Optimization-Based Disassembly Sequence Planning Under Uncertainty for Human–Robot Collaboration

This article presents a disassembly task planning algorithm considering human–robot collaboration (HRC) and human behavior prediction (HBP). Unlike assembly procedures, the disassembly of end-of-life (EOL) products has been a labor-intensive process with uncertainties difficult to cope with. Meanwhile, it is usually challenging to obtain an optimal sequence efficiently without excessive computational cost. Also, the conventional human-centered task planning, in which the robot has to halt frequently due to unsafe interruptions by human motions, may decrease the efficiency of the disassembly process. In this article, a sequence planner is proposed to assign tasks in real time between a human operator and a robot to overcome the aforementioned challenges. The cost function includes the effort of the human and the robot in terms of both movement distance and time spent on the tasks. The constraints include the disassembly rules and the safety of the human operation. The optimal sequence is generated by solving an optimization problem in a receding-horizon way. In particular, at each step, the proposed disassembly sequence planner locates the workers (a human operator and a robot) and the to-be-disassembled components, predicts human movement for the next several steps, and obtains the optimal disassembly sequence for the next several steps following disassembly rules and safety constraints. Experiments have been extensively conducted on the disassembly of a wooden toybox and a used hard disk drive (HDD) to validate the proposed disassembly sequence planner. The planner has successfully generated the disassembly sequence in an HRC setting explicitly considering real-time human motion prediction and assigned the human operator and the robot to collaboratively complete disassembly tasks without violating disassembly rules and safety constraints.

Disassembly planning and sequencing play an important role in recycling a fast-growing number of end-of-life products. Optimal sequences can effectively reduce carbon emissions and save natural resources in the remanufacturing industry. Considering the development of intelligent manufacturing technology, this work deals with the optimization problem of selective disassembly sequences with an objective of maximizing disassembly profit. Disassembly sequences are generated based on AND/OR graphs. After setting up an environment matrix based on such graphs, this proposes a Q-learning technique to find an selective optimal disassembly sequence. The algorithm is applied to real-life disassembly cases. Experimental results show that the algorithm is superior a popularly-used genetic algorithm (GA) in both computing speed and solution quality through their various comparisons.

This paper addresses the problem of creating intelligent, green, and financially-beneficial disassembly sequences for end-of-life (EOL) electronic products. These complex EOL products contain a broad spectrum of materials including precious metals. Therefore, one would have to process these products to retrieve the value buried in them. EOL processing options include, reuse, remanufacturing, recycling or proper disposal. Each of this option requires a certain level of disassembly. Hence, obtaining an optimal or near optimal disassembly sequence is crucial to increasing the efficiency of EOL processing. Since the complexity of determining the best disassembly sequence increases as the number of parts in a product grows, an efficient methodology is required for disassembly sequencing. In this paper, we present an evolutionary algorithm for generating near-optimal and/or optimal sequences for selective disassembly of EOL products. A numerical example is provided to demonstrate the functionality of the algorithm.

The representation of the disassembly sequence is a key issue in maintenance planning. It involves a highly constrained combinatory problem, which is coupled with varying start and end nodes. These start and end nodes are dependent upon the nature of a maintenance task. In this paper, different representation and modelling schemes for disassembly sequence planning are first reviewed. Then, a novel representation scheme for disassembly sequence, which is generic and can be used to represent both the geometrical and precedence constraints dynamically in product disassembly, is proposed. Based on such a representation scheme, the process for the determination of possible disassembly sequences can be simplifled. By taking into consideration disassembly constraints, the optimal disassembly sequence in relation to the component to be maintained (target component) can be quickly derived. This is achieved by pruning the search space of disassembly sequences, grouping related components into subassemblies, and identifying free components to facilitate disassembly operations. Subsequently, the optimal disassembly sequence in relation to the target component can be obtained using genetic algorithms. In this manner, the disassembly sequences for a complex product comprising a relatively large number of components can be derived within a short time. A case study is used to illustrate the effectiveness of the representation scheme. Comparisons are made using the same case study with the AND/OR graph representation and the Petri net approach for disassembly sequence planning. The results show that the proposed representation scheme is simpler and is more efficient than the rest.

The presented paper suggests a design method which seeks to identify the best scheduling of human robot collaborative (HRC) operations with respect to a required safety level. The human behavior along manufacturing scenarios is effectively forecasted through dedicated computer-aided tools. Consequently, this method stresses the usage of virtual environment to replicate both human postures and robot encumbrances over the manufacturing operations. Moreover, it proposes a safety index formulation for HRC systems based on the minimum distance between human and robot (H-R). As results, the approach returns the safety index for every possible combination of H-R operations. Subsequently, a scheduling algorithm suggests the operations sequence depending on the expected value of the safety index, providing an evaluation of the time needed to complete the process. The method is validated on surface control phase involved in post-processing of parts produced by laser powder bed fusion (L-PBF) Additive Manufacturing.

At the end-of-life (EOL) of a product, there are several options available for its processing including reuse, remanufacturing, recycling and disposing. In almost all cases, a certain level of disassembly may be necessary. Thus, finding an optimal (or near optimal) disassembly sequence is crucial to increasing the efficiency of the process. Disassembly operations are labor intensive, can be costly, have unique characteristics and cannot be considered as the reverse of assembly operations. Since the complexity of determining the best disassembly sequence increases with increase in the number of parts of the product, it is extremely crucial that an efficient methodology for disassembly sequencing be developed. In this paper, we present a genetic algorithm for disassembly sequencing of EOL products. A case example is considered to demonstrate the functionality of the algorithm.

Web-based human-robot collaboration digital twin management and control system

Disassembly Sequence planning is the important module of virtual maintenance. Its foundational task is to test the disassemblability of parts, find out all the feasible disassembly sequences of targeted parts and search for the near-optimal disassembly sequence. In the paper, the spherical algorithm is used to test the disassemblability of parts. And then the disassembly direction of parts is determined by rules which are the simplified version of Gaussian map approach. Thus the near-optimal disassembly sequence is found out by optimizing all feasible disassembly sequences of targeted parts by ant colony algorithm. The simulation result of the sequence optimization proves to be the same with manual calculation considering comprehensive cost of the change of disassembly tools and disassembly directions. The set of approaches can be reference for disassembly sequence planning of large-scale mechanical products.

PurposeThe purpose of this paper is to propose a framework to identify all the feasible disassembly sequences for a multi‐component product and to find an optimal disassembly sequence, according to specific criteria such as cost, duration, profit, etc.Design/methodology/approachTaking into account topological and geometrical constraints of a product structure, an AND/OR disassembly graph is built. Each graph node represents a feasible subassembly. Two nodes i and j are connected by an arc (i, j), called a transition, if the subassembly j can be obtained from the subassembly i by removing one or several connectors. Constraint programming approach is used to generate the feasible subassemblies and related transitions.FindingsIf a cost zij is incurred to perform a transition (i, j), an optimal disassembly sequence can be generated for a given subassembly, using the shortest path algorithm or a linear programming model.Research limitations/implicationsThe proposed approach performs very well compared to other approaches published in the literature, even when applied to products requiring parallel disassembly and including a large number of parts.Practical implicationsThis approach has been successfully applied to assess the wheelchair maintainability at the design stage and will be implemented in CAD systems. One other application, regarding the disassembly process and total revenue maximization for product recycling, is now under consideration.Originality/valueApplying constraint programming to efficiently generate the set of the feasible subassemblies constitutes the main contribution in this paper. This process is the hardest step in the disassembly sequencing problem.

Selection of an optimal disassembly sequence is essential for the efficient processing of a product at the end of its life. Disassembly sequences are listings of disassembly actions (such as the separation of an assembly into two or more subassemblies, or removing one or more connections between components). Disassembly takes place in remanufacturing, recycling, and disposal with a disassembly line being the best choice for automation. In this paper, the disassembly sequencing problem is solved for a cell phone case on a disassembly line, seeking a sequence which is feasible, minimizes the number of workstations (and hence idle times), provides for early removal of high demand/value parts, provides the removal of parts that lead to the access of greatest number of still-installed parts, and early removal of hazardous parts as well as for the grouping of parts for removal having identical part removal directions. Since finding the optimal sequence is computationally intensive due to factorial growth, a heuristic method is used taking into account various disassembly-specific matters. Using the experimentally determined precedence relationships and task times of a real-world cell phone, a MATLAB program is designed and a sequencing solution is generated. Finally, Design for Disassembly (DFD) improvements are recommended with respect to environmentally conscious manufacturing.

Due to the increased responsibility for the end-of-life (EOL) treatments of products by the manufacturers, the ease of disassembly has become a key design issue for mass-produced products. As an extension of the conventional design for disassembly (DFD), this paper presents a computational method for designing assemblies that can be disassembled only in specified sequences. Given an ideal disassembly sequence that maximizes the profit in the absence of geometric constraints, the method simultaneously determines the spatial configurations of components, locaters, and joints, such that disassembly can occur only in the sequence. A multi-objective genetic algorithm is utilized to search for Pareto-optimal designs in terms of the unique realization of the given disassembly sequence, the satisfaction of distance specifications among components, and the efficient use of locators on components. A case study on a simplified laptop computer assembly demonstrates the effectiveness of the method

Remanufacturing has enormous economic and environmental benefits in terms of resource conservation and environmental protection. Disassembly, as an essential step in remanufacturing, is always manually executed, it has the disadvantages of high labor intensive, time consuming and low efficiency while robotic disassembly can cover the shortages of manual disassembly. During the robotic disassembly process, considering the structure and movement characteristics of the industrial robot, the industrial robot need to perform collision avoidance movements considering the obstacle caused by the End-of-Life (EoL) product. The moving time considering the robotic collision avoidance trajectory is a non-negligible part of total disassembly time. In this paper, robotic disassembly sequence planning (RDSP) considering robotic collision avoidance trajectory is proposed. This method is used to obtain the collision avoidance trajectory and the moving time between different disassembly points by the robotic collision avoidance model established in this paper. Afterwards, an optimized discrete bee algorithm (ODBA) is used to generate the optimal disassembly sequence to minimize the total disassembly time. Finally, case studies based on a gear pump verify the effectiveness of proposed methods.

The generation of a set of optimal or near-optimal disassembly sequences is an important task associated with the study of disassembly planning. This task encapsulates actions at the required disassembly depth to reach maximum net revenue while satisfying economic constraints. The Petri net modelling, combined with heuristic search procedures developed in this study, offers an efficient procedure for disassembly sequence generation. The heuristic generates and searches a partial reachability graph to arrive at an optimal or near-optimal disassembly sequence based on the firing sequence of transitions of the Petri net model. The proposed methodology reduces the search space in two areas: (1) pruning the disassembly tree (DT) and, (2) selective tracking of the reachability graph. The efficacy of the proposed methodology is demonstrated by using two examples from the literature.

An increase in human-robot collaboration is expected in the future. It can be assumed that this development will affect both, our working environment and our private lives. An exploratory study (N = 116) was conducted to get more insight in this important future field. The results of the study can help to understand how human-robot collaboration in the working environment and in our private lives is seen today, and should help to improve the acceptance of this important future field.

This paper considers disassembly sequencing problems subjected to sequence dependent disassembly costs. In practice, the methods for dealing with such problems rely mainly on metaheuristic and heuristic methods, which intrinsically generate suboptimum solutions. Exact methods are NP-hard and therefore unsuitable to most of the practical problems. Nevertheless, it is useful to have exact methods available that can be applied in order to check, at least medium sized problems, to what extent the heuristically obtained solutions deviate from the optimum solution. The existing exact approaches, which are based on integer linear programming (ILP), become unmanageable, even for the cases of modest product complexity. To alleviate this problem to some extent, the iterative method that has been proposed by Lambert (2006) is applied here. This method is based on repeatedly solving a binary integer linear programming (BILP) problem instead of an ILP problem. The method appears to converge sufficiently quickly to be valuable for dealing with medium sized problems. We then use the iterative method for the validation of a new heuristic method that is also proposed in this paper. Finally, both the heuristic and the iterative BILP methods are implemented on a cellphone from practice consisting of 25 components that are represented, according to a set of precedence relationships, via a disassembly precedence graph.