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.

A Robust RBF-ANN Model to Predict the Hot Deformation Flow Curves of API X65 Pipeline Steel Keywords:

Hot deformation , Neural Computing , Radial Basis Function , Constitutive equations , Flow stress