基于BP神经网络的玻璃纤维增强塑料腐蚀条件下的寿命预测

王涛; 王俊; 赵迪宇; 刘育建; 侯锐钢

doi:10.11949/0438-1157.20190299

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基于BP神经网络的玻璃纤维增强塑料腐蚀条件下的寿命预测

材料化学工程与纳米技术 | 更新时间：2026-07-24

- 基于BP神经网络的玻璃纤维增强塑料腐蚀条件下的寿命预测
- Life prediction of glass fiber reinforced plastics based on BP neural network under corrosion condition
- 化工学报 2019年70卷第12期页码：4872-4880
- 作者机构：
  
  华东理工大学材料科学与工程学院，上海 200237
- 作者简介：
  
  王涛（1995—），男，硕士，1162495591@qq.com
  侯锐钢（1960—），男，教授，rghou@ecust.edu.cn
- 基金信息：
  
  上海市科学技术委员会科研计划项目(15DZ0504600)
- DOI：10.11949/0438-1157.20190299
  中图分类号： TB 332
- 收稿：2019-04-10，
  
  修回：2019-09-18，
  
  网络首发：2020-06-09，
  
  纸质出版：2019-12-05
- 稿件说明：
移动端阅览
王涛, 王俊, 赵迪宇, 刘育建, 侯锐钢. 基于BP神经网络的玻璃纤维增强塑料腐蚀条件下的寿命预测[J]. 化工学报, 2019, 70(12): 4872-4880

WANG Tao, WANG Jun, ZHAO Diyu, LIU Yujian, HOU Ruigang. Life prediction of glass fiber reinforced plastics based on BP neural network under corrosion condition[J]. CIESC Journal, 2019, 70(12): 4872-4880
王涛, 王俊, 赵迪宇, 刘育建, 侯锐钢. 基于BP神经网络的玻璃纤维增强塑料腐蚀条件下的寿命预测[J]. 化工学报, 2019, 70(12): 4872-4880 DOI： 10.11949/0438-1157.20190299.

WANG Tao, WANG Jun, ZHAO Diyu, LIU Yujian, HOU Ruigang. Life prediction of glass fiber reinforced plastics based on BP neural network under corrosion condition[J]. CIESC Journal, 2019, 70(12): 4872-4880 DOI： 10.11949/0438-1157.20190299.

摘要

通过腐蚀条件下玻璃纤维增强塑料老化前后宏观、微观形貌及力学性能的变化对复合材料使用寿命的影响因素进行分析，分析表明，腐蚀条件下玻璃纤维增强塑料使用寿命受温度、时间和腐蚀介质浓度三种因素影响。结合玻璃纤维增强塑料的弯曲强度保留率建立结构为3-10-1的三层BP神经网络模型对复合材料使用寿命进行预测。通过预测数据和实测数据的对比及误差分析，并随机抽取6组检验数据进行检测，结果表明，所建立的BP神经网络模型得到的预测值与实测值具有较好的拟合度。

Abstract

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.

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