Property Optimization of Impeller Casting Using GRA

Abstract

Ductile irons are important engineering materials because of its high strength to weight ratio and castability. The ductile iron castings are used widely for automobile applications due to its wide spectrum of property range. Weight reduction is important in automobile to improve its fuel efficiency which can be achieved by thinning down the casting sections without altering its functionality. Generally automobile castings are having varying section thickness. Varying thickness castings offers different cooling rates while solidification of the casting. The solidification cooling rate decides the final microstructure in cast components. Cooling rate was found to be affect directly the amount of pearlite and ultimately as cast properties in varying thickness ductile iron castings. In view of this, the automobile impeller casting is selected as it consists of varying section thickness in which small sections are connected to central hub. The casting solidification simulations were performed and analyzed. The solidification cooling rates were analyzed further to correlate the experimental processing parameters. The samples from poured castings were analyzed for microstructure and hardness at different section thickness. Multiple response optimization of microstructure and hardness is carried out by combined taguchi and Grey Relational Analysis (GRA). Contribution of input variables on the output variables is attained using ANOVA.