Study On Process Parameters Optimization Of Gas-Assisted Injection Molding For Complex Shell Parts

How to effectively and quickly determine process parameters of gas-assisted injection molding (GAIM) is a quite important and complex process, which directly affect on quality of products. Up to now, it is still a problem how to choose the key process parameters and optimize them to obtain good product performance.21 inches color front panel is taken as study object. On the basis of orthogonal experimental design, GAIM process parameters optimization system is established by using a hybrid system that combines radical basis function (RBF) neural network with genetic algorithm (GA). It can be used to determine optimum process parameter combination rapidly, and also provide a new solution of process parameter optimization for GAIM.The main contents of this thesis are listed as follows:(1) The influence factors of several quite important process parameters for the quality of GAIM part products are comprehensive studied. The common defects of GAIM are summarized and the relevant solutions are given.(2) The preliminary optimization is done based on orthogonal experimental design and mold-flow 2010 simulation methods.(3) the GAIM process parameters optimization mathematical model is established by using multi-objective optimization method.(4) The RBF neural network is setup to show the relationship of GAIM process parameters and the quality of part products. According to the preliminary optimum GAIM process parameters gotten by using orthogonal experimental design, training sample is generated to train the RBF neural network and the correctness of the RBF neural network is verified.(5) The GAIM multi-objective process parameters optimization system is built with RBF neural network model and GA. The best process parameters combination is obtained by this system. At last, the results of CAE simulation show that the optimization system is feasible and effective.