不确定环境下考虑多约束的拆卸收益概率模型研究

废旧产品拆卸过程中存在许多的不确定因素, 进行目标拆卸时需要兼顾整个拆卸活动的整体收益。在利用拆卸网络图得到目标零部件的所有可行拆卸序列之后, 将零部件质量不确定、拆卸破坏率、基本拆卸时间随机等因素进行综合考虑, 建立了不确定环境下的拆卸收益模型, 在同时满足拆卸破坏率和拆卸时间约束下, 基于拆卸收益概率进行序列优化, 并设计了基于随机模拟的求解方法。最后通过案例分析体现出决策者要求不同时序列优化结果的变化, 验证了所提出模型的可行性。     

基于本体论的产品拆卸信息建模

拆卸序列规划生成是虚拟维修的核心之一,直接关系到虚拟维修的可行性及成本。以最优拆卸树的方法研究拆卸序列规划是拆卸序列生成最行之有效的途径。从虚拟维修中产品拆卸模型的基本特点入手,应用本体论的思想和方法,构建了影响拆卸相关的本体,包括几何本体、关系本体、物理本体、化学本体,并综合考虑各本体之间的相互联系,建立了比较完整的产品拆卸信息模型,同时依据成熟的拆卸树算法生成最优拆卸树,以便后期进行拆卸序列规划的研究。并应用于某产品的拆卸模型建立。     

基于解释结构模型的产品零部件拆卸序列规划

为了快速准确地对复杂产品层次结构进行划分以实现拆卸序列规划,将解释结构模型(ISM)引入到对复杂产品的描述中,提出一种产品零部件拆卸序列规划方法.在对复杂产品零部件连接关系提取与表达、产品零部件连接关系邻接矩阵和有向图进行分析的基础上,构建了基于ISM的产品零部件连接关系模型;通过计算ISM连接关系可达矩阵建立了其有向图,并给出一种产品零部件结构深度的计算及拆卸序列规划方法.最后以确定洗碗机产品门体的拆卸序列规划和目标件的结构深度为例,对文中方法进行了验证,结果表明,该方法能够在短时间内确定复杂产品的结构层次,进而实现其拆卸序列的规划.     

支持复杂产品并行拆卸序列规划的遗传算法

为高效求解复杂产品的并行拆卸序列规划问题,提出基于遗传算法的复杂产品并行拆卸序列规划方法.针对并行拆卸序列规划问题中拆卸序列长度和每步拆卸零部件个数不确定的特点,提出并行序列染色体编码方法,分别将拆卸单元序列和拆卸步长作为染色体的前段和后段,以此表示一个拆卸序列.基于该染色体编码,采用拆卸混合图描述产品零部件间装配约束关系和拆卸优先级,并导出拆卸约束矩阵和邻接矩阵,由矩阵随机获取可行的初始染色体种群;将基本拆卸时间和不可行拆卸惩罚因子作为优化目标来构建适应度函数,确保最优解的可行性;在初始染色体种群的基础上,适应度函数最小为优化目标,通过遗传、交叉和变异遗传算子实现并行拆卸序列的优化.最后通过实例验证了该方法的可行性和实用性.     

虚拟环境中基于约束动态解除的产品拆卸技术研究

虚拟拆卸是验证产品可拆卸性与可维护性、确定产品装配序列的有效手段．提出虚拟拆卸过程中配合约束的自动解除方法．随着拆卸过程的递进，根据设计者的交互意图，适时取消零件所受的配合约束．实现了虚拟拆卸过程中零件的约束运动，将运动传感器检测到的设计者手的实际运动映射到虚拟环境中零件的可自由运动空间，以有效地支持虚拟环境中交互拆卸的完成．相关方法在虚拟设计与装配原型系统的研究与开发中得到实现．     

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,而拆卸方向的确定量是其关键的一步,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和方法,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。     

考虑作业空间约束的并行拆卸序列规划算法

为获取多人同时进行不同拆卸任务的并行拆卸序列,提出考虑拆卸作业空间约束的并行拆卸序列规划方法.首先从零件几何可行性、拆卸时间以及拆卸作业空间约束3个方面构建拆卸序列规划问题模型:为避免产生不可行序列,提出拆卸作业空间的快速提取和干涉检查方法;针对回收产品拆卸时间不确定的特点,引入区间数模型描述拆卸时间,从拆卸基本时间、拆卸工具准备时间和拆卸工位改变时间3个方面构建拆卸时间模型.然后基于协同工作原则设计蚁群搜索的等待机制,以求解并行的拆卸序列;为进一步提高算法求解复杂产品并行拆卸序列的质量和效率,采用具有自适应能力的信息素更新方式和蚂蚁选择策略对基本蚁群算法加以改进.通过一种锥齿轮减速器装配体实例对关键参数的取值进行讨论分析,并验证了该算法各项约束措施的有效性.     

基于蚁群优化算法的目标拆卸序列规划

为了能够以较高的效率求解出产品中目标零件的拆卸方案,基于产品中零件间的拆卸优先约束关系,提出并建立目标零件的拆卸层次信息图模型,将目标零件的拆卸序列规划问题转化为对该图模型中具备最优值的路径的搜索和寻优问题.同时,提出一种改进蚁群优化算法,以实现对目标零件拆卸层次信息图的构建和对拆卸方案的搜索与寻优.最后通过实例验证了该方法的可行性和计算效率.     

基于目标拆卸的虚拟拆装过程建模

针对目前基于层次关系模型和关联关系模型的虚拟拆装系统中未考虑目标拆卸的 问题,在原有层次关系模型基础上引入跳跃拆卸路径的概念,对基于层次关系模型和关联关系 模型的拆装结构模型进行了完善和优化。对引入跳跃拆卸路径后的拆卸模型进行序列规划,并 针对拆装决策过程中拆装单元的组合与还原问题进行论述,最后建立基于关联关系模型的目标 拆卸算法。以船用分油机进行实例验证,验证结果表明该模型能够很好地解决目标拆卸的问题, 对相关应用系统开发具有一定的参考价值。     

虚拟维修拆卸序列规划方法研究

提出一种混沌遗传算法来解决了虚拟维修拆卸序列规划效率不高的问题.针对虚拟维修拆卸过程中拆卸序列规划问题,在遗传算法的基础上,结合混沌优化理论,提出了混沌遗传优化算法.上述算法以遗传算法为主流程,利用混沌现象不重复遍历的特点优化生成初始种群,然后对每次迭代中的个体以一定的概率进行混沌优化.经验证,混沌遗传算法能够有效解决虚拟维修拆卸序列优化问题.     

An integrated approach to selective-disassembly sequence planning

De-manufacturing (DM) is defined as a process to disassemble certain parts or components from a product. The parts or components are selected for recycling, reuse, maintenance or disposal. Selective-disassembly as the disassembly of the selected parts is a key process in DM. Allowing a partial and non-procedural disassembly sequence in DM, selective-disassembly aims to minimize the number of removals regardless of assembly indenture levels. It is necessary for selective-disassembly to have an effective and optimal sequence planning in order to reduce tremendous time and cost involved in product DM. The Wave propagation (WP) method, a dominant approach to selective-disassembly sequence planning, focuses on topological disassemblability of parts. It is inefficient to achieve the aim because of two missed considerations: tool accessibility to a fastener in non-procedural and partial disassembly, and batch removability to directly access a part for separation or replacement.This paper presents an integrated approach to selective-disassembly sequence planning. The two examples presented here demonstrate that the approach is efficient and practical for DM. The implemented approach can efficiently generate a feasible and near-optimal sequence plan for selective-disassembly, with ensuring both batch disassembly of components and tool accessibility to fasteners.     

Genetically optimised disassembly sequence for automotive component reuse

Environmental sustainability through end-of-life recovery has become the main items of contest in the automotive industries. Component reuse as one of the product recovery strategy is now gaining importance in view of its impact on the environment. Disassembly as one of the determinant factors for reuse is a very important and difficult process in life cycle engineering. To enable reuse, a certain level of disassembly of each component is necessary so that parts of the products that have arrived at their end-of life can be easily taken apart. Improvements to the disassembly process of products can be achieved at two levels: in the design phase, making choices that favours the ease of disassembly of the constructional system (design for disassembly) and planning at best and optimising the disassembly sequence (disassembly sequence planning). Hence, finding an optimal disassembly sequence is important to increase the reusability of the product. This paper presents the development work on an optimisation model for disassembly sequence using the genetic algorithms (GA) approach. GA is chosen to solve this optimisation model due to its capability in solving many large and complex optimisation problems compared with other heuristic methods. The fitness function of the GA in this study is dependent on the increment in disassembly time. Comparison of results using different combinatorial operators and tests with different probability factors are shown. This paper will present and discuss the disassembly sequence of an engine block, as a case example which achieves the minimum disassembly time.     

Disassembly planning of mechanical systems for service and recovery: a genetic algorithms based approach

In a perspective of improving the behavior of a product in its whole life cycle, the efficient planning of the disassembly processes acquires strategic importance, as it can improve both the product’s use phase, by facilitating service operations (maintenance and repairs), and the end-oflife phase, by favoring the recycling ofmaterials and the reuse of components. The present paper proposes an approach to disassembly process planning that supports the search for the disassembly sequence best suited for both aspects, service of the product and recovery at the end of its useful life, developing two different algorithms. Notwithstanding their different purposes, the two algorithms share the typology of modeling on which they operate, and the logical structure according to which the genetic search procedure is developed. The choice of implementing genetic algorithms was prompted by the intrinsic complexity of the complete mathematical solution to the problem of generating the disassembly sequences, which suggests the use of a non-exhaustive approach. As is shown in the results of a set of simulations, both algorithms may be used not only for the purposes related to disassembly process planning but also as supporting tools during the product design phases. This is especially so for the second algorithm, that deals with the problem of a recovery-oriented disassembly through an all-encompassing approach, combining economical and environmental considerations, and extending the evaluations to the whole life cycle of the product. This formulation gives this algorithm and autonomous decisional capacity on both the disassembly level to be reached, and the definition of the optimum recovery plan (i.e., the best destination for the disassembled components, based on some significant properties of them).     

Disassembly sequence planning in a disassembly cell context

In this paper a two-phase approach is proposed for determining the optimal disassembly sequence when the disassembly system has a cellular configuration. Operations are first grouped into cells based on the resources they require with the goal of minimizing machine acquisition costs. The aim is to group together those operations that use similar equipment in order to achieve good utilization levels of such equipment. A maximum cell size may be imposed. Once the cells have been formed and the operations have been assigned to them, a metaheuristic algorithm (namely GRASP) is used to search for the disassembly sequence for each product that leads to the minimum number of intercellular movements. To account for uncertainty regarding the condition in which the product may arrive, each disassembly task is assumed to be required with a certain probability, regardless of the other tasks. AND/OR precedence relations among the disassembly tasks are also considered. The proposed approach is illustrated on a randomly generated disassembly problem.     

模糊推理Petri网及其在产品拆卸序列决策中的应用

为了在产品拆卸序列决策时,简化拆卸路径的分析难度,提出一种以模糊推理Petri网为工具的产品拆卸序列决策模型.采用将模糊推理Petri网与矩阵运算相结合的形式化推理算法,对所提出的决策算法进行了论述.实例应用结果表明,此模型在产品拆卸过程规划中具有很强的并行处理能力,可以根据产品在拆卸过程中零部件的最新信息对每一步操作作出适时的智能化决策,从而实现将产品中若干零件作为子装配体进行拆卸的自动聚类识别,减少了产品拆卸的复杂性.     

Interference probability matrix for disassembly sequence planning under uncertain interference

Disassembly of end-of-life products is a common step in remanufacturing and recycling. Disassembly sequence planning is the process that automatically finds the optimal sequence of components being removed. A key element of disassembly sequence planning is a suitable mathematical representation that describes the interference of any two components in a product. Previous studies on disassembly sequence planning have tended to focused on the interference that is fixed and known. However, the interference may be uncertain due to complex end-of-life conditions such as deformation, corrosion and rust. To deal with uncertain interference, this paper proposes an interference probability matrix as a new mathematical representation that uses probability to indicate uncertainty in the interference, and establishes a multi-threshold planning scheme to generate the optimal disassembly sequence plans. Three case studies are given to demonstrate the use of the proposed approach. It is also tested the performance of four multi-objective optimization algorithms that can be adopted in the proposed multi-threshold planning scheme.     

Integrated multi-layer representation and ant colony search for product selective disassembly planning

Selective disassembly plays an important role in product life cycle to meet requirements of the product repairing, reusing and recycling. An efficient disassembly plan is essential to minimize processing time in product maintenance for cost saving. This paper introduces a method for integration of the multi-layer product representation and the optimal search in product selective disassembly planning. The multi-layer representation is based on the product structure formed in product design. The method enables an efficient search for the disassembly sequence. Unlike the existing product representation methods, the multi-layer representation is a dynamic product data model integrated with an ant colony search process for a near optimal solution. Industrial applications have proven the method effectiveness.     

Multi-objective partial disassembly optimization based on sequence feasibility

A sustainable manufacturing system integrates production systems, consumer usage behavior, and End-of-Life (EoL) product value recovery activities. Facilitating multi-objective disassembly planning can be a step toward analyzing the tradeoffs between the environmental impact and profitability of value recovery. In this paper, a Genetic Algorithm (GA) heuristic is developed to optimize partial disassembly sequences based on disassembly operation costs, recovery reprocessing costs, revenues, and environmental impacts. EoL products may not warrant disassembly past a unique disassembly level due to limited recovered component market demand, minimal material recovery value, or minimal functional recovery value. The effectiveness of the proposed GA is first verified and tested using a simple disassembly problem and then applied to the traditional coffee maker disassembly case study. Analyses are disaggregated into multiple disassembly network optimization problems, one for each product subassembly, resulting in a bottom-up approach to EoL product partial disassembly sequence optimization.     

Disassembly sequence structure graphs: An optimal approach for multiple-target selective disassembly sequence planning

Modern green products must be easy to disassemble. Specific target components must be accessed and removed for repair, reuse, recycling, or remanufacturing. Prior studies describe various methods for removing selective targets from a product. However, solution quality, model complexity, and searching time have not been considered thoroughly. The goal of this study is to improve solution quality, minimize model complexity, and reduce searching time. To achieve the goal, this study introduces a new ‘disassembly sequence structure graph’ (DSSG) model for multiple-target selective disassembly sequence planning, an approach for creating DSSGs, and methods for searching DSSGs. The DSSG model contains a minimum set of parts that must be removed to remove selected targets, with an order and direction for removing each part. The approach uses expert rules to choose parts, part order, and part disassembly directions, based upon physical constraints. The searching methods use rules to remove all parts, in order, from the DSSG. The DSSG approach is an optimal approach. The approach creates a high quality minimum-size model, in minimum time. The approach finds high quality, practical, realistic, physically feasible solutions, in minimum time. The solutions are optimized for number of removed parts, part order, part disassembly directions, and reorientations. The solutions remove parts in practical order. The solutions remove parts in realistic directions. The solutions consider contact, motion, and fastener constraints. The study also presents eight new design rules. The study results can be used to improve the product design process, increase product life-cycle quality, and reduce product environmental impact.     

Economical green product design based on simplified computer-aided product structure variation

Environmental issues have become an imperative concern for most companies in relation to modern product development. Special procedures have to be taken during the product development process to comply with recent green directives. Product structure is recognized as a critical factor that provides effective means for reducing environmental impact in product end-of-life. However, most previous studies failed to leverage the vast latitude at the design stage due to the assumption of a fixed product structure. To overcome this deficiency, we propose a CAD-based approach that allows automatic variation of 3D product structure by means of changing the combination of parts, selecting the assembly method, and rearranging the assembly sequence. A computing scheme uses Genetic Algorithm (GA) techniques to produce an optimal product structure from the design alternatives generated by the approach. This corresponds to lower assembly/disassembly costs, while complying with specified recycling and recovering rates. The scheme also chooses a smaller set of parts that needs to be disassembled and determines an economical disassembly process. Implemented in a commercial CAD system, the test results demonstrated the effectiveness of this scheme in green product design in a cost-effective manner.