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Characterization of grain growth behaviors by BP-ANN and Sellars models for nickle-base superalloy and their comparisons 被引量:13
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作者 Guo-zheng QUAN Pu ZHANG +3 位作者 Yao-yao MA Yu-qing ZHANG Chao-long LU Wei-yong WANG 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2020年第9期2435-2448,共14页
In order to deeply understand the grain growth behaviors of Ni80A superalloy,a series of grain growth experiments were conducted at holding temperatures ranging from 1223 to 1423 K and holding time ranging from 0 to 3... In order to deeply understand the grain growth behaviors of Ni80A superalloy,a series of grain growth experiments were conducted at holding temperatures ranging from 1223 to 1423 K and holding time ranging from 0 to 3600 s.A back-propagation artificial neural network(BP-ANN)model and a Sellars model were solved based on the experimental data.The prediction and generalization capabilities of these two models were evaluated and compared on the basis of four statistical indicators.The results show that the solved BP-ANN model has better performance as it has higher correlation coefficient(r),lower average absolute relative error(AARE),lower absolute values of mean value(μ)and standard deviation(ω).Eventually,a response surface of average grain size to holding temperature and holding time is constructed based on the data expanded by the solved BP-ANN model,and the grain growth behaviors are described. 展开更多
关键词 grain growth model BP artificial neural network sellars model average grain size
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