Neural Networks Approach for Hyperelastic Behaviour Characterization of ABS under Uniaxial Solicitation

Recent developments in computer-aided polymer processing have brought along the need for accurate description of the behavior of materials under the conjugated effect of applied stress and temperature. In order to serve this purpose, in this study, experimental data provided by uniaxial tensile technique tests for thermoplastic halter (CTPH) comprised of hyperelastic materials when subjected to combined effects of applied stress and temperature are coupled with numerical simulations to obtain the required parameters for the characterization of such materials. First, stresses and displacements the thermoplastic halter are recorded during experiment. Thereafter, Mooney-Rivlin's and Ogden theory of hyperelastic is employed to define the constitutive model of thermoplastic halter (CTPH) and nonlinear equilibrium equations of the process are solved using finite element method with Abaqus software. As a last step, a neuronal algorithm (ANN model) is employed to minimize the difference between calculated and measured parameters to determine material constants for Mooney-Rivlin and Ogden models. Although the developed procedure can be applied to several polymeric materials, in this paper, this technique is successfully implemented for acrylonitrile–butadiene–styrene (ABS). Using these coefficients, the material behavior of ABS with Mooney-Rivlin and Ogden constitutive laws is reproduced. The material model obtained in this study for ABS can be implemented into industrial and academic softwares for applications and design purposes.