注射成型工艺参数对微结构零件复制度的影响

为改善微结构零件的复制度,以具有广泛应用前景的精细微结构零件——微透镜阵列为案例,将制品成型重量作为制品复制度的量化衡量指标,运用单因素实验方法实验研究了注射成型工艺参数（熔体温度、模具温度、注射时间、保压压力、保压时间）对微结构零件制品重量的影响规律。实验结果表明,增加熔体温度和模具温度能使保压压力更有效地通过浇注系统传递到微型腔,增加制品重量;成型重量大的制品,微结构的填充要好于重量轻的制品,微结构零件复制度与成型重量存在对应关系。制品重量可初步评价微结构零件的复制度,研究各工艺参数对制品重量的影响规律对提高微结构零件的复制度有重要意义。     

Influence of molding conditions on the shrinkage and roundness of injection molded parts

During the plastic injection molding process, one of the biggest challenges is shrinkage which deteriorates the quality of produced parts. To control and reduce this defect, the essential way is to perfectly determine the variables like molding parameters. In this study, the effects of molding parameters including packing pressure, melt temperature, and cooling time on shrinkage and roundness have been investigated experimentally. Also, the relationship among initial molding parameters, the cavity pressure, and mold temperature was investigated. The results of this experimental study and analysis fulfill various requirements of plastic injection molding and clarify the relationship between molding conditions and the overall quality of produced parts. This study illustrated that packing pressure and melt temperature are dominant factors which determine the quality of parts.     

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.

     

CAE技术在注塑产品中的应用

应用CAE软件Moldflow对充电器底座进行流动模拟分析,发现产品成型存在欠注.在分析了欠注产生原因的基础上,通过优化充电器底座结构,以及调整模具温度、熔体温度、注射时间、保压压力和时间等注塑工艺参数,解决了欠注问题,同时改善了翘曲变形,保证了注塑产品的成型质量.     

Optimization and Simulation of Plastic Injection Process using Genetic Algorithm and Moldflow

The use of plastic-based products is continuously increasing. The increasing demands for thinner products, lower production costs, yet higher product quality has triggered an increase in the number of research projects on plastic molding processes. An important branch of such research is focused on mold cooling system. Conventional cooling systems are most widely used because they are easy to make by using conventional machining processes.However, the non-uniform cooling processes are considered as one of their weaknesses. Apart from the conventional systems, there are also conformal cooling systems that are designed for faster and more uniform plastic mold cooling. In this study, the conformal cooling system is applied for the production of bowl-shaped product made of PP AZ564. Optimization is conducted to initiate machine setup parameters, namely, the melting temperature, injection pressure, holding pressure and holding time. The genetic algorithm method and Moldflow were used to optimize the injection process parameters at a minimum cycle time. It is found that, an optimum injection molding processes could be obtained by setting the parameters to the following values: T_M= 180 °C; P_(inj)= 20 MPa;P_(hold)= 16 MPa and t_(hold)= 8 s, with a cycle time of 14.11 s. Experiments using the conformal cooling system yielded an average cycle time of 14.19 s. The studied conformal cooling system yielded a volumetric shrinkage of 5.61% and the wall shear stress was found at 0.17 MPa.The difference between the cycle time obtained through simulations and experiments using the conformal cooling system was insignificant(below 1%). Thus, combining process parameters optimization and simulations by using genetic algorithm method with Moldflow can be considered as valid.     

PLC在注塑机控制系统中的应用

SZ-250A型注塑机是一种中小型塑料注射成型机,它将颗粒状的塑料加热熔化到流动状,用注射装置快速、高压注入模腔,保压一定时间,冷却后成型为塑料制品。根据其工艺流程及工作控制要求,设计了以PLC为控制核心的控制系统,给出了此控制系统软硬件设计的全过程。通过实践使用证明,该控制系统具有快速、高效、高可靠性、抗干扰能力强等特点,实现了注塑机注塑全过程的自动控制。     

A nondestructive online method for monitoring the injection molding process by collecting and analyzing machine running data

Nondestructive online monitoring of injection molding processes is of great importance. However, almost all prior research has focused on monitoring polymers in molds and damaging the molds. Injection molding machines are the most important type of equipment for producing polymeric products, and abundant information about actual polymer processing conditions can be obtained from data collected from operating machines. In this paper, we propose a nondestructive online method for monitoring injection molding processes by collecting and analyzing signals from injection molding machines. Electrical sensors installed in the injection molding machine, not in the mold, are used to collect physical signals. A multimedia timer technique and a multithread method are adopted for real-time large-capacity data collection. An algorithm automatically identifies the different stages of the molding process for signal analysis. Moreover, ultrasonic monitoring technology is integrated to measure the cavity pressures. Experimental results show that our nondestructive method can continuously monitor the injection molding process in real time and automatically identify the different stages of the molding process. The packing parameters, including the filling-to-packing switchover point and the packing time, can be optimized based on these data. Furthermore, the ultrasonic reflection coefficient and the actual cavity pressure have similar trends, and our technique for measuring the cavity pressure is accurate and effective.     

基于Moldflow与正交试验法的注塑参数优化

在塑料产品的开发过程中,涉及到塑料模具进行注塑,注塑模具开发方案确定后,最重要的就是如何选择注塑参数。注塑参数可以在注塑机上直接进行试生产来调试,但必须是模具制造出来之后才能进行,对场地和设备均有要求,而且在试模过程中会浪费一定的塑料原材料。本文采用正交试验法对注塑参数进行优化,利用Moldflow软件的模具CAE技术对正交试验过程各种取值情况进行验证,并对最终优化组合进行验证,检验正交试验的正确性。确定翘曲变形量为实验指标,以注射温度、模具温度、充填时间、冷却时间、保压时间为变量的5因素,取各自允许取值范围进行4均分得到4水平,形成一个5因素4水平的正交试验矩阵设计实验,找出KDC-1型电磁断路器塑料壳体充填优化组合,通过在Moldflow的验证,及时反映了该正交试验结果是正确的注塑参数最优组合。     

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。     

Influential factors on the outer lens color in an industrial injection molding process

The quality and the aspect of outer lens fabricated by an injection molding process are an essential part for the optical properties of vehicular lighting system. This work is accurately addressed to identify the influential factors on the red color of the outer lens made of blends of polycarbonate and acrylonitrile butadiene styrene, which are mixed with a red masterbatch. Seven factors were investigated in this work: masterbatch concentration, mold and nozzle temperatures, holding and packing pressures, and holding and packing times. The main influential factors were found through a design of experiments in a linear approximation. The outer lens's red color is mainly influenced by the masterbatch concentration, the nozzle temperature, holding time, and the holding pressure, in a decreasing order. In contrast, the mold temperature, packing pressure, and packing time are not statically significant in the color appearance.     

Analysis and modeling of effective parameters for dimension shrinkage variation of injection molded part with thin shell feature using response surface methodology

The injection molded housing part with thin shell feature could be produced to increase the internal space for packing more components. In this study, injection velocity, packing pressure, mold temperature, and melt temperature were selected as effective parameters for injection molding process. For the purpose of reducing dimension shrinkage variation of thin shell molded part, the response surface methodology was utilized to determine the relationship between input parameters and responses. Then the optimization condition was obtained according to the desirability function. Results show that melt temperature is the most significant factor on dimension shrinkage variation in transverse direction, followed by packing pressure, mold temperature, and injection velocity. However, in the longitudinal direction, packing pressure has the greatest influence on the dimension shrinkage variation, followed by injection velocity, melt temperature, and mold temperature. In accordance with verification experiments, the difference between the experimental data and predicted values ranges from ?9.8% to 1.8%. To obtain the optimal condition, the overall desirability must be larger than 0.9. Based on analysis of variance, the proposed models look reasonably accurate.     

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.     

Minimization of variation in volumetric shrinkage and deflection on injection molding of Bi-aspheric lens using numerical simulation

The profile of a bi-aspheric lens is such a way that the thickness narrows down from center to periphery (convex). Injection molding of these profiles has high shrinkage in localized areas, which results in internal voids or sink marks when the part gets cool down to room temperature. This paper deals with the influence of injection molding process parameters such as mold surface temperature, melt temperature, injection time, V/P Switch over by percentage volume filled, packing pressure, and packing duration on the volumetric shrinkage and deflection. The optimal molding parameters for minimum variation in volumetric shrinkage and deflection of bi-aspheric lens have been determined with the application of computer numerical simulation integrated with optimization. The real experimental work carried out with optimal molding parameters and found to have a shallow and steep surface profile accuracy of 0.14 and 1.57 mm, 21.38-45.66 and 12.28-26.90 μm, 41.56-157.33 and 41.56-157.33 nm towards Radii of curvatures (RoC), surface roughness (Ra) and waviness of the surface profiles (profile error Pt), respectively.

     

Heat transfer in injection molding for reproduction of sub-micron-sized features

Heat transfer in injection molding was quantitatively measured with micro heat-flux sensors. The 0.1–10 micron-wide micro-grooves with aspect ratios of 0.5–1.0 were etched by focus ion beam on a Ni-plated mold. During the short time just after injecting, heat-flux in the mold was maximized to 10–50 W/cm², and heat transfer coefficient between plastic and mold was 0.27 W/(cm²K) with PMMA and 0.085 W/(cm²K) with PS. The maximum mold surface temperature just after injecting should be above the glass transition temperature of plastic, then reproducing sub-micron-wide micro-ridges.     

Effects of machined cavity texture on ejection force in micro injection molding

The friction force developed in the demolding phase of the micro injection molding process is mainly determined by mold surface finish, which affects the tribological phenomena occurring at the polymer–tool interface. In this work, the effects on the ejection force of two cavity surfaces machined with different technologies (viz. micro milling and micro electro discharge machining), but with similar value of Ra, were investigated. The relations between different surface topography parameters and the ejection force were then analyzed, in order to identify the parameters that most appropriately describe the friction at the polymer–tool interface. The experimental results showed the strong interactions between the mold surface texture and the micro injection molding process parameters that promote the replication, such as mold temperature and holding pressure. The different machining technologies generated two mold textures that have a similar value of Ra, but their influence on friction can be properly described only using several other surface topography parameters.     

An integrated parameter optimization system for MISO plastic injection molding

This paper presents the development of a parameter optimization system that integrates mold flow analysis, the Taguchi method, analysis of variance (ANOVA), back-propagation neural networks (BPNNs), genetic algorithms (GAs), and the Davidon–Fletcher–Powell (DFP) method to generate optimal process parameter settings for multiple-input single-output plastic injection molding. In the computer-aided engineering simulations, Moldex3D software was employed to determine the preliminary process parameter settings. For process parameter optimization, an L₂₅ orthogonal array experiment was conducted to arrange the number of experimental runs. The injection time, velocity pressure switch position, packing pressure, and injection velocity were employed as process control parameters, with product weight as the target quality. The significant process parameters influencing the product weight and the signal to noise (S/N) ratio were determined using experimental data based on the ANOVA method. Experimental data from the Taguchi method were used to train and test the BPNNs. Then, the BPNN was combined with the DFP method and the GAs to determine the final optimal parameter settings. Three confirmation experiments were performed to verify the effectiveness of the proposed system. Experimental results show that the proposed system not only avoids shortcomings inherent in the commonly used Taguchi method but also produced significant quality and cost advantages.     

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.     

高相对密度钨球注射成形过程中的喷射现象

针对金属粉末注射成形过程中喷射所引起的气穴、不良流动等缺陷,对高相对密度钨球合金制品进行了注射成形试验,发现钨球注射成形初期存在喷射现象,喂料一进入型腔即发生剧烈扭曲变形。通过多种成形工艺参数的组合,并且根据非牛顿流体的剪切变稀特性,采用高速高压成形工艺使得注射过程在瞬间完成,从而消除喷射对金属粉末制品的影响。同时,采用Moldflow MPI软件对其成形过程进行仿真,结果表明基于欧拉法的传统模流仿真软件无法模拟喷射现象,从而提出采用拉格朗日法对喷射成形过程进行建模和仿真的构想。将上述喷射现象的研究结果与高相对密度钨球的实际生产现状相结合,提出在定模板的模具分形面一侧开排气道增强其排气能力,和采用高速高压注射成形工艺解决喷射所造成的质量问题。     

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。     

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.