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Alloy Design Based on Computational Thermodynamics and Multi-objective Optimization: The Case of Medium-Mn Steels

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Abstract

A new alloy design methodology is presented for the identification of alloy compositions, which exhibit process windows (PWs) satisfying specific design objectives and optimized for overall performance. The methodology is applied to the design of medium-Mn steels containing Al and/or Ni. By implementing computational alloy thermodynamics, a large composition space was investigated systematically to map the fraction and stability of retained austenite as a function of intercritical annealing temperature. Alloys exhibiting PWs, i.e., an intercritical annealing range, which when applied satisfies the given design objectives, were identified. A multi-objective optimization method, involving Pareto optimality, was then applied to identify a list of optimum alloy compositions, which maximized retained austenite amount and stability, as well as intercritical annealing temperature, while minimized overall alloy content. A heuristic approach was finally employed in order to rank the optimum alloys. The methodology provided a final short list of alloy compositions and associated PWs ranked according to their overall performance. The proposed methodology could be the first step in the process of computational alloy design of medium-Mn steels or other alloy systems.

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References

  1. R.L. Miller: Metall. Mater. Trans. B, 1972, vol. 3, pp. 905–12.

    Article  Google Scholar 

  2. Z.H. Cai, H. Ding, R.D.K. Misra, and S.Q. Qiguan: Mater. Sci. Eng. A, 2016, vol. 652, pp. 205–11.

    Article  Google Scholar 

  3. S. Lee and B.C. De Cooman: Metall. Mater. Trans. A, 2013, vol. 44, pp. 5018–24.

    Article  Google Scholar 

  4. R. Rana, P.J. Gibbs, E. De Moor, J.G. Speer, and D.K. Matlock: Steel Res. Int., 2015, vol. 86, pp. 1139–50.

    Article  Google Scholar 

  5. J. Hu, W. Cao, C. Huang, C. Wang, H. Dong, and J. Li: ISIJ Int., 2015, vol. 55, pp. 2229–36.

    Article  Google Scholar 

  6. K. Sugimoto, H. Tanino, and J. Kobayashi: Steel Res. Int., 2015, vol. 86, pp. 1151–60.

    Article  Google Scholar 

  7. X. Zhao, Y. Shen, L. Qiu, Y. Liu, X. Sun, and L. Zuo: Materials, 2014, vol. 7, pp. 7891–7906.

    Article  Google Scholar 

  8. Z.H. Cai, H. Ding, X. Xue, J. Jiang, Q.B. Xin, and R.D.K. Misra: Scripta Mater., 2013, vol. 68, pp. 865–68.

    Article  Google Scholar 

  9. Z.H. Cai, H. Ding, X. Xue, and Q.B. Xin: J. Mater. Eng. Perform., 2012, vol. 23, pp. 1131–37.

    Article  Google Scholar 

  10. A. Grajcar, P. Skrzypczyk, and D. Wozniak: Arch. Metall. Mater., 2014, vol. 59, pp. 1691–97.

    Google Scholar 

  11. M. Cai, Z. Li, Q. Chao, and P.D. Hodgson: Metall. Mater. Trans. A, 2014, vol. 45, pp. 5624–34.

    Article  Google Scholar 

  12. D.W. Suh and S.J. Kim: Scripta Mater., 2016, vol. 126, pp. 63–67.

    Article  Google Scholar 

  13. S. Kang, E. De Moor, and J.G. Speer: Metall. Mater. Trans. A, 2015, vol. 46, pp. 1005–11

    Article  Google Scholar 

  14. H. Kamoutsi, E. Gioti, G.N. Haidemenopoulos, Z. Cai, and H. Ding: Metall. Mater. Trans. A, 2015, vol. 46, pp. 4841–46.

    Article  Google Scholar 

  15. P.J. Gibbs, E. De Moor, M.J. Merwin, B. Clausen, J.G. Speer, and D.K. Matlock: Metall. Mater. Trans. A, 2011, vol. 42, pp. 3691–3702.

    Article  Google Scholar 

  16. H.L. Lukas, S.G. Fries, and B. Sundman: Computational Thermodynamics the Calphad Method, 1st ed., Cambridge University Press, Cambridge, 2007.

    Book  Google Scholar 

  17. J.O. Andersson, T. Helander, L. Höglund, P. Shi, B. Sundman: Calphad, 2002, vol. 26, pp. 273–312.

    Article  Google Scholar 

  18. K. Andrews: J. Iron Steel Inst., 1965, vol. 203, pp. 721–27.

    Google Scholar 

  19. D. Koistinen and R. Marburger: Acta Metall., 1959, vol. 7, pp. 59–60.

    Article  Google Scholar 

  20. Y. Censor: Appl. Math. Optim., 1977, vol. 4, pp. 41–59.

    Article  Google Scholar 

  21. O. Grodzevich and O. Romanko: Proc. First Fields-MITACS Ind. Probl. Workshop, Toronto, Canada, 2006, pp. 89–101.

  22. W. Jakob and C. Blume: Algorithms, 2014, vol. 7, pp. 166–85.

    Article  Google Scholar 

  23. I.Y. Kim and O.L. de Weck: Struct. Multidiscip. Optim., 2006, vol. 31, pp. 105–16.

    Article  Google Scholar 

  24. R.W. Saaty: Math. Model., 1987, vol. 9, pp. 161–76.

    Article  Google Scholar 

  25. H.A. Taha: Operations Research: An Introduction, 8th ed., Pearson, Upper Sadle River, NJ, 2007, pp. 490–500.

    Google Scholar 

  26. T.L. Saaty: Int. J. Serv. Sci., 2008, vol. 1, pp. 83–98.

    Google Scholar 

  27. H. Huang, O. Matsumura, and T. Furukawa: Mater. Sci. Technol., 1994, vol. 10, pp. 621–26.

    Article  Google Scholar 

  28. E. Gioti, H. Kamoutsi, and G.N. Haidemenopoulos: Proc. 2nd Int. Conf. High Manganese Steel, Aachen, Germany, 2014, pp. 337–40.

  29. S. Chen, Y. Bao, H. Dong, and W. Cao: Adv. Mater. Res., 2014, vol. 1063, pp. 3–6.

    Article  Google Scholar 

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Acknowledgments

The authors are grateful to Dr. A. Alexandridis for his assistance with multi-objective optimization procedures and to Professor N. Aravas for the careful reading of the manuscript. The assistance of Dr. H. Kamoutsi with support on Thermo-Calc, as well as Dr. P. I. Sarafoglou and Mrs. M. I. T. Tzini with helpful discussions on mapping procedures are greatly appreciated. The work has been performed in the framework of IKYDA Program on the design rules for third generation advanced high-strength steels, as collaboration between University of Thessaly and RWTH Aachen University.

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Correspondence to Gregory N. Haidemenopoulos.

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Manuscript submitted October 7, 2016.

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Aristeidakis, J.S., Haidemenopoulos, G.N. Alloy Design Based on Computational Thermodynamics and Multi-objective Optimization: The Case of Medium-Mn Steels. Metall Mater Trans A 48, 2584–2602 (2017). https://doi.org/10.1007/s11661-017-4010-4

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