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

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.

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Acknowledgements

This work is an output of project NETME CENTRE PLUS (LO1202) created with financial support from the Ministry of Education, Youth and Sports under the “National Sustainability Programme I”. The authors would also like to thank the Institute of Physics of Materials of the Academy of Sciences of the Czech Republic, v. v. i. for providing the experimental data.

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Correspondence to František Šebek.

Appendix

Appendix

Results of the flow curve identification performed by the above-mentioned methods are shown in the following Figs. 15, 16, 17, 18, 19, 20, 21 and 22. The flow curves identified by the sequential simulation are designated as SEQ. The label SEQ-fit was used for the fitted curves using Eq. (8) serving as the parametric estimate of the flow curve for the extrapolation or for the optimization task. Curves identified by the neural network are labelled as NN.

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Jeník, I., Kubík, P., Šebek, F. et al. Sequential simulation and neural network in the stress–strain curve identification over the large strains using tensile test. Arch Appl Mech 87, 1077–1093 (2017). https://doi.org/10.1007/s00419-017-1234-0

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  • DOI: https://doi.org/10.1007/s00419-017-1234-0

Keywords

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