基于正交试验法进行注塑制品翘曲模拟分析及工艺优化

翘曲变形是薄壳类塑料件注塑成型中的常见缺陷之一。不同材料、形状及不同成型工艺的注塑件的翘曲变形规律差别很大,翘曲变形问题的存在会影响注塑件的形状精度和表面质量,甚至成为成型缺陷,进而影响产品装配及外观。翘曲作为塑件变形的重要特征之一,其研究有着重要的应用价值。利用数值模拟技术研究制件注塑成型,降低塑件成型的翘曲量,对于提高注塑产品精度、缩短新产品开发周期、降低成本、提高生产率等都有着重要的意义。     

基于复合形法的薄壁注塑件变形预补偿研究

针对薄壁注塑制品存在翘曲变形的问题，提出一种基于复合形法成型参数优化的注塑件变形预补偿方法。该方法综合考虑了模具结构、材料特性参数以及工艺参数对注塑件翘曲变形量的影响，经循环迭代拟合了不同成型参数同翘曲变形量间的回归方程，实现了注塑件翘曲变形的最大化减小。利用Moldflow Plastics Insight翘曲分析(Warping)输出不同比例因子的反向模型，以翘曲补偿值评判反向模型节点各向异性误差，为获得高质量的逆向补偿塑件曲面，运用非均匀有理B样条曲面结合变形预补偿原理，以最优反向模型曲面融合点对应初始模型曲面离散点的方式修正塑件曲面的变形误差。以实际薄壁车用风扇为例，通过实验验证该方法的准确性与可行性。     

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。     

注塑件成型工艺参数优化研究

以手机外壳为例,应用CAE软件模拟了注塑件的注塑成型过程。通过正交试验方法与模拟仿真实验的结合,对注塑成型的填充、保压、冷却和翘曲变形过程进行仿真实验分析,获得了制件不同条件下的翘曲变形值。并通过对试验数据的极差分析,得到了不同工艺参数对注塑过程翘曲变形的影响程度,进而得到一组优化的工艺参数组合,同时对优化后的工艺参数组合进行仿真模拟分析,得到了符合产品要求的翘曲变形量,为同类制品的工艺参数优化提供了依据。     

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。     

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。     

投影仪后盖注塑翘曲变形的优化

投影仪后盖在模具制造方面属于典型的薄壁塑件,而薄壁塑件在制造过程中最难控制或解决的问题就是翘曲变形。本文通过Moldflow软件,对投影仪后盖的三种不同进浇方式进行优化,然后结合正交设计对注塑工艺参数进行优化,得到了最优工艺参数组合,有效控制了投影仪后盖的翘曲变形。     

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。     

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。     

Experimental-based optimization of polymer injection molding process parameters using anfis-ga method

Compared with ordinary injection-molded parts, the slender, cantilevered, and thin-walled plastic parts are harsh on the injection molding process conditions. For complexity and particularity, it is difficult to form such parts. It is also more likely to cause excessive warpage deformation, affecting the molding quality and performance. The automobile audio shell is a typical slender, cantilevered, thin-walled plastic part. When the mold structure and material are determined, optimizing its injection molding process is the most economical and effective method to manufacture the products with the optimum properties. In order to minimize the warpage deformation, the adaptive network based fuzzy inference system (ANFIS) and genetic algorithm (GA) were adopted to optimize the injection molding process parameters. In particular, considering the high-dimensional nonlinear relationship between the process parameters and the warpage, the ANFIS is constructed as the prediction model of the warpage. Then, the GA is used to globally optimize the prediction model to determine the optimal process parameters. The results show that the optimization method based on ANFIS-GA has a good performance. The warpage is reduced to 0.0925 mm while reduced by 88.25 %. The optimal injection molding process parameters are used for simulation and manufacture, verifying the effectiveness and reliability of the optimization method.

     

Optimal injection molding conditions considering the core shift for a plastic battery case with thin and deep walls

The objective of this paper is to examine the influence of injection molding parameters on the core shift to obtain the optimal injection molding conditions of a plastic battery case with thin and deep walls using numerical analyses and experiments. Unlike conventional injection molding analysis, the flexible parts of the mold were represented by 3-D tetrahedron meshes to consider the core shift in the numerical analysis. The design of experiments (DOE) was used to estimate the proper molding conditions that minimize the core shift and a dominant parameter. The results of the DOE showed that the dominant parameter is the injection pressure, and the core shift decreases when the injection pressure decreases. In addition, it was shown that the initial mold temperature and the injection time hardly affect the core shift. The results of the experiments showed that products without warpage are manufactured when the injection pressure is nearly 32 MPa. Comparing the results of the analyses with those of the experiments, optimal injection molding conditions were determined. In addition, it was shown that the core shift should be considered to simulate the injection molding process of a plastic battery case with thin and deep walls.     

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。     

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Moldflow的汽轮机叶片注塑模拟参数优化设计

利用Moldflow软件对汽轮机叶片进行工艺成型分析,通过正交试验的办法来分析各工艺参数对翘曲变形的影响规律,得到一组最佳工艺参数组合.     

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.

     

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...     

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.