Surface roughness analysis of dental ceramics treated with hydrofl uoric acid and aluminum oxide jet

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.

Characterization of aluminum hydroxide (Al(OH)(3)) for use as a porogenic agent in castable ceramics

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

Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks

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.

Tropical soils with high aluminum concentrations cause oxidative stress in two tomato genotypes

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

Mechanical and microstructural characterization of laser-welded joints of 6013-T4 aluminum alloy

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

Thin films produced on 5052 aluminum alloy by plasma electrolytic oxydation with red mud-containing electrolytes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Corrosion resistance of 2024 aluminum alloy coated with plasma deposited a-C:H:Si:O films

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Grinding of AISI 4340 steel with interrupted cutting by aluminum oxide grinding wheel

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