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。     

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.     

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。     

注塑成型填充工艺的CAE分析及应用实施

充填保压阶段是注塑成型过程的两个重要阶段,对制品质量有着很大的影响。本文以一汽车音响框架类产品为例,根据实际注塑机情况,采用Moldflow/MPI软件分析塑料熔体的充填保压阶段,从充填时间、温度、及外观熔接线等结果进行阐述,预估合理的成型工艺参数,并进行实际试模,达到合理注塑,大大缩短了找到所需成型工艺条件的时间,显示了CAE技术在注塑成型过程中的突出作用。     

Ultrasonic torsional guided wave sensor for flow front monitoring inside molds

Measuring the extent of flow of viscous fluids inside opaque molds has been a very important parameter in determining the quality of products in the manufacturing process such as injection molding and resin transfer molding. Hence, in this article, an ultrasonic torsional guided wave sensor has been discussed for monitoring the movement of flow front during filling of resins in opaque molds. A pair of piezoelectric normal shear transducers were used for generating and receiving the fundamental ultrasonic torsional guided wave mode in thin copper wires. The torsional mode was excited at one end of the wire, while the flowing viscous fluid progressively wet the other free end of the wire. The time of flight of the transient reflections of this fundamental mode from the air-fluid interface, where the wire enters the resin, was used to measure the position of the fluid flow front. Experiments were conducted on four fluids with different viscosity values. Two postprocessing algorithms were developed for enhancing the transient reflected signal and for suppressing the unwanted stationary signals. The algorithms were tested for cases where the reflected signals showed a poor signal to noise ratio.     

In-situ density measurement for plastic injection molding via ultrasonic technology

Density variation during the injection molding process directly reflects the state of plastic melt and contains valuable information for process monitoring and optimization. Therefore, in-situ density measurement is of great interest and has significant application value. The existing methods, such as pressure−volume−temperature (PVT) method, have the shortages of time-delay and high cost of sensors. This study is the first to propose an in-situ density measurement method using ultrasonic technology. The analyses of the time-domain and frequency-domain signals are combined in the proposed method. The ultrasonic velocity is obtained from the time-domain signals, and the acoustic impedance is computed through a full-spectral analysis of the frequency-domain signals. Experiments with different process conditions are conducted, including different melt temperature, injection speed, material, and mold structure. Results show that the proposed method has good agreement with the PVT method. The proposed method has the advantages of in-situ measurement, non-destructive, high accuracy, low cost, and is of great application value for the injection molding industry.     

基于深度置信网络的大数据制造过程实时智能监控

针对基于浅层学习模型的过程监控方法难以对大数据制造过程运行状态进行实时智能监控的问题,提出了基于深度置信网络的大数据制造过程实时智能监控方法。利用灰度图建立大数据制造过程质量图谱,以精准表达其过程的运行状态;构建用于识别大数据制造过程质量图谱的深度置信网络;应用离线训练好的深度置信网络模型对当前监控窗口内的过程质量图谱进行识别,实现大数据制造过程实时智能监控。最后,应用该方法对某注塑件大数据制造过程进行实时质量智能监控,结果表明：所提方法的识别性能明显优于基于主成分分析与BP神经网络、支持向量机的识别模型,能有效应用于大数据制造过程实时质量智能监控。     

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.     

单片机在注塑机上的应用

阐述了注塑机的基本原理，根据注塑机的控制流程，利用51单片机实现其动作的自动进行。     

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.     

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.     

Three-dimensional numerical simulation for plastic injection-compression molding

Compared with conventional injection molding, injection-compression molding can mold optical parts with higher precision and lower flow residual stress. However, the melt flow process in a closed cavity becomes more complex because of the moving cavity boundary during compression and the nonlinear problems caused by non-Newtonian polymer melt. In this study, a 3D simulation method was developed for injection-compression molding. In this method, arbitrary Lagrangian- Eulerian was introduced to model the moving-boundary flow problem in the compression stage. The non-Newtonian characteristics and compressibility of the polymer melt were considered. The melt flow and pressure distribution in the cavity were investigated by using the proposed simulation method and compared with those of injection molding. Results reveal that the fountain flow effect becomes significant when the cavity thickness increases during compression. The back flow also plays an important role in the flow pattern and redistribution of cavity pressure. The discrepancy in pressures at different points along the flow path is complicated rather than monotonically decreased in injection molding.     

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。     

基于CMAC和PID复合控制器的精密注塑机保压压力控制

针对传统PID算法对注塑成形过程中保压压力进行控制时存在压力超调、静差和时延等问题,建立了注塑过程保压压力的系统数学模型,针对系统特性设计了新型CMAC和PID复合控制器。实验结果表明,相对于传统PID控制,CMAC和PID复合控制对保压压力的超调、静差和时延有显著的改善,制品质量和重复精度得到明显提高。     

气体辅助注射成形充模流动数值模拟的研究

基于广义Hele—Shaw流动模型,通过引入合理的简化和假设,建立了实现气体辅助注射成形充模流动模拟的数学方程、气体穿透过程的边界条件、CAD/CAE建模关键技术以及系统程序设计方法等。该研究对气体的穿透过程、压力场分布、小同时刻熔体/气体边界的移动状态以及在模壁上形成表层聚合物熔体壁厚的过程进行_了气体辅助注射成形充模流动的实例数值模拟研究。结果表明:增大气体注射压力,在其气体穿透方向所形成的表层熔体厚度比值也增人,降低熔体注射温度和非牛顿指数会增大气体穿透的壁厚值,其值接近试验测定的数值范围,也比较符合实际的气辅注射成形工艺结果。     

基于Moldflow对突变壁厚塑件的注塑成型优化方案的研究

利用Mold flow/MPI技术对摩托车外壳进行CAE注塑模拟分析,预测了成型过程中的填充流动情况,并对型腔的填充时间、压力分布、锁模力大小等进行计算分析,掌握了翘曲变形产生的原因。根据注塑模拟分析结果优化了浇口、冷却系统设计方案和成型工艺参数,使模具设计更趋合理。优化后的成型方案用于实际生产,缩短了试模周期。     

Characteristics analysis of cellular bucket brigades with different constraints

Reconfigurable machines are used in various manufacturing processes increasingly, so that the system could be adapted to successive market changes, changes in customer requirements, and competition among manufacturers. This makes necessary designing reconfigurable machines that can get into the market as soon as possible. Injection molding machines are an example of an equipment that requires reconfigurability, and the mold is one of the machine modules requiring further attention. This article provides a design methodology that helps designers to decide which mold configuration is appropriate to produce a molded part family. Proposed methodology brings together classical methodologies and design modern tools, and establishes a sequence of activities during the product architecture. This adjustment provides a useful tool in the conceptual definition of the mold architecture and facilitates the preliminary design of molds. Decisions are based on nature and predominant features of conventional products which are manufactured by injection molding. A characterization of common products obtained by molding is proposed as a support database for developing the methodology. In addition, in this work, a case study is developed in order to illustrate how the methodology can be performed. Benefits and advantages of the model are summarized as well.     

Development of artificial neural network-based in-process mixed-material-caused flash monitoring (ANN-IPMFM) system in injection molding

This paper describes the development of an artificial neural network-based in-process mixed-material-caused flash monitoring system (ANN-IPMFM) in the injection molding process. This proposed system integrates two sub-systems. One is the vibration monitoring sub-system that utilizes an accelerometer sensor to collect and process vibration signals during the injection molding process. The other, a threshold prediction sub-system, predicts a control threshold based on the process parameter settings, thus allowing the system to adapt to changes in these settings. The integrated system compares the monitored vibration signals with the control threshold to predict whether or not flash will occur. The performance of the ANN-IPMFM system was determined by using varying ratios of polystyrene (PS) and low-density polyethylene (LDPE) in the injection molding process, and comparing the number of actual occurrences of flash with the number of occurrences predicted by the system. After a 180 trials, results demonstrated that the ANN-IPMFM system could predict flash with 92.7% accuracy.     

Grey fuzzy PI control for packing pressure during injection molding process

This study presents a grey fuzzy PI (GFPI) controller for packing pressure control during the injection process. The novel controller was designed for solving the problems of large overshoot, static error, and long delay time on servo motor-driven injection molding machines. Based on reasonable assumptions, a mathematical model of injection system was established. According to the nonlinear, severe interferences, and long delay time characteristics in injection process, this paper integrates predictive grey system, robust fuzzy ratiocination, and PI control. Using these mathematical model and control algorithms, a GFPI controller is implemented into an MCU using C programming techniques. The integral discrete PID controller and solid fuzzy controller were realized for contrast experiments. The experimental results show that GFPI controller had better performance on reducing overshoot and static error, increasing both response rate and repeatable accuracy. Such a developed technology would provide helpful references for designing the controller of energy-saving servo motor-driven injection molding equipment.     

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。