INTEGRATED APPROACH TO GENERATION OF PRECEDENCE RELATIONS AND PRECEDENCE GRAPHS FOR ASSEMBLY SEQUENCE PLANNING

An integrated approach to generation of precedence relations and precedencegraphs for assembly sequence planning is presented, which contains more assembly flexibility. Theapproach involves two stages. Based on the assembly model, the components in the assembly can bedivided into partially constrained components and completely con-strained components in the firststage, and then geometric precedence relation for every component is generated automatically.According to the result of the first stage, the second stage determines and constructs allprecedence graphs. The algorithms of these two stages proposed are verified by two assemblyexamples.

Immune and Genetic Algorithm Based Assembly Sequence Planning

In this paper an assembly sequence planning model inspired by natural immune and genetic algorithm (ASPIG) based on the part degrees of freedom matrix (PDFM) is proposed, and a proto system — DSFAS based on the ASPIG is introduced to solve assembly sequence problem. The concept and generation of PDFM and DSFAS are also discussed. DSFAS can prevent premature convergence, and promote population diversity, and can accelerate the learning and convergence speed in behavior evolution problem.

INTEGRATED APPROACH TO ASSEMBLY SEQUENCE PLANNING OF C0MPLEX PRODUCTS

Assembly sequence planning will be more difficult due to the increasingcomplexity of products. An integrated approach to assembly sequence planning of complex productsapplying de-composition-planning-combination strategy is presented. First, an assembly is decomposedinto a hierarchical structure using an assembly structure representation based on connectors. Then,an assembly planning system is used to generate the sequences that are locally optimal for eachleaf partition hi the structure hierarchy. By combining the local sequences systematically in abottom-up manner and choosing suitable ones from the merged sequences, the assembly sequence of eachparent structure including the whole assembly is generated. An integrated system has beencompleted. A complex product is given to illustrate the feasibility and the practicality of theapproach.

An Approach to Assembly Sequence Plannning Based on Hierarchical Strategy and Genetic Algorithm

Using group and subassembly cluster methods, the hierarchical structure of a product is ?generated automatically, which largely reduces the complexity of planning. Based on genetic algorithm, the optimal of assembly sequence of each structure level can be obtained by sequence-by-sequence search. As a result, a better assembly sequence of the product can be generated by combining the assembly sequences of all hierarchical structures, which provides more parallelism and flexibility for assembly operations. An industrial example is solved by this new approach.

Ant colony optimization for assembly sequence planning based on parameters optimization

As an important part of product design and manufacturing, assembly sequence planning (ASP) has a considerable impact on product quality and manufacturing costs. ASP is a typical NP-complete problem that requires effective methods to find the optimal or near-optimal assembly sequence. First, multiple assembly constraints and rules are incorporated into an assembly model. The assembly constraints and rules guarantee to obtain a reasonable assembly sequence. Second, an algorithm called SOS-ACO that combines symbiotic organisms search (SOS) and ant colony optimization (ACO) is proposed to calculate the optimal or near-optimal assembly sequence. Several of the ACO parameter values are given, and the remaining ones are adaptively optimized by SOS. Thus, the complexity of ACO parameter assignment is greatly reduced. Compared with the ACO algorithm, the hybrid SOS-ACO algorithm finds optimal or near-optimal assembly sequences in fewer iterations. SOS-ACO is also robust in identifying the best assembly sequence in nearly every experiment. Lastly, the performance of SOS-ACO when the given ACO parameters are changed is analyzed through experiments. Experimental results reveal that SOS-ACO has good adaptive capability to various values of given parameters and can achieve competitive solutions.

Optimization of Wind Turbine Hub Assembly Sequence Based on Intelligent Algorithm

A method for hub assembly sequence planning oriented to the fixed position layout is proposed.An assembly relationship model was constructed to describe the relationship between hub assembly components and workstations,considering the layout of the hub assembly line and process constraints,including the assembly precedence matrix,workstation assembly capability table and criticality table of components.The evaluation mechanism for the assembly sequence was established.Values from the evaluation functions with engineering significance were used to select the optimal assembly sequence from the perspective of assembly time,assembly index and assembly path distance.In function,the criticality of components was introduced into the traditional assemblability index,comparing the multi-objective dragonfly algorithm(MODA)with non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ)to complete the assembly sequence planning and assignment for workstations.The optimized results show that the presented method is feasible and efficient for solving the hub assembly sequence planning problem.

Multi-User Collaborative Assembly Planning over the Internet

Due to the increasing complexity of products and for the distributed product development, more closely collaborative work among designers is required. A collaborative assembly planning approach is proposed to support assembly planning in a networked environment. The working procedure is depicted and the key techniques including collaborative-planning-oriented assembly decomposition modeling, assembly assignment modeling, and sub-plans merging are addressed. By incorporating visual models at client side with assembly application models at server side, a web-based supporting environment for collaborative assembly planning has been developed using VRML and Java-EAI techniques. A case study is given to illustrate the feasibility and validity of the idea.

FAST GENERATION OF OPTIMAL ASSEMBLY PLANS

Existing approaches to automatic assembly planning often lead to combinatorial explo- sion. When the parts composing the assembly increase in number, computer-aided planning be- comes much slower than manual planning. Efforts to reduce the computing time by taking into ac- count various constraints and criteria to guide the search for the optimal plan requires too much input information, so as to offset the convenience of automatic assembly planning. In addition, as the planner becomes more complicated, such efforts often fail to reach the objective. This paper presents a new concep── unit , asserting that the intemal structure of an assembly is hierachical. Every disassembly operation only handles several units, no matter how many parts are involved. Furthermore, the scenario of disassembly is brought to light. It relates to only two key data──the liaison type and the assembly direction. The computational cast of this approach is roughly propor. tional to the number of parts. A planner, implementing these principlcs can generate the optimal as- sembly plans dramatically faster than the known approaches.

Research of the Assembly Model Based on Petri Net

Assembly model plans an important role in assembly sequence planning.Flexible assembly system (FAS) is a dis- crete event system (DES),so Petri net,as an important analytical tool for the DES,is used for modeling of an FAS.All of the feasible assembly sequences can be obtained by firing the Petri net model.In order to enhancing the efficiency of the assembly sequence planning,knowledge-based Petri net,combining an usual Petri net with expert’s knowledge and experiences,is proposed to con- struct the assembly model.So the feasible assembly sequences can be reduced greatly,and the optimum assembly sequence could be generated without the combinatorial explosion of solutions.

Application of Assembly Modeling and Simulation Technology to a Large Size Wind-drive Generator

Challenges still remain in carrying out assembly modeling efficiently in virtual assembly （VA） fields. One of the root causes is the apparent weakness in effective description of assembly knowledge and information. The assembly modeling, disassembly modeling, assembly interference inspection, assembly sequence planning and optimization, and assembly simulation display for key techniques is studied theoretically in this paper. An example of product assembly modeling is provided to illustrate the effectiveness of the proposed approach. On the basis of re- search, using assembly simulation techniques and multimedia techniques to finish structure design in linkage design of a large size wind-drive generator. The application of the modeling method has shortened the lead time dramatically.

Assembly Sequence Planning for Mechanical Products

A method for assembly sequence planning is proposed in this paper. First, two methods for assembly sequence planning are compared, which are indirect method and direct method. Then, the limits of the previous assembly planning system are pointed out. On the basis of indirect method, an improved method for assembly sequence planning is put forward. This method is composed of four parts, which are assembly modeling for products, assembly sequence representing, assembly sequence planning, and evaluation and optimization. The assembly model is established by human machine interaction, and the assembly model contains components' information and the assembly relation among the components. The assembly sequence planning is based on the breaking up of the assembly model. And/or graph is used to represent assembly sequence set. Every component which satisfies the disassembly condition is recorded as a node of an and/or graph. After the disassembly sequence and/or graph is generated, heuristic algorithm AO * algorithm is used to search the disassembly sequence and/or graph, and the optimum assembly sequence planning is realized. This method is proved to be effective in a prototype system which is a sub project of a state 863/CIMS research project of China ‘Concurrent Engineering’.

改进布谷鸟算法在装配序列规划中的应用研究

针对装配序列规划问题,建立考虑装配序列的几何可行性、稳定性、聚合性、重定向性的装配关系模型以及基于适应度函数的装配序列优化数学模型。提出一种改进布谷鸟算法对装配序列规划问题进行求解,采用随机键和最小位置规则的方法设计基于零件编号、装配方向、装配工具的3层编码方案;设计基于最小装配成本的初始化策略与随机初始化策略相结合的混合种群初始化策略,提高种群质量;改进种群进化和搜索方式,将种群分为3个子群,并分别采用自适应步长飞行、标准步长飞行和交叉、变异的方式进行种群更新,提高算法的收敛速度和求解精度。最后通过实例应用及与其他算法的比较,验证了所提出的改进布谷鸟算法在求解装配序列规划问题上的有效性和优越性。

航空发动机奇数只叶片的风扇转子动叶装配序列优化技术研究

针对航空发动机风扇转子奇数只叶片装配序列规划过程中存在的对顶角叶片重力矩差难以保证、叶片重力矩分布不够均衡的问题,研究了奇数只叶片的装配序列规划技术。首先,将单只叶片一分为二,形成33对“连体双胞胎叶片”,再转换成“66只虚拟叶片”,实现转子叶片数量“从奇变偶”的转换;然后,为每只“虚拟叶片”找到对顶角叶片,但是不能将“连体双胞胎叶片”真正分开或放在对顶角位置;最后,以对顶角位置上“两只虚拟叶片”的重力矩差为约束,以转子剩余不平衡量最小为优化目标,采用改进模拟退火算法规划动叶的装配序列,为转子装配提供优化的装配序列,优化转子的静平衡质量。实例验证表明,奇数只叶片数量“从奇变偶”转换,精确保证了对顶角叶片重力矩差,满足了设计部门给出的对顶角叶片重力矩差的设计指标(≤1200 g·mm),为奇数只叶片的转子动叶装配提供了优化的装配序列,并优化了转子的平衡质量。

基于语义工艺知识的驱动桥装配序列规划研究

针对工程机械驱动桥装配序列规划中缺乏基于知识的推理决策,难以实现复杂产品智能化装配的问题,提出一种基于语义工艺知识的装配序列规划方法。构建了驱动桥装配语义工艺知识信息模型,表达子装配体层级结构信息、属性信息和装配语义信息。通过建立装配序列规划本体,并将SWRL规则引入装配本体,研究了工艺知识的语义表示和推理决策。采用Neo4j构建驱动桥工艺知识图谱,实现了典型结构子装配体装配序列快速识别。通过量化零件几何、物理属性和装配工艺信息对装配效率的影响,生成装配权重序列,并利用SWRL规则迭代修正形成非典型结构子装配体装配序列。以某型号压路机驱动桥装配序列生成与仿真,验证了该方法的可行性,为知识驱动的复杂产品装配序列规划提供参考。

基于布谷鸟算法的风电机组装配序列优化

装配序列规划(ASP)是风电机组制造的重要技术。ASP是一个组合优化问题,风电机组最优装配序列的搜索空间和计算量均很大。提出基于布谷鸟算法的风电机组装配序列优化方法。首先,从三维装配体模型中提取多种装配约束信息并表示成装配约束矩阵,以降低最优装配序列的搜索空间;继而构造装配序列目标函数,建立装配序列规划模型,方便算法计算出最优装配序列;对离散布谷鸟算法(DCA)改进,求解ASP模型,获得风电机组产品的最优装配序列。最后通过实验验证了ASP模型的有效性和DCA的优良性能。

基于GWO-IWD算法的装配序列规划研究

针对复杂装配体装配序列规划问题,提出一种基于灰狼优化的智能水滴算法。综合考虑零件的属性特征及几何约束,构建装配体的装配信息模型,将装配成本作为优化的目标函数,以装配序列的几何可行性、装配方向改变次数以及装配工具改变次数为约束条件,建立面向装配体的优化模型。采用智能水滴算法对装配体优化模型进行求解,并利用灰狼优化算法来改善智能水滴算法的搜索能力,提高算法求解精度。以柱塞泵装配体单元为例,验证了该算法的可行性及有效性。

基于知识图谱的直升机部件装配序列规划方法

由于直升机组件数量多且组件之间约束关系复杂,传统的装配序列规划方法存在指数爆炸和易陷入局部最优解的问题。为了应对装配序列求解时间随组件数量指数增长和多约束关系矩阵运算复杂的难点,引入知识图谱(KG)建立直观的语义装配信息模型,并提出了一种基于KG的直升机部件装配序列规划方法。首先,基于三维模型解析和知识推理技术实现结构信息和约束关系等关键装配信息的提取。其次,基于本体构建KG形式的装配信息模型。最后,利用带反馈的图规划算法从KG中求解直升机部件的装配序列。KG提供的优先关系减少了算法搜索空间,在优先关系的约束下,带反馈的图规划算法以装配方向改变次数最小和装配工具变化次数最少为目标,逐步规划序列并反馈规划结果以避免重复搜索。直升机的中机身中段部件被作为实验对象以验证该方法的有效性,对比启发式算法能获得更高适应度值的装配序列且求解时间更短。

基于改进人工蜂群算法的装配序列规划技术研究

针对复杂产品装配序列规划耗时、效率低且错误率高等问题,提出了一种基于改进人工蜂群算法的装配序列规划解决方案。首先,综合考虑装配序列的可行性、复杂性、稳定性、重定向性、聚合性等多个因素,构建基于矩阵的装配关系模型及适应性评价函数;其次,通过调用SolidWorks中的应用程序接口(application program interface, API)获取装配对象的相关数据,生成装配关系矩阵,以求装配序列规划问题;最后,以冷藏车的典型零件为对象,验证该方法的可行性。该技术显著缩短了检查时间和减小了错误率,同时提高了效率。

基于蚁群算法的立式行星齿轮传动装置并行装配序列规划研究

装配体中零部件的增加导致装配流程与工艺趋向于复杂化,装配序列的规划需要更加严谨,计算机辅助装配序列规划成为虚拟装配的重要方向。以复杂立式人字齿行星齿轮传动装置为例,通过子装配体和聚族零固件的识别,采用装配连接无向关系图确定装配连接关系,将装配关联矩阵转化成数学信息,采用蚁群算法进行了装配路线规划。结果表明,立式行星齿轮传动装置的装配时间减少了约20%,人力资源节省1/3。

基于改进蝙蝠算法的再制造装配体拆卸序列规划研究

针对再制造装配体人工拆卸比重较高,自动化拆卸使用率低等问题,提出了一种基于改进蝙蝠算法的方法来研究拆卸序列规划问题。在传统蝙蝠算法的基础上,引入罗盘算子改进蝙蝠算法,选择拆卸方向变化数量,拆卸工具更换次数和拆卸操作位置变更数作为评价指标,建立适应度函数模型。通过一个应用案例,对改进蝙蝠算法的优化性能进行了测试和验证,并将改进的蝙蝠算法与GA、PSO和差分蝙蝠算法进行了对比。数值结果表明,该算法相较其他三种算法具有更好的优化性能和更精确的解,采用该方法能获得具有高度自动化的再制造装配体拆卸序列且能够指导操作者高效完成拆卸工序。