Prediction of Hot Deformation Behavior of 7Mo Super Austenitic Stainless Steel Based on Back Propagation Neural Network  

基于反向传播神经网络预测7Mo超级奥氏体不锈钢的热变形行为

作　　者：WANG Fan WANG Xitao XU Shiguang HE Jinshan 王帆;王西涛;徐世光;何金珊(北京科技大学钢铁共性技术协同创新中心,北京100083;齐鲁工业大学先进材料研究所(山东省科学院),山东省轻质高强金属材料省级重点实验室,济南250353)

机构地区：[1]Collaborative Innovation Center of Steel Technology,University of Science and Technology Beijing,Beijing 100083,China [2]Shandong Provincial Key Laboratory for High Strength Lightweight Metallic Materials,Advanced Materials Institute,Qilu University of Technology(Shandong Academy of Science),Jinan 250353,China

出　　处：《材料导报》2024年第17期165-171,共7页Materials Reports

基　　金：国家自然科学基金(U1810207);齐鲁理工大学“科教产融合(国际合作)”创新先导项目(2020KJC-GH03)。

摘　　要：The hot compression tests of 7Mo super austenitic stainless(SASS)were conducted to obtain flow curves at the temperature of 1000-1200℃and strain rate of 0.001 s^(-1)to 1 s^(-1).To predict the non-linear hot deformation behaviors of the steel,back propagation-artificial neural network(BP-ANN)with 16×8×8 hidden layer neurons was proposed.The predictability of the ANN model is evaluated according to the distribution of mean absolute error(MAE)and relative error.The relative error of 85%data for the BP-ANN model is among±5%while only 42.5%data predicted by the Arrhenius constitutive equation is in this range.Especially,at high strain rate and low temperature,the MAE of the ANN model is 2.49%,which has decreases for 18.78%,compared with conventional Arrhenius constitutive equation.对7Mo超级奥氏体不锈钢(SASS)进行热压缩试验,获得其在变形温度为1000~1200℃、应变速率为0.001~1 s^(-1)条件下的流变曲线。为了预测该钢种的非线性热变形行为,提出了具有16×8×8隐层神经元的反向传播人工神经网络(BP-ANN)。根据平均绝对误差(MAE)和相对误差的分布来评估ANN模型的可预测性。BP-ANN模型85%的数据相对误差在±5%之间,而Arrhenius本构方程预测的只有42.5%的数据在此范围内。特别是在高应变速率和低温下,ANN模型的MAE为2.49%,比传统Arrhenius本构方程低18.78%。

关 键 词：7Mo super austenitic stainless steel hot deformation behavior flow stress BP-ANN Arrhenius constitutive equation 

分 类 号：TG142[一般工业技术—材料科学与工程]