Product Disassembly Research Articles

End-of-Life product quality management for efficient design of disassembly lines under uncertainty

The design of a disassembly line is a long-term investment. Therefore, it is important to optimise its performance and demonstrate its financial viability. The design process must consider the unique End-of-Life product characteristics and disassembly operation particularities. These peculiarities are mainly expressed by uncertainty in post-consumer products quality, variability of task processing times and presence of hazardous material. To achieve efficient disassembly line design, efficient decision-making tools are needed. The purpose of this work is to propose such a tool. The objective is to efficiently design a disassembly line, as a disassembly system, that provides optimal income and takes into account Enf-of-Life (EoL) product quality uncertainty and task processing times variability. The proposed tool allows the decision-maker to select the optimal disassembly process and depth while assigning retained tasks to the line workstations. Hazardous material of EoL products is handled and the designed line guarantees a certain level of service set by the decision-maker. The product part revenue depends formally on its EoL state or quality. Several product examples from remanufacturing sectors in industry are utilised to demonstrate interest and relevance of the proposed decision-aiding tool in an industrial context.

Capacitated multi-objective disassembly scheduling with fuzzy processing time via a fruit fly optimization algorithm.

This work proposes a capacitated fuzzy disassembly scheduling model with cycle time and environmental cost as parameters, which has broad applications in remanufacturing and many other production systems. Disassembly scheduling is not always given accurately as a time quota in a production system, particularly in the obsolete product remanufacturing process. It is important to study novel models and algorithms based on uncertainty processing time to solve uncertainty disassembly scheduling problems. In this paper, a mixed-integer mathematical programming model is proposed to minimize the cycle time and environmental cost, whilst a metaheuristic approach based on a fruit fly optimization algorithm (FOA) is developed to find a fuzzy disassembly scheduling scheme. To estimate the effectiveness of the proposed method, the proposed algorithm is tested with different size cases of product disassembly scheduling. Furthermore, experiments are conducted to compare with other multi-objective optimization algorithms. The computational results demonstrate the proposed algorithm outperforms other algorithms on computational efficiency and applicability to different problems. Finally, a case study is described to illustrate the proposed method. The main contribution of this current work shows the proposed algorithm to solve the problem of disassembly scheduling in an uncertain environment practically and efficiently.

Applying Improved Particle Swarm Optimization to Asynchronous Parallel Disassembly Planning

Disassembly Planning (DP) refers to an optimization method to find the most cost-effective disassembly sequence for products based on the disassembly properties of parts. In traditional sequential disassembly planning, only a single part or component is removed. To effectively improve the product disassembly efficiency, this study explores the problem of Asynchronous Parallel Disassembly Planning (aPDP) with multiple manipulators. In the aPDP situation where multiple manipulators are used, the optimization method of arranging the manipulators needs to be considered in addition to the limitation of the priority order of parts. This study proposes an improved particle swarm optimization to discuss the aPDP combination optimization problem. The minimum Make Span is the objective, and the solution status and convergence speed are compared with the results of other particle swarm optimization methods, a Genetic Algorithm and Ant Colony Optimization. The results show that the proposed improved version of the particle swarm optimization algorithm has better solution quality and execution time.

Connector-link-part-based disassembly sequence planning

Under the trend of concurrent engineering, the correspondence between functions and physical structures in product design is gaining importance. Between the functions and parts, connectors are the basic unit for engineers to consider. Moreover, the relationship between connector-liaison-part will help accomplish the integration of information. Such efforts will help the development of the Knowledge Intensive CAD (KICAD) system. Therefore, we proposed a Connector-liaison-part-based disassembly sequence planning (DSP) in this study. First, the authors construct a release diagram through an interference relationship to express the priority of disassembly between parts. The release diagram will allow designers to review the rationality of product disassembly planning. Then, the cost calculation method and disassembly time matrix are established. Last, the greedy algorithm is used to find an appropriate disassembly sequence and seek suggestions for design improvement. Through the reference information, the function and corresponding modules are improved, from which the disassembly value of a product can be reviewed from a functional perspective. In this study, a fixed support holder is used as an example to validate the proposed method. The discussion of the connector-liaison-part will help the integration of the DSP and the functional connector approach.

The role of robots in the green economy

PurposeThis aims to illustrate the role robotic technology is playing in the key sectors of the green economy.Design/methodology/approachFollowing a short introduction, this paper discusses existing and potential robotic applications in three key sectors of green economy: renewable energy, recycling and waste management and sustainable transport. This is followed by a discussion and concluding comments.FindingsRobots are playing critical and growing roles in each of the three sectors of the green economy considered. Uses are expanding in the production of renewable energy systems and in their inspection and maintenance. Advances in AI and machine vision have enabled robotic mixed waste sorting which plays a vital role in recycling, and the robotic disassembly of electronic products is also gaining pace. Robots are being used extensively in the sustainable transport sector in the manufacture of electric vehicles and also in the production and recycling of electric vehicle batteries. Emerging applications include robotic vehicle recharging and battery swapping.Originality/valueThis provides an insight into the many ways in which robots are contributing to key sectors of the green economy.

Comparison of RGB-D and IMU-based gesture recognition for human-robot interaction in remanufacturing

With product life-cycles getting shorter and limited availability of natural resources, the paradigm shift towards the circular economy is being impulsed. In this domain, the successful adoption of remanufacturing is key. However, its associated process efficiency is to date limited given high flexibility requirements for product disassembly. With the emergence of Industry 4.0, natural human-robot interaction is expected to provide numerous benefits in terms of (re)manufacturing efficiency and cost. In this regard, vision-based and wearable-based approaches are the most extended when it comes to establishing a gesture-based interaction interface. In this work, an experimental comparison of two different movement-estimation systems—(i) position data collected from Microsoft Kinect RGB-D cameras and (ii) acceleration data collected from inertial measurement units (IMUs)—is addressed. The results point to our IMU-based proposal, OperaBLE, having recognition accuracy rates up to 8.5 times higher than these of Microsoft Kinect, which proved to be dependent on the movement’s execution plane, subject’s posture, and focal distance.

Self-healing materials: A pathway to immortal products or a risk to circular economy systems?

Product lifetime extension is key strategy for a functioning Circular Economy. Yet, the role of material innovation and how this might contribute to longer product and material lifetimes has not been widely discussed. Considering the significant environmental burden of material resource extraction, the longevity of materials must also be deliberated. Inspired by biological systems, self-healing materials are a type of smart material with the ability to inherently repair themselves when damaged. Although a technology extensively investigated within the material science community, the Circular Economy and product lifetime extension implications of implementing these into products has yet to be debated. Thus, this empirical study investigates: What are the benefits, opportunities, risks and challenges for applying self-healing materials to products within the context of the circular economy? In addition to a literature review, this research was carried out utilising a survey, two round table discussions, a workshop and interviews with industry experts from several different sectors. Through thematic analysis, it was discovered that the key benefits that self-healing materials might offer to circular systems are: the ability to maintain the primary lifetime of the product from both a technical and service lifetime perspective; assistance in the refurbishing and remanufacture of products through increasing the ease of disassembly and reassembly of products; and the potential to enable alternative business models. The key risks and limitations are: issues of persistence within the system; hybridization of materials - technosphere or biosphere? whether they will cycle in the system; limitations regarding their technology development, performance and lastly queries of their liability and compliance. These findings are valuable to both fields of self-healing materials and circular economy, and they not only demonstrate what sustainability factors must be understood when developing novel self-healing compositions but also expand our understanding for how these materials might be utilised to create longer lasting products from an industrial perspective. The paper concludes with a set of recommendations for future research activities which could not only help to advance the field of self-healing but also potentially product longevity and make immortal products a reality.

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.

Optimization of disassembly line balancing using an improved multi-objective Genetic Algorithm

Disassembly activities take place in various recovery operations including remanufacturing, recycling, and disposal. Product disassembly is an effective way to recycle waste products, and it is a necessary condition to make the product life cycle complete. According to the characteristics of the product disassembly line, based on minimizing the number of workstations and balancing the idle time in the station, the harmful index, the demand index, and the number of direction changes are proposed as new optimization objectives. So based on the analysis of the traditional genetic algorithm into the precocious phenomenon, this paper constructed the multi-objective relationship of the disassembly line balance problem. The disassembly line balance problem belongs to the NP-hard problem, and the intelligent optimization algorithm shows excellent performance in solving this problem. Considering the characteristics of the traditional method solving the multi-objective disassembly line balance problem that the solution result was single and could not meet many objectives of balance, a multi-objective improved genetic algorithm was proposed to solve the model. The algorithm speeds up the convergence speed of the algorithm. Based on the example of the basic disassembly task, by comparing with the existing single objective heuristic algorithm, the multi-objective improved genetic algorithm was verified to be effective and feasible, and it was applied to the actual disassembly example to obtain the balance optimization scheme. Two case studies are given: a disassembly process of the automobile engine and a disassembly of the computer components.

Human-robot collaboration disassembly planning for end-of-life product disassembly process

• A method for human-robot collaboration disassembly planning is proposed to improve the disassembly process efficiency of end-of-life products. • New optimization parameters for human-robot collaboration based on the component target and operator change are defined and investigated. • Experiments are carried out to validate the task classification and allocation for human-robot collaboration. • An automotive case study is selected to test the proposed method, which results show a higher efficiency and reliability of the method in comparison with the particle swarm algorithm. The disassembly process is the main step of dealing with End-Of-Life (EOL) products. This process is carried out mostly manually so far. Manual disassembly is not efficient economically and the robotic systems are not reliable in dealing with complex disassembly operations as they have high-level uncertainty. In this research, a disassembly planning method based on human-robot collaboration is proposed. This method employs the flexibility and ability of humans to deal with complex tasks, alongside the repeatability and accuracy of the robot. Besides, to increase the efficiency of the process the components are targeted based on the remanufacturability parameters. First, human-robot collaboration tasks are classified, and using evaluation of components remanufacturability parameters, human-robot collaboration definition and characteristics are defined. To target the right components based on their remanufacturability factors, the PROMETHEE II method is employed to select the components based on Cleanability, Reparability, and Economy. Then, the disassembly process is represented using AND/OR representation and the mathematical model of the process is defined. New optimization parameters for human-robot collaboration are defined and the genetic algorithm was modified to find a near-optimal solution based on the defined parameters. To validate the task classification and allocation, a 6-DOF TECHMAN robot arm is used to test the peg-out-hole disassembly operation as a common disassembly task. The experiments confirm the task classification and allocation method. Finally, an automotive component was selected as a case study to validate the efficiency of the proposed method. The results in comparison with the Particle Swarm algorithm prove the efficiency and reliability of the method. This method produces a higher quality solution for the human-robot collaborative disassembly process.

Disassembly Sequence Planning for Intelligent Manufacturing Using Social Engineering Optimizer

Product disassembly and recycling are important issues in green design. Disassembly sequence planning (DSP) is an important problem in the product disassembly process. The core idea is to generate the best or approximately optimal disassembly sequence to reduce disassembly costs and time. According to the characteristics of the DSP problem, a new algorithm to solve the DSP problem is proposed. Firstly, a disassembly hybrid graph is introduced, and a disassembly constraint matrix is established. Secondly, the disassembling time, replacement frequency of disassembly tool and replacement frequency of disassembly direction are taken as evaluation criteria to establish the product fitness function. Then, an improved social engineering optimizer (SEO) method is proposed. In order to enable the algorithm to solve the problem of disassembly sequence planning, a swap operator and swap sequence are introduced, and steps of the social engineering optimizer are redefined. Finally, taking a worm reducer as an example, the proposed algorithm is used to generate the disassembly sequence, and the influence of the parameters on the optimization results is analyzed. Compared with several heuristic intelligent optimization methods, the effectiveness of the proposed method is verified.

Energy-Efficient Robotic Parallel Disassembly Sequence Planning for End-of-Life Products

End-of-life (EOL) products contain many valuable parts and materials. The timely disassembly and recycling of EOL products can bring considerable economic benefits and reduce environmental pollution. To reduce the cost of manual disassembly, robotic disassembly has become one of the main methods used to dismantle EOL products. In addition, parallel disassembly can shorten the makespan of EOL products. Therefore, this article focuses on a new problem involving robotic parallel disassembly sequence planning for EOL products and establishes a corresponding multiobjective model to minimize the makespan and energy consumption. To obtain high-quality disassembly schemes, a discrete artificial bee colony algorithm based on problem characteristics is proposed. The performance of the proposed algorithm is verified by solving 32 benchmark problems and a comparison with three well-known multiobjective algorithms. The proposed model and method are applied to a real-world LCD TV disassembly case, and multiple preferred disassembly schemes are obtained. The results show that the proposed model and method can effectively shorten the makespan and reduce energy consumption. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Parallel disassembly is one of the most efficient disassembly methods, and robotic disassembly will be one of the main disassembly methods in the future. Therefore, this article focuses on the robotic parallel disassembly planning to achieve an efficient and environmentally friendly treatment of end-of-life products. With the importance of production efficiency and energy saving, it is essential to simultaneously assess the makespan and energy consumption. The results demonstrate that the proposed artificial bee colony algorithm can provide multiple disassembly schemes that balance the makespan and energy consumption. The obtained disassembly schemes can provide references for the disassembly planning of the disassembly enterprise and expand the decision-making space for decision-makers.

Identifying and Analyzing the Factors Affecting Disassembly of Products in Remanufacturing Organizations

Disassembly is a key operation that differentiates remanufacturing from traditional manufacturing. However, various factors that affects the process of disassembly are not clearly understood by the researchers. This study focuses on identifying and analyzing the factors that affect the disassembly process in remanufacturing organizations. A literature survey is conducted to identify the factors that can influence the disassembly of products. A total of 17 factors are obtained which are further classified into three main factors viz. Organizational Capabilities (OC), Process Choices (PC) and Product Attributes (PA). An approach based on Analytical Hierarchy Process (AHP) is utilized to analyze the factors for developing a list of priority of these factors. A team of 8 experts from industry and academia is involved in this research for collecting their opinions. Results of this study show that product attributes play a key role in the disassembly of products for remanufacturing. ‘Value of recovered products’, ‘number of components’, ‘sequence of disassembly’, ‘volume of cores supply’ and ‘implementation of ecodesign principles’ are identified as the top five factors affecting the process of disassembly. The findings of this research can be useful for effective disassembly of products in remanufacturing based organizations. The decision makers of remanufacturing organizations can use the priority list of factors developed in this study for managing the disassembly activities in the organization.

Balancing Stochastic Mixed-Model Two-Sided Disassembly Line Using Multiobjective Genetic Flatworm Algorithm

The disassembly industry is still labor-intensive, and for the disassembly of products with similar assembly structures, achieving mixed disassembly of these products on the same disassembly line is a more economical solution and very popular in disassembly companies. When large-volume end-of-life (EOL) products are disassembled on the two-sided disassembly line, there is uncertainty in the disassembly time during the disassembly process due to the differences in the recovery quality of EOL products. Therefore, a stochastic mixed-integer programming model is formulated that considers workstation activation cost, workload smoothness index between workstations, and total hazard index considering the order of hazardous task removal. Then, a multi-objective genetic flatworm algorithm is developed in which two different mechanisms for new individual generation are embedded, namely the flatworm genetic operations and the regeneration operations. The effectiveness of the model and the efficiency of the algorithm are verified by the disassembly cases of the printer and the classical mixed-model on the two-sided disassembly line. Finally, the proposed model and algorithm are applied to the disassembly of multiple types of cars, which proves their practical industrial application value.

Screw detection for disassembly of electronic waste using reasoning and re-training of a deep learning model

Growing populations, increasing standards of living, and reliance on advancing technologies are resulting in an emerging abundance of electronic waste. Automating disassembly of these electronic products is a vital part of improving end-of-life product treatment where hazardous chemicals and materials are present. Furthermore, non-destructive disassembly is ideal to preserve the embodied energy in components from manufacturing. This requires the removal, and hence detection, of fasteners like screws in a disassembly environment. Crosshead screws are a common fastener type used in LCD monitors. This paper proposes a method of screw detection for disassembly and presents results of detection of crosshead screws on components of various models of LCD monitors in a disassembly cell. The system first applies gamma correction to standardize brightness––or luminance–– of images. Generic knowledge about screw features in the form of a deep learning model is then used to visually detects screws. The results are analyzed against common screw location combinations on product components in order to logically reason about possible undetected screws that are also likely to exist. Finally, true negative detections are collected as new training data and the deep learning model is re-trained on these missed detections to improve performance; tailoring and adapting to the environment of the specific disassembly cell.

Towards planning and control in cognitive factories - A generic model including learning effects and knowledge transfer across system entities

Cognitive abilities allow robots to learn and reason from their environment. The gained knowledge can then be incorporated into the robot’s actions which in turn affect the environment. Therefore, a cognitive robot is no longer a static system that performs actions based on a pre-defined set of rules but a complex entity that dynamically adjusts over time. With this, challenges arise for production systems that need to observe and ideally anticipate the cognitive robot’s behavior. Often, digital twins are employed to test and optimize production control systems. This paper presents a generic approach to characterize, model and simulate learning processes and formalized knowledge in hybrid production systems assuming different station types with learning effects. Thereby, quantitative and qualitative learning processes are mapped including knowledge sharing and transfer across entities. A modular and parameterizable design enables the adjustment to different use cases. Eventually, the model is instantiated as a digital twin of a real production system for product disassembly employing cognitive-autonomous robots among human operators and rigidly automated machines. The model shows great potential to be integrated into test beds for planning and control systems of cognitive factories.

Balancing disassembly lines under consideration of tool requirements and limited space

One of the factors that can enable a shift towards a circular economy is the efficient organization of product disassembly. To this end, we study the disassembly line balancing problem, i.e., the assignment of disassembly tasks to workstations and extend it by practical aspects. We present a profit oriented optimization model, specifically taking into account the resource needs to carry out each task, and the limited space at workstations. The model is applied to a case study from literature and, by means of a sensitivity analysis, we demonstrate the impact of various problem specific parameters on the objective value and solution characteristics.

Disassembly Sequence Planning: A Survey

It is well-recognized that obsolete or discarded products can cause serious environmental pollution if they are poorly be handled. They contain reusable resource that can be recycled and used to generate desired economic benefits. Therefore, performing their efficient disassembly is highly important in green manufacturing and sustainable economic development. Their typical examples are electronic appliances and electromechanical/mechanical products. This paper presents a survey on the state of the art of disassembly sequence planning. It can help new researchers or decision makers to search for the right solution for optimal disassembly planning. It reviews the disassembly theory and methods that are applied for the processing, repair, and maintenance of obsolete/discarded products. This paper discusses the recent progress of disassembly sequencing planning in four major aspects: product disassembly modeling methods, mathematical programming methods, artificial intelligence methods, and uncertainty handling. This survey should stimulate readers to be engaged in the research, development and applications of disassembly and remanufacturing methodologies in the Industry 4.0 era.

Human-oriented maintenance and disassembly in sustainable manufacturing

This paper aims to enhance human involvement in equipment maintenance and product disassembly, which are essential in product end-of-life (EOL) planning and achieving sustainable manufacturing. Maintenance operations can extend the production lifespan of machinery, whereas disassembly is an essential step in EOL processes, such as reusing, recycling and remanufacturing. Notably, most maintenance activities and disassembly operations are still conducted manually. Hence, an integrated framework that considers appropriate human factors and ergonomic conditions in these two phases is proposed. First, a review on current approaches in sustainable manufacturing, such as through-life engineering services and continuous maintenance is presented. Next, an integrated framework for maintenance and disassembly based on advanced human computer interaction and soft computing techniques is described. In the maintenance phase, a sensor network that tracks the steps performed by a user during maintenance operations is implemented. The proposed network can account for situations where the user is far away from the maintenance job area and when the user is carrying out the actual maintenance job. This tracking data and information will serve as a basis for comparison to obtain maintenance quality related information and provide real time ergonomics feedback. This information is utilized for disassembly optimisation with human factors consideration. In line with Industry 4.0 concept, this framework allows (i) improvement of maintenance quality with the inclusion of steps tracking during disassembly and maintenance operations and (ii) monitoring and consideration of the well-being of operators. Case studies are implemented to illustrate the proposed framework followed by discussion and conclusion with future work.

Disassembly-based bill of materials data for consumer electronic products

Consumer electronic products have a complex life cycle, characterized by environmental, social, and economic impacts and benefits associated with their manufacturing, use, and disposal at end-of-life. Accurately analysing these trade-offs and creating sustainable solutions requires data about the materials and components that make up these devices. Such information is rarely disclosed by manufacturers and only exists in the open literature in disparate case study format. This study presents a comprehensive database of bill of material (BOM) data describing the mass of major materials and components contained in 95 unique consumer electronic products. Data are generated by product disassembly and physical characterization and then validated against external benchmarks in the literature. The study also contributes a reproducible framework for organizing BOM data so that they can be expanded as new products enter the market. These data will benefit researchers studying all aspects of electronics and sustainability, including material scarcity, product design, environmental life cycle assessment, electronic waste policy, and environmental health and safety.