| 支持向量回归预测不锈钢的点蚀电位 |
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| 引用本文： | 麦嘉琪,徐鹏程,丁松,孙阳庭,陆文聪. 支持向量回归预测不锈钢的点蚀电位[J]. 上海大学学报(自然科学版), 2021, 28(3): 485-491. DOI: 10.12066/j.issn.1007-2861.2378 |
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| 作者姓名： | 麦嘉琪 徐鹏程 丁松 孙阳庭 陆文聪 |
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| 作者单位： | 1.上海大学 理学院, 上海 200444;2.上海大学 材料基因组工程研究院 材料信息与数据科学中心, 上海 200444;3.上海大学 计算机工程与科学学院, 上海 200444;4.复旦大学 材料科学系, 上海 200433 |
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| 基金项目： | 国家重点研发计划资助项目(2018YFB0704400);云南省重大科技专项资助项目(202002AB080001-2);之江实验室科研攻关资助项目(2021PE0AC02) |
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| 摘 要： | 点蚀是不锈钢的主要腐蚀类型之一, 常用点蚀电位来评价不锈钢腐蚀的难易程度. 点蚀电位会受到多方面因素的影响. 基于不锈钢的元素成分和工艺参数, 采用支持向量回归(support vector regression, SVR)算法建立了预测点蚀电位的模型. 结果表明: 独立测试集的相关系数达到 0.97, 均方根误差(root mean square error, RMSE)仅为 0.07; 通过 Pearson 相关分析和敏感性分析, 元素 Cr、Mo 的含量和温度对点蚀电位的影响较大; 当存在少量稀土元素时可以提高不锈钢的抗腐蚀能力.
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| 关 键 词： | 不锈钢 点蚀电位 机器学习 |
| 收稿时间： | 2022-03-15 |
Prediction of pitting potential for stainless steel by support vector regression |
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| MAI Jiaqi,XU Pengcheng,DING Song,SUN Yangting,LU Wencong. Prediction of pitting potential for stainless steel by support vector regression[J]. Journal of Shanghai University(Natural Science), 2021, 28(3): 485-491. DOI: 10.12066/j.issn.1007-2861.2378 |
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| Authors: | MAI Jiaqi XU Pengcheng DING Song SUN Yangting LU Wencong |
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| Affiliation: | 1. College of Sciences, Shanghai University, Shanghai 200444, China;2. Center of Materials Informatics and Data Science, Materials Genome Institute, Shanghai University, Shanghai 200444, China;3. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China;4. Department of Materials Science, Fudan University, Shanghai 200433, China |
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| Abstract: | Pitting corrosion is a primary corrosion type of stainless steel, and pitting potential is often used to evaluate the difficulty of corrosion of stainless steel. The pitting potential is affected by many factors. Based on the elemental composition and process parameters of stainless steel, support vector regression (SVR) was used to establish a model for predicting the pitting potential. The results showed that the correlation coefficient of the independent test set could reach 0.97 with the corresponding root mean square error (RMSE) of only 0.07. From the Pearson correlation analysis and sensitivity analysis, the element contents of Cr and Mo and the temperature had a crucial influence on the pitting potential, and a small amount of rare earth elements could improve the corrosion resistance of stainless steel. |
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| Keywords: | stainless steel pitting potential machine learning (ML) |
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