Assembly Sequence Planning Research Articles

A novel fine-grained assembly sequence planning method based on knowledge graph and deep reinforcement learning

In the assembly sequence planning (ASP) of aviation products, recalibration of components or sufficient space to assemble subsequent components are critical factors for ensuring product quality. To address this need, a fine-grained ASP (FASP) is defined to take assembly operations as units to plan sequences. Lots of operations have complex sequence constraints that are attended unequally in the FASP. A method based on knowledge graph (KG) and deep reinforcement learning is proposed to plan assembly operations. Firstly, continuous and discrete procedures are defined, and a quantitative characterization method is presented to deduce complex constraints objectively. Then, a dynamic KG is designed to establish and update the information model mainly composed of constraints. Finally, a labeled degree centrality algorithm (LDCA) considers edge labels to minimize the number of assembly tool changes and assembly direction changes for sequences. An improved deep Q-network (IDQN) introduces a convolutional layer to extract local features of technical requirements for planning procedures more efficiently. A helicopter structure assembly is used to verify the effectiveness of the proposed method. The improved algorithms have better performance in solving speed, sequence quality, and convergence speed than ordinary ASP methods, respectively. The fine-grained assembly sequence is more reasonable and feasible by comparing it with the ordinary sequence.

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.

Research on Assembly Sequence Planning of Hybrid Power Transmission Device Based on Improved Genetic Algorithm

Research on Assembly Sequence Planning of Hybrid Power Transmission Device Based on Improved Genetic Algorithm

Automating the assembly planning process to enable design for assembly using reinforcement learning

AbstractThis paper introduces a new concept for the automation of the assembly planning process, to enable Design for Assembly (DfA). The approach involves the application of reinforcement learning (RL) to assembly sequence planning (ASP) based on a 3D-CAD model. The ASP algorithm determines assembly sequences through assembly by disassembly. The assembly sequence is then used for the generation of subassemblies by considering the product contact information. The approach aims to support the creation of the manufacturing bill of materials (MBOM) by automating the assembly planning process.

Sequence planning for on-orbit robotic assembly based on symbiotic organisms search with diversification strategy

The demands for efficiency and stability in space missions have necessitated the development of sequence planning schemes for on-orbit robotic assembly of space structures. A sequence planning method is proposed in this work based on robotic kinematic analysis and Symbiotic Organisms Search (SOS) algorithm with a diversification strategy. With feasibility as the constraint and assembly cost as the objective function, the assembly sequence planning is regarded as an optimization problem. The assembly relationship constraint is depicted as the dependency between assembly steps represented by a directed acyclic graph. An abstract modeling method based on robotic kinematics is proposed to construct geometric feasibility constraint, accommodating the interference stemming from the multi-degree-of-freedom motion of the robotic system. A diversification strategy is exploited to address the issue of local optima arising from solving the optimal sequence based on SOS. A cost prediction method using previously calculated populations as samples is proposed to reduce the reliance on robotic dynamic analysis for cost calculation. Finally, simulations are carried out to validate the practicability of the proposed method.

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.

A Graph-based framework for assembly sequence planning of a cable harness

This paper focuses on assembly sequence planning (ASP) for cable harness, which is an essential but not yet well-addressed problem for automated cable assembly. A systematic approach is proposed to obtain all feasible assembly sequences taking account of the effects of the topological structure as well as various types of assembly tasks. The cable representation is first extended to consider the assembled relations of tasks along the entire cable. Then, the effects of a typical task on the assembly feasibility of the entire cable are analyzed. Finally, a framework is proposed to generate all feasible assembly sequences for the cable assembly. The proposed framework is verified using typical cable harnesses with single and multi-branches.

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.

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.

Assembly Sequence and Assembly Path Planning of Robot Automation Products Based on Discrete Particle Swarm Optimization

The product assembly process in industrial production is a time-consuming and labour-intensive link, so the use of robots for intelligent assembly can achieve high-efficiency operations to a large extent. During the intelligent assembly process, robots need to carry out three key processes: product information modelling, assembly route planning and assembly sequence planning. The experiment examines assembly sequence planning and assembly route planning. In light of the deficiencies of the current assembly sequence planning technique, we propose a new approach, which is difficult to calculate, the experiment proposes to introduce the DPSO algorithm into the assembly sequence planning model. Meanwhile, to prevent the model from entering local optimal, the experiment adds disturbance operation on the basis of DPSO. That is, a part is randomly selected to be inserted into another position in the disassembly sequence and at the same time, the rest of the parts are moved to form a new disassembly sequence to update the particle's location. In addition to the assembly sequence planning, the experiment aims to improve the planning path obtained by the existing RRT algorithm, which is not smooth enough, that is, to simplify the installation path by reducing the change of direction. The simulation experiment findings demonstrate that the addition of the disturbance to DPSO algorithm successfully prevents the model from entering a local optimum state. The moving distance of the parts before path optimization is 52.0012mm and the number of moving direction changes is 8 times, while the moving distance of the parts after path optimization is reduced to 42.8094mm and the moving direction is changed only 2 times.

Assembly sequence planning and evaluating for deep oil and gas corer based on graph theory

Fidelity corers play a crucial role in exploring and evaluating deep oil and gas resources. Due to the complexity of the corer parts, an improper assembly sequence can lead to inefficiencies and reduced reliability, impacting the success of obtaining oil and gas samples. This paper presents a novel assembly sequence planning method based on graph theory and multi-objective evaluation. The graph-theoretic cut set algorithm is utilized to generate all possible assembly sequences based on a directed graph which represents corer component assembly information. To address deep oil and gas corer requirements, the interpretative structural modeling method (ISM) is employed to construct a comprehensive stability-reliability-accuracy assurance-easiness-cost economy (SRAEC) indicator system. Through indicators weight assignment and multi-objective evaluation, the optimal solution of assembly sequence is identified from a global perspective. The proposed method is applied to a deep-sea gas hydrate corer, resulting in the identification of the most suitable assembly sequence for both field drilling and laboratory testing. This optimized sequence improves the assembly reliability and efficiency, contributing to the overall success and efficiency of the corer in obtaining oil and gas samples. The study provides valuable insights for designing and practically applying deep oil and gas coring tools.

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.

Assembly Sequence Validation with Feasibility Testing for Augmented Reality Assisted Assembly Visualization

The recent advances in Industry 4.0 have promoted manufacturing industries towards the use of augmented reality (AR), virtual reality (VR), and mixed reality (MR) for visualization and training applications. AR assistance is extremely helpful in assembly task visualization during the stages of product assembly and in disassembly plan visualization during the repair and maintenance of a product/system. Generating such assembly and disassembly task animations consume a lot of time and demands skilled user intervention. In assembly or disassembly processes, each operation must be validated for geometric feasibility regarding its practical implementation in the real-time product. In this manuscript, a novel method for automated assembly task simulation with improved geometric feasibility testing is proposed and verified. The proposed framework considers the assembly sequence plan as input in the form of textual instructions and generates a virtual assembly task plan for the product; furthermore, these instructions are used to ensure there are no collisions using a combination of multiple linear directions. Once the textual instructions achieve geometric feasibility for the entire assembly operation, the visual animations of the assembly operations are successively produced in a game engine and are integrated with the AR platform in order to visualize them in the physical environment. The framework is implemented on various products and validated for its correctness and completeness.

Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly

Robotic assembly planning enables architects to explicitly account for the assembly process during the design phase, and enables efficient building methods that profit from the robots' different capabilities. Previous work has addressed planning of robot assembly sequences and identifying the feasibility of architectural designs. This paper extends previous work by enabling planning with large, heterogeneous teams of robots. We present a planning system which enables parallelization of complex task and motion planning problems by iteratively solving smaller subproblems. Combining optimization methods to solve for manipulation constraints with a sampling-based bi-directional space-time path planner enables us to plan cooperative multi-robot manipulation with unknown arrival-times. Thus, our solver allows for completing subproblems and tasks with differing timescales and synchronizes them effectively. We demonstrate the approach on multiple case-studies to show the robustness over long planning horizons and scalability to many objects and agents of our algorithm. Finally, we also demonstrate the execution of the computed plans on two robot arms to showcase the feasibility in the real world.

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.

Collaborative Assembly Sequence Planning (CASP) for On-site Assembly of a Photovoltaic Power Station Considering Data Analysis

Collaborative Assembly Sequence Planning (CASP) for On-site Assembly of a Photovoltaic Power Station Considering Data Analysis

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.

Mechanical Assembly Sequence Determination Using Artificial Neural Networks Based on Selected DFA Rating Factors

In this paper, an assembly sequence planning system, based on artificial neural networks, is developed. The problem of artificial neural network itself is largely related to symmetry at every stage of its creation. A new modeling scheme, known as artificial neural networks, takes into account selected DFA (Design for Assembly) rating factors, which allow the evaluation of assembly sequences, what are the input data to the network learning and then estimate the assembly time. The input to the assembly neural network procedure is the sequences for assembling the parts, extended by the assembly’s connection graph that represents the parts and relations between these parts. The operation of a neural network is to predict the assembly time based on the training dataset and indicate it as an output value. The network inputs are data based on selected DFA factors influencing the assembly time. The proposed neural network model outperforms the available assembly sequence planning model in predicting the optimum assembly time for the mechanical parts. In the neural networks, the BFGS (the Broyden–Fletcher–Goldfarb–Shanno algorithm), steepest descent and gradient scaling algorithms are used. The network efficiency was checked from a set of 20,000 test networks with randomly selected parameters: activation functions (linear, logistic, tanh, exponential and sine), the number of hidden neurons, percentage set of training and test dataset. The novelty of the article is therefore the use of parts of the DFA methodology and the neural network to estimate assembly time, under specific production conditions. This approach allows, according to the authors, to estimate which mechanical assembly sequence is the most advantageous, because the simulation results suggest that the neural predictor can be used as a predictor for an assembly sequence planning system.

Multi-objective multi-verse optimiser for integrated two-sided assembly sequence planning and line balancing

Multi-objective multi-verse optimiser for integrated two-sided assembly sequence planning and line balancing