注塑制品的翘曲优化与分析

翘曲变形是注塑制品的一种严重缺陷,而工艺参数直接影响制品的质量,因此建立翘曲与工艺参数之间的关系模型并求得最优的工艺参数对制品质量的改善非常关键。文中运用Fractional Factorial方法从众多的实验因子中找出与塑件翘曲量密切相关的独立因子和交互因子,然后采用具有高度非线性识别能力的人工神经网络与遗传算法相结合的方法,建立塑件翘曲量与主要影响工艺参数之间的关系模型。将人工神经网络预测结果和计算机辅助工程软件模拟结果进行比较和误差分析,显示出该方法的可靠性。实验结果表明,该方法能明显提高塑件的质量,通过优化可使翘曲量减小74.06%。     

基于翘曲的注塑成型工艺参数优化组合

采用Taguchi方法设计注塑成型模拟实验,以注塑成型工艺参数混合优化模块方法确定工艺参数最优组合。先建立注塑成型工艺参数混合优化模块,按L9(34)正交表进行实验,得到熔体温度、保压压力、注射时间、成型周期时间等参数不同水平时的翘曲和收缩率数据,翘曲量和收缩率都属于望小特性,计算出S/N比,通过S/N比反应图分析得出较优工艺参数组合,再实验验证确定最优组合,最后说明注塑成型工艺参数混合优化模块方法可行。     

注塑成型制品翘曲变形优化建模分析

目的 针对传统建模方法在预测的翘曲变形位置与实际偏差较大的问题,开展基于Moldflow的注塑成型制品翘曲变形优化建模分析研究.方法 通过数据模拟分析预处理、浇注体系模型构建、基于Moldflow的注塑成型制品翘曲变形过程模拟等手段,实现对注塑成型制品曲面参数优化.结果 通过对比实验证明,新的建模方法与传统建模方法相比...     

神经网络与混合遗传算法结合的注塑成型工艺优化

注塑成型中,工艺参数直接影响到模具内熔体的流动状态和最终制品的质量,而工艺参数与制品质量之间的关系非常复杂,因此如何建立制品质量与工艺参数之间的关系模型井获得优化的工艺参数是改善制品质量的关键。收缩是衡量制品质量的一个重要指标,制品在型腔中的非均匀收缩是引起制品翘曲的主要原因。文中基于成型过程的数值模拟,采用人工神经网络与混合遗传算法结合优化注塑成型工艺,以改善制品质量。对一工业产品进行分析,以制品内的体收缩率差值为质量指标优化工艺,改善了制品内的体收缩率分布,获得了满意效果。     

基于混沌粒子群的微齿轮注塑精密成形翘曲变形分析

目的 针对注塑加工生产的微齿轮运转一段时间后会出现严重变形的问题,对微齿轮注塑精密成形翘曲变形进行分析。方法 基于混沌粒子群建立微齿轮注塑CAE模型,获取曲面全局最优解,在此基础上,计算微齿轮注塑精密成形翘曲收缩率,得到翘曲变形量,同时优化微齿轮注塑精密成形工艺参数,分析微齿轮注塑精密成形翘曲变形情况。结果 将仿真结果与实际试验结果进行对比,得出该分析方法的预测结果与实际翘曲变化趋势完全一致。结论 微齿轮注塑中心位置的翘曲变形量最大,离中心位置越远,翘曲变形量越小。     

基于DOE和RSM的轴流风扇叶片成型精度分析与优化

轴流风扇通常采用注塑工艺制造,其翘曲变形缺陷影响精度,导致风扇的叶珊结构变化,进而影响动力、噪声、动平衡等方面性能。以一种平直翼型轴流风扇为研究目标,采用聚对苯二甲酸丁二醇酯(PBT)材料为注塑原料,对扇叶的注塑成型过程进行模拟仿真,利用田口实验(DOE)分析工艺参数对翘曲变形的影响,确定了各工艺参数对扇叶结构的影响权重,得出模具表面温度、熔体温度、注射时间、保压压力为主要影响因素;另外,采用响应曲面法(RSM)实验设计确定最佳工艺组合,将最大Z方向翘曲量从0.197 0 mm降低到0.108 1 mm,平均Z方向翘曲量从0.104 0 mm降低到0.035 9 mm,分别优化了0.088 9 mm和0.068 1 mm。     

基于正交试验与BP神经网络-遗传算法的空气净化器外壳注塑工艺参数优化

目的 以某空气净化器外壳为研究对象,进行注塑工艺参数优化,从而提高塑料制品的成型质量。方法 设置4个影响塑料制品成型质量的因素：熔体温度、模具温度、保压压力、保压时间,以最大翘曲值作为衡量塑料制品成型质量的指标,通过Moldflow模流分析软件,基于正交试验及极差分析探究各因素的影响主次顺序;使用BP神经网络表征工艺参数与翘曲变形的非线性映射关系;采用遗传算法寻优获得最佳注塑工艺参数组合与翘曲变形量,并将所得参数组合用于实际生产指导。结果 经极差分析,保压压力对塑料制品质量的影响最为显著,其次分别为模具温度、保压时间、熔体温度。经BP神经网络预测与遗传算法寻优,发现当熔体温度为229.5℃、模具温度为77.9℃、保压压力为84.4 MPa、保压时间为6.5 s时,可以使注塑件达到最优质量,预测的最小翘曲值为2.94 mm,此工艺参数组合下的仿真计算翘曲值为2.91 mm,二者吻合程度较高。将优化后的工艺参数组合用于实际生产指导,获得了质量良好的注塑产品。结论 所提出的方法对产品注塑的成型及优化有良好的工程应用价值。     

基于响应曲面和遗传算法的工艺参数优化

注塑工艺参数严重影响薄壳类塑件的翘曲变形。文中以汽车遮阳板为例,运用中心复合设计(CCD)和Moldflow分析获得大量的实验数据,然后利用响应曲面法建立工艺参数和优化目标之间的多元回归模型,通过方差分析表明预测模型的有效性,最后结合遗传算法对含有惩罚项的数学模型进行寻优,实现工艺参数的优化,从而降低塑件的翘曲变形。     

GPPS材料特性对注塑制品尺寸的影响分析

通过注塑成型数值模拟和Plackett-Burman实验设计相结合,以翘曲度评价注塑制品的翘曲变形情况,分析聚苯乙烯材料的流变特性参数和PVT特性参数对注塑制品变形的影响情况来研究材料特性对注塑制品尺寸的影响。结果表明,同一厂家生产不同牌号的同种材料引起的变形情况差距很大,文中研究的GPPS玻璃化转变时零压下的比容是注塑制品尺寸最重要的影响因素,玻璃化转变温度也有较大的影响。     

注塑成型工艺参数对制品体收缩率变化的影响及工艺参数优化

结合CAE及Taguchi DOE技术研究工艺参数对注塑制品体收缩率变化(制品中体收缩率的最大值与最小值的差值)的影响并获得优化的工艺参数以使制品的体收缩率变化最小。文中采用L9(3^4)正交矩阵进行实验,并研究了各个参数对制品体收缩率变化的影响程度,对于所选参数,保压压力和模具温度对注塑制品的体收缩率变化的影响较大。     

基于多域正交空间协同演进的薄壁注塑件翘曲优化研究

目的 鉴于细长、异形、薄壁注塑件成型机理复杂,且容易产生翘曲变形,以及传统工艺优化方法存在局限性的问题,以某轿车薄壁件为研究对象,优化注射成型工艺,以实现降低翘曲变形,提高效率的目的。方法 首先,对压力、温度因素及多重效应造成细长、异形、薄壁注塑件翘曲变形的机理进行分析。然后,在正交试验设计的基础上,应用Moldflow软件模拟仿真获取数据,并进行方差分析,获得各工艺参数的显著性。为了进一步优化工艺参数,打破传统优化方法存在的局限,提出一种多域正交空间协同演进（MDOSE）的集成优化方法。最后,基于该方法优化某轿车薄壁件的翘曲变形,并将其应用于实际生产制造。结果 与初始试验方案相比,优化后制件z方向上的翘曲从1.022 mm降低到了0.085 7 mm,减小了91.6%,证实了MDOSE优化方法的有效性和实用性。结论 优化结果表明,MDOSE优化方法一定程度上解决了薄壁件的翘曲优化问题,改善了传统工艺优化方法的局限。机理分析和工艺方法为往后的薄壁注塑件的实际生产提供了一定的理论支撑。     

Minimization of warpage and sink index in injection-molded thermoplastic parts using Taguchi optimization method

The objective of this paper consist of minimization of the warpage and sink index in terms of process parameters of the plastic parts have different rib cross-section types, and rib layout angle using Taguchi optimization method. Considering the process parameters such as mold temperature, melt temperature, packing pressure, in addition to rib cross-section types, and rib layout angle, a series of mold analyses are performed to exploite the warpage and sink index data. The polymeric materials were selected PC/ABS, POM, and PA66. Taguchi optimization method was used by exploiting mold analyses based on three level factorial design. Orthogonal arrays of Taguchi, the signal-to-noise (S/N) ratio, the analysis of variance (ANOVA) are utilized to find the optimal levels and the effect of process parameters on warpage and sink index. Confirmation analysis test with the optimal levels of process parameters are carried out in order to demonstrate the goodness of Taguchi method. From this, it can be concluded that Taguchi method is very suitable to solve the quality problem occurring the injection-molded thermoplastic parts.     

注塑成型有限元分析管理和成型工艺优化系统

目的针对注塑成型工艺过程,开发有限元分析项目流程管理和工艺参数优化的集成系统。方法参考企业项目流程,开发项目流程管理模块,使用多元回归拟合配合改进的模拟退火算法进行工艺参数的优化,通过塑料托盘翘曲变形量的优化来验证优化方法的准确性,进行塑料托盘的CAE分析正交试验,使用多元回归拟合试验数据并进行拟合优度检验,分别使用Isight和改进的模拟退火算法进行最优解的求解并对比计算结果,最后对得到的最优参数组合进行CAE分析得到变形量,将变形量与正交试验变形量对比。结果在回归方法中,使用惩罚系数为2的多项式岭回归效果较好,其优度指标R2为0.975,改进的模拟退火算法计算结果与Isight软件自适应模拟退火算法的计算结果基本一致,最优参数组合在Moldflow分析中的变形量为0.6805 mm,与之前的最小变形量0.7436 mm相比更小。结论使用回归拟合配合改进的模拟退火算法开发的工艺参数优化程序能起到较好的工艺参数优化效果。     

A hybrid of mode-pursuing sampling method and genetic algorithm for minimization of injection molding warpage

This paper presents a hybrid optimization method for minimizing the warpage of injection molded plastic parts. This proposed method combines a mode-pursuing sampling (MPS) method with a conventional global optimization algorithm, i.e. genetic algorithm, to search for the optimal injection molding process parameters. During optimization, Kriging surrogate modeling strategy is also exploited to substitute the computationally intensive Computer-Aided Engineering (CAE) simulation of injection molding process. With the application of genetic algorithm, the “likelihood-global optimums” are identified; and the MPS method generates and chooses new sample points in the neighborhood of the current “likelihood-global optimums”. By integrating the two algorithms, a new sampling guidance function is proposed, which can divert the search process towards the relatively unexplored region resulting in less likelihood of being trapped at the local minima. A case study of a food tray plastic part is presented, with the injection time, mold temperature, melt temperature and packing pressure selected as the design variables. This case study demonstrates that the proposed optimization method can effectively reduce the warpage in a computationally efficient manner.     

基于Kriging代理模型的多点加点序列优化方法

基于Kriging 代理模型提出了一种同时考虑预测响应值及其不确定性的多点加点准则,并基于该准则发展了一套序列近似优化方法。多点加点准则基于初始样本信息和所预测的对象函数特征增加新样本集,以在寻优迭代过程中自适应地提高代理模型的精度。该文方法依据多点加点准则在一次迭代中增加多个空间无关的新样本点,适用于多机同时计算或并行计算,从而提高计算效率。以两个经典的数学函数为例,将该优化方法与期望提高准则方法进行了比较,结果表明该文提出的优化方法能够有效地提高最优解的全局性。将方法用于一盒式注塑件的成型工艺优化设计,优化结果也表明了该方法的有效性。     

塑件翘曲度及其计算方法

参考其它行业中翘曲度的应用,引入了评价注塑制品翘曲变形的翘曲度概念。随后对特征尺寸上的翘曲度、平面上特征的翘曲度进行了详细的阐述,并在构建注塑CAE翘曲变形模拟结果的数据结构基础上,提出了基于注塑CAE翘曲变形模拟结果的翘曲度计算方法。用一实例说明了用平面特征上的翘曲度评价翘曲变形的有效性。     

Back Propagation neural network modeling for warpage prediction and optimization of plastic products during injection molding

Warpage of plastic products is an important evaluation index for Plastic Injection Molding (PIM). A Back Propagation (BP) neural-network model for warpage prediction and optimization of injected plastic parts has been developed based on key process variables including mold temperature, melt temperature, packing pressure, packing time and cooling time during PIM. The approach uses a BP neural network trained by the input and output data obtained from the Finite Element (FE) simulations which are performed on Moldflow software platform. In addition, a kind of automobile glove compartment cap was utilized in this study. Trained by the results of FE simulations conducted by orthogonal experimental design method, the prediction system got a mathematical equation mapping the relationship between the process parameter values and warpage value of the plastic. It has been proved that the prediction system has the ability to predict the warpage of the plastic within an error range of 2%. Process parameters have been optimized with the help of the prediction system. Meanwhile energy consumption and production cycle were also taken into consideration. The optimized warpage value is 1.58 mm, which is shortened by 32.99% comparing to the initial warpage result 2.358 mm. And the cooling time has been decreased from 20 s to 10 s, which will greatly shorten the production cycle. The final product can satisfy with the matching requirements and fit the automobile glove compartment well.     

Identification of Crims Model Parameters for Warpage Prediction in Injection Moulding Simulation

Polymer injection moulding is a process widely used to produce components in a lot of different applications. One of the most critical aspects related to this process is to control the warpage of the parts after the extraction from the mould. Numerical simulations can predict a part warpage by using specific warpage models. Among numerical codes, Autodesk Moldflow Insight® uses a Corrected In Mold Residual Stress (CRIMS) model, that calculate the residual stresses develop during the moulding process. Warpage is then predicted calculating the deformations of the component induced by the considered stresses. Using experimental and numerical techniques, a new identification procedure was introduced to evaluate the six parameters of the CRIMS model included in the Moldflow® material properties database. The study was conducted on a box for an automotive application made of polypropylene. On the base of a complete rheological, thermal and physical characterization of the employed material, a numerical simulation of the process was implemented, integrating it with an optimization procedure to identify the values of the CRIMS parameters that force numerical results to fit measured deformations. As this procedure was very time consuming, requiring to run several computationally intensive simulations, artificial neural networks were employed to approximate numerical results with lower computational time. Results were verified with independent samples, showing good correspondence between experimental results and numerical calculated deformations.     

Experimental study of warpage and shrinkage in injection molding of HDPE/rPET/wood composites with multiobjective optimization

In this paper, injection molding of squared parts with 1.25 mm in thickness, composed of wood plastic composites (high-density polyethylene, recycled polyethylene terephthalate, and wood flour), was done. The warpage and volumetric shrinkage in the parts was determined experimentally with various process conditions (packing time, melt temperature, wood content, and packing pressure). The experiments were done based on Box–Behnken design of experiments. The significance of each parameter and model was evaluated by analysis of variance (ANOVA). ANOVA showed that packing time and melt temperature are the most significant parameters on warpage and wood content is the most significant on volumetric shrinkage. Packing pressure and wood content had no considerable effect on warpage and packing time on shrinkage too. To obtain optimal process conditions for minimum warpage and shrinkage, a multiobjective optimization based on Pareto front was developed. Response surface method was used to find the relationships between input parameters and objective functions, and genetic algorithm presented the Pareto front solutions to determine the optimum solution. It was observed that there was a good agreement between the predicted optimum values and the experiments.