摘要
对文献报道的铸态高熵合金的成分和压缩断裂强度进行统计,获得了铸态高熵合金成分(元素种类、含量)、强度(压缩断裂强度)的参数,分别以这些数据作为输入和输出,利用BP人工神经网络建立起其间的关系网络模型。研究表明:所建立的网络很好地反映出铸态高熵合金的成分-强度之间的关系并且具有较好的精度,网络模型可用来预测不同成分铸态高熵合金的压缩断裂强度。该网络对铸态高熵合金的体系设计具有有效的指导作用。
Parameters of composition （type and content of elements） and strength （ fracture strength under compress load） of as-cast high entropy alloy were obtained from literatures ; their relationship network model was built by BP artificial neural network. The results show that the model reflects the relationships precisely and has a relative high accuracy. The model can be used to predict the strength of as-cast high entropy alloy. Meanwhile, it can also serve as a guide for the design of high entropy alloy.
出处
《金属热处理》
CAS
CSCD
北大核心
2013年第2期26-30,共5页
Heat Treatment of Metals
关键词
铸态高熵合金
BP人工神经网络
成分
强度
as-cast high entropy alloy
BP artificial neural network
composition
strength
