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Optimization of the Investment Casting Process Using Genetic Algorithm

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Computational Intelligence in Data Mining - Volume 2

Part of the book series: Smart Innovation, Systems and Technologies ((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.

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Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, VSSUT, Burla, Sambalpur, 768018, Odisha, India

    Sarojrani Pattnaik & Sutar Mihir Kumar

Authors
  1. Sarojrani Pattnaik
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  2. Sutar Mihir Kumar
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Corresponding author

Correspondence to Sarojrani Pattnaik .

Editor information

Editors and Affiliations

  1. University of Canberra, Canberra, Australia and University of South Australia, Adelaide, South Australia, Australia

    Lakhmi C. Jain

  2. Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  3. Computer Science & Engineering, Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

  4. Dept. of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, India

    Durga Prasad Mohapatra

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Pattnaik, S., Mihir Kumar, S. (2015). Optimization of the Investment Casting Process Using Genetic Algorithm. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 2. Smart Innovation, Systems and Technologies, vol 32. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2208-8_19

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  • DOI: https://doi.org/10.1007/978-81-322-2208-8_19

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2207-1

  • Online ISBN: 978-81-322-2208-8

  • eBook Packages: EngineeringEngineering (R0)

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