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中国腐蚀与防护学报  2022, Vol. 42 Issue (3): 447-457    DOI: 10.11902/1005.4537.2021.115
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材料大气环境腐蚀试验方法与评价技术进展
徐迪1, 杨小佳1, 李清1, 程学群1,2, 李晓刚1,2()
1.北京科技大学新材料技术研究院 北京 100083
2.北京科技大学 国家材料腐蚀与防护科学数据中心 北京 100083
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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摘要: 

从大气腐蚀加速试验方法、大气腐蚀电化学方法及大气腐蚀监检测技术对大气腐蚀试验方法进行了综述。大气腐蚀加速试验中，多因子循环复合腐蚀试验已经成为模拟大气腐蚀加速试验的主要发展方向；同时，随着电化学方法空间分辨率的提升，大气腐蚀电化学方法也从宏观电化学技术向微区电化学技术延伸；另外，随着物联网及计算机技术的发展，基于数据挖掘及机器学习等现代化的数据处理技术也在大气腐蚀监检测技术中得到了应用。
关键词 ： 加速腐蚀试验,  电化学方法,  监测技术,  大数据    
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
收稿日期: 2021-06-24     
ZTFLH:  TG174  
基金资助:国家重点研发计划(2021YFB3701701);国家自然科;学基金(52171063)
通讯作者: 李晓刚     E-mail: Lixiaogang99@263.net
Corresponding author: LI Xiaogang     E-mail: Lixiaogang99@263.net
作者简介: 徐迪，女，1996年生，博士生
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徐迪, 杨小佳, 李清, 程学群, 李晓刚. 材料大气环境腐蚀试验方法与评价技术进展[J]. 中国腐蚀与防护学报, 2022, 42(3): 447-457.
Di XU, Xiaojia YANG, Qing LI, Xuequn CHENG, Xiaogang LI. 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.

链接本文:

https://www.jcscp.org/CN/10.11902/1005.4537.2021.115      或      https://www.jcscp.org/CN/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
表1  应用于腐蚀研究的数据挖掘方法介绍
图1  主要数据挖掘方法在腐蚀数据中的应用范围
图2  CCF-WKNNs模型结构[88]
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