Search Results (89)

With growing interest in automated dismantling operations for hazardous environments, automatically planning safe and efficient disassembly sequences is becoming increasingly important. When a large structure is segmented into parts, the removal order must ensure that each part can be extracted safely without destabilising the remaining structure. This paper presents a comparative study of four algorithms for solving the disassembly sequencing problem in two dimensions: First Feasible Random Search (FFRS), Greedy Search (GS), Height-Decreasing Search (HDS), and Stochastic Tree Search (STS). The present study focuses specifically on sequencing feasibility under geometric and physical constraints, namely connectivity, accessibility, and structural stability. The 2D formulation provides a simplified yet computationally efficient testbed for analysing algorithmic behaviour under varying cutting complexities, with the objective of minimising the total removal trajectory length. Results show that while STS consistently finds optimal or near-optimal solutions, its factorial runtime limits scalability. GS produces high-quality solutions efficiently but can become trapped in infeasible configurations, whereas HDS offers strong reliability and speed at the expense of solution quality. Based on these findings, a hybrid height-based backtracking algorithm is proposed as a promising future direction, combining the efficiency of greedy search with the robustness of stochastic exploration. The results provide insight into the relative strengths and limitations of different sequencing strategies and establish a foundation for future extension to more realistic dismantling scenarios, including 3D and radiologically constrained applications. Full article

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

This paper tackles the challenge of disassembly sequence planning (DSP) in energy-efficient remanufacturing by introducing an innovative hybrid optimization framework. The proposed model integrates a Dynamic Time-Varying CRITIC–Entropy (DTVCE) decision-making framework with an Improved Honey Badger Algorithm (IHBA) to optimize disassembly sequences under key operational criteria, including idle rate, line smoothness, and energy consumption. The DTVCE framework constructs a dynamic composite score by normalizing evaluation criteria across time slices and incorporating temporal discounting to capture the evolving importance of each factor. Meanwhile, by establishing a symmetric disassembly constraint matrix to restrict the disassembly sequence and integrating exploration and exploitation mechanisms to enhance the IHBA, the solution process is empowered to efficiently generate feasible disassembly sequences and fulfill task allocation across workstations while satisfying takt time constraints. Experimental validation demonstrates that the proposed framework significantly outperforms traditional disassembly optimization approaches in both energy efficiency and line balance performance. In a case study involving an automotive drive axle, the method achieved a near-optimal configuration using only eight workstations, leading to a marked reduction in both energy consumption and idle times. Sensitivity analysis further verifies the model’s robustness, showing stable convergence and consistent performance under varying takt times and energy parameters. Overall, this study contributes to the advancement of green remanufacturing by offering a scalable, data-driven, and adaptive solution to disassembly optimization—paving the way toward sustainable and energy-aware production environments. Full article

Sarcopenia is a progressive loss of skeletal muscle mass and strength with major clinical and economic consequences. While traditional models emphasize mitochondrial dysfunction, inflammation, and proteostasis imbalance, emerging data highlight a neurogenic component involving motor neuron loss, fiber denervation, neuromuscular junction remodeling, and disrupted trophic signaling. To synthesize current evidence on neurogenic mechanisms of sarcopenia revealed by next-generation sequencing and related multi-omics, to map molecular networks across cell types, and to outline translational opportunities for diagnostics and targeted therapy. A narrative review of human and animal studies indexed in PubMed, Web of Science, and Scopus through November 2025. Search terms combined sarcopenia, denervation, neuromuscular junction, neurotrophic signaling, genomics, transcriptomics, epigenomics, single-cell, and spatial transcriptomics. Eligible studies reported omics or physiological endpoints related to neuromuscular function. Convergent omics data support a central role of the nervous system in the onset and progression of sarcopenia. Genetic and regulatory factors linked to denervation, transcriptomic signatures of junctional disassembly, and cell-specific dysfunctions in motor neurons, Schwann cells, satellite cells, and fibro-adipogenic progenitors have been identified. Epigenetic and transcriptional networks underlying neuromuscular homeostasis, along with candidate circulating biomarkers, provide targets for clinical translation. Neurogenic sarcopenia represents a tractable target for precision prevention and therapy. Integration of multi-omics, artificial intelligence, and advanced models such as innervated organoids and NMJ-on-chip systems can accelerate target validation and enable personalized strategies to preserve neuromuscular function. Full article

To achieve a complete circular economy for used electric vehicle batteries, it is essential to implement a disassembly step. Given the significant diversity of battery geometries and designs, a high degree of flexibility is required for automated disassembly processes. The incorporation of human–robot interaction provides a valuable degree of flexibility in the process workflow. However, human behavior is characterized by unpredictable timing and variable task durations, which add considerable complexity to process planning. Therefore, it is crucial to develop a robust strategy for coordinating human and robotic tasks to manage the scheduling of production activities efficiently. This study proposes a global optimization approach to the scheduling of production activities, which employs a genetic algorithm with the objective of minimizing the total production time while simultaneously reducing the idle time of both the human operator and robot. The proposed approach is concerned with optimizing the sequencing of disassembly tasks, considering both temporal and exclusion constraints, to guarantee that tasks deemed hazardous are not executed in the presence of a human. This approach is based on a two-level adaptation framework developed in RoboDK (Robot Development Kit, v5.4.3.22231, 2022, RoboDK Inc., Montréal, QC Canada). At the first level, offline optimization is performed using a genetic algorithm to determine the optimal task sequencing strategy. This stage anticipates human behavior by proposing disassembly sequences aligned with expected human availability. At the second level, an online reactive adjustment refines the plan in real time, adapting it to actual human interventions and compensating for deviations from initial forecasts. The effectiveness of this global optimization strategy is evaluated against a non-global approach, in which the problem is partitioned into independent subproblems solved separately and then integrated. The results demonstrate the efficacy of the proposed approach in comparison with a non-global approach, particularly in scenarios where humans arrive earlier than anticipated. Full article

Disassembly is a crucial step in the remanufacturing of end-of-life (EoL) electronic products. Disassembly depth refers to the disassembly stop point determined by the disassembly sequence. For the disassembly depth optimization of EoL electronic products, a feasibility model with a fast convergence and low mean squared error (MSE) is needed to improve optimization accuracy. However, the use of a backpropagation neural network (BPNN) model or mathematical model often results in a slow convergence and high MSE due to the randomness of the initial weights and biases. In this study, an improved method for the disassembly depth optimization of smartphones based on a Particle Swarm Optimization-BPNN (PSO-BPNN) predictive model is proposed. Compared with the traditional BPNN optimization method, the proposed method in this study is that the BPNN predictive model is optimized by using PSO, which shows a superior predictive performance and reduces the MSE. The case of ‘Huawei P7’ is used to verify the feasibility of the method. The results show that the method maintains disassembly profit while reducing the disassembly time and carbon emissions by 17.1% and 7.8%, respectively. Compared with the BPNN model, the PSO-BPNN model converges 18.6%, 32.8%, and 16.6% faster in predicting the disassembly time, profit, and carbon emissions, respectively, with MSE reductions of 92.95%, 96.51%, and 92.74%, respectively. Full article

Augmented reality (AR)-based assisted maintenance methods are effective in completing simple equipment maintenance tasks. However, complex equipment typically requires multi-location remote collaboration due to structural complexity, multiple fault states, and high maintenance costs, significantly increasing maintenance difficulty. This paper therefore proposes a remote maintenance support method for complex equipment based on layered-MVC-B/S integrated AR framework (IAR-RMS). First, clearly define the maintenance content and workflow for multi-person remote collaboration and conduct an in-depth analysis of process control within the task workflow to avoid incomplete or unsystematic maintenance guidance information and processes. Second, analyze collaborative management from the perspectives of maintenance role conflicts and maintenance operation conflicts and implement on-demand permission control and operation sequence management to ensure the timeliness and user-friendliness of multi-person collaboration. Then, integrate the layered architecture, MVC, and B/S architecture to construct a remote maintenance support (RMS) model based on an integrated architecture system, ensuring the reliability and timeliness of the model. Finally, demonstrate the main functional modules of the RMS task process, and use power system disassembly and assembly as an experiment to validate the effectiveness and generalizability of the proposed IAR-RMS method. The results indicate that the proposed IAR-RMS method can effectively realize maintenance support tasks in multi-person remote collaboration scenarios. Full article

Today’s economic systems are characterized by overproduction, rapid changes in consumer preferences and the intensive exploitation of natural resources. For this reason, the idea of the circular economy has emerged in recent years as a key strategy for tackling environmental, social and resource problems. At the same time, manufacturers are increasingly trying to fulfill customer requirements, so that products are becoming ever more personalized. This increasing focus on individuality is leading to greater variability in design, while at the same time the complexity of product structures and components is increasing, which poses major challenges for production and assembly processes. Understanding this complexity helps in finding the most effective ways for the disassembly process to enable reuse, repair and high-quality recycling, which are among the key principles of the circular economy. This not only supports environmental and resource sustainability, but also contributes to long-term competitiveness and climate neutrality in manufacturing. This paper outlines how complexity is defined and how this parameter can be used to obtain an optimal solution for minimizing product complexity and maximizing the number of disassembled parts. This problem was modeled using linear programming, where the optimal disassembly sequence was defined taking into account variables and constraints such as the time available within a working day and the complexity of the sub-assemblies. The results showed that the process can be significantly optimized if clear variables and targets are defined. Full article

With the rapid growth of electric vehicles, the efficient and safe recycling of high-energy battery packs, particularly the removal of structural bolts, has become a critical challenge. This study presents a NeuroSymbolic robotic system for battery disassembly, driven by autonomous learning capabilities. The system integrates deep perception modules, symbolic reasoning, and action primitives to achieve interpretable and efficient disassembly. To improve adaptability, we introduce an offline learning framework driven by a large language model (LLM), which analyzes historical disassembly trajectories and generates optimized action sequences via prompt-based reasoning. This enables the synthesis of new action primitives tailored to familiar scenarios. The system is validated on a real-world UR10e robotic platform across various battery configurations. Experimental results show a 17 s reduction in average disassembly time per bolt and a 154.4% improvement in overall efficiency compared with traditional approaches. These findings demonstrate that combining neural perception, symbolic reasoning, and LLM-guided learning significantly enhances robotic disassembly performance and offers strong potential for generalization in future battery recycling applications. 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

Soft sensors have emerged as indispensable tools for predicting dissolved gas concentrations in transformer oil-critical indicators for fault diagnosis that defy direct measurement. Addressing the persistent challenge of prediction inaccuracy in existing methods, this study introduces a novel hybrid architecture integrating time-series decomposition, deep learning prediction, and signal reconstruction. Our approach initiates with variational mode decomposition (VMD) to disassemble original gas concentration sequences into stationary intrinsic mode functions (IMFs). Crucially, VMD’s pivotal parameters (modal quantity and quadratic penalty term) governing bandwidth allocation and mode orthogonality are optimized via a Newton–Raphson-based optimization (NRBO) algorithm, minimizing envelope entropy to ensure sparsity preservation through information-theoretic energy concentration metrics. Subsequently, a bidirectional long short-term memory network with attention mechanism (AM-BiLSTM) independently forecasts each IMF. Final concentration trends are reconstructed through superposition and inverse normalization. The experimental results demonstrate the superior performance of the proposed model, achieving a root mean square error (RMSE) of 0.51 µL/L and a mean absolute percentage error (MAPE) of 1.27% in predicting hydrogen (H₂) concentration. Rigorous testing across multiple dissolved gases confirms exceptional robustness, establishing this NRBO-VMD-AM-BiLSTM framework as a transformative solution for transformer fault diagnosis. Full article

The HIV-1 capsid has emerged as a highly attractive drug target due to its highly conserved sequence and critical role in the viral life cycle. By disrupting interactions between capsid proteins and impairing the proper assembly or disassembly of the capsid, the inhibitors can effectively suppress HIV-1 replication and infection. Based on this mechanism, numerous small-molecule agents targeting the HIV-1 capsid protein have been developed to date. In this review, we report the latest advances in such inhibitors and delve into their molecular mechanisms of action. We find a focus on small molecules modulating capsid stability and their assembly/disassembly. Hopefully this study will further enhance the understanding of HIV-1 inhibition mechanisms, facilitating the future exploration of novel capsid inhibitors. Full article

With the sharp increase in the number of products and the development of the remanufacturing industry, disassembly lines have become the mainstream recycling method. In view of the insufficient research on the layout of multi-form disassembly lines and human factors, we previously proposed a linear-U-shaped hybrid layout considering the constraints of employee posture and a Duel-DQN algorithm assisted by Large Language Model (LLM). However, there is still room for improvement in the utilization efficiency of workstations. Based on this previous work, this study proposes an innovative layout of U-shaped and circular disassembly lines and retains the constraints of employee posture. The LLM is instruction-fine-tuned using the Quantized Low-Rank Adaptation (QLoRA) technique to improve the accuracy of disassembly sequence generation, and the Dueling Deep Q-Network(Duel-DQN) algorithm is reconstructed to maximize profits under posture constraints. Experiments show that in the more complex layout of U-shaped and circular disassembly lines, the iterative efficiency of this method can still be increased by about 26% compared with the traditional Duel-DQN, and the profit is close to the optimal solution of the traditional CPLEX solver, verifying the feasibility of this algorithm in complex scenarios. This study further optimizes the layout problem of multi-form disassembly lines and provides an innovative solution that takes into account both human factors and computational efficiency, which has important theoretical and practical significance. Full article

Applying circular economy principles to the renovation of existing buildings is increasingly recognized as essential to achieving Europe’s climate and energy goals. However, current decision-making frameworks rarely integrate life cycle carbon assessment with multi-criteria evaluation to support circular renovation strategies. This paper introduces an innovative framework that combines life cycle carbon assessment with multi-criteria decision analysis to identify and sequence circular renovation measures. The framework was applied to a residential case study in the Netherlands, using IES VE for operational carbon assessment and One Click LCA for embodied carbon assessment, with results evaluated using PROMETHEE multi-criteria analysis. Renovation measures were assessed based on operational and embodied carbon (including Module D), energy use intensity, cost, payback period, and disruption. The evaluation also introduced the embodied-to-operational carbon ratio (EOCR), a novel metric representing the proportion of embodied carbon, including Module D, relative to operational carbon savings over the building’s lifecycle. The homeowner’s preferences regarding these criteria were considered in determining the final ranking. The findings show that circular insulation options involving reused materials and designed for disassembly achieved the lowest embodied carbon emissions and lowest EOCR scores, with reused PIR achieving a 94% reduction compared to new PIR boards. The impact of including Module D on the ranking of renovation options varies based on the end-of-life scenario. The framework demonstrates how circular renovation benefits can be made more visible to decision-makers, promoting broader adoption. Full article

The remanufacturing of end-of-life products is an effective approach to alleviating resource shortages, environmental pollution, and global warming. As the initial step in the remanufacturing process, the quality and efficiency of disassembly have a decisive impact on the entire workflow. However, the complexity of product structures poses numerous challenges to practical disassembly operations. These challenges include not only conventional precedence constraints among disassembly tasks but also sequential dependencies, where interference between tasks due to their execution order can prolong operation times and complicate the formulation of disassembly plans. Additionally, the inherent uncertainties in the disassembly process further affect the practical applicability of disassembly plans. Therefore, developing reliable disassembly plans must fully consider both sequential dependencies and uncertainties. To this end, this paper employs a chance-constrained programming model to characterise uncertain information and constructs a multi-objective sequence-dependent disassembly line balancing (MO-SDDLB) problem model under uncertain environments. The model aims to minimise the hazard index, workstation time variance, and energy consumption, achieving a multi-dimensional optimisation of the disassembly process. To efficiently solve this problem, this paper designs an innovative multi-objective adaptive large neighbourhood search (MO-ALNS) algorithm. The algorithm integrates three destruction and repair operators, combined with simulated annealing, roulette wheel selection, and local search strategies, significantly enhancing solution efficiency and quality. Practical disassembly experiments on a lithium-ion battery validate the effectiveness of the proposed model and algorithm. Moreover, the proposed MO-ALNS demonstrated a superior performance compared to other state-of-the-art methods. On average, against the best competitor results, MO-ALNS improved the number of Pareto solutions (NPS) by approximately 21%, reduced the inverted generational distance (IGD) by about 21%, and increased the hypervolume (HV) by nearly 8%. Furthermore, MO-ALNS exhibited a superior stability, providing a practical and feasible solution for disassembly optimisation. Full article