Electrochemical behavior of welded joints of 10MnNiCr marine steel under stress in seawater
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LiHua Gong
LiHua Gong, Professor, is engaged in research and teaching in the field of corrosion and protection of metal materials in the School of Materials Science and Engineering, Jiangsu University of Science and Technology.
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WeiMin Guo
WeiMin Guo, Professor of Engineering, is engaged in marine corrosion and protection engineering in the State Key Laboratory for Marine Corrosion and Protection.
Abstract
The microstructure morphology of welded joints of marine steel 10MnNiCr was studied by OM analysis. External stresses were applied to the welded joints using the four-point bending method, and the differences in the electrochemical properties of the welded joints with different stresses applied were compared after 7d and 28d corrosion, respectively, using a combination of polarization curves and EIS tests. It was found that the welded joints showed a general trend of increasing corrosion rate with increasing external stress, but the difference in electrochemical properties between the 40 % σs and 65 % σs rusted specimens with smaller stresses applied was small. The electrochemical property changes of the specimen with applied 90 % σs stress are also not significant in a relatively short period of time, but the electrochemical performance showed a great difference with the increase of corrosion time, the corrosion rate increased significantly. These results are closely related to the influence of the alloying elements contained in this type of material on the corrosion products.
About the authors
LiHua Gong, Professor, is engaged in research and teaching in the field of corrosion and protection of metal materials in the School of Materials Science and Engineering, Jiangsu University of Science and Technology.
WeiMin Guo, Professor of Engineering, is engaged in marine corrosion and protection engineering in the State Key Laboratory for Marine Corrosion and Protection.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: No Large Language Models, AI and Machine Learning Tools were used.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: The raw data can be obtained on request from the corresponding author.
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