基于Moldflow的汽车大型覆盖件注射成型工艺研究

以汽车后保险杠为例,利用Moldflow软件直观地模拟出注射过程中的熔体流动过程.针对后保险杠的结构特点及品质要求,提出了一种针阀控制顺序进料延时保压的热流道浇注系统,分析了填充时间、填充/保压转换点压力、锁模力、翘曲变形、熔接痕分布等情况,消除了该类塑件易存在的熔接痕、翘曲变形大等缺陷,为实际生产提供了理论指导.     

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...     

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。     

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。     

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.     

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。     

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。     

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。     

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。     

基于Moldflow的电器面板翘曲分析

以电器面板外壳为实例,针对目前模具注塑过程中发生的收缩翘曲问题,通过Moldflow软件进行模拟分析,给出了注塑件影响翘曲的主要因素。主要是对电器面板进行工艺分析,建立翘曲理论公式考虑影响翘曲的主要因素,确定合理的浇口方案,然后通过Moldflow对两个不同的浇口方案进行分析比较,包括纤维的取向分析以及各方向变形量进行一定的比较。软件研究表明,浇口的位置和数量对翘曲变形有很大程度的影响,优化浇口方案可以减小翘曲量。     

Digitized die forming system for sheet metal and springback minimizing technique

The design of the runner and gating systems is of great importance to achieving a successful injection moulding process. The subjects of this study are the finite element and abductive neural network methods applied to the analysis of a multi-cavity injection mould. In order to select the optimal runner system parameters to minimize the warp of an injection mould, FEM, Taguchi’s method and an abductive network are used. These methods are applied to train the abductive neural network. Once the runner and gate system parameters are developed, this network can be used to accurately predict the warp of the multi-injection mould. A simulated annealing (SA) optimization algorithm with a performance index is then applied to the neural network in order to search the gate and runner system parameters. This method obtains a satisfactory result as compared with the corresponding finite element verification.     

An experimental work on the effect of injection molding parameters on the cavity pressure and product weight

The injection molding process is one of the most efficient processes where mass production through automation is feasible and products with complex geometry at low cost are easily attained. In this study, an experimental work is performed on the effect of injection molding parameters on the polymer pressure inside the mold cavity. Also, the effect of these parameters on the final products' weight is studied. Different process parameters of the injection molding are considered during the experimental work (packing pressure, packing time, injection pressure, injection time, and injection temperature). Two polymer materials are used during the experimental work (polystyrene (PS) and low-density polyethylene (LDPE)). The mold cavity has a cuboidal form with two different thicknesses. The cavity pressure is measured with time by using pressure Kistler sensor at different injection molding cycles. The results indicate that the cavity pressure and product weight increase with an increase in the packing pressure, packing time, and injection pressure for all the analyzed polymers. They also show that the increase of the filling time decreases the cavity pressure and decreases the product weight in case of PS and LDPE. The results show that the increase of packing pressure by 100 % increases the cavity pressure 50 % in the case of PS and 70 % in the case of LDPE. They also show that the increase of injection pressure by 60 % increases the cavity pressure 36 % in case of PS and 90 % in case of LDPE at an injection temperature of 220 °C. The results indicate that process parameters have an effect on the product weight for LDPE greater than PS. The results obtained specify well the developing of the cavity pressure inside the mold cavity during the injection molding cycles.     

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CAE技术的注塑模浇口优化设计

针对空调底座在实际注塑生产过程中的问题,运用MPI模流分析软件模拟空调底座注塑件在注射成型过程中的工艺条件,对不同浇口位置和数量的流动情况进行分析,从产品的充填时间、注射压力、翘曲变形等方面对浇口设计方案进行分析,以保证塑件成型质量为目的,对浇口位置和数量等进行了优化。     

MATLAB在注塑件翘曲变形工艺参数优化中的应用

本文利用BP神经网络对注塑工艺参数及其相对应的翘曲变形量样本进行训练,得到了描述工艺参数到翘曲量映射关系的ANN模型,并且验证了此模型的准确性,得出了工艺参数与塑件注塑翘曲变形量的内在联系。     

The selection of the optimal design parameters applied to the gas-assisted injection mould of an L-shape plates design

This study examined the finite element and abductive network method applied to thegas-assisted injection mould (GAIM). From these models, the effect of the GAIM condition includes the melt formation by percent volume filled, the base-plane thickness, and the ratio of L-shape plane thickness. The L-shape plane can reduce by about 50 per cent the expense of materials, reduce the injection cycle time, and increase the L-shape structure. In order to select the optimal design parameters to minimize the warp of a GAIM, the experimental design method and abductive network were used. These methods were applied to create an efficient model with functional nodes. Once the GAIM parameters were developed, this network was used to predict GAIM warp accurately. A simulated annealing (SA) optimization algorithm with a performance index was then applied to the neural network for searching the optimal GAIM parameters, obtaining a more satisfactory result compared to the corresponding finite element verification.     

Selection of the Optimal Gate Location for a Die-Casting Die with a Freeform Surface

This study deals with a finite-element method application for die-casting dies with freeform surfaces, in order to find an accurate and fast determination of the material injection gate location. To avoid many of the influencing factors, first the part-line of the parameter equation is created using an abductive neural network to limit the range of the gate locations. From the various gate locations in the part-line of the shell, the location producing the minimum warp is found. A prediction of the warp is then found using an abductive neural network. A simulated annealing (SA) optimisation algorithm with a performance index is then applied to the neural network for searching for the optimal position of the gate on the freeform surface, and to obtain a satisfactory result. The method can be applied in die-casting die design, in order to reduce the high cost of trial dies.