IMPROVING UNIAXIAL DYNAMIC TENSION TESTING OF COMPOSITE SPECIMENS

Abstract

Validation of composite specimens on the order of a few millimeters in length in tension can be examined dynamically via uniaxial test rigs and split Hopkinson pressure bars. For compression testing, hydraulic/gravity drop towers and the Hopkinson bar are permissible for larger specimens (on the order of centimeters in length) and up to 1,000 1/s. Uniaxial tension test methods for composite specimens enabling both configurations, i.e., several centimeters in length and at strain rates up to 1,000 1/s, is much more limited. The goal of this research investigation is to test precisely in this regime by optimizing the composite coupon specimen for dynamic material characterization. For this project, a tension setup using a single Hopkins on bar was adapted to enable up to 6 cm long specimens tested at the requisite strain rates. Coupon specimens were then optimized using machine learning to promote stress in the gage region thereby minimizing testing failures if the specimen were to break near the grips. Using these two systems, we may have a proof of concept for high strain rate tension testing of composites that is optimized for validation studies using machine learning based geometries.