Modeling Precipitation Kinetics During Heat Treatment with Calphad-Based Tools


Sophisticated precipitation reaction models combined with well-developed CALPHAD databases provide an efficient way to tailor precipitate microstructures that maximize strengthening via the optimization of alloy chemistries and heat treatment schedules. The success of the CALPHAD approach relies on the capability to provide fundamental phase equilibrium and phase transformation information in materials of industrial relevance taking into consideration composition and temperature variation. The newly developed TC-PRISMA program is described. The effect of growth modes, alloy chemistries, and cooling profiles on the formation of multimodal microstructures has been examined in order to understand the underlying thermodynamics and kinetics. Practical issues that are critical to the accuracy and applicability of the current simulations, such as modifications that overcome mean field approximations, compatibility between CALPHAD databases, and selections of key parameters (particularly interfacial energy and nucleation site densities), are also addressed.

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  1. 1.

    Z.-K. Liu, A materials Research Paradigm Driven by Computation, JOM, 2009, 61(10), p 18–20 [in English]

    Article  Google Scholar 

  2. 2.

    L. Kaufman and H. Bernstein, Computer Calculation of Phase Diagrams, Academic Press, New York, 1970

    Google Scholar 

  3. 3.

    N. Saunders and A.P. Miodownik, CALPHAD (Calculation of Phase Diagrams): A Comperhensive Guide, Elsevier Science Ltd., New York, 1998

    Google Scholar 

  4. 4.

    Q. Chen, H.J. Jou, and G. Sterner. TC-PRISMA User’s Guide and Examples. Thermo-Calc Software AB, Stockholm, Sweden,, 2011

  5. 5.

    J.O. Andersson, T. Helander, L.H. Hoglund, P.F. Shi, and B. Sundman, THERMO-CALC & DICTRA, Computational Tools for Materials Science, Calphad, 2002, 26(2), p 273–312 [in English]

    Article  Google Scholar 

  6. 6.

    A. Borgenstam, L. Höglund, J. Ågren, and A. Engström, DICTRA, a Tool for Simulation of Diffusional Transformations in Alloys, J. Phase Equilib., 2000, 21(3), p 269–280 [in English]

    Article  Google Scholar 

  7. 7.

    J.S. Langer and A.J. Schwartz, Kinetics of Nucleation in Near-Critical Fluids, Phys. Rev. A, 1980, 21(3), p 948–958 [in English]

    Article  Google Scholar 

  8. 8.

    R. Kampmann and R. Wagner, Kinetics of Precipitation in Metastable Binary Alloys—Theory and Application, Decomposition of Alloys: the Early Stages, P. Haasen, V. Gerold, R. Wagner, and M.F. Ashby, Ed., Pergamon, Oxford, 1984, p 91–103

    Google Scholar 

  9. 9.

    Q. Chen, J. Jeppsson, and J. Ågren, Analytical Treatment of Diffusion During Precipitate Growth in Multicomponent Systems, Acta Mater., 2008, 56(8), p 1890–1896 [in English]

    Article  Google Scholar 

  10. 10.

    H.J. Jou, P.W. Voorhees, and G.B. Olson. Computer Simulations for the Prediction of Microstructure/Property Variation in Aeroturbine Disks, Superalloy 2004, K.A. Green, T.M. Pollock, H. Harada, T.E. Howson, R.C. Reed, J.J. Schirra, and S.Walston Ed., Sep 2004 (Seven Springs, PA), TMS, 2004, p 877–886

  11. 11.

    Thermo-Calc Software AB, Stockholm, Sweden,

  12. 12.

    T. Rojhirunsakool, S. Meher, J.Y. Hwang, S. Nag, J. Tiley, and R. Banerjee, Influence of Composition on Monomodal Versus Multimodal γ’ Precipitation in Ni-Al-Cr Alloys, J. Mater. Sci., 2013, 48(2), p 825–831 [in English]

    Article  Google Scholar 

  13. 13.

    E. Balikci and D. Erdeniz, Multimodal Precipitation in the Superalloy IN738LC, Metall. Mater. Trans. A, 2010, 41(6), p 1391–1398 [in English]

    Article  Google Scholar 

  14. 14.

    R. Radis, M. Schaffer, M. Albu, G. Kothleitner, P. Pölt, and E. Kozeschnik. Evolution of size and morphology of γ’ precipitates in Udimet 720 Li during continuous cooling, Superalloys 2008, R.C. Reed, K.A. Green, P. Caron, T.P. Gabb, M. Fahrmann, E.S. Huron, and S.A. Woodard Ed., Sep 14-18, 2008 (Seven Springs, PA), TMS, 2008, p 829–836

  15. 15.

    R. Radis, M. Schaffer, M. Albu, G. Kothleitner, P. Pölt, and E. Kozeschnik, Multimodal Size Distributions of γ’ Precipitates During Continuous Cooling of UDIMET 720 Li, Acta Mater., 2009, 57(19), p 5739–5747 [in English]

    Article  Google Scholar 

  16. 16.

    P.M. Sarosi, B. Wang, J.P. Simmons, Y. Wang, and M.J. Mills, Formation of Multimodal Size Distributions of γ’ in a Nickel-Base Superalloy During Interrupted Continuous Cooling, Scr. Mater., 2007, 57(8), p 767–770 [in English]

    Article  Google Scholar 

  17. 17.

    Y.H. Wen, J.P. Simmons, C. Shen, C. Woodward, and Y. Wang, Phase-Field Modeling of Bimodal Particle Size Distributions During Continuous Cooling, Acta Mater., 2003, 51(4), p 1123–1132 [in English]

    Article  Google Scholar 

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Correspondence to Paul Mason.

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This article is an invited paper selected from presentations at the 27th ASM Heat Treating Society Conference, held September 16-18, 2013, in Indianapolis, IN, and has been expanded from the original presentation.

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Chen, Q., Wu, K., Sterner, G. et al. Modeling Precipitation Kinetics During Heat Treatment with Calphad-Based Tools. J. of Materi Eng and Perform 23, 4193–4196 (2014).

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  • computational materials development
  • heat treatment
  • modeling and simulation
  • steel
  • superalloys