计算机工程与应用2018，Vol.54Issue(13)：67-72,6.DOI:10.3778/j.issn.1002-8331.1705-0428
基于BP神经网络的有限元应力修匀的研究
Study on finite element stress smothing based on BP neural network
赵亚飞 1韦广梅1
作者信息
- 1. 内蒙古工业大学 理学院,呼和浩特 010051
- 折叠
摘要
Abstract
This paper takes the quadrilateral plane stresses isoparametric element as an example and Gaussian integral points as the sample points, based on the BP neural network, there are two training models investigated:The coordinates of the Gaussian integration points are input and the Mises stress is output. The coordinates and displacements of the Gaussian integral points are input at the same time, and the Mises stress is output. Compared with the traditional finite element global stress smoothing, the Mises stress of the nodes is studied by BP neural network, whether it is the coordinate as the input or the coordinate and the displacement as the input at the same time, the accuracy of the Mises stress is higher than that of the traditional finite element global stress smoothing, and the second type of BP neural network training model has the higher accuracy than the first one.关键词
有限元整体应力修匀/BP神经网络/两种训练模型,Mises应力Key words
finite element integral stress smoothing/BP neural network/two training models/Mises stress分类
数理科学引用本文复制引用
赵亚飞,韦广梅..基于BP神经网络的有限元应力修匀的研究[J].计算机工程与应用,2018,54(13):67-72,6.
