基于GRNN神经网络的GH761合金力学性能预测  

Prediction of Mechanical Properties of GH761 Alloy Based on GRNN

作　　者：姜岩蕾[1] 

机构地区：[1]河南工业职业技术学院,河南南阳473009

出　　处：《装备制造技术》2017年第6期214-216,共3页Equipment Manufacturing Technology

摘　　要：根据不同磷含量下的GH761合金力学拉伸实验,测得了抗拉强度、屈服强度、延伸率和面缩率的实验数据,利用Matlab神经网络工具箱建立了磷含量对GH761合金力学性能预测的GRNN神经网络模型。研究结果表明:GRNN神经网络模型能够很好的反映磷含量对GH761合金力学性能的影响趋势,相对误差很低,网络有很高的稳定性。According to GH761 alloy mechanical tensile experiment under different phosphorus content,the experimental data of tensile strength,yield strength,elongation and shrinkage was measured. A GRNN neural network prediction model was established by Matlab neural network toolbox on phosphorus content to GH761 alloy mechanical properties. The results show that the GRNN neural network model can well match the tendency on the phosphorus content to the influence on mechanical properties of GH761 alloy,the relative error is very small with a high stability.

关 键 词：GRNN神经网络 磷含量 GH761合金 力学性能 

分 类 号：TG146.21[一般工业技术—材料科学与工程]