基于梯度提升决策树的3Ni钢应力诱导腐蚀机理研究

杨小佳; 贾静焕; 李清; 朱仁政; 杨吉可; 刘智勇; 程学群; 李晓刚

doi:10.1007/s12613-023-2661-2

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文章导航 > 矿物冶金与材料学报（英文） > 2024 > 最新录用

Xiaojia Yang, Jinghuan Jia, Qing Li, Renzheng Zhu, Jike Yang, Zhiyong Liu, Xuequn Cheng, and Xiaogang Li, Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method, Int. J. Miner. Metall. Mater.,(2024). https://doi.org/10.1007/s12613-023-2661-2

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Xiaojia Yang, Jinghuan Jia, Qing Li, Renzheng Zhu, Jike Yang, Zhiyong Liu, Xuequn Cheng, and Xiaogang Li, Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method, Int. J. Miner. Metall. Mater.,(2024). https://doi.org/10.1007/s12613-023-2661-2

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研究论文

基于梯度提升决策树的3Ni钢应力诱导腐蚀机理研究

1)
北京科技大学新材料技术研究院, 北京 100083, 中国
2)
中国航发北京航空材料研究院航空材料先进腐蚀与防护重点实验室, 北京 100095, 中国
3)
北京科技大学顺德创新学院, 佛山 528399, 中国

文章亮点

(1) 系统地研究了Mn及Cu改性对3Ni耐候钢腐蚀影响规律。

(2) 开发了基于梯度提升决策树的3Ni钢应力诱导腐蚀研究方法。

(3) 总结并提出了成分、组织结构因素对3Ni钢的耐蚀性能影响规律。

中文摘要

传统的3Ni耐候钢已不能完全满足海洋工程发展的要求，因此在新型3Ni钢的设计中加入Mn或Nb等微合金元素增强强度成为一种趋势。采用腐蚀大数据方法研究了一种新型高强度3Ni钢的应力诱导腐蚀行为，采用电偶腐蚀电流监测方法记录腐蚀过程的信息，采用梯度增强决策树(GBDT)机器学习方法挖掘腐蚀机理，研究了组织结构因素的重要性。为了验证GBDT方法的计算结果，进行了现场暴露试验，结果表明，GBDT方法可以有效地研究组织因素对3Ni钢腐蚀过程的影响。Mn和Cu对3Ni钢应力诱导腐蚀的不同作用机制表明，Mn和Cu在腐蚀初期对非应力3Ni钢的腐蚀速率没有明显影响。当腐蚀达到稳定状态时，Mn元素含量的增加加快了3Ni钢的腐蚀速率，而Cu元素含量的增加降低了3Ni钢的腐蚀速率。在有应力存在的情况下，Mn元素含量的增加和Cu元素的加入可以抑制腐蚀过程。室外暴晒3Ni钢的腐蚀规律与基于腐蚀大数据技术的腐蚀规律一致，验证了大数据评价方法和数据预测模型选择的可靠性。

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Research Article

Stress-assisted corrosion mechanism of 3Ni steel by using gradient boosting decision tree machining learning method

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Abstract

Abstract

Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development, resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend. The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method. The information on the corrosion process was recorded using the galvanic corrosion current monitoring method. The gradient boosting decision tree (GBDT) machine learning method was used to mine the corrosion mechanism, and the importance of the structure factor was investigated. Field exposure tests were conducted to verify the calculated results using the GBDT method. Results indicated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel. Different mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion. When the corrosion reached a stable state, the increase in Mn element content increased the corrosion rate of 3Ni steel, while Cu reduced this rate. In the presence of stress, the increase in Mn element content and Cu addition can inhibit the corrosion process. The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology, verifying the reliability of the big data evaluation method and data prediction model selection.
- weathering steel;
- stress-assisted corrosion;
- gradient boosting decision tree;
- machining learning

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