A new algorithm for non-newtonian flow and its application in mould filling process

The simulation of injection molding process requires a stable algorithm to model the molten polymer with non-isothermal non-Newtonian property. In this paper, a staggered and iterative scheme is particularly designed to solve the velocity-pressure-temperature variables. In consideration of the polymer characteristic of high viscosity and low thermal conductivity, the non-Newtonian momentum-mass conservation equations are solved by the Crank-Nicolson method based split (CNBS) scheme, and the energy conservation equation with convective character is discretized by the characteristic Galerkin (CG) method. In addition, an arbitrary Lagrangian Eulerian (ALE) free surface tracking and mesh generation method is introduced to catch the front of the fluid flow. The efficiency of the proposed scheme is demonstrated by numerical experiments including a lid-driven cavity flow problem and an injection molding problem.     

注射成形填充模拟的修正算法

为提高注射成形过程数值模拟结果的准确性,采用类似迎风法的概念,通过对平流方程作为填充状态控制方程而导致模拟失真原因的分析,提出了相应的数值修正算法.该方法在高效显式数值模拟算法和有限元软件基础上,通过对模型内流体速度场的系统性操作,削弱空气流动速度场对喂料填充的不真实影响,使得填充状态的输送更基于流动前沿面后部的流场作用,并对⊥型和L型模腔进行了数值仿真.结果表明:该方法能有效抑制填充流的失真现象,实现了在流动过程中流向发生大角度变化模腔的正确填充过程.      

基于改进算法的多层神经网络的结构优化设计

简要地介绍了工程中应用较多的多层前向神经网络(BP网络)的结构和网络的设计．通过采用一种改进的训练算法(参考隐层的输出),并结合科学的样本选择方法(“超立方体”法)设计网络,进行网络训练．最后通过数字仿真证明了这个网络模型用于结构优化设计的有效性．     

Phase Behaviors in Bi-phase Simulation of Powder Segregation in Metal Injection Molding

Powder segregation induced by mold filling is an important phenomenon that affects the final quality of metal injection molding （MIM）. The prediction of segregation in MIM requires a bi-phase flow model to describe distinctly the flows of metallic powder and polymer binder. Viscous behaviors for the flows of each phase should hence be determined. The coefficient of interaction between the flows of two phases should also be evaluated. However, only viscosity of the mixed feedstock is measurable by capillary tests. Wall sticking is supposed in the traditional model for capillary tests, while the wall slip is important to be taken into account in MIM injection. Objective of the present paper is to introduce the slip effect in bi-phase simulation, and search the suitable way to determine the viscous behaviors for each phase with the consideration of wall slip in capillary tests. Analytical and numerical methods were proposed to realize such a specific purpose. The proposed method is based on the mass conservation between the capillary flows in mono-phase model for the mixed feedstock and in bi-phase model for the flows of two phases. Examples of the bi-phase simulation in MIM were realized with the software developed by research team. The results show evident segregation, which is valuable for improving the mould designs.     

Effect of packing parameters and gate size on shrinkage of aspheric lens parts

In order to produce the high quality optical aspheric lens,the shrinkage of lens should be strictly controlled during the injection process.Serials of experiments are done to find the factors that affect the lens shrinkage during the injection molding process.The study is focused on the effect of gate size,packing time and pressure on the shrinkage.The gate size decides the effective packing time.Only when the effective packing time is enough,the shrinkage size will be decreased along with the packing press...     

基于神经网络的传感器故障诊断仿真

针对现代工业大型复杂系统的不确定性使得对其进行建模比较困难的特点，提出了一种用神经网络进行传感器故障诊断的方法．以凝汽式汽轮机调速系统中的转速传感器为例进行了传感器故障诊断研究，给出了仿真实现结果，并加以分析．仿真结果表明，所提出的利用神经网络进行传感器的故障诊断的方法具有可行性．     

基于神经网络的公交客流预测

公交客流量具有动态性,受多种因素的影响,不能或无法用精确的数学模型进行预测。通过对公交客流量预测的Elman和BP神经网络的建立、学习和训练。并以前三年的公交客流量、国内生产总值、工业总产值、城市人口数作为两种神经网络的输入神经元,第四年的公交客流量作为输出神经元,同时以合肥市公交客流量为例进行分析,结果表明：所建的Elman模型比EBP模型的预测精度高,效果好。     

神经网络在车辆半主动控制中的应用

针对车辆半主动悬挂模型的非线性特性，研究了采用神经网络的自适应控制在车辆悬挂半主动控制中的应用，设计了采用前馈神经网络的辩识器和控制器，对控制器网络采用的学习规则进行了分析。仿真计算表明，采用神经网络自适应控制方法能有效改善车辆的平稳性，采用的神经网络辩识器和控制器的改进BP算法是有效的。     

基于BP人工神经网络的城市轨道交通线网规划的综合评价

城市轨道交通线网规划传统的评价方法主要有层次分析法和模糊综合评判法等,而这些评价方法因其不可避免的主观性而导致评价结果可靠性不高.本文采用BP人工神经网络模型对城市轨道交通线网的规划方案进行评价,有效避免了评价过程中人工过多参与造成的主观性偏差,并采用仿真软件验证其有效性和可行性.     

基于神经网络的公交客流预测

公交客流量具有动态性,受多种因素的影响,不能或无法用精确的数学模型进行预测。通过对公交客流量预测的Elman和BP神经网络的建立、学习和训练,并以前三年的公交客流量、国内生产总值、工业总产值、城市人口数作为两种神经网络的输入神经元,第四年的公交客流量作为输出神经元,同时以合肥市公交客流量为例进行分析,结果表明:所建的Elman模型比BP模型的预测精度高,效果好。     

On the algorithms of adaptive neural network-based speed control of switched reluctance machines

AbstractA switched reluctance machine(SRM)drive is a time-varying,strongly nonlinear system.High performance control can no longer be achieved by using linear techniques.This paper describes the back-propagation (BP)neural network-based proportional-integral-derivative(PID)speed control of the SRM.It’s the interest of this paper to explore the utilization of the prior empirical knowledge as guidance in the initializing and training of the neural networks.The purpose is to make the networks less sensitive on the initial weights.Two modified algorithms are presented and simulation experiments show some interesting findings about their control effects and their corresponding sensitivity on the initial weights of the networks.     

An Adaptive Sliding Mode Tracking Controller Using BP Neural Networks for a Class of Large-scale Nonlinear Systems

A new type controller, BP neural-networks-based sliding mode controller is developed for a class of large-scale nonlinear systems with unknown bounds of high-order interconnections in this paper. It is shown that decentralized BP neural networks are used to adaptively learn the uncertainty bounds of interconnected subsystems in the Lyapunov sense, and the outputs of the decentralized BP neural networks are then used as the parameters of the sliding mode controller to compensate for the effects of subsystems uncertainties. Using this scheme, not only strong robustness with respect to uncertainty dynamics and nonlinearities can be obtained, but also the output tracking error between the actual output of each subsystem and the corresponding desired reference output can asymptotically converge to zero. A simulation example is presented to support the validity of the proposed BP neural-networks-based sliding mode controller.     

基于Moldflow的仪表盖模具设计

以Pro/E为工具,通过对仪表盖的结构和工艺性分析,论述了仪表盖注射模设计中应考虑的因素.重点介绍了利用Moldflow软件来模拟注射流动过程,根据模拟结果调整了设计方案并给出了解决方案,最后给出了模具装配图.这样设计,可缩短模具设计周期,提高模具的一次成品率.     

基于CML的城市地铁网络相继故障分析

分析和掌握城市地铁网络的基本特性,有助于规划和运营管理者对地铁进行运输组织和紧急情况的处理。基于复杂网络理论,采用matlab仿真分析,对城市地铁网络相继故障进行了研究。结果表明：在相继故障扩散过程中蓄意攻击比随机攻击具有较快的传播速度,但在扰动幅度和故障规模关系中呈现出相似的变化趋势,蓄意攻击效果并不明显;多目标随机攻击加快了地铁网络相继故障的扩散进程,但对于最终引起的故障规模则影响不大。     

Moldflow／MPI在塑料注射成型过程中的应用

介绍了热塑性塑料注射成型过程中,使用Moldflow软件对塑件的成型过程进行模拟分析,确定了浇口位置、充填时间、锁模力情况。分析结果对模具设计及塑件注射成型工艺具有指导作用。     

基于BP神经网络的FRP加固混凝土柱承载力预测

为提高纤维增强复合材料(FRP)加固混凝土轴压柱承载力的计算精度,建立了FRP加固混凝土轴压柱承载力的BP神经网络预测模型.利用大量试验数据对神经网络模型进行训练,并用训练成熟的神经网络模型对FRP加固混凝土轴压柱的承载力进行了预测.通过模型预测值与试验结果的比较,证明该模型的预测结果具有一定的可信度,最大误差不超过15%,比其他计算模型的精度高.     

STUDY ON PARISON CAD OF PLASTIC CONTAINER AND DESIGN FOR ITS CONTROL SYSTEM

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数值模拟技术在铸造工艺中的应用

以生产厂家的典型铸件为例，根据实际生产条件，对其铸造工艺进行数值模拟计算．通过对数值模拟结果的分析，预测可能产生的铸造缺陷，进而提出新的优化铸造工艺的措施，以得到较高质量的铸件．     

复杂生产过程的小故障检测与分类方法

为监测复杂生产过程的状态,根据多元统计过程控制方法和支持向量机理论,将累积和控制图原理扩展为多变量的形式对过程数据进行预处理,并通过主元分析方法提取复杂生产过程的关键信息,得到有效的小故障数据,进而构建计算正常数据的统计量阀值及故障数据的Hotelling T平方统计值（T2）和平方预测误差值,实现了复杂生产过程的小故障模式检测,并采用支持向量机多分类方法将检测到的故障进行了分类.沥青混合料生产过程的仿真研究表明:在集料均值发生小波动、周期性上升和下降3种小故障模式下,故障检测识别率均达到95%,与主元分析方法相比平均提高了75%;分类准确率达到92.5%,与BP神经网络方法相比提高了19.3%.      

Guaranteed Cost Iterative Learning Control for Multi-Phase Batch Processes

Batch process is a typical multi-phase process. Due to the interaction between the phases of the batch process, high precision control in a single phase cannot guarantee high precision control of the whole batch process. In order to solve this problem, the guaranteed cost iterative learning control (ILC) of multi-phase batch processes is studied in this paper. Firstly, through introducing the output error, the state error and the extended information, the multi-phase batch process is transformed into an equivalent 2D switched system which has different dimensions. In addition, under the measurable condition, the guaranteed cost iterative learning control law with extended information is designed. The proposed control law ensures not only the stability of the system but also the optimal control performance. Next, in order to study the stability of the system and the minimum running time under the condition of stable running, the multi-Lyapunov function method is used. By means of the average dwell time method, the sufficient conditions ensuring system to be exponentially stable are given in the form of linear matrix inequality (LMI). Finally, the injection molding process is taken as an example to make simulation, which shows the feasibility and effectiveness of the proposed method.