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。     

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。     

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。     

基于正交试验的注塑制件翘曲变形模拟分析

利用模流分析软件Moldflow对注塑制件葡萄筐盖进行翘曲变形分析,基于正交试验对模拟结果数据进行直观分析和方差分析,得出充填压力、保压时间、熔体温度、模具温度对翘曲变形的显著性,并获得试验水平基础上使制件翘曲量最小的最优参数组合以及最优参数组合下的制件翘曲值,并通过试验验证了结论的正确性.     

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。     

基于Moldflow与均匀试验设计的汽车前格栅注塑工艺参数优化

针对汽车前格栅的翘曲变形的预测,利用Creo软件对其进行初始设计,并运用Moldflow软件模拟浇口位置。通过均匀设计的方法获取注塑过程试验数据,对试验数据仿真结果进行回归分析,获得模具温度、熔体温度、保压压力、保压时间、注射压力、注射时间、冷却时间、开模时间等8个因素对塑件翘曲变形的显著性影响情况。最后,确定关于翘曲变形的回归方程,利用遗传算法求出方程最优解,得到最优工艺参数组合和翘曲变形量。     

注塑工艺参数对汽车右A柱下饰板翘曲变形的影响

以汽车右A柱下饰板作为研究对象,进行正交模拟试验,探讨不同注塑参数对下饰板翘曲变形的影响,模拟结果表明:影响翘曲变形最显著的因素是熔体温度、保压压力;其次是冷却时间、注射时间;翘曲变形几乎不受模具温度、保压时间的影响;确定了下饰板成型的最佳工艺参数组合,在该工艺条件下注塑A柱下饰板翘曲变形量减少到2.206 mm,实际生产与模拟结果相符。     

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。     

壳盖注塑件模具结构优化设计及翘曲优化

以壳盖注塑件翘曲值为目标,对壳盖注塑件设计了不同数量和位置的浇口,通过模流分析确定出在产品两侧设置两个侧浇口能得到较小翘曲变形值。采用控制变量法,设计了不同数量、不同距离的冷却系统,通过分析和对比得到了使翘曲量相对较小的冷却系统。采用正交试验法,通过对极差和均值等数据的分析,得到了翘曲变形量最优时对应的最佳工艺参数组合为:A4B4C3D3E2,明确了影响翘曲变形的因素从大到小顺序为:保压压力、保压时间、注射时间、熔体温度和冷却时间。     

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。     

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.     

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.     

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.     

Analysis of shrinkage and warpage in an injection-molded part with a thin shell feature using the response surface methodology

This paper presents a systematic methodology to analyze the shrinkage and warpage in an injection-molded part with a thin shell feature during the injection molding process. The systematic experimental design based on the response surface methodology (RSM) is applied to identify the effects of machining parameters on the performance of shrinkage and warpage. The experiment plan adopts the centered central composite design (CCD). The quadratic model of RSM associated sequential approximation optimization (SAO) method is used to find the optimum value of machining parameters. One real case study in the injection molding process of polycarbonate/acrylonitrile butadiene styrene (PC/ABS) cell phone shell has been performed to verify the proposed optimum procedure. The mold temperature (M _T), packing time (P _t), packing pressure (P _P) and cooling time (C _t) in the packing stage are considered as machining parameters. The results of analysis of variance (ANOVA) and conducting confirmation experiments demonstrate that the quadratic models of the shrinkage and warpage are fairly well fitted with the experimental values. The individual influences of all machining parameters on the shrinkage and warpage have been analyzed and predicted by the obtained mathematical models. For the manufacture of PC/ABS cell phone shell, the values of shrinkage and warpage present the reduction of 37.8 and 53.9%, respectively, using this optimal procedure.     

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.     

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。     

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。     

Optimization of cutting parameters on drill bit temperature in drilling by Taguchi method

In this study, the optimization of the cutting parameters on drill bit temperature in drilling was performed. Al 7075 work piece and the uncoated and Firex® coated carbide drills in the experimental were used. The optimization of the cutting parameters was evaluated by Taguchi method. The control factors were considered as the cutting speed, feed rate and cutting tool. Taguchi method was used to determining the settings of cutting parameters. The L₁₈ orthogonal array was used in experimental planning. The most significant control factors affected on drill bit temperature measurements was obtained by using analysis of variance (ANOVA). Taguchi design method exhibit a good performance in the optimization of cutting parameters on drill bit temperature measurements. In addition, the empirical equations of drill bit temperatures were derived by using regression analysis. The obtained equations results compared with the drill bit temperature measurement results. The empirical equations results indicated a good agreement with experimental results.