航空精密偶件计算机辅助选择装配方法的研究

为了提高航空精密偶件的装配精度,通过分析航空精密偶件的生产装配现状,提出了一种考虑形状误差的计算机辅助选择装配方法。采用最小质量损失成本为匹配精度的优化目标函数,建立基于蚁群算法的优化选配模型,并详细讨论了蚁群算法的实现过程。通过实例,验证了该方法的有效性。     

基于蚁群算法的电梯导轨选择装配

电梯运行的舒适度很大程度上取决于电梯导轨安装质量,由于标准导轨长度为5 m,因此导轨间的配合间隙大小就会影响电梯轿厢运行过程的振动程度。如何进行选择装配导轨,以使导轨间的间隙均匀且满足国标要求,是一个非常重要的课题。提出一种基于蚁群算法的计算机辅助选择装配导轨的方法,能够很好地解决该问题。蚁群算法(ACO)是一种新型的基于种群的模拟进化算法,属于随机搜索算法。为了对各导轨进行组合优化,提出一个以装配质量综合指标为优化目标的数学模型,作为厂家在包装之前的调配依据。通过实际项目应用,验证了该方法的实效性。     

并行组合模拟退火算法在计算机辅助选配系统的应用

计算机辅助选择装配是利用计算机，并采用有效的算法，对装配尺寸链中各组成环的尺寸进行合理的搭配选择，以达到减小封闭环偏差的变动范围，提高装配精度的目的。现有的选配工作缺少一个理想的物理模型来保证高精度的装配。提出了一个新的计算机辅助选配系统，采用田口博士质量损失模型作为装配匹配精度指标，同时以总成本为优化目标函数，采用并行组合的模拟退火优化方法求解零部件的匹配。事例分析了选配系统的应用。     

一种新的数学模型在计算机辅助选择装配中的应用

计算机辅助选择装配通过计算机选择合适的零件进行匹配。实现了较少的剩余零件和稳定的高装配精度。提出了一种新的计算机辅助选择装配优化模型。以质量损失总成本作为优化目标函数。采用遗传模拟退火组合优化算法求解最优匹配方案。     

基于蚁群算法的引信装配序列优化

蚁群算法作为模拟进化算法具有正反馈和分布式计算的特点,可解决由于经验法确定的装配序列较多考虑几何约束条件,难以兼顾其他影响因素的问题,但是迄今未见应用于引信,为此,将蚁群算法引入引信机械机构进行装配序列优化。为了应用蚁群算法,需要依据引信产品的具体情况调整状态转移概率和信息素更新规则。仿真结果表明:通过评价指标引导算法生成的优化序列能较好地满足预期使用功能,装配方案合理、适用。     

改进蚁群算法求解圆排列问题

圆排列问题是典型的NP完全问题,且蚁群算法已成功地解决了许多组合优化的难题.介绍了一种求解圆排列问题的蚁群算法,并通过改变概率、下一个元素的选择方式以及采用分段交换,对求解圆排列问题的蚁群算法进行了优化.提出了一种改进的蚁群算法,并将其应用于求解圆排列问题.仿真实验的结果表明,该方法有效地改善了蚁群算法的搜索时间较长,且易于过早地收敛于非最优解的缺陷.     

基亏蚁群算法的电子装配工艺优化研究

针对电子装配过程中效率低下的问题,提出了基于蚁群算法的电子装配过程中焊接工艺优化算法。该算法利用蚁群信息素反馈机制和概率选择机制,很好地解决了电子装配过程中不同特性元器件及其对应印制板焊盘操作顺序的优化问题,并应用C＋＋语言编制计算程序,实现对算法的快速求解,最后通过实例验证了该算法的可行性和有效性。通过蚁群算法在电子装配工艺优化中的合理应用,提高了电路板焊接速度与盾量,极大地提升了生产效率和高端电子产品装配的可靠性。     

一种飞机装配作业批量排产的图解蚁群算法

为提高飞机装配现场作业效率、降低成本,提出了一种求解批量作业最优排产方案的图解蚁群算法.分析了作业经验对装配周期的影响.建立了装配批量作业的资源服务站网络模型,并以此为基础给出了图解蚁群算法求解的构造图生成方法.研究并建立了蚁群算法的状态转移规则、信息更新规则和快速求解附加策略,通过映射甬数实现了构造图路径向装配作业周期的转换.以某型飞机襟副翼装配为例,验证了算法的有效性.     

面向自动化装配的轴孔装配参数选择研究

在面向装配的设计中,很少考虑装配工艺参数,在一定程度上削弱了面向装配的设计思想对装配过程的指导作用.对此,提出了一种面向自动化装配设计的圆柱轴孔装配工艺参数优化算法.该算法以装配成本为边界条件,建立了解析几何求解模型,能帮助设计者选择合理的工艺参数,如配合零件的尺寸公差、自动化装配设备的定位精度和转角精度等.最后,编制了相应的程序优化装配工艺参数.     

基于改进蚁群算法的装配序列规划

针对装配序列规划问题,分析了基本蚁群系统的不足,提出了面向装配序列规划的改进蚁群算法,来获得最优或次最优的装配序列.改进蚁群算法中,将装配操作约束作为启发式信息引入状态转移概率中,通过获取零部件之间的装配关系设定可行转移范围.通过信息素残留系数的动态变化和影响转移概率的α、β参数的动态设置,提高了蚁群的收敛速度并有效地避免了其陷入局部最优解.通过实例验证了改进算法的有效性.     

量子蚁群算法在压力容器优化设计中的应用

建立压力容器的优化设计数学模型,利用一种新型的优化算法——量子蚁群算法对压力容器的主要参数进行优化设计。量子蚁群算法在蚁群算法的基础上引入量子理论,该方法能尽快搜索到较理想的下降方向,提高了算法的收敛速度。具体应用实例表明,基于量子蚁群算法的优化设计切实可行,显示量子蚁群算法在化工设备优化设计问题上的可用性。     

An ant colony algorithm for scheduling in flowshops with sequence-dependent setup times of jobs

The problem of scheduling in flowshops with sequence-dependent setup times of jobs is considered and solved by making use of ant colony optimization (ACO) algorithms. ACO is an algorithmic approach, inspired by the foraging behavior of real ants, that can be applied to the solution of combinatorial optimization problems. A new ant colony algorithm has been developed in this paper to solve the flowshop scheduling problem with the consideration of sequence-dependent setup times of jobs. The objective is to minimize the makespan. Artificial ants are used to construct solutions for flowshop scheduling problems, and the solutions are subsequently improved by a local search procedure. An existing ant colony algorithm and the proposed ant colony algorithm were compared with two existing heuristics. It was found after extensive computational investigation that the proposed ant colony algorithm gives promising and better results, as compared to those solutions given by the existing ant colony algorithm and the existing heuristics, for the flowshop scheduling problem under study.     

PCB插装机组装调度优化及其实证研究

研究了印刷电路板的组装调度优化问题.该问题基于插装机的机械特征、生产特性,通过将元器件的插装顺序问题定式化为旅行商问题,建立以生产总时间最小化为目标的数学规划模型,并采用蚁群算法进行优化求解.基于实际生产数据,通过MATLAB进行一系列的仿真实验,结果表明基于所提出的模型及优化算法得到的调度方案能够有效地提高插装机的组装效率.     

Microrobot based micro-assembly sequence planning with hybrid ant colony algorithm

Robot-based assembly sequence planning plays an important role in product design and has been widely researched in the macro world. But in the micro world, the characteristics of microrobot-based assembly, such as complexity and scaling effects, make the assembly problems much more difficult and seldom researched. In this paper, the microrobot-based micro-assembly sequence planning problem is discussed. The problem is transferred as a combinatorial optimization problem with several matrixes, such as the moving wedge matrix, the microrobot performance matrix, and the sensing matrix. Furthermore, the geometrical and visibility constraints of assembly sequence and evaluation criteria for optimization are given. A particle swarm optimization (PSO) algorithm modified ant colony optimization (ACO) algorithm, called a hybrid PS-ACO, is devised to solve the problem efficiently. The combination of local search and global search of PSO is introduced into the ACO algorithm, which can balance the exploration and exploitation performances of searches. The experimental results have shown that the PS-ACO can solve the micro-assembly sequence planning problem with better convergence performance and optimizing efficiency than basic ACO and GA.     

复杂环境下的装配路径求解与优化

针对三维复杂环境下的装配路径规划问题,运用栅格法建立了规划空间模型,基于蚁群算法求解出了一条避开障碍物的初始路径;对求解得到的装配初始路径,提出采用二分法插值优化方法缩短装配路径长度,在规划过程中采用目标零件与障碍物的轴向包围盒进行避障。对装配路径的求解及优化进行了实例测试,获得了一条无碰撞的最短的平滑路径,验证了算法的有效性和可行性。     

改进蚁群算法的斜齿轮传动多目标优化

本文针对传统蚁群算法在优化目标函数和设计变量较多时,收敛速度慢和容易陷入局部最优等缺点,提出了一种改进的蚁群优化算法。并对两级斜齿圆柱齿轮减速器在考虑其动态性能、体积、可靠度多目标下对齿轮参数进行了优化。其结果与传统设计相比,在保持了减速器较高可靠性的同时,获得了较好的动态性能和较小的体积。本文提出的改进蚁群算法为斜齿轮减速器提供了一种新的优化设计方法。     

面向云计算环境任务调度的改进蚁群算法

云计算环境下的任务调度问题是一个NP完全问题,其目的是在各个处理节点上合理分配任务,优化调度策略以保证有效完成任务。以总任务完成时间最短和计算成本最低为优化目标,针对蚁群优化算法易陷入局部最优的缺陷,提出了一种求解该问题的改进蚁群算法。该算法将遗传算法的二点交叉算子融入到蚁群优化算法中,以提高蚁群优化算法的局部搜索能力。通过在云仿真平台Cloud Sim上进行仿真实验,结果表明改进蚁群算法缩短了总任务完成时间,降低了计算成本,从而证明了该算法能有效地解决云计算环境下的任务调度问题,并且其优化能力和收敛速度优于蚁群优化算法和改进离散粒子群算法。     

Particle swarm optimization for minimizing assembly variation in selective assembly

Quality of a product is based on the quality of the mating parts. When the parts are assembled interchangeably, the assembly variation will be the sum of the component tolerances. If the assembly variation is to be less than the sum of the component tolerances, selective assembly is the only solution. In conventional selective assembly, the corresponding selective groups are assembled. In this paper, selective group combinations for assembling the mating parts is obtained using particle swarm optimization (PSO). The combination obtained has resulted in an appreciable reduction in assembly variation. The proposed algorithm has been demonstrated for a linear assembly, which consists of three components having equal dimensional distributions. The assembly variation obtained by interchangeable assembly is 36 μm. By implementing the proposed method, the assembly variations are reduced from 36 to 7.2 μm. However, this algorithm can be extended for assemblies with more number of components and with different dimensional distributions.     

Minimizing clearance variations and surplus parts in multiple characteristic radial assembly through batch selective assembly

An assembly consists of two or more mating parts. The quality of any assembly depends on quality of its mating parts. The mating parts may be manufactured using different machines and processes with different standard deviations. Therefore, the dimensional distributions of the mating parts are not similar. This results in clearance between the mating parts. All precision assemblies demand for a closer clearance variation. A significant amount of research has already been done to minimize clearance variation using selective assembly. Surplus part is one of the important issues, which reduces the implementation of selective assembly in real situation. Surplus parts are inevitable while the assembly is made from components with undesired dimensional distributions. Batch selective assembly is introduced in this paper to reduce surplus parts to zero and it is achieved by using nondominated sorting genetic algorithm-II. For demonstrating the proposed algorithm, a complex assembly which consists of piston, piston ring and cylinder is considered as an example problem. The proposed algorithm is tested with a set of experimental problem datasets and is found outperforming the other existing methods found in the literature, in producing solutions with minimum clearance variations with zero surplus parts.