薄壁注塑件保压工艺的CAE分析及优化

保压阶段是注塑成型工艺的重要环节,保压工艺设置不恰当就会引起模腔中的压力分布不均匀,引起制件的翘曲变形、尺寸精度下降等严重的质量问题。介绍了薄壁注塑成型的定义,分析了保压工艺对薄壁制件成型的影响以及常见的保压方式对模腔压力分布的影响,利用Moldflow软件进行数值模拟,调整保压曲线,均衡模腔中的压力分布,并进行了注塑实验验证,结果表明：保压工艺对注塑件的翘曲变形有着显著的影响,与恒定保压相比,先恒压后线性递减的保压方式可获得较均匀的模腔压力分布,制件的体积收缩较均匀,制件的成型质量较好。     

基于Moldflow的薄壁注塑件优化设计

以薄壁注塑件条形码扫描器为例,对其进行分析,对所产生的熔接痕和翘曲变形进行优化,通过修改保压曲线,减少了产品的熔接痕,使翘曲变形量达到了符合的公差要求。     

鼠标外壳注塑件翘曲变形模拟分析

基于CAE数值分析技术和田口实验方法,研究了保压压力、熔体温度、冷却时间、保压时间和模具温度等因素对翘曲变形的影响.以翘曲变形量最小为目标,通过正交试验方法获得了最佳参数组合以及最佳参数组合条件下的翘曲变形量.结果显示,通过模流分析来预报产品缺陷,优化工艺参数,可以缩短模具开发周期,降低成本,提高企业竞争力.     

CAE技术在注塑产品中的应用

应用CAE软件Moldflow对充电器底座进行流动模拟分析,发现产品成型存在欠注.在分析了欠注产生原因的基础上,通过优化充电器底座结构,以及调整模具温度、熔体温度、注射时间、保压压力和时间等注塑工艺参数,解决了欠注问题,同时改善了翘曲变形,保证了注塑产品的成型质量.     

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。     

CAE技术在注塑产品中的应用

应用CAE软件Moldflow对充电器底座进行流动模拟分析,发现产品成型存在欠注。在分析了欠注产生原因的基础上,通过优化充电器底座结构,以及调整模具温度、熔体温度、注射时间、保压压力和时间等注塑工艺参数,解决了欠注问题,同时改善了翘曲变形,保证了注塑产品的成型质量。     

基于Mold Flow的汽车保险丝盒壳体的结构设计

采用Mold Flow对汽车保险丝盒壳体进行填充、保压、冷却、翘曲仿真分析,获得了最佳浇口位置、最佳流动模式和最小的翘曲变形。优化了产品结构,并给出合理的注塑工艺参数,缩短了产品开发周期,降低了开发风险,提高了产品的可靠性,从而达到了降低开发成本,提高产品竞争力的目的。     

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。     

大型浇注薄壁件的翘曲优化

研究了浇注系统和成型工艺参数对薄壁件翘曲变形的影响,对制件浇注系统进行了优化,再在优化后的浇注系统基础上以模具温度、熔体温度、冷却时间、注射时间、保压压力和保压时间为计算工艺参数,在三维流动分析的研究基础上,对制品缺陷进行了CAE分析,通过采用正交实验法,进行均值分析、极差分析及方差分析,并结合各因素效应曲线图,得出了最优工艺参数组合及各成型工艺参数对翘曲变形影响的主次关系及影响程度。CAE分析与试验结果表明,塑件的翘曲量从1.861mm减少到0.6282mm。     

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.     

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.     

熔体温度及保压压力对塑件翘曲量的影响

利用 Moldflow 分析软件,采用数值模拟试验方法,将正交试验和回归设计相结合,研究了熔体温度和保压压力对塑件翘曲量的影响规律,建立了信噪比回归方程。结果表明,在试验温度范围内,熔体温度越高,保压压力越大,信噪比越大,则塑件翘曲量越小。塑件成型工艺参数最终确定为：模具温度60℃、保压时间10 s、熔体温度240℃、保压压力115 MPa 及注射时间0.4 s。     

剃须刀头架注塑模具优化设计

分析了剃须刀头架翘曲变形原因,对剃须刀头架注射模具进行了优化设计。根据CAE分析结果对工件的注塑工艺进行优化,从而改善了工件的注塑质量。     

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。     

注射模工艺参数对塑件翘曲量的影响及优化

利用Moldflow分析软件,采用数值模拟的方法分析了料温、模具温度、注射时间、冷却时间和保压压力等工艺参数的变化对塑件产品翘曲的影响趋势及其原因。结果表明:对所选参数,保压压力对塑件翘曲的影响最为显著,且保压压力取注射压力的95%左右可使产品的翘曲量达到较小的程度;产品翘曲量随料温升高,注射时间减短而减小;冷却时间对翘曲量的影响甚微;模具温度对翘曲影响较为复杂。根据分析结果,优化了塑件的成型工艺参数,使得翘曲量进一步减小。     

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.     

Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm

During the production of thin shell plastic parts by injection molding, warpage depending on the process conditions is often encountered. In this study, efficient minimization of warpage on thin shell plastic parts by integrating finite element (FE) analysis, statistical design of experiment method, response surface methodology (RSM), and genetic algorithm (GA) is investigated. A bus ceiling lamp base is considered as a thin shell plastic part example. To achieve the minimum warpage, optimum process condition parameters are determined. Mold temperature, melt temperature, packing pressure, packing time, and cooling time are considered as process condition parameters. FE analyses are conducted for a combination of process parameters organized using statistical three-level full factorial experimental design. The most important process parameters influencing warpage are determined using FE analysis results based on analysis of variance (ANOVA) method. A predictive response surface model for warpage data is created using RSM. The response surface (RS) model is interfaced with an effective GA to find the optimum process parameter values.     

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.