Achieving excellent superplasticity and predicting the elongations in ultrafine-grained Ti-4.5Al-3V-2Mo-2Fe titanium alloy prepared by friction stir processing

Titanium and its alloys hold significant industrial importance due to their potential for superplastic formability. However, most titanium and its alloys require high temperatures and low strain rates to achieve superplasticity. Friction stir processing, severe plastic deformation technology, offers an effective approach to achieve low-temperature or high-strain-rate superplasticity in fine-grained titanium alloys. Herein, the effect of rotation speed on the microstructure of the friction stir processed Ti-4.5Al-3V-2Mo-2Fe titanium alloy was investigated for the first time. An ultra-fine-grained Ti-4.5Al-3V-2Mo-2Fe titanium alloy was achieved, exhibiting an average grain size of only 0.26 μm at a rotation speed of 100 r/min and a processing speed of 80 mm/min. Subsequently, the superplastic tensile tests were conducted at temperatures ranging from 550°C-800°C, at an interval of 50°C, and strain rates of 3 × 10⁻⁴ s⁻¹, 1 × 10⁻³ s⁻¹, 3 × 10⁻³ s⁻¹, and 1 × 10⁻² s⁻¹, respectively. The results demonstrated that the ultrafine-grained titanium alloy exhibited excellent superplasticity, achieving an elongation of 1808 ± 52 % at 650°C and 3 × 10⁻³ s⁻¹. This large elongation was the highest reported value in the field of severe plastic deformed titanium alloys. The superior superplasticity was attributed to the fine grains (＜2 μm), a relatively high proportion of β phase (∼20 %), and a high proportion of high-angle grain boundaries (＞80 %) in the α and β phases during superplastic deformation. The primary superplastic deformation mechanism included dislocation slip and grain rotation coordinated with α/α, β/β grain boundary sliding, and α/β phase boundary sliding. Finally, a model correlating temperature, strain rate, and superplastic elongations was developed using backpropagation neural networks and support vector regression algorithms. The correlation coefficient between the predicted and the actual values was higher for support vector regression (0.93) compared to backpropagation neural networks (0.81), indicating that support vector regression was more suitable for predicting the superplastic elongations. This study offers a novel method for achieving superplasticity in SP700 titanium alloy components.