基于灰色关联-熵权法的座体注塑工艺参数优化

Injection process parameters optimization of base by grey correlation-entropy weight method

手持风扇底座具有复杂曲面与多筋结构等复杂的结构特征,这阻碍了材料的流动,可能会使产品产生熔接线、翘曲、体积收缩等缺陷,进而影响产品的表面质量和尺寸精度。为解决上述问题,通过理论算法与有限元仿真相结合开展风扇底座注塑工艺参数的优化研究。首先,根据产品特征确定了型腔数量及浇口位置,初步分析发现产品的缩痕指数相对较大;其次,以翘曲变形和缩痕指数为优化目标,建立中心复合试验方案并开展仿真分析,结合灰色关联与熵权法进行多目标优化,确定了工艺参数对灰色关联度影响的大小程度,依次为熔体温度、模具温度、注射时间、保压压力及保压时间;最后,确定最佳工艺参数组合为熔体温度225℃、模具温度30℃、注射时间5 s、保压压力20 MPa及保压时间30 s。相比初始方案,最佳工艺参数组合下的缩痕指数降低了30.79%,由于加强筋的存在翘曲变形优化前后相差较小。

The handheld-fan base has complex structural characteristics including complex surfaces and multiple reinforce-ment ribs,the characteristics hinder the material flow and produce product defects such as weld lines,warpage and volume shrink-age,which affect the surface quality and dimensional accuracy of the product.To solve the above problems,the research on the opti-mization of injection molding process parameters of the fan base was conducted through the combination of theoretical algorithms and finite element simulation.Firstly,the number of cavities and gate positions were determined based on product characteristics,and preliminary analysis revealed significant shrinkage index in the product.Secondly,with warpage deformation and shrinkage index as optimization objectives,a central composite experiment scheme was established and simulation analysis was carried out,and multi-objective optimization was carried out by combining grey correlation and entropy weight method,the influence degree of the process parameters on grey correlation degree were determined to be in the following order:melt temperature,mould tempera-ture,injection time,holding pressure,holding time.Finally,the optimal process parameters combination was determined to be a melt temperature of 225℃,mould temperature of 30℃,injection time of 5 s,holding pressure of 20 MPa,and holding time of 30 s.Compared with the initial scheme,the shrinkage index under the optimal process parameters combination is reduced by 30.79%,due to the existence of reinforcement ribs,the warpage deformation before and after optimization has a small difference.