Modeling of grain size in isothermal compression of Ti-6Al-4V alloy using fuzzy neural network

Isothermal compression of Ti-6Al-4V alloy was conducted in the deformation temperature range of 1093-1303 K, the strain rates of 0.001, 0.01, 0.1, 1.0, and 10.0 s ^-1, and the height reductions of 20%-60% with an interval of 10%. After compression, the effect of the processing parameters including deformation temperature, strain rate, and height reduction on the flow stress and the microstructure was investigated. The grain size of primary α phase was measured using an OLYMPUS PMG3 microscope with the quantitative metallography SISC IAS V8.0 image analysis software. A model of grain size in isothermal compression of Ti-6Al-4V alloy was developed using fuzzy neural network (FNN) with back-propagation (BP) learning algorithm. The maximum difference and the average difference between the predicted and the experimental grain sizes of primary α phase are 13.31% and 7.62% for the sampled data, and 16.48% and 6.97% for the non-sampled data, respectively. It can be concluded that the present model with high prediction precision can be used to predict the grain size in isothermal compression of Ti-6Al-4V alloy.