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.     

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。     

基于模块化思想的拆卸序列规划

针对在产拆卸序列生成过程中因零件数目过多面造成的组合爆炸问题,提出了一种基于模块化思想的拆卸序列生成方法;建立了模块化产品拆卸模型;自动生成了产品的层次网络图;并针对层次网络图进行了优先约束分析．文中介绍了产品拆卸序列的生成过程,并以一个实例验证了该方法的合理性和有效性．     

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.     

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.     

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.     

基于蚁群算法的产品拆卸序列规划方法

拆卸是回收的前提,为了得到最大的回收效益,对拆卸序列进行规划,得到最优的拆卸序列.根据拆卸的特点构建适合计算和优化的产品拆卸混合图模型,该模型描述了零部件之间的连接关系和优先关系.然后通过几何推理方法产生所有可行的拆卸序列,建立目标函数并构建适合拆卸序列规划的蚁群算法:设计了满足连接关系和优先关系的可拆卸零件搜索空间,得到最优或接近最优的拆卸序列.最后通过实例验证了该方法的实用性和可行性.     

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。     

基于改进蚁群算法的维修拆卸序列规划

维修拆卸序列规划是整个维修性设计的重要内容。为了能够以较高的效率求解出产品中零件的拆卸方案,依据产品的基本信息和零件之间的约束关系,建立拆卸Petri网可达图,将拆卸序列规划问题转化为对Petri网可达图最优路径的搜索和寻优问题。同时利用蚁群优化算法对组合优化具有高强适应性的特征,改进基本蚁群算法,对可达图模型进行路径寻优,得到最优或次优的拆卸序列。最后通过实例验证了该方法的有效性。     

支持再制造的选择性并行拆卸序列规划方法

选择性并行拆卸是再制造的关键步骤,为提高再制造拆卸效率,提出一种基于遗传算法的选择性并行拆卸序列规划方法.根据产品拓扑结构和拆卸优先级关系构建了产品拆卸混合图模型,并表示为关联矩阵、邻接矩阵和约束矩阵;提出了目标驱动推演法的目标组件初始拆卸解集合获取方法,由此构建初始种群,并利用关联矩阵剔除不合理解,以提高算法的收敛速度;改进了选择、交叉、变异等染色体进化规则,由此求出了目标组件(近似)最优的并行拆卸序列.最后,通过实例验证了该方法的可行性和有效性.     

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.     

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.     

A framework for virtual disassembly analysis

Product reuse or recyclability is enhanced by designing the product for inexpensive and efficient disassembly. However, accomplishing enhanced product design requires design for disassembly (DFD) tools. This paper presents a disassembly framework that consists of design modules; both of these are embodied in the geometric DFD tool. These modules consist of different tasks including: selection of the appropriate disassembly method; producing an optimized disassembly sequence; evaluating a disassembly sequence for cost; producing design change recommendations. These considerations make a product easier to disassemble and therefore have potential benefit to the environment.     

Disassembly planning and sequencing for end-of-life products with RFID enriched information

When a product reaches its end of lifecycle, components of the product can be reused, recycled, or disposed, depending on their conditions and recovery value. In order to make an optimal disassembly plan to efficiently retrieve the reusable and recyclable items inside a product, knowing the true condition of each component is essential. Practically, the recovery value of a used product is often estimated roughly via visual inspection, and the inaccurate estimates would lead to suboptimal disassembly plans. This paper proposes the use of radio-frequency identification (RFID) technology to support disassembly decisions for end-of-life products. RFID can track pertinent data throughout a product’s lifecycle. With the enriched information, a fuzzy-based disassembly planning and sequencing model is proposed to maximize net profit. First, a Bayesian method translates the RFID data into a quality index of the components. Then, a fuzzy logic model, solved by genetic algorithm, synthesizes input variables (i.e., product usage, component usage, and component condition) into a solution of optimal disassembly sequence that maximizes profit considering recovery value and disassembly cost. This paper verifies the merits of using RFID to improve disassembly decisions that help reuse and recycle end-of-life products to reduce environmental impact.     

A virtual prototyping approach to product disassembly reasoning

An important aspect of Design for the Life Cycle is assessing the disassemblability of products. After an artifact has completed its useful life, it must be disassembled then recycled, remanufactured or scrapped. Disassemblability of a product can be evaluated by performing disassembly activities on prototypes. Virtual prototyping (VP) is an alternative to hardware prototyping in which analysis of designs can be done without manufacturing physical samples. In recent years, disassembly processes have been generated either by using interactive or automated approaches, but these approaches have limitations. Interactive approaches require extensive user input usually in the form of answering questions, whereas automated approaches can only be used to generate disassembly processes for products with simple component configuration and geometry. In this paper automated and interactive techniques are combined, using VP, to generate complete disassembly processes of a product design. To support generation of disassembly processes of a product, a virtual environment and VP method were developed that will support disassembly activities performed by a designer. The product model of the virtual prototype is generated from the CAD model. The disassembly process model for the prototype is generated using automated reasoning techniques and is completed by interactively disassembling the product in the virtual environment. Extensions to automatic reasoning techniques to compute ranges of feasible directions of component removal were developed to facilitate the generation of the disassembly process. A scheme to represent the disassembly process for disassemblability evaluation was developed and implemented. In this paper a Chrysler LHS center console has been used to illustrate our approach of generating disassembly processes via VP.     

A case-based reasoning approach for automating disassembly process planning

One of the first processes for preparing a product for reuse, remanufacture or recycle is disassembly. Disassembly is the process of systematic removal of desirable constituents from the original assembly so that there is no impairment to any useful component. As the number of components in a product increases, the time required for disassembly, as well as the complexity of planning for disassembly rises. Thus, it is important to have the capability to generate disassembly process plans quickly in order to prevent interruptions in processing especially when multiple products are involved. Case-based reasoning (CBR) approach can provide such a capability. CBR allows a process planner to rapidly retrieve, reuse, revise, and retain solutions to past disassembly problems. Once a planning problem has been solved and stored in the case memory, a planner can retrieve and reuse the product's disassembly process plan at any time. The planner can also adapt an original plan for a new product, which does not have an existing plan in case memory. Following adaptation and application, a successful plan is retained in the case memory for future use. This paper presents the procedures to initialize a case memory for different product platforms, and to operate a CBR system, which can be used to plan disassembly processes. The procedures are illustrated using examples.     

考虑零件功能性削减的多目标拆卸方案决策

为解决零件价值高低界限模糊、废旧产品拆卸深度不合理的问题,提出一种考虑零件功能性削减的产品多目标拆卸方案决策方法。分析零件具体损伤对拆卸工具、方向、约束状态的影响,建立损伤情况下的拆卸信息模型;结合各损伤形式对零件接触面功能性的削减作用,提出一种考量零件在产品中综合重要性的剩余价值排序方法,并由此建立一种动态的拆卸目标件选取模型;面向拆卸过程的经济性,建立包含“价值构成”和“成本构成”的目标函数,采用自适应遗传算法进行序列规划求解,并反向决策零件的回收去向;以受损的减速器为例,获得产品的最佳拆卸深度和各零件的回收去向,充分挖掘零件的利用价值,验证所提方法的经济性与科学性。     

Adaptive disassembly sequence control by using product and system information

This contribution will introduce a conception for a product-driven control sequence generation by using information derived from a product accompanying information system, called Life Cycle Unit. The algorithm being developed for the sequence generation takes into account the availability and technological feasibility of disassembly processes, the actual disassembly system condition, and the actual disassembly step of a product. In order to verify the functionality of the developed algorithm for sequence generation an emulation environment is introduced. An actually existing hybrid disassembly system for home appliances serves as an example for the control sequence generation.     

Disassembly line balancing under high variety of end of life states using a joint precedence graph approach

Disassembly is an important aspect of end of life product treatment, as well as having products disassembled in an efficient and responsible manner. Disassembly line balancing is a technique that enables a product to be disassembled as efficiently and economically viable as possible; however, considering all possible end of life (EOL) states of a product makes disassembly line balancing very difficult. The EOL state and the possibility of multiple recovery options of a product can alter both disassembly tasks and task times for the disassembly of the EOL product. This paper shows how generating a joint precedence graph based on the different EOL states of a product is beneficial to achieving an optimal line balance where traditional line balancing approaches are used. We use a simple example of a pen from the literature to show how a joint disassembly precedence graph is created and a laptop example for joint precedence graph generation and balancing. We run multiple scenarios where the EOL conditions have different probabilities and compare results for the case of deterministic task times. We also consider the possibility where some disassembly task times are normally distributed and show how a stochastic joint precedence graph can be created and used in a stochastic line balancing formulation.