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

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.

Keywords

weathering steel / stress-assisted corrosion / gradient boosting decision tree / machining learning

References

[[1]] Li XG, Zhang DW, Liu ZY, Li Z, Du CW, Dong CF. Materials science: Share corrosion data. Nature, 2015, 527(7579): 441,

[[2]] Yang XJ, Yang Y, Sun MH, et al.. 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., 2022, 104: 67,

[[3]] Pei ZB, Cheng XQ, Yang XJ, et al.. Understanding environmental impacts on initial atmospheric corrosion based on corrosion monitoring sensors. J. Mater. Sci. Technol., 2021, 64: 214,

[[4]] Yang XJ, Yang JK, Yang Y, et al.. 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., 2022, 29(4): 825,

[[5]] Y.J. Zhi, Z.H. Jin, L. Lu, et al., Improving atmospheric corrosion prediction through key environmental factor identification by random forest-based model, Corros. Sci., 178(2021), art. No. 109084.

[[6]] Z.B. Pei, D.W. Zhang, Y.J. Zhi, et al., Towards understanding and prediction of atmospheric corrosion of an Fe/Cu corrosion sensor via machine learning, Corros. Sci., 170(2020), art. No. 108697.

[[7]] D.D. Macdonald, Y.K. Zhu, J. Yang, et al., Corrosion of rebar in concrete. Part IV. On the theoretical basis of the chloride threshold, Corros. Sci., 185(2021), art. No. 109460.

[[8]] Y.K. Zhu, D.D. Macdonald, J. Qiu, and M. Urquidi-Macdonald, Corrosion of rebar in concrete. Part III: Artificial Neural Network analysis of chloride threshold data, Corros. Sci., 185(2021), art. No. 109438.

[[9]] D.R. Feenstra, A. Molotnikov, and N. Birbilis, Utilisation of artificial neural networks to rationalise processing windows in directed energy deposition applications, Mater. Des., 198(2021), art. No. 109342.

[[10]] Jiménez-Come MJ, Muñoz E, García R, et al.. Pitting corrosion behaviour of austenitic stainless steel using artificial intelligence techniques. J. Appl. Log., 2012, 10(4): 291,

[[11]] Koo B, La S, Cho NW, Yu Y. Using support vector machines to classify building elements for checking the semantic integrity of building information models. Autom. Constr., 2019, 98: 183,

[[12]] H.Y. Wu, H.G. Lei, and Y.F. Chen, Grey relational analysis of static tensile properties of structural steel subjected to urban industrial atmospheric corrosion and accelerated corrosion, Constr. Build. Mater., 315(2022), art. No. 125706.

[[13]] J.M. Yao, W. Liang, and J.Y. Xiong, Novel intelligent diagnosis method of oil and gas pipeline defects with transfer deep learning and feature fusion, Int. J. Press. Vessels Pip., 200(2022), art. No. 104781.

[[14]] Fu HD, Zhang HT, Wang CS, Yong W, Xie JX. Recent progress in the machine learning-assisted rational design of alloys. Int. J. Miner. Metall. Mater., 2022, 29(4): 635,

[[15]] Zhang HT, Fu HD, Shen YH, Xie JX. Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu–Ni–Co–Si–X alloy via Bayesian optimization machine learning. Int. J. Miner. Metall. Mater., 2022, 29(6): 1197,

[[16]] Pan GF, Wang FY, Shang CL, et al.. Advances in machine learning- and artificial intelligence-assisted material design of steels. Int. J. Miner. Metall. Mater., 2023, 30(6): 1003,

[[17]] 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.

[[18]] X.J. Yang, J.M. Shao, Z.Y. Liu, et al., Stress-assisted microbiologically influenced corrosion mechanism of 2205 duplex stainless steel caused by sulfate-reducing bacteria, Corros. Sci., 173(2020), art. No. 108746.

[[19]] 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.

[[20]] J.H. Jia, Z.Y. Liu, X.G. Li, C.W. Du, and W. Li, Comparative study on the stress corrosion cracking of a new Ni-advanced high strength steel prepared by TMCP, direct quenching, and quenching & tempering, Mater. Sci. Eng. A, 825(2021), art. No. 141854.