Constructing processing map of Ti40 alloy using artificial neural network  

应用人工神经网络构造Ti40合金加工图(英文)

作　　者：孙宇[1] 曾卫东[1] 赵永庆[2] 张学敏[1] 马雄[1] 韩远飞[1] 

机构地区：[1]西北工业大学材料学院凝固技术国家重点实验室,西安710072 [2]西北有色金属研究院,西安710016

出　　处：《Transactions of Nonferrous Metals Society of China》2011年第1期159-165,共7页中国有色金属学报（英文版）

基　　金：Project(2007CB613807)supported by the National Basic Research Program of China;Project(NCET-07-0696)supported by the New Century Excellent Talents in University,China;Project(35-TP-2009)supported by the Fund of the State Key Laboratory of Solidification Processing in Northwestern Polytechnical University,China

摘　　要：Based on the experimental data of Ti40 alloy obtained from Gleeble-1500 thermal simulator,an artificial neural network model of high temperature flow stress as a function of strain,strain rate and temperature was established.In the network model,the input parameters of the model are strain,logarithm strain rate and temperature while flow stress is the output parameter.Multilayer perceptron（MLP） architecture with back-propagation algorithm is utilized.The present study achieves a good performance of the artificial neural network（ANN） model,and the predicted results are in agreement with experimental values.A processing map of Ti40 alloy is obtained with the flow stress predicted by the trained neural network model.The processing map developed by ANN model can efficiently track dynamic recrystallization and flow localization regions of Ti40 alloy during deforming.Subsequently,the safe and instable domains of hot working of Ti40 alloy are identified and validated through microstructural investigations.以Gleeble-1500热模拟试验机获得的Ti40钛合金压缩试验数据为基础,应用人工神经网络对数据进行训练和预测,建立该合金的高温流动应力与应变、应变速率和温度对应关系的预测模型,其中,应变、应变速率(对数形式)和变形温度作为模型的输入参数,流动应力作为模型的输出参数。结果发现,运用BP反向传播算法进行训练的神经网络模型具有良好的预测功能,其预测值与实验测量值基本吻合。同时,采用神经网络模型预测的数据构造Ti40合金的加工图,其安全区和失稳区的范围与实测数据获得的加工图基本相符,并对各自区域的相应组织状态进行金相观察。

关 键 词：Ti40 alloy processing map artificial neural network 

分 类 号：TG146.23[一般工业技术—材料科学与工程]