正交试验法在气辅注射中的应用

采用正交试验法对气体辅助注射成型工艺参数进行优化，并用Moldflow软件对各种工艺参数组合进行模拟试验。通过对伞柄的模拟结果表明，采用正交试验法并结合计算机辅助工程软件模拟，可在短时间内以较低的成本、较少的试模次数找到合适的工艺参数组合。     

气辅注射工艺参数对气体穿透能力的影响

针对一具体零件，利用Moldtlow软件和正交实验法，分析评价主要工艺参数对气体穿透能力和气辅零件质量的影响。发现延迟时间对气体穿透能力和零件质量的影响最大；得出的最优工艺参数为：预注射量98％，延迟时间0．1s，气注压力50MPa，熔体温度236℃。     

复杂壳体类塑料件气体辅助注射成型工艺参数优化研究

以21英寸彩电前壳作为研究对象,将Moldflow 2010作为CAE模拟试验平台,以熔体温度、模具温度、熔体注射时间、气体延迟时间、气体压力为关键工艺因素,考察了复杂壳体类塑料件气体辅助注射成型(GAIM)时制件的翘曲变形量和气体穿透情况。以正交试验设计方法为基础,利用遗传算法并结合径向基神经网络建立GAIM工艺参数优化系统,可用于工艺参数组合的快速确定,为GAIM过程中工艺参数优化提供了一种新的求解思路。     

基于正交实验法的气体辅助注射成型工艺参数优化

以CD机体盖为例,以Moldflow作为主要分析研究工具,考察了气体辅助注射成型时制件的气体穿透长度和气指效应情况.以熔体的预注射量、气体延迟时间、熔体注射温度、注气压力等为关键工艺因素,采用正交实验法,利用Moldflow对正交实验方案进行模拟试验,得到不同工艺因素对气体穿透长度、气指效应的影响情况,并确定了最佳工艺参数组合.     

基于正交试验的气体辅助注射成型工艺参数优化

以21英寸彩电前壳为例,将Moldflow 2010作为CAE模拟试验平台,以熔体温度、模具温度、熔体注射时间、气体延迟时间、气体压力为关键工艺因素,考察了气体辅助注射成型时制品的翘曲变形量和气体穿透情况.采用正交试验法及极差分析法对Moldflow模拟试验结果进行分析,确定并验证了较优工艺参数组合的正确性.     

优化带加强筋ABS材料制件沉降斑的气体辅助成型技术

详细介绍了气体辅助注射成型的物理过程及其关键工艺参数对成型的影响。以带加强筋的绣花机外壳为例，考察了传统注射成型与气体辅助注射成型的优缺点。针对气体辅助注射引入的新工艺参数，介绍了一种CAE技术与统计学的Taguchi正交实验相结合的方法，设计L9(34)正交实验表在可成型范围内寻求最优工艺参数。结果表明，气体辅助技术能减少原料，对有加强筋的制件能有效地去除沉降斑、减弱不均匀收缩及减少内应力，大大改善制件的表面质量，增强制件尺寸的稳定性。     

基于气体穿透效果数值模拟的气体辅助注射成型工艺优化

对气体辅助注射成型工艺进行分析，阐述了气体辅助注射成型的关键技术要求在分析熔料流动、气体穿透物理模型的基础上，探讨了气体辅助注射成型数值分析的实现原理运用MPI／Gas模块对一T型支架进行了气体辅助注射成型CAE分析，模拟不同工艺条件下的气体穿透效果，确定了合理的工艺参数。     

气辅注射成型工艺参数正交试验CAE模拟设定

介绍了气辅注射成型中的CAE分析方法;阐述了气辅制品缺陷与工艺参数的关系;运用正交试验方法对待定气辅成型工艺参数进行合理的实验设计,采用塑料成型过程模拟仿真软件MoldflowPlasticInsight5.0对气辅成型塑料周转箱制品进行FLOW-COOL-FLOW分析,代替传统试模方法,并对CAE后处理结果进行综合分析,得到一组优化的工艺参数组合。该组气辅成型工艺参数组合可作为实际生产中的气辅成型工艺参数设定值。     

基于Taguchi的气体辅助注射成型工艺参数优化

运用Taguchi试验结合MPI模拟技术,以带筋板类零件的气体辅助注射成型为例,以模具型腔有无短射、气指程度和气道气体穿透率为质量指标,分析模具温度、熔体注射温度、熔体预注射量、气体延迟时间、气体注射压力以及气体注射时间等工艺参数对气体辅助注射成型制品质量的影响规律。运用变量分析确定工艺参数对质量指标的影响度。最后利用加权综合评分法,获得最优的工艺参数组合,为气辅工艺设计提供指导。     

气体辅助注射成型过程中工艺参数对产品质量的影响

以一个管状与平板相结合的气体辅助注射成型制品作为研究对象,引入了正交实验法和CAE技术,首先筛选出了影响制品质量的关键因素,再通过实际的气体辅助注塑成型实验,着重讨论了气体注射延迟时间和气体注射压力两个工艺参数对制品质量的影响,通过调节气体注射延迟时间和气体注射压力两个工艺参数的设置,参考气体穿透的变化情况,均能获得较好气体穿透效果的工艺参数组合.     

Thermal and geometry impacts on the structure and mechanical properties of part produced by polymer additive manufacturing

A relatively new additive manufacturing machine called Freeformer was employed as an alternative to common Fused Filament Fabrication (FFF) machines. While FFF machines are fed with expensive and few commercially available filament feedstock, Freeformer is fed with cheap and common polymer pellets. In this study, more than 400 dumbbells made of Acrylonitrile Butadiene Styrene (ABS) were processed varying many processing conditions to evaluate their impacts on the structure and so on, the mechanical properties of 3D parts. Among processing parameter, nozzle temperature, manufacturing chamber temperature, discharge parameters, filling density, raster geometry, slicing distance, number of contour lines, processing speed, filling raster-contour lines overlap, and processing angles were studied. Images obtained with a scanning electron microscope and 3D part density estimations reveal strong changes on the 3D part structure as a function of the processing parameters so that tensile tests exhibit high variations between the 3D part mechanical properties.     

工艺参数及交互作用对塑料件收缩异向性影响的研究

采用部分因子实验设计法,考察了工艺参数对一维流动平板在平行流动方向和垂直流动方向上收缩的交互影响。通过交互作用图筛选了重要的交互影响,并基于影响度来分析各参数及重要交互作用对制品质量的影响程度。实验证明,因子之间的两两交互作用对制品质量的影响是不可忽略的,在分析工艺参数对注塑制品收缩的影响时,应该考虑收缩的异向性。     

基于CAE的厚壁塑件注塑工艺参数优化

利用Moldflow软件对某厚壁塑料制件的注射成型过程进行分析,选取反映制品收缩与翘曲的多个评价指标,结合正交实验法,优化充填时间、熔体温度、保压时间、保压压力、冷却时间等工艺参数,通过均值分析与极差分析研究各因素对各评价指标的影响,并通过综合评分法得到一组最佳的工艺参数。结果表明,增加保压时间与保压压力能减小产品的收缩和翘曲,且得出的最佳工艺组合为注射时间为2.5 s,熔体温度为280 ℃,保压时间为130 s,保压压力为110 MPa,冷却时间为40 s,该工艺下产品的质量疏松度、体积收缩率、平面误差、翘曲分别降低了6.66 %、7.90 %、12.5 %、20.83 %,产品整体成型品质得到有效提高。     

基于神经网络和遗传算法的注射成型工艺优化

论述人工神经网络和遗传算法在塑料注射成型工艺优化中的应用,首先利用人工神经网络建立注射成型工艺参数与塑件翘曲量之间关系的数学模型,然后用遗传算法对工艺参数优化.其中由正交法设计得到实验样本,由数值模拟软件计算得到塑件翘曲量,将其作为优化目标.按优化后的工艺参数进行实验,获得较高质量的塑料制品,从而为建立和控制注射模工艺参数提供一种行之有效的途径.     

气辅成型模具与工艺的优化

运用MoldFlow软件对把手零件的气辅注塑成型过程进行CAE分析,结合气辅成型原理优化了浇口、进气口的位置,总结了气辅CAE的工作流程,分析了各工艺参数对产品壁厚、气道长度的影响,并调整参数,得到了满足设计要求的制品。     

Effects of rheological properties and processing parameters on ABS thermoforming

Understanding effects of material and processing parameters on the thermoforming process is critical to the optimization of processing conditions and the development of better materials for high quality products. In this study we investigated the influence of both rheological properties and processing parameters on the part thickness distribution of a vacuum snap‐back forming process. Rheological properties included uniaxial and biaxial elongational viscosity and strain hardening and/or softening while processing parameters included friction coefficient, heat transfer coefficient, and sheet and mold temperatures. The Wagner two parameter nonlinear viscoelastic constitutive model was used to describe rheological behavior and was fit to shear and elongational experimental data. The linear viscoelastic properties along with the Wagner model were utilized for numerical simulation of the thermoforming operation. Simulations of pre‐stretched vacuum thermoforming with a relatively complex mold for a commercial refrigerator liner were conducted. The effects of nonlinear rheological behavior were determined by arbitrarily changing model parameters. This allows determination of which rheological features (i.e., elongational mode, viscosity, and strain hardening and/or softening) are most critical to the vacuum snap‐back thermoforming operation. We found that rheological and friction properties showed a predominant role over other processing parameters for uniform thickness distribution.     

基于神经网络和遗传算法的塑料热压成型多目标优化

论述了人工神经网络和遗传算法在塑料热压成型工艺优化中的应用,首先利用人工神经网络建立热压成型工艺参数与零件性能之间关系的数学模型,然后用遗传算法对工艺参数优化。根据多目标函数优化问题的单目标化思想,对优化后的单目标进行分解,得到最优工艺参数条件下的塑料热压产品性能,从而为建立和控制塑料热压成型工艺参数提供了一种行之有效的方法。     

Influence of processing on quality of injection-compression-molded disks

A modified injection molding machine with a compression mechanism, and a mold with a movable wall and shut-off mechanism, were used to investigate the effects of processing parameters on the quality of injection-compression-molded polystyrene disks. The compression start-up time, compression force, melt temperature, and part thickness were selected processing parameters. The disk moldings were evaluated based on dimensional accuracy and birefringence. It is found that the compression start-up time affects packing time, and thus greatly affects the residual orientation. If the transition from packing to compression could start before peak cavity pressure, disks with low residual orientation could be obtained. High compression force improves part quality and reduces thickness. Since both compression-induced reduction and cooling-induced shrinkage are involved, the effects of temperature and thickness are not as straightforward as the trends in conventional injection molding.     

Injection-Compression Molded Part Shrinkage Uniformity Comparison between Semicrystalline and Amorphous Plastics

Both semicrystalline polypropylene (PP) and amorphous polystyrene (PS) parts were molded by injection-compression molding. The Taguchi method was utilized to investigate the effects of six processing parameters, including mold temperature, compression speed, compression time, compression distance, delay time, and compression force, on part shrinkage uniformity (SU), which was represented by standard deviation of shrinkage. Analyses of means and variance showed that the compression force is the most important parameter for SU of both parts. The compression distance is the second most significant parameter of SU on the PS parts, but it is the least important parameter on the PP part. The optimal processing parameters for improving the SU of both parts were found and verified experimentally.     

A Dynamic Injection-Molding Process Model for Simulating Mold Cavity Pressure Patterns

The quality of injection-molded parts is typically controlled in the plant using statistical techniques that involve measuring parts as well as monitoring processing parameters. Part quality is also controlled by machine operators who adjust processing conditions in response to trends in process behavior. To achieve direct on-line monitoring and automatic control of part quality, a multivariable, nonlinear process model must be developed that relates process behavior to machine-controllable parameters. Presented in this work are the details of such a model derived from first principles and proven correlations. Because recent work has shown that complete mold cavity pressure patterns are good indicators of part quality, the focus of this lumped-parameter model is to simulate mold cavity pressure patterns observed during the filling, packing, and cooling stages of the process, given machine set points for barrel temperature and holding pressure. The model is validated experimentally using a production injection-molding machine and parameter-sensitivity case studies are presented.