A comparative study of a back propagation artificial neural network and a Zerilli–Armstrong model for pure molybdenum during hot deformation

Abstract

Abstract In this study, the hot deformation behavior of molybdenum was investigated by means of thermal simulation on a Gleeble-1500 machine. The experiments were carried out under different temperatures, ranging from 1100 to 1400 °C, and with a strain rate of 1S−1 to 50S−1. The flow stress under the above mentioned hot deformation conditions was predicted using a back propagation (BP) artificial neural network. The architecture of the network included three input parameters: strain rate, temperature and true strain, and just one output parameter: the flow stress. One hidden layer was adopted, which include nine neurons. Compared with the prediction method of flow stress using the Zerilli–Armstrong model, the prediction method using the BP artificial neural network had higher accuracy.