Simulation and experimental study in determining injection molding process parameters for thin-shell plastic parts via design of experiments analysis

This paper deals with the application computer-aided engineering integrating with statistical technique to reduce warpage variation depended on injection molding process parameters during production of thin-shell plastic components. For this purpose, a number of Mold-Flow analyses are carried out by utilizing the combination of process parameters based on three level of L₁₈ orthogonal array table. In the meantime, apply the design of experiments (DOE) approach to determine an optimal parameter setting. In addition, a side-by-side comparison of two different approaches of simulation and experimental is provided. In this study, regression models that link the controlled parameters and the targeted outputs are developed, and the identified models can be utilized to predict the warpage at various injection molding conditions. The melt temperature and the packing pressure are found to be the most significant factors in both the simulation and the experimental for an injection molding process of thin-shell plastic parts.     

A novel CACD/CAD/CAE integrated design framework for fiber-reinforced plastic parts

This work presents a novel CACD/CAD/CAE integrated framework for design, modeling, and optimization of fiber-reinforced plastic parts, which can greatly enhance the current design practice by realizing partial automation and multi-stage optimization. To support this framework, a new heterogeneous feature model (HFM) has been developed to model the fiber-reinforced objects and to be transferred between engineering modules. To be specific, the CACD (computer-aided conceptual design) module employs the level-set structure and material optimization to produce the initial design with thickness control, and also the initial HFM; the CAD (computer-aided design) module allows manual editing on the HFM to reflect various design intents; then, the injection molding CAE (computer-aided engineering) simulates the manufacturing process, and the response surface method (RSM) is applied to optimize the process parameters of gate location, injection flow rate, mold temperature and melt temperature, to approach the manufactured fiber orientation distribution close to the optimized result produced by the CACD module; besides, the structural analysis CAE module generates the mechanical performance result to support the CACD module, as well as to validate the final design. By applying this framework, the final structural design including the fiber orientation distribution, will perform better in mechanical properties, and consume less matrix and fiber materials; besides, the design maturity can be approached in shorter time. To prove the effectiveness, a plastic gripper design will be comprehensively studied.     

A comparative study of nonlinear optimization and Taguchi methods applied to the intelligent control of manufacturing processes

current investigation focused on neural-network-based control of manufacturing processes utilizing an optimization scheme. In an earlier study, Demirci and Coulter introduced the utilization of neural networks for the intelligent control of molding processes. In that study, a forward model neural network, employed with a search strategy based on the factorial design of experiments method, was shown to successfully control the flow progression during injection molding processes. Recently, Demirciet al. showed that the search mechanism based on the factorial design of experiments method can be intolerable in time during on-line control of manufacturing processes, and suggested an inverse model neural network. This inverse model neural network was shown to be beneficial as it totally eliminated time-consuming parameter searches, but it required a harder mapping than the forward model neural network and thus its performance was inferior. In the present study, the authors investigated two different optimization methods that were utilized in making the search method of the forward control scheme more efficient. The first method was Taguchi's method of parameter design, and the second method was a nonlinear optimization method known as Nelder and Mead's downhill simplex method. These two methods were separately utilized in creating an efficient search method to be used with the forward model neural network. The performance of the resulting two control methods was compared with each other as well as with that of the forward control scheme utilizing a search strategy based on the factorial design of experiments method. Although the applications in this study were on molding processes, the method can be applied to any manufacturing process for which a process model and anin-situ sensing scheme exists.     

Design and implementation of a process optimizer: a case study on monitoring molding operations

Abstract: To cope with the requirements of high dimensional accuracy for injection molding components, it is important to optimize the process parameters in order to sustain the high level dimensional quality of the molded parts. In this respect, a study in the domain of process optimization is of paramount importance in terms of determining the optimal set of injection molding parameters. To this end, a methodology to establish an integrated model which consists of both fuzzy logic reasoning and a genetic algorithm is proposed. These two artificial intelligence techniques can complement each other to form an integrated model which capitalizes on the merits and at the same time offsets the pitfalls of the involved technologies. To validate the feasibility of the proposed model, a case study related to injection molding optimization is also covered in this paper.     

Cost estimation of plastic injection molding parts through integration of PSO and BP neural network

Plastic injection molding technology has been widely used in a variety of high-tech products, auto parts and generic household products. Against the waves of globalization, the plastic injection enterprises must shorten time-to-market to enhancement of competence, and launch products ahead of all other competitors, and thus they can quickly seize a big target market and lead the price. The backpropagation (BP) neural network was used in this study to construct an estimating model for the cost of plastic injection molding parts so as to reduce the complexity in the traditional cost estimating procedures. Because the parameters of BP neural network have a significant influence on results, and particle swarm optimization (PSO) is capable of quickly finding optimal solutions. We integrated PSO and BP neural network so that the convergence rate was improved and precision was relatively enhanced through particle evolutions based on the optimum parameter combination from BP neural network.     

Multi-objective optimization of volume shrinkage and clamping force for plastic injection molding via sequential approximate optimization

Process parameters in plastic injection molding (PIM), such as the packing pressure, the mold temperature, the melt temperature, and so on, are generally determined by a trial-and error method through the experiments. Computer-aided engineering (CAE) in the PIM is an alternative approach to determine the optimal process parameters. In cap-type plastic product, large volume shrinkage makes the clamping difficult. Furthermore, small clamping force leads to high productivity as well as cost reduction. Both volume shrinkage and clamping force should then be minimized simultaneously, and a multi-objective design optimization is formulated. Inappropriate process parameters easily lead to short shot that the melt plastic is not fully filled into cavity. In this paper, short shot is handled as the design constraint. Numerical simulation of the PIM is so expensive that the response surface approach is valid. In particular, a sequential approximate optimization (SAO) that the response surface is repeatedly constructed and optimized with some new sampling points is recognized as one of the most powerful tools available. In this paper, the radial basis function (RBF) network is adopted for the SAO, and the pareto-frontier is identified with a small number of simulation runs. Numerical result shows that the pareto-frontier is well identified with a small number of simulation runs.     

Model predictive control of servo motor driven constant pump hydraulic system in injection molding process based on neurodynamic optimization简

In view of the high energy consumption and low response speed of the traditional hydraulic system for an injection molding machine, a servo motor driven constant pump hydraulic system is designed for a precision injection molding process, which uses a servo motor, a constant pump, and a pressure sensor, instead of a common motor, a constant pump, a pressure pro-portion valve, and a flow proportion valve. A model predictive control strategy based on neurodynamic optimization is proposed to control this new hydraulic system in the injection molding process. Simulation results showed that this control method has good control precision and quick response.     

Generalized quadratic multiple knapsack problem and two solution approaches

The Quadratic Knapsack Problem (QKP) is one of the well-known combinatorial optimization problems. If more than one knapsack exists, then the problem is called a Quadratic Multiple Knapsack Problem (QMKP). Recently, knapsack problems with setups have been considered in the literature. In these studies, when an item is assigned to a knapsack, its setup cost for the class also has to be accounted for in the knapsack. In this study, the QMKP with setups is generalized taking into account the setup constraint, assignment conditions and the knapsack preferences of the items. The developed model is called Generalized Quadratic Multiple Knapsack Problem (G-QMKP). Since the G-QMKP is an NP-hard problem, two different meta-heuristic solution approaches are offered for solving the G-QMKP. The first is a genetic algorithm (GA), and the second is a hybrid solution approach which combines a feasible value based modified subgradient (F-MSG) algorithm and GA. The performances of the proposed solution approaches are shown by using randomly generated test instances. In addition, a case study is realized in a plastic injection molding manufacturing company. It is shown that the proposed hybrid solution approach can be successfully used for assigning jobs to machines in production with plastic injection, and good solutions can be obtained in a reasonable time for a large scale real-life problem.     

基于控制参数化的注塑工业过程最优反馈控制方法

本文针对一类典型的注塑工业过程系统, 研究了注塑填充过程中产生的熔体流动速度最优跟踪控制问题, 提出了一种基于控制参数化的计算最优反馈控制器设计方法以实现注塑过程中熔融聚合物流动前沿位移的最优跟 踪控制, 进而达到改善注塑零件性能的高效生产目标. 首先, 面向注塑工艺复杂生产过程建立了动态过程系统数学 模型, 提出了注塑机内部熔融聚合物流动前沿位置的动态最优跟踪控制问题; 其次, 设计了一种多级反馈控制律, 通 过控制参数化方法将控制反馈核进行了参数化表示, 将控制器设计问题转化为一序列最优参数决策问题; 然后, 通 过状态灵敏度方程分析方法, 求解出了目标函数及约束条件关于决策变量参数梯度信息的显式表达式, 并基于所提 供的梯度信息结合序列二次规划算法进行了高效优化迭代求解; 最后, 通过实验仿真验证了本文所提出的最优反 馈控制器设计方法的可行性和有效性.     

Controller tuning with Bayesian optimization and its acceleration: Concept and experimental validation

This work discusses a data-driven approach to controller parameter tuning based on Bayesian optimization. In particular, we propose to design the prior mean function based on a model of the plant. By encoding the information on the model, the optimization needs a much fewer iterations than standard approaches. The effectiveness of the proposed method is demonstrated with a practical experiment.     

The application of surface demoldability and moldability to side-core design in die and mold CAD

In casting, molding and forming processes, the surface geometries of the fabricated products are formed/molded by different functional components of tooling. In plastic injection molding, they are molded by core, cavity or side-cores. In die and mold CAD, how to identify the product surfaces formed/molded by the corresponding tool components for a given product CAD model is critical, as it affects the determination of parting directions, parting lines and parting surfaces, the generation of core and cavity blocks, and finally the design of side-cores and their actuating mechanisms. In this paper, the concepts of surface visibility, demoldability, and moldability are first presented and formulated. The surfaces formed/molded by core, cavity and side-cores are then defined based on the plastic injection molding process. The methodology to identifying and classifying them is further developed. By employing the proposed notions of the demoldability map of surfaces and undercut features, the most preferred demolding direction, the grouping of undercut features, and how to conduct the side-core design is articulated succinctly, and the detailed procedures and processes are presented. Through an industrial case study, the developed methodology for side-core design is systematically presented and the feasibility of the developed approaches is verified.     

LCD后背板的注射成型参数正交优化设计

针对注射成型过程中最常见的制件翘曲问题,尝试以LCD后背板为研究对象,对影响薄壳塑件翘曲变形的因素(如模具温度、熔体温度、注射时间、保压压力、保压时间、冷却时间)进行分析,以正交实验法找出最佳工艺参数组合,通过极差分析,确定实现低翘曲变形的最优方案为:模具温度85℃,注射时间0.6s,保压压力100%。     

Parametric design and process parameter optimization for bra cup molding via response surface methodology

Seamless and traceless undergarments have rendered foam sheet molding as an important manufacturing technique for the intimate apparel industry. Seamless bra cups are made by one-step forming technology. The three-dimensional (3D) cup shape is formed by using high temperatures and pressures with flexible polyurethane foams. Nevertheless, the mold head design process and control of the bra cup molding process are highly complicated and error prone. There is limited knowledge about the effects of foam properties, molding parameters and foam cup geometric parameters on molding process optimization. This research presents a response surface methodology as the approach for parametric design and process parameter optimization of bra cup molding. The proposed approach integrates 3D scanning via reverse engineering, parameterized-based remeshing and registration algorithm, non-linear mathematical prediction models for cup shape conformity, a model of foam shrinkage and example-based bra cup design and grading to optimize the bra cup development and production process. The experimental results show that this method is highly effective and more timesaving in the design and development of new products, as well as providing consistent quality control of the bra cup molding process.     

有限单元法在塑料注射充模流动模拟中的应用

对塑料熔体注射成型充模流动过程的流变方程进行了熔体充模过程流变方程参数基于3维有限单元法的数值求解研究,得到基于形函数的压力刚度矩阵、温度刚度矩阵的方程式,进而得出压力场、速度场、温度场数值解的方程式。据此完成了注射熔体流动充模过程由M atlab编程实现的有限元分析本体程序。用具体的注射成型塑料制品作为检验实例,用2种不同的塑料进行注射成型模拟,其结果与国际著名的M old flow商品化软件和文献数据进行比较,表明本研究的设计软件算法正确、程序运行速度快。     

基于CAD的注塑模具设计与仿真研究

研究了基于CAD技术的注塑模具设计流程,并对模具的工艺参数进行了分析和优化,具体采用CAD注塑模集成系统实现。完成了最优工艺参数方案的确定,通过UG的CAE分析模块的应用并以模拟结果为依据完成了对注塑件应力应变分布情况的分析,应力最大值及位置通过实验对比完成确定,为模具结构的优化方案提供参考。最佳注塑制品工艺参数通过分析相关工艺参数获取,并以音箱零件为例,对注塑模具型腔设计方法进行了探讨。     

A CAD-CAE Integrated Injection Molding Design System

. In the injection molding design process, interaction between design and analysis is very intensive. This is to ensure that the plastic part being designed is manufacturable by the injection molding process. However, such interaction is not supported by current computer-aided systems (CAD and CAE), because design and analysis are realized as isolated modules. Although most of CAE systems provide built-in modeling tools, these are only meant for developing an analysis model with very limited CAD functionality. On the other hand, some CAD systems have allowed certain CAE systems to run under their environments, but inherently they use different data models, thus communication between them is poor. This paper presents an innovative, CAD-CAE integrated, injection molding design system. This system uses an integrated data model for both design and analysis. The system is built on top of existing CAD and CAE systems, which not only greatly saves development effort, but also makes full use of the strong functionality of commercial computer aided systems. The system architecture consists of four layers: a CAD and CAE platform layer; a CAD-CAE feature layer; a model layer; and a GUI layer. Two design cases were studied to illustrate the iterative design-analysis process and use of the developed system.     

基于数据的流程工业生产过程指标预测方法综述

生产过程关键指标的预测对于流程工业生产调度,安全生产和节能环保有着重要作用.目前,已有多种基于工业生产数据提出的生产过程指标预测方法,主要涉及特征（变量）选择,预测模型构建及其模型参数优化这三方面.本文分别针对以上三方面论述了基于数据的工业生产过程指标预测国内外研究现状,分析了各种方法的优缺点.最后,指出了流程工业生产过程指标预测方法在工业大数据及知识自动化等方面的未来研究方向和前景.     

A novel algorithm for layout optimization of injection process with random demands and sequence dependent setup times

Injection molding is an ideal manufacturing process for producing high volumes of products from both thermoplastic and thermo setting materials. Nevertheless, in some cases, this type of manufacturing process decelerates the production rate as a bottleneck. Thus, layout optimization plays a crucial role in this type of problem in terms of increasing the efficiency of the production line. In this regard, a novel computer simulation–stochastic data envelopment analysis (CS-SDEA) algorithm is proposed in this paper to deal with a single row job-shop layout problem in an injection molding process. First, the system is modeled with discrete-event-simulation as a powerful tool for analyzing complex stochastic systems. Then, due to lack of information about some operational parameters, theory of uncertainty is imported to the simulation model. Finally, an output-oriented stochastic DEA model is used for ranking the outputs of simulation model. The proposed CS-SDEA algorithm is capable of modeling and optimizing non-linear, stochastic, and uncertain injection process problems. The solution quality is illustrated by an actual case study in a refrigerator manufacturing company.     

虚拟塑料注塑成型教学系统

塑料注射成型技术的发展与注射模具设计人员需求的增长十分迅速，模具设计人员学习必要的塑料成型基本原理是掌握注射模具设计方法的现实需要。该文在虚拟现实（VR）技术的基础上，结合基于三维模型的注塑成型FEA技术、面向对象的编程技术和OpenGL图形库，研究并实现了沉浸式、交互式的虚拟塑料注射成型教学仿真系统（VPIM），提出了立体成像算法，给出了模拟注射成型过程运动仿真的方法，借助VR外设及通过建立知识化、数字化、可视化、现实化的教学平台来提高设计人员的学习效率、学习兴趣及对问题理解的深入程度。     

多维关注分离的模型驱动过程框架设计方法

当前,许多模型驱动软件项目过程采用多种开发方式相结合的形式.但开发方式的比较、选择、组合操作却缺少系统化的方法指导.提出一种多维关注分离的开发过程框架设计方法.采用一般化、行为化和抽象化作为元关注维,对开发方式进行比较.结合这三维的期望演化曲线,给出过程实现模型框架.对于模型驱动开发过程的提高开发效率、增强可跟踪性和保证一致性等非功能性需求有一定的参考意义.