Product Assembly Sequence Research Articles

Research on complex products assembly sequence planning and its application based on PSO-GA

Abstract A PSO-GA-based method is proposed to improve the efficiency of complex product assembly sequence planning. We minimize total assembly time by optimizing the assembly sequence, considering the number of tool changes and assembly direction changes as evaluation criteria. A genetic algorithm (GA) is introduced into particle swarm optimization (PSO) to solve and verify assembly sequence planning. Finally, the effectiveness and rationality of the hybrid algorithm are verified by taking a certain type of gear pump as an example.

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

Effective GA Operator for Product Assembly Sequence Planning

Effective GA Operator for Product Assembly Sequence Planning

Research on dynamic decision-making for product assembly sequence based on Connector-Linked Model and deep reinforcement learning

Assembly sequence planning (ASP) is a critical challenge in the manufacturing industry, involving the arrangement of components to maximize production efficiency and reduce costs. Solving the ASP problem is not only hindered by its NP-hard nature, making it difficult to obtain optimal solutions, but also requires making rapid decisions in the face of dynamic changes in resource availability during the actual assembly process. This paper proposes a Deep reinforcement learning (DRL) approach to tackle the ASP problem. DRL learns optimal assembly strategies through interactions between an intelligent agent and its environment, using a reward function as a long-term objective to guide decision-making, ultimately enabling the acquisition of near-optimal solutions. DRL exhibits high scalability and reusability, leveraging previously acquired knowledge to adapt to dynamic changes in the assembly environment. First, a connector-based graph structure is introduced to decompose assembly products into a series of connector-based assembly elements. These elements are then transformed into a matrix format for use as input to the DRL method. Second, an encoder-decoder architecture tailored to the ASP problem is developed. This architecture utilizes mask mechanism to ensure that the generated assembly sequences adhere to assembly priority constraints while adapting to dynamic changes in resource availability during assembly. Finally, policy gradient methods are employed to train the DRL model, enabling it to generate assembly sequences with lower costs and higher efficiency. Experimental results demonstrate the effectiveness of the proposed approach. Furthermore, compared to heuristic algorithms, DRL exhibits faster decision-making capabilities and better adaptability to dynamic resource changes during the assembly process.

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.

THE ASSEMBLY SEQUENCE OF AXISYMMETRIC DETAILS SIMULATION

To ensure the reliability and accuracy of products, an automated construction method for the assembly sequence of high-precision axisymmetric products is presented. The work purpose is to verify the acceptability of using an oriented assembly graph, which includes adjacency and incidence matrices, for structuring and planning the theoretical schematization of the assembly technological process. The considered method coincides with a possible mathematical implementation of the problem. The specifics of structural modeling and the assembly process using mathematical methods were assessed. The technique of creating graphs reflecting the relationship between details and the assembly process is considered. Solving the tasks of the automated assembly process is performed using algorithms and computer software based on graphs constructed in matrix form. The achieved results make it possible to adjust the sequence of products assembly in real-time mode, based on which it becomes possible to monitor parameters and control decision-making when planning assembly processes.

Multiple-objective optimization of a reconfigurable assembly system via equipment selection and sequence planning

With the development of society and technology, the modern manufacturing system is required to handle unpredictable and rapidly changing market demands during various demand periods. This is a new challenge to the production scheduling of the production line, which requires a faster and smarter response to changes in production demand. To meet the challenge, the reconfigurable manufacturing system (RMS) has emerged as a competitive and promising manufacturing model. The reconfigurable assembly system (RAS), a critical feature of RMS, can be seamlessly incorporated into multi-variety, variable-batch, multi-functional, and fast-delivery manufacturing modes, opening up the possibility of rapid changes for production scheduling. In this paper, we first build a RAS model that can more accurately represent scheduling problems that may occur in real-world industrial operations. More specifically, we consider the assembly process of products could be divided into a number of major steps which are performed in different stations and optimize the assembly sequence of products and the equipment selection within individual assembly stations. After that, three objective functions corresponding to the workload balance, reconfiguration/assembly cost, and lead time are proposed to jointly optimize the assembly sequence and the equipment selection in individual assembly stations. Finally, a hybrid particle swarm optimization (MOHPSO) method is utilized to solve the multiple-objective optimization problem, which combines the advantages of particle swarm optimization and the hybrid method. Experimental results show the superiority of the proposed MOHPSO method over other alternatives including MOPSO and NSGA-II, achieving lower Spacing Metric (SM) and Mean Ideal Distance (MID) indexes and is suitable for solving production scheduling problems.

An Automatic Generation Method of Exploded View Based on Projection

Exploded view is a kind of schematic view which decomposes the assembly into parts and arranges according to certain rules to explain the product structural relationship and guide the relevant technical personnel to carry out product assembly or maintenance. It is necessary to consider the visibility constraints and explosion sequence rules to construct the exploded view. Manual creation will lead to repeated modification and low efficiency. In this paper, a projection based algorithm for automatic generation of exploded view is proposed. In this method, the explosion set of products is constructed based on disassembly matrix and the assembly sequence of complex products is represented as a set of ordered hierarchical exploded view, so as to ensure that the exploded view can clearly express the feasible assembly sequence. The projection interference detection method is proposed to ensure the visibility of the exploded view under a given angle of view, and the method of selecting the explosion direction is proposed to improve the compactness of the generated exploded view, and the explosion direction guideline is established to improve the readability of the exploded view. The algorithm has small storage space, fast search speed, and is not limited to a single specific perspective, so it is more general. The feasibility and efficiency of the algorithm are verified by an example of automatic generation of explosive drawing of fixture model.

The Extraction of Product Assembly Feature Information for Intelligent Assembly Sequence Planning

The assembly feature information contained in the CAD model of the product is an important basis for the generation of intelligent assembly sequence. Taking the assembly model of the product as the research object, the research on the acquisition of assembly feature information of the product by intelligent assembly sequence planning is proposed in this paper. Microsoft Visual Studio 2005 and SolidWorks API are used as implementation means to access the internal data of the product CAD model and realize the extraction of the hierarchical information, mating relation and mating line and surface assembly features of the assembly model. An application example of product assembly feature information extraction for the locking device of an elevator landing door shows that the established method of acquiring product assembly feature information is effective and provides a theoretical and technical basis for the establishment of product design and assembly sequence planning knowledge base.

Influence of dimension chains on product assembly sequences

The paper considers the influence of design dimension chains on the assembly sequence of a complex product. When assembling products, the rule of connectivity of the dimension chain must be followed. For this reason, the dimension chain induces a binary partial order relation on the set of product parts. The assembly sequences for which the connectivity rule is applied are linear extensions of this order relation. The number of the linear extensions for the orders generated by the main methods of dimensioning is calculated. It is shown that the chain method of dimensioning is the most rigid, and the coordinate method is the most “tolerant”. The combined method occupies an intermediate position. The principle of matching the dimension and mechanical structures of a complex product is proposed. The results obtained can be used to generate rational dimension schemes in integrated CAD / CAM systems.

Intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge

PurposeThe implied assembly constraints of a computer-aided design (CAD) model (e.g. hierarchical constraints, geometric constraints and topological constraints) represent an important basis for product assembly sequence intelligent planning. Assembly prior knowledge contains factual assembly knowledge and experience assembly knowledge, which are important factors for assembly sequence intelligent planning. This paper aims to improve monotonous assembly sequence planning for a rigid product, intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge is proposed.Design/methodology/approachA spatio-temporal semantic assembly information model is established. The internal data of the CAD model are accessed to extract spatio-temporal semantic assembly information. The knowledge system for assembly sequence intelligent planning is built using an ontology model. The assembly sequence for the sub-assembly and assembly is generated via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge. The optimal assembly sequence is achieved via a fuzzy comprehensive evaluation.FindingsThe proposed spatio-temporal semantic information model and knowledge system can simultaneously express CAD model knowledge and prior knowledge for intelligent planning of product assembly sequences. Attribute retrieval and rule reasoning of spatio-temporal semantic knowledge can be used to generate product assembly sequences.Practical implicationsThe assembly sequence intelligent planning example of linear motor highlights the validity of intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge.Originality/valueThe spatio-temporal semantic information model and knowledge system are built to simultaneously express CAD model knowledge and assembly prior knowledge. The generation algorithm via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge is given for intelligent planning of product assembly sequences in this paper. The proposed method is efficient because of the small search space.

Search algorithm of the assembly sequence of products by using past learning results

In the future smart factory, the production system will further head in the direction of on-demand production. Products will be assembled one by one based on different specifications of customers. In these recognitions, this paper considers a method for raising productivity of the robot work cell. Under the assumption that the dual-arm robot assembles products in the work cell where one robot is in charge of all steps of assembling the product, we propose a computational algorithm for searching the efficient assembly sequence and work assignment to the robot hands utilizing reinforcement learning. Furthermore, we intend to use past learning results to determine work plans of robots more effectively. The proposed methods can eliminate or decrease the workload of the robot teaching. In addition, they can contribute to shorten the assembly time of products by giving the efficient work plan. In this research, the basic theory for automating the work planning of actual assembled products is considered using a building block model.

A structured methodology for the design of a human-robot collaborative assembly workplace

The trend towards mass customization puts traditional automation solutions under pressure. In addition, an aging working population increases the need to improve ergonomics at the workplace. Human-robot collaboration is considered as a solution for these challenges at the workstation level, as it combines the flexibility of the human with the consistency of robots. While the technology supporting the implementation of close human robot collaboration is maturing rapidly, the development of supporting design methodologies is lagging behind. The aim of this paper is to provide a generic methodology including a chain of four supporting procedure blocks for information extraction and processing and collaborative assembly solution generation and evaluation. The first block extracts product and assembly sequence constraints from CAD models. This information is fed into the second block where the previously identified tasks are decomposed into lower level work elements, for which the functional requirements are identified. These requirements are then used in the third block, in order to determine resource capability and safe collaboration possibilities. In the fourth block, the previous information is combined to generate and evaluate possible collaborative product assembly sequences. These sequences consist of work allocation, temporal distribution of work, and corresponding layout constraints.

Two-Stage Assembly Scheduling with Batch Setup Times, Time-Dependent Deterioration, and Preventive Maintenance Activities Using Meta-Heuristic Algorithms

This article considers a two-stage assembly scheduling problem (TSASP) with batch setup times, time-dependent deterioration, and preventive maintenance activities (PMAs). The objective of this problem is to simultaneously determine the optimal component-manufacturing sequence (CMS), product-assembly sequence (PAS), number of setups, and number and position of PMAs (PPMA). First, to determine the optimal solution, a novel mixed integer linear programming model (MILP) for the proposed problem is derived. Then, a standard genetic algorithm (SGA), hybrid genetic algorithm (HGA), standard harmony search (SHS), hybrid harmony search (HHS), and harmony-search-based evolutionary algorithm (HSEA) were proposed owing to the intractability of the optimal solution for large-scale problems. SGA and SHS provide a chromosome to represent a complete solution including three decisions (CMS, PAS, and PPMA). HGA, HHS, and HSEA provide a chromosome to represent a partial solution including PAS. CMS and PPMA are found by an effective local search heuristic based on the partial solution. A computational experiment is then conducted to evaluate the impacts of the factors on the performance of the proposed algorithms.

A method for planning human robot shared tasks

A multi criteria method, in support of the planning of shared human robot assembly tasks is presented in this paper. CAD models are used for the extraction of a product's assembly sequence, whilst an algorithm is applied for the generation and examination of alternative assignment scenarios. The design algorithm performs the joint planning of task-to-resource assignment and cell layout at the same time. The evaluation of each planning scenario against a set of ergonomics, quality and productivity criteria is made for the identification of the most efficient ones. The method's implementation, in the form of a prototype planning tool (Task Planner), enables its application to a pilot case from the automotive industry. The results indicate that the method can provide high quality solutions with respect to the end users' criteria and has potential applicability as a decision making support tool in the planning stage.

Task formalization of constructing rational product assembly sequence

Запропоновано математичні моделі, що відображають зв’язки базування та розмірні зв’язки між складальними елементами, враховують обмеження доступу, конструкторсько-технологічні та екологічні критерії складальних процесів. З їх використанням розроблено метод синтезу раціональної послідовності складання виробів, який відрізняється тим, що при побудові варіантів послідовності складання враховується комплекс технологічних та екологічних обмежень, що дозволяє забезпечити задану якість виробу та мінімізувати тривалість виробничого циклу.

A formal ontology-based spatiotemporal mereotopology for integrated product design and assembly sequence planning

This paper introduces the development of a formal ontology based on spatiotemporal mereotopology in the context of integrated assembly design and sequence planning. The main objective is to make assembly information accessible and exploitable by information management systems and computer-aided X tools in order to support product architects and designer’s activities. Indeed product design information and knowledge as well as the related assembly sequence require a semantic and logical foundation in order to be managed consistently and processed proactively. In this context, product relationships are considered and described in the part-whole theory supported by mereology and its extension, mereotopology. Firstly, a literature survey on formalisms in assembly-oriented design, mereotopological theories and spatiotemporal ontologies is presented so as to highlight the current research issue. Then, a mathematical description approach of product relationships based on mereotopology and temporal relationships is introduced. Built on this, an ontological development of the proposed theory using description logic rules is proposed. Finally, a mechanical assembly, as a case study, is introduced to illustrate the relevance of the proposed ontology.

Research on Building Customized Furniture Product Model and Assembly Sequence

With the improvement of people’s living standard, mass customization production model has been the trend of the furniture industry. A model of furniture product design and assembly for customization is proposed. It can decompose customers’ needs and transform them into homologous modules set in the structure tree. Then through the assembly relationship graph and matrix of these product parts, an assembly sequence which can guide the assembly of products will be built by a genetic algorithm. This process will not only save manpower and resources, but also improve enterprise efficiency. In this article, an instance was given in order to verify the correctness of product design and assembly processes based on product family of mass customization.

Assembly Sequence Planning for Products with Enclosed Shell

Aiming at the special structure of products with enclosed shell, the limitations of the usual methods are analyzed and concepts of axial subassembly and radial subassembly are proposed, the subassemblies are identified base on the adjacent matrix and interference matrix. A new kind of assembly relation matrix with the subassembly information is proposed to express the assembly model，the assembly transition probability in the ant colony algorithm is modified based on the assembly relation matrix and assembly precedence relations. The assembly sequence of products with enclosed shell can be planned rapidly with the algorithm aforementioned. Finally, the effectiveness was verified by an example.

Product Family Formation for Reconfigurable Assembly Systems

Appropriate formation of product families for Reconfigurable Manufacturing Systems (RMS) is of a great importance for a cost-effective and productive manufacturing. One key aspect that differentiates assembly systems from other manufacturing systems is that they often require parallel operations, which is not common for other types of manufacturing systems such as dedicated manufacturing systems. This paper introduces the first product family formation method that particularly addresses Reconfigurable Assembly Systems (RAS). Product assembly sequences are used, along with product demand and commonality, as similarity coefficients. Product assembly sequences are represented in the form of binary rooted trees and, based on well-established tree matching techniques used in Biology and Phylogenetics, a new sequence-based similarity coefficient is introduced to measure the distance between any given pair of assembly sequence trees. Hierarchical clustering is then applied to generate various groups of product families that may be formed based on each similarity coefficient. A novel consensus tree-based method is applied to find the best aggregation for the three different hierarchical clustering trees. The proposed method is applied to an example of eight products. Applying the proposed method to a Reconfigurable Assembly System should significantly improve system efficiency and productivity and hence supporting cost effective production.