811 resultados para Autogenous grinding
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The purpose of this study was to evaluate the physical and mechanical properties of particleboard made with pruning wastes from Ipê (Tabebuia serratifolia) and Chapéu-de-Sol (Terminalia catappa) trees. Particleboards were prepared with both wood species, using all the material produced by grinding the pruning wastes. The particleboards had dimensions of 45×45 cm, a thickness of approximately 11.5 mm and an average density of 664 kg/m3. A urea-formaldehyde adhesive was used in the proportion of 12% of the dry particle mass. The particleboards were pressed at a temperature of 130 C for 10 mins. The physical and mechanical properties analyzed were density, moisture content, thickness swelling, percentage of lignin and cellulose, modulus of resilience, modulus of elasticity and tensile strength parallel to the grain, accordingly to the standards NBR 14810 and CS 236-66 (1968). The particleboards were considered to be of medium density. The particle size significantly affected the static bending strength and tensile strength parallel to the grain. Ipê presented better results, demonstrating a potential for the production and use of particleboard made from this species. © The Author(s) 2013.
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Grinding is a workpiece finishing process for advanced products and surfaces. However, the constant friction between workpiece and grinding wheel causes the latter to lose its sharpness, thereby impairing the result of the grinding process. When this occurs, the dressing process is essential to sharpen the worn grains of the grinding wheel. The dressing conditions strongly influence the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The purpose of this study was to classify the wear condition of a single-point dresser using intelligent systems whose inputs were obtained by digitally processing acoustic emission signals. Two multilayer perceptron (MLP) neural networks were compared for their classification ability, one using the root mean square (RMS) statistics and another the ratio of power (ROP) statistics as input. In this study, it was found that the harmonic content of the acoustic emission signal is influenced by the condition of the dresser, and that the condition of the tool under study can be classified by using the aforementioned statistics to feed a neural network. © IFAC.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Pós-graduação em Engenharia Elétrica - FEB
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Pós-graduação em Odontologia - FOA
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Pós-graduação em Odontologia - FOA
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Pós-graduação em Odontologia - FOA