No description available.

Durch den Einsatz des 3D-Drucks mit Beton kann die Produktivität auf der Baustelle enorm gesteigert, der gesamte Bauablauf optimiert und zugleich geometrische Freiheit ohne zusätzliche Kosten realisiert werden. Jedoch bevor der Beton-3D-Druck eine breite Anwendung finden kann, müssen einige Fragen in Bezug auf die Dauerhaftigkeit sowie die Gebrauchsfähigkeit gedruckter Betonelemente erforscht werden. Im vorliegenden Aufsatz wird die Problematik der Verformungen und Rissbildung bei gedruckten Betonelementen infolge des plastischen Schwindens beleuchtet. Außerdem werden geeignete Messverfahren für die Quantifizierung von gehindertem sowie ungehindertem plastischem Schwinden von gedrucktem Beton vorgestellt.

Mit der neuartigen Bauweise der additiven Fertigung wurden verschiedene Strukturen aus Carbonbeton hergestellt. Die in diesem Beitrag thematisierte Besonderheit ist die Verwendung von Carbonbewehrung mit mineralischer Tränkung, die es ermöglicht, den Verbund zur Betonmatrix zu verbessern und das volle Potential der digitalen Fertigung im Hinblick auf die geometrische Freiheit auszuschöpfen. Nach der Vorstellung des verwendeten 3D-Druckverfahrens und der Tränkung der Bewehrung wird auf die unterschiedlichen Möglichkeiten der Bewehrungsintegration eingegangen. Im Anschluss werden die Ergebnisse der mechanischen Untersuchungen an den mittels der bevorzugten Herstellungsweise gefertigten Prüfkörpern unter Einbeziehen von CT-Aufnahmen diskutiert. Die entwickelte Technologie hat das Potential, den Bauprozess effizienter und umweltfreundlicher zu gestalten.

Angetrieben von vielversprechenden Effizienzsteigerungen wird der Beton-3D-Druck stetig weiterentwickelt. Um die gewonnenen Erkenntnisse niederschwellig in die Baupraxis zu überführen, empfehlen sich druckbare Betone im Rahmen des geltenden Regelwerks. Dabei stellt die Limitierung des Mehlkorngehalts eine Herausforderung dar. Um diese zu meistern, wird eine verallgemeinerbare, numerisch unterstützte Anwendung der Korngrößenverteilung nach Andreasen und Andersen als Basis für den Betonentwurf vorgeschlagen. Experimentelle Untersuchungen haben eine gute Verbaubarkeit und hinreichende Extrudierbarkeit eines Betons mit einem 16 mm Größtkorn und einem Mehlkorngehalt von 500 kg/m³ demonstriert.

<p>Extrusion Deposition Additive Manufacturing is a process by which short fiber-reinforced polymers are extruded in a screw and deposited onto a build platform using a set of instructions specified in the form of a machine code. The highly non-isothermal process can lead to undesired effects in the form of residual deformation and part delamination. Process simulations that can predict residual deformation and part delamination have been a thrust area of research to prevent the repeated trial and error process before a useful part has been produced. However, populating the material properties required for the process simulations require extensive characterization efforts. Tackling this experimental bottleneck is the focus of the first half of this research.</p><p>The first contribution is a method to infer the fiber orientation state from only tensile tests. While measuring fiber orientation state using computed tomography and optical microscopy is possible, they are often time-consuming, and limited to measuring fibers with circular cross-sections. The knowledge of the fiber orientation is extremely useful in populating material properties using micromechanics models. To that end, two methods to infer the fiber orientation state are proposed. The first is Bayesian methodology which accounts for aleatoric and epistemic uncertainty. The second method is a deterministic method that returns an average value of the fiber orientation state and polymer properties. The inferred orientation state is validated by performing process simulations using material properties populated using the inferred orientation state. A different challenge arises when dealing with multiple extrusion systems. Considering even the same material printed on different extrusion systems requires an engineer to redo the material characterization efforts (due to changes in microstructure). This, in turn, makes characterization efforts expensive and time-consuming. Therefore, the objective of the second contribution is to address this experimental bottleneck and use prior information about the material manufactured in one extrusion system to predict its properties when manufactured in another system. A framework that can transfer thermal conductivity data while accounting for uncertainties arising from different sources is presented. The predicted properties are compared to experimental measurements and are found to be in good agreement.</p><p>While the process simulations using finite element methods provide a reliable framework for the prediction of residual deformation and part delamination, they are often computationally expensive. Tackling the fundamental challenges regarding this computational bottleneck is the focus of the second half of this dissertation. To that end, as the third contribution, a neural network based solver is developed that can solve parametric partial differential equations. This is attained by deriving the weak form of the governing partial differential equation. Using this variational form, a novel loss function is proposed that does not require the evaluation of the integrals arising out of the weak form using Gauss quadrature methods. Rather, the integrals are identified to be expectation values for which an unbiased estimator is developed. The method is tested for parabolic and elliptical partial differential equations and the results compare well with conventional solvers. Finally, the fourth contribution of this dissertation involves using the new solver to solve heat transfer problems in additive manufacturing, without the need for discretizing the time domain. A neural network is used to solve the governing equations in the evolving geometry. The weak form based loss is altered to account for the evolving geometry by using a novel sequential collocation sampling method. This work forms the foundational work to solve parametric problems in additive manufacturing.</p>

Additive manufacturing is described in literature as a disruptive technology for spare parts supply chains and shows the potential to impact business models for spare parts production through various advantages over conventional manufacturing. Understanding changes in business models is important for companies to adopt any technology and explore business opportunities around it. This research aims to fill the gap in literature on how business models change when additive manufacturing is used for spare parts production in the automotive industry. To investigate those changes a qualitative research design with semi-guided interviews with experts in automotive companies was chosen. The findings showed that the adoption of additive manufacturing in the automotive industry is generally slow and only a few spare parts are specifically developed for additive manufacturing. Especially for niche low-volume and high-value applications AM is already used. At the same time identifying new business cases is needed for broader adoption. Contrasting opinions in literature that additive manufacturing is a disruptive technology, the adopters referred to it rather as a tool. Moreover, the findings showed that the potential of additive manufacturing described in literature is still there but cannot be utilized yet due to various identified barriers at the current stage. The main identified barriers are lack of knowledge, suitable manufacturing data, and resources to explore additive manufacturing.

Computed x-ray tomography is one of the most common medical imaging modalities andas such ways of improving the images are of high relevance. Applying deep learningmethods to denoise CT images has been of particular interest in recent years. In thisstudy, rather than using traditional denoising metrics such as MSE or PSNR for evaluation, we use a radiomic approach combined with 3D printed phantoms as a "groundtruth" to compare with. Our approach of having a ground truth ensures that we withabsolute certainty can say what a scanned tumor is supposed to look like and compareour results to a true value. This performance metric is better suited for evaluation thanMSE since we want to maintain structures and edges in tumors and MSE-evaluationrewards over-smoothing. Here we apply U-Net networks to images of 3D printed tumors. The 4 tumors and alung phantom were printed with PLA filament and 80% fill rate with a gyroidal patternto mimic soft tissue in a CT-scan while maintaining isotropicity. CT images of the 3Dprinted phantom and tumors were taken with a GE revolution DE scanner at KarolinskaUniversity Hospital. The networks were trained on the 2016 NIH-AAPM-Mayo ClinicLow Dose CT Grand Challenge dataset, mapping Low Dose CT images to Normal DoseCT images using three different loss functions, l1, vgg16, and vgg16_l1. Evaluating the networks on RadiomicsShape features from SlicerRadiomics® we findcompetitive performance with TrueFidelityTM Deep Learning Image Reconstruction (DLIR)by GE HealthCareTM. With one of our networks (UNet_alt, vgg16_l1 loss function with32 features, and batch size 16 in training.) outperforming TrueFidelity in 63% of caseswhen evaluated by counting if a radiomic feature has a lower relative error comparedto ground truth after our own denoising for four different kind of tumors. The samenetwork outperformed FBP in 84% of cases which in combination with the majority ofour networks performing substantially better against FBP than TrueFidelity shows theviability of DLIR compared to older methods such as FBP.

Tato dizertační práce se zabývá přípravou a charakterizací nanokompozitních polymerních pěn se zaměřením na strukturu materiálu a aplikaci v 3D tisku. Cílem práce je studium materiálu s vysoce organizovanou hierarchickou strukturou – od nanoměřítka, přes mikroskopickou strukturu po makroskopická tělesa. V první části práce byly řešeny strukturní vlastnosti nanokompozitů připravených z polymerních skel roztokovou metodou. Byl hledán obecně platný trend, pomocí kterého by bylo možné předpovídat disperzi nanočástic v kompozitu. Ukázalo se, že řídícím faktorem může být závislost na rozdílu parametrů rozpustnosti polymeru a rozpouštědla. Tento poznatek byl ověřen na systémech obsahujících různé nanočástice, polymery a rozpouštědla. Se znalostí principů pro řízení struktury nanokompozitů byly připraveny nanokompozity impaktního polystyrenu plněného nanosilikou. Tyto nanokompozity posloužily jako základ pro přípravu polymerních nakompozitních pěn. Porézní struktury bylo dosaženo pomocí termálního chemického nadouvadla azodikarbonamidu. Z těchto materiálů byly extrudovány filamenty, které byly následně zpracovány pomocí 3D tisku do požadovaných tvarů a vypěněny. Výsledkem byla hierarchická struktura s organizací struktury od nano (organizace nanočástic), přes mikro (struktura dvoukomponentní polymerní směsi a struktura pěny) po makroměřítko (struktura pěny a design 3D tisku). Byl pozorován vliv nanočástic na strukturu a termální a mechanické vlastnosti polymerních pěn. Nanočástice fungují při tvorbě pěny jako nukleační činidlo, na jejich povrchu snadno dochází k tvorbě pórů, takže s obsahem nanočástic v materiálu bylo vytvořeno více menších pórů, což napomohlo k homogenitě pěnové struktury. Přítomnost nanočástic změnila povrchovou energii zrn nadouvadla, díky čemuž docházelo k jeho rozkladu za nižích teplot a pěnění bylo i rychlejší. Nanočástice mají zároveň potenciál vyztužit stěny pěny a zlepšit tak mechanické vlastnosti. 3D tisk je oblíbená a hojně rozšířená technika, díky své jednoduchosti je v mnoha laboratořích a zkušebnách, proto roste poptávka po filamentech se speciálními vlastnostmi. Materiál vyvinutý v této dizertační práci je v podstatě hotovým a charakterizovaným produktem, který by mohl přispět k uspokojení této pohledávky.

<p>  </p> <p>Diabetes, a chronic condition characterized by elevated blood glucose levels, arises when pancreatic β-cells lose capacity to produce a robust, dynamic glucose-stimulated insulin secretion (GSIS) response. Accurate measurement of β-cell health and function <em>ex vivo</em> is thus fundamental to diabetes research, including studies evaluating disease mechanisms, novel drug candidates, and replacement β-cell populations. However, present-day dynamic GSIS assays typically represent end-point measurements, involve expensive commercial perifusion machines, and require time-consuming enzyme-linked immunosorbent assays (ELISA) for insulin detection. Microfluidic devices developed as accessible, low-cost alternatives still rely on secondary ELISAs and suspend islets in liquid medium, limiting their survival <em>in vitro</em>. Here, we present a novel, 3D-printed microphysiological system (MPS) designed to recreate components of <em>in-vivo</em> microenvironments through encapsulation in fibrillar type I collagen and restoration of favorable molecular transport conditions. Following computational-informed design and rapid prototyping, the MPS platform sustained collagen-encapsulated mouse islet viability and cytoarchitecture for 5 days and supported <em>in-situ</em> measurements of dynamic β-cell function. To rapidly detect insulin secretion from β-cells in the MPS, we then developed a highly sensitive electrochemical sensor for zinc (Zn2+), co-released with insulin, based on glassy carbon electrodes modified with bismuth and indium and coated with Nafion. Finally, we validated sensor detection of Zn2+ released from glucose-stimulated INS-1 β-cells and primary mouse islets, finding high correlation with insulin as measured by standard ELISA. Together, the 3D MPS and Zn2+ sensor developed in this dissertation represent novel platforms for evaluating β-cell health and function in a low-cost, user-friendly, and physiologically-relevant manner. </p>

[ES] En los últimos años, la fabricación aditiva a través de la extrusión de materiales ha experimentado un desarrollo y adopción acelerados gracias a la amplia disponibilidad de máquinas y materiales de bajo costo. El tamaño de estas máquinas se ha reducido del tamaño del taller al tamaño del escritorio, lo que permite su uso en configuraciones de oficina o en el hogar. Este cambio ha permitido la adopción de la tecnología por la gama más amplia de usuarios que nunca, con o sin experiencia en diseño de ingeniería. Este nuevo paradigma ha creado el desafío de cómo habilitar que estos nuevos usuarios aprovechen las capacidades proporcionadas por esta tecnología. Esta tecnología permite la creación de geometrías complejas y productos personalizados con un coste inferior a los procesos de fabricación convencionales. Además, la gran cantidad de usuarios dispuestos a compartir sus diseños permite encontrar soluciones de diseño desde otros diseñadores. Sin embargo, la amplia gama de configuraciones de máquina, parámetros y materiales requiere brindar soporte para obtener resultados exitosos para cualquier combinación. Esta tesis aborda este desafío identificando las características de diseño y fabricación a considerar e investigando las consideraciones mecánicas y de pos procesamiento. Se propone y evalúa un nuevo marco de diseño que permite a los nuevos usuarios aprovechar las capacidades y considerar las limitaciones. Esta investigación encuentra que es posible crear un conjunto de herramientas de diseño que permita a los usuarios no capacitados diseñar productos utilizando la complejidad habilitada por la tecnología al tiempo que garantiza la funcionalidad y la capacidad de fabricación del producto. / [CA] En els últims anys, la fabricació additiva a través de l'extrusió de materials ha experimentat un desenvolupament i adopció accelerats gràcies a l'àmplia disponibilitat de màquines i materials de baix cost. La grandària d'aquestes màquines s'ha reduït de la grandària del taller a la grandària de l'escriptori, la qual cosa permet el seu ús en configuracions d'oficina o en a casa. Aquest canvi ha permés l'adopció de la tecnologia per la gamma més àmplia d'usuaris que mai, amb o sense experiència en disseny o enginyeria. Aquest nou paradigma ha creat el desafiament de com habilitar que aquests nous usuaris aprofiten les capacitats proporcionades per aquesta tecnologia. Aquesta tecnologia permet la creació de geometries complexes i productes personalitzats amb un cost inferior als processos de fabricació convencionals. A més, la gran quantitat d'usuaris disposats a compartir els seus dissenys permet trobar solucions de disseny des d'altres dissenyadors. No obstant això, l'àmplia gamma de configuracions de màquina, paràmetres i materials requereix brindar suport per a obtindre resultats reeixits per a qualsevol combinació. Aquesta tesi aborda aquest desafiament identificant les característiques de disseny i fabricació a considerar i investigant les consideracions mecàniques i de post processament. Es proposa i avalua un nou marc de disseny que permet als nous usuaris aprofitar les capacitats i considerar les limitacions. Aquesta investigació troba que és possible crear un conjunt d'eines de disseny que permeta als usuaris no capacitats dissenyar productes utilitzant la complexitat habilitada per la tecnologia al mateix temps que garanteix la funcionalitat i la capacitat de fabricació del producte. / [EN] In recent years, additive manufacturing through material extrusion has experienced accelerated development and adoption thanks to the wide availability of low-cost machines and materials. The size of these machines has been reduced from shop floor to desktop size, enabling their usage in office setups or at home. This change has allowed the adoption of the technology by the broadest range of users than ever, with or without an engineering design background. This new paradigm has created the challenge of how to enable these novel users to leverage the capabilities provided by this technology. This technology allows the creation of complex geometry and customised products with a cost lower than conventional manufacturing processes. Furthermore, the large number of users willing to share their designs allows finding design solutions from other designers. However, the wide range of machine configurations, parameters and materials requires providing support to obtain successful results under any combination. This thesis addresses this challenge by identifying the design and manufacturing characteristics to be considered and investigating the mechanical and post-processing considerations. A new design framework that enables new users to leverage the capabilities and consider the limitations is proposed and evaluated. This research finds that it is possible to create a design toolkit that enables untrained users to design products using the complexity enabled by the technology whilst ensuring the product's functionality and manufacturability. / Fernández Vicente, M. (2022). Analysis to Support Design for Additive Manufacturing with Desktop 3D Printing [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/185344 / TESIS