典型文献
Data-mining and atmospheric corrosion resistance evaluation of Sn- and Sb-additional low alloy steel based on big data technology
文献摘要:
Machine-learning and big data are among the latest approaches in corrosion research. The biggest challenge in corrosion research is to accurately predict how materials will degrade in a given environment. Corrosion big data is the application of mathematical methods to huge amounts of data to find correlations and infer probabilities. It is possible to use corrosion big data method to distinguish the influence of the minimal changes of alloying elements and small differences in microstructure on corrosion resistance of low alloy steels. In this research, cor-rosion big data evaluation methods and machine learning were used to study the effect of Sb and Sn, as well as environmental factors on the corrosion behavior of low alloy steels. Results depict corrosion big data method can accurately identify the influence of various factors on cor-rosion resistance of low alloy and is an effective and promising way in corrosion research.
文献关键词:
中图分类号:
作者姓名:
Xiaojia Yang;Jike Yang;Ying Yang;Qing Li;Di Xu;Xuequn Cheng;Xiaogang Li
作者机构:
Institute for Advanced Materials and Technology,University of Science and Technology Beijing,Beijing 100083,China;Shunde Graduate School,University of Science and Technology Beijing,Foshan 528399,China;Beike Research Center for Advanced Corrosion Resistant Materials,Guangzhou 511400,China
文献出处:
引用格式:
[1]Xiaojia Yang;Jike Yang;Ying Yang;Qing Li;Di Xu;Xuequn Cheng;Xiaogang Li-.Data-mining and atmospheric corrosion resistance evaluation of Sn- and Sb-additional low alloy steel based on big data technology)[J].矿物冶金与材料学报,2022(04):825-835
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B类:
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AB值:
0.470233
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