1,721,016 research outputs found
In-depth analysis of electric vehicles battery pack structure and disassembly procedure for the application of circular economy strategies
Full text linkThe battery pack is the most valuable component of the electric vehicle and its disassembly is the key process to recover the inner value of the product and apply circular economy strategies, from repair to recycling and remanufacturing. Different models of EV battery packs have been analyzed to assess criticalities in the product structure and disassembly procedure. Regardless the absence of a standardized design, some similarities can be identified and considered for the implementation of disassembly procedures. From the comparison of the disassembly procedures of four in-depth analyzed battery pack models emerged that it is possible to identify six disassembly blocks, grouped in two main disassembly stages. The first stage includes the disassembly of the battery covers, the coolant removal (in case of liquid cooling) and the service plug removal, while the second stage involves the removal of the junction block, the battery management system and the modules. The precedencies and criticalities in the execution of the six disassembly blocks were deeply analyzed and the results reported in the paper
Performance analysis of unreliable manufacturing systems with uncertain reliability parameters estimated from production data
Full text linkSystem engineering methods for the performance evaluation of manufacturing systems require the estimation of input parameters, which is either based on real data or experts’ knowledge. In both cases, the input parameters are subjected to uncertainty. However, most of the systems engineering methods, which are based on analytical models assume that machine reliability parameters such as Mean Time to Failure and Mean Time to Repairs are precisely known. In order to overcome this limitation, this paper proposes an approach for the performance evaluation of unreliable manufacturing systems that considers uncertain machine reliability estimates. The method enables to calculate the distribution of the output performance, given the distribution of the input parameters’ uncertainty. The evaluation procedure is based on the combined use of Bayesian estimation, probability density function discretization and existing decomposition-based techniques for analyzing transfer lines composed of unreliable machines and capacitated buffers. Experimental results obtained by using the method show that neglecting uncertainty in the input parameter estimates generates significant errors in the output performance measure estimation, thus making the subsequent system operation and reconfiguration decisions sub-performing. Finally, a real case study is presented to demonstrate the potential benefits of the proposed method for real industrial applications
Production quality performance of manufacturing systems with in-line product traceability and rework
No full textIn-line product inspection and identification are emerging technologies supporting the observability and traceability of the product state evolution in multi-stage manufacturing systems. These technologies enable the dynamic implementation of defect management actions, such as product rework. However, their impact on the system quality and production logistics performance needs to be assessed in order to justify the related investments. This paper presents a theory and a methodology to predict the effective throughput of manufacturing systems with product traceability and rework, jointly considering the product and system state dynamics. The industrial benefits are validated in a real system in the semiconductor industry
Dynamic implementation of function-oriented selective and adaptive assembly in small-lot production
No full textThe increasing trend towards high-variety, complex assembled products combined with the growing attention to sustainable manufacturing call for novel methods to reduce defects in small-lot productions. Selective assembly may improve assembled product quality, but the inherent rigidity of the underlying assembly strategy bounds its industrial applications. The paper presents a methodology to dynamically implement selective and adaptive assembly of complex products. Based on cyber-physical system capabilities, the assembly components are dynamically selected to match evolving quality requirements of the assembled product. The method is validated in a real opto-electronics industry case, showing significant benefits in quality and production logistics performance
Towards smart e-waste demanufacturing systems exploiting multisensor vision system capabilities
No full textWaste electrical and electronic equipment (WEEE) is one of the fastest growing waste streams in the EU, expected to reach more than 12 million tons per year by 2020 [1]. End-of-Life (EoL) products and material mixtures found in E-waste are highly variable and in continuous evolutions. For example Cathod Ray Tube (CRT) televisions are rapidly being replaced by Liquid Crystal Display (LCD) and Light Emitting Diode (LED) TVs, cellular phones are being replaced by smart phones, traditional Hard Disk Drive (HDD) are being replaced by Solid State Disks (SSD). Moreover, new waste flows are entering the recycling streams, including photovoltaic panels and tablet PCs. Under the current business model and waste collection mechanism, the mechanical pre-treatment of these EoL products is usually performed by recyclers which apply (i) manual dismantling processes, and (ii) mechanical shredding and separation processes to reduce the material size and refine the input material into purified material streams which can be sold either to the market or to end-processing plants for further refinement. According to recent studies, the major causes for losses of recoverable metals, including precious and key metals, is due to mechanical pre-treatments (from 40% to 100% depending on the material), while only marginal losses are due to the downstream chemical end-processes. For recycling industries (typically SMEs), having dedicated treatment lines for each specific product type flow is impossible due to space and budget limitations. Therefore, batch production and high utilization of a single or few recycling lines is the typically adopted solution. The process parameters are then selected as a compromise between the different EoL product types. As a matter of fact, in spite of the high variability in the EoL products and material mixtures to be recovered, state of the art mechanical recycling systems are extremely rigid. The process parameters are traditionally set by the recycling machine designer based on a, typically small, material sample provided by the end-user, in the design phase. These parameters are normally kept constants throughout the systems life cycle. This rigidity of the system coupled with the high variability in the input material composition ultimately causes (i) poor recycling rates, (ii) abuse of landfill, also for materials that are potentially recyclable, (iii) lack of competitiveness and, ultimately, (iv) untapped market potentials. This scenario calls for a new generation of highly adaptable demanufacturing systems, endowed with the required level of adaptability to cope with evolving products and variable materials’ market conditions. In this perspective, the integration of specific technologies and on-line control strategies ensure the reconfigurability of process flow and machine parameters according to the specific mixture under treatment, allowing the improvement of process efficiency. In this framework, processes flexibility and reconfigurability capabilities are becoming increasingly important especially when they have to cope with ever-increasing EoL products complexity and variability. However, the lack of appropriate technologies, tools and methods to recover and re-use materials from industrial wastes and post-consumer products can negatively affect the sustainability potential of demanufacturing processes. This paper discusses the advantages related to the installation of a customized multisensor system to support the on-line identification of different material wastes in a new generation of smart demanufacturing systems. An example of integrated multisensor system implemented at the De- and Remanufacturing Pilot Plant at ITIA-CNR is presented
Editorial: The challenge towards more sustainable lithium ion batteries: from their recycling, recovery and reuse to the opportunities offered by novel materials and cell design
Full text linkCyber-Physical Systems formalization in de- and remanufacturing and application to size reduction stage
Full text linkCircular economy aims to shift from traditional linear approach of "take, make, dispose" to a closed loop scenario with the final goals of waste reduction and improvement of environmental friendly processes, creating new adaptable and resilient systems. Recycling processes, fundamental in circular economy, are facing issues related to the variability both in input and in output, showing mechanical treatments as the most promising in terms of environmental impacts and costs. First step in every recycling line is shredding. The objective is to obtain liberated particles (composed only by target materials) at a desired dimensional distribution. Cyber-Physical Systems are the most promising technology in recycling due to their capability to enable working always with optimized parameters, with a continuous exchange of information and actuations between hardware and software parts. The scope of this work is to present an architecture to optimize comminution processes, divided in two different steps, also based on Cyber-Physical Systems. First one aims to achieve the target optimal distribution, while the second one to minimize operational costs
Hyperspectral Imaging for the On-line Identification and Classification of End-of-Life Lamps
No full textThe lighting market is expected to have revenues of over 100 billion euros in 2020. Despite the evident economical
potentialities of this sector, the high products variability, complexity and the presence of materials with potential
negative environmental impacts, make lamps recycling processes a challenging task. The Directive 2002/96/EC
defines 80% as minimum recovery target for lamps containing mercury. Recovery processes currently adopted for
End-Of-Life lamps treatment consist of a preliminary manual phase for sorting different lamp typologies, strictly
correlated to the operator ability of recognize the lamps category. The present work proposes a new approach based
on the application of hyperspectral vision systems for the automatic identification of End-of-Life lamps, aimed at
optimizing recycling processes. These solutions could facilitate the securing of lamps containing hazardous materials,
with the aim of maximizing the degree of purity of the recovered key metals and rare earths, stimulating potential
secondary raw materials markets
Production quality improvement during manufacturing systems ramp-up
Full text linkIn the current manufacturing context, characterized by short product life-cycles, large product variety, product customization and short innovation cycles, achieving target production quality performance is challenging, especially due to the frequent ramp-up phases the system undergoes along its life-cycle. Available production quality methods focus on high-volume productions and long-term system performance, while they lose effectiveness during the system ramp-up, where instability and unknown disturbances affect the system dynamics. This paper proposes a reference framework for improving production quality performance during the system ramp-up phase. Two strategies for properly dealing with this problem are discussed, consisting in anticipating ramp-up problems during the design phase and performing continuous improvement of production quality performance measures during the system ramp-up. The most effective approaches following these strategies are revised and future research directions in this new research area are drawn
Application of high voltage fragmentation to treat end-of-life wind blades
Full text linkThe use of composites is constantly increasing in several sectors, from wind energy to automotive, thanks to their mechanical properties, lightweight, and resistance to corrosion. Despite this, the recycling and reuse of these materials in high-added value applications is not yet performed at the industrial level. In particular, End-of-Life (EoL) products are sent to landfills (if possible), incinerated, or inserted in co-processing in cement plants. This work presents an experimental approach to treat End-of-Life wind blades based on High Voltage Fragmentation (HVF). This technology, based on the creation of electric spark channels, is able to generate localized shock waves at the interface between two different materials. The potential of its application has been shown in the literature, but an experimental campaign is needed to find the optimal parameters to obtain an output material with proper characteristics to feed specific output products, following a demand-driven approach
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