Life prediction of glass fiber reinforced plastics based on BP neural network under corrosion condition

被引：0
|
作者：
Wang T. [1 ]
Wang J. [1 ]
Zhao D. [1 ]
Liu Y. [1 ]
Hou R. [1 ]
机构：
[1] School of Materials Science and Engineering, East China University of Technology, Shanghai
来源：
Huagong Xuebao/CIESC Journal | 2019年 / 70卷 / 12期
关键词：
Composites; Corrosion; Fiber reinforced plastics; Life prediction; Neural network;
D O I：
10.11949/0438-1157.20190299
中图分类号：
学科分类号：
摘要：
The factors affecting the service life of composites were analyzed by the changes of macroscopic, microscopic and mechanical properties before and after aging of glass fiber reinforced plastics under corrosive conditions. The analysis shows that the service life of fiberglass reinforced plastics under corrosion conditions is affected by three factors of temperature, time and corrosion medium concentration. Based on the bending strength retention rate of composites, a three-layer BP neural network model with a structure of 3-10-1 is used to predict the service life of composites. Through the comparison and error analysis of the forecast data and the measured data, and the random extraction of 6 sets of test data for detection, the results show that the predicted value obtained by BP Neural network model has a good fitting fit with the measured value. © All Right Reserved.
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页码：4872 / 4880
页数：8
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