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Interpreting weathering acceleration factors for automotive coatings using exposure models
Polymer Degradation and Stability 69 (2000) 307±316
Interpreting weathering acceleration factors for automotive coatings using exposure models David R. Bauer * Research Laboratory, MD 3182, Ford Motor Company, PO Box 2053, Dearborn, MI 48121, USA Received 20 January 2000; received in revised form 6 March 2000; accepted 13 March 2000
Abstract A number of exposure models have been developed to account for the variation in photo-oxidation rate with light intensity, wavelength distribution, temperature and humidity. These models have proven useful in accounting for the dierent observed rates of photo-oxidation and other weathering phenomena in dierent outdoor locations. In this paper, the same models are applied to calculate acceleration factors of various accelerated weathering tests relative to standard outdoor Florida weathering. The predicted acceleration factors are in reasonable agreement with the ranges commonly observed for various accelerated tests. The models also can be used to assess the sensitivity of the acceleration factors to material dierences and to variations in environmental conditions within a given chamber and from chamber-to-chamber. The critical importance of variability to the reproducibility and reliability of accelerated test results are discussed. Diculties of comparing measurements made outdoors with values for accelerated tests are also discussed. Of particular note is the need for all exposures to measure and report actual sample temperatures rather than air or black panel temperatures. # 2000 Elsevier Science Ltd. All rights reserved. Keywords: Weathering; Photooxidation; Paints; Coatings; Accelerated tests; Accelerated factors
1. Introduction In a previous paper, hereinafter referred to as I, several models were developed to interpret the relative rates of coating photo-oxidation and other weathering phenomena in dierent parts of the world [1]. The simplest model assumed that the extent of change was simply related to the dose of UV light received. While this model did not predict observed variations in photooxidation rate in most polymers and coatings, it did correctly predict variations in the rate of loss of ultraviolet light absorber (UVAs) from most coatings and polymers. Two more complex models included the eects of variations in ozone cut-o wavelength, temperature, and humidity on photo-oxidation. Both models used generic values for the sensitivity of materials to dierent wavelengths of UV light and for activation energy. The models diered in the predicted sensitivity to humidity. One model ignored humidity eects while another used a humidity parameter developed from acrylic melamine coating data. The predictions of these generic models were reasonably successful in accounting for the relative * Tel.: +1-313-594-1756. E-mail address: [email protected]
rates of photo-oxidation for dierent polymeric systems used in coatings and plastics. A critical ®nding was that a relatively small change in photo-oxidation stability of a given coating can have a dramatic eect on the percentage of failures at long times in service. The above ®nding raises signi®cant questions for accelerated testing. Accelerated test results ultimately have to be related to ®eld performance. Typically, accelerated test results are related to standard outdoor test results (i.e. exposure in Florida). Such results are calibrated through the use of an ``acceleration factor'' (that is, how many hours of a given accelerated test is equal to 1 year in Florida) [2]. Such acceleration factors are found to vary over time, with dierent test chambers running the ``same'' test, and for dierent materials. Large variations contribute to the general perception that accelerated testing often provides data of dubious value. In this paper, the exposure models in I are extended to attempt to understand acceleration factors for common accelerated weathering tests relative to standard outdoor Florida testing. In particular, we will attempt to understand the sources of variability that plague all weathering test results. We ®rst summarize the models, with emphasis on the types of outdoor environmental
0141-3910/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved. PII: S0141-3910(00)00074-4
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information required. We then describe the dierent accelerated tests, summarize typical test results, and discuss the parameters that are available to support prediction by the models. The models are then used to calculate acceleration factors to compare to experimental values. One critical factor is that the correct parameters necessary for accurate comparison are often not measured in either outdoor or accelerated tests (e.g. sample temperature). Nevertheless, it is possible to account for many accelerated test observations. Of particular importance is the discussion of factors that cause variation in accelerated test results. Based on the models, a number of suggestions to improve accelerated testing protocols are made. 2. Models for UVA loss and photo-oxidation The most important chemical processes that control weathering performance of current automotive coatings are the rates of photo-oxidation and loss of ultraviolet light absorber (UVA) [3]. Photo-oxidation of the clearcoat in basecoat/clearcoat coatings leads to loss of gloss and ultimately to cracking of the clearcoat. Loss of UVA leads to loss of protection of the basecoat which in turn can result in photo-oxidation of the basecoat and delamination of the clearcoat from the basecoat. The most extensive studies of UVA loss have been made by Pickett [4] and by Gerlock et al. [5]. Aside from volatility, which can be eliminated by judicious choice of UVA molecular weight, there appear to be two main mechanisms for loss of UVA. The ®rst is direct photolysis and the second is free radical attack on the excited state of the UVA. The rate of direct photolysis appears to depend almost solely on the total UV intensity. In any particular exposure, it sets a lower limit on the loss rate of a given UVA. For example, a typical benzotriazole in a stable polymer such as PMMA loses absorbance on exposure in Florida at a rate around 0.35A/year. If the matrix has a relatively high rate of photo-oxidation, free radical attack can increase the rate by an amount that is proportional to the rate of photo-oxidation. Thus, UVA loss in general can be expressed as a sum of a term that depends only on UV light intensity and a term that depends on photo-oxidation rate. For reasonably stable coatings, the term in light intensity appears to dominate and the relative extents of UVA loss in dierent locations correlate well with UV light dose [1]. It should be noted that measured UV doses are only available in limited locations. For modeling purposes, we have used total sunload to estimate relative rates of UVA loss as a function of location. The validity of this assumption was discussed in I. It is also important to note that in addition to location, UV dose depends on the angle of exposure. For Florida exposures, the harshest exposure for UV dose is a hor-
izontal or near-horizontal exposure. This is consistent with the observation that automotive coating failures tend to occur ®rst on horizontal surfaces. In this paper, all quoted doses are for horizontal exposures. Like UVA loss, the rate of photo-oxidation in polymers and coatings is, in general, proportional to the UV light intensity. However, the proportionality constant depends on the wavelength distribution of the UV light, on temperature, and, in some cases, on humidity. For comparison with dierent outdoor sites, sunload was used in I as a surrogate for UV light dose. Seasonal UV doses are available for important outdoor test sites such as Florida and Arizona. Seasonal data are necessary due to the dependence of photo-oxidation rate on wavelength distribution, temperature, and humidity. The relative quantum eciency of light at dierent wavelengths for photo-oxidation is illustrated in Fig. 1 for two dierent kinds of polymers commonly used as topcoats. In general, at constant light intensity, the longer the wavelength of light, the less photo-oxidation will occur. A survey of many common polymers used in exterior applications suggests that above 300 nm, the relative sensitivity with wavelength is not very material speci®c. This may be a result of the fact that for these polymers, the main chromophores are impurities common to all polymeric systems (ketones, hydroperoxides,...). For these polymers, the possibility of photooxidation induced by visible light is very small and only the eects of UV light need be considered.1 The ``acrylic'' quantum eciency function is an average of several non-absorbing polymers like ABS [6]. Some polymers based on aromatic functionality (particularly phthalate based esters and bisphenol-A polycarbonates) have strong transitions just below 300 nm leading to a dramatic increase in sensitivity to light below 300 nm [7]. The ``ester'' quantum eciency function is estimated from polycarbonate quantum eciency measurements [7] and the absorption curve for a phthalate ester coating [8]. These functions represent reasonable limits of sensitivity to short wavelength UV light. Most materials will likely fall somewhere in between these two limits. Thus, calculating the dierence in sensitivity between an accelerated test and sunlight for these two limits provides a good estimate of the material sensitivity of the test caused by improper wavelength distribution. The light wavelength distribution of sunlight is controlled by ozone concentration, the angle of the sun in
1 Three caveats should be mentioned. First, pigments and dyes may be sensitive to long-wavelength UV and visible light fastness issues. Second, some oxidation products may be destroyed by visible light. Third, some polymers used in coating primer and undercoats are sensitive to long-wavelength UV and even visible light. Penetration of this light through topcoats can result in determination. All of these caveats suggest that tests that employ only UV light rather than full spectrum light have to be interpreted carefully.
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Fig. 1. Representative action spectra for a short wavelength insensitive (acrylic) and sensitive (ester) materials. The long wavelength behavior (>300 nm) is assumed to be the same. The ester material has a transition below 300 nm that makes it much more susceptible to short wavelength UV.
the sky, and by altitude and other eects of scattering. The total UV and, in particular, the amount of shortwavelength UV is lower in the winter than in the summer. The loss of short-wavelength UV is a result of the fact that the sun is lower in the sky leading to a longer ozone path length which greatly attenuates the shortwavelength (<330 nm) UV light. It is important to note that even in the summer, there is very little light below 300 nm, so that the dierent material sensitivity to short-wavelength UV light shown in Fig. 1 is not a signi®cant issue for outdoor exposures. The increase in UV cut-o from 300 nm in the summer to 310 nm in the winter reduces the photo-oxidation rate by a signi®cant amount over and above the overall decrease in UV intensity (see Fig. 7 of I). The rate of photo-oxidation also increases with sample temperature with an Arrenhius rate dependence. Typical activation energies for photo-oxidation of different polymers appear to range between 5 and 8 kcal/ mol. For the purposes of outdoor modeling, an average value of 6.5 kcal/mol was used in I. Sample temperatures are usually not measured during outdoor exposure. The only temperature data that are readily available are air temperatures. Sample temperatures are generally hotter during the day than air temperatures due to the absorption of solar radiation. A simple relationship involving mean daily high air temperatures and maximum light intensities was used in I to estimate the sample temperature in dierent locations and dierent times of the year. Sample temperatures are also not generally measured in accelerated testing. As will be discussed in more detail below, this leads to signi®cant
309
problems in comparing outdoor and accelerated test results. Humidity and moisture can aect weathering performance in a number of ways. It can induce hydrolysis and can aect the mechanical performance of the paint through swelling and deswelling. In some cases, humidity and moisture can aect photo-oxidation directly. For example, it has been found in acrylic melamine coatings that the rate of photooxidation increases with increasing humidity at constant light intensity and temperature [9]. This can account for the observation that Florida is a harsher environment for these coatings than Arizona even though the light intensity and temperatures are higher in Arizona. Other polymers exhibit no humidity dependence, and Arizona is the harsher environment for such materials [10]. It is important to note that the relative humidity in the coating sample is signi®cantly lower than the relative humidity reported for based on the air temperature because the sample temperature is generally signi®cantly higher than the air temperature [11]. The easiest way to estimate the relative humidity in the sample is to compare the average atmospheric water content in a given location and season (as measured in mm H2O) with water vapor pressure at the sample temperature. For example, average relative humidity during the summer in Florida of 70% is equivalent to a water content of 25 mm H2O. Sample temperatures may range from 38 to 66 C depending on sample color. Vapor pressures at these temperatures are 49 and 196 mm H2O respectively leading to sample relative humidites of 50% for the lower temperature sample and 13% for the higher temperature sample. Calculation of the ``harshness'' factors for an average Florida year is done based on three models. The ®rst considers only the total UV dose. The second corrects the UV dose for eects of temperature and wavelength distribution of UV light. The third adds a correction for the eects of humidity using acrylic melamine coating data for the humidity sensitivity. The process is as follows and the data are summarized in Table 1: . The total UV dose for each season (winter, spring, summer, and fall) is taken from available data. For Model 1, the dose is summed. . For Model 2, the seasonal UV doses are multiplied by factors to correct for temperature and wavelength eects. The wavelength correction is derived from Fig. 7 of I. The temperature factors assume an activation energy of 6.5 kcal/mol and are computed for light, medium and dark colors by the model described above. Mean daily air temperatures, part temperatures and temperature factors are given in Table 1 along with the ``weighted'' UV doses which are then summed to give a yearly weighted dose. . For Model 3, the seasonal weighted doses of
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Model 2 are furthered modi®ed by a term in relative humidity. The method is described in detail in I (see Fig. 9). The humidity weighted factors are summed as before to get a yearly weighted humidity dependent dose. For comparison, similar values for doses are computed for an Arizona exposure. The eect of humidity in increasing the Florida dose is apparent from Table 1. 3. Accelerated weathering exposures Basic accelerated testing protocols have been reviewed by Wypych [12]. There are three basic types of accelerating weathering exposures that will be considered in this work. The ®rst uses high intensity sunlight (focused on the sample using Fresnel mirrors) to accelerate photooxidation. Since sunlight is employed, the wavelength distribution is considered to be equivalent to conventional outdoor exposures.2 Based on the seasonal doses, a weighted average factor for wavelength distribution can be computed for this exposure. As is the case for the conventional outdoor exposures, this factor is predicted to be independent of material type (``acrylic'' vs. ``ester''). Temperature is controlled to a high temperature set point. It is not clear what the average sample temperature is or how it varies from color to color relative to outdoor samples. Based on the high temperature set point of 65 C, we assume that the part temperatures for light medium and dark colors are 45, 55 and 65 C respectively. These are probably accurate to at best 5 C. Depending on the cycle, samples are periodically exposed to moisture. The relative humidity during the light cycle is also variable depending on the cycle but is on average likely to be signi®cantly lower than Florida. Exposure is quoted in MJ of UV radiation that can be compared directly to that for a typical year in Florida. Based on these values, acceleration factors can be calculated relative to a standard Florida exposure. Values for Models 1±3 are given in Table 3. The second type of accelerated test chamber employs UV active ¯uorescent bulbs together with a dark condensing humidity cycle. UV irradiance is controlled, and exposure times are quoted in hours. The most common bulbs are the 340-UVA and 313-UVB. The intensity distributions of these bulbs have been published elsewhere (see Ref. 12). The 340-UVA bulb is a very close match to sunlight in the short-wavelength part of the UV spectrum, but does not contain a long wavelength 2
The Frensel exposure consists solely of direct UV radiation while conventional outdoor exposures are a combination of direct and diffuse radiation. While the ozone cuto wavelength will be the same, the relative intensities at dierent wavelengths may be somewhat different due to dierent scattering ecencies and re¯ection coecients with wavelength.
UV or visible component. The total UV dose and wavelength distribution factors are calculated based on an 8 h light and 4 h dark cycle. The wavelength distribution factor is greater than 1 due not to the presence of very short wavelength UV but due to the lack of intensity at long wavelengths. Long wavelengths have relatively low sensitivity to degradation. A larger fraction of sunlight UV is at the longer wavelengths compared to the UVA-340 distribution. This leads to a higher distribution factor for UVA-340. The lack of intensity below 300 nm suggests that the wavelength sensitivity factor will be the same for ``acrylic'' and ``ester'' materials. By contrast, the 313-UVB bulb uses very harsh short-wavelength light (as low as 275 nm) to accelerate degradation. This is re¯ected in a much higher value for the wavelength distribution factor due to the higher quantum eciencies at low wavelength. The high value for the ``ester'' material must be considered as a crude approximation. Nevertheless, the wide variation in material sensitivity to short wavelength makes this exposure inappropriate for most materials. The lack of visible light in the outputs of both bulbs not only aects certain failure modes (see previous footnote) but also aects the part temperature relative to the controlled black panel temperature and the variation in part temperature due to color. Basically, these exposures will have much less variation in temperature with color than actual outdoor samples since the total intensity of light heating the panel is much lower. Based on a black panel temperature of 60 C, it is assumed that light and dark colored sample temperatures range from 55 to 60 C. Another important consideration for this exposure is that the relative humidity is not controlled during the light cycle and will be in¯uenced by the external humidity in the laboratory. The average part relative humidity used in Table 2, must be considered no more than a rough guess. The last but probably most important weathering chamber for coating evaluation is that based on a xenon arc light source. These chambers control light intensity (at 340 nm), black panel temperature, and relative humidity during all parts of the cycle. The wavelength distribution depends on the ®lter package used. The boro-boro ®lter (Xenon BB) provides a much closer match to sunlight than does the quartz-boro ®lter (Xenon QB), however, even the Xenon BB contains UV light that is shorter than that observed outdoors. The cuto for the Xenon BB is around 290 nm while that for the Xenon QB is around 280 nm. Light intensity is monitored and accumulated doses are measured at 340 nm. This dose can be converted to a total UV exposure that can be related to an outdoor dose (1 MJ/m2/nm at 340 corresponds to 110 MJ/m2 total UV). Typical chambers are operated at 0.35±0.55 W/m2 at 340 nm which is similar to that observed outdoors (noon, summer Florida is 0.65 W/m2). Some boxes employ a
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Table 1 Photo-oxidation models for Florida and Arizonaa Winter
Spring
Summer
Fall
Total
Florida UV dose (MJ/m2) Wavelength sens. Air temperature C mm H2O
48 0.7 23 15
84 95 27 19
88 1.0 31 25
60 95 28 22
280 (Model 1)
Temperature Ð light Temperature Ð medium Temperature Ð dark
28 37 46
33 46 58
38 52 66
34 47 59
Model 2 Ð light Model 2 Ð medium Model 2 Ð dark
38 51 69
104 163 237
139 217 329
78 121 174
359 552 809
Model 3 Ð light Model 3 Ð medium Model 3 Ð dark
78 85 96
207 244 303
278 326 415
163 186 227
726 841 1041
Arizona UV dose (MJ/m2) Wavelength sens.
55 0.62
98 .91
105 1.0
75 0.91
Air temperature C mm H2O
18 4
28 5
38 10
30 6
333=1.19 Relative to Florida (Model 1)
Temperature Ð light Temperature Ð medium Temperature Ð dark
22 30 38
34 46 57
45 59 73
36 48 59
Model 2 Ð light, relative to Florida Model 2 Ð medium, relative to Florida Model 2 Ð dark, relative to Florida
1.29 1.28 1.27
Model 3 Ð light, relative to Model 3 Ð medium, relative to Florida Model 3 Ð dark, relative to Florida
0.83 0.95 1.05
a Sample temperatures calculated using Eq. (1) of I using C values of 7, 21 and 35 for light, medium and dark colors. Arrhenius temperature factors normalized to 25 C=1. Sample RH calculated from part temperature and mm H2O values. Factor calculated using Eq. (2) of I.
Table 2 Accelerated test environmental factors used to calculate acceleration factorsa Fresnel
340-UVA
313-UVB
Xenon QB
Xenon BB
UV dose (MJ/m2) (1 year of operation) Wavelength sens. (``acrylic'') Wavelength sens. (``ester'') Black panel C
1430 0.92 0.92 65
750 1.3 1.3 60
725 11. >80 60
1260 2.5 >20 70
1260 1.05 2.0 70
mm H2O
15
20
20
33
33
Temperature Ð light Temperature Ð medium Temperature Ð dark
45 55 65
55 58 60
55 58 60
50 60 70
50 60 70
a The yearly UV dose assumes continuous operation using cycles described in the text. The Fresnel number is a long-term average that varies from year-to-year. The wavelength sensitivity is calculated relative to sunlight at a declination angle of 0 . The Fresnel value is a weighted average.
signi®cantly higher light intensity (up to 1.3 W/m2 at 340 nm). Typical cycles run with a black panel temperature of 60±70 C. The air temperature is monitored and tends to be in the range 40±45 C. Sample temperatures are not monitored. The relative humidity is controlled at 50% relative to air temperature. Typical dew points are 31 C. For calculation purposes, the
total UV dose assumes a 340 nm intensity of 0.55 W/m2 with a 2 h light cycle followed by a 1 h dark cycle which is typical for automotive coatings. Due to the presence of UV light below 300 nm, the wavelength sensitivity factor is dierent for ``acrylic'' and ``ester'' materials. Both factors are much larger for the Xenon QB exposure than for the Xenon BB exposure. Based on a black
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panel temperature of 70 C, it is estimated that the sample temperatures range from 50 to 70 C for light to dark samples. The relative humidity in the samples can be estimated from sample temperatures and the measured dew point. The values are given in Table 2. 4. Comparision of predicted acceleration factors with experimental results Before comparing the predictions of acceleration factors for the dierent accelerated tests and models with measured acceleration factors, it is necessary to discuss issues related to how acceleration factors should be measured. The ®rst issue is the variability of the Florida exposure itself. The ratio of two numbers cannot be determined any more accurately than the individual numbers themselves. All outdoor exposures are known to be variable. Since performance outdoors is reported in years rather than dose, it is necessary to determine how much variation there is between dose and time in standard Florida exposures. Year-to-year variation in UV dose is on the order of 10% [13]. The actual variation in harshness is likely to be even larger since variations in temperature and variations in UV light intensity can be coupled. This could lead to yearly variations in weighted dose (as calculated by models 2 or 3) of as large as 15±20%. Longer-term measurements would tend to average out this variability; however, even 4±6 year measurements may have variations in the 5±10% range. This places an upper limit on the accuracy of acceleration factors based on comparing timesto-failure. Another issue that needs to be discussed is the dierence between measurements of the chemistry of weathering and measurements of the physical appearance changes that result from weathering. The models are based solely on eects of environmental factors on photo-oxidation rate. While these chemical changes generally drive the appearance changes, they need not be exactly correlated with one another for dierent tests. For example, within a given test and coating system, there is generally good correlation between FTIR measurements of photo-oxidation and gloss loss [8]. Gloss loss can be, however, sensitive to factors other than photo-oxidative weathering including moisture and acid rain [14]. While important to understanding the actual physical failure, the direct comparison of outdoor and accelerated test gloss data may not re¯ect true acceleration factors for photo-oxidation. Gloss measurements especially on outdoor samples also suer from reproducibility issues. This is especially a problem for basecoat clearcoat systems where the gloss loss is relatively small. Sample-to-sample variations in gloss readings can be a signi®cant fraction of the total gloss loss leading to potentially large variations in accelera-
tion factors based on gloss loss measurements. Cracking and peeling are similarly aected by exposure conditions. If the applied stresses are not correct in the various tests, they will not induce failure at the correct level of chemical damage. While it is ultimately critical to understand these relationships, it is clearly necessary as a ®rst step to determine how the dierent accelerated tests accelerate weathering chemistry relative to Florida. Compared to gloss retention data, there is only a small amount of direct or even indirect measurements of relative acceleration factors for coatings using chemical measurements [8,15±18]. For the purposes of comparison to the models, these are the most desirable. Table 3 reports acceleration factors based in FTIR measurements of photo-oxidation and UVA loss along with typical ranges observed by comparisons of visual changes such as gloss loss [8,19±21]. Using the data from Tables 1 and 2, it is possible to calculate predicted acceleration factors for the dierent accelerated tests for the dierent models of weathering. Recall that Model 1 is intended to correlate with UVA loss while Models 2 and 3 were developed to predict photooxidation. One important consideration for acceleration is that the UVA loss rate and the photooxidation rate should be accelerated by a similar amount to avoid problems with incorrect failure modes. The predicted values are listed in Table 3. For the Fresnel mirror based exposure, predicted UVA loss and photooxidation rate acceleration factors range from 5 to 7 depending on the humidity sensitivity of the material. This is in good agreement with the FTIR measurement of photooxidation of a polyester urethane coating (5.7) [8] and the range of factors seen for gloss loss in a variety of coatings (4.5±7) [19,21]. For the ¯uorescent bulb exposures, the UV light dose is only accelerated by a factor of 2.5. The only UVA loss data for this type of test was reported by Pickett and Moore [22] for FS-40 (a lower light intensity version of 313-UVB bulbs). Based on their measurements, it appears that the measured loss of UVA from PMMA using a 313-UVB exposure would be 5 times that for Florida. The larger than predicted factor may be due to contributions from photo-oxidation to UVA loss for this test. It is also possible that the quantum eciency for UVA loss is larger for the very short wavelengths used in this exposure [4]. The predicted acceleration factor for photo-oxidation of ``acrylics'' using the 313UVB exposure range from 30 to 60 depending on humidity sensitivity and color. This is much larger than that for UVA loss and is a direct result of the wavelength sensitivity to short wavelength UV light. The predicted values are within the range observed by FTIR measurements on acrylic coatings and gloss retention data, but there is signi®cant material sensitivity that cannot be accounted for by the model [15,19]. This variability will be discussed in more detail below. The
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Table 3 Experimental and predicted acceleration factors Fresnel
340-UVA
Experimental valuesa UVA-loss
313-UVB
Xenon QB
5
Xenon BB 4.5
Acrylic Chemical change (FTIR measurements of photo-oxidation) Physical change (gloss loss and color change)
4.5±7.5
Ester Chemical change (FTIR measurements of photo-oxidation) Physical change (gloss loss and color change)
5.7 5.7
Predicted values Model 1
5.1
2.7
2.6
4.5
4.5
Acrylic Model 2 Ð light Model 2 Ð medium Model 2 Ð dark
7.2 6.5 5.9
7.4 5.1 3.8
60 42 31
20.4 18. 16.6
8.5 7.5 6.9
Model 3 Ð light Model 3 Ð medium Model 3 Ð dark
5.0 5.2 5.3
4.9 4.4 3.8
40 36 31
17.3 17. 16.6
7.2 7.1 6.9
Ester Model 2 Ð medium Model 3 Ð medium
6.5 5.2
5.1 4.4
>300 >250
>180 >140
15 14
5 5±8
25±50 15±50
19 8±20
7 6±9 11 15
a Values based on limited data. Actual ranges are likely to be signi®cantly larger, especially for 313-UVB and Xenon QB. Data are from Refs. 8 and 15±21. See text for details.
acceleration factor for photo-oxidation of esters is extremely large and the chemistry of degradation is not representative of that observed outdoors. This test is not suitable for such materials. By contrast, the predicted photo-oxidaton acceleration factor for the 340-UVA bulb ranges from 4 to 7.5 which is in good agreement with FTIR measured values of 5 and color shift values which range from 5 to 8 [16,20]. Because of dierences in panel temperature with color relative to that observed outdoors, the acceleration factors for this test vary signi®cantly with color. The relative UV dose for the xenon exposures is 4.5 times that of Florida. This factor is very close to that observed in UVA loss measurements in Xenon BB exposures [23]. UVA loss measurements for Xenon QB exposures tend to be somewhat higher, likely a consequence of the contribution of photo-oxidation to UVA loss in this exposure. Predicted photo-oxidation acceleration factors for ``acrylic'' materials range from 7 to 8.5 for the Xenon BB exposure and from 16 to 20 for the Xenon QB exposure in excellent agreement with the FTIR measurements of 7 and 19 respectively on acrylic based coatings [17,18]. The FTIR numbers are reasonably consistent with the range seen for gloss retention and other performance factors [19,20]. The dierence in photo-oxidation acceleration factor versus UVA loss appears to be a result of the higher than typical outdoor temperatures for the Xenon BB exposure and to a combination of short-wavelength light and high temperature for the Xenon QB exposure. The vast dier-
ence in UVA loss and photo-oxidation rate for the Xenon QB makes it an inappropriate weathering test for basecoat clearcoat materials. The acceleration factors for ``ester'' materials are roughly 15 for the boro-boro and very large for quartzboro that is consistent with both FTIR and gloss measurements on a phthalate-ester based coating [8]. The relatively small excess of short-wavelength UV light in the boro-boro exposure is sucient to cause signi®cant material sensitivity for certain materials. It should be clear from the above discussion that accelerated tests that use short-wavelength light (313UVB and Xenon QB) to accelerate photo-oxidation have serious issues in regards to their ability to predict in-service weathering of coatings in general. First, the acceleration factors for UVA loss and photo-oxidation are signi®cantly dierent. These tests would not be sensitive to failures based on UVA loss. Second, dierent materials have dierent sensitivity to short wavelength light. This leads to unknown acceleration factors for dierent material classes. Finally, there is another source of material sensitivity that is observed in this type of accelerated test, namely dierences in eectiveness of HALS stabilizers. It has been observed in FS-40 UVB exposures that acrylic melamine photo-oxidation acceleration factors vary by more than a factor of two relative to Xenon BB exposures due to dierences in HALS eectiveness[15]. In some cases, particular HALS are eective in the UVB exposures but not in outdoor exposures [24]. Large dierences in HALS stabilization
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eectiveness have also been seen between 313-UVB tests and outdoor exposures in urethane coatings. For example, HALS can reduce photo-oxidation in 313-UVB exposures by more than a factor of 10 [25]. In outdoor exposures, the factor is rarely greater than 3. This over estimation of stabilizer performance is the worst kind of failure since it predicts that a potentially bad coating will perform well. For these reasons, accelerated tests that employ excessive short wavelength UV light have almost no utility in predicting in service coating performance. Although there is reasonable agreement between measured acceleration factors and those predicted by the dierent models, there is a substantial degree of variability in both experimental and predicted acceleration factors that must be interpreted. Understanding and controlling sources of test variability is important because of the high sensitivity of the potential for in-service failures to relatively small changes in overall durability. For example, it has been estimated, that in the US market, a coating that survives 8 years in Florida will have a failure rate after 10 years in service of less than 5%. Increasing the durability to 10 years decreases the failure rate to less than 1% while decreasing the durability to 6 years increases the failure rate to 20%. If decisions were based solely on accelerated test results, a 25% error in acceleration factor (8 versus 6 say) could lead to an increase of in-service failure from 5 to 20%. To protect for this uncertainty in performance, it is either necessary to wait for actual outdoor results or to build in a sucient (25%) safety margin in the accelerated test requirements. The additional durability will add to the cost of the product without necessarily providing any discernable customer bene®t. Improving reproducibility of test results could result in dramatic reductions in cost. One factor that has already been discussed is the variability of outdoor weathering harshness. Measuring actual doses in the outdoor exposure rather than time can in principle, minimize this variability. There is also substantial variability both from material-to-material and test-to-test for a given material. The models are useful in interpreting the importance of the dierent variations and determining which factors require additional control. Variations in material sensitivity resulting from dierent sensitivity to UV light below 300 nm have already been discussed. There is also the possibility that dierent materials have action spectra above 300 nm that are dierent from those in Fig. 1. This would not dramatically aect the result in a Xenon BB exposure since the light intensities above 300 nm are a reasonable match to sunlight. It could signi®cantly aect the acceleration factor of the 340-UVA test, however. Another material sensitivity that must be considered is the activation energy. The models assumed an activation energy of 6.5 kcal/mol. Available data suggest that most photo-oxidation in polymers has an activation
energy between 5 and 8 kcal/mol. Dierent activation energies will cause dierent acceleration factors if the test temperature is signi®cantly dierent from outdoor test temperatures. The Xenon BB test evaluated here uses a black panel temperature of 70 C. This leads to part temperatures that are 5±10 C higher than the highest temperatures observed outdoors. An activation energy range of 5±8 kcal/mol leads to a variation of 8% in acceleration factor for this test solely due to uncertainty in activation energy. Tests that use even higher temperatures would have even higher variations. Lowering the black panel temperature 5 C would not only reduce this variability, it would also reduce the dierence between the UVA loss and photo-oxidation acceleration factors for this test. It is also important that dierences in temperature that result from dierent color absorption of radiation outdoors also occurs to the same extent in the accelerated chamber. This appears to be a serious problem for the ¯uorescent bulb exposures since they do not have the visible/IR component which is responsible for the temperature dependence on color. The predicted variation given in Table 3 for the 340-UVA test of 15-30% (depending on the model) is too large to be acceptable. If the activation energy and actual sample temperature dierences are known, it is possible to correct for this eect. A ®nal material parameter that can aect the acceleration factor is the moisture or humidity sensitivity. The degree of variation caused by dierences in humidity sensitivity can be determined by comparing the acceleration test time necessary to achieve customer satisfaction as predicted by Models 2 and 3. Test time is determined by dividing the required exposure time in Florida by the acceleration factor. For Model 2, 10.5 years of Florida exposure is required to insure 95% satisfaction after 10 YIS. For Model 3, 8 years of Florida exposure is required. For an average color this translates to 1.5±1.6 years for the Fresnel exposure, 1.8± 2 years for the 340-UVA exposure, and 1.1±1.4 years for the Xenon BB exposure. If the humidity sensitivity is not known, the longer exposure requirement would have to be used, further increasing the safety margin and associated costs of over-engineering. It was suggested in I that if the moisture/humidity sensitivity for the dierent failure modes of a given material is not known, it is advisable to expose the samples in both a humid and dry environment. The same conclusion can be drawn for accelerated tests. The current Xenon BB exposure reasonably reproduces the relative sample humidity that occurs in Florida. It does not replicate Arizona. It would seem desirable to develop a ``dry'' cycle (possibly a modi®ed version of the current interior cycle). Adding a ``dry'' test that mimics Arizona would reduce Xenon testing by 25% from 1.4 to 1.1 years. Once the variability due to dierent material sensitivity is accounted for, it is necessary to account for variations
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due to the accelerated test itself. These variations can result from varying environments within a single chamber, and more importantly, variations from chamber to chamber. Variations in acceleration of identical materials from chamber-to-chamber have been measured to be on the order of 20% [26]. The key variables are intensity, temperature, dew point and wavelength distribution. The intensity, temperature, and dew point are controlled at single points. The values at the sample may vary with location in chamber. For example intensities in ¯uorescent devices have been found to vary by 30% over the length of the bulb [27]. Even diligent rotation of samples may leave signi®cant variability. For Xenon devices, a location with a higher than normal intensity may also have higher than normal temperature leading to a even higher variation in photooxidation rate. Values may also vary from chamber-tochamber as a result of dierences in calibration. Individual variations of 5% in the dierent variables could easily account for the observed dierence from chamber-to-chamber. In the case of xenon weathering, lamp aging also could contribute to variations. As these lamps age, they lose intensity in the shorter wavelengths. To maintain constant light intensity at 340 nm, the wattage increases leading to higher long wavelength intensity. This aects not only the wavelength distribution parameter (the parameter can decrease by as much as 15% over the life of the ®lter) but also may aect the sample temperature. In view of the need for high accuracy in predicting in-service performance and the costs associated with over-engineering, further eorts are indicated for improving the reproducibility of accelerated testing. 5. Conclusion The photo-oxidation models proposed previously in I to account for variations in weathering as a function of outdoor location have been adapted to predict acceleration factors for accelerated weathering tests that agree reasonably well with measured values. The models can account for observed dierences in UVA loss and photooxidation acceleration factors and can also account for the variability that is observed in a given test with dierent materials and from test-to-test. The analysis leads to certain conclusions about dierent accelerated tests and speci®c needs for improvement in tests and testing protocols. First, relatively small variations in weathering performance can lead to signi®cant variations in failure rates at a given time in service. This implies that acceleration factors must be known accurately for the test results to be at all useful. Uncertainty or variability in acceleration requires engineering in larger safety margins that result in longer testing times and higher material costs.
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Second, the use of excessive levels of UV light shorter than 300 nm results in acceleration factors that are highly dependent on speci®cs of material formulation. Also, the acceleration factor for UVA loss will be much smaller than that for photooxidation leading to incorrect failure modes. Tests based on ¯uorescent bulbs at 313-UVB or on Xenon QB are inappropriate for predicting in-service performance. Even Xenon BB light contains sucient UV light intensity below 300 nm to induce some material sensitivity. It is desirable to develop a ®lter package for the xenon arc that provides a better match to sunlight. Fluorescent 340 UVA bulbs are a better match to sunlight between 300 and 360 nm, however, the lack of long wavelength UV and visible light has implications for some speci®c failure modes. Third, current tests that do not employ excessive short wavelength UV light (Fresnel, 340-UVA, and Xenon BB) require very long testing times (>1 year) to produce sucient weathering damage to relate to in-service performance. Shortening test time requires increasing the harshness of the test. As discussed above, increasing harshness by using short wavelength UV is not acceptable. That leaves increasing intensity, temperature, and humidity. As will be discussed in more detail below, temperatures and humidities of current tests are already at or above reasonable limits. That leaves increasing intensity. The models suggest that increasing intensity should not unduly distort the weathering chemistry. Higher (1.75±2.4x) versions of Xenon and ¯uorescent sources are available. A very high intensity (up to 400 suns) source using focussed sunlight has also been reported [28]. A critical factor for such high intensity devices is control of sample temperature for dierent sample colors. Finally, sources of acceleration variability must be reduced. Using the correct wavelength distribution of UV light essentially reduces variability due to dierences in material action spectra. Variations in activation energy can be compensated for by either measuring the activation energy and correcting the acceleration factor using the appropriate model or by insuring that the accelerated test sample temperature is within 5 C of average peak Florida (or Arizona) sample temperature during the summer (May±August) for all colors. This is a formidible task. At the very least, sample temperatures should be monitored for all exposures (accelerated and outdoor). Even if activation energies are determined, sample temperatures should not be allowed to rise signi®cantly higher than what is observed outdoors due to concerns about reproducing the appropriate physical failure. More attention to stresses during accelerated testing versus what occurs outdoors is warranted. This will help insure that failures that occur in the accelerated test will actually represent those that occur outdoors. Humidity levels should be controlled or monitored during all parts of the weathering cycle. If
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the humidity dependence is not known, it is necessary to weather samples in both ``wet'' and ``dry'' cycles to determine material sensitivity to moisture/humidity. This can dramatically reduce test time and costs due to premature failure or over-engineering. Variation of environmental conditions during accelerated testing must be even more tightly controlled to insure that the overall variation in acceleration factor is small enough not to cause issue with predicting in-service performance. Total variation from chamber-to-chamber should be less than 10%. This will require reducing the variability of sample light intensity, temperature and humidity and improving calibration. A new chamber has been proposed that may result in improved control over these variables [29]. References [1] Bauer DR. Global exposure models for coating photooxidation. Submitted to Polym Degrad Stab. [2] Simms JA. Acceleration shift factor and its use in evaluating weathering data. J Coat Technol 1987;59(748):45. [3] Bauer DR. J Coat Technol 1994;66(835):57±65. [4] Pickett JE. Macromol Symposia 1997;115:127±141. [5] Gerlock JL, Smith CA, Nunez EM, Cooper VA, Liscombe P, Cummings DR, et al. In: Clough RL, Billingham NC, Gillen KT, editors. Polymer durability degradation, stabilization, lifetime prediction. USA: American Chemical Society (Adv in Chem 249), 1996. p. 335±348. [6] Kerouac K. Spectral and thermal characterization of automotive interior. In advanced symposium on automotive materials testing, Heraeus DSET Laboratories, 1993. [7] Searle NS. Wavelength sensitivity of polymers. In: Patsis AV, editor. Advances in the Stabilization and Controlled Degradation of Polymers. Technomic Publishing 1989. [8] Bauer DR, Paputa Peck MC, Carter III RO. J Coat Technol 1987;59(755):103±109. [9] Bauer DR, Mielewski DF. Polym Degrad Stab 1993;40:349±55.
[10] Davis A. Polym Degrad Stab 1981;3:187. [11] Burch DM, Martin JW. In: Bauer DR, Martin JW, editors. Service Life Prediction of Organic Coatings: A Systems Approach. Oxford: Oxford University Press (ACS Symp. Ser. 722), 1999. p. 85±107. [12] Wypich G. Handbook of material weathering. Toronto, Canada: ChemTec Publishers, 1995. [13] Martin JW, Saunders SC, Floyd FL, Wineburg JP. Methodologies for predicting the service lives of coating systems. Federation Series on Coatings Technology. Federation of Societies of Coatings Technology. PA: Blue Bell, 1996. [14] Wernstahl KM, Carlsson B. J Coat Technol 1997;69(865):69±75. [15] Bauer DR, Gerlock JL, Mielewski DF. Polym Degrad Stab 1992;36:9. [16] Bauer DR, Mielewski DF, Gerlock JL. Polym Degrad Stab 1992;38:57. [17] Dusbiber T, Gerlock JL. Personal communication. [18] Smith CA, Gerlock JL. Personal communication. [19] Wineburg JP. Advanced symposium on automotive materials testing. Hereaus DSET Laboratories, 1993 (Chapter 5). [20] Brennan PJ. J Vinyl Technol 1990;13:73±7. [21] Putman WJ, Pekara D, McGreer M. In: Lacasse MA, editor. Science and technology of building seals and sealants; 5th volume. ASTM STP 1271. Philadelphia (PA): ASTM 1995. [22] Pickett JE, Moore JE. Polym Degrad Stab 1993;42:231. [23] Gerlock JL, Smith CA. Personal communication. [24] Bauer DR, Gerlock JL, Mielewski DF, Paputa Peck MC, Carter IIIRO. Polym Degrad Stab 1990;28:39±51. [25] Bauer DR, Dean MJ, Gerlock JL. Ind & Eng Chem Research 1988;27:65±70. [26] Fischer RM, Ketola WD. In: Ketola WD, Grossman D, editors. Accelerated and outdoor durability testing of organic materials. ASTM STP 1202. Philadelphia (PA): ASTM, 1994. p. 88. [27] Fischer RM. In: Ketola WD, Grossman D, editors. Accelerated and outdoor durability testing of organic materials. ASTM STP 1202. Philadelphia (PA): ASTM, 1994. p. 112. [28] Lewandowski A, Jorgensen G, Bingham C, Netter J, Goggin R. In: Bauer DR, Martin JW, editors. Service life prediction of organic coatings: a systems approach. Oxford University Press (ACS Symp. Ser. 722), 1999. p. 170±185. [29] Martin JW, Chin JW, Byrd E, Embree E, Kraft KM. Polym Degrad Stab 1999;63:297±304.
Interpreting weathering acceleration factors for automotive coatings using exposure models David R. Bauer * Research Laboratory, MD 3182, Ford Motor Company, PO Box 2053, Dearborn, MI 48121, USA Received 20 January 2000; received in revised form 6 March 2000; accepted 13 March 2000
Abstract A number of exposure models have been developed to account for the variation in photo-oxidation rate with light intensity, wavelength distribution, temperature and humidity. These models have proven useful in accounting for the dierent observed rates of photo-oxidation and other weathering phenomena in dierent outdoor locations. In this paper, the same models are applied to calculate acceleration factors of various accelerated weathering tests relative to standard outdoor Florida weathering. The predicted acceleration factors are in reasonable agreement with the ranges commonly observed for various accelerated tests. The models also can be used to assess the sensitivity of the acceleration factors to material dierences and to variations in environmental conditions within a given chamber and from chamber-to-chamber. The critical importance of variability to the reproducibility and reliability of accelerated test results are discussed. Diculties of comparing measurements made outdoors with values for accelerated tests are also discussed. Of particular note is the need for all exposures to measure and report actual sample temperatures rather than air or black panel temperatures. # 2000 Elsevier Science Ltd. All rights reserved. Keywords: Weathering; Photooxidation; Paints; Coatings; Accelerated tests; Accelerated factors
1. Introduction In a previous paper, hereinafter referred to as I, several models were developed to interpret the relative rates of coating photo-oxidation and other weathering phenomena in dierent parts of the world [1]. The simplest model assumed that the extent of change was simply related to the dose of UV light received. While this model did not predict observed variations in photooxidation rate in most polymers and coatings, it did correctly predict variations in the rate of loss of ultraviolet light absorber (UVAs) from most coatings and polymers. Two more complex models included the eects of variations in ozone cut-o wavelength, temperature, and humidity on photo-oxidation. Both models used generic values for the sensitivity of materials to dierent wavelengths of UV light and for activation energy. The models diered in the predicted sensitivity to humidity. One model ignored humidity eects while another used a humidity parameter developed from acrylic melamine coating data. The predictions of these generic models were reasonably successful in accounting for the relative * Tel.: +1-313-594-1756. E-mail address: [email protected]
rates of photo-oxidation for dierent polymeric systems used in coatings and plastics. A critical ®nding was that a relatively small change in photo-oxidation stability of a given coating can have a dramatic eect on the percentage of failures at long times in service. The above ®nding raises signi®cant questions for accelerated testing. Accelerated test results ultimately have to be related to ®eld performance. Typically, accelerated test results are related to standard outdoor test results (i.e. exposure in Florida). Such results are calibrated through the use of an ``acceleration factor'' (that is, how many hours of a given accelerated test is equal to 1 year in Florida) [2]. Such acceleration factors are found to vary over time, with dierent test chambers running the ``same'' test, and for dierent materials. Large variations contribute to the general perception that accelerated testing often provides data of dubious value. In this paper, the exposure models in I are extended to attempt to understand acceleration factors for common accelerated weathering tests relative to standard outdoor Florida testing. In particular, we will attempt to understand the sources of variability that plague all weathering test results. We ®rst summarize the models, with emphasis on the types of outdoor environmental
0141-3910/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved. PII: S0141-3910(00)00074-4
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information required. We then describe the dierent accelerated tests, summarize typical test results, and discuss the parameters that are available to support prediction by the models. The models are then used to calculate acceleration factors to compare to experimental values. One critical factor is that the correct parameters necessary for accurate comparison are often not measured in either outdoor or accelerated tests (e.g. sample temperature). Nevertheless, it is possible to account for many accelerated test observations. Of particular importance is the discussion of factors that cause variation in accelerated test results. Based on the models, a number of suggestions to improve accelerated testing protocols are made. 2. Models for UVA loss and photo-oxidation The most important chemical processes that control weathering performance of current automotive coatings are the rates of photo-oxidation and loss of ultraviolet light absorber (UVA) [3]. Photo-oxidation of the clearcoat in basecoat/clearcoat coatings leads to loss of gloss and ultimately to cracking of the clearcoat. Loss of UVA leads to loss of protection of the basecoat which in turn can result in photo-oxidation of the basecoat and delamination of the clearcoat from the basecoat. The most extensive studies of UVA loss have been made by Pickett [4] and by Gerlock et al. [5]. Aside from volatility, which can be eliminated by judicious choice of UVA molecular weight, there appear to be two main mechanisms for loss of UVA. The ®rst is direct photolysis and the second is free radical attack on the excited state of the UVA. The rate of direct photolysis appears to depend almost solely on the total UV intensity. In any particular exposure, it sets a lower limit on the loss rate of a given UVA. For example, a typical benzotriazole in a stable polymer such as PMMA loses absorbance on exposure in Florida at a rate around 0.35A/year. If the matrix has a relatively high rate of photo-oxidation, free radical attack can increase the rate by an amount that is proportional to the rate of photo-oxidation. Thus, UVA loss in general can be expressed as a sum of a term that depends only on UV light intensity and a term that depends on photo-oxidation rate. For reasonably stable coatings, the term in light intensity appears to dominate and the relative extents of UVA loss in dierent locations correlate well with UV light dose [1]. It should be noted that measured UV doses are only available in limited locations. For modeling purposes, we have used total sunload to estimate relative rates of UVA loss as a function of location. The validity of this assumption was discussed in I. It is also important to note that in addition to location, UV dose depends on the angle of exposure. For Florida exposures, the harshest exposure for UV dose is a hor-
izontal or near-horizontal exposure. This is consistent with the observation that automotive coating failures tend to occur ®rst on horizontal surfaces. In this paper, all quoted doses are for horizontal exposures. Like UVA loss, the rate of photo-oxidation in polymers and coatings is, in general, proportional to the UV light intensity. However, the proportionality constant depends on the wavelength distribution of the UV light, on temperature, and, in some cases, on humidity. For comparison with dierent outdoor sites, sunload was used in I as a surrogate for UV light dose. Seasonal UV doses are available for important outdoor test sites such as Florida and Arizona. Seasonal data are necessary due to the dependence of photo-oxidation rate on wavelength distribution, temperature, and humidity. The relative quantum eciency of light at dierent wavelengths for photo-oxidation is illustrated in Fig. 1 for two dierent kinds of polymers commonly used as topcoats. In general, at constant light intensity, the longer the wavelength of light, the less photo-oxidation will occur. A survey of many common polymers used in exterior applications suggests that above 300 nm, the relative sensitivity with wavelength is not very material speci®c. This may be a result of the fact that for these polymers, the main chromophores are impurities common to all polymeric systems (ketones, hydroperoxides,...). For these polymers, the possibility of photooxidation induced by visible light is very small and only the eects of UV light need be considered.1 The ``acrylic'' quantum eciency function is an average of several non-absorbing polymers like ABS [6]. Some polymers based on aromatic functionality (particularly phthalate based esters and bisphenol-A polycarbonates) have strong transitions just below 300 nm leading to a dramatic increase in sensitivity to light below 300 nm [7]. The ``ester'' quantum eciency function is estimated from polycarbonate quantum eciency measurements [7] and the absorption curve for a phthalate ester coating [8]. These functions represent reasonable limits of sensitivity to short wavelength UV light. Most materials will likely fall somewhere in between these two limits. Thus, calculating the dierence in sensitivity between an accelerated test and sunlight for these two limits provides a good estimate of the material sensitivity of the test caused by improper wavelength distribution. The light wavelength distribution of sunlight is controlled by ozone concentration, the angle of the sun in
1 Three caveats should be mentioned. First, pigments and dyes may be sensitive to long-wavelength UV and visible light fastness issues. Second, some oxidation products may be destroyed by visible light. Third, some polymers used in coating primer and undercoats are sensitive to long-wavelength UV and even visible light. Penetration of this light through topcoats can result in determination. All of these caveats suggest that tests that employ only UV light rather than full spectrum light have to be interpreted carefully.
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Fig. 1. Representative action spectra for a short wavelength insensitive (acrylic) and sensitive (ester) materials. The long wavelength behavior (>300 nm) is assumed to be the same. The ester material has a transition below 300 nm that makes it much more susceptible to short wavelength UV.
the sky, and by altitude and other eects of scattering. The total UV and, in particular, the amount of shortwavelength UV is lower in the winter than in the summer. The loss of short-wavelength UV is a result of the fact that the sun is lower in the sky leading to a longer ozone path length which greatly attenuates the shortwavelength (<330 nm) UV light. It is important to note that even in the summer, there is very little light below 300 nm, so that the dierent material sensitivity to short-wavelength UV light shown in Fig. 1 is not a signi®cant issue for outdoor exposures. The increase in UV cut-o from 300 nm in the summer to 310 nm in the winter reduces the photo-oxidation rate by a signi®cant amount over and above the overall decrease in UV intensity (see Fig. 7 of I). The rate of photo-oxidation also increases with sample temperature with an Arrenhius rate dependence. Typical activation energies for photo-oxidation of different polymers appear to range between 5 and 8 kcal/ mol. For the purposes of outdoor modeling, an average value of 6.5 kcal/mol was used in I. Sample temperatures are usually not measured during outdoor exposure. The only temperature data that are readily available are air temperatures. Sample temperatures are generally hotter during the day than air temperatures due to the absorption of solar radiation. A simple relationship involving mean daily high air temperatures and maximum light intensities was used in I to estimate the sample temperature in dierent locations and dierent times of the year. Sample temperatures are also not generally measured in accelerated testing. As will be discussed in more detail below, this leads to signi®cant
309
problems in comparing outdoor and accelerated test results. Humidity and moisture can aect weathering performance in a number of ways. It can induce hydrolysis and can aect the mechanical performance of the paint through swelling and deswelling. In some cases, humidity and moisture can aect photo-oxidation directly. For example, it has been found in acrylic melamine coatings that the rate of photooxidation increases with increasing humidity at constant light intensity and temperature [9]. This can account for the observation that Florida is a harsher environment for these coatings than Arizona even though the light intensity and temperatures are higher in Arizona. Other polymers exhibit no humidity dependence, and Arizona is the harsher environment for such materials [10]. It is important to note that the relative humidity in the coating sample is signi®cantly lower than the relative humidity reported for based on the air temperature because the sample temperature is generally signi®cantly higher than the air temperature [11]. The easiest way to estimate the relative humidity in the sample is to compare the average atmospheric water content in a given location and season (as measured in mm H2O) with water vapor pressure at the sample temperature. For example, average relative humidity during the summer in Florida of 70% is equivalent to a water content of 25 mm H2O. Sample temperatures may range from 38 to 66 C depending on sample color. Vapor pressures at these temperatures are 49 and 196 mm H2O respectively leading to sample relative humidites of 50% for the lower temperature sample and 13% for the higher temperature sample. Calculation of the ``harshness'' factors for an average Florida year is done based on three models. The ®rst considers only the total UV dose. The second corrects the UV dose for eects of temperature and wavelength distribution of UV light. The third adds a correction for the eects of humidity using acrylic melamine coating data for the humidity sensitivity. The process is as follows and the data are summarized in Table 1: . The total UV dose for each season (winter, spring, summer, and fall) is taken from available data. For Model 1, the dose is summed. . For Model 2, the seasonal UV doses are multiplied by factors to correct for temperature and wavelength eects. The wavelength correction is derived from Fig. 7 of I. The temperature factors assume an activation energy of 6.5 kcal/mol and are computed for light, medium and dark colors by the model described above. Mean daily air temperatures, part temperatures and temperature factors are given in Table 1 along with the ``weighted'' UV doses which are then summed to give a yearly weighted dose. . For Model 3, the seasonal weighted doses of
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Model 2 are furthered modi®ed by a term in relative humidity. The method is described in detail in I (see Fig. 9). The humidity weighted factors are summed as before to get a yearly weighted humidity dependent dose. For comparison, similar values for doses are computed for an Arizona exposure. The eect of humidity in increasing the Florida dose is apparent from Table 1. 3. Accelerated weathering exposures Basic accelerated testing protocols have been reviewed by Wypych [12]. There are three basic types of accelerating weathering exposures that will be considered in this work. The ®rst uses high intensity sunlight (focused on the sample using Fresnel mirrors) to accelerate photooxidation. Since sunlight is employed, the wavelength distribution is considered to be equivalent to conventional outdoor exposures.2 Based on the seasonal doses, a weighted average factor for wavelength distribution can be computed for this exposure. As is the case for the conventional outdoor exposures, this factor is predicted to be independent of material type (``acrylic'' vs. ``ester''). Temperature is controlled to a high temperature set point. It is not clear what the average sample temperature is or how it varies from color to color relative to outdoor samples. Based on the high temperature set point of 65 C, we assume that the part temperatures for light medium and dark colors are 45, 55 and 65 C respectively. These are probably accurate to at best 5 C. Depending on the cycle, samples are periodically exposed to moisture. The relative humidity during the light cycle is also variable depending on the cycle but is on average likely to be signi®cantly lower than Florida. Exposure is quoted in MJ of UV radiation that can be compared directly to that for a typical year in Florida. Based on these values, acceleration factors can be calculated relative to a standard Florida exposure. Values for Models 1±3 are given in Table 3. The second type of accelerated test chamber employs UV active ¯uorescent bulbs together with a dark condensing humidity cycle. UV irradiance is controlled, and exposure times are quoted in hours. The most common bulbs are the 340-UVA and 313-UVB. The intensity distributions of these bulbs have been published elsewhere (see Ref. 12). The 340-UVA bulb is a very close match to sunlight in the short-wavelength part of the UV spectrum, but does not contain a long wavelength 2
The Frensel exposure consists solely of direct UV radiation while conventional outdoor exposures are a combination of direct and diffuse radiation. While the ozone cuto wavelength will be the same, the relative intensities at dierent wavelengths may be somewhat different due to dierent scattering ecencies and re¯ection coecients with wavelength.
UV or visible component. The total UV dose and wavelength distribution factors are calculated based on an 8 h light and 4 h dark cycle. The wavelength distribution factor is greater than 1 due not to the presence of very short wavelength UV but due to the lack of intensity at long wavelengths. Long wavelengths have relatively low sensitivity to degradation. A larger fraction of sunlight UV is at the longer wavelengths compared to the UVA-340 distribution. This leads to a higher distribution factor for UVA-340. The lack of intensity below 300 nm suggests that the wavelength sensitivity factor will be the same for ``acrylic'' and ``ester'' materials. By contrast, the 313-UVB bulb uses very harsh short-wavelength light (as low as 275 nm) to accelerate degradation. This is re¯ected in a much higher value for the wavelength distribution factor due to the higher quantum eciencies at low wavelength. The high value for the ``ester'' material must be considered as a crude approximation. Nevertheless, the wide variation in material sensitivity to short wavelength makes this exposure inappropriate for most materials. The lack of visible light in the outputs of both bulbs not only aects certain failure modes (see previous footnote) but also aects the part temperature relative to the controlled black panel temperature and the variation in part temperature due to color. Basically, these exposures will have much less variation in temperature with color than actual outdoor samples since the total intensity of light heating the panel is much lower. Based on a black panel temperature of 60 C, it is assumed that light and dark colored sample temperatures range from 55 to 60 C. Another important consideration for this exposure is that the relative humidity is not controlled during the light cycle and will be in¯uenced by the external humidity in the laboratory. The average part relative humidity used in Table 2, must be considered no more than a rough guess. The last but probably most important weathering chamber for coating evaluation is that based on a xenon arc light source. These chambers control light intensity (at 340 nm), black panel temperature, and relative humidity during all parts of the cycle. The wavelength distribution depends on the ®lter package used. The boro-boro ®lter (Xenon BB) provides a much closer match to sunlight than does the quartz-boro ®lter (Xenon QB), however, even the Xenon BB contains UV light that is shorter than that observed outdoors. The cuto for the Xenon BB is around 290 nm while that for the Xenon QB is around 280 nm. Light intensity is monitored and accumulated doses are measured at 340 nm. This dose can be converted to a total UV exposure that can be related to an outdoor dose (1 MJ/m2/nm at 340 corresponds to 110 MJ/m2 total UV). Typical chambers are operated at 0.35±0.55 W/m2 at 340 nm which is similar to that observed outdoors (noon, summer Florida is 0.65 W/m2). Some boxes employ a
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311
Table 1 Photo-oxidation models for Florida and Arizonaa Winter
Spring
Summer
Fall
Total
Florida UV dose (MJ/m2) Wavelength sens. Air temperature C mm H2O
48 0.7 23 15
84 95 27 19
88 1.0 31 25
60 95 28 22
280 (Model 1)
Temperature Ð light Temperature Ð medium Temperature Ð dark
28 37 46
33 46 58
38 52 66
34 47 59
Model 2 Ð light Model 2 Ð medium Model 2 Ð dark
38 51 69
104 163 237
139 217 329
78 121 174
359 552 809
Model 3 Ð light Model 3 Ð medium Model 3 Ð dark
78 85 96
207 244 303
278 326 415
163 186 227
726 841 1041
Arizona UV dose (MJ/m2) Wavelength sens.
55 0.62
98 .91
105 1.0
75 0.91
Air temperature C mm H2O
18 4
28 5
38 10
30 6
333=1.19 Relative to Florida (Model 1)
Temperature Ð light Temperature Ð medium Temperature Ð dark
22 30 38
34 46 57
45 59 73
36 48 59
Model 2 Ð light, relative to Florida Model 2 Ð medium, relative to Florida Model 2 Ð dark, relative to Florida
1.29 1.28 1.27
Model 3 Ð light, relative to Model 3 Ð medium, relative to Florida Model 3 Ð dark, relative to Florida
0.83 0.95 1.05
a Sample temperatures calculated using Eq. (1) of I using C values of 7, 21 and 35 for light, medium and dark colors. Arrhenius temperature factors normalized to 25 C=1. Sample RH calculated from part temperature and mm H2O values. Factor calculated using Eq. (2) of I.
Table 2 Accelerated test environmental factors used to calculate acceleration factorsa Fresnel
340-UVA
313-UVB
Xenon QB
Xenon BB
UV dose (MJ/m2) (1 year of operation) Wavelength sens. (``acrylic'') Wavelength sens. (``ester'') Black panel C
1430 0.92 0.92 65
750 1.3 1.3 60
725 11. >80 60
1260 2.5 >20 70
1260 1.05 2.0 70
mm H2O
15
20
20
33
33
Temperature Ð light Temperature Ð medium Temperature Ð dark
45 55 65
55 58 60
55 58 60
50 60 70
50 60 70
a The yearly UV dose assumes continuous operation using cycles described in the text. The Fresnel number is a long-term average that varies from year-to-year. The wavelength sensitivity is calculated relative to sunlight at a declination angle of 0 . The Fresnel value is a weighted average.
signi®cantly higher light intensity (up to 1.3 W/m2 at 340 nm). Typical cycles run with a black panel temperature of 60±70 C. The air temperature is monitored and tends to be in the range 40±45 C. Sample temperatures are not monitored. The relative humidity is controlled at 50% relative to air temperature. Typical dew points are 31 C. For calculation purposes, the
total UV dose assumes a 340 nm intensity of 0.55 W/m2 with a 2 h light cycle followed by a 1 h dark cycle which is typical for automotive coatings. Due to the presence of UV light below 300 nm, the wavelength sensitivity factor is dierent for ``acrylic'' and ``ester'' materials. Both factors are much larger for the Xenon QB exposure than for the Xenon BB exposure. Based on a black
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panel temperature of 70 C, it is estimated that the sample temperatures range from 50 to 70 C for light to dark samples. The relative humidity in the samples can be estimated from sample temperatures and the measured dew point. The values are given in Table 2. 4. Comparision of predicted acceleration factors with experimental results Before comparing the predictions of acceleration factors for the dierent accelerated tests and models with measured acceleration factors, it is necessary to discuss issues related to how acceleration factors should be measured. The ®rst issue is the variability of the Florida exposure itself. The ratio of two numbers cannot be determined any more accurately than the individual numbers themselves. All outdoor exposures are known to be variable. Since performance outdoors is reported in years rather than dose, it is necessary to determine how much variation there is between dose and time in standard Florida exposures. Year-to-year variation in UV dose is on the order of 10% [13]. The actual variation in harshness is likely to be even larger since variations in temperature and variations in UV light intensity can be coupled. This could lead to yearly variations in weighted dose (as calculated by models 2 or 3) of as large as 15±20%. Longer-term measurements would tend to average out this variability; however, even 4±6 year measurements may have variations in the 5±10% range. This places an upper limit on the accuracy of acceleration factors based on comparing timesto-failure. Another issue that needs to be discussed is the dierence between measurements of the chemistry of weathering and measurements of the physical appearance changes that result from weathering. The models are based solely on eects of environmental factors on photo-oxidation rate. While these chemical changes generally drive the appearance changes, they need not be exactly correlated with one another for dierent tests. For example, within a given test and coating system, there is generally good correlation between FTIR measurements of photo-oxidation and gloss loss [8]. Gloss loss can be, however, sensitive to factors other than photo-oxidative weathering including moisture and acid rain [14]. While important to understanding the actual physical failure, the direct comparison of outdoor and accelerated test gloss data may not re¯ect true acceleration factors for photo-oxidation. Gloss measurements especially on outdoor samples also suer from reproducibility issues. This is especially a problem for basecoat clearcoat systems where the gloss loss is relatively small. Sample-to-sample variations in gloss readings can be a signi®cant fraction of the total gloss loss leading to potentially large variations in accelera-
tion factors based on gloss loss measurements. Cracking and peeling are similarly aected by exposure conditions. If the applied stresses are not correct in the various tests, they will not induce failure at the correct level of chemical damage. While it is ultimately critical to understand these relationships, it is clearly necessary as a ®rst step to determine how the dierent accelerated tests accelerate weathering chemistry relative to Florida. Compared to gloss retention data, there is only a small amount of direct or even indirect measurements of relative acceleration factors for coatings using chemical measurements [8,15±18]. For the purposes of comparison to the models, these are the most desirable. Table 3 reports acceleration factors based in FTIR measurements of photo-oxidation and UVA loss along with typical ranges observed by comparisons of visual changes such as gloss loss [8,19±21]. Using the data from Tables 1 and 2, it is possible to calculate predicted acceleration factors for the dierent accelerated tests for the dierent models of weathering. Recall that Model 1 is intended to correlate with UVA loss while Models 2 and 3 were developed to predict photooxidation. One important consideration for acceleration is that the UVA loss rate and the photooxidation rate should be accelerated by a similar amount to avoid problems with incorrect failure modes. The predicted values are listed in Table 3. For the Fresnel mirror based exposure, predicted UVA loss and photooxidation rate acceleration factors range from 5 to 7 depending on the humidity sensitivity of the material. This is in good agreement with the FTIR measurement of photooxidation of a polyester urethane coating (5.7) [8] and the range of factors seen for gloss loss in a variety of coatings (4.5±7) [19,21]. For the ¯uorescent bulb exposures, the UV light dose is only accelerated by a factor of 2.5. The only UVA loss data for this type of test was reported by Pickett and Moore [22] for FS-40 (a lower light intensity version of 313-UVB bulbs). Based on their measurements, it appears that the measured loss of UVA from PMMA using a 313-UVB exposure would be 5 times that for Florida. The larger than predicted factor may be due to contributions from photo-oxidation to UVA loss for this test. It is also possible that the quantum eciency for UVA loss is larger for the very short wavelengths used in this exposure [4]. The predicted acceleration factor for photo-oxidation of ``acrylics'' using the 313UVB exposure range from 30 to 60 depending on humidity sensitivity and color. This is much larger than that for UVA loss and is a direct result of the wavelength sensitivity to short wavelength UV light. The predicted values are within the range observed by FTIR measurements on acrylic coatings and gloss retention data, but there is signi®cant material sensitivity that cannot be accounted for by the model [15,19]. This variability will be discussed in more detail below. The
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Table 3 Experimental and predicted acceleration factors Fresnel
340-UVA
Experimental valuesa UVA-loss
313-UVB
Xenon QB
5
Xenon BB 4.5
Acrylic Chemical change (FTIR measurements of photo-oxidation) Physical change (gloss loss and color change)
4.5±7.5
Ester Chemical change (FTIR measurements of photo-oxidation) Physical change (gloss loss and color change)
5.7 5.7
Predicted values Model 1
5.1
2.7
2.6
4.5
4.5
Acrylic Model 2 Ð light Model 2 Ð medium Model 2 Ð dark
7.2 6.5 5.9
7.4 5.1 3.8
60 42 31
20.4 18. 16.6
8.5 7.5 6.9
Model 3 Ð light Model 3 Ð medium Model 3 Ð dark
5.0 5.2 5.3
4.9 4.4 3.8
40 36 31
17.3 17. 16.6
7.2 7.1 6.9
Ester Model 2 Ð medium Model 3 Ð medium
6.5 5.2
5.1 4.4
>300 >250
>180 >140
15 14
5 5±8
25±50 15±50
19 8±20
7 6±9 11 15
a Values based on limited data. Actual ranges are likely to be signi®cantly larger, especially for 313-UVB and Xenon QB. Data are from Refs. 8 and 15±21. See text for details.
acceleration factor for photo-oxidation of esters is extremely large and the chemistry of degradation is not representative of that observed outdoors. This test is not suitable for such materials. By contrast, the predicted photo-oxidaton acceleration factor for the 340-UVA bulb ranges from 4 to 7.5 which is in good agreement with FTIR measured values of 5 and color shift values which range from 5 to 8 [16,20]. Because of dierences in panel temperature with color relative to that observed outdoors, the acceleration factors for this test vary signi®cantly with color. The relative UV dose for the xenon exposures is 4.5 times that of Florida. This factor is very close to that observed in UVA loss measurements in Xenon BB exposures [23]. UVA loss measurements for Xenon QB exposures tend to be somewhat higher, likely a consequence of the contribution of photo-oxidation to UVA loss in this exposure. Predicted photo-oxidation acceleration factors for ``acrylic'' materials range from 7 to 8.5 for the Xenon BB exposure and from 16 to 20 for the Xenon QB exposure in excellent agreement with the FTIR measurements of 7 and 19 respectively on acrylic based coatings [17,18]. The FTIR numbers are reasonably consistent with the range seen for gloss retention and other performance factors [19,20]. The dierence in photo-oxidation acceleration factor versus UVA loss appears to be a result of the higher than typical outdoor temperatures for the Xenon BB exposure and to a combination of short-wavelength light and high temperature for the Xenon QB exposure. The vast dier-
ence in UVA loss and photo-oxidation rate for the Xenon QB makes it an inappropriate weathering test for basecoat clearcoat materials. The acceleration factors for ``ester'' materials are roughly 15 for the boro-boro and very large for quartzboro that is consistent with both FTIR and gloss measurements on a phthalate-ester based coating [8]. The relatively small excess of short-wavelength UV light in the boro-boro exposure is sucient to cause signi®cant material sensitivity for certain materials. It should be clear from the above discussion that accelerated tests that use short-wavelength light (313UVB and Xenon QB) to accelerate photo-oxidation have serious issues in regards to their ability to predict in-service weathering of coatings in general. First, the acceleration factors for UVA loss and photo-oxidation are signi®cantly dierent. These tests would not be sensitive to failures based on UVA loss. Second, dierent materials have dierent sensitivity to short wavelength light. This leads to unknown acceleration factors for dierent material classes. Finally, there is another source of material sensitivity that is observed in this type of accelerated test, namely dierences in eectiveness of HALS stabilizers. It has been observed in FS-40 UVB exposures that acrylic melamine photo-oxidation acceleration factors vary by more than a factor of two relative to Xenon BB exposures due to dierences in HALS eectiveness[15]. In some cases, particular HALS are eective in the UVB exposures but not in outdoor exposures [24]. Large dierences in HALS stabilization
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eectiveness have also been seen between 313-UVB tests and outdoor exposures in urethane coatings. For example, HALS can reduce photo-oxidation in 313-UVB exposures by more than a factor of 10 [25]. In outdoor exposures, the factor is rarely greater than 3. This over estimation of stabilizer performance is the worst kind of failure since it predicts that a potentially bad coating will perform well. For these reasons, accelerated tests that employ excessive short wavelength UV light have almost no utility in predicting in service coating performance. Although there is reasonable agreement between measured acceleration factors and those predicted by the dierent models, there is a substantial degree of variability in both experimental and predicted acceleration factors that must be interpreted. Understanding and controlling sources of test variability is important because of the high sensitivity of the potential for in-service failures to relatively small changes in overall durability. For example, it has been estimated, that in the US market, a coating that survives 8 years in Florida will have a failure rate after 10 years in service of less than 5%. Increasing the durability to 10 years decreases the failure rate to less than 1% while decreasing the durability to 6 years increases the failure rate to 20%. If decisions were based solely on accelerated test results, a 25% error in acceleration factor (8 versus 6 say) could lead to an increase of in-service failure from 5 to 20%. To protect for this uncertainty in performance, it is either necessary to wait for actual outdoor results or to build in a sucient (25%) safety margin in the accelerated test requirements. The additional durability will add to the cost of the product without necessarily providing any discernable customer bene®t. Improving reproducibility of test results could result in dramatic reductions in cost. One factor that has already been discussed is the variability of outdoor weathering harshness. Measuring actual doses in the outdoor exposure rather than time can in principle, minimize this variability. There is also substantial variability both from material-to-material and test-to-test for a given material. The models are useful in interpreting the importance of the dierent variations and determining which factors require additional control. Variations in material sensitivity resulting from dierent sensitivity to UV light below 300 nm have already been discussed. There is also the possibility that dierent materials have action spectra above 300 nm that are dierent from those in Fig. 1. This would not dramatically aect the result in a Xenon BB exposure since the light intensities above 300 nm are a reasonable match to sunlight. It could signi®cantly aect the acceleration factor of the 340-UVA test, however. Another material sensitivity that must be considered is the activation energy. The models assumed an activation energy of 6.5 kcal/mol. Available data suggest that most photo-oxidation in polymers has an activation
energy between 5 and 8 kcal/mol. Dierent activation energies will cause dierent acceleration factors if the test temperature is signi®cantly dierent from outdoor test temperatures. The Xenon BB test evaluated here uses a black panel temperature of 70 C. This leads to part temperatures that are 5±10 C higher than the highest temperatures observed outdoors. An activation energy range of 5±8 kcal/mol leads to a variation of 8% in acceleration factor for this test solely due to uncertainty in activation energy. Tests that use even higher temperatures would have even higher variations. Lowering the black panel temperature 5 C would not only reduce this variability, it would also reduce the dierence between the UVA loss and photo-oxidation acceleration factors for this test. It is also important that dierences in temperature that result from dierent color absorption of radiation outdoors also occurs to the same extent in the accelerated chamber. This appears to be a serious problem for the ¯uorescent bulb exposures since they do not have the visible/IR component which is responsible for the temperature dependence on color. The predicted variation given in Table 3 for the 340-UVA test of 15-30% (depending on the model) is too large to be acceptable. If the activation energy and actual sample temperature dierences are known, it is possible to correct for this eect. A ®nal material parameter that can aect the acceleration factor is the moisture or humidity sensitivity. The degree of variation caused by dierences in humidity sensitivity can be determined by comparing the acceleration test time necessary to achieve customer satisfaction as predicted by Models 2 and 3. Test time is determined by dividing the required exposure time in Florida by the acceleration factor. For Model 2, 10.5 years of Florida exposure is required to insure 95% satisfaction after 10 YIS. For Model 3, 8 years of Florida exposure is required. For an average color this translates to 1.5±1.6 years for the Fresnel exposure, 1.8± 2 years for the 340-UVA exposure, and 1.1±1.4 years for the Xenon BB exposure. If the humidity sensitivity is not known, the longer exposure requirement would have to be used, further increasing the safety margin and associated costs of over-engineering. It was suggested in I that if the moisture/humidity sensitivity for the dierent failure modes of a given material is not known, it is advisable to expose the samples in both a humid and dry environment. The same conclusion can be drawn for accelerated tests. The current Xenon BB exposure reasonably reproduces the relative sample humidity that occurs in Florida. It does not replicate Arizona. It would seem desirable to develop a ``dry'' cycle (possibly a modi®ed version of the current interior cycle). Adding a ``dry'' test that mimics Arizona would reduce Xenon testing by 25% from 1.4 to 1.1 years. Once the variability due to dierent material sensitivity is accounted for, it is necessary to account for variations
D.R. Bauer / Polymer Degradation and Stability 69 (2000) 307±316
due to the accelerated test itself. These variations can result from varying environments within a single chamber, and more importantly, variations from chamber to chamber. Variations in acceleration of identical materials from chamber-to-chamber have been measured to be on the order of 20% [26]. The key variables are intensity, temperature, dew point and wavelength distribution. The intensity, temperature, and dew point are controlled at single points. The values at the sample may vary with location in chamber. For example intensities in ¯uorescent devices have been found to vary by 30% over the length of the bulb [27]. Even diligent rotation of samples may leave signi®cant variability. For Xenon devices, a location with a higher than normal intensity may also have higher than normal temperature leading to a even higher variation in photooxidation rate. Values may also vary from chamber-tochamber as a result of dierences in calibration. Individual variations of 5% in the dierent variables could easily account for the observed dierence from chamber-to-chamber. In the case of xenon weathering, lamp aging also could contribute to variations. As these lamps age, they lose intensity in the shorter wavelengths. To maintain constant light intensity at 340 nm, the wattage increases leading to higher long wavelength intensity. This aects not only the wavelength distribution parameter (the parameter can decrease by as much as 15% over the life of the ®lter) but also may aect the sample temperature. In view of the need for high accuracy in predicting in-service performance and the costs associated with over-engineering, further eorts are indicated for improving the reproducibility of accelerated testing. 5. Conclusion The photo-oxidation models proposed previously in I to account for variations in weathering as a function of outdoor location have been adapted to predict acceleration factors for accelerated weathering tests that agree reasonably well with measured values. The models can account for observed dierences in UVA loss and photooxidation acceleration factors and can also account for the variability that is observed in a given test with dierent materials and from test-to-test. The analysis leads to certain conclusions about dierent accelerated tests and speci®c needs for improvement in tests and testing protocols. First, relatively small variations in weathering performance can lead to signi®cant variations in failure rates at a given time in service. This implies that acceleration factors must be known accurately for the test results to be at all useful. Uncertainty or variability in acceleration requires engineering in larger safety margins that result in longer testing times and higher material costs.
315
Second, the use of excessive levels of UV light shorter than 300 nm results in acceleration factors that are highly dependent on speci®cs of material formulation. Also, the acceleration factor for UVA loss will be much smaller than that for photooxidation leading to incorrect failure modes. Tests based on ¯uorescent bulbs at 313-UVB or on Xenon QB are inappropriate for predicting in-service performance. Even Xenon BB light contains sucient UV light intensity below 300 nm to induce some material sensitivity. It is desirable to develop a ®lter package for the xenon arc that provides a better match to sunlight. Fluorescent 340 UVA bulbs are a better match to sunlight between 300 and 360 nm, however, the lack of long wavelength UV and visible light has implications for some speci®c failure modes. Third, current tests that do not employ excessive short wavelength UV light (Fresnel, 340-UVA, and Xenon BB) require very long testing times (>1 year) to produce sucient weathering damage to relate to in-service performance. Shortening test time requires increasing the harshness of the test. As discussed above, increasing harshness by using short wavelength UV is not acceptable. That leaves increasing intensity, temperature, and humidity. As will be discussed in more detail below, temperatures and humidities of current tests are already at or above reasonable limits. That leaves increasing intensity. The models suggest that increasing intensity should not unduly distort the weathering chemistry. Higher (1.75±2.4x) versions of Xenon and ¯uorescent sources are available. A very high intensity (up to 400 suns) source using focussed sunlight has also been reported [28]. A critical factor for such high intensity devices is control of sample temperature for dierent sample colors. Finally, sources of acceleration variability must be reduced. Using the correct wavelength distribution of UV light essentially reduces variability due to dierences in material action spectra. Variations in activation energy can be compensated for by either measuring the activation energy and correcting the acceleration factor using the appropriate model or by insuring that the accelerated test sample temperature is within 5 C of average peak Florida (or Arizona) sample temperature during the summer (May±August) for all colors. This is a formidible task. At the very least, sample temperatures should be monitored for all exposures (accelerated and outdoor). Even if activation energies are determined, sample temperatures should not be allowed to rise signi®cantly higher than what is observed outdoors due to concerns about reproducing the appropriate physical failure. More attention to stresses during accelerated testing versus what occurs outdoors is warranted. This will help insure that failures that occur in the accelerated test will actually represent those that occur outdoors. Humidity levels should be controlled or monitored during all parts of the weathering cycle. If
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the humidity dependence is not known, it is necessary to weather samples in both ``wet'' and ``dry'' cycles to determine material sensitivity to moisture/humidity. This can dramatically reduce test time and costs due to premature failure or over-engineering. Variation of environmental conditions during accelerated testing must be even more tightly controlled to insure that the overall variation in acceleration factor is small enough not to cause issue with predicting in-service performance. Total variation from chamber-to-chamber should be less than 10%. This will require reducing the variability of sample light intensity, temperature and humidity and improving calibration. A new chamber has been proposed that may result in improved control over these variables [29]. References [1] Bauer DR. Global exposure models for coating photooxidation. Submitted to Polym Degrad Stab. [2] Simms JA. Acceleration shift factor and its use in evaluating weathering data. J Coat Technol 1987;59(748):45. [3] Bauer DR. J Coat Technol 1994;66(835):57±65. [4] Pickett JE. Macromol Symposia 1997;115:127±141. [5] Gerlock JL, Smith CA, Nunez EM, Cooper VA, Liscombe P, Cummings DR, et al. In: Clough RL, Billingham NC, Gillen KT, editors. Polymer durability degradation, stabilization, lifetime prediction. USA: American Chemical Society (Adv in Chem 249), 1996. p. 335±348. [6] Kerouac K. Spectral and thermal characterization of automotive interior. In advanced symposium on automotive materials testing, Heraeus DSET Laboratories, 1993. [7] Searle NS. Wavelength sensitivity of polymers. In: Patsis AV, editor. Advances in the Stabilization and Controlled Degradation of Polymers. Technomic Publishing 1989. [8] Bauer DR, Paputa Peck MC, Carter III RO. J Coat Technol 1987;59(755):103±109. [9] Bauer DR, Mielewski DF. Polym Degrad Stab 1993;40:349±55.
[10] Davis A. Polym Degrad Stab 1981;3:187. [11] Burch DM, Martin JW. In: Bauer DR, Martin JW, editors. Service Life Prediction of Organic Coatings: A Systems Approach. Oxford: Oxford University Press (ACS Symp. Ser. 722), 1999. p. 85±107. [12] Wypich G. Handbook of material weathering. Toronto, Canada: ChemTec Publishers, 1995. [13] Martin JW, Saunders SC, Floyd FL, Wineburg JP. Methodologies for predicting the service lives of coating systems. Federation Series on Coatings Technology. Federation of Societies of Coatings Technology. PA: Blue Bell, 1996. [14] Wernstahl KM, Carlsson B. J Coat Technol 1997;69(865):69±75. [15] Bauer DR, Gerlock JL, Mielewski DF. Polym Degrad Stab 1992;36:9. [16] Bauer DR, Mielewski DF, Gerlock JL. Polym Degrad Stab 1992;38:57. [17] Dusbiber T, Gerlock JL. Personal communication. [18] Smith CA, Gerlock JL. Personal communication. [19] Wineburg JP. Advanced symposium on automotive materials testing. Hereaus DSET Laboratories, 1993 (Chapter 5). [20] Brennan PJ. J Vinyl Technol 1990;13:73±7. [21] Putman WJ, Pekara D, McGreer M. In: Lacasse MA, editor. Science and technology of building seals and sealants; 5th volume. ASTM STP 1271. Philadelphia (PA): ASTM 1995. [22] Pickett JE, Moore JE. Polym Degrad Stab 1993;42:231. [23] Gerlock JL, Smith CA. Personal communication. [24] Bauer DR, Gerlock JL, Mielewski DF, Paputa Peck MC, Carter IIIRO. Polym Degrad Stab 1990;28:39±51. [25] Bauer DR, Dean MJ, Gerlock JL. Ind & Eng Chem Research 1988;27:65±70. [26] Fischer RM, Ketola WD. In: Ketola WD, Grossman D, editors. Accelerated and outdoor durability testing of organic materials. ASTM STP 1202. Philadelphia (PA): ASTM, 1994. p. 88. [27] Fischer RM. In: Ketola WD, Grossman D, editors. Accelerated and outdoor durability testing of organic materials. ASTM STP 1202. Philadelphia (PA): ASTM, 1994. p. 112. [28] Lewandowski A, Jorgensen G, Bingham C, Netter J, Goggin R. In: Bauer DR, Martin JW, editors. Service life prediction of organic coatings: a systems approach. Oxford University Press (ACS Symp. Ser. 722), 1999. p. 170±185. [29] Martin JW, Chin JW, Byrd E, Embree E, Kraft KM. Polym Degrad Stab 1999;63:297±304.