Corrosion Science and Technology

The Corrosion Science Society of Korea (한국부식방식학회)
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Prediction of Pitting Corrosion Characteristics of AL-6XN Steel with Sensitization and Environmental Variables Using Multiple Linear Regression Method

다중선형회귀법을 활용한 예민화와 환경변수에 따른 AL-6XN강의 공식특성 예측

  • Jung, Kwang-Hu (Mokpo branch, Korea institute of maritime and fisheries technology) ;
  • Kim, Seong-Jong (Division of marine engineering, Mokpo national maritime university)
  • 정광후 (한국해양수산연수원 목포분원) ;
  • 김성종 (목포해양대학교 기관시스템공학부)
  • Received : 2020.12.08
  • Accepted : 2020.12.17
  • Published : 2020.12.31
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Abstract

This study aimed to predict the pitting corrosion characteristics of AL-6XN super-austenitic steel using multiple linear regression. The variables used in the model are degree of sensitization, temperature, and pH. Experiments were designed and cyclic polarization curve tests were conducted accordingly. The data obtained from the cyclic polarization curve tests were used as training data for the multiple linear regression model. The significance of each factor in the response (critical pitting potential, repassivation potential) was analyzed. The multiple linear regression model was validated using experimental conditions that were not included in the training data. As a result, the degree of sensitization showed a greater effect than the other variables. Multiple linear regression showed poor performance for prediction of repassivation potential. On the other hand, the model showed a considerable degree of predictive performance for critical pitting potential. The coefficient of determination (R2) was 0.7745. The possibility for pitting potential prediction was confirmed using multiple linear regression.

Keywords

Acknowledgement

이 논문은 해양수산부 재원으로 해양수산과학기술진흥원의 지원을 받아 수행된 연구임(선박 배출 대기오염물질 동시저감 후처리시스템 실증 및 인증체계 구축).

References

  1. J. S. Chou, N. T. Ngo, and W. K. Chong, Eng. Appl. Artif. Inte., 65, 471 (2017). https://doi.org/10.1016/j.engappai.2016.09.008
  2. H. Khatak and B. Raj, Corrosion of austenitic stainless steels: mechanism, mitigation and monitoring, 1st ed., p.75-80, Woodhead publishing, (2002).
  3. T. Hong and M. Nagumo, Corros. Sci., 39, 285-293 (1997). https://doi.org/10.1016/S0010-938X(96)00127-8
  4. E. A. Abd El Meguid, N. A. Mahmoud, and S. S. Abd El Rehim, Mater. Chem. Phys., 63, 67 (2000). https://doi.org/10.1016/S0254-0584(99)00206-0
  5. C. Alonso, M. Castellote, and C. Andrade, Electrochim. Acta., 47, 3469 (2002). https://doi.org/10.1016/S0013-4686(02)00283-9
  6. A. Pardo, M. C. Merino, A. E. Coy, F. Viejo, R. Arrabal, and E. J. C. S. Matykina, Corros. Sci., 50, 1796 (2008). https://doi.org/10.1016/j.corsci.2008.04.005
  7. H. S. Klapper, J. Goellner, A. Burkert, and A. Heyn, Corros. Sci., 75, 239 (2013). https://doi.org/10.1016/j.corsci.2013.06.005
  8. Y. Wang, G. Cheng, W. Wu, Q. Qiao, Y. Li, and X. Li, Appl. Surf. Sci., 349, 746 (2015). https://doi.org/10.1016/j.apsusc.2015.05.053
  9. Y. S. Kim, Corros. Sci. Tech., 9, 20 (2010). https://www.j-cst.org/main/abstract_view.htm?scode=C&code=C00090100020&vol=9&no=1&type=aissue https://doi.org/10.14773/cst.2010.9.1.020
  10. B. Beverskog and I. Puigdomenech, Corros. Sci., 39, 43 (1997). https://doi.org/10.1016/S0010-938X(97)89244-X
  11. K. T. Kim, S. B. Um, and S. Y. Kim, Corros. Sci. Tech., 16, 305 (2017). https://doi.org/10.14773/cst.2017.16.6.305
  12. T. O. Kowang and C. S. Long, Appl. Mech. Mater., 789, 1201 (2015). https://doi.org/10.4028/www.scientific.net/AMM.789-790.1201
  13. R. Baboian, Corrosion tests and standards: application and interpretation, 2nd ed., p. 502, ASTM International (1995).
  14. A. A. Dastgerdi, A. Brenna, M. Ormellese, M. Pedeferri, and F. Bolzoni, Corros. Sci. 159, 108160 (2019). https://doi.org/10.1016/j.corsci.2019.108160
  15. J. W. Osborne, Pract. Assess. Res. Evaluation, 7, 2 (2000).
  16. H. Ezuber, A. Alshater, S. O. Nisar, A. Gonsalvez, and S. Aslam, Surf. Eng. Appl. Elect., 54, 73 (2018). https://doi.org/10.3103/S1068375518010039