薄壁注塑件翘曲的正交分析及优化

分析了注塑制件翘曲的原因,采用著名的CAE软件Moldnow与正交试验方法,对不同工艺条件下的注塑成型过程进行模拟分析并对正交实验数据进行极差分析,确定注射时间、保压压力、保压时间、冷却时间、熔体温度、模具温度以及冷却液温度等注塑成型工艺参数对制件翘曲变形的影响程度,得出最优的注塑成型工艺参数组合,并以一薄壁导光板对该工艺组合方案进行模拟验证与实际注塑实验验证。     

基于正交试验薄壁件注塑分析及优化设计

利用Moldflow软件对薄壁件的注塑成型过程进行了模拟分析,设计了两种注塑成型方案,并进行了模流分析和翘曲情况分析,选择出最优的注塑方案。使用正交试验法分析翘曲变形的影响因素,寻找最优参数使薄壁件的翘曲变形最小。分析结果表明:薄壁件最优的注塑方案为两个浇口注塑方案;各因素对翘曲变化的影响程度为保压压力保压时间熔体温度模具温度;最优工艺参数为A2B1C2D2,即熔体温度280℃、模具温度60℃、保压时间10s、保压压力140MPa。最大翘曲变化量由优化前的2.781mm降到优化后的1.661mm。     

PC+ABS工程塑料合金薄壁制件注塑工艺参数的优化

以某畅销手机后盖为例,采用正交试验方法,应用MoldFlow软件模拟了注射时间、熔体温度、模具温度、保压压力等对PC+ABS工程塑料合金制件最大翘曲变形量的影响,得到最佳的注塑工艺参数;采用模拟得到的最佳工艺参数进行试制生产,以验证模拟结果的可靠性。结果表明:注塑工艺参数对手机后盖薄壁制件翘曲变形影响的主次顺序为注射时间、熔体温度、模具温度、保压压力;模拟得到制件的最佳注塑工艺参数为注射时间0.40s,熔体温度280℃,模具温度72℃,保压压力60MPa,此时制件的最大翘曲变形量最小,为0.509 0mm,翘曲变形主要出现在手机后盖四角处,耳机插孔旁的翘曲变形量最大;在优化工艺参数下试制产品的最大翘曲变形量为0.530mm,翘曲变形位置与有限元模拟结果一致,这验证了模拟结果的可靠性。     

ABS塑料在成型薄壁件时的变形研究

ABS塑料在注塑成型薄壁件时，制品常因复杂的变形而产生翘曲现象。根据翘曲变形理论，通过Pro／E建立薄壁件模型，利用Moldflow软件模拟研究了浇口位置、保压和冷却过程对翘曲变形的影响，进行翘曲变形预测，以优化薄壁件注塑成型工艺过程设计，提高生产效率和成形质量。     

ABS塑料在成型薄壁件时的变形研究

ABS塑料在注塑成型薄壁件时,制品常因复杂的变形而产生翘曲现象.根据翘曲变形理论,通过Pro/E厄建立薄壁件模型,利用Moldflow软件模拟研究了浇口位置、保压和冷却过程对翘曲变形的影响,进行翘曲变形预测,以优化薄壁件注塑成型工艺过程设计,提高生产效率和成形质量.     

基于Moldflow的薄壁件翘曲变形研究

翘曲变形是注塑件的主要缺陷,利用电器后盖对薄壁成型工艺进行研究。采用Moldflow软件对塑件成型过程进行数值模拟,研究了保压压力、塑件材料对注塑件翘曲变形的影响。对薄壁注塑件的数值仿真模拟结果进行统计分析,并且对影响注塑翘曲变形量的工艺参数进行综合分析,得到最优的工艺参数组合。研究结果表明:最佳的工艺参数组合可以使得塑件翘曲量变得最小。     

浅谈排插内托注塑成型工艺优化及模具设计

文章以排插内托注塑件为例,基于CAD/CAE技术,以熔体温度、注射压力、保压压力、充填速率等参数为变量,以最大翘曲变形量为指标,利用正交实验法进行成型工艺优化,获得最佳参数组合方式和最优翘曲变形量,依据模流分析结果进行注塑模具结构设计,采用一模四腔整体镶嵌式成型结构,采用推管推杆复合脱模方式,实践检验模具结构合理,为其他类似注塑模具设计提供参考。     

大型薄壁塑件翘曲变形分析及工艺参数优化

在注塑成型大型薄壁塑料产品过程中,由于熔料流动路程长,流动阻力大,产品极易产生翘曲变形,从而影响到使用性能.应用Moldflow软件分析了影响产品翘曲变形的主要原因是收缩率不均引起.通过优化工艺参数,即优化保压曲线,减小了产品翘曲变形.方珐是将保压设置由恒压保压调整为先恒压再线性递减的两段保压,然后再多次调整保压曲线各...     

薄壁注塑件翘曲影响因素分析及优化研究进展

分析了薄壁注塑成型翘曲变形产生的原因,介绍了几种优化算法的基本原理及特点。重点讨论了薄壁注塑件翘曲变形的模具优化设计和工艺参数优化方法。注塑模具优化设计主要通过保证流动平衡来间接实现翘曲优化。注塑工艺参数优化是减少翘曲的可行而有效的途径,影响翘曲最主要的工艺参数是保压压力、注射速率及模具温度。总结了注塑工艺参数优化的几种方法,对“代理模型”翘曲优化进行分析,步进式代理模型能更好地提高计算效率。     

塑料插座面板的模流分析及参数优化

将生活中常见的塑料插座面板作为主要研究分析的对象,利用模流分析软件对产品注塑过程中的翘曲变形进行分析,得出引起翘曲变形的主要原因。选取模具表面温度、熔体温度、冷却时间、保压压力、保压时间作为影响因素,进行三水平五因素的正交试验优化,通过方差和极差结果的分析,得出各个因素对翘曲变形的影响规律。试验结果表明,最佳的工艺参数组合为熔体温度317℃,模具表面温度94℃,冷却时间25 s,保压压力161 Pa,保压时间7 s。对最佳工艺参数组合再次进行分析验证,翘曲值明显降低,这一结果为生产实际中的注塑工艺参数设置提供了参考。     

Mold design and forming process parameters optimization for passenger vehicle front wing plate

The main objective of the present article is to solve the problems of poor molding quality, large warpage, inadequate cooling effect and unsuitable selection of process parameters, in the injection molding process for passenger vehicle front-end plastic wing plate. The thickness and parting surface of the vehicle front-end fender were determined, the injection mold and its cooling system were designed. The relevant process parameters, affecting the product molding quality, were tested, according to orthogonal experimental approach, while their influence on the warpage was obtained, by analyzing the data. Finally, the BP neural network of warpage model was established and globally optimized using genetic algorithm. The optimal parameter combination of the injection molding process was derived as: melt temperature 236 °C, mold temperature 51 °C, cooling time 32 s, packing pressure 97 MPa and packing time 16 s.

     

Optimization of injection molding process parameters using integrated artificial neural network model and expected improvement function method

In this study, an adaptive optimization method based on artificial neural network model is proposed to optimize the injection molding process. The optimization process aims at minimizing the warpage of the injection molding parts in which process parameters are design variables. Moldflow Plastic Insight software is used to analyze the warpage of the injection molding parts. The mold temperature, melt temperature, injection time, packing pressure, packing time, and cooling time are regarded as process parameters. A combination of artificial neural network and design of experiment (DOE) method is used to build an approximate function relationship between warpage and the process parameters, replacing the expensive simulation analysis in the optimization iterations. The adaptive process is implemented by expected improvement which is an infilling sampling criterion. Although the DOE size is small, this criterion can balance local and global search and tend to the global optimal solution. As examples, a cellular phone cover and a scanner are investigated. The results show that the proposed adaptive optimization method can effectively reduce the warpage of the injection molding parts.     

Warpage phenomenon of thin-wall injection molding

This study investigates warpage of electronic dictionary battery covers fabricated using thin-wall injection molding as a replacement for conventional insert molding. The primary concern is the molding window in thin-wall injection molding for acrylonitrile?Cbutadiene styrene (ABS) and polycarbonate (PC)+ABS plastics. Finally, the process parameters for thin-wall injection molding that eliminate warpage of electronic dictionary battery covers are identified. Experimental results demonstrate that the area of the molding window for ABS exceeds that for PC+ABS. Analysis of the molding window reveals that ABS is more appropriate than PC+ABS for battery covers. Low melt temperature, high injection speed, and high packing pressure eliminate battery cover warpage. Melt temperature is the most important process parameter for eliminating warpage when using both ABS and PC+ABS.     

基于Taguchi试验设计的储箱箱盖注射工艺参数研究

注射成型受众多因素影响,在制件结构和模具结构确定的条件下,通过合理的注射工艺参数,可消除或减少塑件成型中出现的缺陷。针对某企业在试生产一种储物箱箱盖时产生翘曲变形的问题,采用Taguchi试验方法,应用Moldflow对注射过程进行模拟,获得了塑件在熔料温度、模具温度、注射时间和保压压力四因素三水平下成型的翘曲变形量。采用极差分析,比较了不同工艺参数对翘曲变形量的影响程度,得到了优化的工艺参数组合。经试验验证,其效果良好,产品的翘曲变形得到了一定的改善。     

基于类神经网络注塑成型翘曲和收缩值之预测

以熔融温度、模具温度、射出时间、保压压力、保压时间等5个制程参数作为控制因子。利用Moldflow来模拟塑料薄壳挡板不同的成型制程参数下的翘曲与收缩值。基于仿真所得翘曲及收缩值数据,使用田口方法结合倒传递神经网络5-14-14-2建立预测模型。再利用测试样本来验证的倒传递神经网络模型的准确性。运用所建立的倒传递神经网络模型预测其他成型制程参数的翘曲及收缩值。结果证明,田口法结合倒传递神经网络,不仅可以有效的优化倒传递神经网络,而能成功的预测翘曲及收缩值,与Moldflow仿真值相比平均误差都在±1%内。     

An experimental work on the effect of injection molding parameters on the cavity pressure and product weight

The injection molding process is one of the most efficient processes where mass production through automation is feasible and products with complex geometry at low cost are easily attained. In this study, an experimental work is performed on the effect of injection molding parameters on the polymer pressure inside the mold cavity. Also, the effect of these parameters on the final products' weight is studied. Different process parameters of the injection molding are considered during the experimental work (packing pressure, packing time, injection pressure, injection time, and injection temperature). Two polymer materials are used during the experimental work (polystyrene (PS) and low-density polyethylene (LDPE)). The mold cavity has a cuboidal form with two different thicknesses. The cavity pressure is measured with time by using pressure Kistler sensor at different injection molding cycles. The results indicate that the cavity pressure and product weight increase with an increase in the packing pressure, packing time, and injection pressure for all the analyzed polymers. They also show that the increase of the filling time decreases the cavity pressure and decreases the product weight in case of PS and LDPE. The results show that the increase of packing pressure by 100 % increases the cavity pressure 50 % in the case of PS and 70 % in the case of LDPE. They also show that the increase of injection pressure by 60 % increases the cavity pressure 36 % in case of PS and 90 % in case of LDPE at an injection temperature of 220 °C. The results indicate that process parameters have an effect on the product weight for LDPE greater than PS. The results obtained specify well the developing of the cavity pressure inside the mold cavity during the injection molding cycles.     

基于Moldflow对突变壁厚塑件的注塑成型优化方案的研究

利用Mold flow/MPI技术对摩托车外壳进行CAE注塑模拟分析,预测了成型过程中的填充流动情况,并对型腔的填充时间、压力分布、锁模力大小等进行计算分析,掌握了翘曲变形产生的原因。根据注塑模拟分析结果优化了浇口、冷却系统设计方案和成型工艺参数,使模具设计更趋合理。优化后的成型方案用于实际生产,缩短了试模周期。     

Influence of molding conditions on the shrinkage and roundness of injection molded parts

During the plastic injection molding process, one of the biggest challenges is shrinkage which deteriorates the quality of produced parts. To control and reduce this defect, the essential way is to perfectly determine the variables like molding parameters. In this study, the effects of molding parameters including packing pressure, melt temperature, and cooling time on shrinkage and roundness have been investigated experimentally. Also, the relationship among initial molding parameters, the cavity pressure, and mold temperature was investigated. The results of this experimental study and analysis fulfill various requirements of plastic injection molding and clarify the relationship between molding conditions and the overall quality of produced parts. This study illustrated that packing pressure and melt temperature are dominant factors which determine the quality of parts.     

基于神经网络和遗传算法的薄壳件注塑成型工艺参数优化

建立基于神经网络和遗传算法并结合正交试验的薄壳件注塑成型工艺参数优化系统.正交试验法用来设计神经网络的训练样本,人工神经网络有效创建翘曲预测模型;遗传算法完成对影响薄壳塑件翘曲变形的工艺参数(模具温度、注射温度、注射压力、保压时间、保压压力和冷却时间等)的优化,并计算出其优化值.按该参数进行试验,效果良好,可以有效地减小薄壳塑件翘曲变形,其试验数值与计算数值基本相符,说明所提出的方法是可行的.