Carpi, Federico and Gei, Massimiliano ![]() |
Abstract
Nonlinear stress–stretch characteristic curves of elastomers are described by means of constitutive equations derived from hyperelastic models. Despite their descriptive ability, these models are not intrinsically predictive a priori, due to their parametric nature, which requires data fitting a posteriori. To overcome this limitation, analytical laws with inherent predictive ability are needed. Here, we present a simple predictive uniaxial law and a simple predictive hyperelastic model. They stem from the experimental evidence that during uniaxial tensile loading of different soft elastomers the true stress has a linear dependence on the engineering strain, up to the characteristic oblique flex that shows up in the nominal stress versus engineering strain plot. We show that this behaviour is captured by a predictive hyperbolic stress–stretch law that requires just a single material constant (the Young's modulus), determinable from few data at very low strains. Also, we formulate a predictive hyperelastic constitutive model, able to accurately describe the stress–stretch curve up to the flex, still just by using the initial elastic modulus. The paper contextualizes the predictive law and model within the field of hyperelastic modelling and presents a comparative experimental validation on three types of elastomers, currently used for electromechanically active polymer devices known as dielectric elastomer transducers. We show that the accuracy of the new predictive models is higher than that of the neo-Hookean equation, and we discuss the potential, as well as the limitations, of the derived laws as tools possibly useful to designers.
Item Type: | Article |
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Date Type: | Publication |
Status: | Published |
Schools: | Engineering |
Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
Publisher: | Institute of Physics |
ISSN: | 0964-1726 |
Last Modified: | 28 Oct 2022 09:17 |
URI: | https://orca.cardiff.ac.uk/id/eprint/74036 |
Citation Data
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