基于装配序列规划的爆炸图自动生成方法

提出利用装配序列规划算法及零部件几何信息自动生成复杂产品爆炸图的方法.为解决零部件在倾斜方向上的拆卸可行性分析的难题,将现有干涉矩阵结构扩展到零部件局部坐标系轴向.从几何可行性和装配成本角度,利用装配关系矩阵及可定制优先级的指标,每次逐级筛选出最优拆卸零部件、方向及并行性,以构造最优序列.按结构紧凑性及序列可读性原则,在读取拆卸序列过程中,利用直接提取的两类零部件包围盒和实时计算的累积包围盒求取零部件位移矢量并驱动零部件移动,从而生成爆炸图,并自动实现装配追踪线及技术图解等相关功能.在UG平台下开发应用系统,结合齿轮减速器爆炸图实例及比较,说明本方法在爆炸图生成效率及可视化效果方面能够满足工程应用要求,可极大地降低手工生成爆炸图的工作强度.     

基于结构模型的拆卸序列规划研究

为了解决装配结构复杂的产品拆卸序列规划中因数据组合爆炸而导致运算量剧增的问题,首先分析了典型装配体的拆卸特点,以零件自由度交集的运算结果作为拆卸方向的判断依据,提出了约束消除的概念,进而通过启发式算法实现了拆卸序列的自动生成;为了提高拆卸序列规划的运算效率和计算结果的实用性,引入了以装配关系为基础的层次化结构模型,通过逐层分解的方法降低了装配结构深度,避免了从整个复杂装配体上求单一零件的拆卸序列,达到了减少运算的目的;最后以简化的汽车为例,阐述了基于结构模型的拆卸序列规划过程。研究结果表明,以层次化结构模型为基础、以约束消除为原则的拆卸序列规划能有效降低运算量,从而控制复杂产品的数据组合爆炸问题。     

面向协同装配规划的装配单元规划方法

作为复杂产品装配规划的有效求解策略,协同装配规划将复杂产品的装配规划结构按照装配要求分解为简单的子装配规划结构,子装配规划结构的最优或次优装配序列采用成熟的装配规划方法求解,通过装配序列合并得到产品的整体装配序列。协同装配规划所要解决的首要问题是装配规划结构分解,针对这个问题,提出装配单元规划方法。首先从产品可装配性的角度分析了影响装配单元规划的装配设计和装配工艺约束,并对归类的部分装配约束进行量化;进而基于模糊层次分析法生成装配单元决策图,采用最小生成树算法生成候选装配单元。同时,建立装配关系干涉矩阵对候选装配单元进一步验证,生成装配过程中互不干涉的装配单元。以一款电动机装配体为例验证提出的方法,结果表明该方法是一种求解装配规划结构分解问题的有效方法。     

基于几何推理有向图的装配序列规划

有向图是表示机械产品装配关系的有力结构,为减少有向图构造时的工作量,从拆装路径的空间可行性出发,提出了一种新的构造方法,从产品模型中提取装配关系矩阵并初始化有向图,建立层次化的结构,对产品中的零件进行路径规划,结合路径查找结果逐层填充节点,根据不同层次中节点的关系补充零件间的优先约束,从而生成相对完善的有向图。该方法基于CATIA二次开发自动进行几何推理,可用于存在复杂拆装路径的机械产品,具有良好的通用性。有向图拓扑排序生成的序列质量较高,运用粒子群算法对这些序列进行启发式搜索,相比传统方式缩小了搜索空间,能够更快找出较优的装配序列。     

装配序列生成的有序二叉决策图技术研究

为提高装配序列规划求解效率,在图论割集法的基础上,引入有序二叉决策图描述装配关联图、装配操作、装配几何可行性和装配稳定性.利用装配优先约束关系,简化装配关联图,设计了基于符号有序二叉决策图技术的装配序列生成算法,分析了该算法的时间复杂度.在每一次割集分解之后,检验子装配体的几何可行性和稳定性,实现装配序列生成过程中状态空间的隐式表示和搜索.研究实例验证了基于符号有序二叉决策图技术的装配序列生成算法的可行性和有效性.     

基于干涉矩阵的装配序列规划

阐述了利用干涉矩阵进行装配方向可行性判断和应用遗传算法进行装配序列规划的方法.干涉矩阵可从三维CAD系统中得到,为减少搜索空间和更快得到能满足装配工艺性的最佳装配序列,可结合装配工艺性和装配的特点对干涉矩阵适当进行处理.该方法有利于实现从三维CAD系统到装配序列的自动规划,最后以实例通过MATLAB编程说明该方法的可行性.     

基于优先约束关系的装配序列规划研究

利用联结图方法建立设备的装配关系模型,生成联结矩阵,并利用子装配体的概念对模型进行简化.利用联结图模型分析零件间的优先约束关系生成优先关系矩阵,利用优先约束矩阵生成设备的可行装配序列,为设备装配序列选优提供基础.     

层状装配系统的自动装配规划研究

对计算机自动装配规划进行了研究,从最底层零件间的优先关系矩阵出发,研究了串联、并联子装配体的识别和检验算法、高层装配体间的优先关系收缩矩阵算法以及装配系列的自动生成算法。通过算法在汽车车身后地板装配实例中的应用,对算法的有效性和实用性进行了验证。研究的算法可自动实现层状装配系统的计算机自动规划,可广泛应用于汽车、机器人及其他各种多零件机械体的装配顺序规划。     

面向装配序列规划的模因算法研究

针对遗传算法在求解装配序列规划问题中收敛速度慢、产生重复解等问题,提出一种基于模因算法的装配序列规划方法。将模因算法中全局搜索与局部搜索相结合动态更新种群的策略引入,采用装配优先约束矩阵和干涉矩阵建立装配规划模型,并以装配单元之间的相异性之和构建适应度函数。在非干涉解空间中进行全局搜索,获得装配规划方案,再通过二叉树中序遍历调序算法将较优方案转化为可行解。通过交叉操作和变异操作后,在可行解空间内进行局部搜索,获得较优解。通过典型柱塞油泵装配规划验证了该算法的可行性和可靠性;并将其与遗传算法进行比较,证明其更有效。     

多工位装配序列粒子群优化算法

针对传统单工位装配序列求解上的不足,将粒子群算法应用于多工位多目标装配序列优化的求解,提出一种面向复杂多工位产品的装配序列优化方法。采用优先序列图(Assembly precedence graph,APG)来描述零件间的优先约束关系,构建优先关系矩阵、装配干涉矩阵、工位能力表和装配信息表,描述装配部件干涉及工位之间的关系;给出粒子群算法编码体系和装配关系算法模型表达方法;综合考虑装配操作成本、装配工具更换成本和装配夹装变更成本和运输成本的影响,提出有工程意义的适应度函数的表达式;根据APG生成随机的可行初始装配序列,并利用粒子群算法(Particle swarm algorithm,PSO)对装配序列和装配工位进行优化。以飞机起落架装配序列规划实例验证多工位粒子群装配序列优化算法有效性。     

Computer aided geometric feasible assembly sequence planning and optimizing

Assembly sequence planning (ASP) is the foundation of the assembly process planning and design for assembly (DFA). In ASP, geometric feasibility is the prerequisite in the valid assembly sequences searching. The assembly precedence relations’ (APRs) deriving and fulfilling are the essential tasks in the geometric feasible assembly sequence planning. In this paper, a systematical approach called geometric constraint analysis (GCA) is proposed and the corresponding software system is developed and integrated with CAD system. Using this system, only with a few mouse clicks on CAD draft, assembly precedence relations (APRs) can be derived correctly and completely. Then, all the geometric feasible assembly sequences can be inferred out automatically. Moreover, an optimal algorithm is designed and realized in the GCA method, by which, the most optimal assembly sequence in terms of the operation convenience can be found out from the immense geometric feasible sequences. An erratum to this article can be found at     

A max–min ant colony system for assembly sequence planning

An improved ant colony optimization (ACO)-based assembly sequence planning (ASP) method for complex products that combines the advantages of ant colony system (ACS) and max–min ant system (MMAS) and integrates some optimization measures is proposed. The optimization criteria, assembly information models, and components number in case study that reported in the literatures of ACO-based ASP during the past 10 years are reviewed and compared. To reduce tedious manual input of parameters and identify the best sequence easily, the optimization criteria such as directionality, parallelism, continuity, stability, and auxiliary stroke are automatically quantified and integrated into the multi-objective heuristic and fitness functions. On the precondition of geometric feasibility based on interference matrix, several strategies of ACS and MMAS are combined in a max–min ant colony system (MMACS) to improve the convergence speed and sequence quality. Several optimization measures are integrated into the system, among which the performance appraisal method transfers the computing resource from the worst ant to the better one, and the group method makes up the deficiency of solely depending on heuristic searching for all parallel parts in each group. An assembly planning system “AutoAssem” is developed based on Siemens NX, and the effectiveness of each optimization measure is testified through case study. Compared with the methods of priority rules screening, genetic algorithm, and particle swarm optimization, MMACS is verified to have superiority in efficiency and sequence performance.     

基于最大-最小蚁群系统的装配序列规划

提出一种结合了蚁群系统与最大-最小蚂蚁系统优点的装配序列规划(Assembly sequence planning, ASP)方法。对近十年基于蚁群优化的ASP文献中采用的优化指标、装配信息模型、实例零件数等进行综述和比较。为提高序列的装配效率区分度,研究方向性、并行性、连续性、稳定性和辅助行程等5项指标的自动量化方法,将其融入到蚁群优化多目标启发式函数和适应值函数中。为提高对最优序列的搜索能力,以装配几何可行性为基础,从蚂蚁数量的确定、最大-最小信息素的界定、初始零件分配位置的绩效考核机制以及对并行零件组强制优化机制等方面,设计针对性解决方案,提出基于最大-最小蚁群系统的ASP算法。开发基于Siemens NX的装配规划系统AutoAssem。以阀门为实例,验证了算法内部各项优化措施的有效性,同时与优先规则筛选法、遗传算法及粒子群算法进行比较,分析该算法在运行效率和序列性能方面的优势。     

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.     

An intelligent interactive approach for assembly process planning based on hierarchical classification of parts

Assembly process planning is a difficult problem in the product design and development process. In order to reduce the difficulty of assembly process planning (APP), this paper presents an approach to finding a practical plan for assembly process through the intelligent interactive method based on hierarchical classification of parts. First, two hierarchical classification principles towards interactive disassembly are presented based on the key assembly constraint (KAC). Then, the initial assembly relationship of the product is readjusted to a new hierarchical structure which is more suitable for assembly planning according to a novel hierarchical classification algorithm. On the basis of the new hierarchy of product, an intelligent disassembly-based approach is presented, which not only takes full advantage of the ant colony algorithm for searching and constructing an initial assembly sequence with three available assembly principles, but also makes full use of the experience and knowledge of the human being for achieving a better assembly process. The intelligent disassembly approach can achieve an optimized assembly sequence, paths and scheme necessary information regarding the required assembly fixtures and tools. Finally, a case study shows that the proposed approach is feasible and practical.     

粒子群算法在装配顺序规划中的应用

装配顺序规划是计算机辅助工艺设计的一个重要环节,影响着轿车车身的装配质量和效率.针对当前装配顺序规划易产生组合爆炸等问题,提出了基于粒子群算法的装配顺序规划算法.装配偏差是影响装配质量的重要因素,因此应用装配体的装配偏差评价装配顺序.在装配顺序规划过程中,首先将装配顺序编码为粒子,根据所建立的判断规则进行识别粒子的可行性,并通过装配偏差评估可行粒子的适应度值,然后根据粒子群算法过程规划装配顺序,最后采用前翼子板案例阐述装配顺序的生成和优化过程.     

Assembly sequences merging based on assembly unit partitioning

Assembly sequence planning is a typical of combinatorial optimization problem which is difficult to be tackled when the number of parts of assembly becomes large. To reduce the searching space of assembly sequence planning of complex products, assembly sequences merging based on assembly unit partitioning is suggested. Assembly unit partitioning is presented to decompose the complex products into a group of assembly units containing a reduced number of parts or components, and the assembly design constraints and the assembly process constraints are comprehensively taken into account. The global optimal assembly sequences can be acquired through three steps. Firstly, the assembly units and decision graph of assembly unit are generated utilizing fuzzy analytical hierarchy process approach. Secondly, the optimal or near-optimal subsequences of assembly units can be obtained with current efficient methods of assembly sequence planning. Thirdly, under the assembly interference of assembly relations (geometrical constraints) of the whole products and the assembly precedence concluded by subsequences of assembly units, the assembly sequence merging is implemented to generate the global assembly sequences, and the optimal sequence is obtained through assembly sequences evaluation. The assembly constraints considered at the two previous steps is represented by the evaluation function. The effectiveness of the method is verified by an illustrative example and the results show that the searching space of assembly sequence merging of complex products is reduced remarkably and the optimal assembly sequence of the whole produces is obtained.     

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.