面向工程的专用产品参数化CAD系统的研究

 针对目前参数化CAD系统的不足，提出了面向工程的参数化思想，引入了功能约束，分析了对功能约束进行处理的3种方法．采用了基于特征的参数化造型方法，用应用层、描述层和图形层三层体系表达基于特征的参数化模型，将该模型作为参数驱动的基础．将设计中的约束分为3类：工程约束、适配约束和几何约束．通过适配约束在工程约束与几何约束间建立映射关系，使得参数化设计系统既可以受几何参数的驱动，又可以受工程参数的驱动，从而扩大了CAD技术对设计过程支持的范围。     

参数化CAD中参数有效范围的算法研究

为了解决在参数化CAD系统中由于参数赋值不合理而导致的几何实体重建失败的问题,提出了确定一类二维参数化CAD模型中参数有效范围的代数算法。该算法可以实时求解所有简单多边形中距离约束参数的有效取值范围,并利用几何变换简化求解规模,提高求解效率。研究结果表明,该算法提供的有效取值范围内的任一赋值,均可保证重建后几何实体的拓扑形状不发生改变,在一定程度上提高了参数化CAD软件的设计效率和人机交互的智能化水平。同时对算法复杂度进行了分析,该算法的复杂度为O(n~2)。     

基于ARX实体驱动的方法及参数化变动设计的实现

AutoCAD系统为每个实体对象提供了唯一的标识符（ID）。笔者基于ARX技术对AutoCAD图形数据库的访问机理，通过实体标识符（ID）及实体对象的特征点、图元几何约束及图元间的约束联动，实现了图元实体驱动的参数化变动设计；通过尺寸实体对象标识符（ID）及尺寸与图元的约束关系，实现了尺寸驱动的参数化变动设计。此方法在水利工程流道CAD中加以了应用，其效果良好。     

几何约束求解的去并拟合方法

依据有向图理论提出了去并拟合方法,由此导出了对于几何约束求解问题的去并拟合的并行处理及串行处理策略,进而得到了一套求解几何约束问题的完备算法。通过将该理论引入到参数化CAD的设计中,使得在算法复杂度增加不大的情况下,几何自动作图的范围大大拓宽。还将其应用于智能动态几何软件的设计中,取得了较好的结果。     

CAD参数化自动绘图中解决图形不确定性的一种新方案

运用消隐的概念，处理计算机辅助设计中参数化自动绘制零件图所产生的图形不确定性。即用不透明的消隐平面，隐去由于不确定性而产生的多余图形。可在参数化绘图的程序编制中，省去对输入参数的判别归类的功能模块，对任何参数所对应的图形，实现单一化处理。从而简化了CAD系统的程序设计，提高了系统的灵活性、绘图效率和适应度。     

机床夹具定位元件图形库的开发与实现

本系统用Auto CAD内嵌语言AutoLISP为机床夹具CAD建立了夹具定位元件的参数图形库。 本软件运用数据库文件与LISP图形图程序相分离的方式,运用参数化设计,根据零件的主参数值及必要的信息,由CAD系统自动查询或计算出该零件其余各参数并绘出图形。用参数化程序建库既可大量节省存储空间,明显地提高设计效率和设计质量,又便于用户随需要对程序或数据库中的数据作灵活改动。 在设计方法上,对系统中图形库和数据库之间的数据传递、图形制作及尺寸标注等运用了简洁的程序设计方式。由于考虑到零件图与装配图有所不同,本系统在制作中也为这两种方式的转换和修改提供了途径。 本系统利用弹出式菜单或图标菜单与用户对话,使用极为方便,随时可以调用。 本课题的研究,体现了现代CAD的主要思想——参数化设计。从理论和实践上都为夹具CAD系统的开发提供了一条方便之路。     

基于约束图的三维服装约束问题求解

采用样条曲线作为基本几何元素,建立了三维服装几何约束关系。以此为基础构造了三维服装的约束图,有效地表达了几何元素及其相互关系。提出了基于约束图的改进的三维服装几何约束求解方法,完成了构造服装和对服装的交互参数化修改问题。通过应用实例将参数化方法向高层次图素进行了推广,并成功应用于服装CAD系统中,运行效果良好。     

智能化尺寸驱动法在参数绘图中的探索应用

讨论了二维参数化绘图中的智能化尺寸驱动问题。首先讨论了几何约束、结构约束与约束判定的关系，基于此关系，利用图形尺寸参数、结构参数实现图形的尺寸驱动，进而利用智能化尺寸驱动法实现参数化绘图。以AutoCAD为平台，通过高级语言C与AutoLISP语言编程，在开发的圆柱齿轮减速器壳体自动化设计系统软件之参数法绘图中得以实现。     

基于Web的框架木箱CAD系统开发与实现

利用VB语言对Solidworks软件进行二次开发,完成框架木箱的参数化设计,在本地参数化设计系统的基础上,利用PHP语言,并结合MySQL数据库,进一步实现了基于Web的框架木箱CAD系统。详细介绍了系统的整体结构框架以及实现过程中的关键技术,用户只要访问网站输入基本参数,即可生成符合要求的木箱模型,并能进行浏览及下载,从而实现了框架木箱设计的共享化与快捷化。     

周转箱CAD系统的研究与实现

以VC 6.0为开发平台,设计一个基于OpenGL三维图形接口的包装周转箱CAD系统.该系统以"图"为数据结构建立了箱体模型,程序内置箱型库中具备一些常用箱型,也可由用户自行设计,从而实现了周转箱的参数化,缩短了设计与制造周期.     

Robust computer-aided parameter and tolerance determination for an electronic circuit design

Conventionally, parameter design precedes tolerance design in the course of product design or process planning. To lower the production costs, as well as to improve quality, this study proposes the simultaneous determination of parameter and tolerance values when designing an electronic circuit. With the current development of CAD (Computer-Aided Design) software for electronic circuit design, engineers can determine parameter and tolerance values without providing transfer functions for circuit analysis. In this study, a computer experiment is performed by using CAD software (PSpice) to obtain outputs that will be converted into the total cost, which includes the quality loss, the tolerance cost and the failure cost. Then, Response Surface Methodology (RSM) is employed to minimize the total cost and to find the optimal parameter and tolerance values statistically. Consequently, a parameter and tolerance design for quality improvement and cost reduction can be achieved for any complex electronic circuit during the early stages of design.     

悬臂输流管的颤振和浑沌运动分析

研究了具有弹性支承和运动约束的悬臂输流管的稳定性问题。在分析参数对系统稳定性影响的同时，特别研究了动态失稳区域内的不同运动。结果表明，在一定条件下该系统不仅因颤振而动态失稳，在某些参数域内还可发生浑沌运动。本文用数值分析的方法确定了浑沌发生区域。     

Physiologically-Based Kinetic Modeling of Intravenously Administered Gold (Au) Nanoparticles

Physiologically-based kinetic (PBK) modeling is a valuable tool to understand the kinetics of nanoparticles (NPs) in vivo. However, estimating PBK parameters remains challenging and commonly requires animal studies. To develop predictive models to estimate PBK parameter values based on NP characteristics, a database containing PBK parameter values and corresponding NP characteristics is needed. As a first step toward this objective, this study estimates PBK parameters for gold NPs (AuNPs) and provides a comparison of two different NPs. Two animal experiments are conducted in which varying doses of AuNPs attached with polyethylene glycol (PEG) are administered intravenously to rats. The resulting Au concentrations are used to estimate PBK model parameters. The parameters are compared with PBK parameters previously estimated for poly(alkyl cyanoacrylate) NPs loaded with cabazitaxel and for LipImage 815. This study shows that a small initial database of PBK parameters collected for three NPs is already sufficient to formulate new hypotheses on NP characteristics that may be predictive of PBK parameter values. Further research should focus on developing a larger database and on developing quantitative models to predict PBK parameter values.     

An investigation of noise factor effects in parameter design

Parameter design aims to determine nominal values of a set of design parameters so as to minimize variability in one or more key performance measures, in the presence of uncertainties in the design parameters, whilst maintaining the required nominal (design point) performance and the overall design concept. A theoretical analysis of some aspects of parameter design and of some related approximate methods is carried out and the results studied with the aim of determining the effect of changes in the magnitudes of the uncertainties in the design parameters on the resulting values of nominal parameter values in the optimum design. It is shown that there is reason to expect that, allowing for the effects of constraints, a given parameter design would be robust against overall changes, by a constant factor, in the parameter uncertainties but not in the presence of changes in individual parameter uncertainties, or by numerous such changes when the change factor varies between individual parameters. Some numerical results are obtained for problems in beam design and are shown to support the above assertions. The results imply that nominal values in a previously determined robust design may continue to be valid when parameter tolerances change, provided such changes are by the same factor for all free design parameters, i.e. those not fixed on a constraint boundary. Copyright © 2002 John Wiley & Sons, Ltd.     

Surrogate-assisted Bayesian inference inverse material identification method and application to advanced high strength steel

Proper definition of certain material properties is a paramount issue for accurate simulation. However, the values of a material parameter are commonly uncertain due to multiple factors in practice. To obtain reliable material parameters, parameter identification via Bayesian theory has become an attractive framework and received more attention recently. Based on this frame, the determination of likelihood function is critical for posterior probability. Unfortunately, it is commonly difficult to be determined directly, especially for complex engineering problems. In this study, Bayesian formulas for material parameter identification are given. To make it feasible for real engineering problems, the least square-support vector regression surrogate and Monte Carlo Simulation are integrated to obtain the maximum likelihood estimation of likelihood function. The uncertainty of parameter identification is quantified via the Bayesian method. In two benchmarks, two cases with single and multiple uncertainty sources are used to propagate and quantify uncertainties in material parameters based on Bayesian approach. Moreover, the proposed method is used to identify the material parameters of advanced high strength steel used in vehicle successfully.     

Controller design and FPGA implementation of a speed‐sensorless vector‐controlled induction motor drive based on stability consideration

Abstract

The purpose of this paper is to determine the controller parameter tuning range for a speed sensorless vector‐ controlled induction motor drive from the system stability point of view. The tuning rules for conventional PI controllers are mostly based on experience. Trial‐and‐error procedures are used to tune the values of the controller parameters. The relationship between the tuned controller parameters and the stable operating range of the control system is generally not known. This paper starts from establishing complete dynamic models for a sensorless vector‐controlled induction motor drive. The nonlinear dynamic models are linearized around a chosen operating point. The characteristic equation is then derived, which is used to determine the values of the controller parameters corresponding to the marginal system stability. Based on these critical values, the tuning ranges of the controller parameters are obtained, which assures stable operation of the drive in the entire operating region and provides a reference for controller parameter tuning. The proposed method is further extended to include the effect of parameter sensitivity due to motor parameter variation. An experimental setup based on a DSP‐FPGA system is implemented. The simulation and experiments confirm the validity of the proposed approach.     

Accounting for Parameter Uncertainty in Simulation Input Modeling

We formulate and evaluate a Bayesian approach to probabilistic input modeling for simulation experiments that accounts for the parameter and stochastic uncertainties inherent in most simulations and that yields valid predictive inferences about outputs of interest. We use prior information to construct prior distributions on the parameters of the input processes driving the simulation. Using Bayes' rule, we combine this prior information with the likelihood function of sample data observed on the input processes to compute the posterior parameter distributions. In our Bayesian simulation replication algorithm, we estimate parameter uncertainty by independently sampling new values of the input-model parameters from their posterior distributions on selected simulation runs; and we estimate stochastic uncertainty by performing multiple (conditionally) independent runs with each set of parameter values. We formulate performance measures relevant to both Bayesian and frequentist input-modeling techniques, and we summarize an experimental performance evaluation demonstrating the advantages of the Bayesian approach.     

Parameter estimation and thermodynamic model fitting for components in mixtures for bio-diesel production

In the search of clean, sustainable and renewable energy sources, at present the use of bio-diesel from vegetable oils is one of the possibilities being considered. The knowledge of phase equilibrium in mixtures found in the bio-diesel production is essential for the correct design and operation of the process. However, there is still a lack of information about the parameter values for the models used to describe the phase equilibrium, which could lead to incorrect design of the reactors and the separation processes, and to low efficiency caused by low mass transfer due to the presence of heterogeneous mixtures. In this study, two algorithms are described for the model fitting and parameter estimation, which were used to estimate the values of the parameters in the UNIQUAC and NRTL model for a mixture of vegetable oils and bio-diesel. One of the algorithms uses the Simulated Annealing (SA) method, to find good initial estimates, without any previous knowledge of the parameters. Another algorithm uses Successive Quadratic Programming, using the estimates calculated in the SA algorithm to refine these estimates. In both algorithms, the criterion for fitting is the minimization of the square of the difference between calculated and experimental values. In both algorithms, the equilibrium is calculated using the direct global minimization of the Gibbs free energy with respect to the number of moles of each component, at constant T and P, for a given set of model parameters. The results obtained were able to correctly reproduce the experimental values of phase equilibrium composition.     

Robust design employing a genetic algorithm

The minimization of variability in a key design feature or performance measure, in the presence of variability in the realized values of design parameters, is discussed and an analytic solution for quadratic performance measures is provided. Solutions are based on the determination of optimum nominal (or design point) values for the design parameters, subject to constraints in the form of a given nominal performance at the design point and limits on the nominal values of the design parameters, which preserve the design concept. The more general, numerical, problem solution is addressed and a previously described deterministic procedure which generated multiple local optima is improved by the replacement of a simplex search method with a sophisticated genetic algorithm which, with suitable parameter values and choice of Lagrange multiplier, converges only to the required global minimum within the specified design parameter limits. Further improvements are discussed. Copyright © 2000 John Wiley & Sons, Ltd.     

基于阻抗模型的玻璃棉声学参数反演方法研究

为了能准确获取玻璃棉材料的声学参数,文章对玻璃棉声学参数在不同阻抗模型下的声学参数进行了反演。采用了厚度分别为22 mm和44 mm的玻璃棉样本实测吸声曲线及各声学参数,选取四种常用阻抗模型,通过遗传算法(Genetic Algorithm,GA)对玻璃棉材料进行声学参数的反演,并选择反演效果最优的模型进行敏感性分析。比较各参数反演结果的误差比,并对比不同模型描述的吸声曲线与测试曲线的一致性,最后量化并比较Johnson-Champoux-Allard (JCA)模型中各参数对吸声系数的影响程度。研究表明,使用GA结合JCA模型或Johnson-Champoux-Allard-Lafarge (JCAL)模型反演的参数值与测试值误差较小;JCA模型适用于玻璃棉材料的声学参数反演,模型中流阻率和曲折度的敏感性较高,反演过程需保证其精确度。