Design of a robotic system for battery dismantling from tablets

Due to the rapid increase in sales of mobile electronic devices, the number of batteries ending up in waste electric and electronic equipment (WEEE) is also rapidly increasing. According to the EU legislation, all batteries need to be removed from WEEE, which is currently done manually for tablets, posing potential safety risks for workers and resulting in high processing costs due to the labour intensity of the required dismantling operations. Therefore, a robotic dismantling system is developed in this research to automatically remove both the back covers and batteries from a mixed waste stream of tablets of different models and brands. At the outset of the design process, a total of 47 randomly collected tablets were analyzed to define the location of the battery and the required manual dismantling time. Thereafter, a robotic bending method was tested for removing the back cover. Once the battery is exposed, two different methods are tested: using a heat gun to loosen the glue that fixes the battery to the rest of the tablet and a robotic scraping method with a spatula to mechanically extract the battery. Whereas the required time for only the heating showed to be more than 120s, the results with the bending and scraping tool show that the proposed robotic dismantling system is capable of removing the back cover and battery for 63% of the tested tablets in less than 90s. However, to increase the economic viability and robustness of the proposed method to be able to cope with the high variety in tablet model designs, future work is required to develop algorithms to recognize product models to enable to define and retrieve product specific toolpaths for dismantling.Peer ReviewedPostprint (published version

A Variety Metric Accounting for Unbalanced Idea Space Distributions

AbstractProving the effectiveness of an idea generation method is key to its acceptance in an industrial and academic environment. This necessitates the development of a set of widely accepted metrics covering the different aspects on which idea generation methods can be characterized. This paper gives an overview of the existing metrics, and demonstrates a number of shortcomings in the variety metric, such as not accounting for the fairness of the distribution of ideas over nodes on an abstraction level. A level-based, correctly normalized variety metric, based on the Shannon entropy, is proposed which is shown to resolve the identified issues

eDIM: further development of the method to assess the ease of disassembly and reassembly of products: Application to notebook computers

The goal of this research is to further develop the eDIM method based on a new application to some exemplary laptops, also referred to as notebooks, which is a product group that is under review for the Eco-design Directive. This study aims at evaluating the applicability of the eDIM method as a standardised method for the assessment of the ability to access or non-destructively remove and reassemble certain components/assemblies from products. The scope of this study is limited to non-destructive, also refered to as reversible, disassembly and reassembly for the purpose of repair, remanufacture and reuse. In addition, the method has been further revised to address comments received from different stakeholders on the technical report outlining the eDIM method and during the presentation of the “Study for a method to assess the ease of disassembly of electrical and electronic equipment”. All comments received, which will be addressed in the presented study, relate to the following main topics: - Applicability of the eDIM method to a broader range of products including small, portable electronics. - Applicability of the eDIM method for other types of connectors, such as glues requiring wedge/pry and peel actions to be released - Applicability of the eDIM method for partial disassembly, different levels of disassembly, reassembly and how to deal with the allocation of the (re)disassembly time for components that need to be disassembled sequentially. - Applicability of the eDIM method to identify potential improvements for product’s designs.JRC.D.3-Land Resource

Sustainable aluminium recycling of end-of-life products: A joining techniques perspective

The sustainable management of aluminium has become crucial due to the exponential growth in global demand. The transition to a sustainable society with lightweight electric vehicles has led to the increasing use of aluminium in the transportation sector. This has consequently led to the importance of aluminium recycling to prevent the valuable material stream going to landfill. In addition, the extraction of primary aluminium has high environmental impact due to the high energy consumption and waste generation in comparison to secondary aluminium processing. Despite being one of the most recycled metals, ongoing trends of multi-material designs and the associated joining choices have caused increasing difficulty of separating aluminium with high purity. This paper evaluates the types of joining techniques causing impurities in the aluminium streams, and the relationship between particle size reduction and the presence of impurities due to joints particularly for end-of-life vehicles. An empirical experiment in a leading European recycling facility was conducted and demonstrated that mechanical fasteners, such as machine screws, socket screws, bolt screws and rivets, are the major types of joining technique causing impurities. Based on the observations from this case study, the characteristics of imperfectly liberated joints are examined. A Life Cycle Assessment (LCA) is also performed to evaluate the environmental impact of recycling different aluminium scrap qualities with varying impurity levels. The outcomes are then used to provide ecodesign guidelines aimed at improving the quality and increase the quantity of recycled aluminium.This study is supported by the Commonwealth Government CRC Program (AutoCRC), the Australian National University, and the Centre for Industrial Management, University of Leuven

Tool Design for Electronic Product Dismantling

AbstractIn industrialized countries, waste electrical and electronic equipment is mostly processed in shredder-based processes, which are characterized by a low recovery of precious metals, rare earth elements and flame retardant plastics. To increase the recycling efficiency for these materials, a dismantling tool has been developed. The development process of the dismantling tool was guided by in-depth analysis of the required disassembly time for LCD TVs and laptops. The results of practical experiments demonstrate that the use of the dismantling tool enable to reduce the dismantling time for plastic housing components and PWBs with respectively 36% and 45% for LCD TVs

Assessing the efficiency of Laser-Induced Breakdown Spectroscopy (LIBS) based sorting of post-consumer aluminium scrap

The aluminium Twitch fraction of a Belgian recycling facility could be further sorted by implementing Laser-Induced Breakdown Spectroscopy (LIBS). To achieve this goal, the presented research identifies commercially interesting output fractions and investigates machine learning methods to classify the post-consumer aluminium scrap samples based on the spectral data collected by the LIBS sensor for 834 aluminium scrap pieces. The classification performance is assessed with X-Ray Fluorescence (XRF) reference measurements of the investigated aluminium samples, and expressed in terms of accuracy, precision, recall, and f1 score. Finally, the influence of misclassifications on the composition of the desired output fractions is evaluated.Peer ReviewedPostprint (published version

Techno-economic assessment of robotic sorting of aluminium scrap

Due to shifting material use in several sectors, such as the automotive sector, the demand for wrought aluminium alloys is significantly increasing. Because of their low weight and desirable mechanical properties, wrought aluminium alloys find their use in many different applications. However, the primary production of aluminium is extremely energy intensive. Therefore, using secondary aluminium yields major environmental benefits. Hence, in order to avoid degradation of the aluminium quality during recycling, sorting aluminium alloys, based on their alloying elements, is necessary. Today, various non-ferrous metal fractions are either still sorted manually in unhealthy working conditions, resulting in either high labour costs, or the export of this waste stream to countries with a lower labour cost. With the emergence of novel spectrometric techniques, such as laser-induced breakdown spectrometry (LIBS) and deep learning computer vision techniques, the technical feasibility of classifying different aluminium alloys has been demonstrated. Therefore, the techno-economic viability of a robotic sorting process, that could be combined with such advanced classification systems, is presented. This study presents the development and evaluation of a robotic sorting system consisting of; a vision system, a conveyor, a SCARA robot and a pneumatic gripper. The vision system recognises the dimensions and positions of the objects on the conveyor and communicates with an innovative sequence planning algorithm. The use of experimental data enables to obtain realistic insights in the sorting efficiencies that can be obtained. The initial economic analysis illustrates the substantial potential of the proposed robotic sorting approach. To overcome saturation of the conveyor belt, two of the proposed systems are assumed to be capable of sorting 20.000 tons of aluminium annually each equipped with 6 robots creating a total added revenue up to 1,95 million euro per year.Peer ReviewedPostprint (published version

Enhanced plastic recycling using RGB+depth fusion with massFaster and massMask R-CNN

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe rapid increase in waste generation from electrical and electronic equipment (WEEE) has created the need for more advanced sensor-based systems to sort this complex type of waste. Therefore, this study proposes a method for object detection, instance segmentation, and mass estimation of plastics and contaminants using the fusion of RGB and depth (D) images. The methodology is based on the Faster and Mask R-CNN with an extra head for the mass estimation. In addition, a pre-processing method to enhance the depth image (ED) is proposed. To evaluate the data fusion and pre-processing method, two data sets of plastics and impurities were created containing images with and without overlapping samples. The first data set contains 174 RGB images and depth (D) maps of 3146 samples, excluding their mass value, while the second data set contains 42 RGB and D images of 766 pieces together with their mass. The first and second data sets were used to evaluate the performance of Mask and Faster R-CNN. Further, the second data set was used to evaluate the network’s performance with the additional head for mass estimation.The proposed method achieved 0.75 R 2 , 1.39 RMSE, and 0.81 MAE with an IoU greater than 50% using the network Resnet50_FPN_RGBED. Hence, it can be concluded that the presented method can distinguish plastics from other materials with reasonable accuracy. Furthermore, the mass of each detected particle can be estimated individually, which is of great relevance for the recycling sector. Knowing the mass distribution and the percentage of contaminants in a waste stream of mixed plastics can be valuable for adjusting the parameters of upstream and downstream sorting processes.Peer ReviewedPostprint (author's final draft

Simultaneous mass estimation and class classification of scrap metals using deep learning

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksWhile deep learning has helped improve the performance of classification, object detection, and segmentation in recycling, its potential for mass prediction has not yet been explored. Therefore, this study proposes a system for mass prediction with and without feature extraction and selection, including principal component analysis (PCA). These feature extraction methods are evaluated on a combined Cast (C), Wrought (W) and Stainless Steel (SS) image dataset using state-of-the-art machine learning and deep learning algorithms for mass prediction. After that, the best mass prediction framework is combined with a DenseNet classifier, resulting in multiple outputs that perform both object classification and object mass prediction. The proposed architecture consists of a DenseNet neural network for classification and a backpropagation neural network (BPNN) for mass prediction, which uses up to 24 features extracted from depth images. The proposed method obtained 0.82 R2, 0.2 RMSE, and 0.28 MAE for the regression for mass prediction with a classification performance of 95% for the C&W test dataset using the DenseNet+BPNN+PCA model. The DenseNet+BPNN+None model without the selected feature (None) used for the CW&SS test data had a lower performance for both classification of 80% and the regression (0.71 R2, 0.31 RMSE, and 0.32 MAE). The presented method has the potential to improve the monitoring of the mass composition of waste streams and to optimize robotic and pneumatic sorting systems by providing a better understanding of the physical properties of the objects being sorted.Peer ReviewedPostprint (author's final draft