Modeling hot deformation behavior of low-cost Ti-2Al-9.2Mo-2Fe beta titanium alloy using a deep neural network

In this study, a deep neural network (DNN) model was developed to correlate flow stress with a wide range of strains (0.025–0.6), strain rates (0.01–10 s-1) and temperatures (750–1000 °C). The model, which was tested with 96 unseen datasets, showed better performance than existing models, with a correlation coefficient of 0.999. The processing map constructed using the DNN model was effective in predicting the microstructural evolution of the alloy. Moreover, it led to the optimization of hot-working conditions to avoid the formation of brittle precipitates (temperatures of 820–1000 °C and strain rates of 0.01–0.1 s-1).Graphical abstract
Source: Journal of Materials Science and Technology - Category: Materials Science Source Type: research