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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

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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Acknowledgement

This work was financially supported by the Postdoctor Research Foundation of Shunde Graduate School of University of Science and Technology Beijing (No. 2022 BH003).

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Authors and Affiliations

  1. Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China

    Xiaojia Yang, Jike Yang, Ying Yang, Qing Li, Di Xu, Xuequn Cheng & Xiaogang Li

  2. Shunde Graduate School, University of Science and Technology Beijing, Foshan, 528399, China

    Xiaojia Yang & Xiaogang Li

  3. Beike Research Center for Advanced Corrosion Resistant Materials, Guangzhou, 511400, China

    Xiaojia Yang & Qing Li

Authors
  1. Xiaojia Yang
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  2. Jike Yang
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  6. Xuequn Cheng
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Correspondence to Xiaojia Yang or Xiaogang Li.

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The authors declared that they have no conflicts of interest.

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Yang, X., Yang, J., 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 29, 825–835 (2022). https://doi.org/10.1007/s12613-022-2457-9

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  • DOI: https://doi.org/10.1007/s12613-022-2457-9

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