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

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

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 ducti...

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Bibliographic Details
Published in:Archive of applied mechanics (1991) Vol. 87; no. 6; pp. 1077 - 1093
Main Authors: Jeník, Ivan, Kubík, Petr, Šebek, František, Hůlka, Jiří, Petruška, Jindřich
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-06-2017
Springer Nature B.V
Subjects:
Accuracy
Classical Mechanics
Computer simulation
Convergence
Elongation
Engineering
Errors
Finite element method
Identification methods
Iterative methods
Mathematical analysis
Modelling
Neural networks
Optimization
Original
Simulation
Strain
Stresses
Test procedures
Theoretical and Applied Mechanics
Training
Elastic–plastic deformation
Constitutive behaviour
Ductility
Metallic materials
Optimization
Numerical algorithms
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Summary: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.
ISSN:0939-1533
1432-0681
DOI:10.1007/s00419-017-1234-0