Abstract:Most existing methods for estimation of cyclic yield stress and cyclic Ramberg-Osgood stress-strain parameters of steels from their monotonic properties were developed on relatively modest number of material datasets and without considerations of the particularities of different steel subgroups formed according to their chemical composition (unalloyed, low-alloy, and high-alloy steels) or delivery, i.e., testing condition. Furthermore, some methods were evaluated using the same datasets that were used for their development. In this paper, a comprehensive statistical analysis and evaluation of existing estimation methods were performed using an independent set of experimental material data compriseding 116 steels. Results of performed statistical analyses reveal that statistically significant differences exist among unalloyed, low-alloy, and high-alloy steels regarding their cyclic yield stress and cyclic Ramberg-Osgood stress-strain parameters. Therefore, estimation methods were evaluated separately for mentioned steel subgroups in order to more precisely determine their applicability for the estimation of cyclic behavior of steels belonging to individual subgroups. Evaluations revealed that considering all steels as a single group results in averaging and that subgroups should be treated independently. Based on results of performed statistical analysis, guidelines are provided for identification and selection of suitable methods to be applied for the estimation of cyclic stress-strain parameters of steels.
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.
Successful prediction of the relevant mechanical properties of steels is of great importance to materials engineering. The aim of this research is to investigate the possibility of reducing the complexity of artificial neural networks-based prediction of total hardness of hypoeutectoid, low-alloy steels based on chemical composition, by introducing the specific Jominy distance as a new input variable. For prediction of total hardness after continuous cooling of steel (output variable), ANNs were developed for different combinations of inputs. Input variables for the first configuration of ANNs were the main alloying elements (C, Si, Mn, Cr, Mo, Ni), the austenitizing temperature, the austenitizing time, and the cooling time to 500 °C, while in the second configuration alloying elements were substituted by the specific Jominy distance. Comparing the results of total hardness prediction, it can be seen that the ANN using the specific Jominy distance as input variable (runseen = 0.873, RMSEunseen = 67, MAPE = 14.8%) is almost as successful as ANN using main alloying elements (runseen = 0.940, RMSEunseen = 46, MAPE = 10.7%). The research results indicate that the prediction of total hardness of steel can be successfully performed only based on four input variables: the austenitizing temperature, the austenitizing time, the cooling time to 500 °C, and the specific Jominy distance.
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