Prediction of the hot flow behavior of AISI 321 stainless steel during dynamic recrystallization using sine hyperbolic constitutive equation and artificial neural network

The hot compression deformation behavior of AISI 321 austenitic stainless steel were studied under the strain rate and temperature ranges of 0.001–1 s −1 and 950–1200°C. The single peak hot flow curves indicated the occurrence of dynamic recrystallization in AISI 321 steel at applied deformation conditions. The microstructural observations showing the development of equaxed austenite grains further approved the results of flow curve analysis. The average size of austenite grains, when deformation is carried out at strain rate of 1 s −1 , increases from 8 μm at 950°C to 61 μm at deformation temperature of 1200°C. After microstructure analysis, the flow behavior of AISI 321 steel was modeled using sine-hyperbolic constitutive equation and feed-forward back propagation artificial neural network. The mean absolute error related to the predicted flow stress values at different strains with the sine-hyperbolic constitutive equation is more than 2, and this value is less than unity for the neural network method. The obtained results efficiently indicate that the ANN model is more accurate in predicting the flow behavior of AISI 321 austenitic stainless steel when the dynamic recrystallization is the prevailing softening mechanism.