Investment casting of nozzle guide vanes from nickel-based superalloys: part II – grain structure prediction

  • Agustin Jose Torroba
  • Ole Koeser
  • Loic Calba
  • Laura Maestro
  • Efrain Carreño-Morelli
  • Mehdi Rahimian
  • Srdjan Milenkovic
  • Ilchat Sabirov
  • Javier LLorcaEmail author
Research article


The control of grain structure, which develops during solidification processes in investment casting of nozzle guide vanes (NGVs), is a key issue for optimization of their mechanical properties. The main objective of this part of the work was to develop a simulation tool for predicting grain structure in the new generation NGVs made from MAR-M247 Ni-based superalloy. A cellular automata - finite element (CAFE) module is employed to predict the three-dimensional (3D) grain structure in the as-cast NGV. The grain structure in the critical sections of the experimentally cast NGV is carefully analyzed, the experimental results are compared with the modeling outcomes, and the model is calibrated via tuning parameters which govern grain nucleation and growth. The grain structures predicted by the calibrated model show a very good accordance with the real ones observed in the critical sections of the as-cast NGV. It is demonstrated that the calibrated CAFE model is a reliable tool for the foundry industry to predict grain structure of the as-cast NGVs with very high accuracy.


Ni-based superalloys Investment casting Nozzle guide vanes Modeling Cellular automata finite element (CAFE) module Grain structure 



This investigation was carried out in frame of the VANCAST project (EU, FP7, ERA-NET MATERA+). SM and IS acknowledge gratefully the Spanish Ministry of Economy and Competitiveness for financial support through the Ramon y Cajal fellowships.

Supplementary material

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  1. 1.
    Janssens KGF, Raabe D, Miodownik Y, Kozeschnik MA, Nestler B: Computational Materials Engineering. Elsevier Academic Press, Burlington, MA, USA; 2007.Google Scholar
  2. 2.
    Penrose O, Fife PC: Thermodynamically consistent models of phase-field type for the kinetic of phase transitions. Phys D 1990, 43: 44–62. doi:10.1016/0167–27890167–2789(90)90015-HCrossRefGoogle Scholar
  3. 3.
    Wang SL, Sekerka RF, Wheeler AA, Coriell SR, Murray BT, Braun RJ, McFadden GB: Thermodynamically-consistent phase-field models for solidification. Phys D 1993, 69: 189–200. doi:10.1016/0167–27890167–2789(93)90189–8CrossRefGoogle Scholar
  4. 4.
    Caginalp G, Fife PC: Phase field methods of interfacial boundaries. Phys Rev B 1986, 33: 7792–7794. doi:10.1103/PhysRevB.33.7792CrossRefGoogle Scholar
  5. 5.
    Lowen H, Bechoefer J, Tuckerman LS: Crystal growth at long times: critical behavior at the crossover from diffusion to kinetics-limited regimes. Phys Rev A 1992, 45: 2399–2415. doi:10.1103/PhysRevA.45.2399CrossRefGoogle Scholar
  6. 6.
    Warren JA, Boettinger WJ: Prediction of dendritic growth and microsegregation patterns in a binary alloy using the phase-field method. Acta Metall Mater 1994, 43: 689–703. doi:10.1016/0956–7151(94)00285-PCrossRefGoogle Scholar
  7. 7.
    Kobayashi R: Modeling and numerical simulations of dendritic crystal-growth. Phys D 1993, 63: 410–423. doi:10.1016/0167–2789(93)90120-PCrossRefGoogle Scholar
  8. 8.
    Wheeler AA, Murray BT, Schaefer RJ: Computation of dendrites using a phase field model. Phys D 1993, 66: 243–262. doi:10.1016/0167–27890167–2789(93)90242-SCrossRefGoogle Scholar
  9. 9.
    Wang SL, Sekerka RF: Algorithms for phase eld computations of the dendritic operating state at large su-. percoolings. J Comp Phys 1996, 127: 110–117. 10.1006/jcph.1996.0161CrossRefGoogle Scholar
  10. 10.
    Provatas N, Goldenfeld N, Dantzig J: Efficient computation of dendritic microstructures using adaptive mesh refinement. Phys Rev Lett 1998, 80: 3308–3311. doi:10.1103/PhysRevLett.80.3308CrossRefGoogle Scholar
  11. 11.
    Chen LQ, Young W: Computer simulation of the domain dynamics of a quenched system with a large number of nonconserved order parameters: the grain-growth kinetics. Phys Rev B 1994, 50: 15752–15756. doi:10.1103/PhysRevB.50.15752CrossRefGoogle Scholar
  12. 12.
    Chen LQ: A novel computer simulation technique for modeling grain growth. Scr Metall Mater 1995, 32: 115–120. doi:10.1016/S0956–716X(99)80022–3CrossRefGoogle Scholar
  13. 13.
    Steinbach I, Pezzolla F, Nestler B, Seesselberg M, Schmitz GJ, Rezende J: A phase field concept for multiphase systems. Phys D 1996, 94: 135–147. doi:10.1016/0167–27890167–2789(95)00298–7CrossRefGoogle Scholar
  14. 14.
    Nestler B, Wheeler AA: Anisotropic multi-phase-field model: interfaces and junctions. Phys Rev E 1998, 57: 2602–2609. doi:10.1103/PhysRevE.57.2602CrossRefGoogle Scholar
  15. 15.
    Kobayashi R, Warren JA, Carter WC: Vector-valued phase field model for crystallization and grain boundary formation. Phys D 1998, 119: 415–423. doi:10.1016/S0167–2789(98)00026–8CrossRefGoogle Scholar
  16. 16.
    Garcke H, Nestler B, Stoth B: On anisotropic order parameter models for multi-phase systems and their sharp interface limits. Phys D 1998, 115: 87–108. doi:10.1016/S0167–2789(97)00227–3CrossRefGoogle Scholar
  17. 17.
    Boettinger WJ, Warren JA: The phase-field method: simulation of alloy dendritic solidification during recalescence. Metall Mater Trans A 1996, 27: 657–669. doi:10.1007/BF02648953CrossRefGoogle Scholar
  18. 18.
    Hesselbarth HW, Göbel IR: Simulation of recrystallization by cellular automata. Acta Metall 1991, 39: 2135–2143. 10.1016/0956-7151(91)90183-2CrossRefGoogle Scholar
  19. 19.
    Wang W, Kermanpur A, Lee PD, McLean M: Simulation of dendritic growth in the platform region of single crystal superalloy turbine blades. J Mater Sci 2003, 38: 4385–4391. doi:10.1023/A:1026303720544CrossRefGoogle Scholar
  20. 20.
    Wang W, Lee PD, McLean M: A model of solidification microstructures in nickel based superalloys: predicting primary dendrite spacing selection. Acta Mater 2003, 51: 2971–2987. doi:10.1016/S1359–6454(03)00110–1CrossRefGoogle Scholar
  21. 21.
    Rappaz M, Gandin CA: Probabilistic modelling of microstructure formation in solidification processes. Acta Metall Mater 1993, 41: 345–360. doi:10.1016/0956–7151(93)90065-ZCrossRefGoogle Scholar
  22. 22.
    Gandin CA, Rappaz M: A coupled finite element-cellular automaton model for the prediction of dendritic grain structures in solidification processes. Acta Metall Mater 1994, 42: 2233–2246. doi:10.1016/0956–7151(94)90302–6CrossRefGoogle Scholar
  23. 23.
    Gandin CA, Rappaz M: A 3D cellular automaton algorithm for the prediction of dendritic grain growth. Acta Mater 1997, 45: 2187–2195. doi:10.1016/S1359–6454(96)00303–5CrossRefGoogle Scholar
  24. 24.
    Kurz W, Giovanola B, Trivedi R: Theory of microstructural development during rapid solidification. Acta Metall 1986, 34: 823–830. doi:10.1016/0001–6160(86)90056–8CrossRefGoogle Scholar
  25. 25.
    Gandin CA, Rappaz M, Desbiolles JL, Lopez R, Swierkosz M, Thevoz PH (1997) 3D modeling of dendritic grain structures in turbine blade investment cast parts. In: Loria EA (ed) Proceedings of the TMS Meeting. TMS, p 121Google Scholar
  26. 26.
    Seo SM, Kim IS, Jo CY, Ogi K: Grain structure prediction of Ni-base superalloy castings using the cellular automaton-finite element method. Mater Sci Eng A 2007, 449–451: 713–716. doi:10.1016/j.msea.2006.02.400CrossRefGoogle Scholar
  27. 27.
    Wang N, Liu L, Gao S, Zhao X, Huang T, Zhang J, Fu H: Simulation of grain selection during single crystal casting of a Ni-base superalloy. J Alloys Compd 2014, 586: 220–229. doi:10.1016/j.jallcom.2013.10.036CrossRefGoogle Scholar
  28. 28.
    Gandin CA, Desbiolles JL, Rappaz M, Thevoz P: A three-dimensional cellular automation-finite element model for the prediction of solidification grain structures. Metall Mater Trans A 1999, 30: 3153–3165. doi:10.1007/s11661–999–0226–2CrossRefGoogle Scholar
  29. 29.
    Lipton J, Glicksman ME, Kurz W: Equiaxed dendrite growth in alloys at small supercooling. Metall Trans A 1987, 18: 341–345. doi:10.1007/BF02825716CrossRefGoogle Scholar
  30. 30.
    ProCast user Manual & Technical Reference:Technical Reference (2007). Version 6.1. ESI software, France; 2007.Google Scholar
  31. 31.
    Qingyan X, Baicheng L, Dong P, Jing Y: Progress on modeling and simulation of directional solidification of superalloy turbine blade casting. Res Develop 2012, 2: 69–77.Google Scholar

Copyright information

© Torroba et al.; licensee Springer. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.

Authors and Affiliations

  • Agustin Jose Torroba
    • 1
  • Ole Koeser
    • 2
  • Loic Calba
    • 2
  • Laura Maestro
    • 3
  • Efrain Carreño-Morelli
    • 1
  • Mehdi Rahimian
    • 4
  • Srdjan Milenkovic
    • 4
  • Ilchat Sabirov
    • 4
  • Javier LLorca
    • 4
    • 5
    Email author
  1. 1.University of Applied Sciences and Arts Western SwitzerlandSionSwitzerland
  2. 2.CALCOM-ESILausanneSwitzerland
  3. 3.Precicast BilbaoBarakaldoSpain
  4. 4.IMDEA Materials InstituteGetafeSpain
  5. 5.Department of Materials SciencePolytechnic University of MadridMadridSpain

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