基于遗传算法的零件工步优化研究

对于复杂零件的加工,其工步顺序直接影响加工质量和生产效率.研究了工步排序原则和典型的工艺路线,以辅助加工时间最短为优化目标,建立了加工中心中零件加工工步排序数需模型.采用遗传算法对工步顺序进行优化,并在隔代映射遗传算法基础上引入自适应策略,使IP_GA中的交叉、变异概率根据适应度大小自动调节,提高了收敛速度及解的质量.实际应用表明,该方法可有效提高工艺规划系统中工步的优化排序能力.     

基于改进遗传算法的压力控制系统参数求解及辨识

该文研究了遗传算法模型,针对遗传算法的特点,在隔代映射遗传算法基础上引入自适应策略,建立了自适应遗传算法数学模型,给出了具体算法流程。通过自适应策略,使IP_GA中的交叉、变异概率能够根据函数适应度大小自动调节,提高了收敛速度及解的质量。通过该算法对液压压力系统传递函数进行参数求解,建立了液压系统的开环模型。利用Matlab/Simulink工具对开环模型进行仿真并与实测输出曲线对比,验证了遗传算法求解液压伺服系统参数的可行性。同时,通过分析仿真结果,找出了材料试验机比例压力控制系统与伺服压力控制系统在辨识方法上的差别。     

并行隔代映射遗传算法及其在材料参数反演中的应用

针对传统遗传算法解决大规模复杂问题效率比较低的问题，提出了一种并行隔代映射遗传算法。该算法采用的是多种群并行进化的方法，在各种群之间引入竞争，既能较好地丰富和保持种群的多样性，有效地避免早熟收敛，又能大大提高求解大规模复杂问题的效率。将该算法应用于冷加工金属板和正交各向异性复合板材料参数的反演问题中，将计算结果与并行化前的算法进行比较，验证了该并行算法具有高效率解决大规模复杂问题的能力。     

双横臂式前独立悬架的改进遗传算法优化

提出了自适应隔代映射遗传算法，使种群的交叉和变异概率根据适应度的大小自我调节，该算法经过了测试函数的测试。建立了基于响应面法的双横臂式前独立悬架的优化模型算法，运用该算法对其进行优化设计，并对设计结果进行了仿真和分析。结果表明：该方法提高了收敛速度和搜索效率，对以车轮接地点侧向滑移量为目标的优化设计有显著效果。     

基于隔代映射算子的差分进化算法

提出一种基于隔代映射算子的差分进化算法以求解优化问题,该方法在保证解的精度的同时具有较快的收敛速度。在经典的差分进化算法基础上,采用反向学习策略产生初始种群,并采用两种差分变异策略产生变异个体,以增加种群的多样性;利用隔代映射算子产生三个新个体替换当前进化种群中最差的三个个体,以实现精英策略提升算法的收敛性;为了保持种群的多样性和避免获得局部解,利用探测算子策略产生新个体加入进化种群。采用11个单峰、多峰测试函数和两个工程实例验证了该方法的有效性。     

基于功能特征结构映射的夹具设计模型与方法

为实现夹具设计的智能化，基于功能论的设计方法，通过对夹具的功能进行分析和分解，研究了夹具功能的表达和夹具的设计过程，建立了基于功能特征映射模型的夹具设计过程和方法。构建了夹具的功能模型，提出用特征作为夹具功能和结构的中介，建立了功能-特征-结构的映射模式。在对夹具设计中的约束进行分析的基础上，总结了夹具各子功能的备选特征集，运用模糊映射的理论实现夹具功能到特征和特征到结构的映射，得到夹具的结构方案。最后，用钻模设计进行了实例验证，证明了该方法的可行性和有效性。     

从制造特征到夹具特征间接映射的中性特征模型研究

应用特征映射理论 ,探讨了将制造特征通过间接映射转换成夹具特征的方法 ,并且以VC ++6 .0和Ac cess2 0 0 0为开发平台 ,研制了具有中性特征信息库的映射子系统 ,它作为基于制造特征的夹具自动规划设计系统的一个重要组成部分已得到了验证。     

面向大规模定制的夹具优化设计系统

针对大规模定制生产环境下夹具相似性的特点,提出了以工件的加工变形最小和变形最均匀为目标的夹具优化设计方法.基于SolidWorks平台,以VC为开发工具,采用遗传算法和有限元方法相结合的优化方法,开发了参数化夹具优化设计系统,并进行了验证.     

模拟退火实参数遗传算法及其工程应用

由于继承性的问题,遗传算法在编码和解码中会花费大量的计算时间;另外,由于缺乏"爬山能力",遗传算法很容易早及局部收敛。提出了一种新的自适应模拟退火遗传算法,具有遗传算法和模拟退火的优点,同时自适应机制的引入,保证了解的质量并提高了收敛速度。将这种方法应用于螺旋弹簧约束优化设计问题中,结果表明,尽管群体规模较小,但在处理复杂问题时,这种混合算法的全局搜索能力和收敛速度显著提高。     

基于变动几何的机床夹具误差预测

将变动几何约束网络应用于夹具误差预测.提出一种针对夹具的变动几何约束网络的建立方法;给出公差向变动几何旋量矩阵的映射关系,以及旋量参数约束式,依据约束式采用极值法计算夹具误差.最后用实例验证方法的有效性.     

LOCATORS OPTIMIZATION FOR MEASURING FIXTURE DESIGN

"N-2-1" principle is widely recognized in the fixture design for deformable sheet metal workpieces, where N, the locators on primary datum, is the key to sheet metal fixture design. However, little research is done on how to determine the positions and the number of N locators. In practice, the N locators are frequently designed from experience, which is often unsatisfactory for achieving the precision requirement in fixture design. A new method to lay out the N locators for measuring fixture of deformable sheet metal workpiece is presented, given the fixed number of N. Finite-element method is used to model and analysis the deformation of different locator layouts. A knowledge based genetic algorithm (KBGA) is applied to identify the optimum locator layout for measuring fixture design. An example of a door outer is used to verify the optimization approach.     

夹具规划技术研究

在计算机辅助夹具设计过程中,探讨了夹具规划的实施方法。考虑了从早期概念设计阶段到后期结构设计阶段多方面的因素,提出了夹具单元构形设计的概念,由此讨论了给定定位表面后夹具结构多方案的生成和优化。这种设计过程为夹具设计自动化提供了一种新的、自顶向下的设计途径。最后通过实例进行了分析验证。     

Machining fixture layout design using ant colony algorithm based continuous optimization method

In any machining fixture, the workpiece elastic deformation caused during machining influences the dimensional and form errors of the workpiece. Placing each locator and clamp in an optimal place can minimize the elastic deformation of the workpiece, which in turn minimizes the dimensional and form errors of the workpiece. Design of fixture configuration (layout) is a procedure to establish the workpiece–fixture contact through optimal positioning of clamping and locating elements. In this paper, an ant colony algorithm (ACA) based discrete and continuous optimization methods are applied for optimizing the machining fixture layout so that the workpiece elastic deformation is minimized. The finite element method (FEM) is used for determining the dynamic response of the workpiece caused due to machining and clamping forces. The dynamic response of the workpiece is simulated for all ACA runs. This paper proves that the ACA-based continuous fixture layout optimization method exhibits the better results than that of ACA-based discrete fixture layout optimization method.     

Simultaneous optimization of fixture and joint positions for non-rigid sheet metal assembly

This paper presents an optimization methodology for non-rigid sheet metal assembly variation by considering part variation, fixture variation, fixture layout, and joint positions, as well as the assembly spring back. The proposed algorithm integrates the finite element analysis (FEA) with a powerful global optimization algorithm, called the mode-pursuing sampling (MPS) method to simultaneously search for the optimal fixture and joint positions in order to minimize the assembly variation. An example application study is presented to demonstrate the optimization procedure and its effectiveness.     

振动夹具的综合优化设计

夹具设计要求质量轻、刚度大。为了在这两者间取得平衡,文中综合使用拓扑优化和尺寸优化两种方法对夹具进行优化设计以实现设计目标。基于拓扑优化结果设计了夹具的拓扑优化模型。虽然相对于初始模型,该模型的第1阶固有频率降低了36.5%,但其质量降低了66.7%,表明拓扑优化效费比显著。以设计参数的合理设计范围作为约束条件,把使第1阶固有频率最大和夹具质量最轻作为优化目标来实施尺寸优化,得到夹具的详细设计模型。与拓扑优化模型相比,该模型的质量降低了3%,第1阶固有频率提升了6%。之后对振动夹具实施了随机振动试验。受试设备安装点的功率谱密度响应曲线的均方根值最大为输入谱均方根值的1.17倍,说明夹具具有良好的动力学传递特性,夹具的综合优化设计方法有效可行。     

FLEXIBLE ASSEMBLY FIXTURING LAYOUT MODELING AND OPTIMIZATION BASED ON GENETIC ALGORITHM

There are many welding fixture layout design problems of flexible parts in body-in-white assembly process, which directly cause body assemble variation. The fixture layout design quality is mainly influenced by the position and quantity of fixture locators and clamps. A general analysis model of flexible assembles deformation caused by fixture is set up based on "N-2-1" locating principle, in which the locator and clamper are treated as the same fixture layout elements. An analysis model for the flexible part deformation in fixturing is set up in order to obtain the optimization object function and constraints accordingly. The final fixture element layout could be obtained through global optimal research by using improved genetic algorithm, which effectively decreases fixture elements layout influence on flexible assembles deformation.     

基于花授粉算法的曲面薄壁件定位布局优化

优化定位布局是减小薄壁件装夹变形的重要手段,现有研究大多以节点法向变形最小为优化目标而忽略其他方向上的变形,为此提出了一种新的基于花授粉算法的夹具布局优化方法。针对曲面薄壁件,在建立法向约束定位模型的基础上,通过应变能来描述所有方向上的变形,以薄壁件的整体应变能最小为目标,结合花授粉算法和基于Python语言的参数化有限元分析,实现薄壁件的定位布局寻优。最后以飞机蒙皮定位布局优化为例验证方法的有效性,并通过与遗传算法的对比表明,花授粉算法在优化薄壁件的定位布局时具有更优的性能。     

Multi-objective optimization design of a fixture layout considering locator displacement and force–deformation

The fixture determines the workpiece position in a machining process; therefore, an increasing amount of attention has been given to fixture layout design. While machining, the workpiece position is affected by two major sources: (a) the locator displacement and (b) the force–deformation of the workpiece–fixture system. In the beginning of this paper, a geometric model considering the shape of a locator is developed to analyze the location performance, followed by the presentation of a simplified solving method and a location layout performance index. Second, to complete the force–deformation analysis, a finite element method-based force–deformation model is built and accelerated by a new method with a lower computer memory cost. Based on these two models, multiple objects of fixture layout optimization problems are proposed, and a multi-objective genetic algorithm-based optimization method is constructed. Finally, testing examples are approved to examine the validity of the method represented in this paper. These methods can provide a more accurate prediction of the locating performance in more widely used cases, and they have faster calculating speeds with lower computer memory costs.     

Design and optimization of machining fixture layout using ANN and DOE

In machining fixtures, minimizing workpiece deformation due to clamping and cutting forces is essential to maintain the machining accuracy. This can be achieved by selecting the optimal location of fixturing elements such as locators and clamps. Many researches in the past decades described more efficient algorithms for fixture layout optimization. In this paper, artificial neural networks (ANN)-based algorithm with design of experiments (DOE) is proposed to design an optimum fixture layout in order to reduce the maximum elastic deformation of the workpiece caused by the clamping and machining forces acting on the workpiece while machining. Finite element method (FEM) is used to find out the maximum deformation of the workpiece for various fixture layouts. ANN is used as an optimization tool to find the optimal location of the locators and clamps. To train the ANN, sufficient sets of input and output are fed to the ANN system. The input includes the position of the locators and clamps. The output includes the maximum deformation of the workpiece for the corresponding fixture layout under the machining condition. In the testing phase, the ANN results are compared with the FEM results. After the testing process, the trained ANN is used to predict the maximum deformation for the possible fixture layouts. DOE is introduced as another optimization tool to find the solution region for all design variables to minimum deformation of the work piece. The maximum deformations of all possible fixture layouts within the solution region are predicted by ANN. Finally, the layout which shows the minimum deformation is selected as optimal fixture layout.