With the recent rise of 3D-printing as a form of manufacturing and their advantages for quick prototyping there is an interest for 3D-printed optical components. This thesis tests how well 3D-printed quasi-optical components preform and verify measurements with electromagnetic simulations. Measurements were made using a 60GHz FM-CW radar as well with a trihedral reflector, and tested on 3D-printed PLA lenses printed using an Ultimaker 2+. The measurements made are of the refractive index of the material, as well as the focal length of the lenses. Results showed PLA having a refractive index close to n = 1.654 in the 60GHz region. Results also showed the lenses having consistent properties like focal length and gain, two lenses having focal length of 23.7 and 23.9 mm which are close to the simulation of 24.05 mm. These findings shows 3D printed quasi-optical components have sufficient performance for use of prototyping or production depending on use. There are also some questions that have arisen like how does crystallisation in the plastic affect the components properties? And how does the PLA degrading affect the quality of the lenses over time? / På grund av framgångar inom 3D-utskrivning på senaste tiden som en form av tillverkning och dess fördelar med snabb framtagande av prototyper finns det ett intresse för utskrivna optiska komponenter. Detta examensarbete testar hur väl 3D-utskrivna kvasioptiska komponenter funger\-ar och verifierar testresultat med elektromagnetiska simuleringar. Mät\-ning\-arna gjordes med en FM-CW radar och en trihedral reflektor, och testade PLA linser utskrivna med en Ultimaker 2+. Mätningar\-na gjordes på materialets brytningsindex samt linsernas brännvidd. Resultaten visade att PLA har ett brytningsindex nära n = 1.654 i 60GHz området. Resultaten visade även hur linserna har konsekventa egenskaper som brännvidd och förstärkning. De två slutgiltiga linserna hade en brännvidd på 23.7 and 23.9 mm vilket är nära simuleringen på 24.05 mm. Dessa resultat visar att 3D-utskrivna komponenter har tillräcklig prestanda för användning i framtagande av prototyper eller produktion beroende på användning. Det finns en del frågor som kommit till, som hur påverkar plastens kristallisation komponentens egenskaper? Och hur påverkar PLAs nedbrytande linsernas kvalité?

This thesis deals with the design of a low-cost robot used for educational purposes at robotic group Robotárna and at the secondary technical school SPŠ and VOŠ Brno, Sokolská, příspěvková organizace. The main intent of the robot is in teaching students programming. The research part deals with collaborational robots phenomenon and their practical usage in present production plants. The thesis covers the mechanical design of the robot, calculations of the required torques for each joints, design of the robot gripper and also a theoretical solution to robot control.

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.

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Telemedical methods enable remote patient monitoring and healthcare at a distance. Besides, fitness tracker and sport watches are currently trending electronic products to generate awareness of health parameters in daily life. Especially, the long-term and continuous measurement of electrophysiological signals such as electrocardiogramm (ECG) becomes increasingly attractive for telemedical applications. Typically used disposable Ag/AgCl wet electrodes for good skin-electrode contact can potentially cause skin irritation and rashes. This paper presents a low cost, individual and flexible substrate for skin electrodes to be applied in future consumer electronic or professional applications. It enables an alternative contact method of the electrode to the skin by applying a pressure during the measurement and hence good contact. If no measurement is needed pressure can be released and the electrode loses skin contact. The 3D printed polymer module is 4 mm thick and comprises a pressure chamber, silver electrodes and insulation layer. The airtight printed membrane of flexible filament, which expands when inflating the chamber, may be printed in different thicknesses and shapes, much thinner than the present 4mm. This enables a high individuality for various applications. Pressure up to 150 kPa was applied and leads to dilatation of 1400 μm. First tests on skin when measuring electrodermal activity (EDA) show promising results for future applications.

<p>Multifunctional additive manufacturing has the immense potential of addressing present needs within structural health monitoring by enabling a new additive manufacturing paradigm that redefines what a sensor is, or what sensors should resemble. To achieve this, the properties of printed components must be precisely tailored to meet structure specific and application specific requirements. However due to the limited number of commercially available multifunctional filaments, this research investigates the in-house creation of adaptable piezoresistive multifunctional filaments and their potential within structural health monitoring applications based upon their characterized piezoresistive responses. To do so, a rigid polylactic acid based-filament and a flexible thermoplastic polyurethane based-filament were modified to impart piezoresistive properties using carbon nanofibers. The filaments were produced using different mixing techniques, nanoparticle concentrations, and optimally selected manufacturing parameters from a design of experiments approach. The resulting filaments exhibited consistent resistivity values which were found to be less variable under specific mixing techniques than commercially available multifunctional filaments. This improved consistency was found to be a key factor which held back currently available piezoresistive filaments from fulfilling needs within structural health monitoring. To demonstrate the ability to meet these needs, the piezoresistive responses of three dog-bone shaped sensor sizes were measured under monotonic and cyclic loading conditions for the optimally manufactured filaments. The characterized piezoresistive responses demonstrated high strain sensitivities under both tensile and compressive loads. These piezoresistive sensors demonstrated the greatest sensitivity in tension, where all three sensor sizes exhibited gauge factors over 30. Cyclic loading supported these results and further demonstrated the accuracy and reliability of the printed sensors within SHM applications.</p>

Increasing demand for structural health monitoring in space highlights the need to make the creation of these systems more accessible. This study investigates the potential of additive manufacturing to achieve this goal by characterizing a self-sensing material made of a commercially available 3D-printed continuous carbon fibre filament. The results demonstrate the feasibility of converting the filament into a strain sensor with improved sensitivity compared to conventional foil strain gauges. Mechanical and electromechanical properties of the self-sensing material were characterized, including an ultimate tensile strength of 45.09 ± 3.45 MPa, a failure strain of 38.93 ± 3.41%, and a base resistance of 759.11Ω. The tensile gauge factor was calculated to be 467.06 ± 375.90 within the strain range of 0% to 3.8% with a linearity (R2) of 0.93. For the first time, a systematic literature review compares mechanical and electromechanical properties to enable material selection for mechanical design incorporating self-sensing material. The study highlights that the spread of material properties in a group of materials indicates how well-developed a material is for self-sensing purposes. This study advances our understanding of the feasibility of using additive manufacturing to create self-sensing materials for structural health monitoring systems and opens up new avenues for further research.