摘要
为减少实验量,降低实验成本,采用人工神经网络BP算法处理了钨合金材料的抗拉强度的实验数据,包括钨含量、变形量对材料抗拉强度的影响,给出了在不同钨含量条件下变形量对材料抗拉强度的关系曲线,和不同变形量条件下钨含量对材料抗拉强度的关系曲线.通过本文的分析可知,采用BP算法来处理钨合金的实验数据是可行的.
In this paper, the tension experimental data of tungsten alloy were processed by BP Neural Network method, including the influences of the W content and deformation magnitude on the tensile strength. Thus two relation curves were drawn, which illustrated the relation between deformation magnitude and material tensile strength when W contents were changed, and the relation between W contents and material tensile strength in the case of different deformation magnitude. It is shown that BP Neural Network may be used to predict the trend of tensile strength of WHA with the changes of shapes and volume fractions of w - phase.
出处
《材料科学与工艺》
EI
CAS
CSCD
北大核心
2006年第1期63-65,共3页
Materials Science and Technology
关键词
人工神经网络
BP算法
抗拉强度
变形量
钨含量
artificial neural network
BP method
tensile strength
deformation magnitude
volume fractions of w - phase
