Prediction of the flow stress of 0.4C–1.9Cr–1.5Mn–1.0Ni–0.2Mo steel during hot deformation

The flow stress of 0.4C–1.9Cr–1.5Mn–1.0Ni–0.2Mo die steel for plastic moulds during hot deformation is predicted using the conventional regression method and the artificial neural network method. The temperatures at which the steel is compressed are 800–1100°C with strain rates of 0.001–10 s−1 and to strains of 0–0.7. Comparisons with the result of physical tests show that the efficiency and accuracy of the flow stress predicted using a multi-layer perceptron network with the back propagation learning algorithm are better than that predicted using the Zener–Holloman parameter and a hyperbolic sine function. It is also shown that the flow stress of die steel for plastic moulds deformed under hot deformation conditions can be predicted very well using the multi-layer perceptron network with the structure of 3–9–10–1.