Development of an Accelerated Test Methodology to the Predict Service Life of Polymeric Materials Subject to Outdoor Weathering

TL;DR

This paper presents a scientifically-based accelerated testing methodology using statistical models to predict the outdoor service life of polymeric materials, reducing testing time while maintaining accuracy.

Contribution

It introduces a nonlinear mixed-effects model incorporating UV spectrum, intensity, temperature, and humidity for accelerated polymer degradation testing.

Findings

The methodology accurately predicts outdoor service life from laboratory tests.

Model validation shows good agreement with outdoor exposure data.

Statistical approach improves reliability of accelerated aging predictions.

Abstract

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.