An integrated parameter optimization system for MIMO plastic injection molding using soft computing

This study proposes an integrated optimization system to find out the optimal parameter settings of multi-input multi-output (MIMO) plastic injection molding (PIM) process. The system is divided into two stages. In the first stage, the Taguchi method and analysis of variance (ANOVA) are employed to perform the experimental work, calculate the signal-to-noise (S/N) ratio, and determine the initial process parameters. The back-propagation neural network (BPNN) is employed to construct an S/N ratio predictor and a quality predictor. The S/N ratio predictor and genetic algorithms (GA) are integrated to search for the first optimal parameter combination. The purpose of this stage is to reduce the process variance. In the second stage, the quality predictor is combined with particle swarm optimization (PSO) to find the final optimal parameters. The quality characteristics, product length and warpage, are dedicated to finding the optimal process parameters. After the numerical analysis, the optimal parameters can meet the lowest variance and the product quality requirements simultaneously. Experimental results show that the proposed optimization system can not only satisfy the quality specification but also improve stability of the PIM process.     

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。     

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.     

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。     

Multi-objective optimization of MIMO plastic injection molding process conditions based on particle swarm optimization

Determining optimal process parameter settings critically influences productivity, quality, and cost of production in the plastic injection molding industry. Selecting the proper process conditions for the injection molding process is treated as a multi-objective optimization problem, where different objectives, such as minimizing product weight, volumetric shrinkage, or flash present trade-off behaviors. As such, various optima may exist in the objective space. This paper presents the development of an experiment-based optimization system for the process parameter optimization of multiple-input multiple-output plastic injection molding process. The development integrates Taguchi’s parameter design method, neural networks based on PSO (PSONN model), multi-objective particle swarm optimization algorithm, engineering optimization concepts, and automatically search for the Pareto-optimal solutions for different objectives. According to the illustrative applications, the research results indicate that the proposed approach can effectively help engineers identify optimal process conditions and achieve competitive advantages of product quality and costs.     

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 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.     

响应面法与遗传算法相结合的注塑工艺优化

应用田口方法进行试验设计,应用计算机辅助工程技术对注塑成形过程进行了分析,建立了注塑成形工艺参数与翘曲度关系的代理模型——响应面模型,对模型进行了验证研究,将响应面法与遗传算法相结合进行了注塑工艺参数优化。结果表明,响应面模型是准确可靠的,将响应面法和遗传算法相结合,可有效提高运算速度和优化效率。     

智能注射成形中工艺参数的多目标自学习优化

注射成形工艺参数是保障产品质量的关键因素。传统试错法严重依赖工艺人员的试模经验,随着注射成形工艺广泛应用于电子、航空航天等国家战略领域,产品的高端化对工艺参数智能化设置水平提出更高的要求。由于成形产品存在多方面的质量要求,且不同质量指标间可能相互制约,因此亟需一种工艺参数多目标智能优化方法,以获得不同优化目标间的帕累托最优。已有学者利用智能优化方法,如非支配排序遗传算法等,对多目标优化问题进行求解,但是此类方法需大量样本数据对质量-参数关系进行建模,存在试验次数多、且对不同材料及模具的适应性较差等问题。为解决上述问题,提出一种注射成形工艺参数多目标自学习优化方法,在优化过程中实时计算并更新各个工艺参数的梯度,并由不同质量指标的多梯度下降算法对多个目标函数进行优化,在优化过程中实现各工艺参数对产品质量影响程度的自主学习,省去了采集大量数据来建立多个质量模型的过程,实现了注射成形工艺参数的高效智能优化。在基准测试函数实验中,所提方法的优化结果与理论解的相对误差小于2%。同时数值仿真与注射成形实验结果表明,所提方法能高效获得多个优化目标的帕累托最优。     

Warpage reduction with variable pressure profile in plastic injection molding via sequential approximate optimization

Warpage reduction is one of the important issues in plastic injection molding (PIM). In order to resolve this issue, there are mainly two ways to reduce warpage: One is to design the mold, and the other is to optimize the process parameters such as the mold temperature, the melt temperature, and so on. In this paper, the latter approach is employed. In particular, variable pressure profile approach is adopted for the warpage reduction. Besides the variable pressure profile, the melt temperature and the mold temperature are taken as the design variables. Also, short shot that the melt plastic is not filled into the cavity is one of the fatal defects in PIM. Unlike the literature, in this paper, the short shot is handled as the design constraint. PIM simulation is generally so costly and time consuming, and then the surrogate-based optimization technique is used. The radial basis function (RBF) network is used throughout sequential approximate optimization (SAO) procedure. Moldex3D is used for PIM simulation. In order to compare the effectiveness of the variable pressure profile, the traditional process parameter optimization considered in the literature is also carried out. Numerical results show that the variable pressure profile is one of the effective ways to warpage reduction compared to the traditional process parameter optimization.     

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.

     

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.     

大型薄壁塑件翘曲变形分析及工艺参数优化

在注塑成型大型薄壁塑料产品过程中,由于熔料流动路程长,流动阻力大,产品极易产生翘曲变形,从而影响到使用性能.应用Moldflow软件分析了影响产品翘曲变形的主要原因是收缩率不均引起.通过优化工艺参数,即优化保压曲线,减小了产品翘曲变形.方珐是将保压设置由恒压保压调整为先恒压再线性递减的两段保压,然后再多次调整保压曲线各...     

鼠标外壳注塑件翘曲变形模拟分析

基于CAE数值分析技术和田口实验方法,研究了保压压力、熔体温度、冷却时间、保压时间和模具温度等因素对翘曲变形的影响.以翘曲变形量最小为目标,通过正交试验方法获得了最佳参数组合以及最佳参数组合条件下的翘曲变形量.结果显示,通过模流分析来预报产品缺陷,优化工艺参数,可以缩短模具开发周期,降低成本,提高企业竞争力.     

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。     

A combined analytical hierarchical process (AHP) and Taguchi experimental design (TED) for plastic injection molding process settings

Injection molding process is without doubt a multi-objective process if processing time, productivity, effectiveness, and the multi-criteria quality of the product are taken into consideration. Process settings affect the degree by which these objectives are realized. This work suggests a new proposal for evaluating optimal process settings through the handling of the plastic injection molding process in the same approach as a traditional multi-objective multi-criteria process. In a sense, there are numerous objective functions including cooling time, volumetric shrinkage, warpage, sink marks, residual stresses, and various process settings including temperature, pressure, etc. Within the suggested proposal, the Taguchi experimental design is used to generate a balanced set of experiments to explore the process; then, the finite element software SIMPOE is used to evaluate the behavior of the injection molding at each experimental setting. Analytical hierarchical process is then employed for multiple comparisons of the objectives and experiments as such to give the overall objective weight for each process setting (experiment). Analysis of variance is then used to evaluate the significant factors and the optimal setting of the process. This technique proved effective to obtain compliance between process design and several common manufacturer preferences, although the considered part was not changed.     

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。     

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.     

基于DOE因子交互作用的注射成型工艺参数优化研究

注射工艺参数不仅以单个因子的作用方式影响注射制品的质量,而且工艺参数之间还存在着非常复杂的交互作用.在分析注射成型工艺对成型制品质量的影响时,必须研究因数对响应的交互效应,考虑影响显著的工艺参数交互因子的作用,通过数值模拟,应用析因试验设计方法,研究得出工艺参数之间存在的两两交互作用,以及工艺参数对注射制品翘曲的影响程度,并从众多的试验因子中初步筛选出与注射制品质量密切相关的若干个独立因子和交互因子.在此基础上,应用正交试验设计,采用L27正交矩阵进行试验,并将这些因子进一步优化,从而为注射工艺参数的合理选取提供科学依据.