Modeling of High Temperature Flow Stress of AZ80 Magnesium Alloy with Support Vector Machines and Artificial Neural Network

Abstract

Support vector machines (SVM) and artificial neural network (ANN) were employed in modeling the flow stress of the AZ80 magnesium. The hot deformation behavior of extruded AZ80 magnesium was investigated by compression tests in the temperature 350-450 and strain rate range 0.01-50 s-1. The maximum relative errors at different temperatures and different strain rates between experimental and predicted flow stresses by SVM and ANN were compared. The results show the SVM derives statistical models have better similar prediction ability to those of ANN, especially at high strain rate. This indicates that SVM can be used as an alternative modeling tool for high temperature rheological behavior studies.