Optimal Assembly Sequence Research Articles

Optimization design of assembly sequence for the proton exchange membrane fuel cell stacks with dimensional errors

Proton exchange membrane fuel cell (PEMFC) stacks are assembled by numerous bipolar plates (BPPs) and membrane electrode assemblies (MEAs). Component manufacturing errors are inevitably caused during the production process and brought into the stack, affecting the mechanical consistency of every cell. Aiming to improve the uniformity of the assembled stack effectively, this paper establishes a novel assembly deviation model based on the asymptotic homogenization (AH) method and proposes an assembly sequence optimization approach for the stack with dimensional errors using the genetic algorithm. The results reveal that dimensional errors significantly affect the equivalent mechanical properties of the stack. Reasonable matching of components with different types of dimensional errors is an effective method to avoid the cumulative negative impacts on the mechanical behavior of the stack. Statistically, the optimization of the assembly sequence can improve contact pressure uniformity in a 10-cell stack by more than 9.00%, while reducing the maximum contact pressure by 15.55%. This methodology is also applicable to improving the assembly uniformity of other stacked structures.

Research on the Digital Maintenance Construction of Hydropower Unit

Abstract At present, the single unit capacity of hydropower unit is increasing, and the problems of difficulty, high precision, large number of labors and high cost in the maintenance process of large unit are becoming increasingly prominent. With the proposal of intelligent construction of hydropower plant, digital maintenance of hydropower unit has become a research hotspot. This article proposes a construction idea for a digitized maintenance system for hydraulic units based on technologies such as RFID, IoT, and artificial intelligence. This system can modularly manage the unit components and realize the integration and interaction of unit maintenance information through the visualization platform. It utilizes IoT sensors to collect comprehensive information on the components involved in the maintenance process. The components’ information is managed in a hierarchical manner using device KKS coding and a hierarchical structure diagram of the hydraulic unit’s equipment components. Through a visualization platform and equipment knowledge base, the component information is processed and analyzed to generate optimal assembly sequences and paths, thus improving the scientific and rational aspects of the unit’s maintenance process. This approach provides theoretical support for maintenance operations and enhances overall work efficiency.

Hybrid Model of Fuzzy Logic and Genetic Algorithm for Product Assembly Sequence Optimization

Hybrid Model of Fuzzy Logic and Genetic Algorithm for Product Assembly Sequence Optimization

An Assembly Sequence Planning Method Based on Multiple Optimal Solutions Genetic Algorithm

Assembly sequence planning (ASP) is an indispensable and important step in the intelligent assembly process, and aims to solve the optimal assembly sequence with the shortest assembly time as its optimization goal. This paper focuses on modular cabin construction for large cruise ships, tackling the complexities and challenges of part assembly during the process, based on real engineering problems. It introduces the multiple optimal solutions genetic algorithm (MOSGA). The MOSGA analyzes product constraints and establishes a mathematical model. Firstly, the traditional genetic algorithm (GA) is improved in the case of falling into the local optimum when facing complex problems, so that it can jump out of the local optimum under the condition of satisfying the processing constraints and achieve the global search effect. Secondly, the problem whereby the traditional search algorithm converges to the unique optimal solution is solved, and multiple unique optimal solutions that are more suitable for the actual assembly problem are solved. Thirdly, for a variety of restrictions and emergencies that may occur during the assembly process, the assembly sequence flexible planning (ASFP) method is introduced so that each assembly can be flexibly adjusted. Finally, an example is used to verify the feasibility and effectiveness of the method. This method improves the assembly efficiency and the diversity of assembly sequence selection, and can flexibly adjust the assembly sequence, which has important guiding significance for the ASP problem.

Building an Information Modeling-Based System for Automatically Generating the Assembly Sequence of Precast Concrete Components Using a Genetic Algorithm

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.

Semantic knowledge-driven A-GASeq: A dynamic graph learning approach for assembly sequence optimization

In the context of an increasingly automated and personalized manufacturing mode, efficient assembly sequence planning (ASP) has emerged as a critical factor for enhancing production efficiency, ensuring product quality, and satisfying diverse market demands. To address this need, our study first transforms the assembly topology and process into a weighted precedence graph, wherein parts represent nodes, and the assembly interconnections between parts constitute weighted edges. Then, we formulate the quantitative models of semantic knowledge, encompassing three facets: assembly direction changes, assembly stability, and part assembly interference, and thus constructs a heuristic function. We propose a novel dynamic graph learning algorithm, i.e., assembly-oriented graph attention sequence (A-GASeq), utilizing the heuristic information as edge weights of the assembly graph structure to incrementally direct the search towards optimal sequences. The performance of A-GASeq is first evaluated utilizing three key metrics: area under the receiver operation characteristic curve (AUC), precision score, and time consumption. The results reveal the superiority of our model over competing state-of-the-art graph learning models using a real-world dataset. Concurrently, we apply the algorithm to actual industrial products of diverse complexity, thereby demonstrating its broad utility across different complex products and its potential for addressing complex assembly sequence planning problems in the field of smart manufacturing.

A Knowledge Graph-Based Approach for Assembly Sequence Recommendations for Wind Turbines

There are various forms of assembly data sources for wind turbines, which contributes to the lack of a unified and standardized expression. Moreover, the reusability of historical assembly data is low, which leads to the poor reasoning ability of a new product assembly sequence. In this paper, we propose a knowledge graph-based approach for assembly sequence recommendations for wind turbines. First, for the multimodal data (text in process manual, image of tooling, and three-dimensional (3D) model) of assembly, a multi-process assembly information representation model is established to express assembly elements in a unified way. In addition, knowledge extraction methods for different modal data are designed to construct a multimodal knowledge graph for wind turbine assembly. Further, the retrieval of similar assembly process items based on the bidirectional encoder representation from transformers joint graph-matching network (BERT-GMN) is proposed to predict the assembly sequence subgraphs. Also, a Semantic Web Rule Language (SWRL)-based assembly process items inference method is proposed to automatically generate subassembly sequences by combining component assembly relationships. Then, a multi-objective sequence optimization algorithm for the final assembly is designed to output the optimal assembly sequences. Finally, taking the VEU-15 wind turbine as the object, the effectiveness of the assembly process information modeling and part multi-source information representation is verified. Sequence recommendation results are better quality compared to traditional assembly sequence planning algorithms. It provides a feasible solution for wind turbine assembly to be optimized from multiple objectives simultaneously.

Graph Database and Matrix-Based Intelligent Generation of the Assembly Sequence of Prefabricated Building Components

The assembly of prefabricated components is a critical process in prefabricated building construction, influencing both progress and accuracy. However, the assembly sequence planning and optimization (ASPO) of prefabricated components have yet to receive sufficient attention from researchers, and current research has displayed limited automation and poor generalization capabilities. Therefore, this paper proposes a framework for intelligently generating assembly sequences for prefabricated components based on graph databases and matrices. The framework utilizes an adjacency matrix and interference matrix-based modeling method to comprehensively describe the connections and constraint relationships between components, enabling better evaluation of assembly difficulty during optimization. The graph database serves as the central hub for data exchange, facilitating component information storage, automatic querying, and summarization. The obtained assembly sequence and progress plan are fed back into the graph database. To accomplish assembly sequence optimization, a genetic algorithm based on the double-elite strategy is employed. Furthermore, the effectiveness of the proposed framework is validated through an actual engineering case. The results demonstrate that the framework can effectively find an optimal assembly sequence to mitigate the assembly challenge of a prefabricated building.

Optimal assembly sequence planning with tool uncertainties

Developing a practical and feasible assembly sequence plan for industrial applications is still challenging during product design and planning. Because of the need for more consideration of practical uncertainties, most of the assembly sequence plans generated by planners are inapplicable to real industrial-based problems. We have considered a crucial practical uncertainty attribute, tool uncertainties, in the current research. A novel approach has been proposed to generate optimal robotic assembly sequence plans by combining tool accessibility and part geometry. This research introduces two essential assembly tool accessibility attributes: tool-integrated geometric feasibility and tool-swept volume. In addition, the automatic extraction of tool accessibility attributes has been proposed. The proposed method is explained with a proper model and essential details such as liaison data, tool-integrated bounding box coordinates, tool-integrated geometric feasibility, and tool-swept volume data. Finally, the practical feasibility of the proposed method is verified for a different product configuration.

Research on Assembly Sequence Optimization Classification Method of Remanufacturing Parts Based on Different Precision Levels

Aiming at resolving the problem of low assembly accuracy and the difficulty of guaranteeing assembly quality of remanufactured parts, an optimization classification method for the assembly sequence of remanufactured parts based on different accuracy levels is proposed. By studying the characteristics of recycled parts, based on the requirement that the quality of remanufactured products not be lower than that of the assembly quality of new products, the classification selection matching constraints of remanufactured parts are determined, and the classification selection matching optimization models of remanufactured parts with different precision levels is established. An algorithm combining particle swarm optimization and a genetic algorithm is proposed to solve the model and obtain the optimal assembly sequence. Taking the remanufacturing assembling of a 1.4 TGDI engine crank and a connecting rod mechanism as an example, the comparison of quality data shows that this method can effectively improve the qualified rate of assembly, reduce the cost of after-sale claims, provide new theories and methods for remanufacturing enterprises that need hierarchical assembly, and provide effective guidance for the development of the remanufacturing industry.

Optimal robotic assembly sequence planning with tool integrated assembly interference matrix

Abstract 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.

Aero-Engine Rotor Assembly Process Optimization Based on Improved Harris Hawk Algorithm

Multi-stage disc rotor assembly is an important part of the aero-engine rotor manufacturing process. To solve the problem that excessive unbalance of assembly affects the vibration of the whole machine, this paper presents an optimization method for aero-engine rotor assembly balance based on an improved Harris Hawk algorithm. Firstly, the assembly sequence model of the single-stage disc blade and the phase assembly model of a multi-stage disc of the engine rotor is established. Secondly, by using the initial population generation based on dynamic opposing learning and the escape energy function of the non-linear logarithmic convergence factor, the search mechanism of the whale optimization algorithm is introduced in the global exploration, and the adaptive weight strategy and mutation strategy of the genetic algorithm is introduced in the development to improve the algorithm. Then, the effectiveness of the algorithm is verified by experiments and compared with particle swarm optimization, genetic algorithm, and Harris Hawk algorithm, the unbalance of the optimal blade assembly sequence is reduced by 91.75%, 99.82%, and 83.39%, respectively. The algorithm comparison and analysis are carried out for all disc-blade assembly optimization of the rotor. The optimal unbalance of the improved Harris Hawk optimization algorithm is reduced by 79.71%, 99.48%, and 54.92% on average. The unbalance of the algorithm in this paper is the best. Finally, the improved Harris Hawk algorithm is used to find the best assembly phase, and the optimized unbalanced force and moment are reduced by 84.22% and 98.05%, respectively. The results of this study prove that the improved Harris Hawk algorithm for aero-engine rotor assembly balance optimization can effectively reduce the unbalance of rotor disc blade assembly and rotor unbalance and provide a powerful solution for solving engine vibration.

Assembly Sequence Optimization of Spatial Trusses Using Graph Embedding and Reinforcement Learning

We consider a truss as a graph consisting of nodes and edges, and combine graph embedding (GE) and reinforcement learning (RL) to develop an agent for generating a stable assembly path for a truss with arbitrary configuration. GE is a method of embedding the features of a graph into a vector space. By using GE, the agent can obtain numerical information on neighboring members and nodes considering their connectivity. Since the stability of a structure is strongly affected by the relative positions of members and nodes, feature extraction by GE should be effective in considering the stability of a truss. The proposed method not only can train agents using trusses with arbitrary connectivity but also can apply trained agents to trusses with arbitrary connectivity, ensuring the versatility of the trained agents' applicability. In the numerical examples, the trained agents are verified to find rational assembly sequences for various trusses more than 1000 times faster than metaheuristic approaches. The trained agent is further implemented as a user-friendly component compatible with 3D modeling software.

Planning to Build Block Structures With Unstable Intermediate States Using Two Manipulators

The work is inspired by the assembly of Soma block puzzles. Soma block puzzles usually include unstable intermediate states that require additional support to maintain stability temporarily. In the puzzles’ solution manual, we can observe that designers consider the characteristics that humans have two hands and can avoid an unstable intermediate state by using one hand to support the finished component and using the other hand to assemble an upcoming workpiece. Motivated by human behavior, this paper develops a planner that automatically finds an optimal assembly sequence for a dual-arm robot to build a woodblock structure while considering various constraints and supporting grasps from a second hand. It uses the mesh model of wood blocks and the final assembly state to generate possible assembly sequences and evaluate the optimal assembly sequence by considering the stability, graspability, assemblability, and the need for a second hand. Especially, the need for a second hand is resolved when supports from worktables and other workpieces are not enough to produce a stable assembly. A second hand can hold and support the unstable components so that the robot can further assemble new workpieces until the structure state becomes stable again. The output of the planner includes the optimal assembly orders, candidate grasps, assembly directions, and the supporting grasps (if needed). The output can help guide a dual-arm robot to perform motion planning and thus generate assembly motion. Experiments using various blocks and structures show the effectiveness of the proposed planner. Note to Practitioners—The presented planner can generate an optimal assembly order for a large variety of structures like decoration accessories, furniture, home interiors, frames, etc., in the practices. They can also be used for scenarios that need stacking or piling up multiple objects. The generated optimal assembly order is more friendly to dual-arm robot systems than previous assembly planners that ignored the merits of robotic collaboration. Also, the proposed assembly planner generates the necessary information for the motion planner, such as grasp poses and optimal assembly directions. A motion planner can directly use the generated results to plan robotic assembly motion.. The proposed assembly planner is expected to significantly reduce human effort and increase the efficiency of robotic assembly lines.

Press-fit analysis of multilayer interference fit

Multilayer interference fits are widely used in the machinery industry, and their performance is closely related to assembly quality. Press-fit is the primary assembly method. The pre-deformation of the previous assembly step can significantly affect the stress distribution (stress concentration) and pressing force in subsequent assembly steps. Therefore, the wear degree of the contact interface and the assembly quality of the components are affected by the assembly sequence. The multilayer thick-walled cylinder theory (MTCT) cannot realize the accurate analysis of stress distribution and pressing force during assembly. Therefore, this paper optimizes the MTCT considering the resistant force generated by the non-contact regions and proposes an assembly matrix generation algorithm and an assembly sequence optimization algorithm. Compared with the simulation analysis, the optimized theoretical model can be used for the analysis of the stress distribution and pressing force of multilayer interference components. The analysis accuracy is significantly improved compared with MTCT. The optimal assembly sequence determined by the proposed algorithm is consistent with simulation results.

Feasibility Study of ITER In-Vessel Coils Bracket Manufacture and Integration

Feasibility study of ITER In-vessel coils bracket manufacture and integration had been developed in Institute of Plasma Physics, Chinese Academy of Sciences. The ITER In-Vessel Coil system is comprised of Edge-Localized Mode (ELM) and Vertical Stabilization (VS) coils. The ELM coils are used to mitigate the Edge Localized Modes and the VS coils are used to provide Vertical Stabilization of the plasma. Designed bracket for IVC coils is a kind of building block type three or four stacked components with arcuate groove matching with round conductor. This paper describes structure design, manufacture and integration process of the ELM and VS bracket. R&D of bracket weld and assembly sequence optimization are carried out to determine the welding and assembly process. At last, three brackets are integrated in ASIPP.

Optimization of assembly sequence of building components based on simulated annealing genetic algorithm

A large number of prefabricated components are composed of different types of houses. The on-site assembly sequence of components is also the key link of the construction organization of the project. The assembly sequence obtained by manual experience is easy to omit components and interfere with each other. According to the weight and assembly process of construction components, the evaluation index of mechanical assembly process is introduced to clarify the relationship between the number of components and the assembly process. In the aspect of intelligent optimization algorithm, the simulated annealing program module is introduced into the existing genetic algorithm to form a new simulated annealing genetic algorithm. The comparative study of the calculation results shows that the simulated annealing genetic algorithm can search the global optimal assembly sequence scheme more accurately and efficiently, and meet the evaluation indexes at the same time. The mechanical simulation technology is innovatively used to simulate the assembly of building components, and the simulation process is used to guide the on-site construction process.

Assembly sequence planning based on structure cells in open design

Advances in computer network technologies have enabled firms to increasingly utilize external resources to remain competitive. Based on the function-behavior-structure cell (FBSC) modeling and computer network technologies, consumers with design knowledge and experience, called co-designers in this research, can involve in the process of open design (OD) to share their requirements, experiences and knowledge. The structure cells (SCs) provided by the co-designers in OD and the relationships among them are critical for generating the optimal design scheme and assembly sequence planning. However, the existing assembly sequence planning (ASP) approaches mainly focus on identification of the assembly plan based on precedence relations of operations from the predefined parts in the design scheme without considering the utilization of resources available in OD. In this study, a new approach for ASP based on SCs in OD is proposed to tackle this problem. First, the assembly features of the SCs and their matching rules are described in OD, and an approach for calculating the matching intensity between SCs is developed for identifying the assembly relationships between SCs. The design scheme is generated according to the SCs and their assembly relationships. Second, the base part of the design scheme is determined by its correlation degree with other parts. The feasible assembly sequences are derived by reversing the disassembly sequences. As the increase of the number of parts in design scheme will result in the combinatorial explosion of feasible assembly sequences, a particle swarm optimization algorithm is presented to achieve the optimal assembly sequence. A case study is provided to demonstrate the feasibility and effectiveness of the proposed approach.

Assembly Sequence Planning Based on Hierarchical Model

With the rapid development of intelligent manufacturing technology and ultraprecision machining, assembly technology has attracted more and more attention. Assembly sequence is an important part of assembly process planning. However, assembly sequence is affected by many complex factors, such as the time required to assemble products, the geometric feasibility of assembly sequence, and other factors (tool replacement times and assembly direction change), so it is very challenging. In this paper, an assembly information model based on geometric features of parts is proposed. The model obtains the hierarchical information of the part through the segmentation and feature reconstruction of the part. Based on the influence of the contact surface between parts on the feasible motion domain and assembly domain, the assembly constraint relationship model is established. The objective function is constructed by changing the assembly direction, changing the assembly tools, and evaluating the assembly fit type. The geometrically feasible assembly sequence is optimized to obtain the optimal assembly sequence under the existing conditions. Taking a block, a secondary reducer spindle, and a cylinder as examples, the effectiveness of the method is verified.

CAD-MBSE Interoperability for the Checking of Design Requirements Based on Assemblability Indicators

A mechanical product is the result of collaboration between different domains. In this paper we focus on the collaborative work that brings together the system engineer and the designer in the realization process of a mechanical system. A design solution, conceived in the CAD environment, is considered valid if all the specification requirements, defined in the MBSE (Model Based System Engineering) domain, are met. Thus, the preliminary study of these requirements has a major influence on the choice and validation of the design solution. In this paper a methodology for handling the requirements has been detailed. These requirements are classified into two main categories. The first one deals with the product performance and the second addresses the process performance in order to generate the best assembly sequence. Depending on the response of the designed solution to these requirements, it becomes easy to decide not only whether the design solution can be validated or not but also to choose the most optimal assembly sequence that ensures the best operation quality. A validation example of a speed reducer is used to demonstrate the added value of the proposed approach.