Modeling of constitutive relationship of Ti-25V-15Cr-0.2Si alloy during hot deformation process by fuzzy-neural network

In this paper, an adaptive fuzzy-neural network model has been established to model the constitutive relationship of Ti-25V-15Cr-0.2Si alloy during high temperature deformation. The network integrates the fuzzy inference system with a back-propagation learning algorithm of neural network. The experimental results were obtained at deformation temperatures of 900-1100 °C, strain rates of 0.01-10 s−1, and height reduction of 50%. After the training process, the fuzzy membership functions and the weight coefficient of the network can be optimized. It has shown that the predicted values are in satisfactory agreement with the experimental results and the maximum relative error is less than 10%. It proved that the fuzzy-neural network was an easy and practical method to optimize deformation process parameters.