张颖骁,张梓杨,宋龙飞,李晓刚.Ti80合金及其热模拟组织在含氟模拟海水中的力学电化学行为研究[J].表面技术,2022,51(5):49-60, 69.
ZHANG Ying-xiao,ZHANG Zi-yang,SONG Long-fei,LI Xiao-gang.Mechanical-electrochemical Study of Ti80 and Heat Treatment Simulated Microstructure in Fluoride-contained Simulated Seawater Environment[J].Surface Technology,2022,51(5):49-60, 69 |
| Ti80合金及其热模拟组织在含氟模拟海水中的力学电化学行为研究 |
| Mechanical-electrochemical Study of Ti80 and Heat Treatment Simulated Microstructure in Fluoride-contained Simulated Seawater Environment |
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| DOI：10.16490/j.cnki.issn.1001-3660.2022.05.006 |
| 中文关键词: Ti80合金 力学电化学 机器学习 |
| 英文关键词:Ti80 alloy mechanical-electrochemical machine learning |
| 基金项目: |
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| Author | Institution |
| ZHANG Ying-xiao | Institute for Advanced Materials and Technology,Key Laboratory for Corrosion and Protection MOE, University of Science and Technology Beijing, Beijing 100083, China |
| ZHANG Zi-yang | Institute for Advanced Materials and Technology,Key Laboratory for Corrosion and Protection MOE, University of Science and Technology Beijing, Beijing 100083, China |
| SONG Long-fei | Institute for Advanced Materials and Technology,Key Laboratory for Corrosion and Protection MOE, University of Science and Technology Beijing, Beijing 100083, China;School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, China |
| LI Xiao-gang | Institute for Advanced Materials and Technology,Key Laboratory for Corrosion and Protection MOE, University of Science and Technology Beijing, Beijing 100083, China |
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| 中文摘要: |
| 目的 研究应变、环境、组织对Ti80合金在含氟海水中电化学行为的影响，为海洋工程装备的安全服役提供数据支持。方法 使用热处理的方式模拟Ti80合金焊接热影响区组织，并通过拉伸机加载至不同应变状态，进行开路电位和极化曲线的测试，最后通过机器学习，挖掘应变、环境、组织与电化学行为的关系。结果 在添加和不添加0.001 mol/L F–的模拟海水(pH=2)中，开路电位随应变的增加而负移。在添加0.01 mol/L F–的模拟海水中，应变对开路电位没有明显影响，应变增加整体上提高维钝电流密度。受应变影响最大的是添加0.01 mol/L F–模拟海水中的1 500 ℃热模拟组织，其最大应变状态下维钝电流密度是无应变状态下的3倍左右。阴极塔菲尔斜率最大值出现在屈服点附近。F–浓度增加显著提高维钝电流密度。决策树和梯度提升树算法预测极化曲线电流值较为准确，随机森林算法的准确度较差。结论 塑性变形显著提高Ti80在模拟海水中的电化学活性，而弹性变形的影响并不明显。F–浓度增加显著提高电化学活性。决策树和梯度提升树算法预测准确度高于随机森林算法。在相对重要性对比中，F–浓度对电化学行为的影响最大，应变状态次之，组织的影响最小。 |
| 英文摘要: |
| In this work, effects of strain, environment, and microstructure on the electrochemical behavior of Ti80 alloy and its simulated heat treatment microstructures in fluoride-contained simulated seawater were studied to provide data support for the safe service of marine engineering equipment. Machine learning method was used to study the influence and compare the relative importance of the affected factors. Results depict that potentiodynamic curves of Ti80 alloy could be accurately predicted under different strain states without additional measurement. To simulate the microstructures of heat affected zone, base metals were kept at 900 ℃ and 1 500 ℃ for 5 min, and then cooled by air to room temperature, as called 900 ℃ and 1 500 ℃ simulated Ti80 microstructure. Tensile test specimens with three different microstructures (base metal, 900 ℃ and 1 500 ℃ simulated Ti80 microstructure) were sectioned, grounded with 2000 grits silicon paper, ultrasonically cleaned by acetone and ethanol, and embedded in a sealant (KAFUTER 704 RTV) to provide 0.3 cm2 as working area. Specimens were loaded to different strain states on a WDML-30 kN with a strain rate of 10–6 s–1 before electrochemical measurement. The simulated seawater in ASTM D1141-98(2013) was used to deploy the solution. The pH value of seawater was adjusted to 2 by HCl. NaF was added to increase F– concentration with two levels:0.001 mol/L and 0.01 mol/L. After polarized at 1.2 V for 120 s, open circuit potential and potentiodynamic curve were tested under different strain states by a CS350H. The machine learning method (Tempodata from Meritdata) was used to mine the relationship between electrochemical behavior and strain, environment, and microstructure. To speed up the model construction, data of current density from potentiodynamic curves were preprocessed in this way:generate a data point every 100 mV form the corrosion potential. Decision tree, random forest, and gradient boosting tree were trained by current density of potentiodynamic curve. Accuracy and relative importance of models were compared. The results showed that open circuit potential shifted negatively as strain increased in seawater without F– addition and with the addition of 0.001 mol/L F–. But strain had little effect on open circuit potential in seawater with the addition of 0.01 mol/L F–. On the whole, strain promoted the increase of passive current density. The condition of 1 500 ℃ simulated Ti80 microstructure in seawater with the addition of 0.01 mol/L F– was the most severely affected by strain, whose passive current density in the maximum strain was about 3 times that without strain. The maximum value of the cathode Tafel slope appeared near the yield point. The increase of F– concentration significantly increased the passive current density. Decision tree and gradient boosting tree algorithms were more accurate in predicting the current value of the polarization curve, while the random forest algorithm was less accurate. In the relative importance comparison, F– concentration had the greatest effect on electrochemical behavior, followed by strain state, and the microstructure had the least effect. In summary, plastic deformation significantly improves the electrochemical activity of Ti80 in simulated seawater, while the effect of elastic deformation is not obvious. The increase in F– concentration significantly promotes the electrochemical activity. The decision tree and gradient boosting tree algorithm could be used to accurately predict potentiodynamic curves with different strains, fluoride ion concentrations, and microstructures of Ti80. For Ti80 in simulated fluoride-contained seawater, the order of importance that affects the electrochemical behavior is:F– concentration> strain> microstructure. |
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