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
Research Article

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

+ Author Affiliations
  • Corresponding authors:

    Xiaojia Yang    E-mail: yangxiaojia@ustb.edu.cn

    Xiaogang Li    E-mail: lixiaogang99@263.net

  • Received: 24 February 2022Revised: 2 March 2022Accepted: 2 March 2022Available online: 4 March 2022
  • 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.
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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 PM2.5 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
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