EPJ Web Conf.
Volume 250, 2021DYMAT 2021 - 13th International Conference on the Mechanical and Physical Behaviour of Materials under Dynamic Loading
|Number of page(s)||7|
|Section||Modelling & Numerical Simulation|
|Published online||09 September 2021|
Predicting the high strain rate behaviour of particulate composites using time-temperature superposition based modelling
Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK
* Corresponding author: firstname.lastname@example.org
Published online: 9 September 2021
Polymeric particulate composites are widely used in engineering systems where they are subjected to impact loading – at a variety of temperatures – leading to high strain rate deformation. These materials are highly rate and temperature dependent, and this dependence must be well understood for effective design. It is not uncommon for many of these materials to display mechanical responses that range from glassy and brittle to rubbery and hyperelastic [1-3], due to their polymeric constituents. This makes accurate measurements and modelling not only necessary, but challenging. This is made more difficult by experimental artefacts present when traditional tools such as the split Hopkinson pressure (SHPB) or Kolsky bar are used to interrogate the high rate response of low-impedance materials. The transition from isothermal to adiabatic conditions as the rate of deformation increases also has an effect on the mechanical response, which cannot be neglected if the high rate behaviour is to be accurately predicted. In this paper, time-temperature superposition based frameworks that have enabled the high rate behaviour of neoprene rubber  and (plasticised) poly(vinyl chloride)  to be captured, will be extended to explore the high strain rate behaviour of unfilled natural rubber and several grades of glass microsphere filled natural rubber particulate composites.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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