THERMAL SCIENCE

International Scientific Journal

Evgenii B. Kuznetsov

,

Sergey S. Leonov

,

Dmitry A. Tarkhov

,

Alexander N. Vasilyev

MULTILAYER METHOD FOR SOLVING A PROBLEM OF METALS RUPTURE UNDER CREEP CONDITIONS

ABSTRACT
The paper deals with a parameter identification problem for creep and fracture model. The system of ordinary differential equations of kinetic creep theory is applied for describing this model. As for solving the parameter identification problem, we proposed to use the technique of neural network modeling, as well as the multilayer approach. The procedures of neural network modeling and multilayer approximation constructing application is demonstrated by the example of finding parameters for uniaxial tension model for isotropic steel 45 specimens at creep conditions. The solution corresponding to the obtained parameters agrees well with theoretical strain-damage characteristics, experimental data, and results of other authors.
KEYWORDS
сreep, fracture, parameter identification, the Cauchy problem, ODE, artificial neural networks, multilayer neural networks
PAPER SUBMITTED: 2018-09-24
PAPER REVISED: 2018-11-21
PAPER ACCEPTED: 2018-12-04
PUBLISHED ONLINE: 2019-05-05
DOI REFERENCE: https://doi.org/10.2298/TSCI19S2575K
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2019, VOLUME 23, ISSUE Supplement 2, PAGES [S575 - S582]
REFERENCES
  1. Rabotnov, Yu. N., Creep Problems in Structural Members, North-Holland Publishing Company, Amsterdam/London, 1969
  2. Gorev, B. V., et al., On Description of Creep and Fracture of the Materials with Non-Monotonic Variation of Deformation-Strength Properties, Physical Mesomechanics Journal, 5 (2002), 2, pp. 17-22
  3. Gorev, B. V., et al., Description of Creep and Fracture of Modern Construction Materials Using Kinetic Equations in Energy Form, J. Appl. Mech. Tech. Phys., 55 (2014), 6, pp. 1020-1030
  4. Burden, R. L., et al., Numerical Analysis, Cengage Learning, Boston, Mass., USA, 2016
  5. Lazovskaya, T., Tarkhov, D., Multilayer Neural Network Models Based on Grid Methods, Proceedings, IOP Conf. Series: Materials Science and Engineering, Kazan, Russia, 2016, Vol. 158
  6. Lazovskaya, T., et al., Multi-Layer Solution of Heat Equation, in: Advances in Neural Computation, Machine Learning, and Cognitive Research, (Ed. Kyzanovski, B., et al.,), Moscow, Russia, 2017, pp. 17-22
  7. Vasilyev, A. N., et al., Semi-Empirical Neural Network Model of Real Thread Sagging, Studies in Computational Intelligence, 736 (2018), Oct., pp.138-146
  8. Zulkarnay, I. U., et al., A Two-Layer Semi-Empirical Model of Nonlinear Bending of the Cantilevered Beam, Journal of Physics: Conference Series, 1044 (2018), Conference 1, 012005
  9. Bortkovskaya, M. R., et al., Modeling of the Membrane Bending with Multilayer Semi-Empirical Models Based on Experimental Data, Proceedings, II International Scientific Conference "Convergent Cognitive Information Technologies" (Convergent'2017), Moscow, Russia, 2017
  10. Lazovskaya, T. V., et al., Parametric Neural Network Modeling in Engineering, Recent Patents on Engineering, 11 (2017), 1, pp. 10-15
  11. Budkina, E. M., Neural Network Approach to Intricate Problems Solving for Ordinary Differential Equations, Optical Memory and Neural Networks, 26 (2017), 2, pp. 96-109
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Multilayer method for solving a problem of metals rupture under creep conditions

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence