Modeling the corrosion behavior of Ni-Cr-Mo-V high strength steel in the simulated deep sea environments using design of experiment and artificial neural network

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	Modeling the corrosion behavior of Ni-Cr-Mo-V high strength steel in the simulated deep sea environments using design of experiment and artificial neural network
	Hu, Qiangfei; Liu, Yuchen; Zhang, Tao; Geng, Shujiang; Wang, Fuhui
	2019
Source Publication	JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY
ISSN	1005-0302
Volume	35 Issue:1 Pages:168-175
Abstract	Corrosion in complex coupling environments is an important issue in corrosion field, because it is difficult to take into account a large number of environment factors and their interactions. Design of Experiment (DOE) can present a methodology to deal with this difficulty, although DOE is not commonly spread in corrosion field. Thus, modeling corrosion of Ni-Cr-Mo-V steel in deep sea environment was performed in order to provide example demonstrating the advantage of DOE. In addition, an artificial neural network mapping using back-propagation method was developed for Ni-Cr-Mo-V steel such that the ANN model can be used to predict polarization curves under different complex sea environments without experimentation. Furthermore, roles of environment factors on corrosion of Ni-Cr-Mo-V steel in deep sea environment were discussed. (C) 2018 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
Keyword	Ni-Cr-Mo-V steel Deep sea corrosion Design of experiment Artificial neural network
Indexed By	SCI
Language	英语
WOS ID	WOS:000449263900023
Publisher	JOURNAL MATER SCI TECHNOL
Citation statistics	Cited Times:7[WOS] [WOS Record] [Related Records in WOS]
Document Type	期刊论文
Identifier	http://ir.imr.ac.cn/handle/321006/81529
Collection	中国科学院金属研究所
Recommended Citation GB/T 7714	Hu, Qiangfei,Liu, Yuchen,Zhang, Tao,et al. Modeling the corrosion behavior of Ni-Cr-Mo-V high strength steel in the simulated deep sea environments using design of experiment and artificial neural network[J]. JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY,2019,35(1):168-175.
APA	Hu, Qiangfei,Liu, Yuchen,Zhang, Tao,Geng, Shujiang,&Wang, Fuhui.(2019).Modeling the corrosion behavior of Ni-Cr-Mo-V high strength steel in the simulated deep sea environments using design of experiment and artificial neural network.JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY,35(1),168-175.
MLA	Hu, Qiangfei,et al."Modeling the corrosion behavior of Ni-Cr-Mo-V high strength steel in the simulated deep sea environments using design of experiment and artificial neural network".JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY 35.1(2019):168-175.

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