基于BP神经网络的塑料齿轮玩具注射成型优化分析

Optimization and Analysis of Injection Molding Process for Gear Toys Based on BP Neural Network

以某塑料拼插齿轮玩具为研究对象,采用自然平衡法设计1模144腔注塑模具。对有限元模型进行合理简化,并采用Moldflow软件进行塑料齿轮注射成型过程中的流动和翘曲分析。针对初始方案中出现的熔接痕和翘曲等缺陷,建立齿轮玩具BP人工神经网络模型,通过BP神经网络算法训练各工艺参数,并对体积收缩率和总翘曲量进行预测。将训练后较优的工艺参数组合应用于注射成型后,使得该塑料齿轮熔接痕分布改变,翘曲变形量明显降低。

In order to produce insert-gear plastic toys more effectively, an injection mold with 144 cavity was designed using natural balance method. The finite element model was reasonably simplified and the flow and warpage in the molding was analyzed by Moldflow software. Focused on the welding marks and warpage and other defects, a BP artificial neural network model was established, the process parameters were trained, the volume shrinkage and the total amount of warpage were predictively analyzed by the BP neural network algorithm. The optimal combination of process parameters after training was applied to the injection molding, the weld mark and warpage were reduced significantly.