基于大数据技术的含锡锑低合金钢腐蚀数据挖掘及耐大气腐蚀性能评价

杨小佳; 杨吉可; 杨颖; 李清; 徐迪; 程学群; 李晓刚

doi:10.1007/s12613-022-2457-9

Volume 29 Issue 4

Apr. 2022

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文章导航 > 矿物冶金与材料学报（英文） > 2022 > 29(4): 825-835

Xiaojia Yang, Jike Yang, Ying Yang, Qing Li, Di Xu, Xuequn Cheng, and Xiaogang Li, Data-mining and atmospheric corrosion resistance evaluation of Sn- and Sb-additional low alloy steel based on big data technology, Int. J. Miner. Metall. Mater., 29(2022), No. 4, pp. 825-835. https://doi.org/10.1007/s12613-022-2457-9

Cite this article as:

Xiaojia Yang, Jike Yang, Ying Yang, Qing Li, Di Xu, Xuequn Cheng, and Xiaogang Li, Data-mining and atmospheric corrosion resistance evaluation of Sn- and Sb-additional low alloy steel based on big data technology, Int. J. Miner. Metall. Mater., 29(2022), No. 4, pp. 825-835. https://doi.org/10.1007/s12613-022-2457-9

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研究论文

基于大数据技术的含锡锑低合金钢腐蚀数据挖掘及耐大气腐蚀性能评价

1)
北京科技大学新材料技术研究院，北京 100083，中国
2)
北京科技大学顺德研究生院，佛山 528399，中国
3)
广州市南沙区贝科耐蚀新材料研究院，广州 511400，中国

通讯作者:
杨小佳 E-mail: yangxiaojia@ustb.edu.cn

李晓刚 E-mail: lixiaogang99@263.net

文章亮点

(1) 研究了基于腐蚀大数据的低合金钢耐蚀性能评价办法。

(2) 研究了基于随机森林的低合金钢关键环境因子数据挖掘方法。

(3) 总结并提出了Sn及Sb对低合金钢耐蚀性能影响的差异。

中文摘要

机器学习和腐蚀大数据技术是腐蚀科学研究领域的最新方法。腐蚀科学研究最大挑战是准确预测材料在既定环境中的腐蚀失效方式。腐蚀大数据技术是利用数学方法探寻挖掘海量腐蚀数据的相关性，并推断其发生腐蚀的概率。本研究采用腐蚀大数据评价方法和机器学习的数据挖掘方法，研究了Sb、Sn以及环境因素对低合金钢腐蚀行为的影响。结果表明，腐蚀时钟图、累计腐蚀量图可反映出Sn及Sb添加的低合金钢腐蚀行为的演变，0.05wt%Sn及0.10wt%Sb添加总体可以提高低合金钢的耐蚀性。同时，基于随机森林的数据挖掘方法分析表明Sn、Sb及环境因子对低合金钢腐蚀行为的影响权重会随着时间的变化而变化。总之，利用腐蚀大数据方法可以区分微合金元素Sn与Sb对低合金钢耐蚀性的影响。

关键词:

Research Article

Data-mining and atmospheric corrosion resistance evaluation of Sn- and Sb-additional low alloy steel based on big data technology

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Abstract

Abstract

Machine-learning and big data are among the latest approaches in corrosion research. The biggest challenge in corrosion research is to accurately predict how materials will degrade in a given environment. Corrosion big data is the application of mathematical methods to huge amounts of data to find correlations and infer probabilities. It is possible to use corrosion big data method to distinguish the influence of the minimal changes of alloying elements and small differences in microstructure on corrosion resistance of low alloy steels. In this research, corrosion big data evaluation methods and machine learning were used to study the effect of Sb and Sn, as well as environmental factors on the corrosion behavior of low alloy steels. Results depict corrosion big data method can accurately identify the influence of various factors on corrosion resistance of low alloy and is an effective and promising way in corrosion research.
- machine-learning;
- corrosion big data;
- low alloy steels;
- corrosion resistance

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[1]	J.H. Jia, X.Q. Cheng, X.J. Yang, X.G. Li, and W. Li, A study for corrosion behavior of a new-type weathering steel used in harsh marine environment, Constr. Build. Mater., 259(2020), art. No. 119760. doi: 10.1016/j.conbuildmat.2020.119760
[2]	S. Raffo, I. Vassura, C. Chiavari, C. Martini, M.C. Bignozzi, F. Passarini, and E. Bernardi, Weathering steel as a potential source for metal contamination: Metal dissolution during 3-year of field exposure in a urban coastal site, Environ. Pollut., 213(2016), p. 571. doi: 10.1016/j.envpol.2016.03.001
[3]	P.J. Wang, L.W. Ma, X.Q. Cheng, and X.G. Li, Influence of grain refinement on the corrosion behavior of metallic materials: A review, Int. J. Miner. Metall. Mater., 28(2021), 7, p. 1112. doi: 10.1007/s12613-021-2308-0
[4]	J.H. Jia, Z.Y. Liu, X.Q. Cheng, C.W. Du, and X.G. Li, Development and optimization of Ni-advanced weathering steel: A review, Corros. Commun., 2(2021), p. 82. doi: 10.1016/j.corcom.2021.09.003
[5]	Q. Li, X.J. Xia, Z.B. Pei, X.Q. Cheng, D.W. Zhang, K. Xiao, J. Wu, and X.G. Li, Long-term corrosion monitoring of carbon steels and environmental correlation analysis via the random forest method, NPJ Mater. Degrad., 6(2022), art. No. 1. doi: 10.1038/s41529-021-00211-3
[6]	L. Wang, C. F. Dong, C. Man, Y.B. Hu, Q. Yu, and X.G. Li, Effect of microstructure on corrosion behavior of high strength martensite steel—A literature review, Int. J. Miner. Metall. Mater., 28(2021), 5, p. 754. doi: 10.1007/s12613-020-2242-6
[7]	K.B. Tayyab, A. Farooq, A.A. Alvi, A.B. Nadeem, and K.M. Deen, Corrosion behavior of cold-rolled and post heat-treated 316L stainless steel in 0.9wt% NaCl solution, Int. J. Miner. Metall. Mater., 28(2021), 3, p. 440. doi: 10.1007/s12613-020-2054-8
[8]	J. Ma, F. Feng, B.Q. Yu, H.F. Chen, and L.F. Fan, Effect of cooling temperature on the microstructure and corrosion behavior of X80 pipeline steel, Int. J. Miner. Metall. Mater., 27(2020), p. 347. doi: 10.1007/s12613-019-1882-x
[9]	E.D. Fan, S.Q. Zhang, D.H. Xie, Q.Y. Zhao, X.G. Li, and Y.H. Huang, Effect of nanosized NbC precipitates on hydrogen-induced cracking of high-strength low-alloy steel, Int. J. Miner. Metall. Mater., 28(2021), p. 249. doi: 10.1007/s12613-020-2167-0
[10]	Y. Yang, X.Q. Cheng, J.B. Zhao, Y. Fan, and X.G. Li, A study of rust layer of low alloy structural steel containing 0.1 % Sb in atmospheric environment of the Yellow Sea in China, Corros. Sci., 188(2021), art. No. 109549. doi: 10.1016/j.corsci.2021.109549
[11]	National Science and Technology Council (US), Materials Genome Initiative for Global Competitiveness, USA, 2011.
[12]	X.G. Li, D.W. Zhang, Z.Y. Liu, Z. Li, C.W. Du, and C.F. Dong, Materials science: Share corrosion data, Nature, 527(2015), No. 7579, p. 441. doi: 10.1038/527441a
[13]	X.J. Yang, Y. Yang, M.H. Sun, J.H. Jia, X.Q. Cheng, Z.B. Pei, Q. Li, D. Xu, K. Xiao, and X.G. Li, A new understanding of the effect of Cr on the corrosion resistance evolution of weathering steel based on big data technology, J. Mater. Sci. Technol., 104(2022), p. 67. doi: 10.1016/j.jmst.2021.05.086
[14]	Y.J. Zhi, Z.H. Jin, L. Lu, T. Yang, D.Y. Zhou, Z.B. Pei, D.Q. Wu, D.M. Fu, D.W. Zhang, and X.G. Li, Improving atmospheric corrosion prediction through key environmental factor identification by random forest-based model, Corros. Sci., 178(2021), art. No. 109084. doi: 10.1016/j.corsci.2020.109084
[15]	X.J. Yang, M.H. Liu, Z.Y. Liu, C.W. Du, and X.G. Li, Failure analysis of a 304 stainless steel heat exchanger in liquid sulfur recovery units, Eng. Fail. Anal., 116(2020), art. No. 104729. doi: 10.1016/j.engfailanal.2020.104729
[16]	X.J. Yang, J.M. Shao, Z.Y. Liu, D.W. Zhang, L.Y. Cui, C.W. Du, and X.G. Li, Stress-assisted microbiologically influenced corrosion mechanism of 2205 duplex stainless steel caused by sulfate-reducing bacteria, Corros. Sci., 173(2020), art. No. 108746. doi: 10.1016/j.corsci.2020.108746
[17]	X.J. Yang, C.W. Du, H.X. Wan, Z.Y. Liu, and X.G. Li, Influence of sulfides on the passivation behavior of titanium alloy TA2 in simulated seawater environments, Appl. Surf. Sci., 458(2018), p. 198. doi: 10.1016/j.apsusc.2018.07.068
[18]	T.G. Dietterich, Machine-learning research, AI Mag., 18(1997), No. 4, p. 97. doi: 10.1609/aimag.v18i4.1324
[19]	J. R and B. P, PIANO: A fast parallel iterative algorithm for multinomial and sparse multinomial logistic regression, Signal Process., 194(2022), art. No. 108459. doi: 10.1016/j.sigpro.2022.108459
[20]	D. Ramos, P. Faria, A. Morais, and Z. Vale, Using decision tree to select forecasting algorithms in distinct electricity consumption context of an office building, Energy Rep., 8(2022), p. 417. doi: 10.1016/j.egyr.2022.01.046
[21]	F. Camastra, V. Capone, A. Ciaramella, A. Riccio, and A. Staiano, Prediction of environmental missing data time series by Support Vector Machine Regression and Correlation Dimension estimation, Environ. Modell. Softw., 150(2022), art. No. 105343. doi: 10.1016/j.envsoft.2022.105343
[22]	H. Li, J.J. Lin, X.B. Lei, and T.X. Wei, Compressive strength prediction of basalt fiber reinforced concrete via random forest algorithm, Mater. Today Commun., 30(2022), art. No. 103117. doi: 10.1016/j.mtcomm.2021.103117
[23]	A.S. Mohammad and M.R. Pradhan, Machine learning with big data analytics for cloud security, Comput. Electr. Eng., 96(2021), art. No. 107527. doi: 10.1016/j.compeleceng.2021.107527
[24]	V. Díaz and C. López, Discovering key meteorological variables in atmospheric corrosion through an artificial neural network model, Corros. Sci., 49(2007), No. 3, p. 949. doi: 10.1016/j.corsci.2006.06.023
[25]	Z.B. Pei, D.W. Zhang, Y.J. Zhi, T. Yang, L.L. Jin, D.M. Fu, X.Q. Cheng, H.A. Terryn, J.M.C. Mol, and X.G. Li, Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning, Corros. Sci., 170(2020), art. No. 108697. doi: 10.1016/j.corsci.2020.108697
[26]	P.P. Sai, T.M.B.R. Balu, R.V. Vignesh, C.V.B. Sastry, and R. Padmanaban, Artificial neural network models for predicting the corrosion behavior of friction stir processed AA5083, Mater. Today Proc., 46(2021), p. 7215. doi: 10.1016/j.matpr.2020.12.340
[27]	S.F. Fang, M.P. Wang, W.H. Qi, and F. Zheng, Hybrid genetic algorithms and support vector regression in forecasting atmospheric corrosion of metallic materials, Comput. Mater. Sci., 44(2008), No. 2, p. 647. doi: 10.1016/j.commatsci.2008.05.010
[28]	Y.J. Lv, J.W. Wang, J.L. Wang, C. Xiong, L. Zou, L. Li, and D.W. Li, Steel corrosion prediction based on support vector machines, Chaos Solitons Fractals, 136(2020), art. No. 109807. doi: 10.1016/j.chaos.2020.109807
[29]	R. Genuer, J.M. Poggi, C. Tuleau-Malot, and N. Villa-Vialaneix, Random forests for big data, Big Data Res., 9(2017), p. 28. doi: 10.1016/j.bdr.2017.07.003
[30]	V.R.S. Mani, A. Saravanaselvan, and N. Arumugam, Performance comparison of CNN, QNN and BNN deep neural networks for real-time object detection using ZYNQ FPGA node, Microelectron. J., 119(2022), art. No. 105319. doi: 10.1016/j.mejo.2021.105319
[31]	Z.B. Pei, X.Q. Cheng, X.J. Yang, Q. Li, C.H. Xia, D.W. Zhang, and X.G. Li, Understanding environmental impacts on initial atmospheric corrosion based on corrosion monitoring sensors, J. Mater. Sci. Technol., 64(2021), p. 214. doi: 10.1016/j.jmst.2020.01.023
[32]	D. Mizuno, S. Suzuki, S. Fujita, and N. Hara, Corrosion monitoring and materials selection for automotive environments by using Atmospheric Corrosion Monitor (ACM) sensor, Corros. Sci., 83(2014), p. 217. doi: 10.1016/j.corsci.2014.02.020
[33]	Y.H. Jin, M.Y. Ha, S.H. Jeon, Y.S. Jeong, and J.H. Ahn, Evaluation of corrosion conditions for the steel box members by corrosion monitoring exposure test, Constr. Build. Mater., 258(2020), art. No. 120195. doi: 10.1016/j.conbuildmat.2020.120195
[34]	Z.G. Shang, T. Deng, J.Q. He, and X.H. Duan, A novel model for hourly PM_2.5 concentration prediction based on CART and EELM, Sci. Total Environ., 651(2019), p. 3043. doi: 10.1016/j.scitotenv.2018.10.193
[35]	D.H. Lee, S.H. Kim, and K.J. Kim, Multistage MR-CART: Multiresponse optimization in a multistage process using a classification and regression tree method, Comput. Ind. Eng., 159(2021), art. No. 107513. doi: 10.1016/j.cie.2021.107513
[36]	J.S. Suh, B.C. Suh, S.E. Lee, J.H. Bae, and B.G. Moon, Quantitative analysis of mechanical properties associated with aging treatment and microstructure in Mg–Al–Zn alloys through machine learning, J. Mater. Sci. Technol., 107(2022), p. 52. doi: 10.1016/j.jmst.2021.07.045