Fernández, J.R., López-Campos, J.A., Segade, A. and Vilán, J.A. (2018) A Genetic Algorithm for the Characterization of Hyperelastic Materials. Applied Mathematics and Computation, 329, 239-250.
https://doi.org/10.1016/j.amc.2018.02.008

has been cited by the following article:

• TITLE: Implementation of Particle Swarm Optimization Algorithm in Matlab Code for Hyperelastic Characterization

  AUTHORS: Talaka Dya, Bale Baidi Blaise, Gambo Betchewe, Mohamadou Alidou

  KEYWORDS: Particle Swarm Optimization, Hyperelastic Models, Tension-Torsion Test, Load Force, Torsional Couple

  JOURNAL NAME: World Journal of Mechanics, Vol.11 No.7, July 26, 2021

  ABSTRACT: The purpose of this paper is to demonstrate the applicability of Particle Swarm Optimization algorithm to determine material parameters in incompressible isotropic elastic strain-energy functions using combined tension and torsion loading. Simulation of rubber behavior was conducted from the governing equations of the deformation of a cylinder composed of isotropic hyperelastic incompressible materials. Four different forms of strain-energy function were considered based respectively on polynomial, exponential and logarithmic terms to reproduce load force (N) and torque (M) trends using natural rubber experimental data. After highlighting the minimization of the objective function generated in the fitting process, the study revealed that a particle swarm optimization algorithm could be successfully used to identify the best material parameters and characterize the behavior of rubber-like hyperelastic materials.