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>

Nature develops several materials with remarkable functional properties composed of comparatively simple base substances. Biological materials are often composites, which optime the conformation to their function. On the other hand, synthetic materials are designed a priori, structuring them according to the performance to be achieved. 3D printing manufacturing is the most direct method for specific component production and earmarks the sample with material and geometry designed ad-hoc for a defined purpose, starting from a biomimetic approach to functional structures. The technique has the advantage of being quick, accurate, and with a limited waste of materials. The sample printing occurs through the deposition of material layer by layer. Furthermore, the material is often a composite, which matches the characteristics of components with different geometry and properties, achieving better mechanical and physical performances. This thesis analyses the mechanics of natural and custom-made composites: the spider body and the manufacturing of metallic and non-metallic graphene composites. The spider body is investigated in different sections of the exoskeleton and specifically the fangs. The study involves the mechanical characterization of the single components by the nanoindentation technique, with a special focus on the hardness and Young's modulus. The experimental results were mapped, purposing to present an accurate comparison of the mechanical properties of the spider body. The different stiffness of components is due to the tuning of the same basic material (the cuticle, i.e. mainly composed of chitin) for achieving different mechanical functions, which have improved the animal adaptation to specific evolutive requirements. The synthetic composites, suitable for 3D printing fabrication, are metallic and non-metallic matrices combined with carbon-based fillers. Non-metallic graphene composites are multiscale compounds. Specifically, the material is a blend of acrylonitrile-butadiene-styrene (ABS) matrix and different percentages of micro-carbon fibers (MCF). In the second step, nanoscale filler of carbon nanotubes (CNT) or graphene nanoplatelets (GNP) are added to the base mixture. The production process of composite materials followed a specific protocol for the optimal procedure and the machine parameters, as also foreseen in the literature. This method allowed the control over the percentages of the different materials to be adopted and ensured a homogeneous distribution of fillers in the plastic matrix. Multiscale compounds provide the basic materials for the extrusion of fused filaments, suitable for 3D printing of the samples. The composites were tested in the configuration of compression moulded sheets, as reference tests, and also in the corresponding 3D printed specimens. The addition of the micro-filler inside the ABS matrix caused a notable increment in stiffness and a slight increase in strength, with a significant reduction in deformation at the break. Concurrently, the addition of nanofillers was very effective in improving electrical conductivity compared to pure ABS and micro-composites, even at the lowest filler content. Composites with GNP as a nano-filler had a good impact on the stiffness of the materials, while the electrical conductivity of the composites is favoured by the presence of CNTs. Moreover, the extrusion of the filament and the print of fused filament fabrication led to the creation of voids within the structure, causing a significant loss of mechanical properties and a slight improvement in the electrical conductivity of the multiscale moulded composites. The final aim of this work is the identification of 3D-printed multiscale composites capable of the best matching of mechanical and electrical properties among the different compounds proposed. Since structures with metallic matrix and high mechanical performances are suitable for aerospace and automotive industry applications, metallic graphene composites are studied in the additive manufacturing sector. A comprehensive study of the mechanical and electrical properties of an innovative copper-graphene oxide composite (Cu-GO) was developed in collaboration with Fondazione E. Amaldi, in Rome. An extensive survey campaign on the working conditions was developed, leading to the definition of an optimal protocol of printing parameters for obtaining the samples with the highest density. The composite powders were prepared following two different routes to disperse the nanofiller into Cu matrix and, afterward, were processed by selective laser melting (SLM) technique. Analyses of the morphology, macroscopic and microscopic structure, and degree of oxidation of the printed samples were performed. Samples prepared followed the mechanical mixing procedure showed a better response to the 3D printing process in all tests. The mechanical characterization has instead provided a clear increase in the resistance of the material prepared with the ultrasonicated bath method, despite the greater porosity of specimens. The interesting comparison obtained between samples from different routes highlights the influence of powder preparation and working conditions on the printing results. We hope that the research could be useful to investigate in detail the potential applications suitable for composites in different technological fields and stimulate further comparative analysis.

Fast-bränsle-raketer (SRM) har funnit sin plats i en stor mängd tillämpningar sedan deras framkomst mer än 2000 år sedan. En SRMs prestanda är förutbestämd av geometrin av drivmedelskrutet och är begränsad av gjutningsmetoden, som idag främst används i produktion. Forskning inom 3D-skrivning av drivmedelskrut har undersökt nya komplexa geometrier som kan öka prestandan. Studier kring 3D-skrivning med UV-härdning är få till antalet och undersöks i detta projekt. Ett bindemedel av polyuretandiakrylat (PUDA) har syntetiserats och därefter karakteriserats, med och utan diverse monomerer genom dragprovsmätningar och differentialkalorimetri (DSC). Tillsats av tvärbindarna 1,6-hexandioldiakrylat (HDDA) samt trimetylolpropantriakrylat (TMPTA) till PUDA producerade ett mer sprött material. Denna skillnaden var mer påtaglig för TMPTA än HDDA, vilket tillskrivs den högre akrylatfunktionaliteten i den förstnämnda. Den kommersiella produkten Ebecryl 113 har karakteriserats med inerta fyllmedel. Härddjupet (DOC) undersöktes med Ebecryl 113 i ett experiment, vilket påvisade en minskning av härddjupet med ökande mängder aluminium. Orsaken är troligen de reflektiva egenskaperna för UV-ljus som aluminium innehar. Reologiska studier utfördes, från vilka en minskning i viskositet påvisades till följd av en ökad polydispersitet i partikelstorlekarna. 3D-skrivning med kolvextrudering och påföljande härdning med UV-strålning utforskades. Detta visade utmaningar med tekniken som behövs bemästras. I synnerhet uppmärksammades fasseparation och residuell härdning från reflekterat UV-ljus som begränsande faktorer för fortsatt arbete. / Solid rocket motors (SRMs) have found their place in many applications since their conception more than 2000 years ago. The performance of SRMs is determined by the geometry of the propellant grain and is limited by the cast-and-mould production method typically used today. Research has been made on 3D printing propellant grains to explore new complex geometries, which may increase performance. Studies on 3D printing techniques using UV curing are limited and are in this work investigated. A polyurethane diacrylate (PUDA) binder was synthesized and then characterized, with and without various monomers by tensile testing and differential scanning calorimetry. Additions of the crosslinkers 1,6-hexanediol diacrylate (HDDA) and trimethylolpropane triacrylate (TMPTA) to PUDA rendered the final product more brittle. This change was more noticeable for TMPTA than HDDA, as the former has a higher acrylate functionality. The commercial product Ebecryl 113 was also characterized with inert fillers added. A depth of cure (DOC) study with Ebecryl 113 was conducted, which showed a decrease in DOC with increasing amounts of aluminium. This is attributed to the reflective properties of aluminium in the UV spectrum. Rheological studies were conducted and a decrease in viscosity could be seen as a result of increasing the polydispersity of particle sizes. A 3D printing technique using plunger extrusion followed by UV curing was explored, which highlighted challenges which need to be overcome. Most notably, phase separation and residual curing from scattered UV rays are limiting factors for future work.

Les polymères conjugués semi-conducteurs sont des matériaux prometteurs pour des applications en optoélectronique et pour la fabrication de dispositifs de conversion d'énergie flexibles. Ils sont toutefois difficilement mis en forme en raison de la rigidité de leur structure. Le poly(3-hexylthiophène) (P3HT) est souvent utilisé comme polymère conjugué organique modèle. Sa mise en forme et ses propriétés peuvent être optimisées en l'incorporant dans une matrice polymère et en favorisant l’orientation moléculaire de ses chaînes. Cette orientation peut être induite dans un matériau lors de sa mise en forme, notamment lors de la préparation de fibres par électrofilage. Le projet vise à préparer des matériaux optimisant l'orientation du P3HT et à développer des outils pour caractériser l'organisation moléculaire du P3HT dans ces matériaux. Plus spécifiquement, la première étude consiste à comprendre l'effet de la matrice polymère sur le comportement du P3HT dans des nanofibres électrofilées. Celles-ci sont préparées en mélangeant le P3HT à une matrice polymère amorphe de poly(méthacrylate de méthyle) (PMMA) atactique ou fortement cristalline de poly(oxyde d'éthylène) (POE), et l’orientation des chaînes de P3HT est mesurée par spectroscopie Raman. Les résultats montrent que la capacité du POE à cristalliser, contrairement au PMMA, contraint les chaînes du P3HT à s'orienter le long de l’axe de la fibre, ce qui devrait améliorer ses propriétés de transport de charge. La calorimétrie différentielle à balayage et la microscopie optique et électronique à balayage permettent respectivement d'analyser les propriétés thermiques et d'imager la morphologie des nanomatériaux. La seconde étude est de développer une approche pour identifier la transition vitreuse du P3HT dans des nanofibres électrofilées et des impressions 3D composées d’un mélange P3HT-POE. Nous suivons alors l'évolution de l’état d'agrégation du P3HT par spectroscopie de fluorescence et le déplacement de sa bande Raman associée au mode d’élongation C=C sur une gamme de températures afin d'observer sa transition de phase vitreuse à l'échelle du nanoobjet individuel. Une preuve de concept est réalisée par des analyses sur des films minces à base de P3HT pour ensuite analyser les échantillons d’intérêt. Les résultats de spectroscopie Raman et de fluorescence sur les nanomatériaux de P3HT sont comparés aux analyses DSC sur les matériaux macroscopiques. Le projet améliorera d'une part notre capacité à caractériser les nanomatériaux de P3HT et, d'autre part, à en optimiser les propriétés. De manière plus générale, nos résultats conduiront à terme à une meilleure compréhension des relations structure-mise en forme-propriété-fonction des polymères conjugués, contribuant à la préparation de nouveaux matériaux électroniques organiques plus performants. / Conjugated polymers are promising semiconducting materials for applications in flexible optoelectronic and energy conversion devices. However, they are difficult to process because of the rigidity of their polymer backbone. Poly(3-hexylthiophene) (P3HT) is often used as a model organic conjugated polymer. Its processing and its properties can be improved by incorporating it into a polymer matrix and by favoring the molecular orientation of its chains. This orientation can be induced in a material during its processing, notably during the preparation of fibers by electrospinning. The project aims to prepare materials optimizing the orientation of P3HT and to develop tools to characterize the molecular organization of P3HT in these materials. More specifically, the first study consists of understanding the effect of the polymer matrix on the behaviour of P3HT in electrospun nanofibers. These nanofibers are prepared by mixing P3HT with an amorphous atactic poly(methyl methacrylate) (PMMA) or highly crystalline poly(ethylene oxide) (PEO) polymer matrix, and the orientation of the P3HT chains is measured by Raman spectroscopy. The results show that the capability of POE to crystallize, unlike PMMA, constrains the chains of P3HT to orient themselves along the fiber axis, which could improve its charge transport properties. Differential scanning calorimetry and optical and scanning electron microscopy make it possible, respectively, to analyze the thermal properties and to image the morphology of the nanomaterials. The second study is to develop an approach to identify the glass transition temperature of P3HT in electrospun nanofibers and 3D prints composed of a P3HT-PEO blend. We then follow the evolution of the aggregation state of P3HT by fluorescence spectroscopy and the shift of the Raman band associated with the C=C elongation mode over a range of temperatures to observe its glass transition temperature at the scale of the individual nanoobject. A proof of concept is first realized by carrying out analyses on thin films based on P3HT, followed by the analysis of the samples of interest. Raman and fluorescence spectroscopy results on P3HT-containing nanomaterials are compared to DSC analyses on macroscopic materials. The project will improve our ability to characterize P3HT nanomaterials and to optimize their properties. More generally, our results will ultimately lead to a better understanding of the structure-processing-property-function relationships of conjugated polymers, contributing to the preparation of new, more efficient organic electronic materials.

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