Abstract: Many aluminum motorcycle parts produced by a high
pressure die casting. Some parts such as fuel caps were a thin and
complex shape. This part risked for porosities and blisters on surface
if it only depended on an experience of mold makers for mold design.
This research attempted to use CAST-DESIGNER software
simulated the high pressure die casting process with the same process
parameters of a motorcycle fuel cap production. The simulated results
were compared with fuel cap products and expressed the same
porosity and blister locations on cap surface. An average of absolute
difference of simulated results was obtained 0.094 mm when
compared the simulated porosity and blister defect sizes on the fuel
cap surfaces with the experimental micro photography. This
comparison confirmed an accuracy of software and will use the
setting parameters to improve fuel cap molds in the further work.
Abstract: The present work analyses different parameters of pressure die casting to minimize the casting defects. Pressure diecasting is usually applied for casting of aluminium alloys. Good surface finish with required tolerances and dimensional accuracy can be achieved by optimization of controllable process parameters such as solidification time, molten temperature, filling time, injection pressure and plunger velocity. Moreover, by selection of optimum process parameters the pressure die casting defects such as porosity, insufficient spread of molten material, flash etc. are also minimized. Therefore, a pressure die casting component, carburetor housing of aluminium alloy (Al2Si2O5) has been considered. The effects of selected process parameters on casting defects and subsequent setting of parameters with the levels have been accomplished by Taguchi-s parameter design approach. The experiments have been performed as per the combination of levels of different process parameters suggested by L18 orthogonal array. Analyses of variance have been performed for mean and signal-to-noise ratio to estimate the percent contribution of different process parameters. Confidence interval has also been estimated for 95% consistency level and three conformational experiments have been performed to validate the optimum level of different parameters. Overall 2.352% reduction in defects has been observed with the help of suggested optimum process parameters.