一种自适应的平面线轮廓度误差评定方法

提出一种基于最小二乘法,结合样条插值函数和优化技术的用于平面线轮廓度误差评定的数据处理方法,该方法的优点在于在轮廓度误差评定过程中能自动地实现被测轮廓与理论轮之间的适应性调整,以此分离并消除被测轮廓与其测量基准之间的位置误差对轮廓误差评定结果的影响。     

基于椭圆线轮廓度误差评定的椭圆提取方法

简要介绍了目标测头视觉坐标测量系统,分析了测量系统成像目标的图像特征。在此基础上,提出了一种识别和提取CCD图像中目标椭圆的新方法。该方法对CCD图像中的每一个封闭轮廓进行椭圆拟合,根据评价椭圆形状误差的线轮廓度大小来判定该轮廓是否为目标椭圆轮廓。实际检测结果表明,该算法原理正确,易于编程实现,能有效剔除噪音图像,准确提取目标椭圆,具有广泛的实用性。     

平面复杂曲线轮廓度误差评定和判别

总结了平面曲线轮廓度误差的测量方法和评定准则,提出了平面曲线轮廓度误差最小区域几何判定准则。给出了平面曲线轮廓度误差评定的数学模型及其计算方法,通过对测量曲线特征点的平移、旋转及最大误差方向移动完成粗配准,进而用坐标轮换法按最小条件原则实现平面曲线的精匹配以消除测量时的定位误差。最后通过实例证明几何准则的正确性和计算方法的可行性。     

线轮廓度和面轮廓度的评定和判别

本文按最小区域和最大实体条件研究了评定和判别线轮廓度、面轮廓度的理论和方法。推导出轮廓度误差评定和判别的描述函数,引入维数的概念,建立了用计算机辅助测量复杂曲面和曲线的数学模型。对轮廓度误差进行了定性分析和定量分析,将其分离成形状误差、参数误差和位姿误差,给出了分离的定量公式。并且还讨论了复杂曲面(线)评定时测头半径的影响及其消除办法。     

基于改进ICP算法的叶片型线轮廓度误差评定

针对传统最近迭代点(ICP)算法存在配准精度较低的问题进行算法改进。首先,考虑到三坐标测量机测量数据呈现有序排列、且一一对应的特点,使用了一种基于矢量对齐法的型线数据初配准方法进行初配准;其次,在传统ICP算法配准的基础上,对待配准数据进行非均匀有理化B样条(NURBS)曲线拟合,再利用自适应粒子群算法对测量数据进一步精配准;最后,采用基于最小区域的叶片型线轮廓度误差评定方法进行误差评定。实验分析结果表明:改进方法相对于传统ICP算法,可在原有收敛值基础上达到进一步收敛的效果,轮廓度误差相对减小28.57%。该方法有效提高了叶片型线轮廓度误差评定的精确度,可为叶片的加工质量提供可靠判定。     

基于实数编码的改进遗传算法及在平面度误差评定中的应用

针对平面度误差计算的特点 ,提出了一种基于实数编码的改进遗传算法。该算法的遗传算子采用确定式良种选择、非一致算术交叉及基本位变异策略 ;交叉和变异概率根据个体适应度大小来自适应地确定 ;同时给出遗传算法评定平面度误差时适应度的计算方法。最后 ,通过不同评价方法对同一平面的平面度误差进行评定 ,结果证明该方法不仅能收敛到全局最优解 ,而且具有较快的收敛速度     

回转体零件轮廓度误差评定方法的实用数学模型研究

本文从实用需要出发,研究提出一种快速而且实用的直接根据最小区域原理来评定回转体轮廓度误差的数学模型。还提出了另一种便于实用的最小二乘评定方法数学模型。     

基于最近点配准的精密零件线轮廓度误差评定

提出一种基于最近点配准的评定方法。利用黄金分割法寻找实测点所对应的理论曲线上的最近点,再用奇异值分解求实测点与最近点配准中需要的旋转矩阵和平移矩阵,从而实现实测点与最近点的配准,在此基础上对线轮廓度误差进行计算。实验结果表明该方法可以高效地对精密零件的线轮廓度误差进行评定。     

自由曲面轮廓度误差评定

在总结面轮廓度误差评定原则的基础上,提出了曲面轮廓度误差最小条件几何判定准则,即“三角形准则”和“交叉准则”。对曲面轮廓度误差最小条件评定算法进行了研究,首先特征点预定位,再用最小二乘原则粗匹配,最后通过最小条件原则精匹配,实现被测曲面与理论曲面之间的自适应调整。实例证明了几何判定准则的正确性和计算方法的可行性。     

基于多种群遗传算法的圆柱度误差评定

分析了最小区域条件下求解圆柱度误差的研究现状,针对现有的圆柱度误差评定方法不能适用于任意位置圆柱度误差计算的情况,提出了一种求解多变量、非线性圆柱度误差的实数编码多种群遗传算法,阐述了算法的基本思想和步骤,建立了基于最小区域条件的优化目标函数。实验结果表明该方法有效地提高了全局搜索能力,进化过程稳定,能够快速获得多变量非线性问题的最优解,适用于各种位置情况的圆柱度误差评定。     

Sustainable operator assignment in an assembly line using genetic algorithm

This paper addresses the operator assignment in predefined workstations of an assembly line to get a sustainable result of fitness function of cycle time, total idle time and output where genetic algorithm is used as a solving tool. A proper operator assignment is important to get a sustainable balanced line. To improve the efficiency and meet the desired target output within the time limit, a balanced assembly line is a must. Real world lines consist of a large number of tasks and it is very time consuming and crucial to choose the most suitable operator for a particular workstation. In addition, it is very important to assign the suitable operator at the right place as his skill of operating machines finally reflects in productivity or in the cost of production. To verify better assignments of workers, a genetic algorithm is adopted here. A heuristic is proposed to find out the sustainable assignment of operators in the predefined workstations.     

U-shaped assembly line balancing problem with genetic algorithm

This paper presents a multi-objective genetic algorithm (moGA) to solve the U-shaped assembly line balancing problem (UALBP). As a consequence of introducing the just-in-time (JIT) production principle, it has been recognized that U-shaped assembly line systems offer several benefits over the traditional straight line systems. We consider both the traditional straight line system and the U-shaped assembly line system, thus as an unbiased examination of line efficiency. The performance criteria considered are the number of workstations (the line efficiency) and the variation of workload. The results of experiments show that the proposed model produced as good or even better line efficiency of workstation integration and improved the variation of workload.     

基于改进量子遗传算法的有源噪声控制方法

提出了一种基于改进量子遗传算法的格型IIR滤波器结构的有源噪声控制方法——改进的量子遗传算法（IQGA）。其核心是在对量子门更新过程进行改进的基础上，引入群体灾变和自适应搜索网格的策略。IQGA不仅收敛速度快，还可以改变基本遗传算法（GA）的局部搜索能力，克服基本遗传算法存在的未成熟收敛问题。仿真结果表明，该算法可以有效地实现噪声控制。     

基于迷宫算法和遗传算法的船舶管路路径规划

船舶管路的多样性和布局环境中约束的复杂性导致管路设计效率低下.为辅助设计人员提高管路设计效率并减少人为错误,提出了一种新的管路设计方法.首先,基于轴平行包围盒简化管路布局空间,利用栅格法对其进行离散化,并赋予空间网格特定的能量值,构建管路布局优化问题的数学模型.其次,基于遗传算法的框架,引入改进迷宫算法,提出管路路径规划方法,其中:迷宫搜索中引入辅助点的概念,增加了遗传算法中初始种群的多样性,有利于提高遗传算法的全局搜索能力;提出了定长度的编码方法,简化了管路染色体处理难度,提高了算法性能;基于引入方向优先搜索策略的迷宫算法,设计定长度编码遗传算子,保证了子代个体的质量,提高算法的收敛速度.最后,基于仿真试验,验证算法的性能.试验结果表明了该方法的可行性和高效率,以及其对实际管路布局工作具有指导意义.     

An adaptive genetic algorithm approach for the mixed-model assembly line sequencing problem

A mixed-model assembly line (MMAL) is a type of production line that is capable of producing a variety of different product models simultaneously and continuously. The design and planning of such lines involve several long- and short-term problems. Among these problems, determining the sequence of products to be produced has received considerable attention from researchers. This problem is known as the Mixed-Model Assembly Line Sequencing Problem (MMALSP). This paper proposes an adaptive genetic algorithm approach to solve MMALSP where multiple objectives such as variation in part consumption rates, total utility work and setup costs are considered simultaneously. The proposed approach integrates an adaptive parameter control (APC) mechanism into a multi-objective genetic algorithm in order to improve the exploration and exploitation capabilities of the algorithm. The APC mechanism decides the probability of mutation and the elites that will be preserved for succeeding generations, all based on the feedback obtained during the run of the algorithm. Experimental results show that the proposed adaptive GA-based approach outperforms the non-adaptive algorithm in both solution quantity and quality.     

本体概念分类的遗传算法

提出了一种基于遗传算法的本体概念分类规则学习方法(GAOCL).该方法从已有的本体库或知识库中获取实例作为训练样本,通过遗传算法获取相关的概念分类规则,实现概念实例的有效分类,以达到扩充和丰富本体的目的.首先,在编码过程中采用了可变长度的规则集合作为个体,以满足不同概念对分类规则数目的不同要求.其次,定义了规则集相对覆盖率,并以此作为适应值函数,评估个体对概念实例的分类效果,实现优化迭代.最后,给出了基于遗传算法的本体分类规则学习算法.利用一组标准样本集对该算法与同类算法进行了比较,实验结果表明该算法具有很好的收敛性,并且能获得较好的分类效果.     

A structural damage identification method based on genetic algorithm and vibrational data

The problem of damage identification in framed structures using vibrational data is considered. The identification problem is modelled as an optimization task and the use of measured natural frequencies as well as modeshape information in the construction of objective functions is discussed. In a first attempt, a standard genetic algorithm is shown to be ineffective in obtaining the correct damage distribution in test problems. Using domain knowledge, modifications are introduced in the coding process, in the initial population generation, in the fitness function, and in the genetic operators, leading to a promising tool to solve this class of problems. Synthetic problems, with the addition of noise in the simulated measured data associated with the damaged structure, are analysed in order to assess the capability of the proposed technique. Copyright © 2006 John Wiley & Sons, Ltd.     

基于误差信号的双模式遗传盲均衡算法研究

DD算法具有收敛速度快,稳态剩余误差小的优点,是一种最常用的盲均衡算法。遗传盲均衡算法是一种新的盲均衡算法,它利用遗传算法来解决盲均衡问题,具有较好的全局优化特性。在综合研究了DD算法及遗传盲均衡算法的特性后,利用误差信号的特性,给出了一种新的双模式遗传盲均衡算法,有效的结合了DD算法以及遗传盲均衡算法的优点,大大提高了算法的运算速度,提升了收敛性能,解决了遗传盲均衡算法运算量大的问题,并通过计算机仿真验证了该算法的有效性。