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Estimation of cyclic behavior of unalloyed, low‐ alloy and high‐alloy steels based on relevant monotonic properties using artificial neural networks (CROSBI ID 249964)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Marohnić, Tea ; Basan, Robert Estimation of cyclic behavior of unalloyed, low‐ alloy and high‐alloy steels based on relevant monotonic properties using artificial neural networks // Materialwissenschaft und Werkstofftechnik, 49 (2018), 3; 368-380. doi: 10.1002/mawe.201700192

Podaci o odgovornosti

Marohnić, Tea ; Basan, Robert

engleski

Estimation of cyclic behavior of unalloyed, low‐ alloy and high‐alloy steels based on relevant monotonic properties using artificial neural networks

Within the framework of this research, a new methodology is proposed for estimation of cyclic Ramberg–Osgood parameters i. e. cyclic stress–strain behavior of steels based on their monotonic properties using artificial neural networks. A large number of experimental data for steels were collected from relevant literature and divided into unalloyed, low-alloy and high-alloy steels, since previous research confirmed that statistically significant differences exist among cyclic parameters of these subgroups of steels. Detailed statistical analysis is performed by means of forward selection and monotonic properties relevant for estimation of each cyclic parameter of each subgroup of steels are determined. Based on results of performed statistical analyses, artificial neural networks are developed, separately for each parameter and each steel subgroup, using only monotonic properties that proved to be relevant for estimation of particular parameter. Neural networks are evaluated on an independent set of data, in comparison with experimental values and values obtained by existing empirical estimation methods. The new approach is more successful than empirical methods for estimation of most of cyclic stress– strain parameters and behavior of different steel subgroups.

steels ; cyclic stress–strain parameters ; cyclic stress–strain curves ; statistical analysis ; artificial neural networks ; estimation

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Podaci o izdanju

49 (3)

2018.

368-380

objavljeno

0933-5137

1521-4052

10.1002/mawe.201700192

Povezanost rada

Strojarstvo

Poveznice
Indeksiranost