Yttria stabilized zirconia (YSZ) is a polymorph with possible phase transformation toughening occurring during impact. The fractography and mechanical properties of laminated alumina and YSZ were studied in this thesis. Five sample types were studied in this thesis: (5:5) Al2O3/YSZ (a sequence of 5 alumina tapes stacked on 5 YSZ tapes), (5:5) Al2O3/YSZ (1 wt.% Pure ZrO2), (7:3) Al2O3/YSZ, Al2O3, and YSZ. Scanning electron microscopy (SEM) and X-ray microscopy (XRM) were used to study morphology and crack propagation with three-point tests performed to study the flexural strength. X-ray diffraction (XRD) spectra of all samples pre and post three-point tests were examined to determine if a change in monoclinic zirconia occurred. The combination of SEM and XRM data found microcracks in the YSZ layers of Al2O3/YSZ laminates with none present on YSZ laminates, leading to the conclusion tensile stress was performed on YSZ during sintering with Al2O3. Fracture patterns show a curving of cracks in Al2O3 layers and abrupt, jagged breaks in YSZ layers with crack forking at major YSZ microcrack regions. YSZ laminates were found to have the highest average flexural strength, but a very high standard deviation and low sample count and Al2O3 laminates having the second highest flexural strength. The (7:3) Al2O3/YSZ laminates had a significant increase in flexural strength compared to both types of (5:5) Al2O3/YSZ laminates. Significant change in monoclinic presence was not found except for the (5:5) Al2O3/YSZ (1 wt.% Pure ZrO2) laminates.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / In the present work we introduce the Mean Shift Clustering (MSC) algorithm as a valuable alternative to perform materials phase classification from hyperspectral images. As opposed to other multivariate statistical techniques, such as principal components analysis (PCA), clustering techniques directly assign a class (phase) label to each pixel, so that their outputs are phase segmented images, i.e. , there is no need for an additional segmentation algorithm. On the other hand, as compared to other clustering procedures and classification methods based on cluster analysis, MSC has the advantages of not requiring previous knowledge of the number of data clusters and not assuming any shape of these clusters, i.e., neither the number nor the composition of the phases must be previously known. This makes MSC a particularly useful tool for exploratory research, allowing automatic phase identification of unknown samples. Other advantages of this approach are the possibility of multimodal image analysis, composed of different types of signals, and estimate the uncertainties of the analysis. Finally, the visualization and interpretation of results are also simplified, since the information content of the output image does not depend on any arbitrary choice of the contents of the color channels. In this paper we apply the PCA and MSC algorithms for the analysis of characteristic X-ray maps acquired in Scanning Electron Microscopes (SEM) which is equipped with Energy Dispersive Detection Systems (EDS). Our results indicate that MSC is capable of detecting minor phases, not clearly identified when only three components obtained by PCA are used. / No presente trabalho será introduzido o algoritmo de Agrupamento por Deslocamento para a Média (ADM) como uma alternativa para executar a classificação de fases em materiais a partir de imagens hiperspectrais de mapas raios X característicos. Ao contrário de outras técnicas estatísticas multivariadas, tal como Análise de Componentes Principais (ACP), técnicas de agrupamentos atribuiem diretamente uma classe de rótulo (fase) para cada pixel, de modo que suas saídas são imagens de fase segmentadas, i.e., não há necessidade de algoritmos adicionais para segmentação. Por outro lado, em comparação com outros procedimentos de agrupamento e métodos classificação baseados em análise de agrupamentos, ADM tem a vantagem de não necessitar de conhecimento prévio do número de fases, nem das formas dos agrupamentos, o que faz dele um instrumento particularmente útil para a pesquisa exploratória, permitindo a identificação automática de fase de amostras desconhecidas. Outras vantagens desta abordagem são a possibilidade de análise de imagens multimodais, compostas por diferentes tipos de sinais, e de estimar as incertezas das análises. Finalmente, a visualização e a interpretação dos resultados também é simplificada, uma vez que o conteúdo de informação da imagem de saída não depende de qualquer escolha arbitrária do conteúdo dos canais de cores. Neste trabalho foram aplicados os algoritmos de ADM e ACP para a análise de mapas de raios X característicos adquiridos em Microscópios de Varredura Eletrônica (MEV) que está equipado com um Espectrômetro de Raios X por Dispersão em Energia (EDS). Nossos resultados indicam que o método ADM é capaz de detectar as fases menores, não claramente identificadas nas imagens compostas pelo três componentes mais significativos obtidos pelo método ACP.