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

作　　者：Talaka Dya Bale Baidi Blaise Gambo Betchewe Mohamadou Alidou Talaka Dya;Bale Baidi Blaise;Gambo Betchewe;Mohamadou Alidou(Faculty of Science, University of Maroua, Maroua, Cameroon;Ecole Nationale Superieure Polytechnique, University of Maroua, Maroua, Cameroon)

机构地区：[1]Faculty of Science, University of Maroua, Maroua, Cameroon [2]Ecole Nationale Superieure Polytechnique, University of Maroua, Maroua, Cameroon

出　　处：《World Journal of Mechanics》2021年第7期146-163,共18页力学国际期刊（英文）

摘　　要：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.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.

关 键 词：Particle Swarm Optimization Hyperelastic Models Tension-Torsion Test Load Force Torsional Couple 

分 类 号：O34[理学—固体力学]