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Circular economy (CE) is a new business model that is pressing manufacturing companies to think about closed loop scenarios for materials and products. Design for End-of-Life (DfEoL) and Design for Disassembly (DfD) are key enabling methods for the effective application of this model. The paper presents a time-based method for the calculation of disassembly sequences, adopting basic theories and techniques in this topic and integrating new concepts for the assessment of the disassembly time. The method consists of five steps and starts from the documentations (e.g., CAD model) generally available early in the product development process. The first three steps encompass the product analysis by including (i) the definition of target components from the general assembly, (ii) the analyses of the virtual model, and (iii) the assessment of the so-called level matrix, which is based on the concept of disassembly levels and liaisons characterization among components. The last two steps allow for the assessment of the time-based disassembly sequence by including (iv) the analysis of feasible sequences and (v) the generation of the best disassembly sequence for target components. The method mainly overcomes two issues highlighted in the literature regarding the reliability of the disassemblability analysis using a time-based approach and the quality of results accounting for the real condition of the product at the time of disassembly. The calculation of the effective disassembly time is grounded on a specific repository developed to gather knowledge about the disassembly tasks and related disassembly time. This is the main contribution and novelty of the proposed approach. By using the proposed method, different target components of a washing machine are analysed with the aim of demonstrating the robustness of the method and its consistency in the estimation of disassembly time. A maximum deviation of 10% between the estimated and measured disassembly times is noticed.

For environmentally conscious and sustainable manufacturing, manufacturers need to incorporate product recovery by designing manufacturing systems to include reverse manufacturing by considering both assembly and disassembly systems. Just as the assembly line is considered the most efficient way to assemble a product, the disassembly line is seen to be the most efficient way to disassemble a product. While having some similarities to assembly, disassembly is not the reverse of the assembly process. The challenge lies in the fact that it possesses unique characteristics. In this paper, we consider a sequence-dependent disassembly line balancing problem (SDDLBP) that is concerned with the assignment of disassembly tasks to a set of ordered disassembly workstations while satisfying the disassembly precedence constraints and optimizing the effectiveness of several measures considering sequence-dependent part removal time increments. SDDLBP is not a trivial problem since it is proven to be NP-complete. Further complications occur by considering multiple objectives including environmental and economic goals that are often contradictory. Therefore, it is essential that an efficient methodology be developed. A new approach based on the particle swarm optimization algorithm with a neighborhood-based mutation operator is proposed to solve the SDDLBP. Case scenarios are considered, and comparisons with ant colony optimization, river formation dynamics, and tabu search approaches are provided to demonstrate the superior functionality of the proposed algorithm.

Remanufacturing automation must be designed to be flexible and robust enough to overcome the uncertainties, conditions of the products, and complexities in the planning and operation of the processes. Machine learning methods, in particular reinforcement learning, are presented as techniques to learn, improve, and generalise the automation of many robotic manipulation tasks (most of them related to grasping, picking, or assembly). However, not much has been exploited in remanufacturing, in particular in disassembly tasks. This work presents the state of the art of contact-rich disassembly using reinforcement learning algorithms and a study about the generalisation of object extraction skills when applied to contact-rich disassembly tasks. The generalisation capabilities of two state-of-the-art reinforcement learning agents (trained in simulation) are tested and evaluated in simulation, and real world while perform a disassembly task. Results show that at least one of the agents can generalise the contact-rich extraction skill. Besides, this work identifies key concepts and gaps for the reinforcement learning algorithms’ research and application on disassembly tasks.

This paper concerns a disassembly line balancing problem (DLBP) in remanufacturing that aims to allocate a set of tasks to workstations to disassemble a product. We consider two objectives in the same time, i.e., minimising the number of workstations required and minimising the operating costs. A common approach to such problems is to covert the multiple objectives into a single one and solve the resulting problem with either exact or heuristic methods. However, the appropriate weights must be determined a priori, yet the results provide little insight on the trade-off between competing objectives. Moreover, DLBP problems are proven NP-complete and thus the solvable instances by exact methods are limited. To this end, we formulate the problem into a multi-objective dynamic program and prove the monotonicity property of both objective functions. A backward recursive algorithm is developed to efficiently generate all the non-dominated solutions. The numerical results show that our proposal is more efficient than alternative exact algorithms proposed in the literature and can handle much larger problem instances.

The disassembly of end-of-life lithium–ion batteries (EOL-LIBs) is inherently complex, owing to their multi-state and multi-type characteristics. To mitigate these challenges, a human–robot collaboration disassembly (HRCD) model is developed. This model capitalizes on the cognitive abilities of humans combined with the advanced automation capabilities of robots, thereby substantially improving the disassembly process’s flexibility and efficiency. Consequently, this method has become the benchmark for disassembling EOL-LIBs, given its enhanced ability to manage intricate and adaptable disassembly tasks. Furthermore, effective disassembly sequence planning (DSP) for components is crucial for guiding the entire disassembly process. Therefore, this research proposes an approach for the generation of HRCD sequences for EOL-LIBs based on knowledge graph, providing assistance to individuals lacking relevant knowledge to complete disassembly tasks. Firstly, a well-defined disassembly process knowledge graph integrates structural information from CAD models and disassembly operating procedure. Based on the acquired information, DSP is conducted to generate a disassembly sequence knowledge graph (DSKG), which serves as a repository in graphical form. Subsequently, knowledge graph matching is employed to align nodes in the existing DSKG, thereby reusing node sequence knowledge and completing the sequence information for the target disassembly task. Finally, the proposed method is validated using retired power LIBs as a case study product.

This paper presents a framework that integrates digital twin and virtual reality (VR) technologies to improve the efficiency and safety of human–robot collaborative systems in the disassembly domain. With the increasing complexity of the handling of end-of-life electronic products and as the related disassembly tasks are characterized by variabilities such as rust, deformation, and diverse part geometries, traditional industrial robots face significant challenges in this domain. These challenges require adaptable and flexible automation solutions that can work safely alongside human workers. We developed an architecture to address these challenges and support system configuration, training, and operational monitoring. Our framework incorporates a digital twin to provide a real-time virtual representation of the physical disassembly process, allowing for immediate feedback and dynamic adjustment of operations. In addition, VR is used to simulate and optimize the workspace layout, improve human–robot interaction, and facilitate safe and effective training scenarios without the need for physical prototypes. A unique case study is presented, where the collaborative system is specifically applied to the disassembly of antenna amplifiers, illustrating the potential of our comprehensive approach to facilitate engineering processes and enhance collaborative safety.

PurposeAlthough disassembly balancing lines has been studied for over two decades, there is a gap in the robotic disassembly. Moreover, combination of problem with heterogeneous employee assignment is also lacking. The hazard related with the tasks performed on disassembly lines on workers can be reduced by the use of robots or collaborative robots (cobots) instead of workers. This situation causes an increase in costs. The purpose of the study is to propose a novel version of the problem and to solve this bi-objective (minimizing cost and minimizing hazard simultaneously) problem.Design/methodology/approachThe epsilon constraint method was used to solve the bi-objective model. Entropy-based Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Preference Ranking Organization methods for Enrichment Evaluation (PROMETHEE) methods were used to support the decision-maker. In addition, a new criterion called automation rate was proposed. The effects of factors were investigated with full factor experiment design.FindingsThe effects of all factors were found statistically significant on the solution time. The combined effect of the number of tasks and number of workers was also found to be statistically significant.Originality/valueIn this study, for the first time in the literature, a disassembly line balancing and employee assignment model was proposed in the presence of heterogeneous workers, robots and cobots to simultaneously minimize the hazard to the worker and cost.

PurposeThis paper aims to illustrate the growing role robots are playing in recycling and product disassembly and provide an insight into recent research activities.Design/methodology/approachFollowing a short introduction, this first considers robotic waste sorting systems and then describes two systems for the disassembly of electronic products. It then provides details of some recent research activities. Finally, brief conclusions are drawn.FindingsRobotic systems exploiting artificial intelligence combined with various sensing and machine vision technologies are playing a growing role in the sorting of municipal and industrial waste, prior to recycling. These are mostly based on delta robots and can achieve pick rates of 60-70 items/min and be configured to recognise and select a wide range of different materials and items from moving conveyors. Electronic waste recycling is yet to benefit significantly from robotics although a limited number of systems have been developed for product disassembly. Disassembly techniques are the topic of a concerted research effort which often involves robots and humans collaborating and sharing disassembly tasks.Originality/valueThis provides an insight into the present-day uses and potential future role of robots in recycling which has traditionally been a highly labour-intensive industry.

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.

As resources become increasingly scarce and environmental demands grow, the recycling of products at the end of their lifecycle becomes crucial. Disassembly, as a key stage in the recycling process, plays a decisive role in the sustainability of the entire operation. Advances in automation technology and the integration of Industry 5.0 principles make the balance of human–robot collaborative disassembly lines an important research topic. This study uses disassembly-precedence graphs to clarify disassembly-task information and converts it into a task-precedence matrix. This matrix includes both symmetry and asymmetry, reflecting the dependencies and independencies among disassembly tasks. Based on this, we develop a multiobjective optimisation model that integrates disassembly-task allocation, operation mode selection, and the use of collaborative robots. The objectives are to minimise the number of workstations, the idle rate of the disassembly line, and the energy consumption. Given the asymmetry in disassembly-task attributes, such as the time differences required for disassembling various components and the diverse operation modes, this study employs an evolutionary algorithm to address potential asymmetric optimisation problems. Specifically, we introduce an advanced multi-objective discrete bee algorithm and validate its effectiveness and superiority for solving the disassembly-line balancing problem through a comparative analysis with other algorithms. This research not only provides innovative optimisation strategies for the product-recycling field but also offers valuable experience and reference for the further development of industrial automation and human–robot collaboration.