Disassembly Sequence Planning:A Survey

It is well-recognized that obsolete or discarded products can cause serious environmental pollution if they are poorly be handled.They contain reusable resource that can be recycled and used to generate desired economic benefits.Therefore,performing their efficient disassembly is highly important in green manufacturing and sustainable economic development.Their typical examples are electronic appliances and electromechanical/mechanical products.This paper presents a survey on the state of the art of disassembly sequence planning.It can help new researchers or decision makers to search for the right solution for optimal disassembly planning.It reviews the disassembly theory and methods that are applied for the processing,repair,and maintenance of obsolete/discarded products.This paper discusses the recent progress of disassembly sequencing planning in four major aspects:product disassembly modeling methods,mathematical programming methods,artificial intelligence methods,and uncertainty handling.This survey should stimulate readers to be engaged in the research,development and applications of disassembly and remanufacturing methodologies in the Industry 4.0 era.

Strategy of Concurrent Optimization for an Assembly Sequence

An effective constraint release based approach to realize concurrent optimization for an assembly sequence is proposed. To quantify the measurement of assembly efficiency, a mathematical model of concurrency evaluation index was put forward at first, and then a technology to quantify assembly constraints was developed by application of some fuzzy logic algorithms. In the process of concurrent optimization of the assembly sequence, two kinds of constraints were involved. One was self-constraints of components, which was used to evaluate the assemble capability of components under the condition of full-freedom. Another was an assembly constraint between components represented by geometric constraints between points, lines and planes under physical restriction conditions. The concept of connection strength degree （CSD） was introduced as one efficient indicator and the value of it was evaluated by the intersection of the two constraints mentioned above. The equivalent constraints describing the connection weights between components were realized by a well designed constraints reduction, and then the connection weights based complete assembly liaison graph was applied to release virtual connections between components. Under a given threshold value, a decomposition and reconstituting strategy for the graph with the focus on high assembly concurrency was used to realize an optimized assembly concurrency evaluation index. Finally, the availability of the approach was illustrated in an example to optimize the assembly of a shift pump.

Sequencing of Mixed Model Assembly Lines Based on Improved Shuffled Frog Leaping Algorithm

Shuffled frog leaping algorithm( SFLA) was used to solve multi-objective sequencing problem of mixed model assembly line( MMAL). Local convergence can be avoided and optimal solution can be obtained to a certain extent. However,the multi-objective sequencing problem of MMAL is an non-deterministic polynomial hard( NP-hard) problem and the shortcomings are slow convergence rate and low precision. To solve the shortcomings for optimization objectives of minimizing total utility time and keeping average consumption rate of parts, a chaos differential evolution SFLA( CDESFLA) is proposed in this study. Because SFLA is easy to fall into local optimum,the evolution operator of differential evolution algorithms is introduced in SFLA as a local search strategy,and differential mutation operator is introduced in chaotic sequence to prevent premature convergence. The examples show that the proposed CDESFLA is better for convergence accuracy than SFLA,genetic algorithm( GA) and particle swarm optimization( PSO)

A Discrete Bat Algorithm for Disassembly Sequence Planning

Based on the bat algorithm(BA), this paper proposes a discrete BA(DBA) approach to optimize the disassembly sequence planning(DSP) problem, for the purpose of obtaining an optimum disassembly sequence(ODS) of a product with a high degree of automation and guiding maintenance operation. The BA for solving continuous problems is introduced, and combining with mathematical formulations, the BA is reformed to be the DBA for DSP problems. The fitness function model(FFM) is built to evaluate the quality of disassembly sequences. The optimization performance of the DBA is tested and verified by an application case, and the DBA is compared with the genetic algorithm(GA), particle swarm optimization(PSO) algorithm and differential mutation BA(DMBA). Numerical experiments show that the proposed DBA has a better optimization capability and provides more accurate solutions than the other three algorithms.

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.

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.

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。

飞机壁板装配偏差传递与演变及其优化方法

壁板的装配准确度直接影响飞机的气动力特性,在装配过程中受到夹紧力、铆接力、回弹力、装配顺序等影响,壁板将发生不同程度的变形,装配变形与零件偏差、夹具偏差等相互耦合,导致壁板最终的装配准确度难以预测和控制。针对以上问题,本文对壁板装配过程中偏差的传递与演变规律进行研究,构建了壁板装配偏差传递与演变的刚柔混合模型与壁板装配准确度的评价准则;采用确定性分析法计算壁板的刚性定位偏差,并通过建立多步夹紧和不同长桁装配顺序下的偏差状态空间模型,揭示了壁板装配过程中偏差的演变规律,实现了壁板装配偏差的准确预测;提出了基于代理模型和智能优化算法的长桁装配顺序稳健优化设计方法,通过装配顺序优化使壁板的装配质量得到了整体提升。

一种基于计算智能的飞机部件装配序列优化方法

针对目前装配资源受限条件下大型部件装配规划存在效率低、性能差的问题,提出一种基于离散烟花算法的装配序列规划模型。建立了装配部件、装配资源和混合干涉矩阵,以描述不同情况下的装配干涉问题。为了加快可行解的求解速度,设计了包括爆炸幅度、烟花爆炸产生的火花数量和火花位置更新的策略。实验阶段,以机翼的装配过程为例对所提模型进行验证。结果表明,与离散粒子群优化算法相比,使用所提出的离散烟花算法获得的解的适应度函数值更优,所提算法适应度值提升约24.5%。

考虑拆卸结构的动车组系统动态机会维修策略

为研究拆卸结构对动车组系统维修计划的影响,运用拆卸混合图模型分析单部件最优拆卸序列。并基于效费比分析方法选择多级维修方式,计算考虑拆卸成本和安装调试成本的部件最优可靠度阈值。在此基础上,引入机会维修理念,针对系统内多部件维修前拆卸序列规划问题,提出多拆卸目标序列优化方法,建立考虑可靠度价值浪费的动态机会维修策略模型。算例分析表明,动态机会维修策略相较于传统顺序预防性维修策略,通过归并维修时机,停机次数减少16次,有效减少因预防性维修和拆卸时间冗长造成的停机成本损耗,且维修总成本节省16.6%,满足维修部门对经济性的要求,为动车组系统考虑复杂相关性的维修策略研究提供理论支持。

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。

涡轮叶片装配智能优化算法

涡轮组件装配是发动机制造过程的重要组成部分,在装配过程中为保证涡轮叶片装配完成后的转子不平衡量符合设计标准,需对叶片进行分组试装等步骤,装配效率因此受到影响。为解决此问题,提出一种基于改进模拟退火算法的叶片装配序列优化方法。建立由轮盘自身不平衡量与叶片不平衡量相结合的数学模型,采用多邻域搜索机制,改进退火机制,增加升温、记忆模块等方法来改进算法,通过实例验证了算法的有效性,并与蝴蝶算法,天鹰算法,原始模拟退火算法进行比较,有效地降低了涡轮转子不平衡量。结果表明,改进的模拟退火算法能够有效的减少叶片装配后的不平衡量。为使该算法能够更方便的服务于装配现场,基于MFC框架开发一款叶片装配软件,为解决叶片装配需进行多次拆装以及装配效率低的问题提供了一种有效的方案。

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。

装配顺序的生成算法研究

基于ＰＡＤＬ—２ 实体造型系统，提出增量干涉检测法，得到任意两个零件间的连接函数及移动函数，进一步求得装配产品的零件连接图、匹配关系图及稳定性有向图。利用图论的割集法，经装配的几何可行性及稳定性判别后。

装配序列规划中拆卸方向的确定

以产品为出发点进行拆卸判断 ,以拆卸顺序的逆生成装配序列是装配序列规划中常用的方法 ,而拆卸方向的确定是其关键的一步 .而将拆卸方向简化为与坐标轴平行的方向不能产生完备的可行装配顺序集 .在分析装配过程的特点基础上 ,提出一种根据装配约束关系用球面几何计算拆卸方向的方法 .首先 ,利用球面几何的方法求解局部拆卸方向集 ,取局部方向集所对应的球面多边形的形心为局部拆卸方向 ;然后 ,利用投影的方法测试此局部拆卸方向是否为全局拆卸方向 .文中提出的方法可以方便地用于工程实践中 .

智能装配规划中的拆卸方向计算

提出了基于装配约束的离散化单位球面的拆卸方向计算方法首先利用离散化的单位球面计算局部拆卸方向集,通过离散点坐标的加权平均求取拆卸方向集的质心;然后通过可拆卸性判断获得可行的全局拆卸方向应用表明。

装配与拆卸序列的自动生成与评价方法研究

采用层次与关系相结合的装配模型 ,研究利用割集法自动求解装配与拆卸的并行序列问题 .为尽量避免图爆炸问题的产生和提高割集法求解装拆序列的运行效率 ,采用分层策略并提出两条新的判断割集可行性的准则 ,通过评价分析 ,给出了从所有可行的装拆序列中 。

Web环境下的协同装配规划系统

异地开发产品要求装配设计成员间进行协同工作。为支持分布式的装配规划活动,提出了协同装配规划网络环境的体系结构,研究了用户界面、通讯管理、应用服务和信息管理等功能模块,并结合虚拟现实建模语言表达的客户端产品显示模型和服务器端装配应用模型,采用Java-EAI等技术,开发了基于Web的协同装配规划原型系统。用一个驱动装配体实例,说明异地人员间同步的协同装配规划过程,验证了协同装配规划方法的可行性和系统的有效性。

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。