Ti-2 Al-9.2 Mo-2 Fe is a low-cost β titanium alloy with well-balanced strength and ductility, but hot working of this alloy is complex and unfamiliar. Understanding the nonlinear relationships among the strain,strain...Ti-2 Al-9.2 Mo-2 Fe is a low-cost β titanium alloy with well-balanced strength and ductility, but hot working of this alloy is complex and unfamiliar. Understanding the nonlinear relationships among the strain,strain rate, temperature, and flow stress of this alloy is essential to optimize the hot working process.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℃). 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℃ and strain rates of 0.01-0.1 s^-1).展开更多
基金supported by grants from the Civil–Military Technology Cooperation Program (16-CM-MA-10) of the Defense Acquisition Program Administrationfrom the Core Material Program (10062485) of the Ministry of Trade, Industry and Energy, Republic of Korea
文摘Ti-2 Al-9.2 Mo-2 Fe is a low-cost β titanium alloy with well-balanced strength and ductility, but hot working of this alloy is complex and unfamiliar. Understanding the nonlinear relationships among the strain,strain rate, temperature, and flow stress of this alloy is essential to optimize the hot working process.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℃). 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℃ and strain rates of 0.01-0.1 s^-1).
