Prediction of the flow stress of high-speed steel during hot deformation using a BP artificial neural network

The hot deformation behavior of T1 (W18Cr4V) high-speed steel was investigated by means of continuous compression tests performed on a Gleeble 1500 Thermomechanical simulator over a wide range of temperatures (950-1150 degrees C) with strain rates of 0.001-10 s(-1) and true strains of 0-0.7. The flow stress under the above-mentioned hot deformation conditions is predicted using a BP artificial neural network. The architecture of the network includes three input parameters: strain rate epsilon, temperature T and true strain epsilon; and just one output parameter: the flow stress sigma. Two hidden layers are adopted, the first hidden layer including nine neurons and the second 10 neurons. It has been verified that a BP artificial neural network with 3-9-10-1 architecture can predict the flow stress of high-speed steel during hot deformation very well. Compared with the prediction method of flow stress using the Zener-Holloman parameter and hyperbolic sine stress function, the prediction method using the BP artificial neural network has higher efficiency and accuracy. (C) 2000 Elsevier Science S.A. All rights reserved.