Search Results (25)

Assembly represents the culminating phase in the product production cycle, accounting for over 40% of production costs. Conventional assembly sequence planning methodologies predominantly prioritize geometric feasibility, tool change frequency, and directional change frequency as primary optimization objectives. Assembly accuracy is rarely systematically considered during the planning phase; instead, it is typically evaluated and optimized retrospectively after the production sequence has been established, making it difficult to effectively mitigate cumulative tolerances. During physical prototyping, failure to meet accuracy standards necessitates re-planning, which delays progress and increases costs. We propose an algorithm that integrates assembly accuracy prediction directly into the assembly sequence generation process. This enables sequence planning to be driven by constraints related to both assembly accuracy and efficiency. First, assembly precedence relationships are established based on the assembly information matrix to identify the base components. During the disassembly process, disassembly feasibility checks are incorporated to prevent the creation of isolated parts with no contact points, thereby enhancing the engineering soundness of the precedence modeling. Second, we propose an improved greedy topological sorting algorithm that incorporates assembly accuracy predictions as a key constraint in the objective function; by merging symmetrical parts in the prediction model to reduce the search space, the algorithm ultimately generates an assembly sequence that balances geometric feasibility, assembly efficiency, and assembly accuracy. Finally, we developed an integrated virtual assembly simulation system that combines assembly information extraction, sequence planning, and accuracy calculation, enabling the rapid generation and closed-loop verification of high-precision assembly sequences. Utilizing a simplified model as a case study, we generate comparison sequences with and without accuracy prediction and validate them through virtual assembly simulation. The experimental results show that, compared to traditional assembly sequences that do not account for precision, the proposed method improves the assembly precision pass rate by approximately 23% while maintaining assembly efficiency and significantly reduces the risk of rework and re-assembly caused by improper sequencing. Simulation software developed using this method can accurately plan assembly sequences for 25 parts in 223.58 s. Full article

Collaborative robots, or cobots, are designed to improve productivity and safety in industrial settings. However, effective Human–Robot Collaboration (HRC) relies heavily on the human operator’s trust in the robotic partner. This study posits that trust is significantly enhanced by the robot’s ability to adapt to unpredictable human behavior. To achieve this adaptability, we propose applying an Adversarial Reinforcement Learning (ARL) framework to the robot’s activity planning. We model the assembly process as a Markov Decision Process (MDP) on a Directed Acyclic Graph (DAG). The robot learns an assembly policy using an on-policy algorithm while a simulated human agent, trained with the same algorithm, acts as an adversary that introduces disturbances and delays. We applied the proposed approach to a simple industrial case study and evaluated it on complex assembly sequences generated synthetically. Although the ARL-trained robot did not outperform conventional assembly optimization algorithms in terms of task completion time, it guaranteed robustness against human variability. This ensured task completion within a bounded timeframe regardless of human actions. By demonstrating consistent performance and adaptability in the face of uncertainty, the robot exhibits the Ability and Benevolence components of the ABI model of trust. This fosters a more resilient and trustworthy collaborative environment. Full article

On large-scale product assembly lines, such as those used in aircraft manufacturing, multiple assembly positions and devices often coexist within a single workstation, leading to complex task interactions. As a result, the problem of parallel task execution within workstations must be effectively addressed. This study focuses on positional and equipment conflicts within workstations. To manage positional and equipment conflicts, a multi-objective optimization model is developed that integrates assembly sequence planning with the first type of assembly line balancing problem. This model aims to minimize the number of workstations, balance task loads, and reduce equipment procurement costs. An improved NSGA-II algorithm is proposed by incorporating artificial immune algorithm concepts and neighborhood search. A selection strategy based on dominance rate and concentration is introduced, and crossover and mutation operators are refined to enhance search efficiency under restrictive parallel constraints. Case studies reveal that a chromosome concentration weight of about 0.6 yields superior search performance. Compared with the traditional NSGA-II algorithm, the improved version achieves the same optimal number of workstations but provides a 5% better workload balance, 2% lower cost, a 76% larger hyper-volume, and a 133% increase in Pareto front solutions. The results demonstrate that the proposed algorithm effectively handles assembly line balancing with complex parallel constraints, improving Pareto front quality and maintaining diversity. It offers an efficient, practical optimization strategy for scheduling and resource allocation in large-scale assembly systems. Full article

Currently, fastener installation within the narrow, confined space of a wing box must be performed manually, as existing robotic systems are unable to adequately meet the internal assembly requirements. To address this problem, a new robot with one prismatic and five revolute joints (1P5R) has been developed for entering and operating inside the wing box. Firstly, the mechanical structure and control system of the robot were designed and implemented. Then, an improved Probabilistic Roadmap (PRM) method was developed to enable rapid and smooth path planning, mainly depending on optimization of sampling strategy based on Halton sequence, an elliptical-region-based redundant point optimization strategy using control points, improving roadmap construction, and path smoothing based on B-spline curves. Finally, obstacle–avoidance path planning based on the improved PRM was simulated using the MoveIt platform, corresponding robotic motion experiments were conducted, and the improved PRM was validated. Full article

Large-scale measurement of key feature points (KFPs) on an aircraft is essential for coordinated movement of locators, which is critical to the final assembly accuracy. Due to the large number and wide distribution of KFPs as well as line-of-sight occlusion, network measurement of laser trackers (LTs) is required. Existing approaches rely on operational experience for the configuration of stations, sequences and station transitions of LTs, which compromises both efficiency and automation capability. To tackle this challenge, this article presents an autonomous mobile measurement method for KFPs in complex scenes of aircraft assembly, incorporating path self-planning and self-positioning capabilities, thereby substantially diminishing temporal expenditure. Firstly, a simultaneous self-planning method of measurement stations and tasks is proposed to determine the minimum number of stations, optimal locations, and their specific KFPs at each station. Secondly, considering obstacles and turning time, a path planning model of mobile LTs combining coarse and fine localization is established to realize automatic station transitions. Finally, an optimal sequence of series of KFPs with a wide spatial distribution is generated to minimize total distance. Aircraft component assembly experiments validated the method, cutting measurement error by 37% and total measurement time by over 50%. Full article

With the rapid development of smart manufacturing, the optimization of real-time monitoring in operating procedures has become a crucial issue in modern industry. Traditional disassembly and assembly (D/A) work, relying on human experience and visual inspection, lacks immediacy and a quantitative basis, further affecting operating quality and efficiency. This study aims to develop a thin-film force sensor and an inertial measurement unit (IMU)-integrated wearable device for monitoring and analyzing operators’ behavioral characteristics during D/A tasks. First, by having operators wear self-made smart gloves and 17 IMU sensors, the work tables with three different heights are equipped with a mechatronics machine for the D/A experiment. Common D/A motions are designed into the experiment. Several subjects are invited to execute the standardized operating procedure, with upper limbs used to collect data on operators’ hand gestures and movements. Then, the measured data are applied to verify the performance measure functional best path of machine D/A. The results reveal that the system could effectively identify various D/A motions as well as observe operators’ force difference and motion mode, which, through the theory of performance indicator optimization and the verification of data analysis, could provide a reference for the best path planning, D/A sequence, and work table height design in the machine D/A process. The optimal workbench height for a standing operator is 5 to 10 cm above their elbow height. Performing assembly and disassembly tasks at this optimal height can help the operator save between 14.3933% and 35.2579% of physical effort. Such outcomes could aid in D/A behavior monitoring in industry, worker training, and operational optimization, as well as expand the application to instant feedback design for automation and smartization in a smart factory. Full article

This study introduces a novel approach for minimizing assembly errors in wind turbine gearboxes using a hybrid optimization algorithm, Particle Swarm-Bacteria Foraging Optimization (PSBFO). By integrating error-driven task sequencing and real-time error feedback with the PSBFO algorithm, we developed a comprehensive framework tailored to the unique challenges of gearbox assembly. The PSBFO algorithm combines the global search capabilities of PSO with the local refinement of BFO, creating a unified framework that efficiently explores task sequencing, minimizing misalignment and torque misapplication assembly errors. The methodology results in a 38% reduction in total assembly errors, improving both process accuracy and efficiency. Specifically, the PSBFO algorithm reduced errors from an initial value of 50 to a final value of 5 across 20 iterations, with components such as the low-speed shaft and planetary gear system showing the most substantial reductions. The 50 to 5 error reduction represents a significant decrease in assembly errors from an unoptimized (50) to an optimized (5) sequence, achieved through the PSBFO algorithm, by minimizing dimensional deviations, torque mismatches, and alignment errors across 26 critical gearbox components. While the primary focus is on wind turbine gearbox applications, this approach has the potential for broader applicability in error-prone assembly processes in industries such as automotive and aerospace, warranting further validation in future studies. Full article

Urban green spaces are integral components of city ecosystems, supporting essential belowground microbial communities such as arbuscular mycorrhizal fungi (AMF). Understanding how green space types influence AMF communities is key to promoting urban ecological function. This study examines AMF diversity, community assembly, and co-occurrence network structures in two urban green space types—park and roadside—in Kaifeng, Henan Province, China. Soil samples were collected from both sites, and AMF community composition was assessed using high-throughput sequencing. Environmental variables, including total nitrogen (TN), available phosphorus (AP), available potassium (AK), water content, and pH, were measured to evaluate their influence on AMF communities. The results indicate marked differences between the two green space types. Park soils support significantly greater AMF species richness and more complex co-occurrence networks than roadside soils. These differences are correlated with higher nutrient levels in park soils. By contrast, AMF communities in roadside soils are more strongly associated with soil water content and pH, resulting in reduced diversity and more homogeneous community structures. Stochastic processes predominantly govern community assembly in both green space types, with roadside green spaces being more influenced by stochastic processes than park green spaces. These findings highlight the influence of urban landscape type on AMF communities and provide guidance for enhancing urban biodiversity through targeted landscape planning and soil management. In future work, we will implement long-term AMF monitoring across different green-space types and evaluate specific management practices to optimize soil health and ecosystem resilience. Full article

In the construction process of large cruise ships, there are numerous cabin components, and the number of assembly sequences will experience a “combinatorial explosion”, which will become a complex NP hard problem. This article proposes an assembly sequence planning method based on practical engineering problems in the construction process of large cruise ships. The cabin components are modularized, and an optimization algorithm is designed for multi-objective problem solving to obtain the optimal assembly sequence of cabin components. This article analyzes the impact of six constraint conditions on the assembly plan, including geometric constraints, sequence constraints, number of assembly reversals, number of tool replacements, stable connection relationships, and selection of reference components. A fitness function is designed and a mathematical model is established. On this basis, a genetic greedy combination algorithm is proposed to solve the optimal assembly sequence. Compared with traditional genetic algorithms, this improves computational efficiency and solves complex problems in a better manner. Multiple unique optimal solutions can be obtained in one solution process. The feasibility and effectiveness of this method were verified through examples. Full article

An assembly sequence evaluation is one of the most important research directions of assembly sequence planning (ASP) for complex mechanical transmission products. Currently, aviation reducers lack a multi-perspective and multi-level evaluation of their assembly sequence. The existing evaluation indicators vary. The evaluation methods have low effectiveness and poor practicability. Therefore, a comprehensive multidimensional evaluation method for complex assembly sequences is proposed in this paper. A multidimensional comprehensive evaluation of the overall assembly quality and performance indices of aviation reducer products is realized. Firstly, the main factors affecting assembly sequence planning are considered: the attributes of the basic unit parts and the cost control of the assembly process. An evaluation index system of assembly sequence planning based on the two dimensions of assembly cost and ontology is constructed. Then, according to the multidimensional evaluation index, fuzzy evaluation theory is used to establish a fuzzy set and a matrix for each dimensional evaluation index. The index weight is divided. A comprehensive evaluation model and the function of each dimension are established. After a comprehensive evaluation, the multidimensional assembly sequence evaluation method for aviation reducers is formed. Finally, the method is applied to the assembly process of the primary reducer of a helicopter’s main reducer, and a comprehensive evaluation of its assembly sequence scheme is completed to verify the feasibility of the proposed method. This article constructs a complex assembly sequence evaluation method that includes 12 evaluation indicators, improves the assembly sequence planning evaluation index system of aviation reducers, and can effectively promote the progress of optimization technology for complex assembly sequences of aviation reducers. Full article

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. Full article

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. Full article

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. Full article

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. Full article

Aviation components play an important role in national defense and aviation development. Bolt connections are widely used in the assembly of aviation components, due to their simple structure and convenient disassembly. In addition to the impact of elastic interaction, the gap between the tightened parts also makes it very difficult to obtain a uniform bolt load, to achieve the required tightness during the tightening process. However, the impact of elastic interaction can be reduced by selecting the best tightening sequence, and the optimal tightening sequence of aviation components under different gaps can be predicted by constructing a neural network surrogate model. Based on the predicted optimal sequence, the elastic interaction matrix corresponding to the sequence can be obtained. In order to obtain a uniform preload, the initial load of each bolt is calculated according to an elastic interaction matrix. This research has improved the tightness of aviation components and the real-life efficiency of tightening process planning. Full article