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 net-work(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.