Yan Luchun, Diao Yupeng, Gao Kewei
School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Materials (Basel). 2020 Jul 23;13(15):3266. doi: 10.3390/ma13153266.
As one of the factors (e.g., material properties, surface quality, etc.) influencing the corrosion processes, researchers have always been exploring the role of environmental factors to understand the mechanism of atmospheric corrosion. This study proposes a random forest algorithm-based modeling method that successfully maps both the steel's chemical composition and environmental factors to the corrosion rate of low-alloy steel under the corresponding environmental conditions. Using the random forest models based on the corrosion data of three different atmospheric environments, the environmental factors were proved to have different importance sequence in determining the environmental corrosivity of open and sheltered exposure test conditions. For each exposure test site, the importance of environmental features to the corrosion rate is also ranked and analyzed. Additionally, the feasibility of the random forest model to predict the corrosion rate of steel samples in the new environment is also demonstrated. The volume and representativeness of the corrosion data in the training data are considered to be the critical factors in determining its prediction performance. The above results prove that machine learning provides a useful tool for the analysis of atmospheric corrosion mechanisms and the evaluation of corrosion resistance.
作为影响腐蚀过程的因素之一（如材料特性、表面质量等），研究人员一直在探索环境因素的作用，以了解大气腐蚀的机制。本研究提出了一种基于随机森林算法的建模方法，该方法成功地将钢材的化学成分和环境因素映射到相应环境条件下低合金钢的腐蚀速率。利用基于三种不同大气环境腐蚀数据的随机森林模型，证明了环境因素在确定露天和遮蔽暴露试验条件下的环境腐蚀性方面具有不同的重要性顺序。对于每个暴露试验场地，还对环境特征对腐蚀速率的重要性进行了排序和分析。此外，还证明了随机森林模型预测新环境中钢样品腐蚀速率的可行性。训练数据中腐蚀数据的数量和代表性被认为是决定其预测性能的关键因素。上述结果证明，机器学习为大气腐蚀机制分析和耐腐蚀性评估提供了一种有用的工具。
