intensity in an individual wavelength range. But this on its own is no explanation for the separable perception of brightness or color: the stimulation of a cone increases in the same manner, if the intensity of the light increases, or if the maximum in the relative spectral power distribution of the incoming light S(l) is shifted closer to the absorption maximum of the cone. But the combination of at least two cone types, which can be stimulated independently from each other with overlapping absorption ranges, allows the desired differentiation between a change in brightness and in color (Funk, 2006). For example, in the (weighted) difference of the M- and L-absorption curves inducing the sensitivity curve of this two-cone system, the resulting values are positive as well as negative and divide, therefore, the full spectral range clearly into two different color ranges (cf. Figure 20.5; Funk, 2006). In this case, the complementary color areas show their maximum in the green or in the red wavelength range, respectively. In the “white point” (cf. Figure 20.5), both cone types are stimulated with the same intensity. Only if the intensity of the incident light is changed, the differential amount of both
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Figure 20.4 Normalized absorption characteristics of the rods (R) and the three types of cones: short-(S), medium(M), and long (L)-wavelength receptors.
absorption curves stays the same, because both cone types are stimulated equally. But, if the wavelength of the incoming light changes, the cone types are stimulated individually. Therefore, a color shift can be easily distinguished from a change in brightness (Funk, 2006). When taking all other cone-system combinations into account, it becomes obvious that the information regarding the absorbed radiation intensity of the
three different cone types can be transformed into four different and independent color signals and one additional brightness signal (Funk, 2006). Even Leonardo da Vinci already ascribed the complementary color pairs red and green as well as yellow and blue, supplemented by the achromatic pair white and black, for the brightness signal for the description of the color space of all colors (cf. Figure 20.6; Richter, 1996).
Figure 20.5 Addition and subtraction of the M- and L absorption curves.
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Figure 20.6 Complementary colors.
Complementary color pairs serve also for the CIE L*a*b* color space with the color coordinate axes a* and b*, supplemented by the brightness parameter L*. For example, it is possible to mix red with a yellow or blue with a red: This causes the impression of a yellowish red or a reddish blue. But if red is mixed with green or blue with yellow, one does not get the impression of a greenish red or yellowish blue (Funk, 2006). The weighted sum of the absorbed radiation power of all cones (“L þ M þ S”) is interpreted as brightness. If the stimulus of all cone types is comparable (“L z M z S”), colors are perceived as white, gray, or black depending on the light intensity. The rede green differentiation is achieved by the complementary colors red and green (“L M þ S”), whereby the blueeyellow differentiation is achieved by “L þ M S” (Funk, 2006). There is no conclusive clarification on which color perception is actually motivated by the introduced color stimulus function. There are numerous and very complex effects interacting with each other: Identical color stimuli can lead to different color perceptions and vice versa (Bergmann et al., 1993). For reproducible and comparable results generated by color matching and spectral measurements, normative conditions must be considered. Some classical examples for distorting effects are: simultaneous contrast, constants in perception of lightness, and lateral inhibition of the retina. Example 20.1 (Simultaneous contrast): If a sheet of gray paper is cut into two pieces, and each piece is placed upon a yellow and upon a blue background, respectively, their visual appearance differs. Against a yellow background, the visual impression of gray will be bluish; against the blue background it will be yellowish (Bach, 2008). Example 20.2 (Constants in the perception of lightness): The two-dimensional image (Figure 20.7,
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Figure 20.7 Two examples for optical effects: constants in perception of lightness (left); Hermann grid (right).
left) is interpreted as a three-dimensional structure with a corresponding shadow. Two fields in this structure are marked with a circle. One circle is within and one is outside the shadow. The one within the shadow appears to be brighter than the other one. Actually, the photometric brightness of both fields is the same (Bach, 2008). Example 20.3 (Lateral inhibition of the retina): When looking at the Hermann grid in Figure 20.7 (right), gray spots can be observed where the white lines cross, which disappear while focusing directly on the crossing (Bach, 2008). In addition, the surrounding conditions and the illumination have to be taken into account for the measurement as well as the visual matching process: Example 20.4 (Chromatic adaption of the eye): Both human eyes can be chromatically adapted independently from each other. Different chromatic adaptions lead to a different color perception in spite of an identical color stimulus. Therefore, if a light non-luminous color is observed with a green adapted or a white adapted eye, respectively, the perception with the green adapted eye appears temporarily reddish compared to the perception of the white adapted eye (Richter, 1980). A green adaption can be realized by focusing on a strong green self-luminous body for about 2 min. This example also proves the need for considering metamerism effects. According to DIN 5033, part 1, metamers are colors that have the same color stimulus values when observed under one illumination (e.g., daylight) and are dissimilar when observed under another illumination (e.g., incandescent light). This is caused by the differing radiance factor b(l) of the metamers. Two isometric or identical colors have identical radiance factors b(l) and, therefore, identical color stimulus values for all illuminants or standard observer (DIN, 1992).
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To consider these and additional potential effects on the perception of color in the context of the characterization of print quality, fundamental basics of color measurement and visual color matching are introduced in the following section.
20.1.2 Color Measurement In 1924, the CIE (Commission Internationale de L’Eclairage) founded an expert group for norms in color measurement (CIE, 1924). This group developed recommendations concerning CIE standard illuminants, CIE standard observer, measurement geometries including a reference absolute white standard and the CIE 1931 XYZ color space (CIE, 1931, pp.19e29; CIE, 1959, p. 91; CIE, 1963, p. 108). In 1971, the CIE made an official recommendation for color measurement in the context of the scientific consideration of the perception of color (CIE, 1971). Finally, these CIE recommendations induce the CIE L*a*b* color space which links the human perception of color in a uniform threedimensional color space with corresponding color deviation formulas (CIE, 1986). CIE standard illuminants: In the context of colorimetric aspects, it is important to avoid undefined illuminations and the related influence on the perception of color. Various illumination conditions, such as daylight, incandescent light, or neon light, are represented by CIE profiles for standard illuminants in laboratory and in industrial usage. Typically, the CIE standard illuminant D50 is used in the prepress and printing industry. This illuminant is specified by
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its relative spectral radiation distribution S(l) between 300 nm and 780 nm, just like all CIE illuminants (cf. Figure 20.8; CIE, 1971). Reference absolute white standard: The CIE recommends using a perfect reflecting diffuser as a reference white. This white is characterized by a loss-free reflection combined with an ideal diffusion. Therefore, the corresponding radiance factor b(l) ¼ 1,0 c l ˛ [380 nm, ., 780 nm] (CIE, 1971). An industrial version of a reference white is integrated in every spectral measurement device. Measurement geometry: The 45 /0 -measurement geometry (directed illumination in a 45 angle to the vertical sampling aperture or vice versa) is used typically for non-transmittance measurements of nontextured surfaces (cf. Figure 20.9). Slightly textured surfaces can be measured reliably with a circular illumination in a 45 angle to the vertical sampling aperture (CIE, 1971). The diffuse/8 -measurement geometry is designed especially for measurements of textured surfaces because of its diffused illumination of the sample. For special materials such as metalized surfaces, a multi-angle spectrophotometer is required. Generated measurements lead to individual colorimetric results concerning every single angle combination of illumination and sample (cf. Figure 20.9). This is caused by the non-diffused reflection of the metalized surface, where information concerning the specular component is often of interest. These and more measurement geometries are defined in DIN 5033 part 8 (DIN, 1992). CIE standard observer: As already shown in Figure 20.1, adjacent color areas with a non-
Figure 20.8 Relative spectral radiation distribution of D65, D50, and A.
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Figure 20.9 Measurement geometries (45 /0 ; 0 /45 ; diffuse d8 ; multiangle).
textured surface in a homogeneous surrounding area are suggested for color matching. Depending on the size of the colored areas, only cones (2 to the fovea) or cones and rods (10 to the fovea) (cf. Figures 20.3, 20.10 and 20.11) are involved (Funk, 2006). This has an impact on the color perception because of the introduced individual behavior of Figure 20.11 Involved zones of the retina for the 2 and 10 standard observer conditions.
Figure 20.10 Density distribution of cones and rods corresponding to their angle difference.
cones and rods. To consider at least these two options, the CIE established two different standard observers. Technical implementation: In the context of color measurement, observer conditions are only relevant with respect to a minimal aperture size depending on the printed screen frequency which is specified in DIN 16,536-2 (DIN, 1993). The individual perception of a sample concerning the 2 - and 10 -retina stimulation is considered in technical implementations by including the 2 - and 10 -CIE standard observer for deriving the color stimulus values. Measurements with a spectral photometer (using a measurement geometry as introduced in the related previous chapter) are
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realized by illuminating a sample and measuring the diffuse share of the reflected light. This is resolved spectrally by using a diffraction grating in front of a silicon photodiode array (CCD element) (Haeuser, 1984). Each spectral band is dedicated to a particular area on a silicon photodiode array, which detects and analyzes the related luminance (light intensity [cd] per area [m2]) (cf. Figure 20.12). This kind of device provides the introduced radiance factor b(l), which is the quotient of the detected luminance of a sample for each wavelength band l and the detected luminance of the reference white, which has been previously illuminated with the same light source (Bergmann et al., 1993). The radiance factor is also referred to as spectral reflectance or remission degree. For ideal matt samples, the observed result is independent of the angle of illumination which is used for the measurement. Because real surfaces are rarely ideal matt and glossy up to a certain extent, the radiation density is rarely fully independent of the illumination and measurement angle. Therefore, devices with different measurement geometries fail to generate identical measurement results. Hence, it is essential to state the actual used measurement geometry, the selected standard observer as well as the corresponding illuminant in the communication of color (Bergmann et al., 1993). In addition, and especially in the case of polymer substrates, the orientation of the device in relation to the orientation of the substrate has an influence on the measured radiation factor. For example, potential irregularities in the material flow during a film extrusion and/or the molecular properties of different
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polymers are responsible for effects like for example, variance in brightness and homogeneity or fluctuations in the surface texture. Measurements with a circular illumination at 45 to the vertical sampling aperture show differences even if the orientation of the device in relation to the substrate is changed. The presented measured radiance factor b(l) combined with an illuminant typedwhich is independent of the actually used measurement illuminationdis part of the introduced color stimuli function 4(l), which is the basis for the following data transfer from spectral data to color stimulus values.
20.1.3 Spectral Data to CIE Lab Data Color stimulus values cannot be derived directly from spectral data. The needed intermediate step is calculating primary or the so-called norm valences, which are related directly to the spectral data. It is possible to describe each color stimulus as the sum of three primary valences, whereby occasionally negative summands occur. These primary valences can be selected freely, but they should be able to create a color gamut as linear independent vectors (Richter, 1980). To avoid the mentioned negative summands, actual existing primary valences like R, G, and B (red, green, and blue) are replaced by the virtual norm valences X, Y, and Z as specified by the CIE. With these norm valences each color, stimulus can be described with only positive (virtual) norm valence fragments (Richter, 1980). The CIE defines this norm valence system by the color matching functions x2 ðlÞ, y2 ðlÞ, z2 ðlÞ or x10 ðlÞ, y10 ðlÞ, z10 ðlÞ to consider the two introduced CIE standard observers (CIE, 1971). Norm valences as the physiological equivalent of the physical color stimuli 4(l) are given by Eq. (20.2): X X :¼ k$ 4ðlÞ$xðlÞ$Dl; l X Y :¼ k$ 4ðlÞ$yðlÞ$Dl; l X Z :¼ k$ 4ðlÞ$zðlÞ$Dl:
(20.2)
l
Figure 20.12 Spectrophotometer with diffraction grating, and CCD element.
mirrors,
Dl describes the discretization step width of the visible spectra into, for example, 10-nm steps
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(Richter, 1980). The normalization factor k ensures that Y calculated for the reference white radiation equals 100 for any norm illuminant type (cf. Eq. (20.3)): 100 SðlÞ$zðlÞ$Dl l
k :¼ P
(20.3)
Thus, the geometrical result of a color stimulus value can be interpreted as color coordinates in a color gamut. This primary and fundamental color system is (among others) by definition the CIE norm valence system XYZ (Richter, 1980). The difference between two color stimuli can be determined by the Euclidean distance in the norm valence system. But the perceptional difference between two colors does not correspond correctly to this distance in the norm valence system, which was identified first by MacAdam. Based on the chromaticity of a color that is specified by the two derived parameters x and y described in Eq. (20.4), it is possible to show that color samples, which have the same color distance to a reference color in different directions, do not lead to a circle around this color in this two dimensional xeysystem: X ; XþY þZ Y y¼ : XþY þZ
x¼
(20.4)
Actually, this leads to elliptical distributions that vary in size, depending on the absolute position of the reference color in the color gamut (cf. Figure 20.13; Schlaepfer, 2002). This yields to ellipsoids in the three-dimensional CIE xeyeY color space, if the luminance variation DY is also taken into account (Macadam & Brown, 1949). By considering these MacAdam ellipsoids, the CIE L*a*b* color space is a mathematical transformation of the XYZ system into a uniform chromaticity scale color space. In this system, the perceptional color characterization is improved due to (intended) equidistant color coordinates (Schlaepfer, 2002). Each color is described by the brightness (L*), the redegreen value (a*) and the blueeyellowvalue (b*) based on the complementary color model (cf. previous chapter “Perception of color”;
Figure 20.13 MacAdam ellipses.
Richter, 1980). These CIE-L*a*b* color values are defined by Eq. (20.5). rffiffiffiffiffi 3 Y 16; L :¼ 116$ Yn rffiffiffiffiffi rffiffiffiffiffi 3 X 3 Y * ; a :¼ 500$ Xn Yn 0sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rffiffiffiffiffi1 3 Y 3 Z A: b* :¼ 200$@ Yn Zn *
(20.5)
Xn, Yn, and Zn are the norm valence values of the related illuminant and are calculated with b(l) ¼ 1,0 c l ˛ [380 nm, ., 780 nm]. If the quotient below the square root is smaller than 216/24,389, the square root is replaced by the term in Eq. (20.6) with P for X, Y, or Z, respectively. 1 24389 P $ $ þ 16 16 27 Pn
(20.6)
20.1.4 Color Deviation Formulas The relationship between the human perception and instrumental measurement results is an important
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subject. The assessment of color differences by means of color difference formulas is one of the most important aims of industrial color measurement. There is no doubt about the advantages such a method can offer: the magnitude and direction of the color difference can be stated in quantitative terms so that colored products can be controlled by objective standards (Strocka, 1971). Unfortunately, the existing color difference formulas have practical limitations. Many articles have shown that the correlation between visual assessment and calculated color difference leaves often much to be desired. It is also a well-known fact that different color difference formulas lead to very diverse results. But it also has to be borne in mind that visual assessment by different observers is also a subject of great variation and that the reproducibility of such a valuation is limited (Strocka, 1971). In general and independent of the type of color deviation formula, two colors can be optically distinguished if DE 1; DE > 3 is perceived as a significant color deviation. Depending on customer demands, even DE > 4 is acceptable under certain circumstances. The CIE DEab formula, in 1976, was the first color-difference formula that relates a measured to a known CIE L*a*b* value. This formula has been succeeded by the DE94 and DE2000 formulas because the CIE L*a*b* space turned out not to be as perceptually uniform as intended, especially in the saturated regions. In 1984, the Color Measurement Committee of the Society of Dyers and Colorists
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defined an additional formula, also based on the CIE L*C*h* color space (cf. Table 20.1 and related literature; CIE, 2001; Cui, Rigg, & Luo, 2001).
20.2 Characterization of Print Quality Additional colorimetric parameters can be derived from the introduced colorimetric values to describe essential properties of a print product, for example, opacity as a measure of impenetrability of light into the printing or tonal value as a measure for halftones. Opacity can be described by the relation between results of the samples measured upon a white and a black background, respectively. This can be used for process control concerning nonopaque film that should be applied upon different base materials. The effective tonal value of a halftone is the quantity of the surface that is actually covered by ink. The tonal value increase, known as “dot gain,” is a well-known parameter in process control. The general and film-related basics concerning colorimetric aspects presented so far allow for an objective and reproducible evaluation and comparison of printings on polymer substrates. The homogeneity of the print or the unprinted surface and the individual influence of different materials on the perception of color will be introduced, before a discussion of data determined by continuous inline measurements for process control finalizes this chapter.
Table 20.1 Color Deviation Formulas * DEab
* DE94
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ ðDL* Þ2 þ ðDa* Þ2 þ ðDb* Þ2 vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ! !2 !2 u u DL* 2 DC * Dh* t ¼ þ þ kL SL kC S C k h Sh
* DE2000
* DECMC
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi ! !2 !2 u * * u DL* 2 DC Dh ¼t þ þ þ RT f ðDC * Dh* Þ kL S L kC SC k h Sh vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ! !2 !2ffi u * * u DL* 2 DC Dh ¼t þ þ l SL c SC Sh
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20.2.1 Homogeneity of Unprinted Substrate Because of tolerances in the production process of substrates, there are variations in the homogeneity of the substrate up to a certain extent. These variations (e.g., variation in the thickness with an orientation) induce discrepancies in the final product, which can be perceived and measured. While these variances occur also in fiber-based substrates, these effects are more critical with polymer substrates because of their opacity and the related special fields of application. The measurement results concerning this inhomogeneity show the influence of these variances also at the characterization of the printing product. If translucent inks are in use, the quality of the print cannot be better than the quality of the substrate itself. Using a printed opaque white intermediate ink layer for printed areas, there is a potential for improvement due to the compensation property of this layer.
20.2.2 Influence of Ink Transfer Uniform ink transfer does not depend only on welladjusted rollers or on electrostatic conditions between the substrate and ink. Typical effects, which have no direct influence on the colorimetric characterization, are, for example, bleeding, smudging, register positioning error, unsteady repeat length, and Moire´ effect, although they will not be discussed in this chapter. Other printing defects, which will be discussed in the following examples, can be distinguished into two groups: invalid or valid for colorimetric characterization. Actual printing defects such as ink mottling, pinholes, missing dots, bridging, deformed dots,
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inaccurate printed dots, halo effect, and doughnut effect, have a direct influence on the measurement result. If these effects occur within the measurement area, a sufficient colorimetric characterization is constrained. Example 20.5 (Ink mottling): The ink mottling effect appears as a dotted or marbling printed image of the solid printing area (cf. Figure 20.14). Possible causes for this effect are: uneven printing plate surface, printing cylinder or substrate; incorrect ink viscosity; too high doctor blade pressure. With regard to the color data, this induces an increase in the colorimetric brightness, which could be misinterpreted as the ink intensity being too low. Sufficient decisions cannot be made if this effect occurs, because the influence of this effect cannot be distinguished from effects that are not related to this printing defect. Example 20.6 (Pinholes): The pinhole effect appears as small bright points in the solid print area (cf. Figure 20.15). Possible causes for this effect are: uneven printing plate surface, printing cylinder or substrate; the ink foams; too low printing pressure. Like the previous effect, this leads to an increase in the colorimetric brightness in the color data, which could be misinterpreted as the ink intensity being too low. Example 20.7 (Bridging): The bridging effect appears as ink connections between individual screen dots (cf. Figure 20.16). Possible causes for this effect are: too high ink viscosity; the transferred volume; the anilox roller and the printing plate screen frequency do not match; excessive pressure. This leads to an increase in the tonal value in the color data, which could be misinterpreted as the dot gain being too high.
Figure 20.14 Ink mottling (left without effect, right with effect).
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Figure 20.15 Pinholes (left without effect, right with effect).
Figure 20.16 Bridging (left without effect, right with effect).
Figure 20.17 Missing dots (left without effect, right with effect).
Example 20.8 (Missing dots): The missing dot effect appears as missing dots in halftone areas (cf. Figure 20.17). Possible causes for this effect are: wrong doctor blade angle; poor ability to wet a film; improper solvent type or mixture ratio. This leads to a decrease in the tonal value in the color data, which could be misinterpreted as the dot gain being too low.
Example 20.9 (Deformed screen dots): The shape of the screen dots deviates considerably from the original shape of the dots (cf. Figure 20.18). Possible causes for this effect are: too high pressure setting between the printing plate/ printing cylinder and the substrate; too low web tension; too high relief depth of the printing plate.
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Figure 20.18 Deformed screen dots (left without effect, right with effect).
Figure 20.19 Inaccurate printed screen dot (left without effect, right with effect).
This leads to an increase in the tonal value in the color data, which could be misinterpreted as the dot gain being too high. Example 20.10 (Inaccurate printed screen dot): The shape of the screen dot deviates considerably from the original shape (cf. Figure 20.19). Possible causes for this effect are: the ink dries on the printing plate; too high pressure setting between the printing plate/printing cylinder and the substrate; too high ink viscosity.
This leads to an increase in the tonal value in the color data, which could be misinterpreted as the dot gain being too high. Example 20.11 (Halo effect): The screen dot is surrounded by a ring (cf. Figure 20.20). Possible causes for this effect are: too high transfer volume of the anilox roller; too high pressure setting between the printing plate and the substrate; too high ink viscosity; too low pressure of the chamber doctor blade system.
Figure 20.20 Halo effect (left without effect, right with effect).
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Figure 20.21 Doughnut effect (left without effect, right with effect).
Figure 20.22 Hazing (left without effect, right with effect).
This leads to an increase in the tonal value in the color data, which could be misinterpreted as the dot gain being too high. Example 20.12 (Doughnut effect): The screen dot shows a gap (cf. Figure 20.21). Possible causes for this effect are: too low ink viscosity; the ink dries too fast; too high ink temperature. This leads to a decrease in the tonal value in the color data, which could be misinterpreted as the dot gain being too low. Furthermore, there are typical effects, which have a direct influence on the colorimetric characterization and can be identified and characterized using colorimetric data; they are hazing effect, wrong color strength, ink transfer into the following inking unit and dot gain. Example 20.13 (Hazing): The ink film shows a foggy appearance (cf. Figure 20.22). Possible causes for this effect are: improper stock tension; poor wipe; poor solvent mix. This effect can be identified and characterized by colorimetric data. If a color shift is detected in a
similar way in unprinted substrates and several colors, the CIE L*a*b* shift can be used to identify the inking unit that is responsible for the hazing effect. For example, if the CIE a* value is increased but the CIE b* value remains the same, it can be assumed that the yellow inking unit is responsible for the hazing effect. Example 20.14 (Color strength too low/too high): The color strength is weaker/too high in comparison to previous prints or samples (cf. Figure 20.23). Possible causes for this effect are: the ink dries on the anilox roller (too low color strength); the ink is too thin (color strength too low); the anilox roller is worn (too low color strength); too high ink volume of the anilox roller (too high color strength); incorrect settings of the doctor blade (too high/low color strength). This leads to a CIE L*a*b* color shift. Determined color deviation values can be used to identify the corresponding inking unit. In contrast to the hazing effect, this effect just affects areas that are covered with the related color.
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Figure 20.23 Color strength (left without effect/right with too high color strength in one inking unit).
Figure 20.24 Ink transfer (left without effect, right with effect).
Example 20.15 (Ink transfer into the following inking unit from the substrate surface): The ink transfer effect leads to a color shift at the following inking units (cf. Figure 20.24). Possible causes for this effect are: the ink dries too slowly; wrong electrostatic conditions between the substrate and ink. This effect can be identified and characterized by colorimetric data. If a color shift is detected, the CIE
L*a*b* shift can be used to identify the inking unit that is responsible for the ink transfer effect. For example, if yellow becomes greenish, the cyan ink dries too slowly, or if a transparent lacquer becomes colored, there is an ink transfer back into the lacquer inking unit. Example 20.16 (Dot gain too high): If the dot gain is too high, this leads to a reduction in the details and the contrast (cf. Figure 20.25).
Figure 20.25 Dot gain too high (left without effect/right with effect).
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Possible causes for this effect are: too soft printing plate; too high pressure setting between the printing plate/printing cylinder and the substrate. This effect can be identified and characterized with colorimetric data. If the dot gain is too high, the measurement results show darker color values with higher color strength, but only in the related halftones.
20.2.3 Different Materials, Different Perception Using the introduced characterization information, it is necessary to take additional surface properties into consideration. These are important if you have to achieve identical color perception with different basis materials. Identical CIE L*a*b* values obtained from different materials do not ensure an identical perception of color because of individual surface effects and vice versa. For example, CIE L*a*b* values obtained from a brushed tin surface can be identical to the CIE L*a*b* values of a gray printed polymer or a transparent film covering a flat, gray surface. One of the main reasons for the still visible difference is the surface glossiness combined with effects that occur because of the surface curvature. Nevertheless, a harmonization of the perception of color printed upon different materials can be realized up to a very high level (cf. Figure 20.26). This holds true for arbitrary surface colors. As is known, a perfect colorimetric match cannot be achieved in any case if printed polymer surfaces have to match surfaces of other materials. Since texture and gloss are per definition not included in the characterization of color (cf.: previous chapter “Perception of color”), these effects cannot be accurately described by colorimetric values, but it is possible to find a theoretical best match in this context by using related databases provided by, for example, ink supplier and measurement system manufacturer. Independent of the introduced challenges in colorimetric characterization of printed polymers, spectral measurements and colorimetric values are necessary in the context of quality assurance and process control. Valuable process control in terms of colorimetric aspects can be realized, for example, in the context of shrink-wrapping by measuring the polymer after printing, but before shrinking upon the aluminum base material. The opacity which can be derived from
Figure 20.26 Best practice in terms of harmonization of color upon different materials.
the colorimetric values is an important parameter to predict the final perception of the combined product.
20.2.4 Inline Spectral Measurements for Continuous Quality Control in Terms of Color All introduced effects can be identified while printing by using an inline measurement system. Highest data comparability between handheld and inline devices is the main issue. The second equally important issue is to identify the effects that interfere with a valuable characterization of the printing in a colorimetric context. Specifically, this means to avoid wrong decisions like compensating a seeming color deviation by modifying an ink recipe instead of adjusting, for example, the pressure setting between the printing cylinder and the substrate to avoid pinholes (cf. Table 20.2). Also the interfering influences of the introduced effects on the colorimetric values of halftones have to be identified before unsuitable corrective actions are initiated (cf. Table 20.3). Tables 20.2 and 20.3 show how the introduced effects can be considered by combining the colorimetric data with images that are related to the measurement position in time and location. The most important homogeneity check can be supported by an automatic
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Table 20.2 Extended Inline Measurement Result for a Solid.
Table 20.3 Extended Inline Measurement Result for a Halftone.
plausibility consideration. This helps to verify decisions based on the inline-generated colorimetric data by interpreting the provided images. Two types of images are shown: images including the surrounding area of the measurement position or images focusing only on the measurement spot (captured while the measurement takes place by using the flash of the spectrophotometer), respectively. The automatic homogeneity check result is visualized by a check
mark or a cross next to the images and in the last line of the tables.
References Bach, M. (2008). Die Hermann-Gitter-Ta¨uschung: Lehrbucherkla¨rung widerlegt. Ophthalmologe, 106, 913e917.
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Bergmann, L., Schaefer, C., & Lang, H. (1993). OptikLehrbuch der Experimentalphysik (9th ed.). Berlin: Walter de Gryter Verlag. Bowmaker, K. (1981). Visual pigments and colour vision in man and monkeys. Journal of the Royal Society of Medicine, 74(5), 348e356. CIE, I. C. O. I. (1924). Empfehlung: Es ist eine Expertengruppe f€ ur Fragen der Farbmessung einzusetzen. CIE-Proceedings, 68. CIE, I. C. O. I. (1931). Empfehlung f€ ur Farbmessung, 1-5. s.l., s.n. p. 19e29. CIE, I. C. O. I. (1959). Bericht des Farbmess Komitees. s.l., s.n. p. 91. CIE, I. C. O. I. (1963). Empfehlung, 1. s.l., s.n. p. 108. CIE, I. C. O. I. (1971). Colorimetry: official recommendations of the international commission on illumination (CIE). Paris: Bureau Central de la CIE. CIE, I. C. O. I. (1986). Colorimetry (2nd ed.). Vienna: Central Bureau of the CIE. CIE, I. C. O. I. (2001). Improvement to industrial colour-difference evaluation. Paris. s.n. p. 15. Cui, G., Rigg, B., & Luo, M. R. (2001). The development of the CIE 2000 colour difference. Color Research and Application, 26, 340e350. DIN, D. I. F. N. (1992). DIN 5033 Farbmessung. Berlin: Beuth Verlag.
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DIN, D. I. F. N. (1993). DIN 16527-3 Kontrollfelder Anwendung im Druck, Teil 3. Berlin: Beuth Verlag. Funk, G. (2006). Digital Media for Artists [Online] Available at: http://www.dma.ufg.ac.at/app/link/ Grundlagen Accessed 2015. Haeuser, F. (1984). Die Entstehung des Farbeindrucks bei der autotypischen Farbmischung. Bonn: Universita¨t Bonn. Macadam, D., & Brown, W. (1949). Visual sensitivities to combined chromaticity and luminance differences. Journal of the Optical Society of America, 39, 808e834. Richter, M. (1980). Einf€ uhrung in die Farbmetrik. Berlin: Walter de Gryter Verlag. Richter, K. (1996). Computergrafik und Farbmetrik. Berlin: VDE Verlag. Schlaepfer, K. (2002). Farbmetrik in der grafischen Industrie. s.l.: UGRA. Strocka, D. (1971 1, June). Color difference formulas and visual acceptability. Applied Optics, 1308e1313. Urban, P. (2005). Metamere und multispektrale Methoden zur Reproduktion farbiger Vorlagen (1st ed.). Hamburg: Books on Demand.
Michael Dattner 1 and Daniel Bohn 2 Innovation Management, BST eltromat International GmbH, Bielefeld, Germany 2 Bergische Universita¨t Wuppertal, Wuppertal, Germany
O U T L I N E 20.1 Colorimetric Aspects 20.1.1 Color Perception 20.1.2 Color Measurement 20.1.3 Spectral Data to CIE Lab Data 20.1.4 Color Deviation Formulas
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20.2 Characterization of Print Quality 20.2.1 Homogeneity of Unprinted Substrate
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20.2.2 Influence of Ink Transfer 20.2.3 Different Materials, Different Perception 20.2.4 Inline Spectral Measurements for Continuous Quality Control in Terms of Color References
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20.1 Colorimetric Aspects
20.1.1 Color Perception
Colorimetric aspects originated with the possibility to describe color in a metrical way; however, a formal definition of color (DIN 5033, part 1) only deals with the perception of color by a human being. It is also defined to ensure that only colorrelated aspects are taken into account for color matching (DIN, 1992). According to DIN 5033, for example, gloss and texture have to be excluded from a sufficient and reproducible color description. Figure 20.1 shows two adjacent color areas with a non-textured surface in a homogeneous surrounding area for color matching, as is suggested in this definition. If differences are still visible, even if the matching is done in the suggested way, these variances are actually related solely to color issues (Richter, 1980). This is the focus of the following sections. Normative and biological fundamentals are introduced especially for describing the perception of color. Measurement geometries and color measurement standards are presented to establish the basis for defining a color gamut with color deviation formulas.
Standardized frameworks for color measurement are based on the introduced specifications of color matching. Science defines light as the band of electromagnetic radiation that is perceptible by the human eye. The relevant wavelength range is l ¼ 380e780 nm (1 nm ¼ 109 m). This range is responsible for the color stimulus and is located between the short-wavelength ultraviolet radiation and the infrared radiation, which is on the other side of the visible spectrum (cf. Figure 20.2). Color perception or in fact the color stimulus occurs because of the absorption of radiation power of the visible light in the cones of the eye. Cones are a type of photoreceptor cells that are responsible for the human color vision. From a physical point of view, the radiation can be described by its spectral composition in the visible wavelength range. This composition is in the context of colorimetry the spectral radiation distribution which defines the color stimulus function 4(l) (Bergmann, Schaefer, & Lang, 1993). In the case of a self-luminous object, 4(l) is equal to its relative spectral power distribution S(l). In the
Printing on Polymers. http://dx.doi.org/10.1016/B978-0-323-37468-2.00020-8 Copyright © 2016 Elsevier Inc. All rights reserved.
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Figure 20.1 Nontextured surface and adjacent color areas for color matching.
Figure 20.2 Electromagnetic spectrum.
case of noneself-illuminant objects (in particular, printed products), the color stimulus is equal to the product of the relative spectral power distribution of the used illumination S(l) and the radiance factor b(l) of the surface (Bergmann, et al., 1993; Richter, 1980; Urban, 2005). Therefore, 4(l) of a so-called nonluminous color is defined by the color stimulus function (cf. Eq. (20.1)): 4ðlÞ :¼ SðlÞ$bðlÞ c l ˛ ½380 nm; :::; 780 nm: (20.1) A closer look at the retina shows that the cones are mainly located inside of the fovea and within a ring of rods (“R”) (cf. Figure 20.3). This is important in the context of the “CIE standard observer” introduced in following section. Furthermore, rods are mainly responsible for the twilight vision, when the light has not enough radiation energy to activate the cones (mesopic vision/scotopic vision).
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Figure 20.3 Distribution of cones and rods in the eye.
There are three types of cones: short-wavelength receptors (“S” with an absorption maximum at 420 nm), medium-wavelength receptors (“M” with an absorption maximum at 534 nm), and long-wavelength receptors (“L” with an absorption maximum at 563 nm) (cf. Figure 20.4; Bowmaker, 1981). The normalized absorption curves of the cones describe how the individual cone types respond if these are stimulated by light with a specific intensity in an individual wavelength range. But this on its own is no explanation for the separable perception of brightness or color: the stimulation of a cone increases in the same manner, if the intensity of the light increases, or if the maximum in the relative spectral power distribution of the incoming light S(l) is shifted closer to the absorption maximum of the cone. But the combination of at least two cone types, which can be stimulated independently from each other with overlapping absorption ranges, allows the desired differentiation between a change in brightness and in color (Funk, 2006). For example, in the (weighted) difference of the M- and L-absorption curves inducing the sensitivity curve of this two-cone system, the resulting values are positive as well as negative and divide, therefore, the full spectral range clearly into two different color ranges (cf. Figure 20.5; Funk, 2006). In this case, the complementary color areas show their maximum in the green or in the red wavelength range, respectively. In the “white point” (cf. Figure 20.5), both cone types are stimulated with the same intensity. Only if the intensity of the incident light is changed, the differential amount of both
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Figure 20.4 Normalized absorption characteristics of the rods (R) and the three types of cones: short-(S), medium(M), and long (L)-wavelength receptors.
absorption curves stays the same, because both cone types are stimulated equally. But, if the wavelength of the incoming light changes, the cone types are stimulated individually. Therefore, a color shift can be easily distinguished from a change in brightness (Funk, 2006). When taking all other cone-system combinations into account, it becomes obvious that the information regarding the absorbed radiation intensity of the
three different cone types can be transformed into four different and independent color signals and one additional brightness signal (Funk, 2006). Even Leonardo da Vinci already ascribed the complementary color pairs red and green as well as yellow and blue, supplemented by the achromatic pair white and black, for the brightness signal for the description of the color space of all colors (cf. Figure 20.6; Richter, 1996).
Figure 20.5 Addition and subtraction of the M- and L absorption curves.
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Figure 20.6 Complementary colors.
Complementary color pairs serve also for the CIE L*a*b* color space with the color coordinate axes a* and b*, supplemented by the brightness parameter L*. For example, it is possible to mix red with a yellow or blue with a red: This causes the impression of a yellowish red or a reddish blue. But if red is mixed with green or blue with yellow, one does not get the impression of a greenish red or yellowish blue (Funk, 2006). The weighted sum of the absorbed radiation power of all cones (“L þ M þ S”) is interpreted as brightness. If the stimulus of all cone types is comparable (“L z M z S”), colors are perceived as white, gray, or black depending on the light intensity. The rede green differentiation is achieved by the complementary colors red and green (“L M þ S”), whereby the blueeyellow differentiation is achieved by “L þ M S” (Funk, 2006). There is no conclusive clarification on which color perception is actually motivated by the introduced color stimulus function. There are numerous and very complex effects interacting with each other: Identical color stimuli can lead to different color perceptions and vice versa (Bergmann et al., 1993). For reproducible and comparable results generated by color matching and spectral measurements, normative conditions must be considered. Some classical examples for distorting effects are: simultaneous contrast, constants in perception of lightness, and lateral inhibition of the retina. Example 20.1 (Simultaneous contrast): If a sheet of gray paper is cut into two pieces, and each piece is placed upon a yellow and upon a blue background, respectively, their visual appearance differs. Against a yellow background, the visual impression of gray will be bluish; against the blue background it will be yellowish (Bach, 2008). Example 20.2 (Constants in the perception of lightness): The two-dimensional image (Figure 20.7,
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Figure 20.7 Two examples for optical effects: constants in perception of lightness (left); Hermann grid (right).
left) is interpreted as a three-dimensional structure with a corresponding shadow. Two fields in this structure are marked with a circle. One circle is within and one is outside the shadow. The one within the shadow appears to be brighter than the other one. Actually, the photometric brightness of both fields is the same (Bach, 2008). Example 20.3 (Lateral inhibition of the retina): When looking at the Hermann grid in Figure 20.7 (right), gray spots can be observed where the white lines cross, which disappear while focusing directly on the crossing (Bach, 2008). In addition, the surrounding conditions and the illumination have to be taken into account for the measurement as well as the visual matching process: Example 20.4 (Chromatic adaption of the eye): Both human eyes can be chromatically adapted independently from each other. Different chromatic adaptions lead to a different color perception in spite of an identical color stimulus. Therefore, if a light non-luminous color is observed with a green adapted or a white adapted eye, respectively, the perception with the green adapted eye appears temporarily reddish compared to the perception of the white adapted eye (Richter, 1980). A green adaption can be realized by focusing on a strong green self-luminous body for about 2 min. This example also proves the need for considering metamerism effects. According to DIN 5033, part 1, metamers are colors that have the same color stimulus values when observed under one illumination (e.g., daylight) and are dissimilar when observed under another illumination (e.g., incandescent light). This is caused by the differing radiance factor b(l) of the metamers. Two isometric or identical colors have identical radiance factors b(l) and, therefore, identical color stimulus values for all illuminants or standard observer (DIN, 1992).
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To consider these and additional potential effects on the perception of color in the context of the characterization of print quality, fundamental basics of color measurement and visual color matching are introduced in the following section.
20.1.2 Color Measurement In 1924, the CIE (Commission Internationale de L’Eclairage) founded an expert group for norms in color measurement (CIE, 1924). This group developed recommendations concerning CIE standard illuminants, CIE standard observer, measurement geometries including a reference absolute white standard and the CIE 1931 XYZ color space (CIE, 1931, pp.19e29; CIE, 1959, p. 91; CIE, 1963, p. 108). In 1971, the CIE made an official recommendation for color measurement in the context of the scientific consideration of the perception of color (CIE, 1971). Finally, these CIE recommendations induce the CIE L*a*b* color space which links the human perception of color in a uniform threedimensional color space with corresponding color deviation formulas (CIE, 1986). CIE standard illuminants: In the context of colorimetric aspects, it is important to avoid undefined illuminations and the related influence on the perception of color. Various illumination conditions, such as daylight, incandescent light, or neon light, are represented by CIE profiles for standard illuminants in laboratory and in industrial usage. Typically, the CIE standard illuminant D50 is used in the prepress and printing industry. This illuminant is specified by
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its relative spectral radiation distribution S(l) between 300 nm and 780 nm, just like all CIE illuminants (cf. Figure 20.8; CIE, 1971). Reference absolute white standard: The CIE recommends using a perfect reflecting diffuser as a reference white. This white is characterized by a loss-free reflection combined with an ideal diffusion. Therefore, the corresponding radiance factor b(l) ¼ 1,0 c l ˛ [380 nm, ., 780 nm] (CIE, 1971). An industrial version of a reference white is integrated in every spectral measurement device. Measurement geometry: The 45 /0 -measurement geometry (directed illumination in a 45 angle to the vertical sampling aperture or vice versa) is used typically for non-transmittance measurements of nontextured surfaces (cf. Figure 20.9). Slightly textured surfaces can be measured reliably with a circular illumination in a 45 angle to the vertical sampling aperture (CIE, 1971). The diffuse/8 -measurement geometry is designed especially for measurements of textured surfaces because of its diffused illumination of the sample. For special materials such as metalized surfaces, a multi-angle spectrophotometer is required. Generated measurements lead to individual colorimetric results concerning every single angle combination of illumination and sample (cf. Figure 20.9). This is caused by the non-diffused reflection of the metalized surface, where information concerning the specular component is often of interest. These and more measurement geometries are defined in DIN 5033 part 8 (DIN, 1992). CIE standard observer: As already shown in Figure 20.1, adjacent color areas with a non-
Figure 20.8 Relative spectral radiation distribution of D65, D50, and A.
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Figure 20.9 Measurement geometries (45 /0 ; 0 /45 ; diffuse d8 ; multiangle).
textured surface in a homogeneous surrounding area are suggested for color matching. Depending on the size of the colored areas, only cones (2 to the fovea) or cones and rods (10 to the fovea) (cf. Figures 20.3, 20.10 and 20.11) are involved (Funk, 2006). This has an impact on the color perception because of the introduced individual behavior of Figure 20.11 Involved zones of the retina for the 2 and 10 standard observer conditions.
Figure 20.10 Density distribution of cones and rods corresponding to their angle difference.
cones and rods. To consider at least these two options, the CIE established two different standard observers. Technical implementation: In the context of color measurement, observer conditions are only relevant with respect to a minimal aperture size depending on the printed screen frequency which is specified in DIN 16,536-2 (DIN, 1993). The individual perception of a sample concerning the 2 - and 10 -retina stimulation is considered in technical implementations by including the 2 - and 10 -CIE standard observer for deriving the color stimulus values. Measurements with a spectral photometer (using a measurement geometry as introduced in the related previous chapter) are
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realized by illuminating a sample and measuring the diffuse share of the reflected light. This is resolved spectrally by using a diffraction grating in front of a silicon photodiode array (CCD element) (Haeuser, 1984). Each spectral band is dedicated to a particular area on a silicon photodiode array, which detects and analyzes the related luminance (light intensity [cd] per area [m2]) (cf. Figure 20.12). This kind of device provides the introduced radiance factor b(l), which is the quotient of the detected luminance of a sample for each wavelength band l and the detected luminance of the reference white, which has been previously illuminated with the same light source (Bergmann et al., 1993). The radiance factor is also referred to as spectral reflectance or remission degree. For ideal matt samples, the observed result is independent of the angle of illumination which is used for the measurement. Because real surfaces are rarely ideal matt and glossy up to a certain extent, the radiation density is rarely fully independent of the illumination and measurement angle. Therefore, devices with different measurement geometries fail to generate identical measurement results. Hence, it is essential to state the actual used measurement geometry, the selected standard observer as well as the corresponding illuminant in the communication of color (Bergmann et al., 1993). In addition, and especially in the case of polymer substrates, the orientation of the device in relation to the orientation of the substrate has an influence on the measured radiation factor. For example, potential irregularities in the material flow during a film extrusion and/or the molecular properties of different
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polymers are responsible for effects like for example, variance in brightness and homogeneity or fluctuations in the surface texture. Measurements with a circular illumination at 45 to the vertical sampling aperture show differences even if the orientation of the device in relation to the substrate is changed. The presented measured radiance factor b(l) combined with an illuminant typedwhich is independent of the actually used measurement illuminationdis part of the introduced color stimuli function 4(l), which is the basis for the following data transfer from spectral data to color stimulus values.
20.1.3 Spectral Data to CIE Lab Data Color stimulus values cannot be derived directly from spectral data. The needed intermediate step is calculating primary or the so-called norm valences, which are related directly to the spectral data. It is possible to describe each color stimulus as the sum of three primary valences, whereby occasionally negative summands occur. These primary valences can be selected freely, but they should be able to create a color gamut as linear independent vectors (Richter, 1980). To avoid the mentioned negative summands, actual existing primary valences like R, G, and B (red, green, and blue) are replaced by the virtual norm valences X, Y, and Z as specified by the CIE. With these norm valences each color, stimulus can be described with only positive (virtual) norm valence fragments (Richter, 1980). The CIE defines this norm valence system by the color matching functions x2 ðlÞ, y2 ðlÞ, z2 ðlÞ or x10 ðlÞ, y10 ðlÞ, z10 ðlÞ to consider the two introduced CIE standard observers (CIE, 1971). Norm valences as the physiological equivalent of the physical color stimuli 4(l) are given by Eq. (20.2): X X :¼ k$ 4ðlÞ$xðlÞ$Dl; l X Y :¼ k$ 4ðlÞ$yðlÞ$Dl; l X Z :¼ k$ 4ðlÞ$zðlÞ$Dl:
(20.2)
l
Figure 20.12 Spectrophotometer with diffraction grating, and CCD element.
mirrors,
Dl describes the discretization step width of the visible spectra into, for example, 10-nm steps
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(Richter, 1980). The normalization factor k ensures that Y calculated for the reference white radiation equals 100 for any norm illuminant type (cf. Eq. (20.3)): 100 SðlÞ$zðlÞ$Dl l
k :¼ P
(20.3)
Thus, the geometrical result of a color stimulus value can be interpreted as color coordinates in a color gamut. This primary and fundamental color system is (among others) by definition the CIE norm valence system XYZ (Richter, 1980). The difference between two color stimuli can be determined by the Euclidean distance in the norm valence system. But the perceptional difference between two colors does not correspond correctly to this distance in the norm valence system, which was identified first by MacAdam. Based on the chromaticity of a color that is specified by the two derived parameters x and y described in Eq. (20.4), it is possible to show that color samples, which have the same color distance to a reference color in different directions, do not lead to a circle around this color in this two dimensional xeysystem: X ; XþY þZ Y y¼ : XþY þZ
x¼
(20.4)
Actually, this leads to elliptical distributions that vary in size, depending on the absolute position of the reference color in the color gamut (cf. Figure 20.13; Schlaepfer, 2002). This yields to ellipsoids in the three-dimensional CIE xeyeY color space, if the luminance variation DY is also taken into account (Macadam & Brown, 1949). By considering these MacAdam ellipsoids, the CIE L*a*b* color space is a mathematical transformation of the XYZ system into a uniform chromaticity scale color space. In this system, the perceptional color characterization is improved due to (intended) equidistant color coordinates (Schlaepfer, 2002). Each color is described by the brightness (L*), the redegreen value (a*) and the blueeyellowvalue (b*) based on the complementary color model (cf. previous chapter “Perception of color”;
Figure 20.13 MacAdam ellipses.
Richter, 1980). These CIE-L*a*b* color values are defined by Eq. (20.5). rffiffiffiffiffi 3 Y 16; L :¼ 116$ Yn rffiffiffiffiffi rffiffiffiffiffi 3 X 3 Y * ; a :¼ 500$ Xn Yn 0sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rffiffiffiffiffi1 3 Y 3 Z A: b* :¼ 200$@ Yn Zn *
(20.5)
Xn, Yn, and Zn are the norm valence values of the related illuminant and are calculated with b(l) ¼ 1,0 c l ˛ [380 nm, ., 780 nm]. If the quotient below the square root is smaller than 216/24,389, the square root is replaced by the term in Eq. (20.6) with P for X, Y, or Z, respectively. 1 24389 P $ $ þ 16 16 27 Pn
(20.6)
20.1.4 Color Deviation Formulas The relationship between the human perception and instrumental measurement results is an important
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subject. The assessment of color differences by means of color difference formulas is one of the most important aims of industrial color measurement. There is no doubt about the advantages such a method can offer: the magnitude and direction of the color difference can be stated in quantitative terms so that colored products can be controlled by objective standards (Strocka, 1971). Unfortunately, the existing color difference formulas have practical limitations. Many articles have shown that the correlation between visual assessment and calculated color difference leaves often much to be desired. It is also a well-known fact that different color difference formulas lead to very diverse results. But it also has to be borne in mind that visual assessment by different observers is also a subject of great variation and that the reproducibility of such a valuation is limited (Strocka, 1971). In general and independent of the type of color deviation formula, two colors can be optically distinguished if DE 1; DE > 3 is perceived as a significant color deviation. Depending on customer demands, even DE > 4 is acceptable under certain circumstances. The CIE DEab formula, in 1976, was the first color-difference formula that relates a measured to a known CIE L*a*b* value. This formula has been succeeded by the DE94 and DE2000 formulas because the CIE L*a*b* space turned out not to be as perceptually uniform as intended, especially in the saturated regions. In 1984, the Color Measurement Committee of the Society of Dyers and Colorists
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defined an additional formula, also based on the CIE L*C*h* color space (cf. Table 20.1 and related literature; CIE, 2001; Cui, Rigg, & Luo, 2001).
20.2 Characterization of Print Quality Additional colorimetric parameters can be derived from the introduced colorimetric values to describe essential properties of a print product, for example, opacity as a measure of impenetrability of light into the printing or tonal value as a measure for halftones. Opacity can be described by the relation between results of the samples measured upon a white and a black background, respectively. This can be used for process control concerning nonopaque film that should be applied upon different base materials. The effective tonal value of a halftone is the quantity of the surface that is actually covered by ink. The tonal value increase, known as “dot gain,” is a well-known parameter in process control. The general and film-related basics concerning colorimetric aspects presented so far allow for an objective and reproducible evaluation and comparison of printings on polymer substrates. The homogeneity of the print or the unprinted surface and the individual influence of different materials on the perception of color will be introduced, before a discussion of data determined by continuous inline measurements for process control finalizes this chapter.
Table 20.1 Color Deviation Formulas * DEab
* DE94
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ¼ ðDL* Þ2 þ ðDa* Þ2 þ ðDb* Þ2 vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ! !2 !2 u u DL* 2 DC * Dh* t ¼ þ þ kL SL kC S C k h Sh
* DE2000
* DECMC
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi ! !2 !2 u * * u DL* 2 DC Dh ¼t þ þ þ RT f ðDC * Dh* Þ kL S L kC SC k h Sh vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ! !2 !2ffi u * * u DL* 2 DC Dh ¼t þ þ l SL c SC Sh
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20.2.1 Homogeneity of Unprinted Substrate Because of tolerances in the production process of substrates, there are variations in the homogeneity of the substrate up to a certain extent. These variations (e.g., variation in the thickness with an orientation) induce discrepancies in the final product, which can be perceived and measured. While these variances occur also in fiber-based substrates, these effects are more critical with polymer substrates because of their opacity and the related special fields of application. The measurement results concerning this inhomogeneity show the influence of these variances also at the characterization of the printing product. If translucent inks are in use, the quality of the print cannot be better than the quality of the substrate itself. Using a printed opaque white intermediate ink layer for printed areas, there is a potential for improvement due to the compensation property of this layer.
20.2.2 Influence of Ink Transfer Uniform ink transfer does not depend only on welladjusted rollers or on electrostatic conditions between the substrate and ink. Typical effects, which have no direct influence on the colorimetric characterization, are, for example, bleeding, smudging, register positioning error, unsteady repeat length, and Moire´ effect, although they will not be discussed in this chapter. Other printing defects, which will be discussed in the following examples, can be distinguished into two groups: invalid or valid for colorimetric characterization. Actual printing defects such as ink mottling, pinholes, missing dots, bridging, deformed dots,
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inaccurate printed dots, halo effect, and doughnut effect, have a direct influence on the measurement result. If these effects occur within the measurement area, a sufficient colorimetric characterization is constrained. Example 20.5 (Ink mottling): The ink mottling effect appears as a dotted or marbling printed image of the solid printing area (cf. Figure 20.14). Possible causes for this effect are: uneven printing plate surface, printing cylinder or substrate; incorrect ink viscosity; too high doctor blade pressure. With regard to the color data, this induces an increase in the colorimetric brightness, which could be misinterpreted as the ink intensity being too low. Sufficient decisions cannot be made if this effect occurs, because the influence of this effect cannot be distinguished from effects that are not related to this printing defect. Example 20.6 (Pinholes): The pinhole effect appears as small bright points in the solid print area (cf. Figure 20.15). Possible causes for this effect are: uneven printing plate surface, printing cylinder or substrate; the ink foams; too low printing pressure. Like the previous effect, this leads to an increase in the colorimetric brightness in the color data, which could be misinterpreted as the ink intensity being too low. Example 20.7 (Bridging): The bridging effect appears as ink connections between individual screen dots (cf. Figure 20.16). Possible causes for this effect are: too high ink viscosity; the transferred volume; the anilox roller and the printing plate screen frequency do not match; excessive pressure. This leads to an increase in the tonal value in the color data, which could be misinterpreted as the dot gain being too high.
Figure 20.14 Ink mottling (left without effect, right with effect).
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Figure 20.15 Pinholes (left without effect, right with effect).
Figure 20.16 Bridging (left without effect, right with effect).
Figure 20.17 Missing dots (left without effect, right with effect).
Example 20.8 (Missing dots): The missing dot effect appears as missing dots in halftone areas (cf. Figure 20.17). Possible causes for this effect are: wrong doctor blade angle; poor ability to wet a film; improper solvent type or mixture ratio. This leads to a decrease in the tonal value in the color data, which could be misinterpreted as the dot gain being too low.
Example 20.9 (Deformed screen dots): The shape of the screen dots deviates considerably from the original shape of the dots (cf. Figure 20.18). Possible causes for this effect are: too high pressure setting between the printing plate/ printing cylinder and the substrate; too low web tension; too high relief depth of the printing plate.
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Figure 20.18 Deformed screen dots (left without effect, right with effect).
Figure 20.19 Inaccurate printed screen dot (left without effect, right with effect).
This leads to an increase in the tonal value in the color data, which could be misinterpreted as the dot gain being too high. Example 20.10 (Inaccurate printed screen dot): The shape of the screen dot deviates considerably from the original shape (cf. Figure 20.19). Possible causes for this effect are: the ink dries on the printing plate; too high pressure setting between the printing plate/printing cylinder and the substrate; too high ink viscosity.
This leads to an increase in the tonal value in the color data, which could be misinterpreted as the dot gain being too high. Example 20.11 (Halo effect): The screen dot is surrounded by a ring (cf. Figure 20.20). Possible causes for this effect are: too high transfer volume of the anilox roller; too high pressure setting between the printing plate and the substrate; too high ink viscosity; too low pressure of the chamber doctor blade system.
Figure 20.20 Halo effect (left without effect, right with effect).
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Figure 20.21 Doughnut effect (left without effect, right with effect).
Figure 20.22 Hazing (left without effect, right with effect).
This leads to an increase in the tonal value in the color data, which could be misinterpreted as the dot gain being too high. Example 20.12 (Doughnut effect): The screen dot shows a gap (cf. Figure 20.21). Possible causes for this effect are: too low ink viscosity; the ink dries too fast; too high ink temperature. This leads to a decrease in the tonal value in the color data, which could be misinterpreted as the dot gain being too low. Furthermore, there are typical effects, which have a direct influence on the colorimetric characterization and can be identified and characterized using colorimetric data; they are hazing effect, wrong color strength, ink transfer into the following inking unit and dot gain. Example 20.13 (Hazing): The ink film shows a foggy appearance (cf. Figure 20.22). Possible causes for this effect are: improper stock tension; poor wipe; poor solvent mix. This effect can be identified and characterized by colorimetric data. If a color shift is detected in a
similar way in unprinted substrates and several colors, the CIE L*a*b* shift can be used to identify the inking unit that is responsible for the hazing effect. For example, if the CIE a* value is increased but the CIE b* value remains the same, it can be assumed that the yellow inking unit is responsible for the hazing effect. Example 20.14 (Color strength too low/too high): The color strength is weaker/too high in comparison to previous prints or samples (cf. Figure 20.23). Possible causes for this effect are: the ink dries on the anilox roller (too low color strength); the ink is too thin (color strength too low); the anilox roller is worn (too low color strength); too high ink volume of the anilox roller (too high color strength); incorrect settings of the doctor blade (too high/low color strength). This leads to a CIE L*a*b* color shift. Determined color deviation values can be used to identify the corresponding inking unit. In contrast to the hazing effect, this effect just affects areas that are covered with the related color.
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Figure 20.23 Color strength (left without effect/right with too high color strength in one inking unit).
Figure 20.24 Ink transfer (left without effect, right with effect).
Example 20.15 (Ink transfer into the following inking unit from the substrate surface): The ink transfer effect leads to a color shift at the following inking units (cf. Figure 20.24). Possible causes for this effect are: the ink dries too slowly; wrong electrostatic conditions between the substrate and ink. This effect can be identified and characterized by colorimetric data. If a color shift is detected, the CIE
L*a*b* shift can be used to identify the inking unit that is responsible for the ink transfer effect. For example, if yellow becomes greenish, the cyan ink dries too slowly, or if a transparent lacquer becomes colored, there is an ink transfer back into the lacquer inking unit. Example 20.16 (Dot gain too high): If the dot gain is too high, this leads to a reduction in the details and the contrast (cf. Figure 20.25).
Figure 20.25 Dot gain too high (left without effect/right with effect).
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Possible causes for this effect are: too soft printing plate; too high pressure setting between the printing plate/printing cylinder and the substrate. This effect can be identified and characterized with colorimetric data. If the dot gain is too high, the measurement results show darker color values with higher color strength, but only in the related halftones.
20.2.3 Different Materials, Different Perception Using the introduced characterization information, it is necessary to take additional surface properties into consideration. These are important if you have to achieve identical color perception with different basis materials. Identical CIE L*a*b* values obtained from different materials do not ensure an identical perception of color because of individual surface effects and vice versa. For example, CIE L*a*b* values obtained from a brushed tin surface can be identical to the CIE L*a*b* values of a gray printed polymer or a transparent film covering a flat, gray surface. One of the main reasons for the still visible difference is the surface glossiness combined with effects that occur because of the surface curvature. Nevertheless, a harmonization of the perception of color printed upon different materials can be realized up to a very high level (cf. Figure 20.26). This holds true for arbitrary surface colors. As is known, a perfect colorimetric match cannot be achieved in any case if printed polymer surfaces have to match surfaces of other materials. Since texture and gloss are per definition not included in the characterization of color (cf.: previous chapter “Perception of color”), these effects cannot be accurately described by colorimetric values, but it is possible to find a theoretical best match in this context by using related databases provided by, for example, ink supplier and measurement system manufacturer. Independent of the introduced challenges in colorimetric characterization of printed polymers, spectral measurements and colorimetric values are necessary in the context of quality assurance and process control. Valuable process control in terms of colorimetric aspects can be realized, for example, in the context of shrink-wrapping by measuring the polymer after printing, but before shrinking upon the aluminum base material. The opacity which can be derived from
Figure 20.26 Best practice in terms of harmonization of color upon different materials.
the colorimetric values is an important parameter to predict the final perception of the combined product.
20.2.4 Inline Spectral Measurements for Continuous Quality Control in Terms of Color All introduced effects can be identified while printing by using an inline measurement system. Highest data comparability between handheld and inline devices is the main issue. The second equally important issue is to identify the effects that interfere with a valuable characterization of the printing in a colorimetric context. Specifically, this means to avoid wrong decisions like compensating a seeming color deviation by modifying an ink recipe instead of adjusting, for example, the pressure setting between the printing cylinder and the substrate to avoid pinholes (cf. Table 20.2). Also the interfering influences of the introduced effects on the colorimetric values of halftones have to be identified before unsuitable corrective actions are initiated (cf. Table 20.3). Tables 20.2 and 20.3 show how the introduced effects can be considered by combining the colorimetric data with images that are related to the measurement position in time and location. The most important homogeneity check can be supported by an automatic
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Table 20.2 Extended Inline Measurement Result for a Solid.
Table 20.3 Extended Inline Measurement Result for a Halftone.
plausibility consideration. This helps to verify decisions based on the inline-generated colorimetric data by interpreting the provided images. Two types of images are shown: images including the surrounding area of the measurement position or images focusing only on the measurement spot (captured while the measurement takes place by using the flash of the spectrophotometer), respectively. The automatic homogeneity check result is visualized by a check
mark or a cross next to the images and in the last line of the tables.
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