注射成型工艺自适应优化技术研究进展

综述了注射成型工艺自适应优化技术的研究现状,从聚合物熔体物性参数（黏度、比容）和熔体质量参数变化的分析与定量表征,以及注射成型工艺自适应优化三方面详细介绍了国内外学者在注射成型工艺自适应优化技术方向的研究进展,最后对现存的问题进行了归纳总结,并对未来的研究方向进行了展望。     

Intelligent injection molding: Parameters self-learning optimization using iterative gradient-approximation adaptive method

Intelligent injection molding consists of three aspects: intelligent parameter optimization, process monitoring and control. The optimal process parameters are critical to guarantee product quality. Injection molding is a typical batch process and has the property that previous runs can provide feedback to optimize subsequent runs. This study proposes a self-learning parameter optimization method named iterative gradient-approximation adaptive optimization (IGAO) method, which adopts the batch-to-batch information to remove the need to establish optimization model with large numbers of experiments. The analysis and the optimization of the parameters can be performed simultaneously. The IGAO method approximates the gradient iteratively and assigns an adaptive step size to each parameter according to gradient accumulation. The experiments conducted in both simulation software and injection molding machine prove that the method has fast convergence speed. Standard product weight can be obtained within 11 runs from three different starting process parameters. Experiment results show that 25% less steps are needed compared with the traditional gradient descent method. The method also has good stability to resist disturbances during the optimization procedure. In general, the proposed IGAO method is fast, stable and robust, and it has good prospects for parameter optimization in batch processes.     

基于改进空间映射方法的注塑过程参数优化

针对现有注塑过程参数优化方法存在的需要付出较高代价的问题,提出将广泛应用于电路设计优化问题的主动空间映射方法应用于注塑过程参数优化问题。针对应用过程中主动空间映射方法所无法解决的粗糙模型与精确模型在响应值空间存在较大差异的问题,提出一种改进空间映射方法,该方法引入一种新的参数提取方法,并将主动空间映射方法与输出空间映射方法相结合,一方面考虑粗糙模型与精确模型在参数空间中存在的差异,另一方面考虑粗糙模型与精确模型在响应值空间中存在的差异。针对注塑制品的尺寸指标,将本文提出的方法在Moldflow模流分析软件上进行仿真,结果证明,该方法能够利用粗糙模型结果以及少量精确模型结果获得满足设定值的注塑过程参数。     

激车后灯外罩注塑工艺优化

灯具外罩通常用透明PMMA制作,外罩采用两次注塑成型,先做成嵌件,然后把嵌件放在男一副模具中注塑合成,通过对嵌件、外罩注塑成型工艺等的介绍,围绕外罩产品特点,进行产品重量的控制,用重量控制法达到产品质量。经生产实践验证,用重量控制法优化工艺能满足外罩成型要求,符合生产需要操作简便可行,产品能得到用户的认同。     

气辅注射成型工艺参数的优化

气辅注射成型的影响因素有很多,选取四因素三水平正交表,通过正交试验法,利用CAE软件Moldflow分析了不同工艺参数对气体穿透的影响,用极差法分析了各因素对气体穿透的影响程度,最后对工艺参数进行了优化组合.     

应用软计算优化气辅注射成型工艺

气体辅助注射成型由于气体的引入使工艺更为复杂,增加了工艺变量,参数选取更为困难。本文基于CAE数值模拟试验结果,采用软计算方法,集成人工神经网络和生物进化遗传算法优化成型工艺,实现了气体辅助注射成型试验样品气体穿透长度的最大化。数值模拟与试验结果一致     

Resin transfer molding process optimization using numerical simulation and design of experiments approach

The art of resin transfer molding (RTM) process optimization requires a clear understanding of how the process performance is affected by variations in some important process parameters. In this paper, maximum pressure and mold filling time of the RTM process are considered as characteristics of the process performance to evaluate the process design. The five process parameters taken into consideration are flow rate, fiber volume fraction, number of gates, gate location, and number of vents. An integrated methodology was proposed to investigate the effects of process prameters on maximum pressure and mold filling time and to find the optimum processing conditions. The method combines numerical simulation and design of experiments (DOE) approach and is applied to process design for a cylindrical composite part. Using RTM simulation, a series of numerical experiments were conducted to predict maximum pressure and mold filling time of the RTM process. A half‐fractional factorial design was conducted to identify the significant factors in the RTM process. Furthermore, the empirical models and sensitivity coefficients for maximum pressure and mold filling time were developed. Comparatively close agreements were found among the empirical approximations, numerical simulations, and actual experiments. These results were further utilized to find the optimal processing conditions for the example part.     

注塑机工艺参数的智能设置与优化

针对工艺人员的试模思路,混合使用实例推理、代理模型和模糊推理技术,建立一种描述注塑机工艺参数设置与优化全过程的混合智能模型。首先采用实例推理技术模拟工艺人员设置初始工艺参数时的“借鉴”思维,在实例推理失败的情况下,采用代理模型模拟工艺人员的“直觉”思维设置初始工艺参数,然后将初始参数用于试模,最后利用模糊推理技术实现工艺人员不断修正缺陷、优化工艺参数的思维过程。基于上述智能模型开发出了相应的软件系统,并通过与控制器的通讯实现与注塑机的集成,实际案例验证表明该系统正确有效,可应用于实际生产。     

A real‐time process optimization system for injection molding

The proposed real‐time intelligent model for the injection molding process control consists of the initial parameters setting and online defects correction. First, preliminary optimization based on a simplified simulation model is used for the initial setting. This simplified model adopts a geometric approximation of the original part by a rectangular edge‐gated plate. Then, the molding trial will be run on the molding machine by using the initial process parameters. And a fuzzy inference model based on expert knowledge is developed for correcting defects during the molding trial. This defects correction procedure will be repeated until the part quality is found satisfactory. A corresponding intelligent system has been developed that is integrated with the injection machine by communicating with the controller. The system can be used to optimize process parameters real time. Experimental studies have been carried out for verification. POLYM. ENG. SCI., 2009. © 2009 Society of Plastics Engineers.     

Adaptive model following control of the mold filling process in an injection molding machine

This paper is concerned with the practical application of the adaptive model following control (AMFC) theory on the cavity pressure control of the mold filling process in an injection molding machine. The experimental results indicate that the AMFC technique based on the modified Popov-Landau method is very useful for the cavity pressure control. Two kinds of control algorithms are implemented on a 16 bit microcomputer to control the cavity pressure. The results also show that the AMFC algorithm is superior to the classical PI control in this system when the acrylonitrile-butadiene-stryrene (ABS) is injected into a test specimen mold which is designed according to the ASTM code.     

Process control of injection molding

This paper discusses the most important input parameters affecting the conventional injection-molding process and describes a closed-loop control system for determining the interaction between ten process inputs and three output parameters. The input parameters are: back pressure, holding pressure, injection time, open mold time, shot size, clamping pressure, injection pressure, screw speed, and boost cut-off. The output parameters studied are: part weight, maximum cavity pressure, and maximum mold deflection.     

BP神经网络在注射成型工艺参数优化中的作用

本文研究的是注射工艺参数对塑件翘曲变形的影响。通过CAE模拟计算,以工艺参数为输入参数,以翘曲量为输出参数,构建BP神经网络模型。以CAE分析结果作为训练样本和检测样本,分析BP神经网络在工艺参数优化方面的作用。     

基于响应面模型的注塑件精密成型工艺优化

为了提高注塑件品质,对注塑件进行了成型工艺优化.结合中心复合试验设计,采用Moldflow软件和响应面模型方法,拟合得到注塑件二阶响应面模型,由响应面模型获得成型最佳产品品质的工艺参数组合.采用Moldflow软件对该工艺参数组合进行了分析.结果表明:采用响应面模型预测与Moldflow软件分析得到的体积收缩率相差约0...     

Analysis of an injection/compression liquid composite molding process

The injection/compression liquid composite molding (LCM) process is simulated by using the control/volume finite element method (CV/FEM). The flow in the runner and the fiber-free areas is simplified by using an equivalent permeability approach. Several molding experiments were conducted using a tub-shaped mold and the structural reaction injection molding (SRIM) process for a poly(urethane/isocyanurate) matrix and a glass fiber preform. Good agreement is found between the experimental results and the simulation.     

Multiple criteria optimization with variability considerations in injection molding

Injection molding (IM) is the most important process for mass‐producing of plastic products. The difficulty of optimizing an IM process is that the performance measures (PMs) usually show conflicting behavior. The aim of this work is to demonstrate a method utilizing CAE, statistical testing, artificial neural networks (ANNs), and data envelopment analysis (DEA) to find the best compromises between multiple PMs, considering the variability in these PMs in an explicit manner. Two case studies are presented. The first case study, based on a virtual part, is discussed in detail in order to illustrate this method. The second case study is experimentally based and makes use of the American Society of Testing Materials (ASTM) mold to illustrate how this approach applies when purely experimental results are available. POLYM. ENG. SCI., 47:400–409, 2007. © 2007 Society of Plastics Engineers     

Modeling of structural reaction injection molding process. I. Mathematical model

A mathematical model of the infusion process in producing reinforced articles is proposed. The model is based on the analysis of flow of a Newtonian liquid inside a rectangular multilayer channel. According to the model, a liquid enters the central (feeding) layer, moves through this layer, and simultaneously impregnates peripheral layers. So, the flow is two‐dimensional. Flow inside the porous layers is treated in terms of the Darcy equation with different permeability coefficients in two directions. Principal solutions for the flow front development and pressure evolution were obtained and analyzed. Then the initial model, developed for a Newtonian liquid, is generalized for the so‐called “rheokinetic” liquid, which changes its rheological properties in time as a result of temperature variation and/or any possible chemical process, in particular, the reaction of curing of a binder. It was proven that in this case the solution is automodel. This means that the solutions obtained for a Newtonian liquid in the dimensionless form are valid for an arbitrary rheokinetic liquid.     

基于小步长搜索法的注射成型工艺参数优化

利用注射充模CAE软件Moldflow Plastic Insight 5.0,应用Taguchi正交试验设计方法,通过对产品翘曲变形量分析得到第一组工艺参数优化组合,再利用小步长搜索法缩小工艺参数优化的搜索范围,生成新的正交试验表并进行二次正交试验,得到更优的工艺参数组合,从而减少翘曲量,提高制品质量.结果表明,选用适当的工艺参数,制品翘曲变形能得到有效控制,而在针对工艺参数优化的正交试验设计中,通过小步长搜索法缩小设计空间并进行二次正交试验,可以得到比采用单步正交试验法时更为优化的工艺参数组合.本文采用的小步长搜索法同样可用于其他领域的正交试验设计之中,使正交试验结果分析更为准确科学.     

注塑成型工艺参数的优化研究进展

在注塑成型过程中,通过合理设置注塑工艺参数可以得到高质量塑料制品。通常可以采用正交试验法对注塑成型工艺参数进行优化。通过对正交试验数据运用极差分析、耦合推广正交算法、灰色关联度分析法以及使用神经网络模型和遗传算法,可以获得注塑成型工艺参数的最优配置组合。     

An integrated dimensionality reduction and surrogate optimization approach for plant-wide chemical process operation

With liquefied natural gas becoming increasingly prevalent as a flexible source of energy, the design and optimization of industrial refrigeration cycles becomes even more important. In this article, we propose an integrated surrogate modeling and optimization framework to model and optimize the complex CryoMan Cascade refrigeration cycle. Dimensionality reduction techniques are used to reduce the large number of process decision variables which are subsequently supplied to an array of Gaussian processes, modeling both the process objective as well as feasibility constraints. Through iterative resampling of the rigorous model, this data-driven surrogate is continually refined and subsequently optimized. This approach was not only able to improve on the results of directly optimizing the process flow sheet but also located the set of optimal operating conditions in only 2 h as opposed to the original 3 weeks, facilitating its use in the operational optimization and enhanced process design of large-scale industrial chemical systems.