journal article·2014-12

Modeling Precipitation Kinetics During Heat Treatment with Calphad-Based Tools

Qing Chen, Kaisheng Wu, Gustaf Sterner, Paul Mason

Publisher page

Abstract

Abstract Traditionally, chemists and metallurgists have used phase diagrams and tables of thermodynamic data for understanding and making predictions related to alloy development and process improvement, such as heat treatment. However, for complex, multi-component systems that extend beyond ternaries, such an approach can be limiting. Computational thermodynamics and specifically the CALPHAD approach allows for the prediction of the thermodynamic properties and phase equilibria of multi-component, multi-phase systems based on mathematical models that describe the Gibbs energy as a function of temperature, pressure and composition for each individual phase in a system. Parameters in the numerical models capture the composition and temperature dependence in binary and ternary systems and are optimized in order to best correspond to the experimental data available and are stored in databases which are then used in conjunction with computer codes whereby extrapolations can be made into the multi-component systems of interest. Additionally, the CALPHAD method can also be extended to model atomic mobilities and diffusivities in a similar way. Thermodynamic and kinetic databases are developed through a hybrid of experiments, first-principles calculations and CALPHAD modelling. By combining the thermodynamic and mobility databases, kinetic reactions during solidification and subsequent heat treatment processes can then be simulated. Through the use of such simulations it is possible to optimize alloy compositions and predict optimal solidification processes and solution heat treatment temperature ranges without performing many time-consuming and costly experiments.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: High Temperature Alloys and Creep · Metallurgical Processes and Thermodynamics · Microstructure and Mechanical Properties of Steels

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (17)

Show source-provided references
1. Z.-K. Liu, A materials Research Paradigm Driven by Computation, JOM, 2009, 61(10), p 18–20 [in English]10.1007/s11837-009-0143-2 ↗
2. L. Kaufman and H. Bernstein, Computer Calculation of Phase Diagrams, Academic Press, New York, 1970
3. N. Saunders and A.P. Miodownik, CALPHAD (Calculation of Phase Diagrams): A Comperhensive Guide, Elsevier Science Ltd., New York, 1998
4. Q. Chen, H.J. Jou, and G. Sterner. TC-PRISMA User’s Guide and Examples. Thermo-Calc Software AB, Stockholm, Sweden, http://www.thermocalc.se/Library.htm , 2011
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]10.1016/S0364-5916(02)00037-8 ↗
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]10.1361/105497100770340057 ↗
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]10.1103/PhysRevA.21.948 ↗
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 91–10310.1016/B978-0-08-031651-2.50018-5 ↗
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]10.1016/j.actamat.2007.12.037 ↗
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 877–88610.7449/2004/Superalloys_2004_877_886 ↗
11. Thermo-Calc Software AB, Stockholm, Sweden, http://www.thermocalc.se/
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]10.1007/s10853-012-6802-7 ↗
13. E. Balikci and D. Erdeniz, Multimodal Precipitation in the Superalloy IN738LC, Metall. Mater. Trans. A, 2010, 41(6), p 1391–1398 [in English]10.1007/s11661-010-0241-3 ↗
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 829–83610.7449/2008/Superalloys_2008_829_836 ↗
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]10.1016/j.actamat.2009.08.002 ↗
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]10.1016/j.scriptamat.2007.06.014 ↗
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]10.1016/S1359-6454(02)00516-5 ↗