Advertisement

Transactions of the Indian Institute of Metals

, Volume 71, Issue 11, pp 2657–2665 | Cite as

Size and Morphology of Gas Porosity in Castings—Insights into Computational Experiments Using a Cellular Automaton Model

  • S. Savithri
  • Roschen Sasikumar
Technical Paper

Abstract

Aluminium castings are known to be prone to micro-porosity formation which appears as fine porosity in the inter-dendritic and inter-granular regions of castings. The size, distribution and morphology of such pores significantly affect mechanical and fatigue properties of castings. We use a cellular automaton simulation model as a virtual experimental set-up to study growth of gas bubbles in solidifying aluminium castings. The model assumes that gas porosity originates from pre-existing micro-bubbles that grow by diffusion of hydrogen from the solid–liquid interfaces into the bubbles. The major factors that limit the growth of the bubbles are the finite time available for the diffusion of hydrogen and the space constraint imposed by the growing solid. While the diffusion limitation to pore growth has been studied well, the effect of the space constraint has not received much attention. Our cellular automaton model with growth rules specially adapted for bubble growth tracks the solid–liquid and bubble–liquid interfaces explicitly on a fine grid. Numerical experiments are performed with a eutectic Al–Si alloy solidified with different grain sizes and solidification rates. The micro-structural environment in which a pre-existing bubble finds itself is seen to be the most critical factor that determines the final size and morphology of porosity.

Keywords

Micro-porosity Aluminium castings Cellular automata Computer simulation 

References

  1. 1.
    Major J F AFS Trans. 105 (1998) 901.Google Scholar
  2. 2.
    Roschen S, and Rohit T, Unpublished work.Google Scholar
  3. 3.
    Asim T, and Michael J Walker, Unpublished work.Google Scholar
  4. 4.
    Roschen S, Markus R, Savithri S, and Hans E E, Z. Metallkd, 92 (2001) 2.Google Scholar
  5. 5.
    Roschen S, Michael J W, Savithri S, and Suresh S, Modelling Simul. Mater. Sci. Eng. 16 (2008) 1.CrossRefGoogle Scholar
  6. 6.
    Zhu J D, Cockroft S L, and Maijer D M, Metall Mater Trans A 37A (2006) 1075.Google Scholar
  7. 7.
    Carlson K D, Lin Z, Beckermann C, Mazurkevich G and Schneider M C, Modeling of casting, welding and advanced solidification processesXI, (eds) Charles-André G, and Michel B, TMS (The Minerals, Metals & Materials Society), (2006) p 627.Google Scholar
  8. 8.
    Carlson K D, Lin Z, and Beckermann C, Metall Mater Trans B 38B (2007) 541.CrossRefGoogle Scholar
  9. 9.
    Jana S, Jakumeit J, and Jouani M Y, in Modeling of Casting, Welding and Advanced Solidificatio ProcessesXII, (eds) Cockroft S L, and Maijer D M, TMS (2009) p 377.Google Scholar
  10. 10.
    Weisstein Eric W. Moore Neighborhood. From MathWorld–A Wolfram Web resource. http://mathworld.wolfram.com/MooreNeighborhood.html.
  11. 11.
    Atwood R C, Sridhar S, Zhang W, and Lee PD, Acta Materialia 48 (2000) 405.CrossRefGoogle Scholar
  12. 12.
    Atwood R C, and Lee P D, Acta Mater 51 (2003) 5447.CrossRefGoogle Scholar

Copyright information

© The Indian Institute of Metals - IIM 2018

Authors and Affiliations

  1. 1.Computational Modeling & Simulation SectionCSIR-National Institute for Interdisciplinary Science and TechnologyThiruvananthapuramIndia
  2. 2.CSIR-National Institute for Interdisciplinary Science and TechnologyThiruvananthapuramIndia

Personalised recommendations