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

作者：Luo Jiao^*; Li Miaoquan

来源：Rare Metals, 2011, 30(6): 555-564.

DOI：10.1007/s12598-011-0429-8

• titanium alloy
• isothermal compression
• grain size
• 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 alpha 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 alpha 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.

• 出版日期2011-12
• 单位西北工业大学