Yan Luchun, Diao Yupeng, Lang Zhaoyang, Gao Kewei
School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, China.
Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, China.
Sci Technol Adv Mater. 2020 Jun 19;21(1):359-370. doi: 10.1080/14686996.2020.1746196.
The empirical modeling methods are widely used in corrosion behavior analysis. But due to the limited regression ability of conventional algorithms, modeling objects are often limited to individual factors and specific environments. This study proposed a modeling method based on machine learning to simulate the marine atmospheric corrosion behavior of low-alloy steels. The correlations between material, environmental factors and corrosion rate were evaluated, and their influences on the corrosion behavior of steels were analyzed intuitively. By using the selected dominating factors as input variables, an optimized random forest model was established with a high prediction accuracy of corrosion rate ( values, 0.94 and 0.73 to the training set and testing set) to different low-alloy steel samples in several typical marine atmospheric environments. The results demonstrated that machine learning was efficient in corrosion behavior analysis, which usually involves a regression analysis of multiple factors.
经验建模方法在腐蚀行为分析中被广泛应用。但由于传统算法的回归能力有限，建模对象往往局限于单个因素和特定环境。本研究提出了一种基于机器学习的建模方法，以模拟低合金钢的海洋大气腐蚀行为。评估了材料、环境因素与腐蚀速率之间的相关性，并直观地分析了它们对钢材腐蚀行为的影响。通过将选定的主导因素作为输入变量，建立了一个优化的随机森林模型，该模型对几种典型海洋大气环境中的不同低合金钢样品具有较高的腐蚀速率预测精度（训练集和测试集的 值分别为0.94和0.73）。结果表明，机器学习在通常涉及多因素回归分析的腐蚀行为分析中是有效的。
