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Journal of Chinese Society for Corrosion and protection  2022, Vol. 42 Issue (3): 447-457    DOI: 10.11902/1005.4537.2021.115
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Review on Corrosion Test Methods and Evaluation Techniques for Materials in Atmospheric Environment
XU Di1, YANG Xiaojia1, LI Qing1, CHENG Xuequn1,2, LI Xiaogang1,2()
1.Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China
2.National Materials Corrosion and Protection Scientific Data Center, University of Science and Technology Beijing, Beijing 100083, China
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Abstract  

In this article, the research progress of atmospheric corrosion test methods is reviewed, namely atmospheric corrosion accelerating test method, atmospheric corrosion electrochemical test method and atmospheric corrosion monitoring and detecting technology. Among the atmospheric corrosion acceleration test methods, the multi-factor cyclic corrosion test has become the main development direction of simulated atmospheric corrosion acceleration tests. At the same time, due to the improvement of the spatial resolution, the electrochemical test methods for atmospheric corrosion have also been extended from macroscopic electrochemical technology to micro-area electrochemical technology. In addition, with the development of the Internet of Things and computer technology, modern data processing technology based on data mining and machine learning has also been applied in atmospheric corrosion monitoring and testing technology.

Key words:  accelerating corrosion experimental      electrochemical experimental methods      monitoring technology      big data     
Received:  24 June 2021     
ZTFLH:  TG174  
Fund: National Key R&D Program of China(2021YFB3701701);National Natural Science Foundation of China(52171063)
Corresponding Authors:  LI Xiaogang     E-mail:  Lixiaogang99@263.net
About author:  LI Xiaogang, E-mail: Lixiaogang99@263.net

Cite this article: 

XU Di, YANG Xiaojia, LI Qing, CHENG Xuequn, LI Xiaogang. Review on Corrosion Test Methods and Evaluation Techniques for Materials in Atmospheric Environment. Journal of Chinese Society for Corrosion and protection, 2022, 42(3): 447-457.

URL: 

https://www.jcscp.org/EN/10.11902/1005.4537.2021.115     OR     https://www.jcscp.org/EN/Y2022/V42/I3/447

MethodsFunctionsAdvantagesDisadvantages
Bayesian NetworkCorrelation analysisExploring the causal relationship between the factors influencing corrosionNeed a certain amount of data to ensure the credibility of the model
Grey Correlation AnalysisCorrelation analysisFinding the key factors affecting the corrosion mechanism of materialsDoes not reflect the general law of material corrosion
Random ForestCorrelation analysisQuantify the size of the effect of each corrosion factor on corrosionAnalysis results are limited and do not reflect the general law of material corrosion
Grey PredictionPredictMinimum sample size requirementUnable to respond to the effect of other factors on corrosion
Multiple Linear RegressionPredictVisually describe the effect of each factor on corrosion rateLimits the use of material corrosion data
Artificial Neural Network (ANN)PredictBetter prediction accuracy than multiple linear regression methodOverfitting can occur with small sample sizes
Support Vector Machine (SVM), Support Vector Regression (SVR)PredictPrediction accuracy higher than a rtificial neural networkThere is a dependence on the sample size for model building
Monte Carlo simulation at the macro scalePredictApplication to service safety assessment of pipeline type engineering facilitiesLarge demand sample size, unable to adjust inputs and outputs for flexible forecasting
Markov ChainPredictSuitable for mining continuous, time-series dataWeak handling of discrete data
Random ForestPredictCorrosion data suitable for high-speed variation characteristicsHigh dependence on sample size for model building
Table 1  Introduction to data mining methods for corrosion research
Fig.1  Scope of application of main data mining methods in corrosion data
Fig.2  CCF-WKNNs model structure[88]
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