Optimization Of Injection Molding Process Parameters For Car Battery Bracket

With increasing requirements of lighted vehicles and environmental protection, the development of the plasticization of automobile accelerates, more and more car metal parts were replaced by the plastic pieces, which have more complex structure and higher quality in the appearance, dimensional accuracy and mechanical behavior. Meanwhile warpage is an important indicator to measure the quality of the plastic parts, so it seems extremely necessary to research on the warpage control of automotive plastic pieces.A car battery bracket was taken as the main object of the study in this paper and the warpage was reduced by optimizing the process parameters. Firstly research status of warpage and optimization of process parameters was introduced in detail. And the finite element model was created. After taking injection molding process simulation analysis, the reasonableness and effectiveness of the gating system and cooling system were verified.Then, the impact of process parameters including melt temperature, mold temperature, injection time, packing time and packing pressure on the workpiece warpage was analysed by orthogonal test, which was established with five factors of and five levels of each factor. After simulation date was dealed with by using range analysis and variance analysis, impact trend of five factors was obtained, and the results showed that packing pressure was the most significant factor on warpage. Meanwhile the optimization set of factors was obtained., and small-scale multi-level optimization was adopted in their vicinity by using the uniform design test with8groups, and the goal to further reduce the amount of warpage was achieved.Thirdly, the data of orthogonal test and uniform design test was used as training samples of PSO-BP (particle swarm optimization and back propagation) artificial neural network, and the neural network prediction model was established with choosing the process parameters as the inputs and choosing the amount of warpage as output variables. The reliability and accuracy of the neural network warpage prediction model was verified by the testing samples.Finally, the PSO algorithm was applied to optimize the process parameters based on the neural network warpage prediction model, and the global minimum amount of warpage was acquired successfully. Then the predicted value was quite close to the result got in the Moldflow software, which indicated that it was feasible using the PSO algorithm to optimize the process parameters based on neural network warpage prediction model.The research shows that the amount of warpage was reduced effectively after optimizing the parameters of forming process. The time of optimizing process parameters can be shorten and the injection parts quality can be improved by using the study method in the paper, which is of certain guiding fignificance for the production.