注塑工艺和玻纤含量对玻纤增强PP注塑制品收缩的影响

文中结合计算机辅助工程(CAE)及Taguchi实验设计(DOE)技术研究了玻纤含量和工艺参数对玻纤增强聚丙烯注塑制品各向异性收缩的影响。基于Taguchi DOE方法采用L18(36)正交矩阵进行了实验以优化制品的收缩,并研究了各个参数对制品收缩的影响程度。对于主实验中所选因素,纤维含量、熔体温度和保压压力对玻纤增强聚丙烯注塑制品各向异性收缩的影响较大。     

注塑成型制品翘曲变形优化建模分析

目的 针对传统建模方法在预测的翘曲变形位置与实际偏差较大的问题,开展基于Moldflow的注塑成型制品翘曲变形优化建模分析研究.方法 通过数据模拟分析预处理、浇注体系模型构建、基于Moldflow的注塑成型制品翘曲变形过程模拟等手段,实现对注塑成型制品曲面参数优化.结果 通过对比实验证明,新的建模方法与传统建模方法相比...     

采用紫外光刻电铸技术的微型腔注塑成型

针对复杂微型腔制作的难点,基于紫外光刻电铸(UV-LIGA)技术和掩膜腐蚀的方法加工了细胞皿微型腔。以聚丙烯(PP)进行单因素充模流动工艺试验,通过极差分析,研究了注射速率、模具温度、熔体温度、保压压力和保压时间对微制品成型质量的影响规律。注射速率对提高充填率起主要作用,模具温度和熔体温度是次要因素,而保压压力和保压时间相对其它因素对充填质量影响较差,但必须保持在一个较高的水平。在获取优化工艺参数后,获得合格的制品。因此基于UV-LIGA技术和掩膜腐蚀的方法制作复杂结构微型腔对于微注塑成型是可行的。     

注塑成型工艺参数对制品体收缩率变化的影响及工艺参数优化

结合CAE及Taguchi DOE技术研究工艺参数对注塑制品体收缩率变化(制品中体收缩率的最大值与最小值的差值)的影响并获得优化的工艺参数以使制品的体收缩率变化最小。文中采用L9(3^4)正交矩阵进行实验,并研究了各个参数对制品体收缩率变化的影响程度,对于所选参数,保压压力和模具温度对注塑制品的体收缩率变化的影响较大。     

工艺因素对RHCM成型制品收缩的宏观影响

开发了车载高光蓝牙快速热响应实验模具和动态模温控制系统,基于单因子实验和Taguchi理论,以无定形ABS塑料为实验材料,研究了模具温度对高光注塑成型(Rapid Heat Cycle Molding,RHCM)制品收缩的影响规律。结果表明:适当升高模具温度可以有效降低制品收缩,最佳模温应控制在塑料热变形温度附近;在高模温作用下,熔体温度(低于210℃)和注射压力(低于100 MPa)较低时,制品收缩性态较为复杂,当二者高于该临界值之后则趋于稳定减小或大体不变状态;保压压力、保压时间和冷却时间的影响呈"V"型小幅波动,且均在塑料热变形温度附近达极小值;模温设定条件下,保压压力对制品收缩影响最大,其次是保压时间,冷却时间影响最小,熔体温度和注射压力的影响效应相当.     

基于正交试验与BP神经网络-遗传算法的空气净化器外壳注塑工艺参数优化

目的 以某空气净化器外壳为研究对象,进行注塑工艺参数优化,从而提高塑料制品的成型质量。方法 设置4个影响塑料制品成型质量的因素：熔体温度、模具温度、保压压力、保压时间,以最大翘曲值作为衡量塑料制品成型质量的指标,通过Moldflow模流分析软件,基于正交试验及极差分析探究各因素的影响主次顺序;使用BP神经网络表征工艺参数与翘曲变形的非线性映射关系;采用遗传算法寻优获得最佳注塑工艺参数组合与翘曲变形量,并将所得参数组合用于实际生产指导。结果 经极差分析,保压压力对塑料制品质量的影响最为显著,其次分别为模具温度、保压时间、熔体温度。经BP神经网络预测与遗传算法寻优,发现当熔体温度为229.5℃、模具温度为77.9℃、保压压力为84.4 MPa、保压时间为6.5 s时,可以使注塑件达到最优质量,预测的最小翘曲值为2.94 mm,此工艺参数组合下的仿真计算翘曲值为2.91 mm,二者吻合程度较高。将优化后的工艺参数组合用于实际生产指导,获得了质量良好的注塑产品。结论 所提出的方法对产品注塑的成型及优化有良好的工程应用价值。     

聚合物注塑成型内应力数值计算的非线性模型

为了提高注塑内应力计算的可靠性,利用粘弹性力学理论建立了新的注塑制品内应力计算的四元件串联力学模型,并推导了其瞬态粘弹性响应的非线性本构方程.通过求解流动及保压控制方程,得到内应力计算所需的温度场和压力场,利用回归分析得到了聚合物弹性模量和粘壶系数的计算公式.用新模型对PS平板注塑制件脱模前的内应力进行了模拟计算.计算结果与固体高聚物的结构和力学性能的相关研究结论相一致.     

集成相对位移自传感磁流变阻尼器性能优化

提出并实现基于Pareto优化原理对体积一定的集成相对位移自传感磁流变阻尼器(IRDSMRD)关键参数进行关于目标函数为IRDSMRD的阻尼力与集成相对位移传感器(IRDS)的线性度优化方法。对影响IRDSMRD阻尼力与IRDS线性度磁通回路的磁特性建模、分析,建立受IRDSMRD关键参数影响的IRDS线性度与IRDSMRD阻尼力数学模型。获得IRDSMRD体积一定时表征权衡IRDSMRD阻尼力性能与相对位移传感性能间关系的Pareto最优曲线(即Pareto前沿)。结果表明,在Pareto前沿上点即为IRDSMRD阻尼力与IRDS线性度最优点。在特定应用条件下,可据Pareto前沿选择具有特定IRDS的线性度最优的IRDSMRD阻尼力,反之也然。     

神经网络与混合遗传算法结合的注塑成型工艺优化

注塑成型中,工艺参数直接影响到模具内熔体的流动状态和最终制品的质量,而工艺参数与制品质量之间的关系非常复杂,因此如何建立制品质量与工艺参数之间的关系模型井获得优化的工艺参数是改善制品质量的关键。收缩是衡量制品质量的一个重要指标,制品在型腔中的非均匀收缩是引起制品翘曲的主要原因。文中基于成型过程的数值模拟,采用人工神经网络与混合遗传算法结合优化注塑成型工艺,以改善制品质量。对一工业产品进行分析,以制品内的体收缩率差值为质量指标优化工艺,改善了制品内的体收缩率分布,获得了满意效果。     

模具温度对快速热循环成型制品复原性的影响

为深入了解模具温度对快速热循环注塑成型制品复原性的影响,以自主开发的车用蓝牙高光模具和成型温控辅助装置为基础,研究了快速热循环注塑成型模具温度对制品复原性的影响,并分析了不同模具温度条件下其他参数对制品复原性的影响及相同品质条件下各参数随模具温度的变化规律.结果表明,当模具温度升至塑料热变形温度附近时,复制率明显提高;在相同模具温度条件下,压力和温度对制品复制率的影响较明显,时间对制品复制率的影响甚微;随着模具温度逐步升高,在熔体温度、注射压力不变条件下,制品复制率呈小幅升高趋势,对于保压压力、保压时间和冷却时间则呈"V"字型小幅波动变化.     

Experimental study of warpage and shrinkage in injection molding of HDPE/rPET/wood composites with multiobjective optimization

In this paper, injection molding of squared parts with 1.25 mm in thickness, composed of wood plastic composites (high-density polyethylene, recycled polyethylene terephthalate, and wood flour), was done. The warpage and volumetric shrinkage in the parts was determined experimentally with various process conditions (packing time, melt temperature, wood content, and packing pressure). The experiments were done based on Box–Behnken design of experiments. The significance of each parameter and model was evaluated by analysis of variance (ANOVA). ANOVA showed that packing time and melt temperature are the most significant parameters on warpage and wood content is the most significant on volumetric shrinkage. Packing pressure and wood content had no considerable effect on warpage and packing time on shrinkage too. To obtain optimal process conditions for minimum warpage and shrinkage, a multiobjective optimization based on Pareto front was developed. Response surface method was used to find the relationships between input parameters and objective functions, and genetic algorithm presented the Pareto front solutions to determine the optimum solution. It was observed that there was a good agreement between the predicted optimum values and the experiments.     

An experimental investigation of class A surface finish of composites made by the resin transfer molding process

Achievement of high class surface finish is important to the high volume automotive industry when using the resin transfer molding (RTM) process for exterior body panels. Chemical cure shrinkage of the polyester resins has a direct impact on the surface finish of RTM molded components. Therefore, resins with low profile additives (LPA) are used to reduce cure shrinkage and improve surface quality of the composite parts. However, little is known about the behaviour of low profile resins during RTM manufacturing and their ultimate effects on the surface quality of molded plaques. In this work, the effects of controlled material and processing parameters on the pressure variations, process cycle times and ultimately on the surface quality of RTM molded components were investigated. Taguchi experimental design techniques were employed to design test matrices and an optimization analysis was performed. Test panels were manufactured using a flat plate steel mold mounted on a press. Pressure sensors were inserted in the mold cavity to monitor pressure variations during different stages of cure and at various locations in the mold cavity. It was found that a critical amount of LPA (10%) was required to push the material against the mold cavity and to compensate for the resin cure shrinkage. A significant increase in pressure was observed during the later stages of resin cure due to the LPA expansion. The pressure increase had a significant effect on the surface roughness of the test samples with higher pressures resulting in better surface finish. A cure gradient was observed for low pressure injections which significantly reduced the maximum pressure levels.     

A method for simulation based optimization using radial basis functions

We propose an algorithm for the global optimization of expensive and noisy black box functions using a surrogate model based on radial basis functions (RBFs). A method for RBF-based approximation is introduced in order to handle noise. New points are selected to minimize the total model uncertainty weighted against the surrogate function value. The algorithm is extended to multiple objective functions by instead weighting against the distance to the surrogate Pareto front; it therefore constitutes the first algorithm for expensive, noisy and multiobjective problems in the literature. Numerical results on analytical test functions show promise in comparison to other (commercial) algorithms, as well as results from a simulation based optimization problem.     

Uncertainty‐based robust aerodynamic optimization of rotor blades

The aerodynamic performance of a compressor is highly sensitive to uncertain working conditions. This paper presents an efficient robust aerodynamic optimization method on the basis of nondeterministic computational fluid dynamic (CFD) simulation and multi‐objective genetic algorithm (MOGA). A nonintrusive polynomial chaos method is used in conjunction with an existing well‐verified CFD module to quantify the uncertainty propagation in the flow field. This method is validated by comparing with a Monte Carlo method through full 3D CFD simulations on an axial compressor (National Aeronautics and Space Administration rotor 37). On the basis of the validation, the nondeterministic CFD is coupled with a surrogate‐based MOGA to search for the Pareto front. A practical engineering application is implemented to the robust aerodynamic optimization of rotor 37 under random outlet static pressure. Two curve angles and two sweep angles at tip and hub are used as design variables. Convergence analysis shows that the surrogate‐based MOGA can obtain the Pareto front properly. Significant improvements of both mean and variance of the efficiency are achieved by the robust optimization. The comparison of the robust optimization results with that of the initial design, and a deterministic optimization demonstrate that the proposed method can be applied to turbomachinery successfully. Copyright © 2012 John Wiley & Sons, Ltd.     

Multiobjective optimization with an adaptive weight determination scheme using the concept of hyperplane

Finding an optimum design that satisfies all performances in a design problem is very challenging. To overcome this problem, multiobjective optimization methods have been researched to obtain Pareto optimum solutions. Among the different methods, the weighted sum method is widely used for its convenience. However, since the different weights do not always guarantee evenly distributed solutions on the Pareto front, the weights need to be determined systematically. Therefore, this paper presents a multiobjective optimization using a new adaptive weight determination scheme. Solutions on the Pareto front are gradually found with different weights, and the values of these weights are adaptively determined by using information from the previously obtained solutions' positions. For an n-objective problem, a hyperplane is constructed in n -dimensional space, and new weights are calculated to find the next solutions. To confirm the effectiveness of the proposed method, benchmarking problems that have different types of Pareto front are tested, and a topology optimization problem is performed as an engineering problem. A hypervolume indicator is used to quantitatively evaluate the proposed method, and it is confirmed that optimized solutions that are evenly distributed on the Pareto front can be obtained by using the proposed method.     

连续体结构裂纹识别的Kriging代理模型求解策略

本文提出了一种基于代理模型的裂纹识别方法,利用初始样本构造Kriging代理模型,建立裂纹模型参数与结构响应的关系,来代替结构的原有的结构参数与动力响应关系,最大程度地减少了反演优化迭代过程中反复网格剖分和冗繁的有限元计算次数。使用最优设计加点准则进行代理模型修正,以改进初始代理模型的准确性。为了识别连续体结构上的裂纹模型参数,采用随机粒子群优化方法搜索代理模型多极值域下的全局最优解。数值算例对具有裂纹的悬臂梁和板结构进行了裂纹识别。结果表明,该方法能有效地识别裂纹参数,并且具有良好的抗噪性能。此外,初始样本数量对裂纹识别效率及识别结果的影响也进行了讨论。     

Uncertainty analysis of trade‐offs between multiple responses using hypervolume

When multiple responses are considered in process optimization, the degree to which they can be simultaneously optimized depends on the optimization objectives and the amount of trade‐offs between the responses. The normalized hypervolume of the Pareto front is a useful summary to quantify the amount of trade‐offs required to balance performance across the multiple responses. To quantify the impact of uncertainty of the estimated response surfaces and add realism to what future data to expect, 2 versions of the scaled normalized hypervolume of the Pareto front are presented. To demonstrate the variation of the hypervolume distributions, we explore a case study for a chemical process involving 3 responses, each with a different type of optimization goal. Results show that the global normalized hypervolume characterizes the proximity to the ideal results possible, while the instance‐specific summary considers the richness of the front and the severity of trade‐offs between alternatives. The 2 scaling schemes complement each other and highlight different features of the Pareto front and hence are useful to quantify what solutions are possible for simultaneous optimization of multiple responses.     

A multi-objective military system of systems architecting problem with inflexible and flexible systems: formulation and solution methods

System of systems (SoS) architecting is the process of bringing together and connecting a set of systems so that the collection of the systems, i.e., the SoS is equipped with a set of required capabilities. A system is defined as inflexible in case it contributes to the SoS with all of the capabilities it can provide. On the other hand, a flexible system can collaborate with the SoS architect in the capabilities it will provide. In this study, we formulate and analyze a SoS architecting problem representing a military mission planning problem with inflexible and flexible systems as a multi-objective mixed-integer-linear optimization model. We discuss applications of an exact and an evolutionary method for generating and approximating the Pareto front of this model, respectively. Furthermore, we propose a decomposition approach, which decomposes the problem into smaller sub-problems by adding equality constraints, to improve both the exact and the evolutionary methods. Results from a set of numerical studies suggest that the proposed decomposition approach reduces the computational time for generating the exact Pareto front as well as it reduces the computational time for approximating the Pareto front while not resulting in a worse approximated Pareto front. The proposed decomposition approach can be easily used for different problems with different exact and heuristic methods and thus is a promising tool to improve the computational time of solving multi-objective combinatorial problems. Furthermore, a sample scenario is presented to illustrate the effects of system flexibility.     

基于代理模型的空投装备气囊缓冲系统多目标优化

基于有限元法和控制体积法建立装备-气囊系统有限元模型,并采用试验数据对模型进行验证。复杂气囊系统着陆缓冲过程仿真计算资源消耗大,难以应用传统迭代方法进行参数优化。为克服这些问题,结合扩展拉丁超立方设计,以最大着陆冲击加速度和最大翻转角度为响应,采用径向基函数构建代理模型。在代理模型基础上,利用多目标遗传算法对主气囊高度、横向宽度及排气孔面积等气囊缓冲系统参数进行了多目标优化。优化结果表明:优化后最大冲击加速度减小了15.5%,最大翻转角度减小了70.3%,缓冲性能与横向稳定性均有所提高。