Optimising assembly planning based on virtual reality and bionic algorithm

The design and verification of assembly operations is essential for planning product production operations. Recently, virtual prototyping has witnessed tremendous progress, and has reached a stage where current environments enable rich and multi-modal interaction between designers and models through stereoscopic visuals, surround sound, and haptic feedback. The benefits of building and using Virtual Reality (VR) models in assembly process verification are discussed in this paper. In this paper, we present the virtual assembly (VA) of an aircraft turbine engine. The assembly parts and sequences are explained using a virtual reality design system. The system enables stereoscopic visuals, surround sounds, and ample and intuitive interaction with developed models. A special software architecture is suggested to describe the assembly parts and assembly sequence in VR. A collision detection mechanism is employed that provides visual feedback to check the interference between components. The system is tested for virtual prototype and assembly sequencing of a turbine engine. We show that the developed system is comprehensive in terms of VR feedback mechanisms, which include visual, auditory, tactile, as well as force feedback. The system is shown to be effective and efficient for validating the design of assembly, part design, and operations planning.

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.

Presents an experiment that investigated the potential benefits of virtual reality (VR) environments in supporting assembly planning. In the experiment, 15 subjects performed an assembly planning task in three different conditions: a traditional engineering (TE) environment, a nonimmersive desktop VR (DVR) environment, and an immersive CAVE (Cave Automatic Virtual Environment) VR (CVR) environment. The effects of the three conditions on the subjects' performance were analyzed. The subjects' performance time in the TE condition was significantly longer than that in the DVR condition and that in the CVR condition, whereas the difference in performance time between the DVR condition and the CVR condition was not significant. The total number of problematic assembly steps in the TE condition was significantly greater than that in the CVR condition. Specifically, the subjects' assembly sequences in the TE condition involved more reorientations than in the DVR condition. The number of difficult assembly steps in the TE condition was significantly greater than that in the DVR condition, which was significantly greater than that in the CVR condition. The number of dissimilar assembly steps in the TE condition was significantly greater than that in the CVR condition, which was significantly greater than that in the DVR condition. Hence, the results revealed advantages of the two VR environments over the TE environment in improving the subjects' overall assembly planning performance and in minimizing the handling difficulty, excessive reorientation and dissimilarity of assembly operations.

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. >

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.

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.

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.

Assembly sequence planning (ASP) and assembly line balancing (ALB) problems are two essential problems in the assembly optimization. This paper proposes an ant colony algorithm for integrating assembly sequence planning and assembly line balancing, to deal with the two problems on parallel, and resolve the possible conflict between two optimization goals. The assembly sequence planning problem and the assembly line balancing problem are discussed, the process of the proposed ant colony algorithm is investigated. The results can provide a set of solutions for decision department in assembly planning.

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.

A physically based approach with human–machine cooperation concept to generate assembly sequences

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.

Manufacturing industries are looking for efficient assembly planners that can swiftly develop a practically feasible assembly sequence while keeping costs and time to a minimum. Most assembly sequence planners rely on part relations in the virtual environment. Nowadays, tools and robotic grippers perform most of the assembly tasks. Ignoring the critical aspect renders solutions practically infeasible. Additionally, it is vital to test the feasibility of positioning and assembling components while employing robotic grippers and tools prior to their implementation. This paper presents a novel concept named by considering both part and tool geometry to propose “tool integrated assembly interference matrices” (TIAIMs) and a “tool integrated axis-aligned bounding box” (TIAABB) to generate practically feasible assembly sequence plans. Furthermore, the part-concatenation technique is used to determine the best assembly sequence plans for an actual mechanical component. The results show that the proposed approach effectively and efficiently deals with real-life industrial problems.

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.

Due to the complexities of calculations based on different representations of constituent parts in an assembly, the combined improvement of efficiency and precision for the riveting has seldom been studied by the sequence planning or dimensional engineering. This article develops an assembly sequence optimization method that simultaneously minimizes the path length and overall dimensional error for the solid riveting. The sequence is denoted by the order of classifications for the nodes around the rivet holes in the finite element model for the assembly. Ant colony algorithm is applied on the optimization by integrating two calculations, that is, the length calculation of riveting path and the calculation of the dimensional error related to rivet upsetting directions and assembly sequence. Results for three assemblies are presented which show a good degree of optimization in performance for different scales of assemblies with different numbers of rivets that share the same solid riveting process.

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.