Optimization of the Investment Casting Process Using Genetic Algorithm

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 32)

Abstract

This paper presents a study in which an attempt has been made to improve the quality characteristic (surface finish) of the wax patterns used in the investment casting process. The wax blend consists of paraffin wax (20 %), carnauba wax (10 %), microcrystalline wax (20 %), polyethylene wax (10 %) and teraphenolic resin (40 %), which provided an improved pattern wax composition. The process parameters considered are injection temperature, holding time and die temperature. The injection process parameters are optimized by genetic algorithm. Further, verification test have been conducted at the obtained optimal setting of process parameters to prove the effectiveness of the method. Finally, a good agreement between the actual and the predicted results of surface roughness of the wax patterns has been found.

Keywords

Investment casting Wax pattern Surface roughness Genetic algorithm Optimization 

References

  1. 1.
    Clegg, A.J.: Precision Casting Processes. Pergamon Press, Oxford (1991)Google Scholar
  2. 2.
    Pattnaik, S., Karunakar, D.B., Jha, P.K.: Developments in investment casting process: a review. J. Mater. Process. Technol. 212, 2332–2348 (2012)Google Scholar
  3. 3.
    Beeley, P.R., Smart, R.F.: Investment Casting, 1st edn. The Institute of Materials, London (1995)Google Scholar
  4. 4.
    Horton, R.A.: Investment casting. In: Lyman, T. (ed.) American Society for Metals (1987)Google Scholar
  5. 5.
    Rezavand, S.A.M., Behravesh, A.H.: An experimental investigation on dimensional stability of injected wax patterns of gas turbine blades. J. Mater. Process. Technol. 182, 580–587Google Scholar
  6. 6.
    Rahmati, S., Akbari, F., Barati, E.: Dimensional accuracy analysis of wax patterns created by RTV silicone rubber molding using the Taguchi approach. Rapid Prototyping J. 13(2), 115–122Google Scholar
  7. 7.
    Tsoukalas, V.D.: Optimization of porosity formation in AlSi9Cu3 pressure die castings using genetic algorithm analysis. Mater. Des. 29, 2027–2033 (2008)CrossRefGoogle Scholar
  8. 8.
    Vijian, P., Arunachalam, V.P.: Modelling and multi objective optimization of LM24 aluminium alloy squeeze cast process parameters using genetic algorithm. J. Mater. Process. Technol. 186, 82–86 (2007)CrossRefGoogle Scholar
  9. 9.
    Kilickap, E., Huseyinoglu, M., Yardimeden, A.: Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm. Int. J. Adv. Manuf. Technol. 52, 79–88 (2011)CrossRefGoogle Scholar
  10. 10.
    Zhou, M., Sun, S.D.: Genetic algorithms: theory and applications. National Defense Industry Press (2002)Google Scholar
  11. 11.
    Reddy, N.S.K., Rao, P.V.: A genetic algorithmic approach for optimization of surface roughness prediction model in dry milling. Mach. Sci. Technol. 9, 63–84 (2005)CrossRefGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringVSSUTBurla, SambalpurIndia

Personalised recommendations