Modeling of the microstructure variables in the isothermal compression of TC11 alloy using fuzzy neural networks

The grain size and volume fraction of prior α phase in high temperature deformation appear highly nonlinear and fuzzy characteristic. The approach to model the grain size and volume fraction of prior α phase and to train the model structure is presented in terms of the fuzzy set and artificial neural networks method using BP learning algorithm. The experimental data of teacher's samples are prepared from the grain size and volume fraction of prior α phase after isothermal compression of TC11 alloy at the deformation temperatures ranging from 1023 to 1323K with an interval of 20K, the strain rates ranging from 0.001 to 10.0s^-1, and the height reductions ranging from 50 to 70%. The predicted grain size and volume fraction are in a good agreement with the experimental results in the isothermal compression of TC11 alloy.