TY - JOUR ID - 3941 TI - A Robust RBF-ANN Model to Predict the Hot Deformation Flow Curves of API X65 Pipeline Steel JO - Iranian Journal of Materials Forming JA - IJMF LA - en SN - AU - Rakhshkhorshid, M. AD - Department of Mechanical Engineering, Birjand University of Technology, POBOX 97175-569, Birjand, Iran Y1 - 2017 PY - 2017 VL - 4 IS - 1 SP - 12 EP - 20 KW - Hot deformation KW - Neural Computing KW - Radial Basis Function KW - Constitutive equations KW - Flow stress DO - 10.22099/ijmf.2017.3941 N2 - AbstractIn 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 was 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 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 that shows the robustness of it to predict the hot deformation flow curves of tested steel. The results can be further used in mathematical simulation of hot metal forming processes. UR - https://ijmf.shirazu.ac.ir/article_3941.html L1 - https://ijmf.shirazu.ac.ir/article_3941_7afa31b6d2b634af9d1556dddf293acd.pdf ER -