Disassembly Tasks Research Articles

Mathematical Formulations for Asynchronous Parallel Disassembly Planning of End-of-Life Products

Disassembly is one of the most time-consuming and labor-intensive activities during the value recovery of end-of-life (EOL) products. The completion time (makespan) of disassembling EOL products is highly associated with the allocation of operators, especially in parallel disassembly. In this paper, asynchronous parallel disassembly planning (APDP), which avoids the necessity to synchronize disassembly tasks of manipulators during the parallel disassembly process, is studied to optimize the task assignment of manipulators for minimal makespan. We utilize four mixed integer linear programming (MILP) formulations to identify the optimal solutions. A set of different-sized instances are used to test and compare the performance of the proposed models, including some real-world cases. Finally, the proposed exact algorithm is further compared with the existing approach to solving APDP. Results indicate that a significant difference exists in terms of the computational efficiency of the MILP models, while three of four MILP formulations can efficiently achieve better solutions than that of the existing approach.

A multi‐objective hybrid evolutionary search algorithm for parallel production line balancing problem including disassembly and assembly tasks

AbstractThis paper proposes a multi‐objective hybrid evolutionary search algorithm to simultaneously optimize the number of workstations, the idle index and the quantity of the production equipment required for the parallel production line balancing problem including disassembly and assembly tasks. The proposed algorithm uses the crossover operation and the mutation operation to generate neighborhood individuals of the current evolutionary population. Based on the mixed population composed of the current evolutionary population and the newly generated neighborhood population, a novel population update method is developed. The individuals of the new generation population are composed of Pareto dominant individuals filtered from the mixed population and several individuals randomly selected from the dominated individuals. Then the local search strategy based on simulated annealing operation is applied to the new generation of population individuals to overcome the defect of premature convergence of the proposed algorithm. By solving test examples of different scales and comparing them with a variety of classical algorithms, the advantages in the aspects of the diversity, the convergence, and the distribution of the solution results of the proposed algorithm are proved. Finally, the established mathematical model and the developed algorithm are applied to the parallel production line designed for the bogie remanufacturing production line of a railway freight car maintenance company. The advantages of the parallel production line in improving the production efficiency and reducing the production cost are identified.

Modeling and solving the parallel mixed-flow remanufacturing disassembly line balancing problem for multi-variety products

The types and numbers of components in end-of-life (EOL) products are often uncertain during remanufacturing, leading to low disassembly efficiencies for traditional remanufacturing disassembly lines. To address this problem, a parallel mixed-flow workstation layout was designed, and a novel parallel mixed remanufacturing disassembly line balancing optimization method for multi-variety products was proposed. A mixed-flow product disassembly task hierarchical assignment matrix was constructed to perform disassembly task allocations for similar components. Furthermore, a parallel mixed-flow remanufacturing disassembly line balancing (PMRDLB) optimization model was developed with the optimization objectives of minimizing the number of workstations, the disassembly line balancing rate, and the remanufacturing value indexes of the components. Furthermore, the multi-objective non-dominated genetic optimization method (NSGA-III) was improved, in which a chromosome construction method, based on the parallel mixed-flow disassembly task allocation matrix, was proposed to conduct mapping between the chromosomes and the PMRDLB model. In addition, non-dominated solution sorting was performed based on a Pareto hierarchy, which increased the searching rate of the algorithm during optimization. Finally, a case study verified the effectiveness and feasibility of the proposed method.

Task allocation and planning for product disassembly with human–robot collaboration

This paper presents a comprehensive disassembly sequence planning (DSP) algorithm in the human–robot collaboration (HRC) setting with consideration of several important factors including limited resources and human workers’ safety. The proposed DSP algorithm is capable of planning and distributing disassembly tasks among the human operator, the robot, and HRC, aiming to minimize the total disassembly time without violating resources and safety constraints. Regarding the resource constraints, we consider one human operator and one robot, and a limited quantity of disassembly tools. Regarding the safety constraints, we consider avoiding potential human injuries from to-be-disassembled components and possible collisions between the human operator and the robot due to the short distance between disassembly tasks. In addition, the transitions for tool changing, the moving between disassembly modules, and the precedence constraint of components to be disassembled are also considered and formulated as constraints in the problem formulation. Both numerical and experimental studies on the disassembly of a used hard disk drive (HDD) have been conducted to validate the proposed algorithm. • Disassembly sequence planning considering human-robot collaboration (HRC) • Consideration of constraints including limited resources and human worker’s safety • Distributing and planning disassembly tasks among human, robot, and HRC • Experimental validation on disassembly of used hard disk drives (video available)

A Predictive Approach for Disassembly Line Balancing Problems.

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.

Product disassembly planning and task allocation based on human and robot collaboration

Product disassembly planning and task allocation based on human and robot collaboration

Multi-objective optimisation for cell-level disassembly of waste power battery modules in human-machine hybrid mode

The rapid global growth in the production of electric vehicles (EVs) will produce numerous waste power battery modules (WPBMs) in the future, which will create significant challenges concerning waste disposal. Therefore, measures to disassemble and recycle WPBMs before using them in other fixed scenarios provide an opportunity for research. First, considering battery components' hazards and complex properties, a human-machine collaborative cell-level disassembly model of WPBMs is proposed. Second, the WPBMs from the Tesla Model S are selected as the case study to verify reliability and validity. Finally, two different disassembly schemes are obtained by solving the proposed model using NSGA-II based on the actual data from resource-recycling companies. The results show that: 1) The proposed model and method can realize the cell-level disassembly of WPBMs and assign the disassembly tasks of hazard components to robots and the disassembly tasks of complex components to humans. 2) The two disassembly schemes obtained are two solutions that do not dominate each other, and the four objectives (number of workstations, workstation idle time, number of workers, and disassembly cost) are optimized simultaneously. 3) The proposed model can provide decision-makers with additional options when incorporating the number of workers into enterprise risk indicators.

Restart genetic flatworm algorithm for two-sided disassembly line balancing problem considering negative impact of destructive disassembly

Large-sized end-of-life products are particularly suitable for disassembly operations on the two-sided disassembly line, and destructive disassembly is often required in the disassembly process. However, the negative impact caused by destructive disassembly during the disassembly process has been ignored. Therefore, in this paper, a probability-based task destructive disassembly model is constructed in the two-sided disassembly line balancing problem, the negative impact on the total disassembly cost, workload smoothness index, and the impact of destructive disassembly tasks on other adjacent but unconnected tasks is defined. Then a mixed-integer programming model for the problem is formulated to minimize the total disassembly cost, smoothness index and the impact on other adjacent tasks by destructive disassembly tasks. A multi-objective restart genetic flatworm algorithm is proposed, which combines the genetic operations and flatworm regeneration processes, and embeds a restart mechanism. The effectiveness of the model and the efficiency of the algorithm are verified by disassembling mobile phones, laptops, printers and engines on the two-sided disassembly line. Finally, through the application of car disassembly, it is proved that the model and algorithm can assist decision-makers to choose the preferred disassembly schemes.

Disassembly Line Balancing of Electronic Waste Considering the Degree of Task Correlation

With growing concerns about the depletion of rare-earth elements, managing End-of-Life products has become a key sustainability initiative in the supply chains of global corporations. Recycling, the process of dismantling, separating, and recovery of components and raw materials from wastes, is technologically challenging and should be planned in such a way as to ensure operational efficiency as well as safety. This study explores the Disassembly Line Balancing Problem with Correlated Tasks (DLBP-CT), which is prevalent in the recycling of the Waste of Electrical and Electronic Equipment (WEEE). For this purpose, an original Integer Nonlinear Programming (INLP) model is proposed to find the optimal configuration for the disassembly lines. Given the NP-hard nature of this problem, the Adaptive Genetic Algorithm (AGA) is developed to solve the problem, minimizing the number of workstations and maximizing the relationship between the disassembly tasks. A case example from electronic waste is provided to test the practicality of the developed optimization approach. Sensitivity analysis is conducted to explore the impact of parameter changes in the optimization outcomes. Results are supportive of the applicability of the developed approach and show that it can serve as a strong decision aid tool when selecting the best disassembly process, workstations, and task assignments.

Asynchronous parallel disassembly sequence planning method of complex products using discrete multi-objective optimization

Disassembly is a necessary link to realize the integrity of product life cycle. Asynchronous Parallel Disassembly (APD) is an important way to achieve efficient disassembly. Due to the asynchronous start of disassembly tasks by operators in APD, the calculation complexity of disassembly sequence planning optimization increases more obviously with the increase of product complexity. Further, the disassembly sequence is a discrete numerical value, which is difficult to be efficiently realized by existing optimization algorithms. To overcome these difficulties, we introduced a complex products APD description. It helped to describe the APD problem with multiple disassembly resources. Based on the APD resources overall matrix, a three-objective, viz., the total disassembly time-consuming, the disassembly direction change times, and the disassembly tools replacement times, optimization model was constructed. To obtain the optimal disassembly planning sequence, the improved discrete NSGA-II (IDNSGA-II) was proposed, which introduced a novel population restart mechanism. The proposed method was verified in a bevel gearbox disassembly process from the EAS4633 rescue inspection equipment. The optimal results showed the APD sequence planning multi-objective optimized results obtained by the IDNSGA-II algorithm can be close to the single object optimized results. For the rest objects, the optimal solution obtained from the multi-objective genetic algorithm is obviously better.

Human–robot collaboration in industrial environments: A literature review on non-destructive disassembly

Nowadays, numerous companies and industries introduce recycling processes in their production, aiming to increase the sustainable use of the planet’s natural resources. Nevertheless, these processes remain inefficient due to the high degree of complexity and variation in the products. In order to remedy this, industry stakeholders adopt the circular economy business model and introduce take-back programmes and remanufacturing processes for their End of Life products in their own supply chains. Take-back programmes enable the re-sourcing of sub-assemblies and components of previously manufactured products while remanufacturing processes encourage non-destructive disassembly. Due to the uncertain conditions of the re-sourced products, fully automated cells cannot cope with the demanding disassembly processes. Therefore, there is a need to establish hybrid disassembly robot cells where humans and robots work closely together in a process known as human–robot collaborative disassembly (HRCD). This paper examines the landscape of HRCD and reviews the progress in the field during the period 2009–2020. The analysis investigates principles and elements of human–robot collaboration in industrial environments such as safety standards and collaborative operation modes, HRI communication interfaces, and the design characteristics of a disassembly process. Additionally, the various technical challenges of HRCD are explored, and a review of existing systems supporting HRCD is presented. This review aims to support the robotics community in the future development of HRCD systems, discuss identified literature gaps, and suggest future research directions in this area. • This paper presents the first extensive review of the latest progress in the area of Industrial human–robot collaboration within the field of disassembly for the period 2009–2020. • The perspective is on how robot control schemes and human–robot interfaces enable human–robot collaborative disassembly tasks. • The outcome of this review identifies gaps in the practice of human–robot collaborative disassembly and provides the future trends and needs for robot cells focused on disassembly tasks.

Human-centred assembly and disassembly systems: a survey on technologies, ergonomic, productivity and optimisation

Despite the increasing use of automation in the current industry 4.0 context, manual assembly and disassembly tasks are still common and in some situations even unavoidable. The interaction between humans and other elements of the Assembly and Disassembly Systems (ADS) has been discussed in the scientific literature with the dual objective of optimising human well-being and system performance. Human Factor (HF) related studies focused on new technologies such as motion tracking systems. These technologies enable the collection of data to improve understanding the work environment and its impact on employee well-being and productivity. In the literature, different metrics were suggested to measure the ergonomic and productivity scores. These measurements influence the global system performance and may allow its optimisation. In this paper, we provide a review of the literature on human-centred ADS. We mainly focus on technologies (used to capture human motion), metrics (used to assess human ergonomic risks and productivity), and operational research models (used to optimise the performance system considering economic and cycle time objectives). Future directions are discussed in the perspectives of this paper.

Dynamic Scene Graph for Mutual-Cognition Generation in Proactive Human-Robot Collaboration

Human-robot collaboration (HRC) plays a crucial role in agile, flexible, and human-centric manufacturing towards the mass personalization transition. Nevertheless, in today’s HRC tasks, either humans or robots need to follow the partners’ commands and instructions along collaborative activities progressing, instead of proactive, mutual engagement. The non-semantic perception of HRC scenarios impedes mutually needed, proactive planning and high-cognitive capabilities in existing HRC systems. To overcome the bottleneck, this research explores a dynamic scene graph-based method for mutual-cognition generation in Proactive HRC applications. Firstly, a spatial-attention object detector is utilized to dynamically perceive objects in industrial settings. Secondly, a linking prediction module is leveraged to construct HRC scene graphs. An attentional graph convolutional network (GCN) is utilized to capture relations between industrial parts, human operators, and robot operations and reason structural connections of human-robot collaborative processing as graph embedding, which links to mutual planners for human operation supports and robot proactive instructions. Lastly, the Proactive HRC implementation is demonstrated on disassembly tasks of aging electronic vehicle batteries (EVBs) and evaluate its mutual-cognition capabilities.

Using work factor method for operational models of disassembly and reassembly evaluation

The purpose of this paper is to study the disassembly task time in the maintenance and recycling context, knowing that only the reassembly task is needed in repairing operation. Dis/reassembly activities are delicate and need precise intervention due to the obligation of equipment refunctioning constraints. Time spent for dis/reassembling faulty components should be well-deducted and standardized. It is not always the case due to the various variant disassembly metrics and contexts. The Work Factor Method during dis/reassembly activities helps to develop the operational dis/reassembly time models. These models contribute to defining effective Time as far as operational dis/re-assembly activity is concerned and can help to optimize maintenance and recycling task planning.

Disassembly task planning for end-of-life automotive traction batteries based on ontology and partial destructive rules

Disassembly is an essential step for the cascade utilization of end-of-life automobile power batteries. The diversity in types and structures of end-of-life automobile power batteries has led to much waste in time and cost in manual disassembly processes. With the incoming large-scale retirement of automobile power batteries, it is urgent to use artificial intelligence technology to enable the automation of battery disassembly planning. In order to establish a complete and open product information model to realize the automatic disassembly task planning of end-of-life automobile power battery, a disassembly task planning method of automobile power batteries is proposed based on ontology and partial destructive rules. This presented approach is in combination with case-based reasoning/rule-based reasoning, which is utilized as the mechanism for disassembly knowledge reuse and reasoning. Firstly, a disassembly information ontology of automobile power batteries is constructed to describe the components information and assembly relation. Then, a set of partial destructive rules are formulated to guide the dismount of parts with destructive connection. Thirdly, a disassembly sequence generation method is presented to infer feasible planning schemes from the rule base. Finally, the effectiveness of the method is tested with a case study of a power lithium-ion battery pack. The case study has indicated that this presented method can generate the disassembly task schemes quickly and effectively, when applied to the disassembly of large-scale heterogeneous automobile power batteries.

Learning and generalising object extraction skill for contact-rich disassembly tasks: an introductory study

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.

Human-robot collaborative disassembly line balancing considering the safe strategy in remanufacturing

Remanufacturing is an important method to realize resource saving and circular economy. Disassembly is a vital step of remanufacturing and it is always finished by manual labor which is low efficiency. Although robotic disassembly covers the shortages of manual disassembly, it is still difficult for the robots to independently finish the disassembly process without the operator's assistance. The human-robot collaborative disassembly is being paid increasing attention and the human-robot collaborative disassembly line balancing is to assign suitable disassembly tasks to each human-robot collaborative disassembly workstation in a balanced manner. In human-robot collaborative disassembly, the operator's safety should be considered when the robot collaboratively executes the disassembly tasks with the operator. In this paper, disassembly information model of human-robot collaborative disassembly considering the safe strategy is studied. After that, this paper solves the human-robot collaborative disassembly line balancing problem using the improved discrete bees algorithm. To verify the proposed method, case study based on a bearing coupler is carried out. The results show the proposed improved discrete bees algorithm can generate the optimal solutions of human-robot collaborative disassembly line balancing problem considering the safe strategy and finds better solutions compared with the other optimization algorithms in terms of running time, average generational distance and Pareto fronts.

An adaptive genetic algorithm-based and AND/OR graph approach for the disassembly line balancing problem

Manufacturers need to arrange the recovery of product components and subassemblies for reuse, remanufacture and recycling to extend the life of materials in use and reduce the disposal volume owing to increasing environmental concerns. The disassembly line balancing problem (DLBP) is the process of allocating a set of disassembly tasks to an ordered sequence of workstations. A novel mathematical model is presented for the DLBP by considering resource and labour constraints. Utilizing a transformed AND/OR graph as the main input ensures the feasibility of the precedence relationships among the tasks. The objective is to minimize the number of labourers used under the predetermined cycle time. This study proposes a three-phase heuristic adaptive genetic algorithm (AGA) to optimize the number of labourers in the disassembly line. Experimental results indicate that the proposed method is superior to the existing approaches for medium- and large-scale DLBPs.

A multi-variable analysis of aircraft structure disassembly ‐ A technico-economic approach to increase the recycling performance

Despite international efforts in maximizing products recycling, complex product reprocessing poses a considerable threat on sustainable development on a macro scale. Thousands of aircrafts reach their End-of-Life each year with most of their components ending up in graveyards and landfill. Airframe disassembly, as a main step, is a complex process in terms of the required effort versus the regained values. This research proposes a disassembly planning model to evaluate the ease of disassembly for complex products. Three key disassembly parameters are employed including time, difficulty and, material compatibility to evaluate the efficiency of the disassembly strategies based on technical, economic and environmental criteria. A mathematical model is presented to find the most efficient solution based on the defined criteria. A mid-range airliner stabilizer was selected as a case study to validate the efficiency of the proposed method. The analysis includes airframe disassembly site visits, aircraft documentation analysis, interviews with experts and disassembly examination over a three-year period. The results from the proposed method shows the selected disassembly plan is 65% easier to perform, 85% less time-consuming and allows the user to gain more material recovery than conventional technics. The scalable findings of this research can be useful to increase the efficiency of the disassembly task on complex products particularly of interests to the disassembly sites, product owners and OEMs.

Disassembly Line Balancing by Using Simulation Optimization

Increasing environmental awareness in today's society and stricter environmental regulations have forced manufacturing firms to take necessary actions for the recovery of end-of-life (EOL) products through different options (e.g., recycling, remanufacturing,). Disassembly is regarded as a critical operation in EOL treatment of used products since all product recovery options require the disassembly of EOL products at certain levels. This critical operation is generally carried out by forming disassembly lines in product recovery facilities. Miscellaneous methodologies based on heuristics, metaheuristics and mathematical programming have been proposed for the balancing of disassembly lines. Majority of those methodologies assume that disassembly line parameters are deterministic by ignoring the fact that a disassembly line involves great deal of uncertainty mainly due to uncertain conditions of arriving EOL products. Considering this high level of uncertainty, simulation modeling can be an effective tool for the modeling of disassembly lines. In this study, a simulation-based disassembly line balancing methodology is proposed for the explicit consideration of stochastic parameters. First, simulation model of a disassembly line is constructed. Since the disassembly line balancing problem has a combinatorial nature, two commonly used metaheuristics (i.e., genetic algorithms (GAs) and simulated annealing (SA)) are integrated with the simulation model in order to balance the disassembly line. The disassembly sequence and task assignments proposed by GA are compared with the sequence and task assignments proposed by SA. This comparison indicates that GA outperforms SA in four of eight performance measures while both algorithms have the same value for line efficiency measure.