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Stress Relaxation Behavior of Thermoset Polymers at Large Strains



Unlike composites used in traditional structural applications, thin ply high strain composites (HSCs) common to deployable space structures often experience long term packing deformations at strains up to, and sometimes exceeding, 2%. Extended stowage of these structures can result in incomplete deployment and often permanent shape change, which is directly related to the stress relaxation of the thermoset matrix materials typical to HSCs. Traditionally, viscoelastic characteristics of polymers are measured at small strains on the order of 0.1% and very little data exists at the much larger 2% strains of HSCs. To address this lack of data, the stress relaxation response of two relevant thermoset matrix materials was characterized, over a range of strains and temperatures, to inform and validate future constitutive models. A large strain test, presented here as the Stepped Strain Extended Recovery (SSER) method, was developed to characterize non-linear viscoelastic and potential plastic polymer behavior. The method was implemented using a dynamic mechanical analyzer (DMA) at a variety of operational strains and temperatures. Specimens were prepared from cured thin films of cyanate ester resin, PMT-F6, and toughened epoxy resin, PMT-F7, for testing in a tensile configuration. Four strains, 0.5, 1.0, 1.5 and 2.0% were sequentially applied to each specimen for 100-minute durations, each separated by a 1000-minute recovery period. Tests were performed isothermally at 30°C, 50°C and 70°C. Despite the test temperatures being well below the glass transition temperature (Tg) of the crosslinked polymers, load decay was measured under constant specimen deformation. Trends in the data indicate a consistent increase in stress relaxation with increasing strain and temperature. Overall, the test protocol offers a consistent, rapid, and relatively automated method for generating stress relaxation data over a range of conditions, a necessity for the development and validation of future predictive engineering tools.


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