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arXiv · 1903.11545

Data-driven, structure-based hyperelastic manifolds: A macro-micro-macro approach

Victor Jesús Amores·José María Benítez·Francisco Javier Montáns
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Abstract

In this paper we introduce a novel approach to obtain the stored energy density of rubber-like materials directly from experimental data. The model is structure-based, in which the only assumption is the existence of an isotropic distribution of fibres, chains or networks. Using a single macroscopic test, we obtain the response of the constituents simply by solving a linear system of equations. This response includes all possible interactions, without an assumption on the nature of that behavior, performing an efficient reverse-engineering of the constituents behavior. With that microstructural behavior, we build constitutive manifolds capable of reproducing accurately the behavior of the continuum under any arbitrary loading condition. To demonstrate the goodness of the proposed non-parametric macro-micro-macro approach, using just one of the macroscopic test curves of the Kawabata et al experiments to compute the fiber (micro) behavior, we reproduce, to very good accuracy, the rest of the series of biaxial tests from Kawabata on vulcanized rubber 8phr sulfur, showing that the computed micromechanical behavior represents an excellent approximation of the actual behavior. We show similar results for the Treloar material and for the Kawamura et al series of experiments on silicone. With the use of constitutive manifolds, the method has similar efficiency in finite element programs to that of analytical models. Julia language code to reproduce the main computations and some figures of the paper is available.

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Victor Jesús Amores, José María Benítez, Francisco Javier Montáns. 2019-08-21. Data-driven, structure-based hyperelastic manifolds: A macro-micro-macro approach. https://doi.org/10.1016/j.compstruc.2020.106209

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