Colour change in OEM coatings after weathering

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1 Colour change in OEM coatings after weathering In this article the colourimetrical meaning of a grey scale is evaluated, aiming to find the key of the correlation between the visual assessment and colour difference formulae. The case of straight shades and effect coatings are considered as well. A simple empirical formula based on the well-known colour tolerances for controlling and supplying paint batches was found and the numerical results of deviation after exposure on a grey scale was computed. A general colourimetrical rule can thus be used for an objective evaluation of the results. Carlos Vignolo. For most paint suppliers providing excellent durability on outdoor exposure of their products is one of the challenges they have to normally overcome. The development of a methodology that will mimic and/or accelerate the effects of weathering itself is already a hard task, because no test can in principle duplicate all of the variables associated with a coatings environment and these tests can not always guarantee a perfect correlation. For example, the UV Accelerated Weathering (or Weather-O-Meter equipments "WOM", etc., similar to this) attempts to reproduce the damage caused by sunlight, rain and dew by exposing them to alternating cycles of light and moisture at controlled, elevated temperatures. The test simulates the effect of sunlight with fluorescent ultraviolet lamps, dew and rain with condensing humidity and water sprays. Exposure conditions can be varied to simulate various end-use environments. It aims to reproduce damage that occurs over months or years outdoors in only a few weeks. Regarding colour, many pigments fade, darken or change their shade after exposure to light. This is mainly due to the ultra-violet rays in sunlight which are sufficiently energetic to break certain chemical bonds and thus change molecules. A change in chemical structure means a change in the ability to absorb light in the visible region of the spectrum, with consequent variation (generally loss) of chroma or deviations in hue. If one considers that the test itself is a hard chapter for the coating industry, the visual assessment of the colour deviation after weathering promises at least a new and intense discussion. Some archaic scales for visual evaluation [1] are basically today the criteria to approve or reject a sample. The subjective intuition of an experienced laboratory assistant serves as guide for this declaration, quantifying it on a scale the deviation seen. Usually, this represents only colour variations on a grey base and one has to imagine its correspondence with the coloured deviation observed on the panels. This is in no extent an easy job. Colourimetrical examination of the grey scale Figure 1 shows schematically the different grades of colour deviation based on the grey scale. The task of the examiner is to determine, by comparing the colour shift between the exposed and a non exposed reference panel against the scale, which degree of colour shift corresponds to the grey scale rating. And this job must be done no matter what kind of colour is being examined. Let us imagine we have tested a very bright red effect shade (hereafter simply metallics) and after 2000 h of accelerated exposure in laboratory equipment, one wants to know which rating of deviation corresponds according to the scale. As we pointed out above, this seems to be not a very simple judgement. How can we do it? And in case of metallics, which angle should be taken for the comparison? Moreover, how are we in fact able to make this decision comparing colour shifts between grey and coloured panels? To have further understanding about the visual assessment, we have measured the colour difference between each pair of grey panels on the standardized grey scale [1]. Figure 2 shows the resulting colour measurements and clarifies how the scale was designed: accordingly to our expectations, no colour differences are observed at the a*-b* plane but only variations in the lightness L*, where the deltas are always positive. For instance, the rating 5 on the scale is associated with dl*=0, the rating 4 with dl*=0.7, etc., but always positive differences on the lightness are subsequently generated. Since da*=db*=0 these differences in lightness are equally converted in de* as also seen in the Figure 2. This fact is one of the first trivial consequences of this scale and will give us the first path to follow: the examiner will quantify the result severely when the exposed panel becomes lighter as when it becomes darker. One cannot simply use the CIELab total colour differences de* to quantify this non-symmetrical situation. Moreover, as it is well known despite the efforts of R. Hunter [2], the tri-stimulus CIE colour model does not give us an uniform spacing of perceived colours. To describe numerically in a consistent manner our human perception of the colour differences and associate their results with ratings on a grey scale, we will need an equally normalised colour space with asymmetrical acceptance limits in the lightness. Case of straight shades: use of CIE94 colour difference formula One major goal by improving non-uniformities in the CIELab colour-difference space was achieved by Berns et al. [3] and it is known as the CIE 94 colour difference formula. R. Berns conducted visual assessments using glossy acrylic paint pairs to perform a pass/fail colour tolerance experiment with different observers, attempting to answer how to improve existing models of colour-difference evaluation. A data set including 156 visual colour tolerances perceptually equivalent to a near-grey anchor pair of 1 CIELab unit was generated, obtaining a similar structure to the corresponding formula of the Colour Measurement Committee of the Society of Dyers and Colourists [4] (CMC) but with simpler weighting functions. The aim was in both cases the same: to develop a unique pass/fail formula for industrial applications. In other words, the same colour difference predicted by the formula for pairs of samples located in different colour regions must give the same perceptual differences. For our purpose of comparing the visual assessment after weathering with colourimetrical results we preferred to use the CIE94 colour difference formula, since in spite of the CMC equation which has different tolerances in the lightness depending on the absolute L*-value, the CIE94 has a constant weight K L. [equation 1 see PDF page four] To equation 1: A L = K L = const., A C = K C ( C* R ), A H = K H = K H ( C* R ). In equation (1) K L, K C and K H are the weighting factors for the lightness, the chroma and the hue, and C* R refers to the standard (or colour reference) for the pair of colours under

2 test. In automotive applications with numerical tolerancing system we observe that the best correlation with the visual perception is achieved precisely when the tolerance in the lightness is independent of its value. For instance, in the whole range of non-chromatic colours going from black (L* 0) to white (L* 90) the acceptance limit is constant and about of 0.3 units. In the opinion of the author this is the major limitation for the use of the CMC equation in automotive tolerancing for solid colours, which as in the case of metallics, introduces an acceptance limit in the lightness depending on its absolute value. Other points of view and discussion topics are also considered in several publications [5]. The comparison between some usual colour tolerances for the batch approval of automotive solid colours and the CIE94 as illustration is given in Table 1. A good correlation is obtained by simply fixing as weighting constants for K L, K C and K H the value 1/3. For that reason, if one wishes an uniform de-based description of the colour acceptability by comparing pairs of samples and this description has to be related with the tolerability of automotive customers, at least for straight shades the CIE94 formula will give us the best chances. Let us then examine again the colour deviation for each grey pair of panels in our normalised grey scale depicted in Figure 2, but now with the CIE94 tolerances and with an additional asymmetry in the lightness of 2 to 1 (that means, we tolerate twice in the direction dl*<0 the limit accepted for deviations in dl*>0). The <de> result according to equation 1 is shown in Figure 3. Moreover, with exception of the colour ratings between 4 and 5, an exponential regression analysis evidences a good fit to equation (2). [equation 2 see PDF page five] In equation (2) CR is the numerically computed colour rating on the grey scale. The correlation coefficient is and the standard deviation Equation (2) means that, at least for colour ratings under 4 we are able to compute the note on the grey scale by measuring the weighted color difference <de> from the equation (1). Furthermore, since for 4 <CR<5 a linear interpolation reaches for an excellent fit, one could cover the whole range of grey colour ratings. Our goal is of course not only to have a good correlation for non-chromatic colours but for the whole CIELab colour space and hence the introduction of the CIE94 formula. As pointed out, this formula guarantees an uniform framework where equal distances in that space correspond to equal perceived colour differences. In other words, a given value of <de> will be perceived for a human observer in the non-chromatic area or in the chromatic one so equally "good" or "bad". The magnitude of the colour deviation is in such sense normalised. Accordingly, equation (2) should give us a good fit also in the "coloured" area outside of the non-chromatic range as well. That assumption can be easily tested examining the correlation of the visual ratings based on the grey scale and the ratings computed with equation (2). Figure 4 shows the results for around 90 different panels in the red area (full shade and different binary admixtures with white) evaluated after weathering 1 year in Florida. The correlation looks like quite good and in comparison with the visual ratings, the numerical ones appear more critically evaluated being thus in average the quotient about 10 to 15% larger than the unity. Case of effect colour shades After the discussion of previous section, the path to effect colour shades seems to be straightforward. It is basically a matter of seeking a tolerancing system suitable with these kinds of colours. Although this topic was a challenge in the last decade, a tolerancing system for effect coatings is already well established in the coatings industry, at least regarding automotive applications [6]. In the present work we use these well-known acceptance values. [equations 3 see PDF page six] Valid for the chromatic area defined by: [formula 4a see PDF page seven] otherwise, [formula 4b see PDF page eight] However, the angular-dependence present in such coatings could appear at first sight as a barrier to apply formulae based on <de> to compute a numerical colour rating. Should the flop or the face angles be used for the numerical evaluation? Or should be the averaged total colour difference the leading magnitude? Let us take a look at the colour deviation after an accelerated weathering in WOM for several panels coated with a metallic shade compared with the original status without stress. Figure 5 show that, when colour measurements at the usual 3 angles 25, 45 and 75 are taken, the 45 geometry gives only an average description of the problem. Sometimes, the weighted colour difference <de> is larger at face than at flop or vice versa. But the crucial question is how does an examiner perform this kind of test visually? In fact, no one will "average" his visual assessment but better provide the judgement based on the worst angle viewed. If at the 25 angle the colour deviation is larger than at the 75 one, why do the average? The visual rating will be defined on the worst observation. Thus, we have examined for metallics the correlation of the visual ratings with those ones obtained using the equation (2), but taking as input for <de> the worse value resulting for the 25, 45 and 75 readings. As in Figure 4, a good correlation is observed also for metallics and these results are depicted by Figure 6. Again, a good correlation between the visual and the computed values is found. Acknowledgements The author thanks to Paul Czornij who kindly examined the manuscript adding very helpful and enlightening comments. REFERENCES [1] See e.g. DIN54001 "Graumaßstab" [2] R. Hunter and R. Harold, The Measurement of Appearance, J.Wiley & Sons (1987). [3] R. S. Berns, Deriving instrumental tolerances from pass-fail and calorimetric data, Color Res. Appl. 21, (1996) p. 459 [4] J. Clarke, R. McDonald and B. Rigg, Modification to the JPC79 color-difference formula, J Soc Dyers Col,100, (1984) p. 128 and p. 281 [5] S. Oglesby, The effectiveness of CIE94 compared with CMC equation, J. Soc. Dyers Col. 111, (1995) p. 380 [6] See e.g. DIN 6175 part 2, "Farbtoleranzen für Automobillackierungen" Results at a glance As concluding remarks, colourimetry can be applied for quantifying the colour deviation after weathering and the resulting values can be correlated with the ratings usually given based on a grey scale, provided a normalised and uniform colour tolerancing system and non-symmetrical acceptance limits on the lightness. This task can be performed for straight shade colours as well as effect colours. In the last case, the worse measured angle must be used as input for the computational colour ratings. A simple equation as (2) together with the individual acceptance limits

3 from equations (1) or (3) can be easily incorporated in the colourimetrical software to automatically transform colour deviations in colour ratings after weathering. The author: -> Carlos E. Vignolo was born in 1962, he studied physics at the University of Buenos Aires, and did his PhD Thesis in Germany, obtaining his degree in Since then, he has been employed by BASF Coatings working at the area of color and colorimetry, the first years in South America and since 2000 in Germany.

4 equation 1.

5 equation 2.

6 equations 3.

7 formula 4a.

8 formula 4b.

9 Figure 1: Grey scale for the visual assessment of colour change after exposure.

10 Figure 2: Colour differences on each pair of panels on the Grey Scale. The dashed lines separate the steps associated with different ratings.

11 Figure 3: Measured <de> according to equation 1 with the weighting factor and asymmetrical lightness tolerance against the colour ratings on the grey scale.

12 Figure 4: Measured colour ratings according to equation 2 as compared with the visual qualification based on the grey scale for panels in the red after weathering in Florida.

13 Figure 5: Multiangular colour measurements at 25, 45 and 75 (defined as usual from the gloss angle) for several metallics after 2000 h in a WOM equipment.

14 Figure 6: Measured colour ratings according to equation 2 as compared with the visual qualification based on the grey scale for panels coated with different metallic shades after weathering in Florida (washed and polished zones). Multiangular colour measurements were performed at 25, 45 and 75 and the worse <de> was selected as input.

15 Table 1: Some usual colour tolerances for straight shades in the automotive coatings industry.

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