Abstract: Regarding heat forming process of 1Cr18Ni9Ti as typical forming process, this paper presents the study of the effect of various parameters on flow stress, grain size and hardness of formed specimen by means of Gleeble-1500 Thermo-simulation machine and metalloscope. On the basis of technical experi- ment this paper, data are proceeded by applying multilayer feedforward back-propagation neural network, a prediction model of technological parameters together with microstructure and property in the heat forming process is established, thus forging property prediction in the heat forming process is realized.

Key words: heat forming, neural network, BP algorithm, prediction