北京航空航天大学学报 ›› 2021, Vol. 47 ›› Issue (6): 1271-1276.doi: 10.13700/j.bh.1001-5965.2020.0158

• 论文 • 上一篇    下一篇

BP神经网络预测复合材料热压罐成型均匀性

林源, 关志东   

  1. 北京航空航天大学 航空科学与工程学院, 北京 100083
  • 收稿日期:2020-04-23 发布日期:2021-07-06
  • 通讯作者: 关志东 E-mail:zdguan@buaa.edu.cn

Predicting the formation uniformity of composite autoclave by BP neural network

LIN Yuan, GUAN Zhidong   

  1. School of Aeronautic Science and Engineering, Beihang University, Beijing 100083, China
  • Received:2020-04-23 Published:2021-07-06

摘要: 复合材料热压罐成型过程中的固化度差值是复合材料固化度均匀性的主要表征参数之一。基于3层BP神经网络,以复合材料双平台固化工艺曲线的加热速率、保温时间和保温温度为输入参数,建立了成型过程任一时刻最大固化度差值的快速估算模型。仿真复合材料热压罐成型过程,得到最大固化度差值作为试验样本数据,对BP神经网络进行训练,训练结束后对该模型的准确性进行验证。结果表明:该BP神经网络估算模型准确性和效率较高,为复合材料热压罐成型最大固化度差值的估算提供了一种快速有效的新方法。

关键词: 复合材料, 神经网络, 估算, 固化工艺, 热压罐, 残余应力

Abstract: The difference in degree of cure in the forming process of composite autoclave is one of the main characterization parameters of degree of cure uniformity of composite. Based on thethree-layer BP neural network, this paper established a rapid estimation model of maximum difference of curing degree at any time in the forming process with heating rate, holding time and holding temperature as input parameters. Maximum difference in degree of cure was obtained by simulating the forming process of composite autoclave as test sample data to train the BP neural network, and the accuracy of the model was verified after the training. The results show that the accuracy and efficiency of this BP neural network model are high, which provides a fast and effective new method for estimating the difference of the maximum curing degree of composite autoclave.

Key words: composite, neural network, estimation, curing process, autoclave, residual stress

中图分类号: 


版权所有 © 《北京航空航天大学学报》编辑部
通讯地址:北京市海淀区学院路37号 北京航空航天大学学报编辑部 邮编:100191 E-mail:jbuaa@buaa.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发