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。     

基于Taguchi技术的注射模工艺参数优化设计

将Taguchi技术和注射模CAE相结合,通过两次正交设计实验,研究了成型工艺参数对塑件翘曲量的影响,并以塑件翘曲量作为考察结果对成型工艺参数进行了优化。对所选参数,熔体温度和保压压力对塑件翘曲量影响高度显著,注射时间显著,其它参数影响甚微,且在实验范围内,塑件翘曲量随着熔体温度和保压压力的升高而减小。     

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。     

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DOE技术的薄壁塑件翘曲变形工艺优化

文中在薄壁注射成型中将CAE技术和DOE(design ofexperiment)相结合,以薄壁盖板塑件为例,利用Moldflow对各工艺参数进行注射成型模拟分析。通过分析塑件翘曲变形的原因,得出保压压力对翘曲变形起主导性作用。并在正交试验的指导下优化工艺参数,有效降低塑件的翘曲变形。     

汽车水室注塑成型多目标工艺参数优化

以汽车水室为研究目标,结合Moldflow和正交试验进行注塑成型数值模拟.通过对正交试验模拟结果进行分析,确定模具温度、熔体温度、保压压力、保压时间、充填时间对产品体积收缩率差值、翘曲变形量、缩痕深度的影响程度.运用加权综合评分建立多指标综合评价数学模型,并对3个指标进行综合评分.通过对综合评分的极差分析确定模具温度、熔体温度、保压压力、保压时间、充填时间对综合评分的影响程度大小,得出最优的工艺参数为模具温度72.5℃,熔体温度270℃,保压压力为注射压力的80％,保压时间6 s,充填时间1.5 s,并对此工艺方案进行了注塑模拟验证,达到预期优化目的.     

注塑工艺参数优化的正交法应用实例

以天线座走线保护盖为研究对象,应用Moldflow有限元分析软件,针对工件质量缺陷,合理设计模具的浇注系统和温度调节系统。以翘曲变形量作为质量指标,采用多因素正交法,获得塑件在熔料温度、模具温度、保压压力、保压时间、注射时间五因素四水平下成型的翘曲变形量。采用方差分析比较不同工艺参数对翘曲变形量的影响程度,得到优化的工艺参数组合。     

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。     

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。     

基于Moldflow与正交试验设计的汽车前格栅注塑工艺参数优化

以汽车前格栅塑件翘曲变形量为研究对象,采用Creo绘图软件对塑件进行初始设计,并利用Mold?ow软件对模型进行模流分析。通过正交设计方法获得试验参数组合,并整理分析数据,对获得的翘曲变形量进行极差分析,得出熔体温度、模具温度、保压时间、保压压力、注射时间、注射压力以及冷却时间对翘曲变形量的影响,从而进一步确定最优方案,获得最佳的工艺参数和翘曲变形量。     

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.     

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。     

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。     

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.     

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.     

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。     

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。     

Multi-objective optimization of injection molding process parameters in two stages for multiple quality characteristics and energy efficiency using Taguchi method and NSGA-II

In this paper, the parameters optimization of plastic injection molding (PIM) process was obtained in systematic optimization methodologies by two stages. In the first stage, the parameters, such as melt temperature, injection velocity, packing pressure, packing time, and cooling time, were selected by simulation method in widely range. The simulation experiment was performed under Taguchi method, and the quality characteristics (product length and warpage) of PIM process were obtained by the computer aided engineering (CAE) method. Then, the Taguchi method was utilized for the simulation experiments and data analysis, followed by the S/N ratio method and ANOVA, which were used to identify the most significant process parameters for the initial optimal combinations. Therefore, the range of these parameters can be narrowed for the second stage by this analysis. The Taguchi orthogonal array table was also arranged in the second stage. And, the Taguchi method was utilized for the experiments and data analysis. The experimental data formed the basis for the RSM analysis via the multi regression models and combined with NSGS-II to determine the optimal process parameter combinations in compliance with multi-objective product quality characteristics and energy efficiency. The confirmation results show that the proposed model not only enhances the stability in the injection molding process, including the quality in product length deviation, but also reduces the product weight and energy consuming in the PIM process. It is an emerging trend that the multi-objective optimization of product length deviation and warpage, product weight, and energy efficiency should be emphasized for green manufacturing.