513 research outputs found

    Systemic Circular Economy Solutions for Fiber Reinforced Composites

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    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

    Concurrent Product and Supply Chain Architecture Design Considering Modularity and Sustainability

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    Since sustainability is a growing concern, businesses aim to integrate sustainability principles and practices into product and supply chain (SC) architecture (SCA) design. Modular product architecture (MPA) is essential for meeting sustainability demands, as it defines detachable modules by selecting appropriate components from various potential combinations. However, the prevailing practice of MPA emphasizes architectural aspects over interface complexity and design production processes for the structural dimension, potentially impending manufacturing, assembly/disassembly, and recovery efficiency. Most MPA has been developed assuming equal and/or fixed relations among modules rather than configuring for SC effectiveness. Therefore, such methods cannot offer guidance on modular granularity and its impact on product and SCA sustainability. Additionally, there is no comparative assessment of MPA to determine whether the components within the configured modules could share multiple facilities to achieve economic benefits and be effective for modular manufacture and upgrade. Therefore, existing modular configuration fails to link modularization drivers and metrics with SCA, hampering economic design, modular recycling, and efficient assembly/disassembly for enhancing sustainability. This study focuses on the study of design fundamentals and implementation of sustainable modular drivers in coordination with SCA by developing a mathematical model. Here, the architectural and interface relations between components are quantified and captured in a decision structure matrix which acts as the foundation of modular clustering for MPA. Again, unlike previous design approaches focused only on cost, the proposed work considers facility sharing through a competitive analysis of commonality and cost. It also evaluates MPA's ease of disassembly and upgradeability by a comparative assessment of different MPA to enhance SCA sustainability. The primary focus is concurrently managing the interdependency between MPA and SCA by developing mathematical models. Consistent with the mathematical model, this thesis also proposes better solution approaches. In summary, the proposed methods provide a foundation for modeling the link between product design and SC to 1) demonstrate how sustainable modular drivers affect the sustainability performance, 2) evaluate the contribution of modularity to the reduction of assembly/disassembly complexity and cost, 3) develop MPA in coordination with SC modularity by trading off modular granularity, commonality, and cost, and 4) identify a sustainable product family for combined modularity considering the similarity of operations, ease of disassembly and upgradability in SCA. Using metaheuristic algorithms, case studies on refrigerators showed that MPA and its methodology profoundly impact SCA sustainability. It reveals that interactions between components with levels based on sustainable modular drivers should be linked with modular granularity for SCA sustainability. Another key takeaway is that instead of solely focusing on cost, facility sharing and ensuring ease of disassembly and upgradeability can help to reap sustainability benefits

    Energy-aware coordination of machine scheduling and support device recharging in production systems

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    Electricity generation from renewable energy sources is crucial for achieving climate targets, including greenhouse gas neutrality. Germany has made significant progress in increasing renewable energy generation. However, feed-in management actions have led to losses of renewable electricity in the past years, primarily from wind energy. These actions aim to maintain grid stability but result in excess renewable energy that goes unused. The lost electricity could have powered a multitude of households and saved CO2 emissions. Moreover, feed-in management actions incurred compensation claims of around 807 million Euros in 2021. Wind-abundant regions like Schleswig-Holstein are particularly affected by these actions, resulting in substantial losses of renewable electricity production. Expanding the power grid infrastructure is a costly and time-consuming solution to avoid feed-in management actions. An alternative approach is to increase local electricity consumption during peak renewable generation periods, which can help balance electricity supply and demand and reduce feed-in management actions. The dissertation focuses on energy-aware manufacturing decision-making, exploring ways to counteract feed-in management actions by increasing local industrial consumption during renewable generation peaks. The research proposes to guide production management decisions, synchronizing a company's energy consumption profile with renewable energy availability for more environmentally friendly production and improved grid stability

    Intelligent robotic disassembly optimisation for sustainability using the bees algorithm

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    Robotic disassembly plays a pivotal role in achieving efficient and sustainable product lifecycle management, with a focus on resource conservation and waste reduction. This thesis discusses robotic disassembly sequence planning (RDSP) and robotic disassembly line balancing (RDLB), with a specific emphasis on optimising sustainability models. The overarching goal was to enhance the efficiency and effectiveness of disassembly processes through intelligent robotic disassembly optimisation techniques. At the heart of this research lies the application of the Bees Algorithm (BA), a metaheuristic optimisation algorithm inspired by the foraging behaviour of honeybees. By harnessing the power of the BA, this research aims to address the challenges associated with RDSP and RDLB, ultimately facilitating sustainable disassembly practices. The thesis gives an extensive literature review of RDSP and RDLB to gain deeper insight into the current research landscape. The challenges of the RDSP problem were addressed in this work by introducing a sustainability model and various scenarios to enhance disassembly processes. The sustainability model considers three objectives: profit, energy savings, and environmental impact reduction. The four explored scenarios were recovery (REC), remanufacture (REM), reuse (REU), and an automatic recovery scenario (ARS). Two novel tools were developed for assessing algorithm performance: the statistical performance metric (SPM) and the performance evaluation index (PEI). To validate the proposed approach, a case study involving the disassembly of gear pumps was used. To optimise the RDSP, single-objective (SO), multiobjective (MO) aggregate, and multiobjective nondominated (MO-ND) approaches were adopted. Three optimisation algorithms were employed — Multiobjective Nondominated Bees Algorithm (MOBA), Nondominated Sorting Genetic Algorithm - II (NSGA-II), and Pareto Envelope-based Selection Algorithm - II (PESA-II), and their results were compared using SPM and PEI. The findings indicate that MO-ND is more suitable for this problem, highlighting the importance of considering conflicting objectives in RDSP. It was shown that recycling should be considered the last-resort recovery option, advocating for the exploration of alternative recovery strategies prior to recycling. Moreover, MOBA outperformed other algorithms, demonstrating its effectiveness in achieving a more efficient and sustainable RDSP. The problem of sequence-dependent robotic disassembly line balancing (RDLBSD) was next investigated by considering the interconnection between disassembly sequence planning and line balancing. Both aspects were optimised simultaneously, leading to a balanced and optimal disassembly process considering profitability, energy savings, environmental impact, and line balance using the MO-ND approach. The findings further support the notion that recycling should be considered the last option for recovery. Again, MOBA outperformed other algorithms, showcasing its capability to handle more complex problems. The final part of the thesis explains the mechanism of a new enhanced BA, named the Fibonacci Bees Algorithm (BAF). BAF draws inspiration from the Fibonacci sequence observed in the drone ancestry. This adoption of the Fibonacci-sequence-based pattern reduces the number of algorithm parameters to four, streamlining parameter setting and simplifying the algorithm’s steps. The study conducted on the RDSP problem demonstrates BAF’s performance over the basic BA, particularly in handling more complex problems. The thesis concludes by summarising the key contributions of the work, including the enhancements made to the BA and the introduction of novel evaluation tools, and the implications of the research, especially the importance of exploring alternative recovery strategies for end-of-life (EoL) products to align with Circular Economy principles

    Application of deep learning methods in materials microscopy for the quality assessment of lithium-ion batteries and sintered NdFeB magnets

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    Die QualitĂ€tskontrolle konzentriert sich auf die Erkennung von Produktfehlern und die Überwachung von AktivitĂ€ten, um zu ĂŒberprĂŒfen, ob die Produkte den gewĂŒnschten QualitĂ€tsstandard erfĂŒllen. Viele AnsĂ€tze fĂŒr die QualitĂ€tskontrolle verwenden spezialisierte Bildverarbeitungssoftware, die auf manuell entwickelten Merkmalen basiert, die von Fachleuten entwickelt wurden, um Objekte zu erkennen und Bilder zu analysieren. Diese Modelle sind jedoch mĂŒhsam, kostspielig in der Entwicklung und schwer zu pflegen, wĂ€hrend die erstellte Lösung oft spröde ist und fĂŒr leicht unterschiedliche AnwendungsfĂ€lle erhebliche Anpassungen erfordert. Aus diesen GrĂŒnden wird die QualitĂ€tskontrolle in der Industrie immer noch hĂ€ufig manuell durchgefĂŒhrt, was zeitaufwĂ€ndig und fehleranfĂ€llig ist. Daher schlagen wir einen allgemeineren datengesteuerten Ansatz vor, der auf den jĂŒngsten Fortschritten in der Computer-Vision-Technologie basiert und Faltungsneuronale Netze verwendet, um reprĂ€sentative Merkmale direkt aus den Daten zu lernen. WĂ€hrend herkömmliche Methoden handgefertigte Merkmale verwenden, um einzelne Objekte zu erkennen, lernen Deep-Learning-AnsĂ€tze verallgemeinerbare Merkmale direkt aus den Trainingsproben, um verschiedene Objekte zu erkennen. In dieser Dissertation werden Modelle und Techniken fĂŒr die automatisierte Erkennung von Defekten in lichtmikroskopischen Bildern von materialografisch prĂ€parierten Schnitten entwickelt. Wir entwickeln Modelle zur Defekterkennung, die sich grob in ĂŒberwachte und unĂŒberwachte Deep-Learning-Techniken einteilen lassen. Insbesondere werden verschiedene ĂŒberwachte Deep-Learning-Modelle zur Erkennung von Defekten in der Mikrostruktur von Lithium-Ionen-Batterien entwickelt, von binĂ€ren Klassifizierungsmodellen, die auf einem Sliding-Window-Ansatz mit begrenzten Trainingsdaten basieren, bis hin zu komplexen Defekterkennungs- und Lokalisierungsmodellen, die auf ein- und zweistufigen Detektoren basieren. Unser endgĂŒltiges Modell kann mehrere Klassen von Defekten in großen Mikroskopiebildern mit hoher Genauigkeit und nahezu in Echtzeit erkennen und lokalisieren. Das erfolgreiche Trainieren von ĂŒberwachten Deep-Learning-Modellen erfordert jedoch in der Regel eine ausreichend große Menge an markierten Trainingsbeispielen, die oft nicht ohne weiteres verfĂŒgbar sind und deren Beschaffung sehr kostspielig sein kann. Daher schlagen wir zwei AnsĂ€tze vor, die auf unbeaufsichtigtem Deep Learning zur Erkennung von Anomalien in der Mikrostruktur von gesinterten NdFeB-Magneten basieren, ohne dass markierte Trainingsdaten benötigt werden. Die Modelle sind in der Lage, Defekte zu erkennen, indem sie aus den Trainingsdaten indikative Merkmale von nur "normalen" Mikrostrukturmustern lernen. Wir zeigen experimentelle Ergebnisse der vorgeschlagenen Fehlererkennungssysteme, indem wir eine QualitĂ€tsbewertung an kommerziellen Proben von Lithium-Ionen-Batterien und gesinterten NdFeB-Magneten durchfĂŒhren

    STRATEGIC PLANNING OF CIRCULAR SUPPLY CHAINS WITH MULTIPLE DOWNGRADED MARKET LEVELS: A METHODOLOGICAL PROPOSAL

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    Recent legislation has recognized the importance of adopting Circular Economy (CE) principles in supply chain (SC) restructuring. The primary objective is to create circular supply chains (CSCs) that effectively reintegrate end-of-life (EOL) products into production networks through processes such as reusing, remanufacturing, and recycling. This paradigm shift toward circularity aims to enhance resource efficiency, extend product lifecycle, and minimise waste, thereby aligning firms with sustainable practices while providing them with a competitive advantage. In line with the goals of the CE, this study focuses on the design and optimisation of strategic decisions within a circular supply chain (CSC). To achieve this aim, a bi-objective mixed-integer linear programming (MILP) model is developed. This model represents a significant contribution as it offers a compact and generalized formulation for dealing with CSC design problems. The proposed MILP model encompasses several key decision variables and considerations. It determines the optimal number of downgraded market levels to be activated, the location of forward and treatment facilities as well as the optimal product flow within the CSC. Furthermore, the model takes into account the cannibalisation effects associated with the demand for both new and recovered products, ensuring a comprehensive analysis of the system dynamics. To solve the complex mathematical model, the augmented epsilon-constraint (AUGMECON2) method is employed. The utilisation of this method enables decision-makers to obtain practical solutions within reasonable time frames. The computational results obtained from applying the MILP model illustrate its encouraging potential and effectiveness in dealing with strategic decision-making problems within CSCs

    Homodyne spin noise spectroscopy and noise spectroscopy of a single quantum dot

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    The steady-state fluctuations of a spin system are closely interlinked with its dynamics in linear response to external perturbations. Spin noise spectroscopy exploits this link to extract parameters characterizing the dynamics without needing an intricate spin polarization scheme. In samples with an accessible optical resonance, the spin fluctuations are imprinted onto a transmitted linearly polarized quasi-resonant probe laser beam according to the optical selection rules, making an all-optical observation of spin dynamics possible. The beam’s detuning and intensity determine whether the system is probed at thermal equilibrium or under optical driving. The technique is uniquely applicable for studying single quantum dots, where a charge carrier’s spin and occupancy dynamics can be observed simultaneously. This thesis presents a step-by-step derivation of the shape and statistical properties of experimental spectra and highlights the experimental limitations faced by the technique at very low probe intensities through uncorrelated broadband technical noise contributions. Optical homodyne amplification is evaluated in a proof-of-principle experiment to determine whether this limitation can be overcome at low frequencies < 5 MHz. Unlike previous attempts, the presented proof-of-principle experiment demonstrates that shot-noise limited spin noise measurements are possible in low-frequency ranges down to ≳ 100 kHz. For even lower frequencies, the suppression of laser intensity noise by the limited common-mode rejection of conventional balanced detectors is found to be the limiting contribution. In the second part of the thesis, optical spin noise spectroscopy is used to conduct a long-term study of spin and occupancy dynamics of an individual hole spin confined in an (In,Ga)As quantum dot with high radial symmetry in the high magnetic fields regime. For magnetic fields ≳ 250 mT, the splitting of the Zeeman branches with an effective g-factor of 2.159(2) exceeds the quantum dot’s trion resonance’s homogeneous line width of 6.3(2) ÎŒeV, revealing a rich spectral structure of spin and occupancy dynamics. This structure reveals a so far neglected contribution of an internal photoeffect to the charge dynamics between the quantum dot and its environment. Previously developed theoretical modeling is extended to incorporate the photoeffect and successfully achieves excellent qualitative correspondence with experimental spectra for almost all detuning ranges. The photoeffect shuffles the charge from and into the quantum dot with two distinct rates. Within the model, the previously required Auger process is unnecessary to describe the experimental data. The rates of discharging and recharging the quantum dot are determined to be on the order of 12(7) kHz·ΌmÂČ·nW⁻Âč and 6(2) kHz·ΌmÂČ·nW⁻Âč, respectively. For magnetic fields < 500 mT, very long T1 hole spin relaxation times ≫ 1 ms are observed, while above 500 mT, T1 falls to 5(2) ÎŒs at 2.5 T, qualitatively confirming the theoretical prediction of a single-phonon mediated relaxation process. Furthermore, the electron spin relaxation time T1 in the trion state shows no pronounced dependence on magnetic fields above 500 mT and stays at a constant value of 101(2) ns. The saturation intensity of the transition also does not depend on the magnetic field and stays at a constant value of 4.8(7) nW·Όm⁻ÂČ
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