Title: Optimal design of die casting process parameters of A713 cast alloy with grain refinement by using genetic algorithm approach for automobile industries
Authors: Rosang Pongen; Anil Kumar Birru; P. Parthiban
Addresses: Department of Mechanical Engineering, National Institute of Technology, Nagaland – 797103, India ' Department of Mechanical Engineering, National Institute of Technology, Manipur – 795004, India ' Department of Production Engineering, National Institute of Technology, Trichy – 620015, India
Abstract: Die casting with aluminium alloy has potential applications but often results in castings with poor properties due lagging in density, and it is mandatory to predict the nature of the output responses before manufacturing. Genetic algorithm (GA) approach prediction model helps in optimal output responses before the actual production in die casting. In this research, GA approach for optimising the theoretical and experimental density of an A713 alloy with Al-3.5Ti-1.5C and Al-3Cobalt as grain refiners is carried out. The selected die casting process parameters are molten metal temperature, Al-3.5Ti-1.5C, Al-3.0 Cobalt, die temperature and injection pressure. Theoretical and experimental densities are considered as outputs for the GA modelling. GA was performed for two cases, it was observed that the optimal prediction model for the theoretical and experimental density of A713 alloy with grain refiners have been accomplished using GA approach.
Keywords: A713alloy; die casting; optimisation; genetic algorithm; grain refinement; density.
International Journal of Heavy Vehicle Systems, 2022 Vol.29 No.2, pp.197 - 211
Received: 22 May 2019
Accepted: 02 Aug 2019
Published online: 07 Sep 2022 *