Guided genetic algorithms for solving a larger constraint assembly problem

Multi-sequence modelling is proposed to optimise assembly sequences for compliant body assemblies such as automotive and aircraft bodies. Multi-sequence modelling is composed of assembly modelling, assembly sequences, tolerance analysis, and assembly operations. Assembly modelling describes the geometric modelling through the liaison graph, which is based on assembly sequence optimisation. Assembly sequences provide information for assembly sequence optimisation. Three-dimensional tolerance analysis is employed to evaluate assembly operations, which is different from previous conceptual tolerance analysis. A genetic algorithm is presented to optimise assembly operations among components. Results show that different sequences lead to a different tolerance of key product characteristics because assembly operations among components are not identical. This paper provides the flowchart of optimising assembly sequences according to the tolerance of key product characteristics.

If a product is made of more than one component, then it needs to be assembled. Complexity of assembling a product is largely subject to the implication of product design as well as the number of assembly components that may also affect attainable assembly sequences. In addition, it is widely understood that efficiency of assembling a product can be partially achieved by reducing assembly times (therefore costs) and this is notably important for small and medium-sized manufacturing companies. Thus, it is useful for seeking an optimal assembly sequence of a product at the early product design stage. This paper presents a study by applying the genetic algorithm (GA) for solving an assembly sequence optimisation problem of a car engine pump valve providing a quick solution in obtaining an optimal or near-optimal assembly sequence of the product.

Assembly sequence planning is a typical of combinatorial optimization problem which is difficult to be tackled when the number of parts of assembly becomes large. To reduce the searching space of assembly sequence planning of complex products, assembly sequences merging based on assembly unit partitioning is suggested. Assembly unit partitioning is presented to decompose the complex products into a group of assembly units containing a reduced number of parts or components, and the assembly design constraints and the assembly process constraints are comprehensively taken into account. The global optimal assembly sequences can be acquired through three steps. Firstly, the assembly units and decision graph of assembly unit are generated utilizing fuzzy analytical hierarchy process approach. Secondly, the optimal or near-optimal subsequences of assembly units can be obtained with current efficient methods of assembly sequence planning. Thirdly, under the assembly interference of assembly relations (geometrical constraints) of the whole products and the assembly precedence concluded by subsequences of assembly units, the assembly sequence merging is implemented to generate the global assembly sequences, and the optimal sequence is obtained through assembly sequences evaluation. The assembly constraints considered at the two previous steps is represented by the evaluation function. The effectiveness of the method is verified by an illustrative example and the results show that the searching space of assembly sequence merging of complex products is reduced remarkably and the optimal assembly sequence of the whole produces is obtained.

A tree-model for assembly sequences planning is proposed after analyzing the characteristic of assembly sequence evaluation, and the generation algorithm of the tree-model is proposed. Based on the tree-model of assembly sequence, the two-phase method for synthetically evaluating and optimizing assembly sequence layer by layer is expounded. Weights of all evaluation indexes are calculated by using the entropy method. Then, by using the fuzzy comprehensive evaluation, the assembly sequence of all data sub-nodes under A or AP in every layer of the assembly sequence tree-model is optimized. Finally, the optimal assembly sequence is generated. Using this method, the calculation extension of the assembly sequence is reduced, and the evaluation efficiency is heightened. The theory is validated by an example of gear reducer.

Nowadays in order to perform assembly process planning by using CAPP (Computer Aided Process Planning), researches to generate the assembly sequence have come under constant interest by many researchers and they are devoted to optimize the assembly sequence according to the novel approach such as Genetic Algorithm and the newly biology inspired principle such as ant colony optimizing method. In this paper, the developed assembly sequence generating system analyzes the relationship between assembled parts based on surface contacting information. Moreover, this system generates the new type of parts liaison graph for the assembly sequences via analyzed information such as common area between parts, related ratio and number of the connected parts. Finally, the optimized assembly sequence is generated logically through the parts liaison graph.KeywordsAssembly SequenceLiaison GraphRelated RatioCommon Area

Facing a significant decrease in economic working processes, Off-Site Construction (OSC) methods have been frequently adopted in response to challenges such as declining productivity and labor shortages in the construction industry. Currently, in most OSC applications, the assembly phase is traditionally managed based on the personal experience and judgment of the site managers. This approach can lead to inaccuracies or omissions, particularly when dealing with a large amount of information on large, complex construction sites. Additionally, there are limitations in exploring more efficient and productive alternatives for rapidly adapting to changing on-site conditions. Given that the assembly phase significantly affects the OSC productivity, a systematic management approach is crucial for expanding OSC methods. Some initial studies used computer algorithms to determine the optimal assembly sequences. However, these studies often focused on geometrical characteristics, such as component weight or spatial occupancy, neglecting crucial factors in actual site planning, such as the work radius and component installation status. Moreover, these studies tended to prioritize the generation of initial assembly sequences rather than providing alternatives for adapting to evolving on-site conditions. In response to these limitations, this study presents a systematic framework utilizing a Building Information Modeling (BIM)–Genetic Algorithm (GA) approach to generate Precast Concrete (PC) component installation sequences. The developed system employs Genetic Algorithms to objectively explore diverse assembly plans, emphasizing the flexibility of accommodating evolving on-site conditions. Real on-site scenarios were simulated using this framework to explore multiple assembly plan alternatives and validate their applicability. Comprehensive interviews were conducted to validate the research and confirm the system’s potential contributions, especially at just-in-time-focused PC sites. Acknowledging a broader range of variables such as equipment and manpower, this study anticipates fostering more systematic on-site management within the context of a digitized construction environment. The proposed algorithm contributes to improving both productivity and sustainability of the construction industry by optimizing the management process of the off-site construction projects.

Currently, neither the efficiency nor the effectiveness is sufficient in the area of the assemble optimization that commonly involves the genetic algorithm. A novel method to solve the cumbersome problem in the optimization of assembly sequences was proposed. On the basis of the assembly constraint matrix, the optimized assembly sequence is obtained with the proposed evaluating factors of the process requirement. That is the evolution of the original genetic algorithm to a certain extent. The effectiveness of the proposed method was proved by the comparison with the ant colony algorithm.

The invention relates to a fixture assembly sequence planning method based on an ant colony algorithm. The fixture assembly sequence planning method comprises the following steps: firstly carrying out product assembly modeling based on a directed graph, thus establishing a foundation for solving assembly sequence planning by using the ant colony algorithm later; constructing the ant colony algorithm oriented to the fixture assembly sequence planning according to the fundamental principles of the ant colony algorithm, utilizing the advantages of the ant colony algorithm to optimize the assembly sequence of fixtures, using redirection times and tool change times as evaluation indexes, and searching out the optimum assembly sequence in numerous feasible assembly sequences; using Matlab programming to realize the optimization procedure of the ant colony algorithm on the fixture assembly sequences to obtain the optimum assembly sequence. The fixture assembly sequence planning method can improve the design efficiency of enterprises, reduces the production cost, shortens the production period, and enhances the market competitiveness.

Assembling a product is a delicate process at the borderline between design and manufacturing. Shortening the time between them plays a central role both for quality assurance and fast time to market. In this paper, we describe an automatic tool based on a new method, integrating assembly design, sequence optimization, and advanced rigid body path planning. First, we introduce a greedy algorithm for assembling a product, part by part, based on state-of-the-art path planning. We exploit all the six degrees of freedom of a rigid body to search for collision-free paths, instead of limited motions. Then, we use assembly design in order to limit the search for an optimal assembling sequence and to guarantee geometrical quality among the sequences examined. Disassembly path planning is used here to further cut the state space and to give a quality measure to the sequences. Eventually, we present results for an industrial test case, which has been successfully solved by applying our method.

Virtual assembly, as a typical application of Virtual Reality (VR), provides an efficient tool to optimise assembly planning and evaluation. In this paper, a VR-based Assembly Planning and Training System (VR-APTS) is introduced. Using a Computer-Aided Design (CAD) assembly model as input, the system can output one or several good assembly sequences with the application of an intelligent bionic algorithm. Key techniques in the development of VR-APTS are discussed. A novel information decomposition and transformation method is put forward to transform data from CAD to VR, and a geometric constraint-based virtual environment is set up to support interactive assembly planning and evaluation. First, an initial optimal assembly sequence is generated by virtual disassembly and ant colony optimisation algorithm. Then, this initial sequence is evaluated by virtual assembly simulation, a more practical and optimal assembly sequence is replaned. This is an iterative process until the best assembly sequence is obtained. An application case is studied to demonstrate the basic advantages of the system for assembly planning and training.

The authors propose the implementation of hybrid Fuzzy Logic-Genetic Algorithm (FL-GA) methodology to plan the automatic assembly and disassembly sequence of products. The GA-Fuzzy Logic approach is implemented onto two levels. The first level of hybridization consists of the development of a Fuzzy controller for the parameters of an assembly or disassembly planner based on GAs. This controller acts on mutation probability and crossover rate in order to adapt their values dynamically while the algorithm runs. The second level consists of the identification of the optimal assembly or disassembly sequence by a Fuzzy function, in order to obtain a closer control of the technological knowledge of the assembly/disassembly process. Two case studies were analyzed in order to test the efficiency of the Fuzzy-GA methodologies.

PurposeAssembly sequence optimization is a difficult combinatorial optimization problem having to simultaneously satisfy various feasibility constraints and optimization criteria. Applications of evolutionary algorithms have shown a lot of promise in terms of lower computational cost and time. But there remain challenges like achieving global optimum in least number of iterations with fast convergence speed, robustness/consistency in finding global optimum, etc. With the above challenges in mind, this study aims to propose an improved flower pollination algorithm (FPA) and hybrid genetic algorithm (GA)-FPA.Design/methodology/approachIn view of slower convergence rate and more computational time required by the previous discrete FPA, this paper presents an improved hybrid FPA with different representation scheme, initial population generation strategy and modifications in local and global pollination rules. Different optimization objectives are considered like direction changes, tool changes, assembly stability, base component location and feasibility. The parameter settings of hybrid GA-FPA are also discussed.FindingsThe results, when compared with previous discrete FPA and GA, memetic algorithm (MA), harmony search and improved FPA (IFPA), the proposed hybrid GA-FPA gives promising results with respect to higher global best fitness and higher average fitness, faster convergence (especially from the previously developed variant of FPA) and most importantly improved robustness/consistency in generating global optimum solutions.Practical implicationsIt is anticipated that using the proposed approach, assembly sequence planning can be accomplished efficiently and consistently with reduced lead time for process planning, making it cost-effective for industrial applications.Originality/valueDifferent representation schemes, initial population generation strategy and modifications in local and global pollination rules are introduced in the IFPA. Moreover, hybridization with GA is proposed to improve convergence speed and robustness/consistency in finding globally optimal solutions.

Unique definition and classification of assembly feature are presented. Assembly feature's definition is provided using the concept of assembly intents. Assembly features classification is done based on useful assembly-specific information. Assembly features provide information that result in a smaller number of assembly sequences that have to be checked in assembly sequence optimisation. Due to the reduction in assembly sequences, AND/OR graph not only reduces considerably but also becomes very simple. This can result in a significant reduction in the computation time for assembly sequence planning. Real-life examples are given to demonstrate how assembly features are useful in sequence planning.

This paper presents a method for the automatic generation of assembly sequences from a liaison graph representation of an assembly through the recursive decomposition of assembly into subassemblies. In order to increase the planning efficiency, the proposed assembly planning system automatically identifies and avoids those decomposition that incur physically infeasible assembly operation. This is achieved by merging those parts that can not be mutually separable at the current stage of assembly planning due to interconnection infeasibility as well as functional dependency. The above merging process transforms the original liaison graph into an abstract liaison graph with smaller number of nodes. Then, weights are assigned to each liaison of abstract liaison graph based on the stability and structural connectivity associated with liaison such that these weights are used to extract the tentative subassemblies. To select preferred subassemblies, the extracted tentative subassemblies are evaluated based on the subassembly selection indices defined in terms of mobility, structural preference, stability, and parallelism. Furthermore, by adjusting the assembly coefficients of subassembly selection indices according to given assembly environment, an optimal assembly sequence can be generated. The application of the proposed planning system to the tabletop vise assembly is illustrated as an example. >

In recent years, many modern methods/techniques have been developed in manufacturing to meet customer needs. Even though many advanced methods have developed, still the cost of manufacturing a product has not come down; this is due to the assembly operation involved to make the final product. Researchers developed many methods to solve the ASP problem, out of which many of them results in local optima and consume more time during execution. In this paper, a new algorithm, namely the crab shell search algorithm (CSSA), is implemented to solve the ASP problem. The CSSA algorithm is developed based on the concept of crab occupying the shells to protect from the foreign bodies and the sun's heat. The proposed algorithm applied to the eleven-part transmission assembly and the 22 part pulley assembly to obtain the optimal assembly sequences, and the obtained results compared with the different well-known algorithms.