ABSTRACT
ABSTRACT: Uneven cooling of injection molded part sets up uneven distribution of stresses. These stresses warp the part and affect its dimensional accuracy. The lack of injection molding machine control leads to this defect in plastic parts. So as to keep the warpage minimum, accurate prediction of optimum process parameters is very important. Finite element analysis software, Moldflow Plastic Insight, Taguchi statistical design of experiment and artificial neural network model are used to find optimum process parameter values. The optimum combination of process parameters that can minimize the warpage is found out. Most effective factor contributing to warpage is the melt temperature for this problem. The value of warpage obtained by using recommended process setting is reduced by 5.3% after optimization. Neural network model, formulated on the basis of finite element simulation data, gives results with acceptable accuracy and can be used as an alternative to the costly and time consuming finite element simulation process.
