Search Results (20)

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

Circular Design methodology is essential for sustainable industrial practices. This study provides a methodology with a Python-based computational tool that optimizes industrial products’ disassembly sequences, focusing on Design for End of Life (DfEoL) and Design for Disassembly (DfD) to promote Circular Design. The tool creates disassembly precedence graphs and shows the best disassembly path for target components, facilitating material recovery and environmental sustainability. The tool was applied to a case study on an Axial Flux Permanent Magnet (AFPM) electric motor. The approach provides a flexible and open access solution for optimizing product design within a Circular Design framework. Full article

Remanufacturing, which benefits the environment and saves resources, is attracting increasing attention. Disassembly is arguably the most critical step in the remanufacturing of end-of-life (EoL) products. Human–robot collaborative disassembly as a flexible semi-automated approach can increase productivity and relieve people of tedious, laborious, and sometimes hazardous jobs. Task allocation in human–robot collaborative disassembly involves methodically assigning disassembly tasks to human operators or robots. However, the schemes for task allocation in recent studies have not been sufficiently refined and the issue of component placement after disassembly has not been fully addressed in recent studies. This paper presents a method of task allocation and sequence planning for human–robot collaborative disassembly of EoL products. The adopted criteria for human–robot disassembly task allocation are introduced. The disassembly of each component includes dismantling and placing. The performance of a disassembly plan is evaluated according to the time, cost, and utility value. A discrete Bees Algorithm using genetic operators is employed to optimise the generated human–robot collaborative disassembly solutions. The proposed task allocation and sequence planning method is validated in two case studies involving an electric motor and a power battery from an EoL vehicle. The results demonstrate the feasibility of the proposed method for planning and optimising human–robot collaborative disassembly solutions. Full article

The rapidly increasing adoption of electric vehicles (EVs) globally underscores the urgent need for effective management strategies for end-of-life (EOL) EV batteries. Efficient EOL management is crucial in reducing the ecological footprint of EVs and promoting a circular economy where battery materials are sustainably reused, thereby extending the life cycle of the resources and enhancing overall environmental sustainability. In response to this pressing issue, this review presents a comprehensive analysis of the role of artificial intelligence (AI) in improving the disassembly processes for EV batteries, which is integral to the practical echelon utilization and recycling process. This paper reviews the application of AI techniques in various stages of retired battery disassembly. A significant focus is placed on estimating batteries’ state of health (SOH), which is crucial for determining the availability of retired EV batteries. AI-driven methods for planning battery disassembly sequences are examined, revealing potential efficiency gains and cost reductions. AI-driven disassembly operations are discussed, highlighting how AI can streamline processes, improve safety, and reduce environmental hazards. The review concludes with insights into the future integration of electric vehicle battery (EVB) recycling and disassembly, emphasizing the possibility of battery swapping, design for disassembly, and the optimization of charging to prolong battery life and enhance recycling efficiency. This comprehensive analysis underscores the transformative potential of AI in revolutionizing the management of retired EVBs. Full article

As the number of end-of-life products multiplies, the issue of their efficient disassembly has become a critical problem that urgently needs addressing. The field of disassembly sequence planning has consequently attracted considerable attention. In the actual disassembly process, the complex structures of end-of-life products can lead to significant delays due to the interference between different tasks. Overlooking this can result in inefficiencies and a waste of resources. Therefore, it is particularly important to study the sequence-dependent disassembly sequence planning problem. Additionally, disassembly activities are inherently fraught with uncertainties, and neglecting these can further impact the effectiveness of disassembly. This study is the first to analyze the sequence-dependent disassembly sequence planning problem in an uncertain environment. It utilizes a stochastic programming approach to address these uncertainties. Furthermore, a mixed-integer optimization model is constructed to minimize the disassembly time and energy consumption simultaneously. Recognizing the complexity of the problem, this study introduces an innovative bees algorithm, which has proven its effectiveness by showing a superior performance compared to other state-of-the-art algorithms in various test cases. This research offers innovative solutions for the efficient disassembly of end-of-life products and holds significant implications for advancing sustainable development and the recycling of resources. Full article

In a hydropower station, equipment needs maintenance to ensure safe, stable, and efficient operation. And the essence of equipment maintenance is a disassembly sequence planning problem. However, the complexity arises from the vast number of components in a hydropower station, leading to a significant proliferation of potential combinations, which poses considerable challenges when devising optimal solutions for the maintenance process. Consequently, to improve maintenance efficiency and decrease maintenance time, a discrete whale optimization algorithm (DWOA) is proposed in this paper to achieve excellent parallel disassembly sequence planning (PDSP). To begin, composite nodes are added into the constraint relationship graph based on the characteristics of hydropower equipment, and disassembly time is chosen as the optimization objective. Subsequently, the DWOA is proposed to solve the PDSP problem by integrating the precedence preservative crossover mechanism, heuristic mutation mechanism, and repetitive pairwise exchange operator. Meanwhile, the hierarchical combination method is used to swiftly generate the initial population. To verify the viability of the proposed algorithm, a classic genetic algorithm (GA), simplified teaching–learning-based optimization (STLBO), and self-adaptive simplified swarm optimization (SSO) were employed for comparison in three maintenance projects. The experimental results and comparative analysis revealed that the proposed PDSP with DWOA achieved a reduced disassembly time of only 19.96 min in Experiment 3. Additionally, the values for standard deviation, average disassembly time, and the rate of minimum disassembly time were 0.3282, 20.31, and 71%, respectively, demonstrating its superior performance compared to the other algorithms. Furthermore, the method proposed in this paper addresses the inefficiencies in dismantling processes in hydropower stations and enhances visual representation for maintenance training by integrating Unity3D with intelligent algorithms. Full article

With the increase in the production of electric vehicles (EVs) globally, a significant volume of waste power battery modules (WPBM) will be generated accordingly, posing challenges for their disposal. An intelligent scrap power battery disassembly sequence planning method, integrated with operational risk perception, is proposed to automate the planning process. Taking into consideration the risk coefficients, energy consumption, and costs during disassembly, this method maximizes profits, minimizes energy usage, and ensures safety. Utilizing an extended part priority graph, an optimized model for integrated risk-aware disassembly sequence planning (IRA-DSP) is constructed. With the Guangqi Toyota LB7A-FX1 as a case study, and using real data from resource recovery enterprises, an improved MOPSO-GA algorithm is proposed to solve the model and generate disassembly plans. The results demonstrate the method’s ability to achieve unit-level disassembly of WPBM, avoid high-risk sequences, and optimize profit and energy consumption, exhibiting its practicality and feasibility. Full article

End-of-life (EoL) electric vehicle (EV) batteries are one of the main fountainheads for recycling rare metal elements like cobalt and lithium. Disassembly is the first step in carrying out a higher level of recycling and processing of EV batteries. This paper presents a knowledge graph of electric vehicle batteries for robotic disassembly. The information extraction of the EV batteries was conducted based on the source data of EV batteries. The semantic ontology structure and the knowledge graph of the EV batteries were constructed. A case study was designed to demonstrate the proposed knowledge graph. The study involved generating a robotic disassembly sequence planning for an EoL EV battery. The results show the feasibility of the constructed knowledge graph. Full article

In a circular economy, strategies for product recovery, such as reuse, recycling, and remanufacturing, play an important role at the end of a product’s life. A sustainability model was developed to solve the problem of sequence-dependent robotic disassembly line balancing. This research aimed to assess the viability of the model, which was optimised using the Multi-Objective Bees Algorithm in a robotic disassembly setting. Two industrial gear pumps were used as case studies. Four objectives (maximising profit, energy savings, emissions reductions and minimising line imbalance) were set. Several product recovery scenarios were developed to find the best recovery plans for each component. An efficient metaheuristic, the Bees Algorithm, was used to find the best solution. The robotic disassembly plans were generated and assigned to robotic workstations simultaneously. Using the proposed sustainability model on end-of-life industrial gear pumps shows the applicability of the model to real-world problems. The Multi-Objective Bees Algorithm was able to find the best scenario for product recovery by assigning each component to recycling, reuse, remanufacturing, or disposal. The performance of the algorithm is consistent, producing a similar performance for all sustainable strategies. This study addresses issues that arise with product recovery options for end-of-life products and provides optimal solutions through case studies. Full article

Disassembly plays a pivotal role in the maintenance of industrial equipment. However, the intricate nature of industrial machinery and the effects of wear and tear introduce inherent uncertainty into the disassembly process. The inadequacy in representing this uncertainty within equipment maintenance disassembly has posed an ongoing challenge in contemporary research. This study centers on disassembly sequence planning (DSP) in the context of industrial equipment maintenance, with a primary aim to mitigate the adverse effects of uncertainty. To effectively address this challenge, we introduce a multi-objective DSP problem and utilize triangular fuzzy numbers from fuzzy logic to manage uncertainty throughout the disassembly process. Our objectives encompass minimizing disassembly time, reducing tool changes and directional reversals, and improving responsiveness to emergency maintenance needs. Recognizing the complexities of this problem, we present an innovative multi-objective enhanced water wave optimization (EWWO) algorithm, integrating propagation, refraction, and breaking wave operators alongside novel local search strategies. Through rigorous validation with real-world industrial cases, we not only demonstrate the algorithm’s potential in solving disassembly maintenance challenges but also underscore its exceptional performance in producing high-quality and efficient solutions. In comparison to other algorithms, EWWO provides significant advantages in multi-objective evaluation metrics, including Hypervolume (HV), Spread, and CPU time. Moreover, the application of triangular fuzzy numbers offers a comprehensive evaluation of solutions, empowering decision makers to make informed choices in diverse scenarios. Our findings lead to the conclusion that this research provides substantial support for addressing uncertainty in the field of industrial equipment maintenance, with the potential to significantly enhance the efficiency and quality of disassembly maintenance processes. Full article

Disassembly sequence planning (DSP) is a key approach for optimizing various industrial equipment-maintenance processes. Finding fast and effective DSP solutions plays an important role in improving maintenance efficiency and quality. However, when disassembling industrial equipment, there are many uncertainties that can have a detrimental impact on the disassembly and subsequent maintenance work. Therefore, this paper proposes a multi-objective DSP problem in an uncertain environment that addresses the uncertainties in the disassembly process through stochastic planning, with the objectives of minimizing disassembly time and enhancing responsiveness to priority maintenance components. Due to the complexity of the problem, an improved peafowl optimization algorithm (IPOA) is proposed for efficient problem-solving. The algorithm is specifically designed and incorporates four customized optimization mechanisms: peafowls’ courtship behavior, the adaptive behavior of female peafowls in proximity, the adaptive search behavior of peafowl chicks, and interactive behavior among male peafowls. These mechanisms enable effective search for optimal or near-optimal solutions. Through comparisons with a real-world industrial case and other advanced algorithms, the superiority of the IPOA in solving DSP problems is demonstrated. This research contributes to improving maintenance efficiency and quality, bringing positive impacts to industrial equipment maintenance. Full article

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. 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

Currently, practical optimisation models and intelligent solution algorithms for solving disassembly sequence planning are attracting more and more attention. Based on the importance of energy efficiency in product disassembly and the trend toward green remanufacturing, this paper proposes a new optimisation model for the energy-efficient disassembly sequence planning. The minimum energy consumption is used as the evaluation criterion for disassembly efficiency, so as to minimise the energy consumption during the dismantling process. As the proposed model is a complex optimization problem, called NP-hard, this study develops a new extension of the whale optimisation algorithm to allow it to solve discrete problems. The whale optimisation algorithm is a recently developed and successful meta-heuristic algorithm inspired by the behaviour of whales rounding up their prey. We have improved the whale optimisation algorithm for predation behaviour and added a local search strategy to improve its performance. The proposed algorithm is validated with a worm reducer example and compared with other state-of-the-art and recent metaheuristics. Finally, the results confirm the high solution quality and efficiency of the proposed improved whale algorithm. Full article

In selective serial disassembly sequence planning, when the target node (component) is reached, the selective disassembly task is completed and the refurbished component is repaired, reused or remanufactured. Since the efficient utilization of existing resources is necessary, it is crucial to predict disassembly operation times and the condition of joints for recycling, reusing or remanufacturing. The method of estimating the disassembly times of a joint if it is intended for remanufacturing, recycling and reuse is an important and urgent requirement for research development and results. The aim of the paper is to investigate the disassembly system with predicted operation times and the quality of product connections (joints) in order to balance the line smoothness index, to minimize a line time factor, line efficiency and profit and minimize an ex post error. Disassembly times for remanufacturing, recycling and reuse are estimated separately based on the historical data of disassembly times and the quality of joints. The presented estimation method of disassembly operation times increases the reliability and efficiency of elaborated balances of tasks in lines. Underestimated disassembly operation times can be compensated for during the idle points in the successive cycles, provided that the transport operations are performed manually and that travel time determines the cycle time. Full article