Modeling hot deformation behavior of low-cost Ti-2Al-9.2Mo-2Fe beta titanium alloy using a deep neural network
来源期刊：JOURNAL OF MATERIALS SCIENCE TECHNOLOG2019年第5期
论文作者：Cheng-Lin Li P.L.Narayana N.S.Reddy Seong-Woo Choi Jong-Taek Yeom Jae-Keun Hong Chan Hee Park
文章页码：907 - 916
摘 要：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).
Cheng-Lin Li1，P.L.Narayana1,2，N.S.Reddy2，Seong-Woo Choi1，Jong-Taek Yeom1，Jae-Keun Hong1，Chan Hee Park1
1. Advanced Metals Division, Korea Institute of Materials Science2. School of Materials Science and Engineering, Gyeongsang National University
摘 要：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).
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