At present, due to their properties, the tungsten carbide-cobalt (WC-Co) composite materials are in huge demand by industry to manufacture special tools, dies/molds and components under erosion. The powder metallurgy is the usual process applied to obtain WC-Co products, but in some cases this process is unable to produce tools of very complex shapes and highly intricate details. Thus, additional conventional and non-conventional machining processes are required. In this context, the electrical discharge machining (EDM) is an efficient alternative process. However, the EDM parameters have to be properly set for any different tungsten carbide-cobalt composition and electrode material to achieve an appropriate level of machining performance. In this work, a special grade of tungsten carbide-cobalt was used as workpiece and a copper-tungsten alloy as electrode. Experiments on important EDM electrical and non-electrical parameter settings with reference to material removal rate, electrode wear ratio and surface roughness were carried out under typical rough and finish machining. This paper contributes with an attempt to provide insightful guidelines to optimize electrical discharge machining of WC-Co composite materials using CuW alloy electrodes.

Neste trabalho novas metodologias de análise para a quantificação de princípios ativos em fármacos são estudadas, empregando técnicas de otimização meta-heurísticas em modelos de regressão multivariada e dados de espectroscopia por reflexão difusa no infravermelho com transformada de Fourier (DRIFTS). Inicialmente foram preparadas amostras do princípio ativo hidroclorotiazida, a partir de um planejamento experimental. Os espectros foram coletados empregando um espectrofotômetro, sendo aplicada a correção multiplicativa de sinal (MSC). Foram construídos modelos de calibração por mínimos quadrados parciais (PLS), sendo implementados métodos de otimização dos Algoritmos Genético (AG) e Busca Tabu (BT), objetivando minimizar o erro das amostras de validação (SEV). Os melhores modelos foram selecionados com base no coeficiente de regressão (R²) e comparados. Ambas as técnicas de otimização resultaram em modelos de calibração com excelente capacidade de quantificação do princípio ativo, sendo adequadas para a utilização no controle de qualidade do processo produtivo.

Two new methodologies of analysis for the antihipertensive drugs quantification are provided in this work, using meta-heuristic algorithms optimization with infrared spectroscopy by diffuse reflectance Fourier transform (DRIFTS) and partial-least squares regression method (PLS). A diffuse reflectance joined to a spectrophotometry in the mid-infrared region was used. The calibration was through PLS in order to use it as a pre-processing. The data were used in multiplicative signal correction (MSC). After the best wavelength selection, which results in models with better coefficient regression (R²), these were improved using routines based on genetic algorithm heuristic and tabu search methods. For the evaluation of the optimization a standard error of validation (SEV) was used. The new technique using DRIFTS/PLS/AG or DRIFTS/PLS/TS shows excellent choices for the quality control of the productive process of factories and drugstore.