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).
类型: 期刊论文
作者: Cheng-Lin Li,P.L.Narayana,N.S.Reddy,Seong-Woo Choi,Jong-Taek Yeom,Jae-Keun Hong,Chan Hee Park
来源: Journal of Materials Science & Technology 2019年05期
年度: 2019
分类: 工程科技Ⅰ辑
专业: 金属学及金属工艺
单位: Advanced Metals Division, Korea Institute of Materials Science,School of Materials Science and Engineering, Gyeongsang National University
基金: supported by grants from the Civil–Military Technology Cooperation Program (16-CM-MA-10) of the Defense Acquisition Program Administration,from the Core Material Program (10062485) of the Ministry of Trade,Industry and Energy,Republic of Korea
分类号: TG146.23
页码: 907-916
总页数: 10
文件大小: 3156K
下载量: 26
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