Modeling of grain growth kinetics in complexly alloyed austenite

A.A. Vasilyev ORCID logo , S.F. Sokolov, D.F. Sokolov, N.G. Kolbasnikov show affiliations and emails
Received 03 September 2019; Accepted 23 September 2019;
Citation: A.A. Vasilyev, S.F. Sokolov, D.F. Sokolov, N.G. Kolbasnikov. Modeling of grain growth kinetics in complexly alloyed austenite. Lett. Mater., 2019, 9(4) 419-423
BibTex   https://doi.org/10.22226/2410-3535-2019-4-419-423

Abstract

A quantitative model is presented to describe the kinetics of grain growth in complexly alloyed austenite. The average relative error in calculating grain size is about 11% that is comparable to the measurement error.A quantitative model is presented to describe the kinetics of grain growth in complexly alloyed austenite. The model assumes that the activation energy of grain growth is proportional to the activation energy of bulk self-diffusion, which is calculated as a function of the chemical composition of the solid solution using the previously obtained formula. The empirical parameters of the model are determined on the basis of experimental data on the kinetics of isothermal grain growth in steels with the chemical composition varying in a wide range: C (0.05 ÷ 0.32), Mn (0.30 ÷1.88), Si (0.01÷ 0.29), Ni (0.0 ÷ 4.0), Cr (0.0 ÷ 2.0), Mo (0.0 ÷ 0.5), Nb (0.00 ÷ 0.05) available in the literature. The model allows one to obtain good agreement with the experiment for the considered steels in which the minimum (~ 79.7 kJ / mol) and maximum (~ 243.7 kJ / mol) values of the activation energy of grain growth differ by 3 times. The average absolute value of the relative error in calculating the grain size is about 11 % that is comparable to the measurement error. Taking into account the influence of the chemical composition on the activation energy of grain growth, implemented in the developed model, it is possible to obtain agreement with the experiment without accounting for the solid-solution pinning of moving boundaries (the solute drag effect) requires a large number of additional empirical parameters (two exponential parameters for each alloying element). This result deserves further consideration from the physical viewpoint and verification on both simple carbon steels and steels with various quantities of Mn, Mo and Nb, which, according to literature, exert the strongest solute drag effect.

References (25)

1. C. M. Sellars, J. A. Whiteman. Metal Sci. 13 (3-4), 187 (1979). Crossref
2. P. A. Manohar, D. P. Dunne, T. Chandra, C. R. Killmore. ISIJ Int. 36 (2), 194 (1996). Crossref
3. S. Uhm, J. Moon, C. Lee, J. Yoon, B. Lee. ISIJ Int. 44 (7), 1230 (2004). Crossref
4. M. Shome, O. Gupta, O. Mohanty. Scr. Mater. 50 (7), 1007 (2004). Crossref
5. S.-J. Lee, Y.-K. Lee. Mater. Des. 29 (9), 1840 (2008). Crossref
6. Y. Xu, D. Tang, Y. Song, X. Pan. Mater. Des. 36, 275 (2012). Crossref
7. G.-W. Yang, X.-J. Sun, Q.-L. Yong, Z.-D. Li, X.-X. Li. J. Iron Steel Res. Int. 21 (8), 757 (2014). Crossref
8. I. Andersen, Ø. Grong. Acta Metall. Mater. 43 (7), 2673 (1995). Crossref
9. M. Militzer, T. R. Meadowcroft, Е. B. Hawbolt, А. Giumelli. Metall. Mater. Trans. A. 27 (11), 3399 (1996). Crossref
10. J. Moon, J. Lee, C. Lee. Mater. Sci. Eng. A. 459 (1-2), 40 (2007). Crossref
11. S. Sarkar, A. Moreau, М. Militzer, W. J. Poole. Metall. Mater. Trans. A. 39 (4), 897 (2008). Crossref
12. M. Maalekian, R. Radis, M. Militzer, A. Moreau, W. J. Poole. Acta Materialia. 60 (3), 1015 (2012). Crossref
13. Н. Pous-Romero, I. Lonardelli, D. Cogswell, H. K. D. H. Bhadeshia. Mater. Sci. Eng. A. 567, 72 (2013). Crossref
14. D. Dong, F. Chen, Z. Cui. J. Mater. Eng. Perform. 25 (1), 152 (2016). Crossref
15. B. Jiang, M. Wu, H. Sun, Z. Wang, Z. Zhao, Y. Z. Liu. Met. Mater. Int. 24 (1), 15 (2018). Crossref
16. Z.-y. Liu, Y.-p. Bao, M. Wang, X. Li, F.-z. Zeng. Int. J. Miner. Metall. Mater. 26 (3), 282 (2019). Crossref
17. A. Graux, S. Cazottes, D. De Castro, D. San Martin, C. Capdevila, J. M. Cabrera et al. Materialia. 5, 100233 (2019). Crossref
18. F. J. Humphreys, M. Hatherly. Recrystallization and related annealing phenomena, 2nd ed. Oxford, United Kingdom, Pergamon Press Ltd (2004) 658 p. Crossref
19. Ø. Grong. Metallurgical modeling of welding (Materials modelling series), 2nd ed. The Institute of Materials (1997) 605p.
20. J. E. Burke, D. Turnbull. Prog. Metal Phys. 3, 220 (1952). Crossref
21. J. W. Cahn. Acta Metall. 10 (9), 768 (1962). Crossref
22. E. Hersent, K. Marthinsen, E. Nes. Metall. Mater. Trans. A. 44 (7), 3364 (2013). Crossref
23. E. Hersent, K. Marthinsen, E. Nes. Model. Numer. Sim. Mater. Sci. 4, 8 (2014). Crossref
24. A. A. Vasil’ev, S. F. Sokolov, N. G. Kolbasnikov, D. F. Sokolov. Phys. Solid State. 53 (11), 2194 (2011). Crossref
25. А. К. Giumelli, M. Militzer, Е. B. Hawbolt. ISIJ Int. 39 (3), 271 (1999). Crossref

Similar papers

Funding

1. Russian Science Foundation - Project No.19-19-00281