a robust rbf-ann model to predict the hot deformation flow curves of api x65 pipeline steel

In this research, a radial basis function artificial neural network (rbf-ann) model was developed to predict the hot deformation flow curves of api x65 pipeline steel. the results of the developed model were compared with the results of a new phenomenological model that has recently been developed based on a power function of zener-hollomon parameter and a third order polynomial function of strain power m (m is a constant). root mean square error (rmse) criterion was used to assess the prediction performance of the investigated models. according to the results obtained, it was shown that the rbf-ann model has a better performance than that of the investigated phenomenological model. very low rmse value of 0.41 mpa was obtained for rbf-ann model, which was less than one-tenth of the rmse value of 4.74 mpa obtained for the investigated constitutive equation. the results can be further used in mathematical simulation of hot metal forming processes.