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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
R.L. Miller: Metall. Mater. Trans. B, 1972, vol. 3, pp. 905–12.
Z.H. Cai, H. Ding, R.D.K. Misra, and S.Q. Qiguan: Mater. Sci. Eng. A, 2016, vol. 652, pp. 205–11.
S. Lee and B.C. De Cooman: Metall. Mater. Trans. A, 2013, vol. 44, pp. 5018–24.
R. Rana, P.J. Gibbs, E. De Moor, J.G. Speer, and D.K. Matlock: Steel Res. Int., 2015, vol. 86, pp. 1139–50.
J. Hu, W. Cao, C. Huang, C. Wang, H. Dong, and J. Li: ISIJ Int., 2015, vol. 55, pp. 2229–36.
K. Sugimoto, H. Tanino, and J. Kobayashi: Steel Res. Int., 2015, vol. 86, pp. 1151–60.
X. Zhao, Y. Shen, L. Qiu, Y. Liu, X. Sun, and L. Zuo: Materials, 2014, vol. 7, pp. 7891–7906.
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.
Z.H. Cai, H. Ding, X. Xue, and Q.B. Xin: J. Mater. Eng. Perform., 2012, vol. 23, pp. 1131–37.
A. Grajcar, P. Skrzypczyk, and D. Wozniak: Arch. Metall. Mater., 2014, vol. 59, pp. 1691–97.
M. Cai, Z. Li, Q. Chao, and P.D. Hodgson: Metall. Mater. Trans. A, 2014, vol. 45, pp. 5624–34.
D.W. Suh and S.J. Kim: Scripta Mater., 2016, vol. 126, pp. 63–67.
S. Kang, E. De Moor, and J.G. Speer: Metall. Mater. Trans. A, 2015, vol. 46, pp. 1005–11
H. Kamoutsi, E. Gioti, G.N. Haidemenopoulos, Z. Cai, and H. Ding: Metall. Mater. Trans. A, 2015, vol. 46, pp. 4841–46.
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.
H.L. Lukas, S.G. Fries, and B. Sundman: Computational Thermodynamics the Calphad Method, 1st ed., Cambridge University Press, Cambridge, 2007.
J.O. Andersson, T. Helander, L. Höglund, P. Shi, B. Sundman: Calphad, 2002, vol. 26, pp. 273–312.
K. Andrews: J. Iron Steel Inst., 1965, vol. 203, pp. 721–27.
D. Koistinen and R. Marburger: Acta Metall., 1959, vol. 7, pp. 59–60.
Y. Censor: Appl. Math. Optim., 1977, vol. 4, pp. 41–59.
O. Grodzevich and O. Romanko: Proc. First Fields-MITACS Ind. Probl. Workshop, Toronto, Canada, 2006, pp. 89–101.
W. Jakob and C. Blume: Algorithms, 2014, vol. 7, pp. 166–85.
I.Y. Kim and O.L. de Weck: Struct. Multidiscip. Optim., 2006, vol. 31, pp. 105–16.
R.W. Saaty: Math. Model., 1987, vol. 9, pp. 161–76.
H.A. Taha: Operations Research: An Introduction, 8th ed., Pearson, Upper Sadle River, NJ, 2007, pp. 490–500.
T.L. Saaty: Int. J. Serv. Sci., 2008, vol. 1, pp. 83–98.
H. Huang, O. Matsumura, and T. Furukawa: Mater. Sci. Technol., 1994, vol. 10, pp. 621–26.
E. Gioti, H. Kamoutsi, and G.N. Haidemenopoulos: Proc. 2nd Int. Conf. High Manganese Steel, Aachen, Germany, 2014, pp. 337–40.
S. Chen, Y. Bao, H. Dong, and W. Cao: Adv. Mater. Res., 2014, vol. 1063, pp. 3–6.
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.
Manuscript submitted October 7, 2016.
About this article
Cite this article
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
- Pareto Optimal Solution
- Intercritical Annealing
- Alloy Design