Made available in DSpace on 2014-10-09T12:33:22Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:06:31Z (GMT). No. of bitstreams: 0 / Dissertacao (Mestrado) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP

Este trabalho foi estruturado visando apresentar os efeitos causados pelo shot peening (jateamento por esferas) em componentes mecânicos. Este processo introduz tensões residuais compressivas na superfície do material, o que retarda a nucleação de trincas de fadiga, promovendo aumento na vida em fadiga. O material utilizado foi o aço SAE 1060, laminado, com um perfil de 15 por 5mm. Para o jateamento foram selecionados dois tamanhos de esferas de aço: S110 e S280. Para cada tamanho foram realizadas três condições de cobertura superficial no material: 80%, 100% e 500%. Foram realizados metalografia, ensaio de tração, análise de tensões residuais, curvas de fadiga, rugosidade, perfil de microdureza e efetuadas análises da fratura e da superfície do material com e sem jateamento. Os resultados apontaram uma melhoria significativa no desempenho em fadiga com a esfera S110 na condição de 100% de cobertura associado às tensões residuais compressivas obtidas. / This work study the effects of shot peening on mechanical components. This process introduces compressive residual stresses in the surface of the material, and cause delays the nucleation of cracks of fatigue, promoting increase in the life fatigue. The material used was SAE 1060 steel, cold rolled, with section of 15x5mm. For the shot peening two sizes of steel spheres had been selected: S110 and S280. For each size three conditions of superficial covering in the material had been carried through: 80%, 100% and 500%. Had been realized analysis of metallography, tensile test, analysis of residual stresses, fatigue test, analysis of roughness, hardness test, surface analysis in the material with and without shot peening. The results indicated a significant improvement in the fatigue performance with the S110 sphere in the 100% of covering condition associated with the compressive residual stresses.

Made available in DSpace on 2014-10-09T12:48:51Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:01:09Z (GMT). No. of bitstreams: 1 09601.pdf: 14581150 bytes, checksum: 039930ea0467d8cb98d0f2a68ecb3072 (MD5) / Tese (Doutoramento) / IPEN/T / Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP

Made available in DSpace on 2014-10-09T12:33:22Z (GMT). No. of bitstreams: 0 / Made available in DSpace on 2014-10-09T14:06:31Z (GMT). No. of bitstreams: 0 / O tratamento superficial de shot peening tem por objetivo aumentar a resistência à fadiga sendo comparada pelas medidas de tensão residual. O processo peen forming é uma variante do processo shot peening onde se obtém uma curvatura na placa produzida pelo jateamento das esferas através da compressão dos grãos localizados próximos à superfície. Foi estudado neste trabalho a influência dos parâmetros pressão e tamanho de granalha, utilizado no processo de peen forming, no perfil de tensão residual e no raio de curvatura em amostras de alumínio 7050. A avaliação do perfil de tensão de residual foi efetuada por difração de raios-x utilizando o método de sen2 . Os resultados mostram que a formação da altura do arco de curvatura é proporcional a pressão de jateamento e ao tamanho das esferas e inversamente proporcional a espessura da amostra, e que o fator de concentração de tensões é maior para amostras jateadas com menores esferas. Na seção final deste trabalho apresenta um estudo complementar sobre microdeformação e tamanho médio de cristalito, podendo avaliar o perfil das amostras após jateamento. / Dissertacao (Mestrado) / IPEN/D / Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP

Orientador: Fernão Vitor Pessoa de Almeida Ramos / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Artes / Made available in DSpace on 2018-08-24T17:56:11Z (GMT). No. of bitstreams: 1 Braz_BeatrizNogueiradeLimaD'Angelo_M.pdf: 1145799 bytes, checksum: 6dfccd626955893285744da67e3fb1bc (MD5) Previous issue date: 2013 / Resumo: O presente trabalho propõe um estudo analítico de O Escafandro e a borboleta (Le Scaphandre et le papillont), 2007, de Julian Schnabel, baseado no livro homônimo e autobiográfico de Jean-Dominique Bauby. Esse estudo tem como objetivo analisar a forma com a qual o filme transpõe para o cinema a narrativa de Bauby, ao transformar a voz narrativa em primeira pessoa no olhar-câmera, que conduz o filme. Busca-se, assim, refletir sobre o ponto de vista no cinema, em especial sobre o plano ponto-de-vista, como forma de materializar em imagem o monólogo subjetivo e o confinamento que caracterizam as memórias de Bauby / Abstract: This piece aims to study and analyze the film adaptation of The Diving bell and the butterfly, 2007, directed by Julian Schnabel. Moreover, it intends to analyze how the film turns the narrative of Jean-Dominique Bauby¿s book into film by transforming the first person - the literary I in the book - into the camera, making the framing play a key role in the adaptation process. We seek to discuss and analyze point of view in the cinema, shedding light to the point of view shot, and how it made it possible to turn the subjective monologue and the sense of confinement that characterize Bauby¿s memoir into film / Mestrado / Multimeios / Mestra em Multimeios

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Despite the significant progress in 3D vision in recent years, collecting large amounts of high-quality 3D data remains a challenge. Hence, developing solutions to extract 3D object information efficiently is a significant problem. We aim for an effective shape classification algorithm to facilitate accurate recognition and efficient search of sizeable 3D model databases. This thesis has two contributions in this space: a) a novel meta-learning approach for 3D object recognition and b) propose a new compositional 3D recognition task and dataset. For 3D recognition, we proposed a few-shot semi-supervised meta-learning model based on Pointnet++ representation with a prototypical random walk loss. In particular, we developed the random walk semi-supervised loss that enables fast learning from a few labeled examples by enforcing global consistency over the data manifold and magnetizing unlabeled points around their class prototypes. On the compositional recognition front, we create a large-scale, richly annotated stylized dataset called 3D CoMPaT. This large dataset primarily focuses on stylizing 3D shapes at part-level with compatible materials. We introduce Grounded CoMPaT Recognition as the task of collectively recognizing and grounding compositions of materials on parts of 3D Objects.

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Graphs are commonly employed to model complex data and discover latent patterns and relationships between entities in the real world. Canonical graph learning models have achieved remarkable progress in modeling and inference on graph-structured data that consists of nodes connected by edges. Generally, they leverage abundant labeled data for model training and thus inevitably suffer from the label scarcity issue due to the expense and hardship of data annotation in practice. Data-efficient graph learning attempts to address the prevailing data scarcity issue in graph mining problems, of which the key idea is to transfer knowledge from the related resources to obtain the models with good generalizability to the target graph-related tasks with mere annotations. However, the generalization of the models to data-scarce scenarios is faced with challenges including 1) dealing with graph structure and structural heterogeneity to extract transferable knowledge; 2) selecting beneficial and fine-grained knowledge for effective transfer; 3) addressing the divergence across different resources to promote knowledge transfer. Motivated by the aforementioned challenges, the dissertation mainly focuses on three perspectives, i.e., knowledge extraction with graph heterogeneity, knowledge selection, and knowledge transfer. The purposed models are applied to various node classification and graph classification tasks in the low-data regimes, evaluated on a variety of datasets, and have shown their effectiveness compared with the state-of-the-art baselines.