Disassembly Planning Research Articles

Toolchain for Automated Disassembly for Recycling of Electric Vehicle Batteries

In the context of the increasing focus on the circular economy and the importance of responsible use of resources, more and more attention is being paid to disassembly. While today a large part of the products are shredded and recycled as a single unit, individual components of complex products must be dismantled to reuse or recycle their materials by type. As disassembly is still done manually for many products today, this is only cost-effective for a few. To accelerate the planning process for disassembly and to make automated disassembly using multi-functional robot cells more lucrative, this paper presents a toolchain consisting of a disassembly planning assistant on the one hand and a robot programming assistant on the other. The goal is to simplify and accelerate disassembly planning. As input data, the toolchain receives the product model in a neutral data format and determines disassembly-relevant information from it. A GUI enables the manual adaptation of the data as well as the addition of additional information that cannot be extracted directly from the product model. This information is passed on to a simulation environment, which can be used to intuitively create a template program for a robot in a disassembly cell offline. In addition, evaluation criteria are derived to validate the toolchain.

Suitability of CAD-based Disassembly Sequence Plans for Flexible Remanufacturing

When products reach the end-of-first-life (EoFL), often recovery of valuable components is ignored because of unprofitable disassembly processes. Extracting the wanted component is a highly manual try-and-error process of disassembling multiple components. The creation of dis- and reassembly plans (e.g. for workers or robots) based on CAD files is currently too labour extensive and individually dependent on the EoFL state. Several studies exist for the automatic generation of assembly plans by CAD files with collision-based approaches. However, whether these approaches are suitable for industrial remanufacturing planning has not been discovered. This research compares the suitability of an AND/OR graph (AOG) and a precedence graph (PG) approach on ten exemplary examples. The suitability is measured with graph-based and visual feasibility checks as well as performance measures. The AOG approach is more promising than the PG approach because it considers the flexible formation of subassemblies and thus, better flexibility for scheduling in mixed-model disassembly lines. The results contribute to automatically deriving disassembly and reassembly plans for lot size one and saving labour-extensive high-wage planning effort. Overall, the resulting quality of assembly plans is highly dependent on the CAD model quality. Only two of the ten models resulted in suitable assembly plans. A preliminary CAD quality control during the design phase is needed to improve the applicability of the examined approaches.

A Systematic Approach to Task Assignment and Production Planning in Disassembly with Employee Skills

An emerging shortage of resources fosters a development for strategies for the circularity of products and resources. Due to the different states of returned end-of-life products, the complexity for employees in disassembly increases. This work aims to provide an approach for an optimal allocation of disassembly tasks to individual employees and therefore enable a basis for planning and control in disassembly. At first, a task description is provided based on which a standardized time for operations is considered. Second, a link is created between the task description and the product state. Depending on the product state the time determined prior can be adjusted. Third, human skills are considered in manufacturing. It is assumed that within a production system there are different employees with different, developing skill sets. Based on specific skills, a task-to-person-is conducted. Using the information gathered, a Digital Twin (DT) that includes the human nature of the employees and their state is created to enable a simulation of tasks and thereby also a learning system for “first-time-seen” products. When facing complex tasks, the cognitive load and human's fatigue are decisive for performance and thereby the time required for execution. Completing these steps, a multistage concept is created that enables a more precise disassembly planning that can be shown in the case study on the example of components of electric vehicles.

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.

Neighborhood Modularization-based Artificial Bee Colony Algorithm for Disassembly Planning with Operation Attributes

The recycling and remanufacturing of end-of-life products are significant for environmental protection and resource conservation. Disassembly is an essential process of remanufacturing end-of-life products. Effective disassembly plans help improve disassembly efficiency and reduce disassembly costs. This paper studies a disassembly planning problem with operation attributes, in which an integrated decision of the disassembly sequence, disassembly directions, and disassembly tools are made. Besides, a mathematical model is formulated with the objective of minimizing the penalty cost caused by the changing of operation attributes. Then, a neighborhood modularization-based artificial bee colony algorithm is developed, which contains a modular optimized design. Finally, two case studies with different scales and complexities are used to verify the performance of the proposed approach, and experimental results show that the proposed algorithm outperforms the two existing methods within an acceptable computational time.

Optimisation of robotic disassembly plans using the Bees Algorithm

Remanufacturing is a key component of a circular economy. It enables the reduction of landfill and the preservation of natural resources, therefore benefiting the environment. Disassembly is the first stage in the remanufacturing process, but, so far, has been mostly a manual operation. Transitioning from manual to robotic disassembly is desirable in the Industry 4.0 era. A model for planning the sequence of steps a robot needs to take to dismantle a product is proposed in this study. The model has three objectives: to maximise profits, energy savings and reduction in greenhouse gas emissions. The Bees Algorithm, an algorithm inspired by the foraging behaviour of honey bees, was used to obtain near-optimal plans for disassembling gear pumps. Statistical tests were performed on the results obtained with different optimisation parameters to establish statistical significance. They show that the Bees Algorithm was able to optimise disassembly plans for profits, energy savings and reductions in greenhouse gas emissions. • Optimisation of robotic disassembly plans to maximise profits, energy savings and reduction in greenhouse gas emissions. • Application of the Bees Algorithm to an NP-complete optimisation problem. • Bibliometric literature review of robotic disassembly revealings that the field is understudied.

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.

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

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

Detection Method of End-of-Life Mobile Phone Components Based on Image Processing

The number of end-of-life mobile phones is increasing every year, which includes parts that have high reuse values and various dangerous and toxic compounds. An intellectualized and automatic upgrade of the disassembly process of the end-of-life mobile phones would enhance the recycling value as well as efficiency. It would reduce the pollution in the environment. The detection of end-of-life mobile phone parts plays a critical role in automatic disassembly and recycling. This study offers an image processing-based approach for identifying important parts of mobile phones that are nearing the end of their useful lives. An image enhancement approach has been utilized for generating disassembly datasets of end-of-life mobile phones from several brands and models, and different retirement states. The YOLOv5m detection model is applied to train as well as validate the detection model on the customized datasets. According to the results, the proposed approach allows the intelligent detection of battery, camera, mainboard and screw. In the validation set, the Precision, Recall and mAP@.5 are 99.4%, 98.4% and 99.3%, respectively. Additionally, several path planning algorithms are utilized for the disassembly plan of screws which indicates that the genetic algorithm’s use increases the efficiency of disassembly.

Computational design of high-level interlocking puzzles

Interlocking puzzles are intriguing geometric games where the puzzle pieces are held together based on their geometric arrangement, preventing the puzzle from falling apart. High-level-of-difficulty , or simply high-level , interlocking puzzles are a subclass of interlocking puzzles that require multiple moves to take out the first subassembly from the puzzle. Solving a high-level interlocking puzzle is a challenging task since one has to explore many different configurations of the puzzle pieces until reaching a configuration where the first subassembly can be taken out. Designing a high-level interlocking puzzle with a user-specified level of difficulty is even harder since the puzzle pieces have to be interlocking in all the configurations before the first subassembly is taken out. In this paper, we present a computational approach to design high-level interlocking puzzles. The core idea is to represent all possible configurations of an interlocking puzzle as well as transitions among these configurations using a rooted, undirected graph called a disassembly graph and leverage this graph to find a disassembly plan that requires a minimal number of moves to take out the first subassembly from the puzzle. At the design stage, our algorithm iteratively constructs the geometry of each puzzle piece to expand the disassembly graph incrementally, aiming to achieve a user-specified level of difficulty. We show that our approach allows efficient generation of high-level interlocking puzzles of various shape complexities, including new solutions not attainable by state-of-the-art approaches.

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

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

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

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

A mathematical formulation for processing time computing in disassembly lines and its optimization

Disassembly is the first practice in maintenance and recycling of industrial products. For productivity and efficiency, it is necessary to optimize its operative manners by reducing: change of tools and directions, process variation, wastes, etc. The simulation of Disassembly Plan (DP) allows the detection and identification of difficulties from the design stage in order to avoid them.This paper proposes a mathematical formulation which combines two principal indicators: the index of Quality (Qi) and the index of processing Time (Ti) in order to choose the best and feasible disassembly plan. The Failure Mode, Effects and Criticality Analysis method is implemented to compute Qi. Ti is obtained according to real manufacturing constraints (workspace, layout, tools, machines, etc.). Based on 5S method, the workspace can be optimized which directly impacts the timing index and contribute to the selection of the best DP. A gear box is used to show up the efficiency of the proposed approach.

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.

CAD-based AND/OR Graph Generation Algorithms in (Dis)assembly Sequence Planning of Complex Products

Several studies in production engineering and operations research focused on disassembly and assembly planning for improving the profitability of remanufacturing. The representation of feasible (dis)assembly sequences by analyzing geometrical and technical precedence constraints is an essential necessity of (dis)assembly planning. An AND/OR graph (AOG) is commonly used to represent a product’s feasible (dis)assembly sequences, including alternative subassemblies and parallel operations. In recent years, researchers studied the automatic extraction of geometrical precedence constraints by collision analysis within 3D models. However, since most of the existing approaches focused on collision analysis to identify complex precedence constraints, the generation of AOGs for complex products from collision analysis results remains too inefficient for industrial use cases. In this study, a Computer-Aided Design (CAD) interface from previous studies is used to extract liaison and moving wedge information from 3D models. A top-down and a bottom-up approach for generating complete AOGs from the extracted data are introduced. The approaches’ computing performances are analyzed on sample test cases and CAD models. The bottom-up approach performed better for the given samples. It has been found that the amount of moving wedge constraints has a strong effect on computing performance. It is a strong indicator to estimate the complexity of products under examination. The exponential behavior of the needed computing resources can be estimated beforehand. For complex products, graph simplification or alternative graph representations with less information richness should be considered. The results contribute to automated (dis)assembly sequence planning from complex products and, consequently, increasing remanufacturing profitability.

Feature modeling for configurable and adaptable modular buildings

Current design approaches for new buildings do not sufficiently plan for or adapt to changing conditions that could be used to extend the useful life of buildings, as part of a circular economy. While notable advances have emerged for using BIM-based configurators to improve building design and project execution, there is a need expand such configurators to look at how buildings can be adapted and re-configured across their lifecycle. This paper develops and demonstrates an innovative feature modeling approach for configuring and adapting modular buildings. This study uses BIM for structuring intricate feature relationships of specific design aspects in terms of product circularity. The design aspects considered are structural design, dimensional variation control, and disassembly planning design. Feature parameter maps, which are a general constraint relation representation, are implemented to describe the data models since they are an efficient way to visualize feature elements and interdependencies, to avoid the creation of redundant information, and to improve data structure consistency. The application of the proposed methodology is validated with a functional demonstration of a conceptual design and optimization for a single module that is meant to be part of a modular construction project. The product model was synthesized in a parametric BIM environment for iterative configuration, analysis of results, and final optimization of the single module assembly. The demonstration case study shows that BIM can be adapted to assist on modeling specific design aspects for modular buildings and to create design alternatives. Also, the method shows a considerable benefit that the designer can produce diverse accurate design alternatives within a reduced amount of time.

A sustainability-based model for robotic disassembly sequence planning in remanufacturing using the Bees Algorithm

Remanufacturing, as a way to achieve the circular economy paradigm, can help save the environment by reducing the use of raw materials and energy, cutting greenhouse gas emissions and virtually eliminating the need for landfill. Disassembly is a critical first step in the remanufacturing process. This research uses the Bees Algorithm to optimise robotic disassembly sequence planning. Three sustainability strategies are addressed in the proposed model-based system for robotic disassembly planning: reuse, remanufacturing, and recycling. The results of two case studies based on end-of-life industrial gear pumps demonstrate that the Bees Algorithm can find the best solution for robotic disassembly sequence planning.

An efficient disassembly process generation method for large quantities of waste smartphones

Disassembly is one of the most important steps throughout the life cycles of many waste electronics. Since mobile phones are one of the most popular electronic devices but have a short service life, they are one of the largest contributors to the fastest-growing waste streams worldwide, and the problems of recycling waste smartphones urgently need to be properly resolved. To improve the disassembly efficiency of waste smartphones, it is essential to pay attention to the disassembly process generation. In this study, an efficient process generation method is proposed to provide a solution for improving the disassembly efficiency for large quantities of waste smartphones. The disassembly processes of each smartphone are generated based on the composite model with all disassembly features. Five different types of waste smartphones, namely Huawei P7, honor 6, Xiaomi 3, Xiaomi 5 and iPhone 6, were investigated to verify the applicability and efficiency of the proposed method. The experimental results demonstrate that the proposed method can provide feasible processes for disassembly planning, which could definitely improve the disassembly efficiency for large quantities of waste smartphones.

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

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

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.