97 resultados para aluminum tube


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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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Researches concerning cooling-lubrication optimization in grinding have been conducted to contribute to a more sustainable process. An alternative to flood coolant is minimum quantity lubrication (MQL), which spray oil droplets in a compressed air jet. However, problems related to wheel cleaning were reported, due to wheel loading by a mixture of chips and oil, resulting in worsening of surface quality. This work aims to evaluate the viability of Teflon and aluminum oxide for wheel cleaning, compared to MQL without cleaning and MQL with cleaning by compressed air, through the following output variables: surface roughness, roundness, wheel wear, grinding power and acoustic emission. Vickers microhardness measurements and optical microscopy were also carried out. The results showed that both materials were efficient in cleaning the wheel, compared to MQL without cleaning, but not as satisfactory as compressed air. Much work is to be done in order to select the right material for wheel cleaning.

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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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Magnetic fields can be produced by natural magnets, artificial magnets, and by circulating electric currents in wires and solenoids. An interesting experiment to observe the interaction between the magnetic field and free charges in a conductor, a magnet falling inside a tube made of conductive materials. The slowing down of the magnet by the appearance of a field in the opposite direction to the original one (Lenz's Law) is function the number of free electrons in the conductor and the electrical properties of this. Based on this, the objective of this study is to analyze the relationship between the electrical properties of conductors, copper and aluminum, with magnetic force on a neodymium magnet-iron-boron magnet falling inside a copper tube and aluminum, positioned vertically. In performing this experiment, we observed that it is a demonstration of Lenz-Faraday’s Law

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This experimental study aimed the evaluation of the use of anthropomorphic phantoms in the analysis of the influence of the acquisition parameters in the contrast of radiographic images. The analyzed factors were the variation of the peak kilovoltage (kVp) and the product of the filament circuit by the time of exposition (mAs). The influence of these factors was verified for different anthropomorphic phantoms: foot, knee and chest. The image contrast behavior with the simulators was compared to values obtained with the use of an aluminum ladder being the behavior of this reference for analysis and discussion. To assure the reproducibility of images, quality control tests were made and evaluation of procedure conditions before the experiments. The results obtained are shown in a scale of images where it was possible to evaluate the impact in the darkness of images. Regions with different compositions were analyzed which were different in image, this way the values of optical density and contrast are represented as charts and graphics. We conclude that the use of anthropomorphic phantoms in the evaluation of the influence of tension of the tube and time of exposition in the contrast of the radiographic images is not indicated for a quantitative analysis through optical density, once they present incompatible results with the data as reference as the aluminum ladder even so these simulators present a great property in qualitative analysis regarding the differentiation of structures and subjective evaluations

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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The aim of this study was to evaluate the surface roughness of 5 indirect restorative materials treated with hydrofluoric acid to 10%, with aluminum oxide jet and a combination of both. The specimens was prepared with 10 mm in diameter and 2 mm thickness, divided into fi ve groups: (1) Ceromer (CeseadII-Kuraray), (2) Leucite crystals ceramics (IPS EmpressIIIvoclarforcasket), (3) glass ceramic with fluorapatite (IPS D. Sign-Ivoclar), (4) lithium disilicate ceramic (IPS Empress II-Ivoclar restorations), (5) ceramics (Cergogold-Degussa). For all groups were performed the controls, and the surfaces with the 3 types of treatment. For testing roughness used the rugosimeter Taylor/Hobson-Precision, model form tracerSV-C525 high sensitivity. After confi rmation of variance analysis with a signifi cance level of 1% (p < 0.01), there was equality between the average roughness of materials from groups 1, 3 and 5, and the group 2 was different from the others. It was also found that the ceramics of the group 5 behaved similar to group 4. However the lowest average roughness was observed in group 2 ceramic. In the evaluation between the types of treatment, the aluminum oxide jet and associations and blasting with hydrofl uoric acid were similar, and different isolated hydrofl uoric acid, and 3 types of treatment signifi cantly higher than the control group. All treatments promoted superfi cial alterations in all tested materials.

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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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The grinding operation gives workpieces their final finish, minimizing surface roughness through the interaction between the abrasive grains of a tool (grinding wheel) and the workpiece. However, excessive grinding wheel wear due to friction renders the tool unsuitable for further use, thus requiring the dressing operation to remove and/or sharpen the cutting edges of the worn grains to render them reusable. The purpose of this study was to monitor the dressing operation using the acoustic emission (AE) signal and statistics derived from this signal, classifying the grinding wheel as sharp or dull by means of artificial neural networks. An aluminum oxide wheel installed on a surface grinding machine, a signal acquisition system, and a single-point dresser were used in the experiments. Tests were performed varying overlap ratios and dressing depths. The root mean square values and two additional statistics were calculated based on the raw AE data. A multilayer perceptron neural network was used with the Levenberg-Marquardt learning algorithm, whose inputs were the aforementioned statistics. The results indicate that this method was successful in classifying the conditions of the grinding wheel in the dressing process, identifying the tool as "sharp''(with cutting capacity) or "dull''(with loss of cutting capacity), thus reducing the time and cost of the operation and minimizing excessive removal of abrasive material from the grinding wheel.

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