Simulation Analysis And Optimization Research Of Injection Molding For Front Bumper

With the rapid development of science and technology, the requirements of injection mold become higher and higher, as plastic products are widely used. Some possible defects could be found in the design stage of plastic products by making full use of injection molding CAE technology, through which, mold design could be predicted, plastic products could be simulated and analyzed, and the effect of injection molding parameters on the products could be evaluated. So the design of the product could be more reasonable, and also, the successful rate and efficiency of new product development could be improved.In this paper, the optimization targets are the amount of warpage and shrinkage volume of the front bumper of the car. By using Moldflow software to simulate the process of injection molding, the effect of five process parameters, such as melt temperature, mold temperature, mold time, dwell time and dwell pressure parameters on the two optimization targets were analyzed by orthogonal experiment, uniform experiment, comprehensive balance method, comprehensive evaluation method, gray relational analysis method and so on. The optimal combination of parameters was obtained. Detailed contents are as follows:Firstly, the effect of the five process parameters on the amount of warpage was analyzed through the orthogonal experiment and uniform experiment. The optimal combination of parameters was obtained and the amount of warpage got optimized. At the same time, the amount of warpage of the front bumper was predicted and the maximum and the minimum value were obtained by analyzing regressive model of SPSS.Secondly, the amount of warpage and shrinkage rate was multi-objective optimized by comprehensive balance method, comprehensive evaluation method and gray relational analysis method. The optimal result was verified by Moldflow software. And also, the best multi-objective optimal result was obtained by comparing with these three methods, which is the result simulated from gray relational analysis method.Finally, a BP neural network model was designed by Matlab programming. A prediction model of warpage amount and volume shrinkage was established at the base of the data sample simulated from orthogonal experiment. The veracity of the BP neural network model was confirmed through experiments. Thus, the amount of warpage and volume shrinkage under different combinations of process parameters could be predicted and caculated based on this BP neural network model.