Abstract
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.
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