文献详情
Kohonen Neural Network Classification for Failure Process of Metallic Organic Coating in Corrosion Environment
文献类型期刊
作者Xu, Yuanming[1];Ran, Junshuang[2];Chen, Hao[3]
机构
通讯作者Ran, JS (reprint author), Beihang Univ, Sch Aeronaut Sci & Engn, Beijing 100191, Peoples R China.
来源信息年:2017  卷:7  期:4  
期刊信息METALS影响因子和分区  ISSN:2075-4701
关键词coating; corrosion; EIS; Kohonen neural network; classification
增刊正刊
摘要A deeper insight into the changing states of corrosion during certain exposure circumstances has been investigated by applying Kohonen networks. The Kohonen network has been trained by four sets of samples and tested using another sample. All the sample data were collected during accelerated corrosion experiments and the network took the changing rate of impedance of each cycle as an input. Compared with traditional classification, the Kohonen artificial network method classifies corrosion process into five sub-processes which is a refinement of three typical corrosion processes. The two newly defined sub-processes of corrosion-namely, pre-middle stage and post-middle stage-were introduced. The EIS data and macro-morphology for both sub-processes were analyzed through accelerated experiments. The classification results of the Kohonen artificial network are highly consistent with the predictions based on impedance magnitude at low frequency, which illustrates that the Kohonen network classification is an effective method for predicting the failure cycles of polymer coatings.
收录情况SCIE(WOS:000404051600041)  
所属部门航空科学与工程学院
DOI10.3390/met7040147
学科材料科学:综合;冶金工程
百度学术Kohonen Neural Network Classification for Failure Process of Metallic Organic Coating in Corrosion Environment
语言外文
人气指数40
浏览次数40
基金National High Technology Research and Development Program of China (863 Program); Aeronautical Science Foundation of China [2014ZE21009]; Foundation of the Key Laboratory of Beijing, China
全部评论(0 条评论)
作者其他论文

Haze removal based on Sparse Representation Prior.

Li, Jiafeng;Zhang, Hong;Chen, Hao,等.Proceedings 3rd IAPR Asian Conference on Pattern Recognition ACPR 2015.2015,781-785.

Visual Tracking via Multi-experts Combined with Average Hash Model.

Feng, Yachun;Zhang, Hong;Chen, Hao,等.3rd IAPR Asian Conference on Pattern Recognition (ACPR).2015,331-335.

A light lithium niobate transducer design and ultrasonic de-icing research for aircraft wing.

Wang, Zhenjun;Xu, Yuanming;Gu, Yuting.ENERGY.2015,87,173-181.

Research on flow simulation of fuel systems based on components.

Liu, Li;Ma, Shuyang;Zhou, Yuying,等.Huntsville Simulation Conference, HSC 2008.2008.

登录