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In this study, we present a systematic scheme to identify the material parameters in constitutive model of hyperelastic materials such as rubber. This approach is proposed based on the combined use of general regression neural network, experimental data and finite element analysis. In detail, the finite element analysis is carried out to provide the learning samples of GRNN model, while the results observed from the uniaxial tensile test is set as the target value of GRNN model. A problem involving parameters identification of silicone rubber material is described for validation. The results show that the proposed GRNN-based approach has the characteristics of high universality and good precision, and can be extended to parameters identification of complex rubber-like hyperelastic material constitutive.

This paper investigates the effects of cell wall thickness, initial stress/strain, and cell regularity on the high strain compressive responses of micro-and nano-sized low density random irregular honeycombs. The strain gradient effects at the micrometer scale, and the surface elasticity and initial stress effects at the nanometer scale are incorporated into the dominant deformation mechanisms in finite element simulations. It is found that the dimensionless compressive stress strain relation strongly depends on the thickness of the cell walls at the micron scale, and at the nano-meter scale, this relation is not only size-dependent, but are also tunable and controllable over a large range. It is also found that under high strain compression, the Poisson's ratios of microand nano-sized low density random irregular honeycombs strongly depend on the cell regularity, but are almost independent of the cell wall thickness and the amplitudes of the initial stress or strain.

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