Modeling hot deformation behavior of low-cost Ti-2Al-9.2Mo-2Fe beta titanium alloy using a deep neural network
- Li, Cheng-Lin; Narayana, P. L.; Reddy, N. S.; Choi, Seong-Woo; Yeom, Jong-Taek; Hong, Jae-Keun; Park, Chan Hee
- Issue Date
- JOURNAL MATER SCI TECHNOL
- Deep neural networks; Back propagation; Processing map; Recrystallization; Beta titanium
- JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY, v.35, no.5, pp.907 - 916
- Journal Title
- JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY
- Start Page
- End Page
- Ti-2Al-9.2Mo-2Fe is a low-cost beta 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 degrees 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 degrees C and strain rates of 0.01-0.1 s(-1)). (C) 2019 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
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- 공과대학 > 나노신소재공학부금속재료공학전공 > Journal Articles
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