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

被引：1

作者：

Qiangfei Hu ^{[1
,2
]}

Yuchen Liu ^{[2
]}

Tao Zhang ^{[2
,3
]}

Shujiang Geng ^{[1
]}

Fuhui Wang ^{[2
,3
]}

机构：

[1] School of Metallurgy, Northeastern University

[2] Corrosion and Protection Division, Shenyang National Laboratory for Materials Science, Northeastern University

[3] Laboratory for Corrosion and Protection, Institute of Metal Research, Chinese Academy of Sciences

来源：

Journal of Materials Science & Technology | 2019年 / 35卷 / 01期

基金：

中央高校基本科研业务费专项资金资助; 中国国家自然科学基金;

关键词：

Ni-Cr-Mo-V steel; Deep sea corrosion; Design of experiment; Artificial neural network;

D O I：

暂无

中图分类号：

TG172 [各种类型的金属腐蚀];

学科分类号：

080503 ;

摘要：

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.

引用

页码：168 / 175

页数：8