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<p>Carbon-fiber reinforced polymers (CFRPs) are often used in the aerospace and automotive  industries for their high strength-to-weight ratios and corrosion resistance. A new class of  composites – known as Prepreg Platelet Molded Composites (PPMCs) – offers further  advantageous such as high forming capabilities with modest compromises in strength and stiffness.  One such property of PPMCs that have garnered interest over the years is their apparent  insensitivity to notches. Previous studies have researched the effect of specimen size and platelet  length on its effect on the open-hole performance of PPMCs. Research however has focused on  thinner samples with smaller hole sizes and neglected thicker samples with larger holes.  Additionally, while platelet sizes have been investigated for unnotched samples, platelet width on  notched samples is less clear from the literature. The present thesis offers some investigations to  aid in filling this knowledge gap. </p> <p><br></p> <p>The objective of this work is to study two parameters that could influence the performance of PPMCs under open-hole tension. First, thick (7.6 mm) specimens are subjected to large hole  sizes (up to 19.08 mm) to investigate their behavior in comparison to the smaller sample sizes  previously investigated in the literature. Through-thickness DIC measurements are taken to  investigate strain gradients in these thicker specimens. Second, various platelet widths are tested  to research their influence on notch insensitivity of open-hole tensile PPMC specimens. Lastly, a  finite element based continuum damage mechanics model is implemented to predict macro-level  structural properties using only material properties of the parent prepreg. It is found that large holes  in thick samples increase notch sensitivity compared to other samples of similar diameter-to-width  ratios. Narrower platelets were found to produce higher unnotched strengths, while wider platelets  offered more notch insensitivity. Lastly, the finite element model developed was found to  qualitatively replicate features and failure modes that are exhibited by PPMCs, though strength  predictions became inaccurate at larger specimen sizes. Recommendations are made for future  work on the basis of these findings.   </p>

This thesis deals with the design process of a tensile test specimen geometry with the intention that the specimen will show failure in a shear band during a tensile test. The triggered shear band is linked to a characteristic length lc, which is required for a nonlocal approach to continuum damage mechanics that predicts the life expectancy of a combustion chamber independent of the FEM mesh size. To predict if a specimen will fail in the preferred manner, numerical simulations have been performed and were analysed with the newly defined failure-in-shear-band indicator. Ductile failure modes and the fracture process depend strongly on the stress state. Therefore the indicator is formulated as a function of the Lode parameter and the stress triaxiality. Several double-notched bar specimens have been designed with different notch radii and notch depths. The failure-in-shear-band indicator implies promising values for a small notch radius and larger notch depth. Tensile tests were performed on four specimens which successfully failed in a shear band. Furthermore, a first statement on the magnitude of the characteristic length of CuAgZr is given. / Detta arbete behandlar designprocessen för en dragprovstavskonfiguration framtagen för att uppvisa brott i ett skjuvband under draghållfasthetsprovning. Initiering av skjuvbandet är kopplat till en karakteristisk längd lc, som krävs för att kunna använda en icke lokal metod för att analysera kontinuerlig skademekanik oberoende av maskstorleken i den numeriska modellen. Metoden är utvecklad för att kunna uppskatta den förväntade livslängden för en förbränningskammare. För att förutsäga om ett provobjekt kommer att gå sönder på det sätt som önskashar datorsimuleringar utförts och analyserats med den nyligen definierade indikatorn för skjuvbrott. Plastisk deformation, och så småningom brott, är starkt beroende avspänningstillståndet. Indikatorn är därför formulerad som en funktion av en s.k. Lode parametern och det treaxliga spänningstillståndet. Flera provstavsgeometrier har utformats med dubbla brottanvisningar vars radie och storlek varierats. Indikatorn för skjuvbrott ger lovande värden för små radier och ett större anvisningsdjup. Draghållfasthetsprovning utfördes på fyra provkroppar som uppvisade önskat skjuvbrott. Dessutom erhölls en första indikation om storleken på den karakteristiska längden för CuAgZr.

Compression of elastomer elements are nonlinear due to the cross-linked molecular structure owing to a property known as hyperelasticity. Hyperelasticity is defined as the nonlinear stress-strain behavior shown by rubber like materials which can be strained up to a range of 700% in tension and up to 40% in compression. The stress-strain behavior is modeled by using different material models which predict the behavior very precisely.  Linear actuators from Cascade Drives AB uses a patented load sharing mechanism using elastically deformable elements to distribute the torque evenly between all the gears interacting with a common gear rack. An accurate model predicting the response of elastomer under compression has been developed in this thesis project. The elastomers were loaded in compression to provide flexibility for the system. First a static model was developed, where both a rectangular and a cylindrical roller model were analyzed.  The two models were derived using a continuum mechanics approach and the stiffness of the elastomers along with the torque output of the gearbox was calculated. A MATLAB model and an FEA model using ANSYS was created, and the results were compared. An error estimate between the MATLAB and FEA results for the rectangular and roller model was plotted for a certain β° of rotation of the gear. The models were also checked for different materials and the output torque for the different materials was plotted and analyzed. Finally, the experimental results were compared with the MATLAB results for the rectangular and roller models. The rectangle and roller model can be both used to predict the behaviour of using elastomers as the load sharing elements in applications. / Kompression av elastomerelement är olinjär till följd av den tvärbundna molekylära strukturen, en egenskap som kallas hyperelasticitet. Hyperelasticitet definieras som det icke-linjära spännings-töjningsbeteendet som uppvisas av gummiliknande material vilka kan töjas upp till av 700% och upp till komprimeras upp till 40%. Spänningsbelastningsbeteendet modelleras med hjälp av olika materialmodeller som förutsäger beteendet.  Cascade Drives linjäraktuatorer använder elastiskt komprimerbara element i sin lastfördelningsmekanism för att använda multipla plinjonger på ett kuggrack utan att få ett överbestämt system. Lastfördelningsmekanismen ger även en viss flexibilitet för systemet.  En modell som förutsäger responsen hos elastomerer under kompression har utvecklats i denna avhandling. Två geometriska former undersöktes modeller togs fram för både en rektangulär och cylindrisk rulle. De två modellerna härleddes med en kontinuummekanisk metod och elastomerernas styvhet beräknades. En MATLAB-modell och en FEM-modell i ANSYS skapades och resultaten jämfördes och en feluppskattning modellerna gjordes. Modellerna undersöktes också för olika material och utmattningsegenskaperna för de olika materialen analyserades.  Rektangel- eller rullmodellen kan båda användas för att förutsäga hur en elastomer skulle bete sig i en växellådsapplikation.

The Internet of Things (IoT) has deeply changed how we interact with our world. Today, smart homes, self-driving cars, connected industries, and wearables are just a few mainstream applications where IoT plays the role of enabling technology. When IoT became popular, Cloud Computing was already a mature technology able to deliver the computing resources necessary to execute heavy tasks (e.g., data analytic, storage, AI tasks, etc.) on data coming from IoT devices, thus practitioners started to design and implement their applications exploiting this approach. However, after a hype that lasted for a few years, cloud-centric approaches have started showing some of their main limitations when dealing with the connectivity of many devices with remote endpoints, like high latency, bandwidth usage, big data volumes, reliability, privacy, and so on. At the same time, a few new distributed computing paradigms emerged and gained attention. Among all, Edge Computing allows to shift the execution of applications at the edge of the network (a partition of the network physically close to data-sources) and provides improvement over the Cloud Computing paradigm. Its success has been fostered by new powerful embedded computing devices able to satisfy the everyday-increasing computing requirements of many IoT applications. Given this context, how can next-generation IoT applications take advantage of the opportunity offered by Edge Computing to shift the processing from the cloud toward the data sources and exploit everyday-more-powerful devices? This thesis provides the ingredients and the guidelines for practitioners to foster the migration from cloud-centric to novel distributed design approaches for IoT applications at the edge of the network, addressing the issues of the original approach. This requires the design of the processing pipeline of applications by considering the system requirements and constraints imposed by embedded devices. To make this process smoother, the transition is split into different steps starting with the off-loading of the processing (including the Artificial Intelligence algorithms) at the edge of the network, then the distribution of computation across multiple edge devices and even closer to data-sources based on system constraints, and, finally, the optimization of the processing pipeline and AI models to efficiently run on target IoT edge devices. Each step has been validated by delivering a real-world IoT application that fully exploits the novel approach. This paradigm shift leads the way toward the design of Edge Intelligence IoT applications that efficiently and reliably execute Artificial Intelligence models at the edge of the network.

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