刘成臣,徐胜,王浩伟,张金奎.基于灰色模型和神经网络的铝合金腐蚀预测对比[J].装备环境工程,2013,10(3):1-4,31. LIU Cheng-chen,XU Sheng,WANG Hao-wei,ZHANG Jin-kui.Comparative Study of Prediction Models of Aluminum Alloys Based on Gray Model and Artificial Neural Network[J].Equipment Environmental Engineering,2013,10(3):1-4,31.
基于灰色模型和神经网络的铝合金腐蚀预测对比
Comparative Study of Prediction Models of Aluminum Alloys Based on Gray Model and Artificial Neural Network
投稿时间：2013-01-18  修订日期：2013-05-01
DOI：10.7643/issn.1672-9242.2013.03.001
中文关键词:  铝合金  腐蚀损伤  模型  预测
英文关键词:aluminum alloy  corrosion damage  model  prediction
基金项目:
作者单位
刘成臣 中国特种飞行器研究所，湖北荆门448035 
徐胜 海军装备部航订部，北京100841 
王浩伟 中国特种飞行器研究所，湖北荆门448035 
张金奎 中国特种飞行器研究所，湖北荆门448035 
AuthorInstitution
LIU Cheng-chen China Special Vehicle Research Institute，Jingmen448035，China 
XU Sheng Science Order Department of Naval Aeronautic Equipment，Beijing100841，China 
WANG Hao-wei China Special Vehicle Research Institute，Jingmen448035，China 
ZHANG Jin-kui China Special Vehicle Research Institute，Jingmen448035，China 
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中文摘要:
      采用NaCl溶液对铝合金试验件进行预腐蚀试验，产生腐蚀坑，获取了不同腐蚀时间下的腐蚀数据，然后进行疲劳加载试验。分别利用灰色模型和BP神经网络建立了腐蚀深度及疲劳寿命与腐蚀时间相关性的预测模型，对两种预测模型的精度进行了对比。研究发现，在缺乏足够统计数据的情况下灰色模型预测精度优于神经网络算法。
英文摘要:
      Aluminum alloy was tested through pre-corrosion in NaCl solution. The corrosion pits were detected to get corrosion damage data of different corrosion time. Corrosion fatigue test of the pre-corroded specimen was carried out. Gray prediction model and BP neural network algorithms were selected to establish predictive model of the relation between fatigue lives, corrosion depth, and corrosion time. The accuracy of both two prediction model were compared. The result showed that the prediction accuracy of gray prediction model is higher than BP neural network algorithm when the statistics is lack.
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