Avaliação da resistência à corrosão da liga Ti-30Ta submetida a diferentes taxas de deformação visando aplicações biomédicas

This study aims to evaluate the corrosion resistance of Ti-30Ta alloy when subjected to different strain rates. Samples of the alloy Ti-30Ta were obtained from the melting of pure elements in the arc furnace in inert atmosphere (argon gas). Then, the samples were subjected to a thermal treatment and to cold worked to obtain bars. After forging, the samples were machined in accordance with ASTME9-09 standard for carried out compression tests. To microstructural characterization, samples were sectioned longitudinal and transversally and embedded in resin. After, the wet sanding and polishing were performed, followed by a chemical attack, in order to study the microstructure under an optical microscope. Microhardness was measured on the samples that were subjected to microstructural characterization by using microhardness tester. Phases were evaluated by x-rays diffraction. Corrosion tests were carried out to evaluate the influence of deformation on the corrosion resistance. Results show that microstructure was not influenced by deformation

Previsão de demanda de veículos automotores através do modelo de regressão linear múltipla

This study aimed to model a equation for the demand of automobiles and light commercial vehicles, based on the data from February 2007 to July 2014, through a multiple regression analysis. The literature review consists of an information collection of the history of automotive industry, and it has contributed to the understanding of the current crisis that affects this market, which consequence was a large reduction in sales. The model developed was evaluated by a residual analysis and also was used an adhesion test - F test - with a significance level of 5%. In addition, a coefficient of determination (R2) of 0.8159 was determined, indicating that 81.59% of the demand for automobiles and light commercial vehicles can be explained by the regression variables: interest rate, unemployment rate, broad consumer price index (CPI), gross domestic product (GDP) and tax on industrialized products (IPI). Finally, other ten samples, from August 2014 to May 2015, were tested in the model in order to validate its forecasting quality. Finally, a Monte Carlo Simulation was run in order to obtain a distribution of probabilities of future demands. It was observed that the actual demand in the period after the sample was in the range that was most likely to occur, and that the GDP and the CPI are the variable that have the greatest influence on the developed model