Robotic Disassembly Research Articles

Robotic disassembly of electric vehicle batteries: Technologies and opportunities

The demand for electric vehicle (EV) battery services, such as repair, remanufacturing, and recycling, is rising as more EVs enter the market. Disassembly is an essential step in these services, but it is usually done manually, which is slow and costly. Therefore, robots have been proposed to disassemble batteries. This work provides an extensive overview of existing technologies in EV battery disassembly and potential robotics. The technologies investigated in this paper focus on how robots can enhance automation and flexibility. This paper summarises the characteristics and applicability of the potential technologies and highlights promising research directions to stimulate further exploration by researchers.

Publisher Correction: Scaffold-free cooperative robotic disassembly and reuse of a timber structure in the ZeroWaste project

Publisher Correction: Scaffold-free cooperative robotic disassembly and reuse of a timber structure in the ZeroWaste project

Scaffold-free cooperative robotic disassembly and reuse of a timber structure in the ZeroWaste project

Scaffold-free cooperative robotic disassembly and reuse of a timber structure in the ZeroWaste project

Design for robotic disassembly

AbstractThis study envisions a unified paradigm for design for automated disassembly. The goal is to integrate disassembly insights related to precious material recovery with the design phase for sustainable lifecycle management.Targeting plastic products with embedded electronics, the collaboration between design and robotic engineers aims to program a robot for disassembly for the LEGO® motor (45603) as demonstration, emphasizing a disassembly map as a vital tool. By considering the limitations and strengths of robots, this research pioneers a design for disassembly framework.

Towards robotic disassembly: A comparison of coarse-to-fine and multimodal fusion screw detection methods

To accelerate the transition towards a more circular economy it is believed essential to increase the economic viability and hence the adoption of de- and remanufacturing processes. For this, industry 4.0 technologies, including robotization, computer vision and artificial intelligence, are commonly considered as the main enablers to reduce costs while boosting performances of de- and remanufacturing processes. However, the essential step in de- and remanufacturing of screw detection for robotic disassembly is still confronted with various challenges, such as false negatives due to deep screw holes and false positives caused by similar patterns and colors, e.g. letters in labels. Previous studies improved the detection accuracy by capturing images of only a fine part of the product and neglected the negative impact of this imaging approach on the overall time efficiency of the disassembly process. Therefore, two of the most distinct approaches are evaluated in the presented research: 1. A Coarse-to-Fine detection method, where potential screws across the entire product are detected by an area camera in the coarse stage to generate an optimal unscrewing path and screw types and locations are further refined in a second instance, the fine stage. 2. A multimodal fusion detection method, where a combination of a high-resolution color line and 3D triangulation line camera system captures full-product images allowing a single run screw detection in the RGB, Intensity and Depth (RGBID) images. Furthermore, a pooling-based aligned fusion module (PAlign) is introduced to improve the feature representation and mitigate the encountered spatial misalignment of different modality branches. Two industrially representative datasets comprising images of laptops and hard disk drives are collected for the training and evaluation of the screw detection and unscrewing task. Experimental results show that the RGBID fusion method with PAlign module achieves an unscrewing success rate of 80.4 % with an average detection and unscrewing speed of 2.27 s per screw, which is almost double the speed when compared to the Coarse-to-Fine approach. Through a detailed analysis comparing these most distinct screw detection methods in terms of imaging approach, lighting and camera hardware (1/2, area/line, low/high resolution, RGB/RGBID, 600/25.000€), this study provides concrete insights and recommendations aimed at enhancing the efficiency of disassembly systems for sustainable demanufacturing.

Electric Vehicle Battery Disassembly Using Interfacing Toolbox for Robotic Arms

This paper showcases the integration of the Interfacing Toolbox for Robotic Arms (ITRA) with our newly developed hybrid Visual Servoing (VS) methods to automate the disassembly of electric vehicle batteries, thereby advancing sustainability and fostering a circular economy. ITRA enhances collaboration between industrial robotic arms, server computers, sensors, and actuators, meeting the intricate demands of robotic disassembly, including the essential real-time tracking of components and robotic arms. We demonstrate the effectiveness of our hybrid VS approach, combined with ITRA, in the context of Electric Vehicle (EV) battery disassembly across two robotic testbeds. The first employs a KUKA KR10 robot for precision tasks, while the second utilizes a KUKA KR500 for operations needing higher payload capacity. Conducted in T1 (Manual Reduced Velocity) mode, our experiments underscore a swift communication protocol that links low-level and high-level control systems, thus enabling rapid object detection and tracking. This allows for the efficient completion of disassembly tasks, such as removing the EV battery’s top case in 27 s and disassembling a stack of modules in 32 s. The demonstrated success of our framework highlights its extensive applicability in robotic manufacturing sectors that demand precision and adaptability, including medical robotics, extreme environments, aerospace, and construction.

Robotic Disassembly Platform for Disassembly of a Plug-In Hybrid Electric Vehicle Battery: A Case Study

Efficient processing of end-of-life lithium-ion batteries in electric vehicles is an important and pressing challenge in a circular economy. Regardless of whether the processing strategy is recycling, repurposing, or remanufacturing, the first processing step will usually involve disassembly. As battery disassembly is a dangerous task, efforts have been made to robotise it. In this paper, a robotic disassembly platform using four industrial robots is proposed to automate the non-destructive disassembly of a plug-in hybrid electric vehicle battery pack into modules. This work was conducted as a case study to demonstrate the concept of the autonomous disassembly of an electric vehicle battery pack. A two-step object localisation method based on visual information is used to overcome positional uncertainties from different sources and is validated by experiments. Also, the unscrewing system is highlighted, and its functions, such as handling untightened fasteners, loosening jammed screws, and changing the nutrunner adapters with square drives, are detailed. Furthermore, the time required for each operation is compared with that taken by human operators. Finally, the limitations of the platform are reported, and future research directions are suggested.

Jamming problems and the effects of compliance in dual peg-hole disassembly

Disassembly is a crucial step in remanufacturing and is currently mainly performed by humans. Automating disassembly can reduce labour costs and make remanufacturing more economically attractive. This paper focuses on identifying and characterizing a common disassembly task, dual peg-hole disassembly, with the aim of building a robotic disassembly system for this task. We enumerate the possible contact states and their geometric conditions during the extraction of two studs in a dual peg-hole. This paper focuses on jamming in the extraction and conducts geometrical and quasi-static analyses to determine the boundary conditions of jamming. Based on the analyses, this paper also investigates the role of active compliance as a solution to avoid jamming. We also simulate critical variables and examine key parameters such as the degree of compliance, the location of the compliance centre and initial position errors. Finally, we conduct experimental studies on dual peg-hole extraction with different compliance centres obtained using active compliance.

Predictive exposure control for vision-based robotic disassembly using deep learning and predictive learning

Predictive exposure control for vision-based robotic disassembly using deep learning and predictive learning

To shred or to disassemble – A techno-economic assessment of automated disassembly vs. shredding in lithium-ion battery module recycling

The global adoption of electric vehicles (EVs) is driving a surge in the use of lithium-ion batteries (LIBs), creating an urgent need for sophisticated recycling techniques to recover valuable materials and manage waste. Particularly, automating the laborintensive disassembly process is critical for scaling up recycling efforts. This study provides a techno-economic evaluation of a robotic line for disassembling EV LIB modules, exploring whether it's more profitable to extend automation to the cell level. Three different EV modules are examined, and several scenarios are proposed, showing that investments in robotic disassembly could be financially sound, particularly with the anticipated rise in end-of-life EV LIBs. The key to profitability lies in disassembling to the cell level, which enables the recovery of more valuable materials and reduces downstream processing requirements. This research offers practical guidelines for automating the disassembly process in line with future waste management demands.

Robotic disassembly of screws for end-of-life product remanufacturing enabled by deep reinforcement learning

Robot-assisted screw removal can greatly facilitate the disassembly and remanufacturing automation of end-of-life products to realise circular economies. However, it is challenging to determine the exact positions of disassembling screws in practical remanufacturing environments. To tackle the issue, in this research, a novel approach designed based on reinforcement deep learning (DRL)-based optimisation processes is herein presented. First, to identify the search directions for the exact positions of disassembling screws, an analytical model was established to quantify geometrical relationships between a robotic screwdriver and the screws for removal. Secondly, a Markov model was built to represent key parameters related to robot configuration and the evolving relationships in the analytical model. Furthermore, a proximal policy optimisation (PPO) algorithm, which is a high-performing DRL algorithm, was developed to determine the optimal values of key parameters in the analytical model and the Markov model. Finally, experiments were conducted to identify optimal parameters by applying this approach and to benchmark its effectiveness. Experimental outcomes demonstrated that high success rates in screw positioning were achieved using this approach, and it outperformed the comparative approaches/optimisation algorithms in terms of screw positioning accuracy and efficiency.

A novel Fibonacci-inspired enhancement of the Bees Algorithm: application to robotic disassembly sequence planning

A novel Fibonacci-inspired enhancement of the Bees Algorithm: application to robotic disassembly sequence planning

Robotic ease of Disassembly Metric (Re-DiM) for human robot cooperative disassembly: A case study for a vacuum cleaner

Remanufacturing, a beacon of economic and ecological alignment, hinges on disassembly’s complexity. As both manual and robotic approaches face challenges, systems in which human and robots can work iteratively offer adaptable solutions, curbing the required system flexibility at acceptable capital expenditure. However, today no metrics exist to evaluate the ease of disassembly of product designs when considering a combination of manual and robotic disassembly Therefore, this study extends the existing ease of Disassembly Metric (e-DiM) for designers, policymakers, and recyclers in anticipation of the industrialization of human-robot cooperative disassembly systems. For the development of such a metric the expected limitations of robotic disassembly systems are first discussed based on prior research. Subsequently, a set of criteria are presented to define which operations can be performed successfully in a flexible system. Finally, the Robotic ease of Disassembly metric (Re-DiM) is introduced, which serves to comprehensively evaluate both manual and robotic disassembly times. To validate Re-DiM’s applicability, the ease of both manual and robotic disassembly is evaluated for all critical components of a robotic vacuum cleaner. The presented case study highlights today’s robotic disassembly challenges, underscoring also the relevance of novel design for robotic disassembly approaches.

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.

Advancing Material Separation Efficiency Through Extended Disassembly Depth in Pretreatment Processes for Lithium-ion Batteries Recycling

The use of lithium-ion batteries (LIBs) in electric mobility requires proper end-of-life disposal to avoid environmental risks. Recycling for reusing active materials demands efficient pretreatment. In conventional processes, battery packs are disassembled into cells, shredded, and separated. The ensuing mixed black mass necessitates additional efforts for effective high-purity material recovery. The present research addresses this challenge by focusing on extending the disassembly depth of LIBs. The cells are subjected to a partially automated disassembly process. Based on a robot-assisted disassembly procedure, cells are opened using ultrasonic cutting technology, with various parameters being investigated with the Taguchi method. The assorted components, such as casing, separator, anodes and cathodes are separated. The electrodes subsequently undergo a crushing process to remove the active materials from the metal foils. Compared to the conventional method, disassembly and pre-sorting of the cell components improves the recovery of cathodic active materials by 12.8%. The findings highlight the importance of using robotic disassembly to achieve higher material separation efficiency, promoting sustainable recycling processes for spent LIBs.

Experiment-based conceptualization of an automated disassembly process chain for the recycling of lithium-ion battery modules

As the volume of end-of-life lithium-ion batteries (LIB) used in electric vehicles increases, so does the demand for recycling to recover valuable active materials as secondary resources, reducing the need for primary raw materials and minimizing supply dependencies. In addition to pyro- and hydrometallurgy recycling processes, an increasingly examined approach is the direct recycling route, which involves disassembling battery systems into modules, modules to cells, and ultimately cells to electrodes. To minimize passive materials such as various metals from the housing, joints, arresters, plastics, foams, and adhesives in the subsequent crushed mass, the introduction of module disassembly processes into the existing LIB recycling process chain is promising for the resulting increase in recyclate purity. This study presents the conceptualization of a LIB module disassembly process chain which aims to elevate the depth of disassembly of LIB during early recycling stages. A reverse engineering approach is used to recover individual cells from two battery modules of one specific automotive manufacturer by removing their casing through milling and disconnecting the cells from the arresters. The experimental results are analyzed for influences on the disassembly possibilities for individual components as well as the interactions within the system, enabling the elaboration of a priority graph and a disassembly plan. In order to allow for scalable recycling process chains suitable for very high quantities, a concept for a fully automated and variant-compatible robot application based on the experiment is derived. Furthermore, the depth of disassembly is compared with the corresponding reduction of passive materials in the crushed mass. The findings indicate that several module components can be disassembled, resulting in cell recovery and a reduction of metal and plastic in the crushed mass by about 17 %, highlighting the importance of robotic disassembly in enhancing material separation efficiency during LIB recycling.

Learning Robotic Milling Strategies Based on Passive Variable Operational Space Interaction Control

This paper addresses the problem of robotic cutting during disassembly of products for materials separation and recycling. Waste handling applications differ from milling in manufacturing processes, as they engender considerable variety and uncertainty in the parameters (e.g. hardness) of materials which the robot must cut. To address this challenge, we propose a learning-based approach incorporating elements of interaction control, in which the robot can adapt key parameters, such as feed rate, depth of cut, and mechanical compliance during task execution. We show how a mathematical model of cutting mechanics, embedded in a simulation environment, can be used to rapidly train the system without needing large amounts of data from physical cutting trials. The simulation approach was validated on a real robot setup based on four case study materials with varying structural and mechanical properties. We demonstrate the proposed method minimises process force and path deviations to a level similar to offline optimal planning methods, while the average time to complete a cutting task is within 25% of the optimum, at the expense of reduced volume of material removed per pass. A key advantage of our approach over similar works is that no prior knowledge about the material is required. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by challenges in emerging fields such as recycling of electric vehicles, where products such as batteries adopt a range of designs with varying physical geometry and materials. More generally, this applies when considering robotic disassembly of any unknown component where semi-destructive operations such as cutting are required. Product-to-product variation introduces challenges when planning cutting processes required to disassemble a component, as contemporary planning approaches typically require advance knowledge of the material properties, shape and desired path to select tool speed, feed and depth of cut. In this paper, we show a mathematical model of milling force embedded in a simulation environment can be used as a relatively inexpensive approach to simulate a broad spectrum of cutting processes the robot may encounter. This allows the robot to learn from experience a strategy that can select these key parameters of a milling task online without user assistance. We develop a framework for controlling a robot using this strategy that allows the stiffness of the robot arm to be modulated over time to best satisfy metrics of productivity (e.g. required cutting time), while maintaining safe interaction of the robot with its environment (e.g. by avoiding force limits), similarly to how a human operator can vary muscular tension to accomplish different tasks. We posit that the proposed method can substitute a trial-and-error strategy of selecting process parameters for disassembly of novel products, or integrated with existing planning approaches to adjust the parameters of milling tasks online.

Knowledge Graph Construction of End-of-Life Electric Vehicle Batteries for Robotic Disassembly

End-of-life (EoL) electric vehicle (EV) batteries are one of the main fountainheads for recycling rare metal elements like cobalt and lithium. Disassembly is the first step in carrying out a higher level of recycling and processing of EV batteries. This paper presents a knowledge graph of electric vehicle batteries for robotic disassembly. The information extraction of the EV batteries was conducted based on the source data of EV batteries. The semantic ontology structure and the knowledge graph of the EV batteries were constructed. A case study was designed to demonstrate the proposed knowledge graph. The study involved generating a robotic disassembly sequence planning for an EoL EV battery. The results show the feasibility of the constructed knowledge graph.

Digital Twin-Driven Robotic Disassembly Sequence Dynamic Planning Under Uncertain Missing Condition

Disassembly is an inevitable process of recycling end-of-life products and robotic disassembly sequence planning could improve disassembly efficiency. However, missing condition of component is uncertain and it could not be pre-known before execution of disassembly process. The optimal solution should be dynamically generated according to the recognized condition during disassembly process. In this paper, digital twin is utilized to solve robotic disassembly sequence dynamic planning under uncertain missing condition. First, the framework of the proposed method is studied and digital twin of robotic disassembly process is established. Afterwards, deep Q-learning network is utilized to solve the proposed problem. Finally, case studies are carried out to verify the effectiveness of proposed method. The results show the converged DQN model could dynamically find the optimal solutions after missing condition of component is recognized during disassembly process using less running time, compared with the other meta-heuristics algorithms.

Optimisation of Product Recovery Options in End-of-Life Product Disassembly by Robots

In a circular economy, strategies for product recovery, such as reuse, recycling, and remanufacturing, play an important role at the end of a product’s life. A sustainability model was developed to solve the problem of sequence-dependent robotic disassembly line balancing. This research aimed to assess the viability of the model, which was optimised using the Multi-Objective Bees Algorithm in a robotic disassembly setting. Two industrial gear pumps were used as case studies. Four objectives (maximising profit, energy savings, emissions reductions and minimising line imbalance) were set. Several product recovery scenarios were developed to find the best recovery plans for each component. An efficient metaheuristic, the Bees Algorithm, was used to find the best solution. The robotic disassembly plans were generated and assigned to robotic workstations simultaneously. Using the proposed sustainability model on end-of-life industrial gear pumps shows the applicability of the model to real-world problems. The Multi-Objective Bees Algorithm was able to find the best scenario for product recovery by assigning each component to recycling, reuse, remanufacturing, or disposal. The performance of the algorithm is consistent, producing a similar performance for all sustainable strategies. This study addresses issues that arise with product recovery options for end-of-life products and provides optimal solutions through case studies.