A green assembly sequence planning model with a closed-loop assembly and disassembly sequence planning using a particle swarm optimization method

In a green product life cycle, it is necessary to determine how to disassemble a product before the product is planned to be assembled. In this research, a green assembly sequence planning model is developed. A closed-loop assembly sequence planning model is presented by integrating assembly and disassembly sequence planning models. For an assembled product, an assembly sequence planning model is required for assembling the product at the start, while a disassembly sequence planning model is performed for disassembling the product at the end. In typical assembly and disassembly sequence planning approaches, the two sequences are independently planned and evaluated. In this paper, a closed-loop model is presented to concurrently generate and evaluate the assembly and disassembly sequences. First, a graph-based model is presented for representing the feasible assembly sequences and disassembly sequences. Next, a particle swarm optimization (PSO) method with a new encoding scheme is presented. In the PSO method, the assembly and disassembly sequences can be simultaneously represented and evaluated with an objective of minimizing the total of assembly costs and disassembly costs. The test results show that the presented method is feasible and efficient for solving the integrated assembly and disassembly sequence planning problem. An example product is illustrated and discussed.     

Unification of the a priori inconsistencies checking among assembly constraints in assembly sequence planning

Sequence planning generation is an important problem in assembly line design. A good assembly sequence can help to reduce the cost and time of the manufacturing process. This paper focuses on assembly sequence planning (ASP) known as a hard combinatorial optimization problem. Although the ASP problem has been tackled via even more sophisticated optimization techniques, these techniques are often inefficient for proposing feasible assembly sequences that satisfy the assembly planners’ preferences. This paper presents an approach that makes easier to check the validity of operations in assembly process. It is based on a model of the assembly planners’ preferences by means of strategic constraints. It helps to check a priori the consistency of the assembly constraints (strategic and operative constraints) given by the assembly system designers before and while running an assembly plan generation algorithm. This approach reduces the solution space significantly. A case study is presented to demonstrate the relevance of the proposed approach.     

Imperialist competitive algorithm for assembly sequence planning

Automated generation of all feasible assembly sequences for a given product is highly desirable in manufacturing industry. Many research studies in the past decades described efforts to find more efficient algorithms for assembly sequence planning. Imperialist competitive algorithm for assembly sequence planning is presented in this paper. Population individuals called countries are in two types: colonies and imperialists that all together form some empires. Each assembly sequence is encoded into the country. The proposed algorithm is tested and compared with genetic algorithm and particle swarm optimization. Results show that imperialist competitive algorithm can improve the quality in solution searching and upgrade the opportunity to find optimal or near-optimal solution for assembly sequence planning.     

Integrated assembly and machining planning for electronic products using a genetic algorithm approach

To produce an electronic product, both assembly operations and machining operations are required in the process plan. In most cases, the assembly operations and machining operations need to be combined in a continued order with an integrated sequence. This is different from the traditional process planning approaches in which machining operations and assembly operations are separated as two independent tasks with no interactions. For an electronic product, the two types of operations and the associated costs may affect each other in an interactive way. Therefore, the sequence planning of assembly operations and machining operations must be analyzed with an integrated model. In this research, a graph-based model is presented to represent the assembly and machining operations in an integrated model. The related operation cost functions are developed to evaluate the costs for the integrated assembly and machining sequences. The integrated sequence planning problem is solved using a genetic algorithm approach with an objective of lowest operation costs. As a result, the assembly operations and machining operations can be planned in an integrated sequence suitable for producing electronic products. The result shows that the developed method using the genetic algorithm approach is efficient for solving the integrated sequence planning problem. Example products are demonstrated and discussed.     

Integrated knowledge-based assembly sequence planning

This paper presents a novel approach and system for the automatic generation, selection and evaluation, optimisation, and simulation of assembly plans. The information and knowledge about a product and its assembly (e.g. assembly constraints, solid model and CAD database, heuristic rules) are described using a hybrid approach and model with numeric and symbolic representation. A new methodology is presented to generate all feasible assembly sequences of the product by reasoning and decomposing the feasible subassemblies, and representing them by the assembly Petri net modelling. Qualitative strategic constraints are then used to evaluate the feasible assembly sequences. In order to obtain a good assembly sequence, some quantitative criteria such as assembly time and cost, workstation number, operator number, and part priority index are applied to select the optimal assembly sequence. Based on DFA analysis, MTM time analysis, and assemblability analysis, estimates are made of the assembly time and cost of the product when each of these sequences is used. A knowledge-based system KAPSS has been developed to achieve the integration of generation, selection evaluation, and visualisation of the assembly sequences.     

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 effective integration approach toward assembly sequence planning and evaluation

An integration strategy for assembly sequence planning and sequence scheme evaluation is proposed. This strategy can be used to plan a reasonable assembly sequence, to optimize a sequence scheme, and to predict whether a collision will occur between the assembly tool and assembled components by considering factors like target components and assembly resources. A hybrid method is presented for assembly sequence modeling that combines human-computer interactive operations to manually build a hierarchical assembly sequence main model and a hybrid graph method to automatically generate sub-assembly sequence schemes of the main model. An optimization algorithm based on time-cost is introduced to handle a best candidate components selection. This relieves the problem of limited capability found when handling large size assembly models with traditional methods. The essential issues involved in system implementation are discussed as well; these include a representation method for the assembly consequence model, an optimization model of assembly sequence planning, and an object-oriented system architecture model employed with multi-agent technology for visually evaluating the assembling process. This system, KM computer-aided assembly process planning, KMCAAPP, has been developed on the basis of our previous work, KMCAD3; KMCAAPP uses the presented approach. KMCAAPP can be integrated with CAD model from KMCAD3D. A case study shows that the presented approach can use large CAD assembly models and delivers a feasible and effective way to integrate the assembly sequence planning process with scheme evaluation by visually evaluating the assembling process. This allows the identification of design errors in a timely manner and mitigates economic loss.     

An integrated method for block assembly sequence planning in shipbuilding

Many efforts in the past have been made to find more efficient methods for assembly sequence planning in machining area. While few researches reported in other area such as block assembly in shipbuilding industry. In general, a ship hull is built with hundreds of different blocks, most of which are complicated in structure and different from each other in assembly planning. Additionally, there may be a large number of feasible assembly sequences for any block. A better sequence can help to reduce the cost and time of the manufacture. Therefore, it is necessary to seek out the optimal sequence from all feasible ones. Currently, the assembly sequences are determined manually by some process engineers. Consequently, it is becoming a time-consuming task and cannot make the assembly plan consistent to improve productivity. In this paper, a methodology-integrated case-based reasoning and constraints-based reasoning is proposed to improve the assembly planning for complicated products. Besides, genetic algorithm is designed to evaluate and select the optimal sequence automatically from the reference ones. The validity of the method is tested using real blocks, and the results show that it can facilitate the optimal assembly sequences generation.     

A model-based approach to assembly sequence planning

This paper presents a systematic approach for automatic assembly sequence planning (ASP) by using an integrated framework of assembly relational model (ARM) and assembly process model (APM), which are established by object-oriented method. ARM, consisting of assembly, components and liaisons, is the static model of assembly to describe the geometric relationships between components in terms of contact, constraint and interference matrixes. APM, containing states and sub-processes, is the dynamic model of assembly to represent all potential alternatives in assembly process. An algorithmic procedure is also presented by which APM of an assembly can be constructed systematically by analyzing states from lower levels to higher levels. Through the procedure, all feasible sequences of a product can be generated, and the optimum one can be determined by further evaluating each sequence. To explain the approach more clearly, a practical assembly with five components is used as an illustration in the paper.     

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     

基于遗传算法的飞机装配序列规划优化方法

在基于割集的装配顺序规划方法中，随着零件的增加，装配序列数呈指数增加，由此导致算法的组合爆炸问题。为此，提出了基于．遗传算法的装配序列规划优化方法，研究了零件装配信息的基因组表示方法，将参与装配的零件的基因组排列成序，实现装配顺序的染色体表示。采用模糊集理论建立了适应度函数，并对装配序列进行评价和优化。以某型飞机机翼产品为例，验证了技术的有效性。     

基于知识的智能装配规划系统

为了保证装配工艺的合理性和实用性,提出了基于知识的智能装配规划方法。该方法利用联接件装配工艺的特点和知识,进行装配序列规划,建立了基于联接件知识的装配实例库,生成装配序列的途径有典型实例的匹配、标准实例的匹配和运用有向联接件知识的装配序列规划。为了使产品的装配周期最短,并提高装配系统的柔性和可靠性,提出了基于多代理的装配任务规划方法,该方法综合考虑了装配操作时间和装配工具变换时间等因素。     

A novel ant colony algorithm for assembly sequence planning

An ant colony algorithm-based approach to assembly sequence generation and optimization of mechanical products is presented in this article. For diverse assemblies, the approach generates different amount of ants cooperating to find optimal solutions with the least reorientations during assembly processes. Based on assembly by disassembly philosophy, a candidate list composed by feasible and reasonable disassembly operations that are derived from disassembly matrix guides sequences construction in the solution space expressed implicitly, and so guarantees the geometric feasibility of sequences. The state-transition rule and local- and global-updating rules are defined to ensure acquiring of the optimal solutions. Cases are given to show the effectiveness of the proposed approach, and the characteristics of the algorithm are discussed.     

基于复合装配图进行装配序列规划的研究

为了构建一个有效且便于使用的装配序列规划系统,给出了一个开放式装配模型———复合装配图,以有向装配连接图表述零件之间的优先关系,利用检查有向圈、删除冗余有向边确保该图正确、简洁。在此基础上,用无向边连接没有优先关系的零件,并根据装配代价和装配可行性对有向边、无向边赋权值,得到复合装配图。将装配序列规划描述为在复合装配图中求解特殊的最优H回路,并且基于分支定界法给出了满足优先关系、装配可行性、装配代价优化的装配顺序规划算法。最后通过实例验证了算法的有效性。     

装配序列规划问题求解的一种混合算法

基于蚂蚁算法和的遗传算法特点,给出了一种解决装配序列规划问题的遗传和蚂蚁混合算法.混合算法中利用蚂蚁的每一次周游,快速生成问题的一组可行解,用遗传算法对得到的可行解进行快速优化,并根据优化解的质量,生成路径上的信息素分布,以加速蚂蚁最优路径上信息素的积累,从而引导蚂蚁更快地搜索到问题的最优解.实验结果表明,混合算法在装配序列规划问题求解上具有更好的性能.     

A knowledge-based engineering system for assembly sequence planning

In this study, we developed a knowledge-based engineering (KBE) system to assist engineers in promptly predicting a near-optimal assembly sequence. A three-stage assembly optimization approach with some heuristic working rules was employed to establish the proposed system. In the first stage, Above Graph and a transforming rule were used to create a correct explosion graph of the assembly models. In the second stage, a three-level relational model graph, with geometric constraints and assembly precedence diagrams, was generated to create a completely relational model graph, an incidence matrix, and a feasible assembly sequence. In the third stage, a robust back-propagation neural network engine was developed and embedded in the Siemens NX system. System users can easily access the volume, weight, and feature number through the Siemens NX system interface, input the related parameters such as contact relationship number and total penalty value, and predict a feasible assembly sequence via a robust engine. Three real-world examples were used to evaluate the feasibility of the KBE system. The results show that the proposed system can facilitate feasible assembly sequences and allow designers to recognize contact relationships, assembly difficulties, and assembly constraints of three-dimensional components in a virtual environment type.     

多细节层次装配模型及序列规划

为了建立支持自顶向下的产品设计系统 ,开发一个自顶向下设计的装配模型和相应的高效、快速的装配规划方法 ,通过深入分析自顶向下的设计流程 ,提出了支持自顶向下设计的多细节层次装配模型。该模型中的超零件模型和简化零件模型是对装配体细节的不同描述 ,是装配体中零件几何信息的简化表达。利用多细节层次模型 ,对自上而下、分层分步的装配序列规划以及装配序列规划算法和复杂性进行了分析。应用实例表明 ,该方法避免了装配规划中复杂模型的庞大数据量问题 ,可以提高装配规划的推理效率 ,加快装配仿真中的碰撞检测速度 ,使装配规划的结果更加合理和实用     

Concurrent and collaborative process planning for automotive general assembly

To ensure competitiveness in today’s automotive market, process planning should be performed concurrently with other engineering activities during new car development. In this paper, new business workflows and the support system for process planning of an automotive general assembly are proposed and presented. Since process planning for a whole general assembly shop is a huge and complex job, several process planners should execute planning together. Therefore, planners should consult with one another regarding his/her results through continuous online communication and cooperation. Process planning based on computers, database and Internet is inevitable for reducing manufacturing preparation time and costs through concurrent and collaborative engineering. In this research, the Web-based process planning system for general assembly shops using the bound operation method is implemented and proven. By using this system, savings in time and cost of assembly process planning are possible, and the reliability of the planning results is greatly improved.     

Concurrent and collaborative process planning for automotive general assembly

To ensure competitiveness in today’s automotive market, process planning should be performed concurrently with other engineering activities during new car development. In this paper, new business workflows and the support system for process planning of an automotive general assembly are proposed and presented. Since process planning for a whole general assembly shop is a huge and complex job, several process planners should execute planning together. Therefore, planners should consult with one another regarding his/her results through continuous online communication and cooperation. Process planning based on computers, database and Internet is inevitable for reducing manufacturing preparation time and costs through concurrent and collaborative engineering. In this research, the Web-based process planning system for general assembly shops using the bound operation method is implemented and proven. By using this system, savings in time and cost of assembly process planning are possible, and the reliability of the planning results is greatly improved.     

基于改进蚁群算法的装配序列规划研究

装配序列规划能解决装配过程中所出现的速度和质量问题,降低生产成本。通过考虑装配影响因素,对装配体零件之间关系进行数学建模,提出一种解决该问题的改进蚁群算法。算法提出用加强装配关系矩阵来表示零件之间存在的强约束关系;用加强随机状态转移规则,使蚂蚁能更快地寻找到最优或者近优解;完善信息素更新规则,使算法能更好的利用正反馈机制。通过实例证明,该改进型算法能实际有效地解决装配序列规划问题。