Systemic circular economy solutions for fiber reinforced composites

This open access book provides an overview of the work undertaken within the FiberEUse project, which developed solutions enhancing the profitability of composite recycling and reuse in value-added products, with a cross-sectorial approach. Glass and carbon fiber reinforced polymers, or composites, are increasingly used as structural materials in many manufacturing sectors like transport, constructions and energy due to their better lightweight and corrosion resistance compared to metals. However, composite recycling is still a challenge since no significant added value in the recycling and reprocessing of composites is demonstrated. FiberEUse developed innovative solutions and business models towards sustainable Circular Economy solutions for post-use composite-made products. Three strategies are presented, namely mechanical recycling of short fibers, thermal recycling of long fibers and modular car parts design for sustainable disassembly and remanufacturing. The validation of the FiberEUse approach within eight industrial demonstrators shows the potentials towards new Circular Economy value-chains for composite materials

Application of high voltage fragmentation to treat end-of-life wind blades

The 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

Analytical evaluation of the output variability in production systems with general Markovian structure

Performance evaluation models are used by companies to design, adapt, manage and control their production systems. In the literature, most of the effort has been dedicated to the development of efficient methodologies to estimate the first moment performance measures of production systems, such as the expected production rate, the buffer levels and the mean completion time. However, there is industrial evidence that the variability of the production output may drastically impact on the capability of managing the system operations, causing the observed system performance to be highly different from what expected. This paper presents a general methodology to analyze the variability of the output of unreliable single machines and small-scale multi-stage production systems modeled as General Markovian structure. The generality of the approach allows modeling and studying performance measures such as the variance of the cumulated output and the variance of the inter-departure time under many system configurations within a unique framework. The proposed method is based on the characterization of the autocorrelation structure of the system output. The impact of different system parameters on the output variability is investigated and characterized. Moreover, managerial actions that allow reducing the output variability are identified. The computational complexity of the method is studied on an extensive set of computer experiments. Finally, the limits of this approach while studying long multi-stage production lines are highlighted. © 2013 Springer-Verlag Berlin Heidelberg

Daydreaming factories

Optimisation of factories, a cornerstone of production engineering for the past half century, relies on formulating the challenges with limited degrees of freedom. In this paper, technological advances are reviewed to propose a “daydreaming” framework for factories that use their cognitive capacity for looking into the future or “foresighting”. Assessing and learning from the possible eventualities enable breakthroughs with many degrees of freedom and make daydreaming factories antifragile. In these factories with augmented and reciprocal learning and foresighting processes, revolutionary reactions to external and internal stimuli are unnecessary and industrial co-evolution of people, processes and products will replace industrial revolutions

Design and evaluation of in-line product repair strategies for defect reduction in the production of electric drives

Manufacturing companies are continuously facing the challenge of operating their manufacturing processes and systems in order to deliver the required production rates of high quality products of increasing complexity, with limited use and waste of resources. This aspect is particularly critical in emerging sectors, such as the e-mobility industry, where state of the art quality and process control technologies show strong limitations. This paper proposes new solutions for implementing in-line product repair strategies in the production of electric drives for the automotive industry. Moreover, it develops an innovative quantitative tool to estimate the impact of the proposed strategies on the overall process-chain performance. The benefits of the approach are validated within a real industrial context

Integrated Workstation Design and Buffer Allocation in Disassembly Systems for Remanufacturing

Remanufacturing is recognized as one of the most profitable and environmentally conscious options of the circular economy. A remanufacturing process chain includes disassembly, cleaning, inspection, reconditioning and reassembly stages to recover the functionality and value of post-use products. However, the efficiency and profitability of remanufacturing are significantly affected by the variability of post-use product conditions. Consequently, the disassembly tasks times are highly uncertain, and this leads to a lack of robustness in disassembly lines designed without considering these challenges. This paper aims at finding the optimal disassembly line design under uncertainty of tasks times to support remanu-facturing. A mathematical optimization model with the objective of profit maximization is proposed which jointly optimizes and determines (1) the sequence of components to be disassembled and the assignment of disassembly tasks to workstations and (2) the allocation of buffers in order to provide a disassembly line design which has the maximum profit and satisfies the desired cycle time. The benefits of the proposed model are validated within a real case study dedicated to the remanufacturing of mechatronic components in the automotive industry

Characterization of fine metal particles using hyperspectral imaging in automatic WEEE recycling systems

Waste from electric and electronic equipment (WEEE) represents the fastest growing waste stream in EU. The large amount and the high variability of electric and electronic products introduced every year in the market make the WEEE recycling process a complex task, especially considering that mechanical processes currently used by recycling companies are not flexible enough. In this context, hyperspectral imaging systems (HSI) can represent an enabling technology able to improve the recycling rates and the quality of the output products. This study shows the preliminary results achieved using a HSI technology in a WEEE recycling pilot plant, for the characterization of fine metal particles derived from WEEE shredding

Lithium-ion batteries towards circular economy: A literature review of opportunities and issues of recycling treatments

Nowadays, Lithium-ion batteries are widely used in advanced technological devices and Electric and Hybrid Vehicles, due to their high energy density for weight, reduced memory effect and significant number of supported charging/discharging cycles. As a consequence, the production and the use of Lithium-ion batteries will continuously increase in the near future, focusing the global attention on their End-of-Life management. Unfortunately, wasted Lithium-ion batteries treatments are still under development, far from the optimization of recycling processes and technologies, and currently recycling represents the only alternative for the social, economic and environmental sustainability of this market, able to minimize toxicity of End-of-Life products, to create a monetary gain and to lead to the independence from foreign resources or critical materials. This paper analyses the current alternatives for the recycling of Lithium-ion batteries, specifically focusing on available procedures for batteries securing and discharging, mechanical pre-treatments and materials recovery processes (i.e. pyro- and hydrometallurgical), and it highlights the pros and cons of treatments in terms of energy consumption, recovery efficiency and safety issues. Target metals (e.g. Cobalt, Nickel and Lithium) are listed and prioritized, and the economic advantage deriving by the material recovery is outlined. An in-depth literature review was conducted, analysing the existing industrial processes, to show the on-going technological solutions proposed by research projects and industrial developments, comparing best results and open issues and criticalities

Advancing in the analysis of materials in electr(on)ic equipment

Despite there is a great effort to support strategies for a circular economy of electr(on)ics as maintenance, repair, remanufacture and reuse, recycling keeps being the final ultimate stage reached by them. As the supply of materials has become a key issue for the economic and technology development, more information about the content of materials in electr(on)ics is in order. This is especially for printed circuit boards contained in the majority of electr(on)ics which have a great variety of materials with a significant economic value. This paper discusses two methodologies to quantify the material composition of these parts. The first methodology quantifies the material content using two algorithms to identify the typologies of electr(on)ics components, and the average material composition of some typologies of electr(on)ic components given by original manufacturers. The second methodology uses the Database of SEmiconductors (DoSE) which contains the full material composition of about 250 different electr(on)ic components of printed circuit boards. A case study based on the analysis of two models of battery management systems contained in the batteries of electric vehicles is developed to compare the material composition results obtained from the two methodologies. Although the analysis is limited to some electr(on)ic components, mainly the integrated circuit and capacitors, the results of the composition of the battery management system are given for a list of materials including aluminum, copper, iron, gold, lead, nickel and tantalum. For two of the most economically relevant materials, copper and gold, the results obtained by the two methodologies differ 2% for copper and 4% for gold. To advance towards more automatized and systematic methodologies to estimate the material composition of the battery management systems, there are some further developments needed: to increase datasets for other electr(on)ic components as connectors, and better quantification of the number of layers and finishing of the circuit boards as they are made of significant quantities of copper and gold

Part Variation Modeling to Avoid Scrap Parts in Multi-stage Production Systems

Manufacturing systems for today's products are complex systems requiring a variety of different processes in order to be able to manufacture all necessary part features. This also applies to the production of rotating components, which have experienced increasing demand at the latest due to the growth in mobility. As in almost every manufacturing process, quality-reducing defects can occur due to deviations for example tool wear, which cannot always be avoided. Those, that have accumulated from previous process steps can cause the occurrence of superimposed defects. This leads to complex relationships between quality defects in the end product and the numerous parameters of the manufacturing processes. To remain competitive, production must be optimized in order to identify defects as early as possible, as well as their dependencies and variation patterns. The paper presents an approach to identify and model part variations within multi-stage production systems. Subsequently, based on a detected deviation, a downstream compensation strategy can be proposed at an early stage of the manufacturing process, which uses the capability of the overall system to fundamentally eliminate rejects