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A series of compression tests were performed on Ti-6Al-4V-0.1Ru titanium alloy in nine temperatures between 750 and 1150 °C and a strain rate range of 0.01 to 10s −1 . The hot deformation behaviors of Ti-6Al-4V-0.1Ru showed highly non-linear intrinsic relationships with temperature, strain and strain rate. The flow curves exhibited different softening mechanisms, dynamic recrystallization (DRX) and dynamic recovery (DRV). In this study, the rheological behaviors of Ti-6Al-4V-0.1Ru were modeled using a special hybrid prediction model, where genetic algorithm (GA) was implemented to do a back-propagation neural network (BPNN) weights optimization, namely GA-BPNN. Subsequently, the predicted results were compared with experimental values and GA-BPNN model showed the ability to predict the flow behaviors of Ti-6Al-4V-0.1Ru with superior accuracy. Then a 3-D continuous interaction space was constructed to visually reveal the successive relationships among processing parameters. Finally, the predicted data were applied to process simulation and accuracy results were achieved.

Modeling of the Hot Flow Behaviors for Ti-6Al-4V-0.1Ru Alloy by GA-BPNN Model and Its Application