Variables, Methods and Philosophies Considered in Coatings Durability

Henry K. Hardcastle III
Atlas Weathering Services Group


This presentation broadly overviews several key aspects of coatings weathering and durability. Recent empirical data is included to introduce some fundamental variables and environmental considerations being investigated. The evolution and organization of test methods for characterizing weathering variables effects is reviewed. Finally, the scope of weathering investigations is viewed from a philosophical perspective. This introduction provides a general review of weathering technology from novel perspectives and provides attendees with a warm-up for more specific technical papers that follow.


Coatings durability and weathering technology in general is a very broad and multi-disciplined technology. The purpose of this presentation is to provide an overview and warmup for participants. In this introductory presentation, we provide a view as though from 30,000 feet. This presentation, by necessity, uses broad-brush strokes to construct a skeletal context participants may use to organize more detailed concepts. Some of the most important key aspects of this technology will be identified in order to provide landmarks for navigating through more detailed and focused presentations that follow. This presentation is designed to begin with fundamental information regarding weathering variables familiar to most participants. Next, less familiar organizations of test methods are overviewed for exploring the effect of fundamental variables on coatings as weathering. Finally, unique philosophical considerations useful for long-term work in this technology are introduced.

Figure 1. Organization of Presentation
Organization of Presentation

Fundamental Variables

One place to begin examining weathering technology is with several fundamental variables effecting coatings durability and weathering performance. There are several main areas of coating history that have a direct effect on durability. These areas include Formulation, Raw Materials, Processing History, and End Use Environment. Formulation, Raw Materials, and Processing are all specific for materials under consideration. The environmental exposure variables, however, are often applied to many different materials.

Figure 2. Interplay of Formulation, Raw Materials, Processing, and Environment variables for coating durability.
Interplay of Formulation

Individuals with a background in manufacturing are familiar with the huge number of manufacturing variables that can affect coating quality. Likewise, very large collections of variables exist in the environment that can affect coatings durability. Light, temperature, moisture, pollution, abrasion, solvents, biological, cycling, packaging, substrate, chlorides, pH, etc., represent only a very small number of environmental variables affecting coating performance. The "environment" itself is best described as an n-dimensional hyper-volume [1]. There is an added complexity in that many of the environmental variables interact to cause coating degradation characteristics not observed with only a single variable. Given the characteristics of an n-dimensional hyper-volume, it is not surprising that weathering technologists still have difficulty predicting material behavior in the end use environment. Often, because of this complexity, weathering technologists focus studies on what may be referred to as the big three environmental variables in weathering research; Light, Temperature and Moisture. The following section presents characteristics of these three variables in some detail. The reader needs to keep in mind, however, the following only presents an infinitely thin slice of the n-dimensional hyper-volume of a coatings environment.


Sunlight in a materials exposure environment may play a key role in coatings durability and degradation. Many years ago, scientists characterized a coatings durability by estimating the number of years a coating would fulfill its function. Test exposures were timed using months and years. It soon became obvious that all times were not equivalent for coatings degradation. Five years exposure in Miami was not equivalent to five years exposure in London. Similarly, natural variation in one year to the next at a single location resulted in variability of exposure test results for the same exposure duration during different years. A major improvement in reducing these variabilities was achieved by timing exposures by light dosage rather than by time. Now, coatings durability could be rated by amount of degradation given a specific light dosage rather than a specific duration of exposure. Later, even better correlation and repeatability was achieved when dosages were measured in total UV dosage rather than total solar dosage. Sunlight's total solar spectrum includes 295 to about 2500 nm wavelengths. Solar UV, however, includes 295 to 385 nm wavelengths. Chemists postulate that the UV portion of the solar spectrum is responsible for the majority of light induced coating degradations and the better correlations between coating degradation and solar UV dosage (as opposed to total solar dosage) also indicates this relationship. Understanding some characteristics of how this solar UV dosage is accumulated on coatings surfaces throughout different time slices offers insights into coatings degradation due to the fundamental variable of light.

Total UV dosage and time slice considerations - empirical data 

Atlas Weathering Services Group measures solar radiation and maintains historical radiation data on a professional basis for a variety of surface orientations and locations.

Figure 3 shows historical daily radiation measurements for 26 degree oriented south surface from 1990 to 1998. These measurements were made with calibrated Epply Laboratories TUVR instruments and showed accumulated energy deposited from 295-385 nm. The graph plots median actual values, mean statistical values, and ± 1.96 standard deviations from the mean (95% confidence estimate) for the 1990 to 1998 time period. Interpreting this graph indicates that two different randomly selected days may differ anywhere from 0 to 1.6 MJ/m^2 UV dose. This level of variation may be important for in-service exposures and service life times of one or several days. Testing of skin response and specialty materials designed for rapid decomposition may be required to account for this type of daily variation. Many materials designed for the exterior service environment, however, undergo service lives involving much thicker time slices and require larger UV dosages to show significant photo degradation.

Figure 4 shows historical monthly total UV radiation measurements for a 26 degree south oriented surface from 1990 to 1998. Interpreting this graph indicates that exposures for two different Julys may differ only from approximately 22 to 32 MJ/m^2 UV dose within ± 1.96 standard deviations. Again, this variation may be important for those materials with service life times on the scale of one month. The focus of most materials SLP (Service Life Prediction), however, is on materials with a multi-year in-service life expectancy.

Figure 3. Daily Solar Radiation, Miami
Daily Solar Radiation
Figure 4. Monthly Solar Radiation, Miami (Note: The ± 1.96 Standard Deviation lines widen in the ninth month due to an assignable cause in 1992 when hurricane Andrew passed over the measurement site. Radiation measurements were discontinued for many days reducing n and increasing the standard deviations.)
Monthly Solar Radiation

Figure 5 shows historical yearly total UV radiation measurements for a 26 degree oriented surface from 1990 to 1997. Interpreting this graph indicates that exposures for any two different years may differ only from approximately 250 to 350 MJ/m^2 UV with -in ± 1.96 standard deviations. The actual yearly totals from 1990 to 1997 show a moderately good agreement with the statistical mean for the 1990 to 1997 period. Actual totals for this data show no discernable trends or cyclic patterns. If the variation depicted in Figure 5 were considered analogous to a plot of output of a manufacturing process (i.e., control chart), most manufacturing process engineers would interpret this as evidence of a relatively stable process in a state of statistical control [2].

It is the multi-year time slice and predictability of solar irradiance dosage at the multi-year level of context that is important for the vast majority of materials designed for exterior in-service use conditions. Figures 6, 7, 8, and 9 show distributions of historical yearly total UV radiation dosages for a selection of angles facing due south measured at Atlas Laboratories in Phoenix, Arizona, and Miami, Florida, from calibrated and maintained Epply TUVR radiometers.

Figure 5. Yearly Solar Radiation, Miami, At Latitude
Yearly Solar Radiation
Figure 6. Yearly Solar Radiation, Miami, 5°
Yearly Solar Radiation
Figure 7. Yearly Solar Radiation, Miami, 45°
Yearly Solar Radiation
Figure 8. Yearly Solar Radiation, Phoenix, At Latitude
Figure 9. Yearly Solar Radiation, Phoenix, 45°
Yearly Solar Radiation

The Right Light: New proposed spectral power distributions from empirical data

In addition to characterizing how the solar light is deposited on the coating, understanding what light is causing the dosage is important. Exposing coatings to unrealistic light sources with spectral power distributions very different than the light received in end-use environment can produce unrealistic degradation results. This is especially important in artificial weathering test methods discussed in the next section. In order to characterize the solar UV spectral power distribution, Atlas Weathering Services Group performed an empirical study of light deposited on exposed surfaces over three years in Phoenix and Miami reference environments [3].

Scatter Plots. Initial data analysis involved plotting the entire population of near solar noon measurements - for each wavelength - on x-y scatter plots. These complex plots indicated effects of season (sun angle) and atmospheric trends (clouds) on solar intensities. Examples of these scatter plots are shown in Figure 10 for Miami and Figure 11 for Phoenix. The population of measured solar intensities for the three-year period at 315.6 nm near solar noon is plotted as a function of day of year. A collection of these scatter plots was generated for all 15 wavelengths considered for both Miami and Phoenix.

Figure 10. Scatter plot of solar intensities at 315.6 nm for 3 years in Miami.
Scatter plot of solar intensities
Figure 11. Scatter plot of solar intensities at 315.6 nm for 3 years in Phoenix.
Scatter plot of solar intensities

Histograms and Frequency Distributions. Although interesting, the scatter plots were difficult to apply and therefore were reduced to histograms. For each wavelength, the range of solar intensities was determined. The intensity range was then divided into ten classes. For ease of comparison, the same set of classes was used for both the Miami and the Phoenix data. The number of occurrences of solar intensity within each class was counted. Each class count was then ratioed to the total count of measurements to obtain a frequency for each class of intensities within each wavelength. An example of this histogram frequency distribution is shown at 315.6 nm for Miami and Phoenix in Figures 12 and 13, respectively. A collection of these histograms was generated for the 15 wavelengths considered for both Miami and Phoenix.

Calculation of Grouped Modes. Researchers desired a statistic that would represent the most frequently observed intensity at each of the 15 wavelengths. For each wavelength, a grouped mode was calculated from the frequency distribution using the formula [4]:

Mode = LMo + [d1 / (d1 + d2)] w

LMo = lower limit of the modal class

d1 = frequency of the modal class minus the frequency of the class directly below it

d2 = frequency of the modal class minus the frequency of the class directly above it

w = width of the modal class interval

Figure 12. Histogram frequency distribution of solar intensities at 315.6 nm for 3 years in Miami (n = approx. 2780).
Frequency distribution of solar intensities
Figure 13. Histogram frequency distribution of solar intensities at 315.6 nm for 3 years in Phoenix (n = approx. 2970).
Frequency distribution of solar intensities

Table 1. Proposed Reference Solar Spectral Power Distributions (SPDs) for Miami, FL and Phoenix, AZ - 45° South Near Solar Noon

Miami Subtropical Environment Phoenix Desert Environment
Nominal Wavelength-nm Modal Value in milliwatts/m2-nm Nominal Wavelength-nm Modal Value in milliwatts/m2-nm
296.2 0.35 296.2 0.16
297.6 1.21 297.6 0.38
299.7 6.83 299.7 1.04
301.4 13.64 301.4 16.63
303.7 35.72 303.8 41.75
305.7 55.04 305.8 64.95
307.5 78.32 307.5 96.3
309.7 99.71 309.7 123.76
312.3 164.82 312.3 193.74
314.0 185.77 314.0 217.92
315.7 201.96 315.7 235.72
317.9 237.82 317.9 277.68
320.4 281.32 320.5 323.17
322.2 285.31 322.2 325.86
323.3 301.92 323.3 345.27


Proposed Reference SPDs. The data in Table 1 were then plotted in x-y format to obtain the proposed reference SPDs as shown in Figure 14 and Figure 15. These SPDs are properly denoted as hemispherical solar spectral power distributions for a 45° south facing surface near solar noon in Miami and Phoenix.

It is unclear why these distributions indicate that Miami has a higher grouped mode than Phoenix at wavelengths shorter than 300 nm. Errors due to wavelength calibration do not appear to account for this observation. One possibility may be that this is an artifact of signal-to-noise ratios in either of the two instruments. Another may be that these values are truly representative of differences between the environments. No matter what the root cause of this observation, users of this data are strongly cautioned about the values at wavelengths shorter than 300 nm until these data are successfully explained.

Figure 14. Proposed reference solar spectral power distribution for Miami, FL - 45° south near solar noon.
Proposed reference solar spectral power distribution
Figure 15. Proposed reference solar spectral power distribution for Phoenix, AZ - 45° south near solar noon.
Proposed reference solar spectral power distribution
Figure 16. Comparison of the proposed reference SPDs to other light sources in weathering technology.
Comparison of the proposed reference SPDs

Applications. One application envisioned for the proposed SPDs is comparison of the most frequently observed values near solar noon in actual real world environments to values measured from artificial light sources and values measured at different conditions in the real world. Figure 16 shows a graphical comparison of several different types of data to the reference SPDs developed in this study.

This study enabled exposure laboratories in Miami and Phoenix to obtain reference spectral power distributions based on empirical measurements in the subtropical and desert reference environments. Sufficient measurements were considered (daily measurements for approximately three years) to constitute statistically robust data sets. The reference SPDs were derived from grouped modes and represent the most frequently observed class of intensities for the period of measurements. Only intensities near solar noon for wavelengths between 295 and 324 nm were considered for the 45° tilted surfaces facing south.

Sources of instrumental error appeared less than variation due to environmental factors. It is unclear why the Miami data displayed higher intensities than Phoenix data at wavelengths shorter than 300 nm. The proposed reference SPDs may be useful for comparing artificial light sources to real world measured solar intensities.

Acknowledgement. The UV B data reported in this paper were obtained by AWSG in partnership with The National Institute of Standards and Technology and Smithsonian Environmental Research Center.


The previous section detailed several levels of the environmental variable of light. Similar complexity occurs for the environmental variable of temperature. Temperature can affect coatings durability in many ways. Temperature differences can drastically affect reaction rates, reaction mechanisms, pathways, and co-variables (such as moisture) in coatings degridation. Understanding different characteristics of temperature variables my offer insights into coatings performance. One may look at the different temperatures often measured during materials test exposure.

Ambient Air and material temperature - empirical data 

Engineers consider several different temperatures important to weathering test methods. Exposure laboratories generally report one or several critical temperatures of exposure.

Exposure laboratories typically measure ambient or air temperatures using WMO or NOAA documented techniques. Local and regional geography, ecosystems, and atmospheric conditions influence a shaded measurement device such as thermometer, thermocouple, or RDT sensor enclosed in a ventilated shelter. Measurements obtained indicate the level of energy in the mass of air surrounding the sensor. Figure 17 compares seasonal ambient air temperatures observed in the subtropical and desert reference environments of Miami and Phoenix.

By contrast, materials surface characteristics: reflectance, transmittance, absorptance, (along with convection, conduction and emittance of the material) interact with ambient temperatures and solar irradiance to determine temperature of a material's surface [5]. Anyone picking up a piece of metal pipe warming in the summer's sun can attest to the significant differences between ambient and surface temperatures. Exposure laboratories typically measure surface temperature by attaching thermocouples to, or just below, the surface absorbing radiation. Measurements obtained indicate the level of energy at the absorbing surface. Figure 18 compares material temperatures in un-backed and black box configurations with ambient air temperatures.

Bulk temperature indicates the amount of energy inside the material surrounding the sensor. Environmental, surface and intrinsic properties influence bulk temperatures of the material. Thermocouples or other sensors can be cast or implanted inside materials to measure this property as a function of weathering.

Figure 17. Average Ambient Temperature - Sliding Average in Florida and Arizona Reference Environments
Average ambient temperature
Figure 18. Comparison of Black Box to Un-backed Metal Panel Temperatures - Near Horizontal in Florida - September 17, 1998 Understanding the various aspects of temperature including seasonal, geographical, ambient air and material temperature considerations points out key considerations for this important environmental variable's effect on coatings degradation.
Comparison of Black Box to Un-backed Metal Panel Temperatures


Just as the environmental variables of light and moisture had multiple levels of influence on coatings degradation, moisture is also a multi-dimensional variable. Moisture in gas form, liquid form and solid form has different influences on materials degradation. This manifestation of the temperature-moisture interaction effect causes different coating degradations. Similarly, frequency and duration dependencies also affect coatings durability. Examination of several sets of empirical moisture data indicates some of the important considerations for this environmental variable. Relative humidity and wet time - empirical data.

Exposure laboratories typically employ two main types of moisture measurement; relative humidity and wet time. Exposure laboratories typically measure relative humidity using WMO or NOAA documented techniques. As in ambient temperature, local and regional geography, ecosystems and atmospheric conditions influence a shaded measurement device such as wet bulb/dry bulb or solid-state sensor enclosed in a ventilated shelter. Measurements obtained indicate the amount of water vapor in the air mass relative to the air's maximum capacity for the air temperature. Exposure laboratories measure wet time with a variety of techniques. Wet time is the amount of time liquid water is present on a material's surface due to condensation and precipitation. Sensors use change in resistivity of wick like material, liquid bridging between conductive leads, and activation of voltaic cells to measure presence of liquid water [6]. Water acts both as a chemical reagent in many hydrolytic and galvanic weathering reactions and as a physical stressor in materials degradation. Moisture is a critical weathering variable for many materials. Figure 19 compares seasonal high and low relative humidities observed in the subtropical and desert reference environments of Miami and Phoenix. Figure 20 compares seasonal wet times - hours with liquid water present on coating surfaces - observed in Miami and Phoenix.

Moisture cycling by humidity or liquid water creates mechanical stress cyclic loading in many materials. Absorbed water results in compressive stresses on the outside and tensile stresses in the bulk. Drying creates bulk compressive and surface tensile stress gradients. The daily fatigue due to the night's condensation and day's solar drying, in subtropical and tropical environments may interact with hydrolytic, thermal and photo degradations causing mechanisms significantly different than those observed in arid exposures.

Engineers often underestimate effects of condensation in subtropical environments. ISO 9223 offers an estimate of wet time by measuring the amount of time relative humidity is above 80% with temperatures above freezing. Exposure tests, however, should utilize direct measurement data for critical considerations. The black body properties of the night sky further enhance condensation by causing materials surface temperatures to drop significantly below surrounding air mass temperatures. This phenomena causes materials to act as condensers in the night air.

The effects of rain may be similar to condensation. Whereas condensation often occurs at regular daily intervals, rain can play a more infrequent, seasonal role. The frequency and duration of rain is also closely linked with irradiance and temperature excursions. One example of catastrophic excursions occurs in Arizona's arid climate when materials heated close to heat deflection temperatures in the summer afternoon undergo dramatic thermal shocks due to sudden afternoon seasonal monsoon showers.

The effects of relative humidity, like liquid water, depend on material characteristics. Relative humidity often receives undue attention simply because exposure facilities report this data. In fact, the amount of water molecules available for materials degradation at the highest possible terrestrial relative humidities (vapor pressures) may represent order of magnitudes less than amount of water molecules available in liquid state depending on how the calculation is performed. Test development should consider effects of water vapor with consideration to presence of liquid water in the exposure environment. In sub-tropical environments, high relative humidities often affect biological degradation (algae, mildew, etc.). Relative humidity microclimates present in full scale testing often result in biodegradation not observed in small coupon exposures.

Figure 19. Average Relative Humidity - High and Low in Florida and Arizona
Average Relative Humidity
Figure 20. Average Wet Time - Hours Each Day - Florida and Arizona Near Horizontal
Average Wet Time

Test Methods

The previous section reviewed a primary level of context for coatings durability and weathering. Knowledge of the fundamental variables effects on coatings durability can be used to develop useful test methods. These test methods can be used to study various aspects of coatings durability in order to achieve knowledge of performance. By building up test methods from fundamental variables, weathering technologists obtain powerful tools to study why coatings degrade and what can be done to prevent or slow these degradations. The use of fundamental variables to construct test methods represents a change in the level of context from the previous section of this presentation.

One of the major dichotomies in weathering test methods is between natural methods utilizing naturally occurring weathering factors and artificial methods utilizing man made factors. Another dichotomous grouping separates real-time from accelerated methods. Thus, an exposure utilizing a 45 degree south facing backed rack in Phoenix would represent a natural, real time method. An exposure utilizing a xenon arch weather-o-meter with high irradiance settings would represent an artificial accelerated method. Descriptive organizations of weathering test methods offer insights into these test methods.

Evolutionary Jumps in Natural Weathering Test Methods

Advances in natural weathering test methods can be visualized as levels of evolutionary jumps in weathering test method sophistication:

Car wrack

Level 1-Exposures in reference environments represent a first step in accelerating degradation from traditional end-use markets. Comparison between South Florida and Phoenix, AZ offers an effective technique for understanding these environments with respect to solar radiant exposure, temperature and moisture.

Modern exposure facilities

Level 2-Modern exposure facilities employ racks attached by a single axle to fixed vertical members. The axle allows rack pivoting to the appropriate angles from horizontal as needs arise. Un-backed, backed, under glass or other exposure enhancements then clamp to the pivoted frame. Although simple in retrospect, the pivoting frame advancement provided basis for considerable development of natural weathering test methods. This humble, simple improvement in rack design has allowed more advances in conventional weathering technology than any other single advancement. Static exposures easily relate back to full system end-use exposures. Increases in critical weathering variables can significantly affect the rate at which materials degrade. Simple optimization of location, backing and angles can modestly accelerate degradation rates.

static exposure angles

Level 3-Data from the static exposure angles discussed shows the effect of angle on increasing critical weathering variables. A dynamic exposure, which varies orientation in response to the seasonal variation in the sun's path, can increase critical weathering variables throughout the year.

Exposure evolution

Level 4-The evolution in exposure methods from static to variable angle exposure racks dramatically increased levels of radiant energy deposited on specimens. A similar jump in exposure development occurred with automatic tracking mounts that follow the sun's path from sunrise to sunset. These sun-tracking mechanisms dramatically increase solar irradiance and represent the next milestone in natural weathering acceleration methods.


Level 5-After obtaining maximum acceleration from normal incidence, sun-tracking exposures, engineers often want to accelerate UV degradation beyond these methods using NATURAL sunlight. Inventors developed an elegant solution to concentrate the image of several suns onto a single target area of the test material. This method became known as the "EMMA," an acronym for Equatorial Mount with Mirrors for Acceleration. Standards for this test method include ASTM G90, ASTM D4364 A, ISO 877 and SAE J1961.

accelerated weathering test

Level 6-The next quantum leap in evolution of natural accelerated weathering test methods (like the pivoting rack, follow-the-sun trackers and EMMAqua concentrators) may come from hybrid test methods utilizing combinations of different natural, accelerated, and artificial weathering test methods. The design for special exposures is only limited by the engineer's ability to link back or correlate to the reference environments, imagination, and funding. Remaining within the paradigms represented by standards is important for comparisons of performance between different vendors, materials, processes, quality control issues, and the like. For research and development of materials and processes, however, understandings often come from experimentation outside the standards requirements and utilize novel test method approaches.

Methods of acceleration and increasing critical weathering variables may present considerable risks for correlating testing results back to full system end-use exposures. Engineers should only use accelerated test methods in conjunction with test methods presented so far. ASTM G 90 clearly states, "No accelerated exposure test can be specified as a total simulation of natural field exposures."

Inventory and Logical Organization of Test Methods

The process described above has been ongoing in weathering technology for many years and occurs in artificial methods as well as natural. Similar evolutionary processes have been occurring for analytical measurements used in assessing coatings durability including appearance, chemical, physical, mechanical, etc. The effect of this evolution has been to provide a sizeable collection of diverse test methods researchers may use to study coatings degradation.

Figure 21. One organization of weathering test methods
organization of weathering test methods

Organizing the scope of weathering test methods into logical order helps the engineer visualize tools available for weathering tests. Figure 21 presents one such organization. In general, there appears to be a trade-off between confidence and acceleration of test methods. Between specific methods and materials, however, this trade-off becomes less obvious.

Three general guidelines may assist researchers in selection of various test methods. First, relying on several test methods rather than a single method reduces risks of errors and does not put all the eggs in one basket. Second, preceding accelerated exposures with identical specimens on real time exposure allows direct comparison between test methods. Third, surveillance testing involving regular sampling from production lines for exposure tests reveals durability differences due to drifts in formulation, processing, and raw materials.

The test methods presented herewithin represent a collection of tools for the product engineer. These analytical tools - like all others - if used improperly, can result in erroneous decisions with catastrophic results. Likewise, if used with skill, they can enhance product performance and customer satisfaction. This treatment only reviews a number of tools available to the engineer. This treatment does not present application and methodology details. Proper use of these tools includes reviewing procedures, cautions, and warnings as specified in appropriate standards.

Philosophical Aspects

The previous section reviewed how researchers could use knowledge of fundamental variables to develop test methods in order to achieve knowledge of coatings durability. Many other behaviors used in weathering technology likewise improve our understanding of weathering phenomena. Organizing these behaviors offers powerful approaches to studying coatings durability and weathering technology. These philosophical approaches to studying weathering can enhance a researcher's ability to understand and affect coatings durability at an advanced level [8]. Often, understanding and appreciation of weathering knowledge results in new approaches and philosophies in coatings durability. In this presentation, several important philosophical perspectives are offered as an aid for navigating through weathering technology.

Levels of Context

One of the most important philosophical aspects involves levels of context in weathering research. Often weathering researchers become distracted from their primary research goals. Considering the different levels of context involved in the weathering research project may represent an important tool for preventing these distractions. An illustration of different levels of context uses the behavior of sand. For a researcher to effectively study behavior of sand, it is important he understand what level of context includes the behavior of question. Maintaining primary focus on the level of context of important behavior, with occasional efforts in other levels, as needed, to explain behavior on the focus level, is prudent. Spinning off the level of primary focus is often too easy, represents blue-sky research, and is usually better left to academics than to industrial technologists in commercial settings.

The following four photographs consider the same subject (sand) from four different levels of context. Different sets of variables affect sand at different levels of context.

Representation of atomic structure of silica. Covalent bond strength, crystal lattice structure, atomic diameters, inclusions, etc., affect sand at the atomic level.
Atomic structure of silica
Photograph of Sand particles at 30X. Surface energies, inclusions, fracture planes, matrix, etc., affect sand at the agglomerate level.
Photograph of Sand particles at 30X
Photograph of Ft. Lauderdale Beach. Boardwalks, sanitary facilities, lifeguards, populations, etc., affect sand at the commercial level.
Photograph of Ft. Lauderdale Beach
Photograph of the Florida Peninsula from satellite. Ecologies, plate tectonics, ocean currents, hurricanes, etc., affect sand at a planetary level.
Photograph of the Florida Peninsula from satellite

Studying bond strength of the Si-O complex may offer some insights at the commercial level but should probably not be the primary focus of a study trying to understand human interactions with sand during summer months in Ft. Lauderdale.

Because of the diversity of weathering phenomena and the ability to study weathering at a large number of different levels of context, researchers often become distracted from original goals. Because of this diversity and complexity of weathering phenomena, researchers should understand which level of context they are working at and make sure efforts at other levels of context lead to understandings at the level under investigation. Using this tool of identifying levels of context can help researchers identify root causes, important variables, interactions, and critical behaviors of a materials response to the environment more effectively. Contextual understanding may help separate the critical from the trivial.

Means vs. Variation

Another philosophical aspect of weathering and coatings durability involves consideration of mean failures vs. variation of failures. Typically, weathering researchers employ experiments aimed at characterizing mean values. For example, a researcher might wish to determine what the mean change in gloss is as a function of Arizona exposure. Developing experiments to characterize variation (the range of degradations) also represents an important and often under-utilized tool for weathering researchers. Many of the experimental tools outlined in this presentation, and generally used throughout manufacturing, can be easily modified to study variations of weathering phenomena. Also, variables that affect mean characteristics will often have effects on dispersion and can be studied using simple experimental designs. Figure 22 illustrates different issues of location (means) and dispersion (variation). Many of today's robust automotive materials have mean degradation characteristics well within customer's satisfaction tolerance. Generally, it is the infrequent dramatic variations from these mean values that result in customer dissatisfaction. Studies of variation represent an important set of tools for weathering researchers to understand and characterize these events.

Figure 22. Means and variations of weathering failures
Means and variations of weathering failures
Comparison of Two Coatings

Often, identifying and controlling input variables that narrow variation in degradations can represent greater return on research investment than marginal changes in degradation means. The concept of the Taguchi Loss Function [9], as illustrated in Figure 23, may apply to variation in weathering degradations; L=K (Yi - T)2 where K is a constant that converts deviation to a monetary value, Yi is the weathering characteristic of interest for the product and T is the weathering degradation characteristic target.

Figure 23. Graphic representation of Taguchi Loss Function
Graphic representation of Taguchi Loss Function

Interactions of Formulation, Processing and End Use

Currently much of the work being done in service life prediction is only focusing on the environment and a part of formulation (blending and compounding). Most current approaches leave out formulation variations and processing aspects even though it is generally accepted that many end-use failures are rooted in processing and formulation variations.

An example of an interaction at a simple level that occurs in weathering is of temperature and UV or moisture and UV. Most modern weathering researchers acknowledge this level of interactions and plan for them in models and experiments. An example of an interaction at a complex level that occurs in weathering is of processing history and exposure history. Many modern weathering researchers acknowledge this level of interaction also, however, more often do not plan for these interactions in models and experiments.

Throughout manufacturing technology today, there is an increasing need to understand how interactions affect processes. The inherent inability of single variable experiments to characterize behavior of complex manufacturing processes has led process engineers to evolve experimental techniques for characterizing these interactions. Similarly, in weathering technology, single variable experiments do not provide the sophistication to characterize the effects of weathering interactions in many materials end-use environments. Today, some weathering researchers have recognized the overwhelming role of complex interactions in weathering phenomena and are developing new tools specifically designed to look for and characterize interactions.

Quantitative Evidence for Interacting Variables

Simply put, if we find the difference in response between the levels for one variable is not the same at all levels of the other variables, there is an interaction between variables. This concept is often easier to visualize in a square design as shown in figure 24 [10].

Figure 24. Quantitative Evidence for Interacting Variables
Quantitative Evidence for Interacting Variables

Often in weathering, very complex interactions between three or more variables may occur. The temperature, moisture, UV interactions represent one classic example that weathering researchers strive to characterize for many materials. By designing weathering experiments to seek out the natural interactions occurring on materials in end- use environments, weathering researchers will develop important tools for characterizing materials behavior.

The Interface of Man-made and Natural Phenomena

A final philosophical consideration can be used to summarize many of the concepts in this presentation. Most people have heard the expression, "That is about as exciting as watching paint dry". Occasionally, to the uninterested, a career in weathering technology may seem about as exciting as watching paint fade.

This author makes the following proposition: In past presentations, weathering has been defined as "the adverse response of a material to climate". A previous tool outlined in this presentation has described a materials weathering behavior as interplay between formulation, processing, and end-use environment. Whatever the literal definition, conceptually, weathering phenomena takes place at a very critical interface between the man-made world and the natural world. Weathering researchers would do well to pause and compare these two very large and very different bodies of knowledge as represented in Figure 25.

On one hand, technological aspects of man-made materials embody modern design and manufacturing characteristics. Variation is controlled, tolerances are held, theoretical engineering applied and modern industrial practices form raw materials into finished engineered products within economical constraints. This structured and controlled system is the world of material manufacture.

On the other hand, the variety inherent in natural science effects the environment of exposure. Environmental variations are not generally controlled by man and can be considered n-dimensional. Statistical distributions of nature's environmental variables result in what are called ecological niches in biological sciences. Many aspects of the "n-dimensional hyper-volume" pressuring evolution in ecological niches also pressures man-made materials on exposure. (Consider a number of different material failure modes at a similar order of magnitude as the number of biological species!). In many ways, biological sciences seem better equipped to deal with natural variation found in environments than the current industrial sciences approaches to weathering.

It is at the interface of these two great concepts, of the man-made and of the natural, that the weathering phenomena play out. Researchers must acquire the skills to not only deal effectively in both arenas but also to relate them to each other. Interfacing these two great concepts is difficult, at best (given their different understandings) but is made even more difficult by researchers who elect to deal predominately with only one of these two bodies of knowledge. Understanding and characterizing the exposure environment without regard to the manufacturing aspects embodied in the material will not provide the researcher with an accurate perception of weathering phenomena. Controlling and affecting the manufacturing process without accounting for the variety and dimensionality of the "in service" environment will not provide the researcher with predictive understandings of weathering phenomena. Only when the researcher accounts for the real interactions between the numerous variables in both the man-made and the natural will the understanding of this complex interface advance to the next level. This point of view is a very important tool for weathering researchers to understand.


  1. Pianka, E. Evolutionary Ecology, 2nd ed.; Harper & Row: New York, NY 1978.
  2. Juran's Quality Control Handbook, J.M. Juran and Frank M. Gryna, 4th ed., McGraw-Hill, New York, NY, 1988
  3. Hardcastle, H.K. Proposed Reference Solar Spectral Power Distributions for Miami and Phoenix From Three Years of Measurements, Proceedings of The American Chemical Society Division of Polymeric Materials: Science and Engineering (2000) 83, p. 152-154
  4. Levin, R.I. Statistics For Management; 4th ed.; Prentice-Hall: Englewood Cliffs, NJ, 1987.
  5. Hardcastle, H.K. Predicting Maximum Field Service Temperatures from Solar Reflectance Measurements of Vinyl, Journal of Vinyl and Additive Technology (1998) 4, No. 3 p. 169.
  6. Grossman, P.R. Investigation of Atmospheric Exposure Factors that Determine Time-of-Wetness of Outdoor Structures - ASTM STP No. 646 (1978) ASTM, Philadelphia, PA
  7. Annual Book of ASTM Standards; various Standards; American Society for Testing and Materials: Philadelphia, PA, 1997, various volumes.
  8. Hardcastle, H.K. Weathering Experimenter's Toolbox, Paper presented at the Atlas School for Natural and Accelerated Weathering (ASNAW), Phoenix, AZ, (October 1999)
  9. Phadke, M. Quality Engineering Using Robust Design; Prentice-Hall: Englewood Cliffs, NJ, 1989
  10. Douglas C. Montgomery, Design and Analysis of Experiments, 3rd ed., John Wiley & Sons, New York, NY, 1991.
Related Links