New methods for predicting lifetimes. Part 2 -- The Wear-out approach for predicting the remaining lifetime of materials

GILLEN,KENNETH T. ; CELINA,MATHIAS C.

Sandia National Laboratories

关键词: Aging;    Polymers;    36 Materials Science;    Thermal Degradation;    Mathematical Models;   

DOI  :  10.2172/754007
RP-ID  :  SAND2000-0715
RP-ID  :  AC04-94AL85000
RP-ID  :  754007

美国|英语

来源: UNT Digital Library

【 摘 要 】

The so-called Palmgren-Miner concept that degradation is cumulative, and that failure is therefore considered to be the direct result of the accumulation of damage with time, has been known for decades. Cumulative damage models based on this concept have been derived and used mainly for fatigue life predictions for metals and composite materials. The authors review the principles underlying such models and suggest ways in which they may be best applied to polymeric materials in temperature environments. The authors first consider cases where polymer degradation data can be rigorously time-temperature superposed over a given temperature range. For a step change in temperature after damage has occurred at an initial temperature in this range, they show that the remaining lifetime at the second temperature should be linearly related to the aging time prior to the step. This predicted linearity implies that it may be possible to estimate the remaining lifetime of polymeric materials aging under application ambient conditions by completing the aging at an accelerated temperature. They refer to this generic temperature-step method as the Wear-out approach. They then outline the expectations for Wear-out experiments when time-temperature superposition is invalid, specifically describing the two cases where so-called interaction effects are absent and are present. Finally, they present some preliminary results outlining the application of the Wear-out approach to polymers. In analyzing the experimental Wear-out results, they introduce a procedure that they refer to as time-damage superposition. This procedure not only utilizes all of the experimental data instead of a single point from each data set, but also allows them to determine the importance of any interaction effects.