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。     

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.     

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.     

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。     

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。     

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。     

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.     

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.     

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.

     

An effective warpage optimization method in injection molding based on the Kriging model

In this paper, an effective optimization method using the Kriging model is proposed to minimize the warpage in injection molding. The warpage deformations are nonlinear, implicit functions of the process conditions, which are typically evaluated by the solution of finite element (FE) equations, a complicated task which often involves huge computational effort. The Kriging model can build an approximate function relationship between warpage and the process conditions, replacing the expensive FE reanalysis of warpage in the optimization. In addition, a “space-filing” sampling strategy for the Kriging model, named rectangular grid, is modified. Moldflow Corporation’s Plastics Insight software is used to analyze the warpage deformations of the injection-molded parts. As an example, the warpage of a cellular phone cover is investigated, where the mold temperature, melt temperature, injection time, and packing pressure are regarded as the design variables. The result shows that the proposed optimization method can effectively decrease the warpage deformations of the cellular phone cover and that the injection time has the most important influence on warpage in the chosen range.     

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。     

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.     

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.

     

OPTIMIZATION STUDY IN MANUFACTURING PROCES SFOR PC／ABS BLENDS

The optimization of injection molding process for polycarbonate/acrylonitrile-butadiene-styrene (PC/ABS) blends is studied using Taguchi method and principal component analysis (PCA). Four controllable process factors are studied at three levels each in the manufacturing process. The L9 orthogonal array is conducted to determine the optimum process factor/leve! combination for single quality of mechanical properties. In addition, the principal component analysis is employed to transform the correlated mechanical properties to a set of uncorrelated components and to evaluate a comprehensive index for multi-response cases. Then the optimum process factor/level combination for multiple qualities can be determined. Finally, the analysis of variance is used to find out the most influential injection molding parameter for single and multiple qualities problems.     

Three-dimensional numerical modeling of an induction heated injection molding tool with flow visualization

Using elevated mold temperature is known to have a positive influence of final injection molded parts. Induction heating is a method that allow obtaining a rapid thermal cycle, so the overall molding cycle time is not increased. In the present research work, an integrated multi-turn induction heating coil has been developed and assembled into an injection molding tool provided with a glass window, so the effect of induction heating can directly be captured by a high speed camera. In addition, thermocouples and pressure sensors are also installed, and together with the high speed videos, comparison of the induction heating and filling of the cavity is compared and validated with simulations. Two polymer materials ABS and HVPC were utilized during the injection molding experiments carried out in this work. A nonlinear electromagnetic model was employed to establish an effective linear magnetic permeability. The three-dimensional transient thermal field of the mold cavity was then calculated and compared with the experiments. This thermal field was transferred to an injection molding flow solver to compare simulations and experimental results from the high speed video, both with and without the effect of induction heating. A rapid thermal cycle was proved to be feasible in a mold with an integrated induction coil. Furthermore, it was shown that the process can be modeled with good accuracy, both in terms of the thermal field and of the flow pattern.     

精密注塑模具的注塑成型模拟

在精密注塑模具设计过程中应用计算机辅助分析的方法模拟注塑成型过程,对保证注塑成型产品的质量,提高生产率有极大的优越性。利用MoldflowPlasticInsight(MPI)软件对精密注塑模具的注塑成型过程进行了模拟,分别介绍了注塑参数、浇注系统和冷却系统的设计过程,比较了两套不同设计方案的注塑成型模拟结果。并对影响翘曲变形的各个因素进行了详细的分析。     

超薄塑件注塑成形特性的试验研究与数值模拟

薄壁注塑成形技术具有节约材料、降低成本、减少制品重量和外形尺寸等优点,可促进移动电话等电子产品的迅速发展,特别是超薄塑件的注塑成形技术在微机电领域具有巨大的应用潜力。但随着制品厚度的减小也使注射成形难度加大,填充过程更为复杂,成形特性有待探索。设计制造出可成形超薄塑件的模具,利用正交试验方法(田口方法)进行充模试验和数值模拟技术研究各工艺参数(注射速度、注射压力、熔体温度、注射量和制品厚度等)对超薄塑件注塑成形充模过程的影响。研究结果表明,制品厚度对超薄塑件的填充起决定性作用;注射量及注射速度对超薄塑件注塑成形的填充起主导作用,提高注射速度能大幅度地提高填充率;熔体温度和注射压力相对于注射量和注射速度只起次要作用,但在填充过程中,高的熔体温度和注射压力也是必要的。     

模具先进制造技术在塑料成型模具设计方法中的应用研究

以塑料电器壳体为例,论述了基于Moldex 3D模流分析CAE的塑料注射成型模具设计方法。针对该薄壳类塑料制品的塑料注射成型加工工艺进行了研究,包括零件电器壳体用ABS树脂材料性能分析、聚碳酸酯（PC）材料性能分析、零件的塑料注射成型工艺过程分析、针对该薄壳类塑料制品的侧向分型注射成型模具的设计与制造研究及相关技术图样资料分析等。     

Injection mold design of reverse engineering using injection molding analysis and machine learning

Plastic composites are used in vehicle components to improve fuel efficiency. Thus, the warpage of injection-molded plastic parts has become a quality issue. Factors, such as product shape and thickness, resin, and other injection molding conditions, can be modified to improve the warpage problem. However, if these factors are set with no possible adjustments, reverse engineering may be required. Reverse engineering is a difficult process that requires many trials and errors; thus, it is only used as a last resort. With respect to the warpage issue, reverse engineering considers the following: (1) Predicting and (2) modeling the warpage in opposite directions. Autodesk Moldflow Insight accommodates these key considerations, but many researchers are reluctant to use it. Although existing injectionmolding analysis programs are mainly used to predict qualitative results, computer-aided engineering (CAE) for reverse engineering requires quantitative analysis. Hence, the considerations are different from the existing analyses. An error in warpage prediction may lead to a costly mold modification because of the molds' complex structures. Quantitative warpage prediction for reverse engineering depends on process variables; thus, understanding how warpages are affected by uncertain process variables is important to improve the reliability of reverse engineering. Moreover, even if appropriate process variables are set, they cannot be applied due to tolerance in lengths. For this reason, mold shrinkage must be identified before designing a mold. This study conducted injection molding analysis for a radiator tank that uses glass fiber-reinforced plastic using Autodesk Moldflow Insight 2018.2. Data for warpage prediction were generated in accordance with five process variables to identify the relationship between the level of warpage and process variables. CAE also showed the level of mold shrinkage that can reduce warpage. In addition, a predictive model was created using the multilayer perceptron (MLP)- supervised learning technique, which is a deep learning method for artificial neural networks. The predictive model was compared with typical regression models, such as polynomial regression (also known as response surface model), EDT and RBF, to determine the optimal approximation model. The real modeling time for a radiator tank product is 1 h, but the MLP approximation model required only 1 min and 8 s to perform 8530 iterations with a similar reliability.