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.

     

An enhanced optimization approach based on Gaussian process surrogate model for process control in injection molding

A stepwise optimization approach based on Gaussian process (GP) surrogate model is proposed to determine the process parameters and improve the quality control for injection molding. In order to improve the global performance in this optimization, an enhanced probability of improvement criterion is also introduced. Firstly, GP surrogate model is constructed with the initial samples which are obtained from an optimal design of experiment method. GP is capable of giving both a prediction and an estimate of the confidence for the prediction simultaneously. Secondly, an enhanced probability of improvement criterion is used to find the direction of adding training samples and optimize the surrogate model. Since the global optimal region of the model become accurate efficiently after steps of optimizing the surrogate model, the proposed enhanced probability of improvement criterion can switch more swiftly to global optima compared with other improvement criterion. Finally, an auto front grille molding process is taken as an example to illustrate the method. The results show that the proposed optimization method can effectively decrease the warpage of injection-molded parts.     

A modified global optimization method based on surrogate model and its application in packing profile optimization of injection molding process

This article introduces a step-by-step optimization method based on the radial basis function (RBF) surrogate model and proposes an improved expected improvement selection criterion to better the global performance of this optimization method. Then it is applied to the optimization of packing profile of injection molding process for obtaining best shrinkage evenness of molded part. The idea is first, to establish an approximation function relationship between shrinkage evenness and process parameters by a small size of design of experiment with RBF surrogate model to alleviate the expensive computational expense in the optimization iterations. And then, an improved criterion is used to provide direction in which additional training samples could be added to better the surrogate model. Two test functions are investigated and the results show that stronger global exploration performance and more precise optimal solution could be obtained with the improved method at the expense of increasing the infill data properly. Furthermore the optimal solution of packing profile is obtained for the first time which indicates that the type of optimal packing profile should be first constant and then ramp-down. Subsequently, the discussion of this result is given to explain why the optimal profile is like that.     

基于BP-NSGA的注塑参数多目标智能优化设计

为获得成型性能最优的注塑参数设计方案,提出了基于BP神经网络和非支配排序遗传算法的注塑参数多目标优化方法。将注塑模结构尺寸参数和注塑工艺参数作为待优化的设计变量,建立了以高质量、低成本、高效率为优化目标的注塑参数优化设计模型。基于非支配排序遗传算法获取给定参数范围内的所有Pareto最优解,并通过建立多输入和多输出的BP神经网络来快速获得非支配排序遗传算法优化进程中所有个体的适应度值。开发了基于BP神经网络与非支配排序遗传算法集成的注塑参数智能优化设计系统,并通过鼠标注塑参数设计实例,验证了其适用性和有效性。     

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。     

用注射分析对悬浮填料结构优化

悬浮填料的结构对其挂膜性能影响较大,设计时需要重点考虑其结构优化.在现有悬浮填料结构的基础上进行改进,设计三种新型填料结构,采用注射分析软件对这些悬浮填料的注塑性能进行模拟计算,了解塑胶熔体在填料模腔中的流动状况,得到这些填料结构上各点的注塑时间、塑胶压力、温度和塑胶流动状况等数据,并根据这些数据比较它们之间的注塑优劣,数据显示,加筋多面空心球结构注射性能比较好.     

Study of water penetration length and processing parameters optimization in water-assisted injection molding

Water penetration length is one of the most important indexes of the water-assisted injection molding parts, the maximization of which is a particularly significant optimization objective. The effects of processing parameters, such as the short shot size, melt temperature, water pressure, and delay time, on water penetration length were exploited by using single factor experiment method and computational fluid dynamics analysis. In addition, the maximization of water penetration length on dimensional transition and curved-section parts by integrating the Taguchi orthogonal array design, radial basis function neural network, and particle swarm optimization was investigated. The research results showed that the two primary parameters affecting the water penetration length were the short shot size and water pressure, and that the effects of the melt temperature and delay time were little. Furthermore, the maximum water penetration length after optimization was slightly bigger than that of the confirmation experiment, which indicated that the optimization methodology was reliable and effective.     

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.     

Multiobjective optimization of injection molding parameters based on soft computing and variable complexity method

The objective of this study is to propose an intelligent methodology for efficiently optimizing the injection molding parameters when multiple constraints and multiple objectives are involved. Multiple objective functions reflecting the product quality, manufacturing cost and molding efficiency were constructed for the optimization model of injection molding parameters while multiple constraint functions reflecting the requirements of clients and the restrictions in the capacity of injection molding machines were established as well. A novel methodology integrating variable complexity methods (VCMs), constrained non-dominated sorted genetic algorithm (CNSGA), back propagation neural networks (BPNNs) and Moldflow analyses was put forward to locate the Pareto optimal solutions to the constrained multiobjective optimization problem. The VCMs enabled both the knowledge-based simplification of the optimization model and the variable-precision flow analyses of different injection molding parameter schemes. The Moldflow analyses were applied to collect the precise sample data for developing BPNNs and to fine-tune the Pareto-optimal solutions after the CNSGA-based optimization while the approximate BPNNs were utilized to efficiently compute the fitness of every individual during the evolution of CNSGA. The case study of optimizing the mold and process parameters for manufacturing mice with a compound-cavity mold demonstrated the feasibility and intelligence of proposed methodology.     

正交试验设计的注塑成型工艺参数多目标优化设计

结合正交试验设计和注塑成型模拟软件Moldflow,对不同工艺条件下的注塑成型过程进行模拟分析,并运用模糊数学中的综合评判法,对塑件成型后的体积收缩率变化、表面缩痕指数和最大翘曲变形量三个目标值进行综合评判,得到综合评分.通过对综合评分的极差分析,确定模具温度、熔体温度、注塑时间、保压参数、冷却时间等工艺参数对综合评分的影响程度,并绘制因素水平影响趋势图,分析得出最优的注塑工艺参数组合方案,并对该工艺组合方案进行模拟验证.     

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.     

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.     

A study of gate location optimization of plastic injection molding using sequential linear programming

A gate location is one of the most important design variables controlling the product quality of injection molding. In this paper, the numerical simulation of injection mold filling process is combined with the design optimization method to find the optimum gate location to achieve balanced flow. The objective function is expressed in terms of the difference between the maximum and minimum times of boundary filling. The coordinates of gate are chosen as design variables, and a constraint is employed to limit the clamp force lower than the reference value. The optimization problem is solved with the sequential linear programming algorithm, and design sensitivities are evaluated via the finite difference approximation. Finally, numerical examples are given to demonstrate the effect of proposed methods.     

Improved state space model predictive fault-tolerant control for injection molding batch processes with partial actuator faults using GA optimization

A novel model predictive fault-tolerant control (MPFTC) strategy adopting genetic algorithm (GA) is proposed for batch processes under the case of disturbances and partial actuator faults. Based on the extended state space model in which the tracking error is contained, there are more degrees of freedom provided for the controller design and better control performance is obtained. In order to enhance the control performance further, the GA is introduced to optimize the relevant weighting matrices in the cost function. The effectiveness of the proposed MPFTC approach is tested on the injection velocity regulation of the injection molding process.     

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.     

注塑工艺参数快速确定方法研究

工程上采用不断试验来调整并确定注塑工艺参数的方法不仅费时费料,效率低下,而且由于参数间的相互影响,难以得到最合适的参数值。对人工神经网络技术在注塑工艺参数快速确定方面的应用进行了研究。在CAE模拟的基础上,利用Matlab下的神经网络工具箱建立了BP网络模型,编制了应用程序,对参数非线性映射过程进行求解。结果表明人工神经网络技术应用于注塑参数的快速确定方面是可行的。