基于Moldflow和BP神经网络的超薄塑料制品流程长度预测

FLOW LENGTH PREDICTION FOR ULTRATHIN PLASTIC PRODUCT BASED ON MOLDFLOW AND BP NEURAL NETWORK

基于Moldflow/MPI软件,结合正交试验,研究了注塑压力、注射速率、模具温度、熔体温度、保压压力、保压时间等6个重要工艺参数对超薄塑料制品注塑流程长度的影响规律。然后建立一个三层BP神经网络,仿真结果表明,利用该神经网络模型,可预测超薄塑料制品的流程长度。

On the basis of Moldflow/MPI software, the effect of six important process parameters（ injection pressure, injec- tion rate, mold temperature, plastic melt temperature, packing pressure, packing time） on the flow length of the ultrathin plastic prod- uct was studied with orthogonal test. A three-layer BP neural network was established and the simulation results showed that the flow length of the uhratbin plastic product could be predicted by means of the neural network model.