Neuronal Networks Approach for Characterization of Softened Polymers

Abstract

Recent progress in computer-aided polymer processing analysis demonstrates the need for an accurate description of the material behavior under the conjugated effect of applied stress and temperature. In this work, we are interested in the characterization of circular thermoplastic membranes, ABS and HIPS thermoforming grade, under biaxial deformation using the bubble inflation technique. Hyperelastic (Mooney-Rivlin, Ogden) models are considered. First, the governing equations for the inflation of a flat circular membrane are solved using a finite difference method with deferred corrections and thereafter, a neuronal algorithm (ANN model) is employed to determine the material constants.

Keywords

softened polymer biaxial characterization neural networks ABS HIPS hyperelasticity

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