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

• 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
• ISSN: 0939-1533; 1432-0681
• DOI: 10.1007/s00419-017-1234-0

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.