Literature DB >> 34301009

Inverse Parameter Identification for Hyperelastic Model of a Polyurea.

Yihua Xiao1,2, Ziqiang Tang1, Xiangfu Hong1.   

Abstract

An inverse procedure was proposed to identify the material parameters of polyurea materials. In this procedure, a polynomial hyperelastic model was chosen as the constitutive model. Both uniaxial tension and compression tests were performed for a polyurea. An iterative inverse method was presented to identify parameters for the tensile performance of the polyurea. This method adjusts parameters iteratively to achieve a good agreement between tensile forces from the tension test and its finite element (FE) model. A response surface-based inverse method was presented to identify parameters for the compression performance of the polyurea. This method constructs a radial basis function (RBF)-based response surface model for the error between compressive forces from the compression test and its FE model, and it employs the genetic algorithm to minimize the error. With the use of the two inverse methods, two sets of parameters were obtained. Then, a complete identified uniaxial stress-strain curve for both tensile and compressive deformations was obtained with the two sets of parameters. Fitting this curve with the constitutive equation gave the final material parameters. The present inverse procedure can simplify experimental configurations and consider effects of friction in compression tests. Moreover, it produces material parameters that can appropriately characterize both tensile and compressive behaviors of the polyurea.

Entities:  

Keywords:  experiment; finite element; hyperelastic model; inverse procedure; polyurea

Year:  2021        PMID: 34301009     DOI: 10.3390/polym13142253

Source DB:  PubMed          Journal:  Polymers (Basel)        ISSN: 2073-4360            Impact factor:   4.329


  1 in total

1.  Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN-GA).

Authors:  Mingxun Zhu; Zhigang Meng
Journal:  Comput Intell Neurosci       Date:  2021-11-22
  1 in total

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