Feasible Disassembly Sequences Research Articles

Selective disassembly sequence optimization based on the improved immune algorithm

PurposeThe purpose of this paper is to improve the automation of selective disassembly sequence planning (SDSP) and generate the optimal or near-optimal disassembly sequences.Design/methodology/approachThe disassembly constraints is automatically extracted from the computer-aided design (CAD) model of products and represented as disassembly constraint matrices for DSP. A new disassembly planning model is built for computing the optimal disassembly sequences. The immune algorithm (IA) is improved for finding the optimal or near-optimal disassembly sequences.FindingsThe workload for recognizing disassembly constraints is avoided for DSP. The disassembly constraints are useful for generating feasible and optimal solutions. The improved IA has the better performance than the genetic algorithm, IA and particle swarm optimization for DSP.Research limitations/implicationsAll parts must have rigid bodies, flexible and soft parts are not considered. After the global coordinate system is given, every part is disassembled along one of the six disassembly directions –X, +X, –Y, +Y, –Z and +Z. All connections between the parts can be removed, and all parts can be disassembled.Originality/valueThe disassembly constraints are extracted from CAD model of products, which improves the automation of DSP. The disassembly model is useful for reducing the computation of generating the feasible and optimal disassembly sequences. The improved IA converges to the optimal disassembly sequence quickly.

An improved multi-objective evolutionary algorithm for multiple-target asynchronous parallel selective disassembly sequence planning

The product’s service life has been shortened rapidly along with the development of modern technologies and human esthetic evolutions, resulting in a great number of end-of-life (EOL) products. Disassembling renewable parts from EOL products for remanufacturing is of great significance to save resources and protect the environment. The existing studies on selective disassembly sequence planning (SDSP) largely focus on the sequential disassembly planning, which is inefficient especially for complex products because it is a linear process where only one component can be removed at a time. Thus, this work focuses on parallel SDSP and further proposes a multiple-target asynchronous parallel selective DSP (APSDSP) with the objectives of minimizing disassembly time and maximizing disassembly profit simultaneously. In APSDSP, operators can remove multiple components simultaneously as long as disassembly constraints are not violated, and without synchronization requirement between operators. The feasible disassembly sequence and disassembly direction sequence are generated by a space interference matrix method (SIMM) to meet the actual disassembly environment. Based on SIMM, an improved multi-objective evolutionary algorithm based on multiple neighborhood search strategy is developed to create the pareto frontier of the problem. Finally, two cases study are presented to validate the effectiveness of the proposed methodology. It gives enterprises new insights to reduce disassembly time and improve disassembly profit.

Disassembly assessment from CAD-based collision evaluation for sequence planning

Disassembly sequence planning is the foundation for disassembly process planning. Furthermore, integrating efficient geometrical disassembly feasibility of non-destructive operations can contribute to designing robotic disassembly and remanufacturing systems. Non-destructive operations increase the chance of repurposing parts with less cost but require collision-free trajectories between components. Thus, generating geometrically feasible disassembly sequences is critical for determining the optimal disassembly sequencing and disassembly trajectories. In this work, geometric collision data has generated to develop the disassembly precedence matrix that provides fundamental information for disassembly planning methods. Current methods often require significant manual resources to locate geometrically feasible sequences for disassembly of products. This paper describes the results of utilizing disassembly collision tests to automatically extract interference data from STEP CAD assembly files to determine feasible disassembly operations. A method to determine a precedence matrix that describes all geometrically feasible disassembly operations for disassembly of a selected part or a whole assembly to its components has been developed and tested. Extracted data from STEP files is used to characterize the spatial properties of a component in an assembly and is the basis for the collision test to determine the geometrically feasible disassembly operations. The precedence matrix also identifies the feasible disassembly directions for each component based on the outcomes of the collision tests.

Disassembly sequence planning based on a modified grey wolf optimizer

Disassembly sequence planning (DSP) can effectively increase the disassembly efficiency, shorten the disassembly cycle, reduce disassembly costs, and reduce environmental hazards of end-of-life (EOL) products, playing an important role in manufacturing industries. Thus, it is urgent to propose an approach to solve the DSP problem. DSP is a famous NP-hard combinatorial optimization problem. As the size of components increases, exact algorithms can hardly obtain the optimal disassembly sequence. Therefore, we propose a promising intelligence algorithm, modified grey wolf optimizer (MGWO), to solve the DSP problem. MGWO inherits the main idea of the hierarchy and hunting mechanism of the original grey wolf optimizer (GWO). Three new operators are designed in MGWO to ensure the feasibility of solutions under the complex constraint of disassembly precedence. The feasible solution generator (FSG) is designed to obtain feasible disassembly sequences, the neighborhood search operator (NSO) is developed to make wolves (solutions) self-evolving, and the guided search operator (GSO) is used to make the wolf group guided by three leaders of wolves. Two engineering cases are applied to validate the effectiveness of the proposed operators. Then, they and two real-world applications are used to compare the MGWO with other reported methods. The results demonstrate that MGWO can solve the DSP problem effectively.

Asynchronous parallel disassembly sequence planning for multi-manipulator based on improved shuffled frog leaping algorithm

To solve the problem of low disassembly efficiency of complex products, an asynchronous parallel disassembly sequence planning method of multi-manipulator based on improved shuffled frog leaping algorithm was proposed. Depending on the characteristics of this method, the disassembly manipulators are taken as the research object, and the minimum completion time of disassembly is taken as the optimization objective. Based on the basic disassembly time of parts, the disassembly preparation time caused by the change of disassembly tools and directions is considered, so that the objective function evaluation model is more realistic. Then, the mathematical model of asynchronous parallel disassembly sequence planning is constructed and the disassembly information modeling method is optimized. We proposed a new method based on the fastener constraint matrix and the part constraint matrix to build the information model of product disassembly. Based on this model, the detachable conditions are derived and the feasible disassembly sequence is obtained. In order to improve the performance of the proposed algorithm, a double-link encoding method and an efficient decoding operator is proposed. The adjustment orders which based on adjustment position and the substitution formulas are constructed to carry out the new segmental local evolution of frog individuals, and the update formula of frog position is reconstructed. Furthermore, the two-point mutation operator is added to the global information exchange strategy to enhance the search ability of the algorithm. The proposed algorithm is applied to a screw lifting mechanism and four products of varying complexity, and the results are compared with two algorithms. The results show that the proposed method can solve disassembly problems of different scales and is especially effective in solving large-scale disassembly problems.

Disassembly sequence planning using discrete Bees algorithm for human-robot collaboration in remanufacturing

Abstract Remanufacturing helps to improve the resource utilization rate and reduce the manufacturing cost. Disassembly is a key step of remanufacturing and is always finished by either manual labor or robots. Manual disassembly has low efficiency and high labor cost while robotic disassembly is not flexible enough to handle complex disassembly tasks. Therefore, human-robot collaboration for disassembly (HRCD) is proposed to flexibly and efficiently finish the disassembly process in remanufacturing. Before the execution of the disassembly process, disassembly sequence planning (DSP), which is to find the optimal disassembly sequence, helps to improve the disassembly efficiency. In this paper, DSP for human-robot collaboration (HRC) is solved by the modified discrete Bees algorithm based on Pareto (MDBA-Pareto). Firstly, the disassembly model is built to generate feasible disassembly sequences. Then, the disassembly tasks are classified according to the disassembly difficulty. Afterward, the solutions of DSP for HRC are generated and evaluated. To minimize the disassembly time, disassembly cost and disassembly difficulty, MDBA-Pareto is proposed to search the optimal solutions. Based on a simplified computer case, case studies are conducted to verify the proposed method. The results show the proposed method can solve DSP for HRC in remanufacturing and outperforms the other three optimization algorithms in solution quality.

An efficient metaheuristics for a sequence-dependent disassembly planning

Abstract Disassembly planning (DP) is critical in remanufacturing and value recovery from end-of-life products and has attracted increasing attention due to the recent resurgence of research on circular economy. DP problem is NP-hard and its complexity increases exponentially with the size of problem. Sequence-dependent cost due to varying quality of the parts to be retrieved further increases the complexity of DP problems. This paper investigates the DP considering sequence-dependent costs among disassembly operations. A mathematical model is proposed with the objective to maximize the recovery profit using an AND/OR graph (AOG) subject to sequence-dependent costs. A novel two-phase heuristic method is developed to effectively generate feasible disassembly sequence according to the AOG in reasonable computation time. In addition, an improved genetic algorithm (IGA) is proposed to solve the problem, in combination with the presented two-phase heuristic. The performance of IGA is measured on a series of test problem instances against exact methods including CPLEX and an iterative method. Results indicate that IGA successfully find the near-optimal/optimal solutions and outperforms the other methods in terms of computation time. Finally, the proposed method is applied to compute the disassembly solution of a HG5-20 triaxial five speed mechanical transmission. Compared to the existing disassembly solutions of the transmission, the obtained solutions by IGA can shorten about 11% disassembly time and increase by approximately 7% recovery profit.

Hybrid bidirectional ant colony optimization (hybrid BACO): An algorithm for disassembly sequence planning

In traditional disassembly sequence planning (DSP), a disassembly sequence is planned based on the description of the product design and personal experience. Research on disassembly sequence planning needs to take into account the relationship between the parts and related factors, such as changes in orientation and tools, so as to optimize the order of the disassembly sequence. Among the numerous feasible disassembly sequences, it is important for researchers to find the most economical DSP, which becomes more difficult as the number of parts increases. In this study, a hybrid bidirectional ant colony optimization (Hybrid BACO) algorithm is proposed and compared with four related algorithms. Simulated cases show that the hybrid BACO algorithm provides a better solution than other ant algorithms. The five ant algorithms are also compared from the viewpoint of a reverse assembly sequence, and the results again show that the Hybrid BACO algorithm provides the best solution quality.

Modeling and Planning for Dual-Objective Selective Disassembly Using and/or Graph and Discrete Artificial Bee Colony

Disassembly sequencing is important for remanufacturing and recycling used or discarded products. AND / OR graphs (AOGs) have been applied to describe practical disassembly problems by using “ AND ” and “ OR ” nodes. An AOG-based disassembly sequence planning problem is an NP-hard combinatorial optimization problem. Heuristic evolution methods can be adopted to handle it. While precedence and “ AND ” relationship issues can be addressed, OR (exclusive OR ) relations are not well addressed by the existing heuristic methods. Thus, an ineffective result may be obtained in practice. A conflict matrix is introduced to cope with the exclusive OR relation in an AOG graph. By using it together with precedence and succession matrices in the existing work, this work proposes an effective triple-phase adjustment method to produce feasible disassembly sequences based on an AOG graph. Energy consumption is adopted to evaluate the disassembly efficiency. Its use with the traditional economical criterion leads to a novel dual-objective optimization model such that disassembly profit is maximized and disassembly energy consumption is minimized. An improved artificial bee colony algorithm is developed to effectively generate a set of Pareto solutions for this dual-objective disassembly optimization problem. This methodology is employed to practical disassembly processes of two products to verify its feasibility and effectiveness. The results show that it is capable of rapidly generating satisfactory Pareto results and outperforms a well-known genetic algorithm.

A hybrid conjugated method for assembly sequence generation and explode view generation

PurposeThe purpose of this paper is to develop an efficient hybrid method that can collectively address assembly sequence generation (ASG) and exploded view generation (EVG) problem effectively. ASG is an act of finding feasible collision free movement of components of a mechanical product in accordance with the assembly design. Although the execution of ASG is complex and time-consuming in calculation, it is highly essential for efficient manufacturing process. Because of numerous limitations of the ASG algorithms, a definite method is still unavailable in the computer-aided design (CAD) software, and therefore the explosion of the product is not found to be in accordance with any feasible disassembly sequence (disassembly sequence is reverse progression of assembly sequence). The existing EVG algorithms in the CAD software result in visualization of the entire constituent parts of the product over single screen without taking into consideration the feasible order of assembly operations; thus, it becomes necessary to formulate an algorithm which effectively solves ASG and EVG problem in conjugation. This requirement has also been documented as standard in the “General Information Concerning Patents: 1.84 Standards for drawings” in the United States Patent and Trademark office (2005) which states that the exploded view created for any product should show the relationship or order of assembly of various parts that are permissible.Design/methodology/approachIn this paper, a unique ASG method has been proposed and is further extended for EVG. The ASG follows a deterministic approach to avoid redundant data collection and calculation. The proposed method is effectively applied on products which require such feasible paths of disassembly other than canonical directions.FindingsThe method is capable of organizing the assembly operations as linear or parallel progression of assembly such that the assembly task is completed in minimum number of stages. This result is further taken for EVG and is found to be proven effective.Originality/valueAssembly sequence planning (ASP) is performed most of the times considering the geometric feasibility along canonical axes without considering parallel possibility of assembly operations. In this paper, the proposed method is robust to address this issue. Exploded view generation considering feasible ASP is also one of the novel approaches illustrated in this paper.

An improved multi-objective discrete bees algorithm for robotic disassembly line balancing problem in remanufacturing

Remanufacturing is an effective way to realize the reutilization of resources. Disassembly, as an essential step of remanufacturing, is usually finished by manual work which is low efficiency and high labor cost. Robotic disassembly provides an alternative way to reduce labor intensity and disassembly cost. Disassembly line is an efficient method to deal with end-of-life products on a large scale. Balancing the workload of robotic workstations is the main objective of robotic disassembly line balancing problem. In this paper, an improved multi-objective discrete bees algorithm is proposed to solve robotic disassembly line balancing problem. The feasible disassembly sequence is obtained by space interference matrix method. It is used to generate robotic disassembly line solution by robotic workstation assignment method. After that, the multi-objective robotic disassembly line balancing problem is proposed. With the help of efficient non-dominated Pareto sorting method, the improved multi-objective discrete bees algorithm is proposed to find Pareto optimal solutions. Based on a gear pump and a camera, the performance of the improved multi-objective discrete bees algorithm is analyzed under different parameters and compared with the other optimization algorithms. In addition, Pareto fronts of robotic disassembly line balancing problem are also compared with those of the other two cases. The result shows the proposed method can find better solutions using comparable running time compared with the other optimization algorithms.

Backtracking Algorithm-based Disassembly Sequence Planning

Disassembly sequence planning (DSP) is one of the core research themes of product life cycle design. Effective DSP can reduce product life cycle costs as well as their environmental effects and provides a theoretical future foundation for recycling and improving sustainability performance. The problem with current heuristic artificial intelligence algorithms is that the outcome may have a local extremum; therefore, it may not generate the exact or even the approximate optimum solution. For a specific sequence planning problem, some algorithm parameters need to be adjusted in order to generate a perfect solution. In this study, the objective is to create new DSP by integrating a constraint satisfaction problem (CSP) based on backtracking algorithms. First, the relationship between DSP and the CSP was analysed. Then, the DSP was converted into a CSP based on a disassembly constraint graph (DCG). The solution flowchart of DSP and the CSP-associated algorithms, based on backtracking algorithms, are presented in this article for a dishwasher recycling case study. The outcome of this study shows that the created method can effectively find all of the feasible disassembly sequences as well as the optimum sequence for recycling. Ultimately, these results can be effectively applied to more appliances for future use.

Selective cooperative disassembly planning based on multi-objective discrete artificial bee colony algorithm

Disassembly sequencing has significant effects on the performance of remanufacturing and recycling of used or discarded products. Studies on disassembly sequence optimization have largely focused on sequential disassembly. However, for large or complex products sequential disassembly takes long time to complete and is rather inefficient since it removes only one part or subassembly at a time with only one operator assigned to disassemble a product. This work studies selective cooperative disassembly sequence planning (SCDSP) problem which is essential to disassemble large or complex products in an efficient way. Similar to sequential disassembly planning, SCDSP aims at finding the optimal disassembly task sequence, but is more complicated. SCDSP is a nonlinear NP-complete combinatorial optimization problem, and evolutionary algorithms can be adopted to solve it. In this paper exclusive and cooperative relationships are introduced as additional constraints besides the common precedence relationship. A novel procedure to generate feasible cooperative disassembly sequences (GFCDS) is proposed. A mathematical programming model of SCDSP is developed based on the parallel disassembly characteristics with two optimization objectives i.e. disassembly time and profit, considered. A multi-objective evolutionary algorithm (MOEA), i.e., multi-objective discrete artificial bee colony optimization (MODABC), is adopted to solve the problem to create the Pareto frontier. This approach is applied to real-world disassembly processes of two products (a small product and a medium/large one) to verify its feasibility and effectiveness. Also, the proposed method is compared with the well-known NSGA-II. For our comparative study, the nondominated solutions of the two MOEAs are compared in both cases, and two quantitative metrics, i.e., inverted generational distance (IGD) and spacing (SP), are adopted to further measure the algorithm performance. Results indicate that the set of nondominated solutions from MODABC are better for each instance tested, and the Pareto front is overall superior to that from NSGA-II. For both cases, IGD and SP are decreased by up to 81.5% and 62.2%, respectively.

Optimization of disassembly sequence planning for preventive maintenance

Disassembly sequence planning is considered an important research topic in the manufacturing automation field. In recent years, in close collaboration with manufacturers, many investigations have been conducted to design robust and profitable dismantling systems. Therefore, the designers of new products have to consider the disassembly constraints in the design phase of products. Moreover, considering the disassembly constraints is important not only in the context of life end of a product but also in its life cycle in order to reduce problems related to its exploitation and maintenance. Consequently, the optimization of the disassembly process of a product is a crucial task for improving the product design. This article presented an automated disassembly sequence planning approach based on ant colony algorithm. The developed method permits the generation of an optimal and feasible disassembly sequence planning of a product from its computer-aided design (CAD) model. Several criteria were introduced in this approach such as part volume, tool change, disassembly directions, and the maintainability of wearing part. A comparison between genetic and ant colony algorithms was conducted to reveal the effectiveness of the proposed approach. A case study is presented and an implemented tool was developed. The obtained results demonstrate the satisfactory side of the considered criterions to identify the feasible disassembly sequence plan.

A Method for Improving the Process and Cost of Nondestructive Disassembly

The ever-increasing global environmental concerns demand that strong emphasis be given to the decision-making process pertaining to the strategy of design for nondestructive disassembly during the product evaluation stage. The ultimate objective is to considerably increase the percentage of product components and materials suitable for recycling, recovery, and/or reuse. In this context, this paper proposes a method for improving the nondestructive disassembly of a final product. This method analyzes the nondestructive disassembly by determining a disassembly interference matrix, feasible disassembly sequences, and improved nondestructive disassembly sequences. The innovative element of the nondestructive disassembly method proposed in this paper is integrating the generated conceptual design solutions for a given technical device with a software package developed for determining its improved disassembly sequence embedded within a 3D CAD platform. The developed procedure is based on information obtained from a 3D CAD model of a product, such as geometric constraints, automatic identification of fasteners and components, determination of component-to-component and component-to-fastener connection graphs, and AND/OR logic operations. The goal is for product designers to predict, evaluate, and define improved disassembly sequences while minimizing the cost of disassembly operations as early in the design stage as possible after a CAD model of the product becomes available. The applicability of the integrated method for determining the improved disassembly sequence is presented through an illustrative example.

A systematic selective disassembly approach for Waste Electrical and Electronic Equipment with case study on liquid crystal display televisions

Waste Electrical and Electronic Equipment is one of the major waste streams in terms of quantity and toxicity, and a critical step in Waste Electrical and Electronic Equipment end-of-life processing is through disassembly. Compared with full disassembly, which is a sub-optimal solution due to its high operational cost, selective disassembly is more economic and practical as only selected parts with recycling potential are considered. In this article, a systematic selective disassembly approach for handling Waste Electrical and Electronic Equipment with a maximum disassembly profit in accordance to the Waste Electrical and Electronic Equipment and Restriction of Hazardous Substances Directives has been developed. First, a space interference matrix is generated based on the interference relationship between individual components in the three-dimensional computer-aided design model of Waste Electrical and Electronic Equipment. A matrix analysis algorithm is then applied to obtain all the feasible disassembly sequences through the obtained space interference matrix in a three-dimensional environment. Second, an evaluation and decision-making method is developed to find out an optimal selective disassembly sequence from the obtained feasible disassembly sequences. The evaluation takes into account the disassembly profit and requirements of the Waste Electrical and Electronic Equipment and Restriction of Hazardous Substances Directives, which regulate on recycling rates of different types of products and removal requirements of (1) hazardous, (2) heavy and (3) high-value components. Thus, an optimal solution is a selective disassembly sequence that can achieve the maximum disassembly profit, while complying with the Waste Electrical and Electronic Equipment and Restriction of Hazardous Substances restrictions based on a brute-force search method. Finally, an industrial case on Changhong liquid crystal display televisions of the type LC24F4 is used to demonstrate the effectiveness of the developed approach.

Selective disassembly sequence generation based on lowest level disassembly graph method

Integration of disassembly operations during product design is an important issue today. As known, the number of possible disassembly sequences increases significantly with the number of parts in a product. Thus, generating proper disassembly sequences is critical. Most existing methods often require tremendous computational resources, while, at the same time, they often fail to find realistic and optimal solutions for complex product disassembly. For selective disassembly, for instance, it is important to eliminate the components which are unrelated with the target prior to sequence generation. In order to address this configuration, this paper deals with a method for generating the feasible disassembly sequences for selective disassembly. It is based on the lowest levels of a disassembly product graph. Instead of considering the geometric constraints for each pair of components, the proposed method considers the geometric contact and collision relationships among the components in order to generate the proposed disassembly geometry contacting graph (DGCG). The generation of the disassembly sequences is based on the investigated three cases called micro-units, which consider all the possible situations of relationships among the components in the DGCG. The latter is then used for disassembly sequence generation, thus allowing decreasing the number of possible disassembly sequences. For this purpose, a mixed Virtual Reality Disassembly Environment (VRDE) is developed based on Python programming language using mixed Visualization Toolkit (VTK) and Open Dynamics Engine (ODE) libraries. It is applied for automatic generation of the selective disassembly sequences and is illustrated within two examples.

Generating complete disassembly sequences by utilising two-dimensional views

This research work proposes a novel method to generate the complete disassembly sequences for mechanical products by utilising the part interference matrix which contains the removal directions of the parts and the part connection graph which indicates the contact among the parts in the assembly. Contrary to the earlier methods, the proposed method considers the two-dimensional views generated from the computer-aided design assembly model for automatically identifying the part removal directions and for generating the part connection graph. Rules are formulated in the proposed method to identify the part removal directions effectively and also to reduce the size of part connection graph, since the method of identifying the part removal directions and the size of the formulated graph plays a vital role in the generation of disassembly sequence. Finally, a heuristic method is developed to generate the best feasible disassembly sequences. The effectiveness of the approach is illustrated with three examples.

Disassembly sequence planning based on a genetic algorithm

Disassembly sequence planning is a relatively new subject and has only recently gained interest in the research literature. Disassembly simulations are important not only for improving product design, but also for maintaining an updated process plan, which becomes a tedious and complicated task given the complexity of today’s mechanisms. The crucial issue, in the generation of disassembly plans, is the rise in the amount of sequences, especially with the increase of the number of parts in the mechanism. For this reason, it is crucial to seek an optimal feasible method for the disassembly process. In this paper, an optimized disassembly process based on a genetic algorithm is proposed. The method takes into account several criteria such as maintainability of usury component, part volume, tools change and the change of disassembly directions. The results obtained demonstrate the satisfactory side of these criteria to identify a feasible disassembly sequence.

Disassembly sequence planning using a Simplified Teaching–Learning-Based Optimization algorithm

Disassembly Sequence Planning (DSP) is a challenging NP-hard combinatorial optimization problem. As a new and promising population-based evolutional algorithm, the Teaching–Learning-Based Optimization (TLBO) algorithm has been successfully applied to various research problems. However, TLBO is not capable or effective in DSP optimization problems with discrete solution spaces and complex disassembly precedence constraints. This paper presents a Simplified Teaching–Learning-Based Optimization (STLBO) algorithm for solving DSP problems effectively. The STLBO algorithm inherits the main idea of the teaching–learning-based evolutionary mechanism from the TLBO algorithm, while the realization method for the evolutionary mechanism and the adaptation methods for the algorithm parameters are different. Three new operators are developed and incorporated in the STLBO algorithm to ensure its applicability to DSP problems with complex disassembly precedence constraints: i.e., a Feasible Solution Generator (FSG) used to generate a feasible disassembly sequence, a Teaching Phase Operator (TPO) and a Learning Phase Operator (LPO) used to learn and evolve the solutions towards better ones by applying the method of precedence preservation crossover operation. Numerical experiments with case studies on waste product disassembly planning have been carried out to demonstrate the effectiveness of the designed operators and the results exhibited that the developed algorithm performs better than other relevant algorithms under a set of public benchmarks.