高光成型制品体积收缩率的数值模拟优化与结果预测  

Numerical Simulation Optimization and Consequence Prediction of Volume Shrinkage Rate for High-gloss Product

作　　者：张正彬[1] 陈晨[2] 陈从升[1,2] 

机构地区：[1]安徽建筑工业学院机械与电气工程学院,安徽合肥230601 [2]合肥工业大学材料科学与工程学院,安徽合肥230009

出　　处：《塑料工业》2011年第5期117-120,共4页China Plastics Industry

基　　金：安徽省高校自然科学研究重点项目(KJ2009A011)

摘　　要：以高光液晶前壳为研究对象,以高光注射成型制品体积收缩率为成型性能指标,基于CAE仿真试验和正交试验设计获得相关数据并研究了工艺参数对高光制品体积收缩率的影响趋势及影响程度,得出保压压力为最显著性影响因素,并获得了使制品体积收缩率最小的最优工艺参数组合。在数值模拟和正交试验获得的样本数据基础上,采用径向基(RBF)神经网络建立了工艺参数与制品体积收缩率之间的非线性映射关系,实现了在工艺参数变化情况下能够快速、准确地预测出高光成型制品的体积收缩率,对实践生产具有一定的指导意义。A high-gloss LCD front shell was taken as the research object with the aim of reducing its volume shrinkage rate of the high gloss injection moulding products. CAE simulation and orthogonal experimental design were combined to investigate the influence of the processing parameters on the volume shrinkage rate of the high gloss products including influential trend and the order of significance. It was found that packing pressure was the most significant factor and the optimum parameters were obtained. Based on sample data acquired by numerical simulation and orthogonal method, RBF neural network was utilized to establish the non- linear relationship between process parameters and volume shrinkage rate, and it was realized that volume shrinkage rate of high-gloss product could be predicted quickly and accurately under the change of process parameters, thus having a significant effect on the practical production.

关 键 词：高光注射成型 体积收缩率 CAE 正交试验 RBF神经网络 

分 类 号：TQ320.63[化学工程—合成树脂塑料工业]