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2 2023

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Modelling the high‑temperature deformation characteristics of S355 steel using artificial neural networks

100%

Olejarczyk-Wożeńska I. , Mrzygłód B. , Hojny M.

tom Vol. 23, no. 1

art. no. e1, 2023

In this study, artificial neural networks were used to predict the plastic flow behaviour of S355 steel in the process of high-temperature deformation. The aim of the studies was to develop a model of changes in stress as a function of strain, strain rate and temperature, necessary to build an advanced numerical model of the soft-reduction process. The high-temperature characteristics of the tested steel were determined with a Gleeble 3800 thermo-mechanical simulator. Tests were carried out in the temperature range of 400-1450 °C for two strain rates, i.e. 0.05 and 1 s-1. The test results were next used to develop and verify a rheological model based on artificial neural networks (ANNs). The conducted studies show that the selected models offer high accuracy in predicting the high-temperature flow behaviour of S355 steel and can be successfully used in numerical modelling of the soft-reduction process.

The Use of the XGBoost and Kriging Methods in the Prediction of the Microstructure of CGI Cast Iron

88%

Sztangret Ł. , Olejarczyk-Wożeńska I. , Regulski K. , Gumienny G. , Mrzygłód B.

2023

tom Vol. 23, iss. 4

22--33

Compacted Graphite Iron (CGI) is a unique casting material characterized by its graphite form and extensive matrix contact surface. This type of cast iron has a tendency towards direct ferritization and possesses a complex set of intriguing properties. The use of data mining methods in modern foundry material development facilitates the achievement of improved product quality parameters. When designing a new product, it is always necessary to have a comprehensive understanding of the influence of alloying elements on the microstructure and consequently on the properties of the analyzed material. Empirical studies allow for a qualitative assessment of the above-mentioned relationships, but it is the use of intelligent computational techniques that allows for the construction of an approximate model of the microstructure and, consequently, precise predictions. The formulated prognostic model supports technological decisions during the casting design phase and is considered as the first step in the selection of the appropriate material type.