  Service life prediction is of great importance to manufacturers of coatings
and other polymeric materials. Photodegradation, driven primarily by
ultraviolet (UV) radiation, is the primary cause of failure for organic paints
and coatings, as well as many other products made from polymeric materials
exposed to sunlight. Traditional methods of service life prediction involve the
use of outdoor exposure in harsh UV environments (e.g., Florida and Arizona).
Such tests, however, require too much time (generally many years) to do an
evaluation. Non-scientific attempts to simply "speed up the clock" result in
incorrect predictions. This paper describes the statistical methods that were
developed for a scientifically-based approach to using laboratory accelerated
tests to produce timely predictions of outdoor service life. The approach
involves careful experimentation and identifying a physics/chemistry-motivated
model that will adequately describe photodegradation paths of polymeric
materials. The model incorporates the effects of explanatory variables UV
spectrum, UV intensity, temperature, and humidity. We use a nonlinear
mixed-effects model to describe the sample paths. The methods are illustrated
with accelerated laboratory test data for a model epoxy coating. The validity
of the methodology is checked by extending our model to allow for dynamic
covariates and comparing predictions with specimens that were exposed in an
outdoor environment where the explanatory variables are uncontrolled but
recorded.
