Characterization of grain growth behaviors by BP-ANN and Sellars models for nickle-base superalloy and their comparisons  

基于BP-ANN和Sellars模型的镍基高温合金晶粒长大行为表征及其比较

作　　者：Guo-zheng QUAN Pu ZHANG Yao-yao MA Yu-qing ZHANG Chao-long LU Wei-yong WANG 权国政;张普;马遥遥;张钰清;鹿超龙;王卫永(重庆大学材料科学与工程学院,重庆400044;重庆大学土木工程学院,重庆400045)

机构地区：[1]School of Material Science and Engineering,Chongqing University,Chongqing 400044,China [2]School of Civil Engineering,Chongqing University,Chongqing 400045,China

出　　处：《Transactions of Nonferrous Metals Society of China》2020年第9期2435-2448,共14页中国有色金属学报（英文版）

基　　金：Project(cstc2018jcyjAX0459)supported by Chongqing Basic Research and Frontier Exploration Program,China;Projects(2019CDQYTM027,2019CDJGFCL003)supported by the Fundamental Research Funds for the Central Universities,China。

摘　　要：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.为了深入理解Ni80A的晶粒长大行为,在不同温度(1223~1423 K)和不同保温时间(0~3600 s)下进行一系列的晶粒长大实验。基于实验数据,建立BP神经网络并求解Sellars模型。使用4个统计指标比较和评价这两个模型的预测与泛化能力。结果表明,所建立的BP神经网络具有更高的r值、更低的AARE值、更低的绝对μ值和ω值。最后,基于求解的BP神经网络扩展的数据建立平均晶粒尺寸对保温温度和保温时间的响应面,并描述晶粒的长大行为。

关 键 词：grain growth model BP artificial neural network Sellars model average grain size 

分 类 号：TG132.3[一般工业技术—材料科学与工程]