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。     

基于Taguchi的悬浮式割草机叶轮的注射工艺参数优化

以悬浮式割草机叶轮塑件为研究对象,采用Moldflow软件分析出最佳浇口位置,并对其构建浇注系统和冷却系统。以翘曲变形量为参考,采用多因素Taguchi法,获得了塑件最佳注射工艺参数组合,即注射温度为230℃,模具温度为75℃,保压时间为120s,保压压力为100% Velocity/Pressure转换点压力,注射时间为6s。     

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。     

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.     

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。     

基于多因子交互作用的PP/EPDM复合材料注射成型工艺优化

通过Moldflow仿真软件建立了汽车侧踏板聚丙烯/三元乙丙烯(PP/EPDM)复合材料端盖注射成型仿真模型,采用析因设计筛选出对工件翘曲、体积收缩、表面缩痕等缺陷影响最显著的主因子与交互因子,应用正交试验设计方法模拟得到最佳注射成型工艺,并进行了验证。结果表明:对目标值影响显著的主因子为熔体温度、模具温度、注射时间、保压时间和保压压力,交互因子为熔体温度与模具温度、注射时间与保压时间;最优注射成型工艺为熔体温度220℃、模具温度40℃、注射时间2s、保压时间25s、保压压力60 MPa,模拟得到的最大翘曲量比原工艺方案的减小了33.21%,体积收缩率降低了39.61%,表面质量显著提高。     

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。     

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.     

打印机底壳注塑工艺参数优化设计

以打印机底壳为研究对象,借助Moldflow有限元分析软件和正交实验设计方法,研究熔体温度、模具温度、注射时间、保压时间和保压压力对产品翘曲变形量的影响,确定最佳工艺参数组合。实验结果表明注塑工艺参数对翘曲变形影响程度顺序为保压压力(E)注射时间(C)熔体温度(A)保压时间(D)模具温度(B);最佳工艺参数组合为A_3B_4C_4D_1E_4(下标为正交实验水平参数),最佳工艺参数组合的翘曲变形量2.308mm,翘曲变形有较大改善。     

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。     

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.     

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.

     

An effective warpage optimization method in injection molding based on the Kriging model

In this paper, an effective optimization method using the Kriging model is proposed to minimize the warpage in injection molding. The warpage deformations are nonlinear, implicit functions of the process conditions, which are typically evaluated by the solution of finite element (FE) equations, a complicated task which often involves huge computational effort. The Kriging model can build an approximate function relationship between warpage and the process conditions, replacing the expensive FE reanalysis of warpage in the optimization. In addition, a “space-filing” sampling strategy for the Kriging model, named rectangular grid, is modified. Moldflow Corporation’s Plastics Insight software is used to analyze the warpage deformations of the injection-molded parts. As an example, the warpage of a cellular phone cover is investigated, where the mold temperature, melt temperature, injection time, and packing pressure are regarded as the design variables. The result shows that the proposed optimization method can effectively decrease the warpage deformations of the cellular phone cover and that the injection time has the most important influence on warpage in the chosen range.     

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.     

注射成型参数对微结构阵列导光板翘曲量的影响

为研究不同的工艺参数对微结构阵列导光板翘曲变形的影响,以微结构阵列导光板的翘曲量为质量目标,利用MoldFlow MPI5,仿真研究了不同工艺参数下,尺寸规格为11 mm×3 mm×0.8 mm导光板的翘曲变形。采用正交实验法找出影响微结构阵列导光板翘曲变形最小参数组合,然后采用单因素法仿真研究不同工艺参数对微结构阵列导光板翘曲变形的影响。结果表明,保压压力对微结构阵列导光板翘曲变形的贡献率最大(60.19％),其次是注射时间(13.13％),成型工艺参数对微结构阵列导光板翘曲量的影响顺序为:保压压力>注射时间>保压时间>熔体温度>冷却时间。结果表明,在微结构阵列导光板注射成型阶段,就应考虑不同工艺参数对微结构导光板注射成型翘曲变形的影响,并优先考虑保压压力的设置,以减少微结构阵列导光板微注射成型的翘曲量。