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以汽车手套箱翘曲问题的解决为例,针对产品翘曲变形问题,运用CAE有限元分析对其注塑工艺进行了仿真分析,首先优化并确定了产品的浇注方案,进一步的保压、冷却及翘曲分析表明,保压和冷却能得到有效保证,但翘曲变形大为产品注塑的主要质量问题。依据CAE分析的产品翘曲的分离因素结果,运用GRA灰色关联分析法并结合RBF神经网络对其工艺影响因素进行权重取值,调整后的GRA-RBF神经网络对工艺参数因素水平与翘曲变形量之间的关系具有较为准确的预测,依据此神经网络模型,得到产品注塑的优化工艺参数,获得了高质量的注塑产品,降低模具制造成本,有效缩短了模具生产周期。
Taking the solution of warping problem of automobile glove box as an example, this paper analyzes and simulates the warpage of the product by using CAE finite element analysis. Firstly, the pouring scheme of the product is optimized and determined, and further packing, cooling and Warpage analysis shows that the packing and cooling can be effectively guaranteed, but the warpage is the major quality problem of product injection molding. According to the results of CAE analysis of product warpage, GRA gray relational analysis and RBF neural network were used to determine the influence factors of the process. The adjusted GRA-RBF neural network was used to evaluate the factors of process parameters and warpage According to the neural network model, the optimal process parameters of the product injection molding are obtained, the high quality injection molding products are obtained, the manufacturing cost of the mold is reduced, and the production cycle of the mold is effectively shortened.