基于改进粒子群算法的多工位装配序列规划

针对现有单工位装配序列规划结果难以满足工位需求的问题,提出一种基于改进粒子群算法的多工位装配序列规划方法。建立多工位装配模型来描述零部件的几何信息及其与工位的关系;用装配序列可行性、装配方向一致性、装配聚合性及工位间平衡性4个评价指标构建适应度函数。为解决一般粒子群算法易陷入局部最优解的问题,对惯性权重进行了改进,提出粒子相似度和相似度阈值的概念,并通过相似度阈值控制粒子的变异,提高了算法的全局搜索能力。以某型发动机为装配实例,验证了改进粒子群算法应用于多工位装配序列规划的可行性;同时将该算法和遗传算法、一般粒子群算法进行比较,证明了该算法的优越性。     

考虑工序刚性约束的自动化装配生产线多目标优化研究

为了对具有工序刚性约束的装配线进行优化配置,提出一种基于并行工位设计和装配序列规划的自动化装配生产线多目标优化方法,主要包括:①针对工序刚性约束的自动化装配线的特殊性,搭建了以成本和生产节拍为多目标的装配线优化数学模型;②针对数学模型中决策变量的离散性特点,提出基于随机键的两段式编/解码方法,综合考虑了装配工艺中的优先关系、直接优先关系、完全分配约束和最大并行度限制等,实现了对工序刚性约束的描述;③将基于拥挤度的Pareto评价方法嵌入回溯搜索优化算法,实现了工序刚性约束下的装配线多目标优化;④通过断路器自动化装配线进行案例验证。结果表明,在装配生产线上各作业元素时间和设备单元成本固定且均已知的条件下,所提出的方法能够为该类装配线计算出最优配置方案,且将计算出的优化配置方案(成本517.5万元,节拍3.2秒/级)与现有方案(成本636.0万元,节拍5.4秒/级)对比,发现生产效率提高了40.7%,装配线成本降低了18.6%。     

基于改进粒子群算法的锂电池模块装配序列规划

根据电动汽车中用到的锂电池模块装配特点,提出了模块内部零件的编码规则,并综合几何可行性、装配方向改变次数、装配连续性、装配工具改变次数和装配优先性五个指标设计了目标函数,结合改进的粒子群算法迭代更新得出最优装配序列组.通过电池模块实例对该算法进行了验证,结果表明本文提出的改进粒子群算法可以有效地求解该锂电池模块的装配序列规划问题.     

基于混合粒子群算法的复杂机械产品装配质量控制阈优化方法

为提高复杂机械产品的装配精度和服役安全性,提出一种多载荷作用下的基于混合粒子群算法的复杂机械产品装配过程质量控制点控制阈优化方法。分析复杂机械产品装配及服役的特点,定义关键质量控制点的多载荷影响因子;综合考虑装配质量损失成本和装配时间调整成本,建立基于多载荷影响因子的关键质量控制点装配损失-控制阈函数,揭示了质量控制点总损失与控制阈的关系;在保证装配精度的情况下,以总成本最小化为目标,构建多载荷影响因子的装配质量控制阈优化模型,并给出了基于混合粒子群算法的模型求解过程。最后以某型发动机的缸盖拧紧过程为例,将基于多载荷影响因子的模型求解出的产品装配方案结果与应用该方法之前装配结果做了对比分析,结果表明该方法能够显著提高装配质量及服役安全性能,为复杂机械产品装配质量控制阈优化提供了一种方法。     

考虑工具操作空间的装配序列生成方法

介绍了基于有向图的装配模型,该模型记录了零件间的优先关系,并对普通零件、紧固件进行不同的描述。在UG环境中对装配工具进行建模,在完整装配体中对工具的安装动作进行仿真和干涉检查,记录下工具同周围零件的潜在干涉信息,利用该信息快速检查装配序列是否满足工具的操作空间要求。然后应用改进的蚁群算法,在优先关系的指导下求解装配序列。针对妨碍工具操作等不可行的序列,提出信息素的惩罚蒸发策略,帮助蚁群避开不可行解。最后通过实例验证了算法的实用性。     

基于粒子群算法的多质量特性下的选装优化方法研究

提出了一种基于粒子群算法的分组选装的优化方法。针对复杂产品分组装配中多质量特性的特点,构建了以装配间隙波动最小化为目标的优化模型。利用引入共享机制和动态归档机制的多目标粒子群算法对该模型进行求解。最后,通过发动机的活塞和气缸的选择装配实例对所提出方法进行了验证,结果表明,采用该方法可以有效提高产品的装配精度。     

装配工序的多准则群决策协调权评价算法

为获得最佳的飞机装配工序,提出了基于多准则群决策协调权的装配工序评价算法.首先多名决策专家给出装配工序优先关系,以及面向装配工序的性能、工艺性、成本、质量、工时等准则的决策系数矩阵I然后求解装配工序群决策协调权和协调指数,当协调指数为非负时,决策过程实现协调,则可实现多条装配工序排序,获得最合理的装配工序.最后,以某民用机机翼为例.验证了算法的正确性和有效性.     

基于粒子群—遗传混合算法的装配操作优化

为通过装配工艺优化提高车身装配尺寸质量,针对车身众多几何可行装配顺序,应用多属性有向图描述零件间的优先关系和装配控制特征数量,来去除非工程可行装配顺序。以装配尺寸质量为目标函数,提出粒子群—遗传混合算法优化零件间装配操作,通过线性装配偏差分析模型进行装配偏差累积运算,获得了最优装配顺序。通过车身侧围装配体阐述了装配控制特征的优化过程,结果表明,不同的装配顺序将影响装配控制特征的选择,从而影响最终的产品装配偏差。     

面向装配序列规划的装配成本评价研究

针对装配序列的特点提出了一个装配成本评价的模型,认为装配成本与产品装配过程中装配方向的变化次数,产品组成零件装配后的运动不确定度总和有关.提出了基于零件的装配方向矩阵确定装配方向变化次数的方法,并根据装配基础件确定零件装配后的运动不确定度.采用了基于优先关系矩阵的干涉推理方法检验装配序列的可行性,并计算装配序列中可能产生的装配干涉数目,最后用实例验证了所提模型的有效性.     

基于递归循环的层次化爆炸图自动生成方法

提出以递归循环和深度优先搜索层次化装配序列的策略自动生成复杂产品层次化爆炸图的方法。为使设计结构符合装配规划的要求,设计了可方便定义子装配体和编辑装配树的装配结构重构功能。为避免重复的干涉检测,研究干涉矩阵合成方法,通过对原始装配关系信息的整合,柔性化生成面向层次化结构的装配关系矩阵。为降低大规模序列规划问题的复杂性,将整体规划任务分解为若干个及若干层级子任务,子任务内采用多规则筛选算法,以基于干涉矩阵的几何可行性为前提,并行性、连续性、稳定性和方向性等为优化筛选指标,循环构建序列。提出递归爆炸算法,循环对层次化装配序列进行深度优先搜索,实时计算已爆炸零部件的累积包围盒,从而确定待爆炸零件位移矢量,自动生成间隔均匀有序、结构紧凑且空间层次感强的爆炸图。开发了基于Siemens NX的装配规划系统Auto Assem,以减速器及汽车部件实例验证了方法的有效性。     

模糊环境下基于Vague集的虚拟企业伙伴选择研究

为解决模糊完工时间和交货期下的虚拟企业伙伴选择问题,提出一种基于Vague集的伙伴选择方法。在给出基于Vague集的交货时间满足度概念的基础上,建立了以极大化交货时间满足度指数为优化目标的虚拟企业伙伴选择模型,该模型考虑了交货时间满足度、成本和任务间的时序关系等因素。为解决标准粒子群优化算法容易陷入局部极值的问题,引入混沌搜索方法,设计了一种混沌粒子群优化算法进行模型求解。实例分析表明,混沌粒子群优化算法比标准粒子群优化算法具有更强的局部搜索能力。     

An approach to multi-criteria assembly sequence planning using genetic algorithms

This paper focuses on multi-criteria assembly sequence planning (ASP) known as a large-scale, time-consuming combinatorial problem. Although the ASP problem has been tackled via a variety of optimization techniques, these techniques are often inefficient when applied to larger-scale problems. Genetic algorithm (GA) is the most widely known type of evolutionary computation method, incorporating biological concepts into analytical studies of systems. In this research, an approach is proposed to optimize multi-criteria ASP based on GA. A precedence matrix is proposed to determine feasible assembly sequences that satisfy precedence constraints. A numerical example is presented to demonstrate the performance of the proposed algorithm. The results of comparison in the provided experiment show that the developed algorithm is an efficient approach to solve the ASP problem and can be suitably applied to any kind of ASP with large numbers of components and multi-objective functions.     

A hybrid assembly sequence planning approach based on discrete particle swarm optimization and evolutionary direction operation

Assembly sequence planning (ASP) has always been an important part of the product development process, and ASP problem can usually be understood as to determine the sequence of assembly. A good assembly sequence can reduce the time and cost of the manufacturing process. In view of the local convergence problem with basic discrete particle swarm optimization (DPSO) in ASP, this paper presents a hybrid algorithm to solve ASP problem. First, a chosen strategy of global optimal particle in DPSO is introduced, and then an improved discrete particle swarm optimization (IDPSO) is proposed for solving ASP problems. Through an example study, the results show that the IDPSO algorithm can obtain the global optimum efficiently, but it converges slowly compared with the basic DPSO. Subsequently, a modified evolutionary direction operator (MEDO) is used to accelerate the convergence rate of IDPSO. The results of the case study show that the new hybrid algorithm MEDO-IDPSO is more efficient for solving ASP problems, with excellent global convergence properties and fast convergence rate.     

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.     

基于改进粒子群算法的隐式广义预测控制

针对大多数工业系统的控制输入输出都存在约束的情况,提出一种基于改进粒子群算法的隐式广义预测控制算法(IGPC)。粒子群算法(PSO)是一种基于群体的智能优化算法,解决受约束的优化问题具有精度高、收敛速度快等优点;为了避免粒子群算法陷入早熟,提高精度,引入细菌觅食算法中的自适应迁徙机制。在隐式广义预测控制的滚动优化环节引入改进粒子群算法,弥补了传统GPC在处理受约束控制问题上的缺陷。仿真结果表明了该方法的有效性和良好的控制性能。     

The modified firefly algorithm considering fireflies’ visual range and its application in assembly sequences planning

This paper proposes a modified discrete firefly algorithm (MDFA) to solve the problem of assembly sequence planning. Firstly, to improve the performance of the firefly algorithm (FA), we proposed a MDFA by endowing the fireflies with the capability of changeable visual range. The computing case shows the proposed algorithm is more effective and robust than standard FA, genetic algorithm and particle swarm optimization algorithm. Secondly, a method of how to set parameters for FA and MDFA is proposed. This method is practical in the application of FA to solve discrete problem. Thirdly, to make the sequences more closer to real industrial requirements, a so called process precedence relations (PPR) evaluation function is presented, which not only considering the interference between parts, assembly tools and clamps, but also regarding the assembly order between parts and their reference parts. Finally, the evolution performance of the MDFA is investigated, and the performance of the proposed approach to solve ASP is verified through two cases study.     

An assembly sequence planning approach with a discrete particle swarm optimization algorithm

In this paper, a discrete particle swarm optimization (DPSO) algorithm is proposed to solve the assembly sequence planning (ASP) problem. To make the DPSO algorithm effective for solving ASP, some key technologies including a special coding method of the position and velocity of particles and corresponding operators for updating the position and velocity of particles are proposed and defined. The evolution performance of the DPSO algorithm with different setting of control parameters is investigated, and the performance of the proposed DPSO algorithm to solve ASP is verified through a case study.     

混合复合材料飞轮转子成本优化设计

基于平面应力理论,计算多层过盈装配混合纤维增强复合材料飞轮转子的应力分布;指出多层过盈装配复合材料转子多种可能的失效形式;结合增广Lagrange乘子法、粒子群优化(Particle swarm optimization,PSO)算法和经典梯度算法各自的优点,提出可求解非线性约束问题的且具有全局收敛性和较高计算效率的两阶段增广Lagrange粒子群优化算法(Two-stage augmented Lagrange particle swarm optimization,TS-ALPSO);利用TS-ALPSO优化算法,以多层过盈装配混合复合材料转子的分层半径、层间过盈量和转速为设计变量,研究多层混合复合材料转子的成本优化问题;并分析转子材料价格不变时,材料次序、分层数及过盈量对转子储能性能的影响;揭示出转子单位成本储能最大时,转子分层半径、层间过盈量及转速随转子材料价格变化的规律。     

Correction of sensor's dynamic error caused by system limitations

The method based on particle swarm optimization (PSO) integrated with functional link articial neural network (FLANN) for correcting dynamic characteristics of sensor is used to reduce sensor's dynamic...