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The purpose of this paper is to enhance the method developed by previous researchers. In addition to using the combined interference matrix, the combined connection matrix and the combined contact matrix of product components, the disassembly sequence matrix and the combined instability matrix with platform to evaluate instability of sub‐assemblies are built, and effects of changes of sub‐assembly disassembly directions or tools and the effect of gravity are considered to obtain the best disassembly sequence for a product with many components. A computer program is generated and results of two cases are compared with those of the available studies.

The methodology includes the combined interference matrix, the combined contact matrix and the combined connection matrix of components for a product. The combined instability matrix of sub‐assemblies, changes of sub‐assembly disassembly direction or tools, and the effect of gravity during operation are considered. The binary number system is used to simplify relations among components of a product.

This methodology enhances the existing method and software is generated. Results of two cases are compared and show the same optimum disassembly processes as those obtained from other researchers.

All matrices are defined by the directions of x, y and z with three axes perpendicular to each other. The computer program generated cannot be used for a product with components that must be disassembled in the directions different from the axes.

Two cases are used to investigate feasibility of the proposed methodology with the computer program generated. The first one is an electric drill, and the second one is a flash lighter.

The methodology described in this paper is feasible for study of disassembly processes of products. The software generated can be used to obtain the optimum disassembly process of products.

Maintenance disassembly that involves separating failed components from an assembly or system plays a vital role in line maintenance of civil aircraft, and it is necessary to have an effective and optimal sequence planning in order to reduce time and cost in maintenance. The purpose of the paper is to develop a more effective disassembly sequence planning method for maintenance of large equipment including civil aircraft systems.

The methodology involves the following steps: a component‐fastener graph is built to describe the equipment in terms of classifying components into two categories that are functional components and fasteners; interference matrix is developed to determine the removable component, and a disassembly sequence planning of functional components is proposed based on Dijkstra's algorithm; the disassembly sequence planning including fasteners is presented based on particle swarm optimization.

An application case, which takes the nose landing gear system of a regional jet as a study object, shows that the disassembly sequence planning method proposed in the paper can reduce the calculation complexity greatly, and its effectiveness is greater than that of a genetic algorithm‐based method, in most situations.

The method proposed herein can acquire the optimal maintenance disassembly sequence, which can reduce the cost and time for maintenance of large equipment.

A novel and effective disassembly sequence planning solution for maintenance of large equipment is presented, which can be applied to the line maintenance of civil aircraft.

The purpose of this paper is to review the fundamental methodology and its development of intelligent disassembly planning research.

Following a brief introduction, this paper first discusses the fundamental problems associated with disassembly planning and analysis. And then considers the role of intelligent optimization methods in the disassembly planning field. This is followed by a summary and conclusion.

Many advances have been made in computerized intelligent disassembly planning research, which is a natural evolutionary result of both traditional solving methodology and much research effort over past two decades. But as yet, some fundamental limitations are also rooted in this computational model‐based methodology.

The paper provides a fundamental review on the development of computerized intelligent disassembly planning research.

Aims to develop a selective disassembly methodology for generating an optimum disassembly sequence for end‐of‐life (EOL) products.

The paper presents a selective disassembly methodology for EOL products. In order to achieve this, Nevins and Whitney's methodology for assembly was modified. In addition, a Java‐based software was developed to speed up the generation of all possible disassembly sequences.

Finds that the methodology developed by Nevins and Whitney for assembly is applicable to disassembly process. In addition, the winnowing process for disassembly is much easier than for assembly because of the selective disassembly approach, which automatically provides a significant constraint on possible sequences.

Provides an easy to use and visual disassembly sequence generation tool for end‐of‐life products.

Disassembly is one of the significant cost drivers in achieving close loop manufacturing. Application of the methodology proposed in this paper will significantly reduce the disassembly time by providing a disassembly sequence for the selected components with reuse potential.

Provides a graphical representation of disassembly sequences at different stages of the process, which allows the user to visualize the disassembly process.

The purpose of this paper is to improve the automation of selective disassembly sequence planning (SDSP) and generate the optimal or near-optimal disassembly sequences.

The 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.

The 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.

All 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.

The 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.

The purpose of this paper is to propose a novel method under the name of genetic simulated annealing algorithm (GSAA) and ant colony optimization (ACO) algorithm for assembly sequence planning (ASP) which is possessed of the competence for assisting the planner in generating a satisfied and effective assembly sequence with respect to large constraint assembly perplexity.

Based on the genetic algorithm (GA), simulated annealing, and ACO algorithm, the GSAA are put forward. A case study is presented to validate the proposed method.

This GSAA has better optimization performance and robustness. The degree of dependence on the initial assembly sequence about GSAA is decreased. The optimization assembly sequence still can be obtained even if the assembly sequences of initial population are infeasible. By combining GA and simulated annealing (SA), the efficiency of searching and the quality of solution of GSAA is improved. As for the presented ACO algorithm, the searching speed is further increased.

Traditionally, GA heavily depends on the choosing original sequence, which can result in early convergence in iterative operation, lower searching efficiency in evolutionary process, and non‐optimization of final result for global variable. Similarly, SA algorithms may generate a great deal of infeasible solutions in the evolution process by generating new sequences through exchanging position of the randomly selected two parts, which results in inefficiency of the solution‐searching process. In this paper, the proposed GSAA and ACO algorithm for ASP are possessed of the competence for assisting the planner in generating a satisfied and effective assembly sequence with respect to large constraint assembly perplexity.

This paper seeks to present a methodology to design products for disassembly. This would facilitate end‐of‐life product disassembly with a view to maximizing material usage in the supply chain at a low cost to the environment.

The methodology presented in the paper draws on fundamentals related to task analysis and motion time measurement. The methodology was practically applied to disassemble several different consumer products with significant savings in time.

Several improvements in product design resulted from various perspectives including functionality, assembly, aesthetics and disassembly.

The paper identifies several areas of future research including design optimization and designing work fixtures for disassembly.

This work presents in part an improvement in current methodologies related to disassembly as well as original work based on task analysis and suggestion of design alternatives. The paper is therefore valuable to practitioners and researchers alike.

To improve the efficiency of end-of-life product’s disassembly process, this paper aims to propose a disassembly sequence planning (DSP) method to reduce additional efforts of removing parts when considering the changes of disassembly directions and tools.

The methodology has three parts. First, a disassembly hybrid graph model (DHGM) was adopted to represent disassembly operations and their precedence relations. After representing the problem as DHGM, a new integer programming model was suggested for the objective of minimizing the total disassembly time. The objective takes into account several criteria such as disassembly tools change and the change of disassembly directions. Finally, a novel hybrid approach with a chaotic mapping-based hybrid algorithm of artificial fish swarm algorithm (AFSA) and genetic algorithm (GA) was developed to find an optimal or near-optimal disassembly sequence.

Numerical experiment with case study on end-of-life product disassembly planning has been carried out to demonstrate the effectiveness of the designed criteria and the results exhibited that the developed algorithm performs better than other relevant algorithms.

More complex case studies for DSP problems will be introduced. The performance of the CAAFG algorithm can be enhanced by improving the design of AFSA and GA by combining them with other search techniques.

DSP of an internal gear hydraulic pump is analyzed to investigate the accuracy and efficiency of the proposed method.

This paper proposes a novel CAAFG algorithm for solving DSP problems. The implemented tool generates a feasible optimal solution and the considered criteria can help the planer obtain satisfactory results.

Smart materials (SMs) have the potential for facilitating active disassembly (AD). Select SMs are used in the design of devices to aid product disassembly. The purpose of this paper is to compare different AD approaches and highlight future work and potential.

This work is a survey of the collated AD research employing only Smart and “made Smart” materials work from various published work in the field from companies and academia since its original invention. The introduction gives general discussion of AD with cost implications and how the technology could offer very lean dismantling. An overview of the history of the work is given with the context of the implications for the need for a technology like AD to retain critical materials.

Besides a survey to date, comparisons were made of each AD technology application highlighting advantages and challenges. Comparisons were also made prior to this in alternative disassembly strategies to give context to the potential usefulness of the technology.

Only AD with SMs or “made Smart” were highlighted with some considerations for potential candidates.

A survey of AD work only employing SMs and “made‐Smart” materials to date. Comparisons of each AD application were made highlighting advantages and challenges. Comparisons were made between AD and alternative disassembly strategies to give context to the potential usefulness of the technology. The conclusion included an overview of work with consideration for future work. A candidate technology with the most potential was discussed.

The purpose of this paper is to present an interactive system to enable product design for disassembly and to offer robust and quick design solutions based on designers’ input.

The system utilizes an interactive questionnaire to communicate with the designer. The questionnaire is in the form of binary questions (Yes/No) and design questions that would enable the system to learn the objectives of the design. Solutions are based on a CAD supported design platform. The efficiency of each design is calculated using disassembly time as the metric of measurement using motion-time measurement (MTM). The designer would be able to make an informed decision with respect to component functionality, ease of disassembly and disassembly time. The paper presents a detailed framework and structure of this system.

The value of the system is corroborated by means of a case study of an actual product design. The system is structured to offer multiple solutions to a design problem so as to enable the designer to choose the option that best serves their needs.

This novel interactive system would accept customers’ design preferences as input and offer multiple solutions in order to solve the design problem. Process time is directly calculated using the MTM system of measurement by converting design features into time measurement units. Disassembly time can then be easily converted into disassembly cost by using standard conversion rates. The value to designers is obvious.