A Comparative study of the fitting performance of hyperelastic constitutive models

Date

2019

Author

Badienia, Yashar

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Hyperelastic materials are widely used over the last decades. Studies on molecular structure and stress-stretch response of such materials goes back to 1940. Since then, many researchers have developed various material models to represent the response of hyperelastic materials undergoing different loading scenarios. Generally phenomenological and micromechanically based material models are the two main categories considered during the modeling steps. Among the hyperelastic material models micromechanically based network models are known to have high performance and reliability over the purely phenomenological models dealing with the analysis of unfilled rubber. Number of available experimental data sets under different loading cases, maximum stretch level reached by each loading case, and additives with percentage of fillers, on the other hand, play an important role choosing the appropriate model for further analysis of technical rubber. Therefore, a well defined hyperelastic material model should have physically interpretable and minimum number of parameters. During the last decades number of hyperelastic material models has been increased, therefore, comparison among the material models and choosing an appropriate one turns to be crucial factor for researchers of the field. One may access to large number of review papers comparing strength and weakness of constitutive material models, implying the importance of making decision between different types of constitutive models suiting the specific analysis. In this study, fitting performance of 40 hyperelastic material models has been presented. In order to obtain parameters for each constitutive model a genetic algorithm is developed. Further improvement of the results are achieved using FMINCON utility of MATLAB. Four set of distinct and well known data for uniaxial tensile, equi-biaxial, pure shear, and biaxial tension loads has been considered during parameter optimization

Subject Keywords

Elasticity., Hyperelasticity, parameter optimization, rubber-like material, genetic algorithm, multi-objective optimization.

URI

http://etd.lib.metu.edu.tr/upload/12624479/index.pdf
https://hdl.handle.net/11511/44209

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Graduate School of Natural and Applied Sciences, Thesis