Disassembly Operations Research Articles

A two-stage approach for modeling, performance evaluation, and decision support of disassembly processes

ABSTRACT This paper tackles an issue of disassembly lot-sizing within the framework of stochastic batch demand for multi-products featuring a two-level structure. The suggested methodology relies on a batch deterministic and stochastic Petri net as a modeling tool to address such problems in the context of stochastic processes. The primary objective is to determine the optimal sequencing of disassembly for each product type to meet stochastic batch demands of root items across multiple criteria. A novel mathematical model is introduced to minimize the cumulative costs associated with disassembly operations and extra costs, as well as holding costs for diverse products. In a subsequent stage, a decision support tool, namely the preference ranking organization method for enrichment evaluation method, is put forward for prioritizing different scenarios. For assessing the efficiency of the suggested approach, a real case study from the manufacturing industry is simulated. The simulation results prove the suggested approach’s capability to adapt to dynamic demands for leaf items throughout the planning horizon.

Graph and Network Methods for Automating the Design of Disassembly Processes for Complex Technical Systems. Review

During the operation of a complex machine or device, it is necessary to perform complete or partial disassembly of the product and its individual components and assemblies. Disassembly operations are required for repair, maintenance and disposal of technical system components at the end of their service life. Modern technical systems can consist of several thousand (or more) parts and components. Therefore, it is necessary to use modern computing and design automation tools to plan disassembly processes and operations. The sequence of disassembly and the content of disassembly operations largely depend on the mechanical structure of the product. To model the mechanical structures of complex products in CAD-systems, various graph and network models are mainly used. The paper gives an overview of the most popular models of this type. The main carrier of information about the mechanical structure of a product is the so-called liaison graph. The review considers design procedures for synthesizing disassembly processes using the liaison graph. Procedures of expert ordering and procedures based on cuts in the liaison graph.

Impact of bolt positioning on the stiffness of angular support brackets

Bolts and screws are usually preferred over other joining techniques when assembly and disassembly operations are required. However, they add extra weight to the system, so it is essential to reduce their size and weight by optimizing bolt parameters. Additionally, the stiffness of bolted members is very crucial; those with low stiffness may affect the correct functioning of the other connected components. This study focuses on the impact produced by bolt positions, specifically their center of gravity and spacing among them, on the stiffness of angular L-shaped brackets using Finite Element Analysis. Bending and torsional loads were applied to the free end of the member and deformations were recorded under these loads. The results revealed that a triangular bolt pattern is recommended for three bolts, whereas for four or more bolts, the pattern should be uniform to avoid stress concentrations. Moreover, the center of gravity and bolt spacing have a significant impact on stiffness. By optimizing bolt spacing and the center of gravity, a combination of six bolts can be replaced with four bolts of the same size while maintaining the same stiffness.

A rule-driven method for disassembly sequence real-time planning of intelligent mixed-flow disassembly line

Intelligent mixed-flow disassembly serves an important role in enhancing the flexible operation capability of the disassembly line for multi-variety and small-batch used electromechanical products, and in forming the scale benefit of recycling. However, in mixed-flow disassembly lines, pre-planning the disassembly sequence for products is typically unfeasible and can lead to “over-disassembly” or “under-disassembly”, resulting in economic losses. This is attributed to significant variations in disassembly features, such as internal structural damage and subassembly recycling income, even within the same type of used electromechanical product. These differences contribute to considerable uncertainties in disassembly sequence planning. To this end, a method is proposed to leverage dynamic disassembly feature information for mining disassembly rules, efficiently driving disassembly sequences real-time planning, and realizing intelligent optimization of product disassembly profit on mixed-flow disassembly line. First, a multi-matrix collaboration based dynamic representing method of product disassembly feature information is proposed. It aims to eliminates the influence of uncertain information on the disassembly sequence planning by recording real-time disassembly feature information, such as disassembly operation time and subassembly recycling income. On this basis, a rule-driven disassembly sequences real-time planning model is built. This model realizes disassembly sequence real-time planning based on offline mined rules using the updated disassembly feature information in matrices. Finally, an improved gene expression programming-based disassembly rule offline mining algorithm is proposed, which incorporates a mutation probability mapping function to improve the efficiency and effectiveness of disassembly rule mining. A case study containing two products with differences in structure and function are used to confirm the effectiveness of the proposed method.

AR/VR assisted integrated framework of autonomous disassembly system for industrial products

An immersive Augmented Reality (AR) technology enhances productivity and flexibility in manufacturing by providing real-time instructions within workers’ field of view, aiding in complex tasks. According to cognitive load and attention-based theories, AR guidance can reduce cognitive strain, enhancing task performance. The affordance of AR in industrial processes like assembly or disassembly for repair/maintenance has been exploited broadly. However, the prevailing studies on automated user interfaces for AR instruction development, specifically virtual scene creation and target generation for object mapping rendering, still need to be explored. Notably, production industries currently rely on outsourcing skilled professionals to create AR overlaying instructions using manufacturer input. However, the exactness of the disassembly repository is substantial to avoid defects in AR guidance particulars. This underscores the significance of generalized automated AR instruction development and emphasizes the demands for further research. In this work, the autonomous AR-integrated framework is developed for the disassembly context, empowering manufacturers to acquire desired outcomes across various modules. These modules include the validation of the manual user disassembly sequence repository during the prefatory phase of AR instruction creation and the provision of Disassembly Sequence Planning (DSP) input. Additionally, the framework supports the simulation/rendering of disassembly efforts in a virtual platform and the generation of rendering virtual display scenes for appropriate AR-guided object mapping. Furthermore, it enables the generation of essential model targets for registering the AR scenes. Subsequently, the extracted repositories (virtual scenes and model targets) are input into the Unity AR tool to create AR mapping guidance for disassembly operations. The performance of the autonomous disassembly module is evaluated through three case studies with different AR visualizations. Furthermore, the potential of the proposed framework is exploited by extracting feedback from experts regarding flexibility, compatibility, completion time, and cognitive level.

HOLONIC MULTI-AGENT SYSTEMS ALGORITHM FOR AUGMENTED REALITY INTERACTIVE ASSISTANCE

In this paper is presented an algorithm based on the holonic multi-agent approach concerning augmented reality assistance to be applied in assembly or disassembly operations. The variation in production and fast start of production requires operators to learn assembly operations for large range of products. In order to avoid errors, operators might be trained and corrected using augmented reality application able to assist and provide important information to allow the operation according to technical required specification. The holonic multi-agent system allows solving complex calculus concerning the best choice. The proposed algorithm is easier to apply when the operator needs to be assisted in assembly operations while meeting technical specifications. By applying the proposed algorithm, the operators learn very fast the operations for assembly/disassembly.

Hybrid Genetic-ADAM Optimizer to Improve Disassembly Line Balancing Problem

Modern products are designed to meet the needs of customized and short-lived products, after a short period of time, these products become waste products, these waste products are accumulating at an exponential rate, resulting in environmental degradation as a result of pollution, in order to keep this environment clean, it is necessary to have disassembly lines for those waste products, so in this research a Hybrid Genetic ADAM optimizer Algorithm was proposed to facilitate the disassembly operations at work stations placed in the disassembly lines, the proposed algorithm used to solve multi-objective disassembly line balancing problem, and Pareto optimal solution was used to determine non inferior solutions from a population. The trend of results reveal 6.1 percent a reduction in disassembly workstations, reduction of at least 0.05 percent of the idle time, reducing to minimum run time 1.5 percent comparing proposed and other meta heuristics algorithms. Furthermore, the plans of disassembly data are created to be valuable regarding trace ability and disassembly improvement processes in the future.

Coupled response analysis of dual lifting vessels during collaborative lifting topside module

Offshore assembly and disassembly operations represent a multi-billion-dollar market potential. Collaborative lifting by multiple vessels has emerged as a new operating paradigm for integrated offshore facilities assembly and disassembly. Hence, this paper investigates the hydro-dynamic interaction of dual lifting vessels in collaborative lifting operations. The coupled motions during multi-body operations are simulated using the commercial software SESAM. The feasibility of the numerical model for coupled motions in collaborative lifting is verified by comparing the numerical results of topside motions, vessel motions, and vertical lifting arm loads against experimental measurements. The effects of wave heading and period on the hydrodynamic responses of the topside module, dual lifting vessels, and lifting arms during collaborative operations are studied. Their influence patterns and mechanisms are analysed in detail. The results show that transverse waves and head wave induce significant heave and pitch motions of the topside module and vessels, but the maximum vertical loads on the lifting arms occur in oblique waves. The motion responses of the topside module and vessels increase with longer wave periods under the oblique sea condition, and roll motions are more sensitive to large periods compared to the gradual rise in heave and pitch.

Design and analysis of serviceable cantilever fit snap in automotive plastic parts

Purpose Snap fits are crucial in automotive applications for rapid assembly and disassembly of mating components, eliminating the need for fasteners. This study aims to focus on designing and analyzing serviceable cantilever fit snap connections used in automobile plastic components. Snap fits are classified into permanent and semi-permanent fittings, with permanent fittings having a snap clipping angle between 0° and 5° and semi-permanent fittings having a clipping angle between 15° and 45°. Polypropylene random copolymer is chosen for its exceptional fatigue resistance and elasticity. Design/methodology/approach The design process includes determining dimensions, computing assembly, disassembly pressures and creating three-dimensional computer-aided design models. Finite element analysis (FEA) is used to evaluate the snap-fit mechanism’s stress, deformation and general functionality in operational scenarios. Findings The study develops a modified snap-fit mechanism with decreased bending stress and enhanced mating force optimization. The maximum bending stress during assembly is 16.80 MPa, requiring a mating force of 7.58 N, while during disassembly, it is 37.3 MPa, requiring a mating force of 16.85 N. The optimized parameters significantly improve the performance and dependability of the snap-fit mechanism. The results emphasize the need of taking into account both the assembly and disassembly processes in snap-fit design, because the research demonstrates greater forces during disassembly. The approach developed integrates FEA and design for assembly (DFA) concepts to provide a solution for improving the efficiency and reliability of snap-fit connectors in automotive applications. Originality/value The research paper’s distinctiveness comes from the fact that it presents a thorough and realistic viewpoint on snap-fit design, emphasizes material selection, incorporates DFA principles and emphasizes the specific requirements of both assembly and disassembly operations. These discoveries may enhance the efficiency, reliability and sustainability of snap-fit connections in plastic automobile parts and beyond. In conclusion, the idea that disassembly needs to be done with a lot more force than installation in a snap-fit design can have a good effect on buzz, squeak and rattle and noise, vibration and harshness characteristics in automobiles.

A Salp Swarm Algorithm for Parallel Disassembly Line Balancing Considering Workers With Government Benefits

Proper disassembly operations organization and workstation assignment can help increase the efficiency of disassembly systems that are critical for recycling and remanufacturing of end-of-life (EOL) products. A parallel disassembly system layout allows diversification of disassembly tasks and increases flexibility. In this work, a parallel disassembly balancing model considering hiring workers with government benefits (WGB) is established. To quickly find an optimal solution to the model, a salp swarm algorithm (SSA) with a new encoding and decoding process is developed. Moreover, we use the well-known mathematical optimization technique CPLEX to verify the correctness of the proposed model and use a genetic algorithm (GA), a constrained decomposition approach with grids’ optimization (CDG), and a random search (RS) algorithm to show the effectiveness of the proposed algorithm. Experimental results show that the proposed algorithm can perform well on the proposed problem, which is conducive to the society accepting more WGB into the workplace.

Visual-triggered contextual guidance for lithium battery disassembly: a multi-modal event knowledge graph approach

Lithium battery disassembly is a complex task that presents numerous challenges, including the wide variety of battery types, intricate manufacturing processes, and the absence of standardised procedures. These challenges pose significant obstacles in achieving efficient and accurate disassembly operations. Consequently, there is a clear need for guidance and support throughout the disassembly process. Conventional strategies for providing disassembly guidance often struggle to adapt to the dynamic changes that occur during the process. Therefore, it is essential to develop an approach that offers flexibility, efficiency, and adaptability to real-time visual cues in order to effectively address these challenges. This paper introduces a novel real-time visual guidance scheme that utilises a sophisticated multi-modal event knowledge graph. By leveraging outputs from computer vision models, the proposed scheme monitors the disassembly process in real-time and provides visually triggered, context-sensitive guidance through the multi-modal event knowledge graph. Additionally, this paper presents an auxiliary training method for visual state detection, guided by the large-scale visual model known as the Segment Anything Model (SAM), which helps mitigate the costs associated with data annotation during model development. The efficacy of the proposed framework is validated through experimental evaluations, demonstrating its potential in enhancing disassembly efficiency.

Integrated remanufacturing scheduling of disassembly, reprocessing and reassembly considering energy efficiency and stochasticity through group teaching optimization and simulation approaches

The energy crisis and environmental pollution are receiving increasing attention from governments and communities. This study researches energy-aware remanufacturing systems. Remanufacturing aims to reuse valuable resources from end-of-life products and produce as-new products. Since remanufacturing systems involve a series of disassembly, processing and assembly operations, remanufacturing schedule integrates disassembly, processing and assembly shops. A multi-objective scheduling of remanufacturing systems is proposed, considering workstation use, energy consumption and customer satisfaction simultaneously. A chance-constrained programming model is established to minimize makespan and energy consumption while satisfying total tardiness requirements. A hybrid method is developed, using group teaching optimization and a discrete event simulation system, which can seek and evaluate potentially favourable solutions. The approach is validated on a group of test instances using well-known methods. The results reveal that this method can find non-dominated solutions with well-converged and well-diversified performance, verifying its advantages in providing informed decisions for managers and engineers.

Smart disassembly cell for circularity: Turn industry 4.0 technologies for disassembly and recovery of components.

Circular Economy initiatives have introduced a solution demand for the treatment of End of Life (EoL) products, switching from shredding and material recycling to the recovery of modules/components that remain functional. The process needs product recovery and diagnostic, disassembly, and requalification. These tasks are today carried out by human labour due to their complexity and uncertainties concerning the working conditions of the received product. But it implies time and costs that hardly balance the economical balance in developed countries context. Human – Cobot collaborative disassembly are promising solution to overcome the uncertainties without compromising the flexibility and the rate of disassembly operation. The Smart Disassembly Cell for Circularity (SDC2) project aims to provide a semi–automated disassembly solution for EoL electronic products. This paper portrays the requirements for the implementation of a Human-Cobot collaborative cell for electronic products. The goal of this project is to implement a shared workplace where the robot and the worker can work in parallel or share a common task with safe interaction. A literature study on available solutions was made. From there, the study and implementation of the decision system, database and service platform started. This document describes the function of the database and decision system as well as the service platform to establish global communication. MySQL and Python with the FLASK package were used to frame the database and service platform, Genetic algorithm was implemented for providing the robotic disassembly planning solution. A case study using a personal computer and a Power inverter was conducted to verify the development in each stage. The systems are under development with an aim to provide a global disassembly cell which will be able to disassemble various products rather than focusing on a specific product.

Line balancing problem with multi-manned workstations and resource constraints: The case of electronics waste disassembly

The increasing public awareness of environmental protection and the scarcity of rare earth elements have made closed-loop supply chains a necessity in many sectors. In particular, recycling components and parts from end-of-life consumer electronics have drawn the attention of both academics and practitioners. Disassembly line balancing improves the cost efficiency of recycling operations and hence helps waste management businesses that operate on very narrow profit margins. This study proposes a new mathematical formulation and hybrid metaheuristics to solve the Disassembly Line Balancing Problem (DLBP) considering multi-manned workstations and resource constraints. The transformed AND/OR graph is used for prioritizing disassembly tasks in the modeling process. The method is applied for optimizing a real-world case of laptop disassembly to showcase the usefulness of the proposed approach. The performance of the developed metaheuristics is evaluated for minimizing the number of workstations, operators, and machines involved in the disassembly operations. Further, the results are analyzed through sensitivity analysis. This study is concluded by providing practical insights and suggestions for the future development of DLBPs.

A Review of Prospects and Opportunities in Disassembly With Human–Robot Collaboration

Abstract Product disassembly plays a crucial role in the recycling, remanufacturing, and reuse of end-of-use (EoU) products. However, the current manual disassembly process is inefficient due to the complexity and variation of EoU products. While fully automating disassembly is not economically viable given the intricate nature of the task, there is potential in using human–robot collaboration (HRC) to enhance disassembly operations. HRC combines the flexibility and problem-solving abilities of humans with the precise repetition and handling of unsafe tasks by robots. Nevertheless, numerous challenges persist in technology, human workers, and remanufacturing work, which require comprehensive multidisciplinary research to address critical gaps. These challenges have motivated the authors to provide a detailed discussion on the opportunities and obstacles associated with introducing HRC to disassembly. In this regard, the authors have conducted a review of the recent progress in HRC disassembly and present the insights gained from this analysis from three distinct perspectives: technology, workers, and work.

A Self-Adaptive Learning Approach for Uncertain Disassembly Planning Based on Extended Petri Net

Disassembly is the first phase to demanufacture end-of-life (EOL) products that are separated into parts/components for recovery. The quality conditions of EOL products are highly uncertain, which would result in some uncertain information during the disassembly process, e.g., the disassembly time and recovering revenue of each subassembly. It is quite challenging to determine the optimal/near-optimal disassembly solutions under uncertain information. This paper studies uncertain disassembly planning (UDP) and proposes a self-adaptive learning approach to quickly identify the near-optimal disassembly solutions. First, we model the UDP by extending Petri nets, where not only disassembly operations but also EOL options of each subassembly are represented in the extended Petri Net. Based on the UDP model, we develop the self-adaptive learning approach, which integrates an approximation procedure for estimating uncertain disassembly information, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -learning algorithm for training disassembly samples, and a heuristic method for selecting the best disassembly solution. Finally, a hybrid Li-ion battery pack of Audi A3 Sportback e-tron is selected as the case study and applied to test the proposed self-adaptive learning approach. The experimental results demonstrate that our proposed method can efficiently find a better disassembly solution than the existing disassembly solution within 200 trainings in the case study.

Domain Generalization-Based Dynamic Multiobjective Optimization: A Case Study on Disassembly Line Balancing

The objective of disassembly lines is to disassemble End-of-Life products in a remanufacturing field. The disassembly line balancing problem (DLBP) considers how to allocate disassembly operations to operators on the disassembly line to optimize predetermined goals, such as cycle time. In practice, various environmental uncertainties (e.g., uncertain product quality) exist in the disassembly line. These uncertainties entail DLBP essentially a dynamic multi-objective optimization problem. This study presents a dynamic DLBP (D-DLB) to model the effect of environmental uncertainties on the assignment of disassembly operations. Furthermore, a prediction-based dynamic optimization algorithm, termed domain generalization-based dynamic multi-objective evolutionary algorithm (DG-DMOEA), combining meta-learning with multi-objective optimization, is proposed to solve D-DLB. In DG-DMOEA, a meta-learning algorithm is employed to learn the parameters of a solution-generative model from the Pareto-optimal sets in all historical environments. Subsequently, the solution-generative model is applied to generate a high-quality initial population that can assist multi-objective optimization algorithms in finding the Pareto-optimal set in the new environment faster. Since no information in the new environment is required, learning can begin before the new environment arrives, significantly reducing computational time. Moreover, different solution-generative models can be designed for different DMOPs. Therefore, DG-DMOEA can thoroughly combine real-world problem properties to represent knowledge. The experimental results show that, compared with state-of-the-art methods, DG-DMOEA can considerably improve the quality of solutions and significantly enhance the ability to react quickly to environmental changes.

An Augmented Reality-Assisted Disassembly Approach for End-of-Life Vehicle Power Batteries

The rapid expansion of the global electric vehicle industry has presented significant challenges in the management of end-of-life power batteries. Retired power batteries contain valuable resources, such as lithium, cobalt, nickel, and other metals, which can be recycled and reused in various applications. The existing disassembly processes rely on manual operations that are time-consuming, labour-intensive, and prone to errors. This research proposes an intelligent augmented reality (AR)-assisted disassembly approach that aims to increase disassembly efficiency by providing scene awareness and visual guidance to operators in real-time. The approach starts by employing a deep learning-based instance segmentation method to process the Red-Green-Blue-Dept (RGB-D) data of the disassembly scene. The segmentation method segments the disassembly object instances and reconstructs their point cloud representation, given the corresponding depth information obtained from the instance masks. In addition, to estimate the pose of the disassembly target in the scene and assess their disassembly status, an iterative closed point algorithm is used to align the segmented point cloud instances with the actual disassembly objects. The acquired information is then utilised for the generation of AR instructions, decreasing the need for frequent user interaction during the disassembly processes. To verify the feasibility of the AR-assisted disassembly system, experiments were conducted on end-of-life vehicle power batteries. The results demonstrated that this approach significantly enhanced disassembly efficiency and decreased the frequency of disassembly errors. Consequently, the findings indicate that the proposed approach is effective and holds promise for large-scale industrial recycling and disassembly operations.

Selective disassembly sequence planning under uncertainty using trapezoidal fuzzy numbers: A novel hybrid metaheuristic algorithm

Nowadays, recycling end-of-life (EoL) products has emerged as a vital approach to address resource scarcity. Within the recycling process, disassembly plays a pivotal role and has garnered substantial attention from researchers. Disassembly sequence planning (DSP) is a crucial method to enhance disassembly efficiency. Among the various DSP models, selective disassembly sequence planning (SDSP) has gained prominence as a means to save time and reduce costs. It empowers operators to locate specific components or materials in real time, thereby boosting efficiency and minimising resource wastage. However, a notable research gap exists in the domain of SDSP, particularly in uncertain environments. To bridge this gap and render SDSP solutions more practical for real-world disassembly operations, this study adopts trapezoidal fuzzy numbers to represent uncertain information within the disassembly process and formulates a comprehensive SDSP model. In response to the intricate challenges posed by this problem, we propose a hybrid approach termed nondominated sorting genetic algorithm-II with simulated large neighborhood search (NSGA–II–SLNS). This innovative algorithm leverages the strengths of the nondominated sorting genetic algorithm-II (NSGA-II), simulated annealing algorithm (SA), and large neighborhood search (LNS). Additionally, we introduce several novel search operators into NSGA–II–SLNS, including a crossover and mutation strategy based on chaotic mapping, as well as a local search operator founded on the SA criterion and LNS. To assess the effectiveness of the proposed algorithm and model, extensive numerical case studies are conducted in this research. The outcomes contribute to the advancement of rapid, nearly optimal SDSP strategies in the face of uncertainty and ambiguity in problem settings.

Human-robot interaction for extraction of robotic disassembly information

ABSTRACT Disassembly of end-of-use products is critical to the economic feasibility of circular feedstock, reusing, recycling and remanufacturing loops. High-volume disassembly operations are constrained by disassembly complexity and product variability. The capability to buffer against timing, quantity and quality uncertainties of end-of-use products impacts the efficiency and profitability of demanufacturing systems. To achieve a competitive operation in the manufacturing life-cycle, disassembly systems need automated lines, however, the unpredictability of core supply challenges automation adaptability. Disassembly robot trajectories that are programmed manually or controlled by vision systems can be time intensive and subject to variability in lighting conditions and image recognition models. Alternatively, this paper presents a novel human-robot disassembly framework to systematically extract and generate robot trajectories derived from human-collaborative robot (cobot) disassembly. The collaborative training station proposed classifies trajectory segments and then adjusts trajectories to station-specific robots in a high-volume disassembly line. Virtual and physical collaborative disassembly case studies are presented and discussed. Results demonstrate the effectiveness of the disassembly data extraction method but indicate a disparity between the expected and ideal disassembly trajectories due to variability from human handling, which is further discussed in this paper.