Modeling the High Temperature Deformation Constitutive Relationship of TC4-DT Alloy Based on Fuzzy-neural Network

By analyzing the high temperature TC4-DT titanium alloys' deformation temperature, strain rate and deformation degree with the parameters of the experimental data flow stress, an adaptive fuzzy-neural network model has been established to predict flow stress data to model the high temperature deformation constitutive relationship of TC4-DT titanium alloy. The experimental results were obtained at deformation temperature of 750∼1150 °C, strain rates of 0.001∼ 10 s−1, and height reduction of 50%. The network integrates the fuzzy inference system with a back-propagation (BP) learning algorithm of neural network. Results show that the predicated values are in satisfactory agreement with the experimental results and the maximum relative error is less than 6%. It proves that the fuzzy-neural network is a very effective and practical method to achieve more optimized TC4 - DT titanium alloy constitutive relation model and optimize deformation process parameters.