Application of An Electrical Resistance Sensor-Based Automated Corrosion Monitor in the Study of Atmospheric Corrosion.

Authors:
Zhuolin Li
Zhuolin Li
Affiliated Hospital of Guiyang Medical College
Dongmei Fu
Dongmei Fu
Dalian Institute of Chemical Physics
China
Ying Li
Ying Li
School of Public Health
New Haven | United States
Dawei Zhang
Dawei Zhang
Nanyang Technological University
Singapore
Zhaohui Yang
Zhaohui Yang
University of Pennsylvania School of Medicine
United States
Guoqing Ding
Guoqing Ding
Zhejiang University

Materials (Basel) 2019 Apr 1;12(7). Epub 2019 Apr 1.

Research Institute, Nanjing Iron & Steel Co., Ltd., Nanjing 210035, China.

An automated corrosion monitor, named the Internet of Things atmospheric corrosion monitor (IoT ACM) has been developed. IoT ACM is based on electrical resistance sensor and enables accurate and continuous measurement of corrosion data of metallic materials. The objective of this research is to study the characteristics of atmospheric corrosion by analyzing the acquired corrosion data from IoT ACM. Employing data processing and data analysis methods to research the acquired corrosion data of steel, the atmospheric corrosion characteristics implied in the corrosion data can be discovered. Comparing the experiment results with the phenomenon of previous laboratory experiment and conclusions of previously published reports, the research results are tested and verified. The experiment results show that the change regulation of atmospheric corrosion data in the actual environment is reasonable and normal. The variation of corrosion depth is obviously influenced by relative humidity, temperature and part of air pollutants. It can be concluded that IoT ACM can be well applied to the conditions of atmospheric corrosion monitoring of metallic materials and the study of atmospheric corrosion by applying IoT ACM is effective and instructive under an actual atmospheric environment.

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Source
http://dx.doi.org/10.3390/ma12071065DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480216PMC
April 2019
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References

(Supplied by CrossRef)

Li et al.
2014

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