A comparative study on constitutive relationship of as-cast 904L austenitic stainless steel during hot deformation based on Arrhenius-type and artificial neural network models

Constitutive relationship of as-cast 904L austenitic stainless steel is comparatively investigated by the Arrhenius-type constitutive model incorporating the strain effect and back-propagation (BP) neural network. The experimental true stress–true strain data were obtained from hot compression tests on the Gleeble-1500D thermo-mechanical simulator in the temperature range of 1000–1150 °C and strain rate range of 0.01–10 s−1. The corrected data with the friction and the temperature compensations were employed to develop the Arrhenius-type model and BP neural network respectively. The accuracy and reliability of the models were quantified by employing statistical parameters such as the correlation coefficient and absolute average error. The results show that the proposed models have excellent predictabilities of flow stresses for the present steel in the specified deformation conditions. Compared with the Arrhenius-type model, the optimized BP neural network model has more accuracy and capability in describing the compressive deformation behavior at elevated temperature for as-cast 904L austenitic stainless steel.