PhD, EngD and MSc by research theses (SATM)

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Browsing PhD, EngD and MSc by research theses (SATM) by Course name "MSc by Research"

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    Advanced carbon/flax/epoxy composite material for vehicle applications: vibration testing, finite elements modelling, mechanical and damping characterization.
    (Cranfield University, 2015-05) Ampatzidis, Theofanis; Blackburn, Kim; Abhyankar, Hrushikesh
    Nowadays, research in automotive and construction industries focuses on materials that offer low density along with superior dynamic and static performance. This goal has led to increasing use of composites in general, and carbon fibre (CF) composites in particular. CF composites have been adopted widely in the space industry and motorsports. However, their high stiffness and low density leads to low damping performance, which is responsible for increased levels of noise and reduction in service life. On the other hand, natural fibres (NF) like flax fibres (FF) are capable of delivering a much better damping performance. A hybrid composite comprising of FF and CF can potentially deliver both on strength and higher damping performance. In this study the mechanical and damping properties of CF, FF and their hybrid composites were examined. Composites' anisotropic nature affects their response to vibrations and so traditional damping experimental setups used for metals had to be ruled out. A damping set up based on Centre Impedance Method (CIM) was adopted for the purpose of this study which was based on an ISO standard originally developed for glass laminates. Standard tensile and flexural tests were conducted in order to characterise the performance of the hybrid composite. The experimental work was accompanied by finite elements analysis (FEA). The experimental data and FEA were used to optimize the hybrid structure layup with respect to damping and structural response.
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    Biomimetic vision-based collision avoidance system for MAVs.
    (2017-05) Isakhani, Hamid; Aouf, Nabil; Whidborne, James F.
    This thesis proposes a secondary collision avoidance algorithm for micro aerial vehicles based on luminance-difference processing exhibited by the Lobula Giant Movement Detector (LGMD), a wide-field visual neuron located in the lobula layer of a locust’s nervous system. In particular, we address the design, modulation, hardware implementation, and testing of a computationally simple yet robust collision avoidance algorithm based on the novel concept of quadfurcated luminance-difference processing (QLDP). Micro and Nano class of unmanned robots are the primary target applications of this algorithm, however, it could also be implemented on advanced robots as a fail-safe redundant system. The algorithm proposed in this thesis addresses some of the major detection challenges such as, obstacle proximity, collision threat potentiality, and contrast correction within the robot’s field of view, to establish and generate a precise yet simple collision-free motor control command in real-time. Additionally, it has proven effective in detecting edges independent of background or obstacle colour, size, and contour. To achieve this, the proposed QLDP essentially executes a series of image enhancement and edge detection algorithms to estimate collision threat-level (spike) which further determines if the robot’s field of view must be dissected into four quarters where each quadrant’s response is analysed and interpreted against the others to determine the most secure path. Ultimately, the computation load and the performance of the model is assessed against an eclectic set of off-line as well as real-time real-world collision scenarios in order to validate the proposed model’s asserted capability to avoid obstacles at more than 670 mm prior to collision (real-world), moving at 1.2 msˉ¹ with a successful avoidance rate of 90% processing at an extreme frequency of 120 Hz, that is much superior compared to the results reported in the contemporary related literature to the best of our knowledge.