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Bayesian Hierarchical Models for Service Life Prediction of Polymers



Adam L. Pintar, Christopher C. White, Li Piin Sung


Service life prediction for polymers remains an elusive goal. In this chapter, a general methodology first described in a previous study \cite{pintar2018predicting} is recapitulated and extended. A central component of the methodology is a Bayesian hierarchical model that describes the data generating process for the samples weathered in the laboratory. This model allows for the propagation of uncertainties to the predictions of outdoor degradation. A shortcoming of the implementation of the methodology in \cite{pintar2018predicting} is the necessity of a calibration for the outdoor predictions that was found to be unstable. That is, the calibration for the outdoor predictions changes over time. An approach to remove the calibration is presented here. The accuracy of the new implementation is assessed on two polymer systems, a sealant and polyethylene.
Service Life Prediction of Polymers and Plastics Exposed to Outdoor Weathering
Publisher Info
Elsevier, Cambridge, MA


Service Life Prediction, Bayesian Hierarchical Model, Polymer Material, Reliability
Created August 4, 2020, Updated August 25, 2020