基于优劣解距离法⁃灰色关联分析的注射成型质量多目标优化

为提高座厕椅面板注射成型质量,将优劣解距离法（TOPSIS）与灰色关联分析相结合,提出了基于TOPSIS的灰色关联综合评价模型。首先,优化并确定了塑件浇注系统,然后进行正交试验设计,选择模具温度、熔体温度,注射时间、保压压力、保压时间为试验因素,以翘曲变形量、缩痕指数、体积收缩率为评价指标,运用Moldflow软件进行模拟分析;根据正交试验数据,利用基于指标相关性的指标权重确定（CRITIC）法确定了各评价指标权重系数,采用基于TOPSIS的灰色关联综合评价方法,将多目标优化转化为单目标优化问题,获得了塑件的最佳注塑工艺参数组合。结果表明,优化后的塑件体积收缩率降低14.6 %、缩痕指数降低43.3 %,翘曲变形量与优化前基本一致,塑件综合质量显著提高。     

基于改进的BP神经网络的注塑成型翘曲优化设计

注塑成型是制造塑料产品应用最广泛的一种方法。整个注塑成型过程一般分为注射、保压和冷却3个阶段。成型过程中的翘曲变形是注塑制品一种严重的缺陷。由于注塑制品质量主要受工艺条件影响,所以如何确定最佳工艺条件来减少翘曲变形成为改进注塑制品质量的一个关键。以模具温度、熔体温度、注射时间、保压时间、保压压力和冷却时间为设计变量,运行Moldflow软件进行制品的翘曲变形分析,用BP神经网络模型来建立翘曲变形与设计变量的函数关系,加权形式的期望提高加点准则实现序列的迭代优化设计。这种加点准则能调整局部和全局搜索,在保证计算效率的同时提高对全局最优解的逼近程度。通过实例验证,所提出的优化方法能有效地减小注塑制品的翘曲变形。     

基于Kriging代理模型的气辅注射成型工艺优化

针对气辅成型工艺设定及控制的复杂性,使用Kriging代理模型近似拟合气辅成型工艺参数与气体穿透长度之间的非线性函数关系,建立起Kriging代理模型与APSO算法相结合的优化策略。应用所建立的优化方案,以气体的穿透深度为优化的目标,模拟了某汽车后视镜的气辅成型工艺优化过程。算例表明,基于Kriging代理模型与APSO算法耦合的优化策略可以在小样本情况下获取较高的求解精度和较快的收敛速度。     

基于CAE模拟的注塑成型浇口设计优化与应用

利用Moldflow软件建立仿真平台,对聚丙烯材质遥控器外壳的注塑加工进行了模拟,主要评估了浇口位置、数量和浇口横截面积对制件翘曲变形量的影响。通过正交试验模拟发现:浇口位置、数量和横截面尺寸均会对聚丙烯材质遥控器外壳的翘曲变形量产生影响,当在模具长短边各设置1个浇口时,有利于降低制件的翘曲变形量;随着浇口横截面尺寸增加,相应制件的翘曲变形量降低。模拟仿真结果表明:当浇口横截面为梯形,短边边长为5 mm,长边边长为6 mm,高为7 mm,且浇口数量为2个,分别位于模具长边中心和短边中心时,相应聚丙烯材质遥控器外壳的总翘曲变形量最低,为0.932 mm。     

Multi-objective optimization of injection molding parameters,based on the Gkriging-NSGA-vague method

Plastic production quality, manufacturing cost, and molding efficiency are three important indices for a new product development. In addition to injection molding process parameters (IMPP), runner system also has an important role in the injection molding process. In this study, the plastic production quality, manufacturing costs, and molding efficiency are considered as the optimized objectives. The design parameters include runner diameters and IMPP. The improved Kriging surrogate model (Gkriging), nondominated sorting genetic algorithm (NSGA-II), and multicriteria fuzzy decision-making approach (vague sets) are combined, and the Gkriging-NSGA-vague scheme is proposed to optimize the runner diameters and the IMPP. Firstly, the Gkriging model is established to map the correlation between design parameters and optimized objectives. Based on the Gkriging model, the NSGA-II is combined with predictive models to obtain the Pareto-optimal solutions. Then, the optimal Pareto-optimal solution is obtained by the vague approach. A multicavity mold with two different plastic parts is utilized as the design case. The optimization results indicate that the Gkriging-NSGA-vague method is a powerful method for solving the multi-objective optimization problems. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 137, 48659.     

基于ANSYS的注塑机定模板的有限元-拓扑优化分析

运用ANSYS工程分析软件，首先对-已有注塑机定模板进行有限元分析，得出应力和变形的分布图。其次，列该定模板简化图长方形板钵进行拓扑优化设计，根据拓扑优化得到的单元伪密度分布图绘制定模板三维图。再对绘制的定模板模型进行有限元分析，得出应力和变形分布图。最后，通过比较可知，优化之后不但产品的设计刚度提高了，也达到了结构轻化的目的。它有针对性地解决了工程实际问题，也就是在满足最大刚度的前提下，找出最佳的材料分配方案，即最佳的筋板分布形式-X形。     

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.     

Mechanical and physical properties of protein-starch based plastics produced by extrusion and injection molding

Processing conditions, ingredient ratio, and moisture content were optimized for making soy protein/starch based plastics using a twin-screw extruder and an injection molding machine. A metering pump and a high speed mixer were used for ingredient mixing. The optimal processing temperature for injection molding was 130°C, and the moisture content of extruded pellets was 10–14%. Processing effects were investigated by measuring the tensile properties and water resistance of specimens. Reduction of water and glycerol in mixtures increased the barrel pressure of the extruder. Mold release was improved by incorporating 0.25 parts tallow per 100 parts of solid material (soy protein and starch). The water absorption of the specimens was reduced by adding acids to adjust the pH to the isoelectric point of soy proteins (pH 4.5). Effects of storage at different relative humidities were studied. The processibility of pellets was stable after a 4-wk-storage period, despite some moisture loss. Injection-molded specimens, after being stored for up to 6 mon at dry conditions [50 and 11% relative humidity (RH)] at room temperature and for 4 wk in a 50°C oven, showed no surface crack. However, humid (93% RH) storage at room temperature promoted fungal growth after storage for 3 mon, indicating that preservatives such as potassium sorbate and propionic acid were needed.     

基于一类组织P系统的模拟移动床的多目标优化

The binaphthol enantiomers separation process using simulation moving bed technology is simulated with the true moving bed approach （TMB）. In order to systematically optimize the process with multiple productive objectives, this article develops a variant of tissue P system （TPS）. Inspired by general tissue P systems, the special TPS has a tissue-like structure with several membranes. The key rules of each membrane are the communication rule and mutation rule. These characteristics contribute to the diversity of the population, the conquest of the multimodal of objective function, and the convergence of algorithm. The results of comparison with a popular algorithm——the non-dominated sorting genetic algorithm 2（NSGA-2） illustrate that the new algorithm has satisfactory performance. Using the algorithm, this study maximizes synchronously several conflicting objectives, purities of different products, and productivity.     

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。     

基于信噪比与灰色关联分析的注塑工艺参数多目标优化

以分线器盖为研究对象,基于信噪比和灰色关联分析,提出了一种注塑工艺参数多目标优化方法.针对顶出时的体积收缩率和总翘曲变形量等质量指标要求,建立5因素4水平的正交试验,得到每组实验的质量指标数值及其对应的信噪比.对信噪比进行无量纲化处理后,计算各因素对质量指标的灰色关联系数和灰色关联度.对灰色关联度进行极差分析,得到最优...     

A physical model for a quality control concept in injection molding

In this work, a model is presented, which is the basis of a quality control concept for the injection molding process. Contrary to statistical methods, this model uses physical dependencies of two quality parameters on four influencing parameters. The influences of holding pressure, holding time, melt temperature, and mold temperature on part mass and dimensions are described based on the fundamental material behavior such as pvT‐data or energy equation. Furthermore, the influence of viscosity changes is indirectly taken into account using the injection work. Assuming only small deviations of the influencing parameters around an optimized operating point, the four parameters are treated as being independent from each other. With this assumption, a product ansatz was chosen with different functions for each influencing factor. Applying basic algebra, the starting equation was transformed into a form that describes either the change in part mass or characteristic part dimensions as a function of the influencing factors. The final equation for the part mass contains six model parameters, whereas nine model parameters are necessary for the equation for the part length. To obtain those model parameters some systematic experiments are required. Once the parameters are known, the model is able to calculate the change of the target values when the influencing factors vary around the operating point. The model was tested experimentally with focus on dimensions using a plastic cover made of an acrylonitrile butadiene styrene (ABS) grade. For the investigated part geometry and material grade, the process behavior was described well by the model. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Process optimization of injection molding using an adaptive surrogate model with Gaussian process approach

This article presents an integrated, simulation‐based optimization procedure that can determine the optimal process conditions for injection molding without user intervention. The idea is to use a nonlinear statistical regression technique and design of computer experiments to establish an adaptive surrogate model with short turn‐around time and adequate accuracy for substituting time‐consuming computer simulations during system‐level optimization. A special surrogate model based on the Gaussian process (GP) approach, which has not been employed previously for injection molding optimization, is introduced. GP is capable of giving both a prediction and an estimate of the confidence (variance) for the prediction simultaneously, thus providing direction as to where additional training samples could be added to improve the surrogate model. While the surrogate model is being established, a hybrid genetic algorithm is employed to evaluate the model to search for the global optimal solutions in a concurrent fashion. The examples presented in this article show that the proposed adaptive optimization procedure helps engineers determine the optimal process conditions more efficiently and effectively. POLYM. ENG. SCI., 47:684–694, 2007. © 2007 Society of Plastics Engineers.     

基于Moldflow和Ansys联合仿真对进气歧管注射成型及进气性能优化的研究

以优化聚酰胺6/玻璃纤维（PA6/GF30）进气歧管的翘曲变形来改善歧管的进气性能为目标，利用BoxBenhnken设计响应曲面试验，再构建随机森林回归模型，并以响应曲面实验所得数据为原始数据进行寻优预测。最后，通过Moldflow和Ansys联合仿真对歧管的进气过程进行模拟。结果表明，歧管的翘曲变形量的预测值与实际值拟合度较高，说明随机森林回归模型是有效的；优化后的歧管翘曲变形量由原来的1.413 mm下降到1.032 mm，下降了27%，进气流量的偏差率和不均匀度分别由原来的[-3.944 153 2%,3.554 111%]、7.498 3%下降到[-2.545 082 2%,1.697 694 7%]、4.242 8%，其进气性能得到明显改善。     

基于SPH方法的微注射成型数值模拟

基于无网格方法和黏性本构方程,开展微注射成型数值模拟的研究。采用光滑粒子流体动力学的粒子近似法离散N-S 控制方程组,求解速度场、压力场、温度场等物理场的变化规律。以应用于生物医学领域带有细微针头的聚合物微针为例,进行充型过程的数值模拟,模拟结果与实验结果基本一致。     

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.     

On‐line multivariate optimization of injection molding

An auxiliary process controller was designed, implemented, and validated for on‐line process and quality optimization. The objective function included terms related to the process variation, model uncertainty, and control energy. The controller architecture relied on characterized models including both process transfer functions and principal components analysis to perform on‐line optimization in parallel with the physical molding process. New process and quality observations were input to the controller to update the models and provided new settings for the machine controller. Experimentation included characterization with a D‐optimal design of experiments followed by a validation to measure the controller's performance with respect to controller stability, extrinsic material variation, cycle time reduction, and other common manufacturing goals. In every case, the controller was able to reduce the value of the objective function while also improving the part dimensions relative to tight tolerance specifications. While characterization experiments could be costly, the use of the resulting process models greatly speeds convergence and facilitates the consideration of various cost and quality terms in the objective function. POLYM. ENG. SCI., 55:2743–2750, 2015. © 2015 Society of Plastics Engineers     

Presentation of a novel sensor based on acoustic emission in injection molding

Injection molding is the most important process to produce plastic parts. Because of increasing complexity of the plastic parts and the aim to reach zero‐defect production it is a must to control the dynamic injection molding process. Therefore information from the inside of the mold, measured with sensors, is necessary. State of the art is to implement wired mold cavity pressure sensors as well as wired cavity temperature sensors. This article presents a novel wireless measurement setup which uses structure borne sound as transport medium. The sound is generated by an acoustic actor which is activated by the passing flow front at certain predetermined positions in the cavity (or cavities). Beside the mechanical setup of the sensor proof of concept measurements with a prototype setup are shown in this article. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

基于模腔压力的注塑过程注射速度设定曲线优化

注塑成型中,把熔前速度恒定(熔体均匀填充模具)作为设定注射速度参数的重要依据。针对熔前速度无法测量问题,提出对于一类注塑制品,用模腔压力随时间线性增加来近似熔前速度恒定。其次,利用实验数据,设计并建立了模腔压力递归神经网络(RNN)模型。在此基础上,提出一种注射速度设定曲线的分级优化策略,其中曲线分级是利用Douglas-Peucker方法自动确定速度分级数目和位置,优化过程则采取基于滑动窗口的迭代优化方法来实现。实验结果表明,该优化方法可以根据模腔复杂程度自动选择合适的注射曲线分级数目,并且其优化精度较常规等间隔分级优化方法更高。