全自动模切机动平台机构优化设计

对新型全自动模切机动平台机构进行优化设计,解决高速加工过程中从动凸轮轮廓曲线损坏变形,导致加工精度和效率下降问题。基于多项式三次样条函数反转法设计从动凸轮轮廓线,离散轮廓线为多个点,对应的点生成多条曲线,再通过交集运算得到更接近理想的凸轮轮廓曲线。通过Mat Lab对基于模切机间隙运动、动平台上下运动和位移误差进行了运动仿真分析,从动件位移最大误差小于0.1,整体误差比例控制在0.5%以内。从而让凸轮运动速度达到750r/min,从动件推程在20mm内,运动性能平稳,位移误差在0.1mm以内,达到高速模切性能加工要求。     

凸轮符合“最小条件”误差值的求解

1.概述 凸轮轮廓的形状误差,是通过凸轮机构从动件（挺柱）的运动误差（升程误差）来反映的。根据形状误差的定义,凸轮的升程误差是指被测实际凸轮对其理想凸轮的变动量。因为处于不同方位理想凸轮下的实际凸轮会得到不同的升程误差值,所以应按最小区域法并考虑凸轮升程公差大小、公差带形状的影响,     

求解凸轮当量转角-升程的方法

本文针对采用非设计要求测头 (与凸轮机构从动件 (挺柱 )不同形式和形状的测头 )进行凸轮测量所引发的当量转角—升程的求解方法问题进行了分析。并强调指出 ,在进行凸轮测量时 ,不能因为采用了非设计测头而改变凸轮设计受检点的位置 ,即测头切换应遵守凸轮受检点位置不变原则。     

包装机械用空间凸轮的设计与加工

根据包装机械中常用的空间凸轮从动件在升程与降程都有较大负载的特殊运动要求,运用Pro/ENGINEER及MasterCAM软件进行参数化设计与数控加工,解决了一般编程方法难以解决的问题.     

基于B样条曲线的充氮加塞包装机凸轮曲线设计

根据通常采用的凸轮曲线设计方法是根据从动件运动关系进行分段表述,不能从整体上对凸轮特性进行控制的情况,采用样条曲线函数表示从动件运动关系,并将其应用在充氮加塞包装机凸轮设计中。实现了充氮加塞包装机凸轮曲线的连续性设计,为其它包装机凸轮设计提供了参考。     

高效节能型摆臂式小凸轮圆织机的开发

通过对现有四梭圆织机经线开口传动装置的改进和对凸轮轮廓线进行优化设计,并采用摆动从动件共轭圆柱凸轮机构代替原有的直动从动件共轭圆柱凸轮机构,将凸轮双曲线凹槽改进为单曲线凸台,对凸轮机构的动力学性能优化和凸轮加工方法等方面的研究,成功开发出高效节能型摆臂式小凸轮圆织机,减少了圆织机的驱动电机功率和启动电流,有效地解决现有圆织机运行能耗高、振动和噪声大的缺点。     

基于Matlab机器人工具箱的摆动从动件盘形凸轮设计方法

对欧空局计划的LISA-Pathfinder探测器中所需一次性运动凸轮的设计方法进行研究。针对盘形凸轮外部轮廓曲线设计中复杂函数的求解问题，提出一种基于Matlab机器人工具箱的摆动从动件盘形凸轮设计方法。该方法设计过程简便，对作复杂函数运动的凸轮来说设计精度高。设计了符合使用要求的凸轮和专用的实验装置，并通过实验验证了所设计凸轮的运动规律。     

基于样条函数拟合的凸轮升程误差评定方法研究

在凸轮升程误差设计要求与测量评定要求的基础上，提出了凸轮升程误差的二义性；进而分析现有圆柱凸轮升程误差的几种测量评定方法如“敏感点法”、“桃尖点法”的缺点，提出基于三次样条函数拟合的凸轮升程误差评定方法。理论分析和比较测量实验结果表明，该方法的评定精度比传统的敏感点法提高约13．3％。     

基于6σ的车身噪声传递函数稳健优化设计

为提高设计稳健性,将6σ稳健优化设计引入车身噪声传递函数优化过程。将6σ质量管理、可靠性设计稳健设计相结合,考虑设计变量、约束条件目标函数在内所有不确定性信息,不仅满足优化目标函数、提高系统可靠性要求,使系统响应均方差最小化,即提高稳健性。以某型汽车为例,在汽车声固耦合有限元模型基础上采用基于试验设计的二阶多项式响应面模型,以车身总质量一阶模态频率为约束条件驾驶员耳旁声压级响应均方根值为目标函数,在基本随机变量概率特性已知情况下对车身噪声传递函数进行6σ稳健优化设计,与传统确定性优化设计相比表明该方法的有效性。     

基于MATLAB的波纹膨胀节的优化设计

通过设计变量的选取、目标函数和约束条件的确定,建立U型波纹膨胀节的优化设计数学模型,编辑波纹膨胀节设计软件,利用MATLAB优化工具箱进行寻优计算.对设计公式中一些由经验曲线获得的参数,采用多项式拟合法进行求解.     

An uncertain multidisciplinary design optimization method using interval convex models

This article proposes an uncertain multi-objective multidisciplinary design optimization methodology, which employs the interval model to represent the uncertainties of uncertain-but-bounded parameters. The interval number programming method is applied to transform each uncertain objective function into two deterministic objective functions, and a satisfaction degree of intervals is used to convert both the uncertain inequality and equality constraints to deterministic inequality constraints. In doing so, an unconstrained deterministic optimization problem will be constructed in association with the penalty function method. The design will be finally formulated as a nested three-loop optimization, a class of highly challenging problems in the area of engineering design optimization. An advanced hierarchical optimization scheme is developed to solve the proposed optimization problem based on the multidisciplinary feasible strategy, which is a well-studied method able to reduce the dimensions of multidisciplinary design optimization problems by using the design variables as independent optimization variables. In the hierarchical optimization system, the non-dominated sorting genetic algorithm II, sequential quadratic programming method and Gauss–Seidel iterative approach are applied to the outer, middle and inner loops of the optimization problem, respectively. Typical numerical examples are used to demonstrate the effectiveness of the proposed methodology.     

Fast numerical methods for robust optimal design

A fast numerical approach for robust design optimization is presented. The core of the method is based on the state-of-the-art fast numerical methods for stochastic computations with parametric uncertainty. These methods employ generalized polynomial chaos (gPC) as a high-order representation for random quantities and a stochastic Galerkin (SG) or stochastic collocation (SC) approach to transform the original stochastic governing equations to a set of deterministic equations. The gPC-based SG and SC algorithms are able to produce highly accurate stochastic solutions with (much) reduced computational cost. It is demonstrated that they can serve as efficient forward problem solvers in robust design problems. Possible alternative definitions for robustness are also discussed. Traditional robust optimization seeks to minimize the variance (or standard deviation) of the response function while optimizing its mean. It can be shown that although variance can be used as a measure of uncertainty, it is a weak measure and may not fully reflect the output variability. Subsequently a strong measure in terms of the sensitivity derivatives of the response function is proposed as an alternative robust optimization definition. Numerical examples are provided to demonstrate the efficiency of the gPC-based algorithms, in both the traditional weak measure and the newly proposed strong measure.     

Hybrid approximation algorithm with Kriging and quadratic polynomial‐based approach for approximate optimization

This paper describes a new hybrid algorithm that uses a Kriging and quadratic polynomial‐based approach for approximate optimization. The Kriging method is used for generating a global approximation model, and the polynomial‐based approximation method is used for generating a local approximation model. The Kriging system is only used to construct a polynomial‐based locally approximate model by estimating some function values and Hessian components of an estimated surface. The number of Kriging estimations can be reduced in comparison with direct Kriging‐based optimization, and a local optimum solution on an approximated surface can be clearly estimated without use of an optimization procedure based on a local appropriate quadratic polynomial model. Numerical examples of engineering optimization using the proposed method illustrate validity and effectiveness of the proposed method. Copyright © 2006 John Wiley & Sons, Ltd.     

A MODIFIED DEMPSTER-SHAFER THEORY FOR MULTICRITERIA OPTIMIZATION

Abstract

A new methodology, based on a modified Dempster-Shafer (DS) theory, is proposed for solving multicriteria design optimization problems. It is well known that considerable amount of computational information is acquired during the iterative process of optimization. Based on the computational information generated in each iteration, an evidence-based approach is presented for solving a multiobjective optimization problem. The method handles the multiple design criteria, which are often conflicting and non-commensurable, by constructing belief structures that can quantitatively evaluate the effectiveness of each design in the range 0 to 1. An overall satisfaction function is then defined for converting the original multicriteria design problem into a single-criterion problem so that standard single-objective programming techniques can be employed for the solution. The design of a mechanism in the presence of seven design criteria and eighteen design variables is considered to illustrate the computational details of the approach. This work represents the first attempt made in the literature at applying DS theory for numerical engineering optimization.     

Hybrid real-code population-based incremental learning and approximate gradients for multi-objective truss design

In this article, real-code population-based incremental learning (RPBIL) is extended for multi-objective optimization. The optimizer search performance is then improved by integrating a mutation operator of evolution strategies and an approximate gradient into its computational procedure. RPBIL and its variants, along with a number of established multi-objective evolutionary algorithms, are then implemented to solve four multi-objective design problems of trusses. The design problems are posted to minimize structural mass and compliance while fulfilling stress constraints. The comparative results based on a hypervolume indicator show that the proposed hybrid RPBIL is the best performer for the large-scale truss design problems.     

Multi-objective optimization of engineering systems using game theory and particle swarm optimization

This study proposes particle swarm optimization (PSO) based algorithms to solve multi-objective engineering optimization problems involving continuous, discrete and/or mixed design variables. The original PSO algorithm is modified to include dynamic maximum velocity function and bounce method to enhance the computational efficiency and solution accuracy. The algorithm uses a closest discrete approach (CDA) to solve optimization problems with discrete design variables. A modified game theory (MGT) approach, coupled with the modified PSO, is used to solve multi-objective optimization problems. A dynamic penalty function is used to handle constraints in the optimization problem. The methodologies proposed are illustrated by several engineering applications and the results obtained are compared with those reported in the literature.     

Probability of failure sensitivity analysis using polynomial expansion

Probability of failure sensitivity analysis is a subject of major interest in uncertainty based optimization. However, the computational effort required to obtain accurate results is frequently very high. In this work we present a novel strategy for probability of failure sensitivity analysis, that is based on polynomial expansions of both the performance function and its derivatives. Sensitivity analysis is then made using the obtained polynomial expansions together with standard expressions. Since the simulation step requires only evaluation of closed form polynomials, very large samples can be used to obtain accurate results with small computational effort. The proposed approach is expected to be efficient when polynomial expansion methods are suitable. Four numerical examples are presented in order to show the effectiveness of the proposed approach.     

Explicit level‐set‐based topology optimization using an exact Heaviside function and consistent sensitivity analysis

This paper presents a level‐set‐based topology optimization method based on numerically consistent sensitivity analysis. The proposed method uses a direct steepest‐descent update of the design variables in a level‐set method; the level‐set nodal values. An exact Heaviside formulation is used to relate the level‐set function to element densities. The level‐set function is not required to be a signed‐distance function, and reinitialization is not necessary. Using this approach, level‐set‐based topology optimization problems can be solved consistently and multiple constraints treated simultaneously. The proposed method leads to more insight in the nature of level‐set‐based topology optimization problems. The level‐set‐based design parametrization can describe gray areas and numerical hinges. Consistency causes results to contain these numerical artifacts. We demonstrate that alternative parameterizations, level‐set‐based or density‐based regularization can be used to avoid artifacts in the final results. The effectiveness of the proposed method is demonstrated using several benchmark problems. The capability to treat multiple constraints shows the potential of the method. Furthermore, due to the consistency, the optimizer can run into local minima; a fundamental difficulty of level‐set‐based topology optimization. More advanced optimization strategies and more efficient optimizers may increase the performance in the future. Copyright © 2012 John Wiley & Sons, Ltd.     

一种新型模切机间歇机构的设计与运动学

目的设计一种新型的固定凸轮连杆间歇运动机构,以实现高速模切机的间歇输纸功能。方法首先,分析间歇机构的工作原理及组合框图;然后,在对固定凸轮五杆机构的运动连续性进行研究之后,根据运动连续性条件确定间歇机构的杆长尺寸;接着,应用ADAMS软件中相对轨迹曲线生成实体的方法设计固定凸轮轮廓曲线;最后,对基于简谐运动、摆线运动和五次多项式运动的间歇机构分别进行运动学分析。结果基于五次多项式的固定凸轮连杆间歇机构不但可以实现预期的动静比,而且在高速运转下运动性能平稳。结论固定凸轮连杆间歇机构具有良好的运动性能,能够满足高速模切机的工作要求。     

Novel orthogonal simulated annealing with fractional factorial analysis to solve global optimization problems

A classical simulated annealing (SA) method is a generic probabilistic and heuristic approach to solving global optimization problems. It uses a stochastic process based on probability, rather than a deterministic procedure, to seek the minima or maxima in the solution space. Although the classical SA method can find the optimal solution to most linear and nonlinear optimization problems, the algorithm always requires numerous numerical iterations to yield a good solution. The method also usually fails to achieve optimal solutions to large parameter optimization problems. This study incorporates well-known fractional factorial analysis, which involves several factorial experiments based on orthogonal tables to extract intelligently the best combination of factors, with the classical SA to enhance the numerical convergence and optimal solution. The novel combination of the classical SA and fractional factorial analysis is termed the orthogonal SA herein. This study also introduces a dynamic penalty function to handle constrained optimization problems. The performance of the proposed orthogonal SA method is evaluated by computing several representative global optimization problems such as multi-modal functions, noise-corrupted data fitting, nonlinear dynamic control, and large parameter optimization problems. The numerical results show that the proposed orthogonal SA method markedly outperforms the classical SA in solving global optimization problems with linear or nonlinear objective functions. Additionally, this study addressed two widely used nonlinear functions, proposed by Keane and Himmelblau to examine the effectiveness of the orthogonal SA method and the presented penalty function when applied to the constrained problems. Moreover, the orthogonal SA method is applied to two engineering optimization design problems, including the designs of a welded beam and a coil compression spring, to evaluate the capacity of the method for practical engineering design. The computational results show that the proposed orthogonal SA method is effective in determining the optimal design variables and the value of objective function.