神经网络预测搅拌摩擦加工TC4超塑性行为  

Neural Network Prediction of Superplastic Behavior of Friction Stir Processing TC4

作　　者：门月 王鑫[1] 张浩宇 周舸 陈立佳[1] 刘海建[2] MEN Yue;WANG Xin;ZHANG Hao-yu;ZHOU Ge;CHEN Li-jia;LIU Hai-jian(School of Materials Science and Engineering,Shenyang University of Technology,Shenyang 110870,China;Shanghai Spaceflight Precision Machinery Institute,Shanghai 201600,China)

机构地区：[1]沈阳工业大学材料科学与工程学院,沈阳110870 [2]上海航天精密机械研究所,上海201600

出　　处：《精密成形工程》2022年第6期59-64,共6页Journal of Netshape Forming Engineering

基　　金：国家自然科学基金(51805335)。

摘　　要：目的研究搅拌摩擦加工工艺改性的Ti-6Al-4V双相钛合金的超塑性变形行为。方法对360 r/min、30 mm/min工艺条件下搅拌摩擦加工处理的TC4钛合金在不同的变形条件下进行超塑性拉伸实验,在实验数据的基础上构建以变形温度、应变速率和晶粒尺寸为输入参数且以峰值应力为输出参数的3-16-1结构的BP人工神经网络模型。应用所构建的BP人工神经网络模型对不同变形条件的Ti-6Al-4V钛合金的超塑性行为进行预测。结果BP人工神经网络预测的精准度较高,实验应力值与预测应力值吻合度较高,相关系数R=0.9913,相对误差为1.91%~12.48%,平均相对误差为5.92%。结论该模型预测的准确性较高,能够客观真实地描述Ti-6Al-4V合金的超塑性变形行为。The paper aims to study the superplastic deformation behavior of Ti-6Al-4V dual-phase titanium alloy modifies by friction stir processing.The superplastic tensile test of TC4 titanium alloy after friction stir processing at 360 r/min,30 mm/min was carried out under different deformation conditions.Based on the experimental date,a BP artificial neural network model of 3-16-1 structure with deformation temperature,strain rate and grain size as input parameters and peak stresses as output parameters was constructed.The established BP neural network model was used to predict the superplastic behavior of Ti-6Al-4V titanium alloy under different deformation conditions.BP neural network prediction accuracy was high.The experimental stress value was in good agreement with the predicted stress value,and for the correlation coefficient R=0.9913,the relative error range was between 1.91%-12.48%,and the average relative error was 5.92%.The model has high prediction accuracy and can objectively and truly describe the superplastic deformation behavior of Ti-6Al-4V alloy.

关 键 词：TI-6AL-4V钛合金 BP人工神经网络 超塑性变形 搅拌摩擦加工 

分 类 号：TG306[金属学及工艺—金属压力加工]