Premium
Estimation of cyclic behavior of unalloyed, low‐alloy and high‐alloy steels based on relevant monotonic properties using artificial neural networks
Author(s) -
Marohnić T.,
Basan R.
Publication year - 2018
Publication title -
materialwissenschaft und werkstofftechnik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.285
H-Index - 38
eISSN - 1521-4052
pISSN - 0933-5137
DOI - 10.1002/mawe.201700192
Subject(s) - monotonic function , alloy , artificial neural network , materials science , alloy steel , stress (linguistics) , metallurgy , computer science , mathematics , artificial intelligence , mathematical analysis , linguistics , philosophy
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.