摘要
利用径向基神经网络建立了TC4钛合金TIG焊焊接工艺参数与接头力学性能关系的网络模型。训练模型使用了27组数据,并对另外9组数据进行仿真。结果表明,以焊接电流、焊接速度和氩气流量作为网络输入参数,利用所建的模型能够对该焊接接头抗拉强度、抗弯强度和断后伸长率进行较为准确的预测。通过与常用的标准BP神经网络模型比较发现,径向基网络相对于BP网络预测精度有了大幅度的提高,克服了BP网络训练时间长和容易陷入局部极小的缺点,为实现焊接接头力学性能预测提供了一条有效途径。
A RBF neural network model on the welding parameters and the mechanical properties of TC4 titanium alloy joints welded by TIG welding was established.The 27 sets of experimental data are used to train this model,and other 9 sets are used to simulation.The results show that the welding parameters including welding current,welding speed and argon gas flow rate as network input parameters can predict mechanical properties including tensile strength,bend strength and ductility.The efficiency and accuracy of the RBF network predictions have improved comparing with common standard BP neural network,which overcome the BP network's disadvantage of long time to train and plunge in part smallest easily.
出处
《焊接学报》
EI
CAS
CSCD
北大核心
2008年第7期81-84,共4页
Transactions of The China Welding Institution
基金
教育部"春晖计划"(Z2005-2-01002)
关键词
RBF神经网络
钛合金
预测
RBF neural network
titanium alloy
prediction
