A multilayer Method for Identifying Creep Characteristics in the Stress Range for the Problem of Metals Creep Rupture
Abstract
The paper considers the coefficient inverse problem of identifying the creep characteristics for metal structures deformed under high-temperature creep conditions. Creep deformation is widely used in various engineering problems, including the shaping of structural elements and the calculation of long-term strength. The creep phenomenon is exhibited by most known materials, including metals, concrete, and composites. Herein, there is no unified creep theory, which is explained by the complexity of the described process. The constitutive creep equations, as a rule, contain a set of tunable parameters (material constants) necessary for identification. There are many methods for determining material constants, but most of them are suitable only for specific creep models. In previous works, the authors proposed a method of multilayer functional systems, which in combination with neural network technique led to an original approach in the application of deep learning neural networks to the construction of approximate solutions of differential equations. Such approach allows combining the flexibility of artificial neural networks and the performance of traditional numerical methods for solving inverse and Cauchy problems. With regard to the creep models identification problem multilayer method allows naturally obtain a deep network, giving an approximate solution to the problem under consideration with an explicit occurrence of material constants. The identification of material constants is reduced to approximating experimental data, for example, by the least-squares method using the constructed multilayer solution. The advantages of the multilayer method are shown in solving the identification problem of the material constants for stress range. We selected the creep model describing the tension of cylindrical specimens of steel 45 at a constant stress level and temperature T = 850°С as a test problem for our multilayer method. Authors carry out analysis of obtained results and a comparison of the calculated data with the experiment and the results of other authors to confirm the reliability of the method used.
References
[2] Fridman Ya.B. Mekhanicheskiye svoystva metallov. V dvukh chastyakh. Chast’ vtoraya. Mekhanicheskiye ispytaniya. Konstruktsionnaya prochnost’ [Mechanical Properties of Metals. Part 2. Mechanical Tests. Structural Strength]. Mashinostroenie, Moscow, 1974. (In Russ.)
[3] Simonyan A.M. Nekotoryye voprosy polzuchesti [Some questions of creep]. Gitutiun, Yerevan, 1999. (In Russ.)
[4] Sosnin O.V., Gorev B.V., Nikitenko A.F. Energeticheskiy variant teorii polzuchesti [Energy Variant of Creep Theory]. Inst. of Hydrodynamics, USSR Acad. of Sci., Novosibirsk, 1986. (In Russ.)
[5] Vasilyev А.N., Kuznetsov E.B., Leonov S S. Nejrosetevoj metod identifikatsii i analiza modeli deformirovaniya metallicheskikh konstruktsij v usloviyakh polzuchesti [Neural network method of identification and analysis of the model of deformation of metal structures under creep conditions]. Sovremennye informacionnye tehnologii i IT-obrazovanie = Modern Information Technologies and IT-Education. 2015; 2(11):360-370). Available at: https://www.elibrary.ru/item.asp?id=26167516 (accessed 16.08.2019). (In Russ.)
[6] Kuznetsov E.B., Leonov S.S., Tarkhov D.A., Vasilyev A.N. Multilayer method for solving a problem of metals rupture under creep conditions. Thermal Science. 2019; 23(Suppl. 2):S575-S582. (In Eng.) DOI: 10.2298/TSCI19S2575K
[7] Lazovskaya T., Tarkhov D. Multilayer neural network models based on grid methods. IOP Conf. Series: Materials Science and Engineering. 2016; 158:012061. (In Eng.) DOI: 10.1088/1757-899X/158/1/012061
[8] Lazovskaya T., Tarkhov D., Vasilyev A. Multi-Layer Solution of Heat Equation. In: Kryzhanovsky B., Dunin-Barkowski W., Redko V. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research. NEUROINFORMATICS 2017. Studies in Computational Intelligence, vol. 736. Springer, Cham, 2018. (In Eng.) DOI: 10.1007/978-3-319-66604-4_3
[9] Vasilyev A.N., Tarkhov D.A., Tereshin V.A., Berminova M.S., Galyautdinova A.R. Semi-empirical Neural Network Model of Real Thread Sagging. In: Kryzhanovsky B., Dunin-Barkowski W., Redko V. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research. NEUROINFORMATICS 2017. Studies in Computational Intelligence, vol. 736. Springer, Cham, 2018. (In Eng.) DOI: 10.1007/978-3-319-66604-4_21
[10] Zulkarnay I.U., Kaverzneva T.T., Tarkhov D.A., Tereshin V.A., Vinokhodov T.V., Kapitsin D.R. A two-layer semi-empirical model of nonlinear bending of the cantilevered beam. Journal of Physics: Conference Series. 2017; 1044(conference 1):012005. (In Eng.) DOI: 10.1088/1742-6596/1044/1/012005
[11] Bortkovskaya M.R., Vasilyev P.I., Zulkarnay I.U., Semenova D.A., Tarkhov D.A., Udalov P.P., Shishkina I.A. Modeling of the membrane bending with multilayer semi-empirical models based on experimental data. In: CEUR Workshop Proceedings: Proceedings of the II International scientific conference "Convergent cognitive information technologies" (Convergent’2017), Moscow, Russia, November 24-26, 2017, vol. 2064, 2017, pp. 150-156. Available at: http://ceur-ws.org/Vol-2064/paper18.pdf (accessed 16.08.2019). (In Eng.)
[12] Rabotnov Yu.N. Creep Problems in Structural Members. North-Holland Publishing Company, Amsterdam/London. 1969. (In Eng.) DOI: 10.1002/zamm.19710510726
[13] Gorev B.V., Zakharova T.E., Klopotov I.D. On description of creep and fracture of the materials with non-monotonic variation of deformation-strength properties. Physical Mesomechanics. 2002; 5(2):17-22. Available at: https://elibrary.ru/item.asp?id=12912517 (accessed 16.08.2019). (In Russ., abstract in Eng.)
[14] Gorev B.V.,Lyubashevskaya I.V., Panamarev V.A.,Iyavoynen S.V. Description of creep and fracture of modern construction materials using kinetic equations in energy form.Journal of Applied Mechanics and Technical Physics. 2014; 55(6):1020-1030. (In Eng.) DOI: 10.1134/S0021894414060145
[15] Kuznetsov E.B., Leonov S.S. Technique for selecting the functions of the constitutive equations of creep and long-term strength with one scalar damage parameter. Journal of Applied Mechanics and Technical Physics. 2016; 57(2):369-377. (In Eng.) DOI: 10.1134/S0021894416020218
[16] Kuznetsov E.B., Leonov S.S. Examples of parametrization of the Cauchy problem for systems of ordinary differential equations with limiting singular points. Computational Mathematics and Mathematical Physics. 2018; 58(6):914-933. (In Eng.) DOI: 10.1134/S0965542518060076
[17] Panteleyev A.V., Letova T.A. Metody optimizatsii v primerakh i zadachakh [Optimization methods in the examples and problems]. Vysshaya shkola, Moscow. 2005. Available at: https://elibrary.ru/item.asp?id=21462687 (accessed 16.08.2019). (In Russ.)
[18] Kuznetsov E.B., Leonov S.S. On the analytical solution of one creep problem. Zhurnal Srednevolzhskogo Matematicheskogo Obshchestva = Middle Volga Mathematical Society Journal. 2018; 20(3):282-294. Available at: https://www.elibrary.ru/item.asp?id=36328837 (accessed 16.08.2019). (In Russ., abstract in Eng.)
[19] Panteleyev A.V. Metaevristicheskiye algoritmy poiska global'nogo ekstremuma [Metaheuristic algorithms for finding the global extremum]. MAI-PRINT, Moscow. 2009. Available at: https://elibrary.ru/item.asp?id=24226401 (accessed 16.08.2019). (In Russ.)

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