Journal of Powder Materials (한국분말재료학회지)

The Korean Powder Metallurgy & Materials Institute (한국분말재료학회 (*구 분말야금학회))
권호 표지
DOI QR코드

DOI QR Code

Machine Learning-based Data Analysis for Designing High-strength Nb-based Superalloys

고강도 Nb기 초내열 합금 설계를 위한 기계학습 기반 데이터 분석

  • Eunho Ma (Department of Materials Science & Engineering, Seoul National University of Science & Technology) ;
  • Suwon Park (School of Materials Science & Engineering, Kookmin University) ;
  • Hyunjoo Choi (School of Materials Science & Engineering, Kookmin University) ;
  • Byoungchul Hwang (Department of Materials Science & Engineering, Seoul National University of Science & Technology) ;
  • Jongmin Byun (Department of Materials Science & Engineering, Seoul National University of Science & Technology)
  • 마은호 (서울과학기술대학교 신소재공학과) ;
  • 박수원 (국민대학교 신소재공학부) ;
  • 최현주 (국민대학교 신소재공학부) ;
  • 황병철 (서울과학기술대학교 신소재공학과) ;
  • 변종민 (서울과학기술대학교 신소재공학과)
  • Received : 2023.06.13
  • Accepted : 2023.06.26
  • Published : 2023.06.28
Download PDF
⟨ Previous Next ⟩

Abstract

Machine learning-based data analysis approaches have been employed to overcome the limitations in accurately analyzing data and to predict the results of the design of Nb-based superalloys. In this study, a database containing the composition of the alloying elements and their room-temperature tensile strengths was prepared based on a previous study. After computing the correlation between the tensile strength at room temperature and the composition, a material science analysis was conducted on the elements with high correlation coefficients. These alloying elements were found to have a significant effect on the variation in the tensile strength of Nb-based alloys at room temperature. Through this process, a model was derived to predict the properties using four machine learning algorithms. The Bayesian ridge regression algorithm proved to be the optimal model when Y, Sc, W, Cr, Mo, Sn, and Ti were used as input features. This study demonstrates the successful application of machine learning techniques to effectively analyze data and predict outcomes, thereby providing valuable insights into the design of Nb-based superalloys.

Keywords

Acknowledgement

본 연구는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(NRF-2022M3H4A1A04085307, NRF-2022R1A4A5033917).

References

  1. J. Wadsworth, T. G. Nieh and J. J. Stephens: Int. Mater. Rev., 33 (1988) 131.
  2. S. Zhao, X. Xie, G. D. Smith and S. J. Patel: Mater. Sci. Eng. A, 355 (2003) 96. 
  3. E. Akca and A. Gursel: Period. Eng. Nat. Sci., 3 (2015). 
  4. M. R. Jackson, B. P. Bewlay, R. G. Rowe, D. W. Skelly, and H. A. Lipsitt: JOM, 48 (1996) 39. 
  5. B. P. Bewlay, M. R. Jackson, P. R. Subramanian and J.-C. Zhao: Metall. Mater. Trans. A, 34 (2003) 2043. 
  6. X. He, X. Zhang, Y. Li and J. Huang: J. Alloys Compd., 551 (2013) 578. 
  7. R. Ding, H. Jiao and I. P. Jones: Mater. Sci. Eng. A, 483 (2008) 199. 
  8. J. Sha, H. Hirai, H. Ueno, T. Tabaru, A. Kitahara and S. Hanada: Metall. Mater. Trans. A, 34 (2003) 2861. 
  9. J. H. Kim, T. Tabaru and H. Hirai: Met. Mater. Int., 8 (2002) 233. 
  10. K. Zelenitsas and P. Tsakiropoulos: Mater. Sci. Eng. A, 416 (2006) 269. 
  11. J. Liu, L. Chang, H. Liu, Y. Li, H. Yang and J. Ruan: Mater. Sci. Eng. C, 71 (2017) 512. 
  12. Y. Kang, F. Guo and M. Li: Mater. Sci. Eng. A, 760 (2019) 118. 
  13. Q. Wang, X. Wang, R. Chen, Z. Zhou, Y. Su and H. Fu: J. Alloys Compd., 892 (2021) 162127. 
  14. Q. Wang, C, Zhou, S. Wang and R. Chen: J. Mater. Res. Technol., 9 (2020) 15585. 
  15. S. Zhang, L. Jia, Y. Guo, B. Kong, C. Zhou and H. Zhang: Corros. Sci., 163 (2020) 108294. 
  16. Y. Guo, L. Jia, H. Zhang, F. Zhang and H. Zhang: Intermetallics, 101 (2018) 165. 
  17. S. Majumdar: Int. J. Refract. Met. Hard Mater., 78 (2019) 76. 
  18. X. Zhang, X. He, C. Fan, Y. Li, G. Song, Y. Sun and J. Huang: Int. J. Refract. Met. Hard Mater., 41 (2013) 185. 
  19. G. Sun, L. Jia, Y. Wang, Z. Jin and H. Zhang: Prog. Nat. Sci., 32 (2022) 248. 
  20. D. Chen, Q. Wang, R. Chen, Z. Zhou, Y. Su and H. Fu: J. Alloys Compd., 895 (2022) 162707. 
  21. N. Sekido, Y. Kimura, S. Miura, F. Wei and Y. Mishima: J. Alloys Compd., 425 (2006) 223. 
  22. L. Su, L. Jia, Y. Feng, H. Zhang, S. Yuan and H. Zhang: Mater. Sci. Eng. A, 560 (2013) 672. 
  23. C. Ye, H. Li, L. Jia, Z. Jin, G. Sun and H. Zhang: Int. J. Refract. Met. Hard Mater., 94 (2021) 105378. 
  24. B. Guo and X. Guo: Mater. Sci. Eng. A, 617 (2014) 39. 
  25. M. S. El-Genk and J. M. Tournier: J. Nucl. Mater., 340 (2005) 93. 
  26. L. Duan, P. Feng and X. Dang: Powder Metall. Technol., 39 (2021) 223. 
  27. Y. Tan, C. L. Ma, R. Tanaka and J.-M. Yang: Mater. Trans., 47 (2006) 1527. 
  28. L. Zhang and X. Guo: Rare Met., 36 (2017) 174. 
  29. X. Chong, S. Shang, A. M. Krajewski, J. D. Shimanek, W. Du, Y. Wang, J. Feng, D. Shin, A. M. Beese and Z. Liu: J. Phys.: Condens. Matter, 33 (2021) 295702. 
  30. J. Peng, R. Pillai, M. Romedenne, B. A. Pint, B. Muralidharan, J. A. Haynes and D. Shin: NPJ Mater. Degrad., 5 (2021) 41. 
  31. J. Peng, Y. Yamamoto, M. P. Brady, S. Lee, J. A. Haynes and D. Shin: JOM, 73 (2021) 164. 
  32. D. N. Reshef, Y. A. Reshef, H. F. Finucane, S. R. Grossman, G. Mcvean, P. J. Turnbaugh, E. S. Lander, M. Mitzenmacher and P. C. Sabeti: Science, 334 (2011) 1518. 
  33. D. D. Mari and S. Kotz: Correlation and Dependence, Imperial College Press, London, (2001). 
  34. T. K. Ho: IEEE T. Pattern Anal., 20 (1998) 832. 
  35. T. Conver and P. Hart: IEEE T. Inform. Theory, 13 (1967) 21. 
  36. D. J. C. Mackay: Neural Comput., 4 (1992) 415. 
  37. M. A. Hearst, S. T. Dumains, E. Osuna, J. Platt and B. Scholkopf: IEEE Intel. Syst. App., 13 (1998) 18. 
  38. K. P. Murphy: Machine Learning: A Probabilistic Perspective, The MIT Press, Cambridge, London (2012) 60. 
  39. T. Hastie and R. Tibshirani: Adv. Neural Inf. Process. Syst., 8 (1995) 409. 
  40. V. Cherkassky and Y. Ma: Neural Netw., 17 (2004) 113. 
  41. S. Lee, J. Peng, A. Williams and D. Shin: J. Open Source Softw., 5 (2020) 1656. 
  42. J. Peng, S. Lee, A. Williams, J. A. Haynes and D. Shin: Calphad, 68 (2020) 101733. 
  43. J. P. Hankwitz, C. Leoford, C. Rock, S. O'Dell and T. J. Horn: Materials, 14 (2021) 5536. 
  44. S. Knittel, S. Mathieu and M. Vilasi: Intermetallics, 47 (2014) 36. 
  45. N. Vellios and P. Tsakiropoulos: Intermetallics, 15 (2007) 1518. 
  46. L. Cretegny, B. P. Bewlay, A. M. Retter and M. R. Jackson: MRS Online Proc. Libr., 842 (2005) 1. 
  47. Y. Huang, L. Jia, Z. Jin, B. Kong, Y. Guo, J. Sha and H. Zhang: Mater. Des., 160 (2018) 671.