Publication detail

Sequential simulation and neural network in the stress–strain curve identification over the large strains using tensile test

JENÍK, I. KUBÍK, P. ŠEBEK, F. HŮLKA, J. PETRUŠKA, J.

Original Title

Sequential simulation and neural network in the stress–strain curve identification over the large strains using tensile test

English Title

Sequential simulation and neural network in the stress–strain curve identification over the large strains using tensile test

Type

journal article in Web of Science

Language

en

Original Abstract

Two alternative methods for the stress–strain curve determination in the large strains region are proposed. Only standard force–elongation response is needed as an input into the identification procedure. Both methods are applied to eight various materials, covering a broad spectre of possible ductile behaviour. The first method is based on the iterative procedure of sequential simulation of piecewise stress–strain curve using the parallel finite element modelling. Error between the computed and experimental force–elongation response is low, while the convergence rate is high. The second method uses the neural network for the stress–strain curve identification. Large database of force–elongation responses is computed by the finite element method. Then, the database is processed and reduced in order to get the input for neural network training procedure. Training process and response of network is fast compared to sequential simulation. When the desired accuracy is not reached, results can be used as a starting point for the following optimization task.

English abstract

Two alternative methods for the stress–strain curve determination in the large strains region are proposed. Only standard force–elongation response is needed as an input into the identification procedure. Both methods are applied to eight various materials, covering a broad spectre of possible ductile behaviour. The first method is based on the iterative procedure of sequential simulation of piecewise stress–strain curve using the parallel finite element modelling. Error between the computed and experimental force–elongation response is low, while the convergence rate is high. The second method uses the neural network for the stress–strain curve identification. Large database of force–elongation responses is computed by the finite element method. Then, the database is processed and reduced in order to get the input for neural network training procedure. Training process and response of network is fast compared to sequential simulation. When the desired accuracy is not reached, results can be used as a starting point for the following optimization task.

Keywords

Ductility; Constitutive behaviour; Metallic materials; Numerical algorithms; Optimization; Elastic–plastic deformation

Released

08.06.2017

Pages from

1077

Pages to

1093

Pages count

17

BibTex


@article{BUT136827,
  author="Ivan {Jeník} and Petr {Kubík} and František {Šebek} and Jiří {Hůlka} and Jindřich {Petruška}",
  title="Sequential simulation and neural network in the stress–strain curve identification over the large strains using tensile test",
  annote="Two alternative methods for the stress–strain curve determination in the large strains region are proposed. Only standard force–elongation response is needed as an input into the identification procedure. Both methods are applied to eight various materials, covering a broad spectre of possible ductile behaviour. The first method is based on the iterative procedure of sequential simulation of piecewise stress–strain curve using the parallel finite element modelling. Error between the computed and experimental force–elongation response is low, while the convergence rate is high. The second method uses the neural network for the stress–strain curve identification. Large database of force–elongation responses is computed by the finite element method. Then, the database is processed and reduced in order to get the input for neural network training procedure. Training process and response of network is fast compared to sequential simulation. When the desired accuracy is not reached, results can be used as a starting point for the following optimization task.",
  chapter="136827",
  doi="10.1007/s00419-017-1234-0",
  howpublished="print",
  number="6",
  volume="87",
  year="2017",
  month="june",
  pages="1077--1093",
  type="journal article in Web of Science"
}