Monitoring of emulsion properties is important in many applications, like in foods and pharmaceutical products, or in emulsion polymerization processes, since aged and broken emulsions perform worse and may affect product quality. In machining processes, special types of emulsions called metalworking fluids (MWF) are widely used, because of its combined characteristics of cooling and lubrication, increasing the productivity, enabling the use of higher cutting speeds, decreasing the amount of power consumed and increasing tool life. Even though emulsion quality monitoring is a key issue in manufacturing processes, traditional methods are far from accurate and generally fail in providing the tools for determining the optimal useful life of these emulsions, with high impact in costs. The present study is dedicated to the application of a spectroscopic sensor to monitor MWF emulsion destabilization, which is related to changes in its droplet size distribution. Rapeseed oil emulsions, artificially aged MWF and MWF in machining application were evaluated, using optical measurements and multivariate calibration by neural networks, for developing a new method for emulsion destabilization monitoring. The technique has shown good accuracy in rebuilding the droplet size distribution of emulsions for monomodal and bimodal distributions and different proportions of each droplet population, from the spectroscopic measurements, indicating the viability of this method for monitoring such emulsions. This study is part of a joint project between the University of São Paulo and the University of Bremen, within the BRAGECRIM program (Brazilian German Cooperative Research Initiative in Manufacturing) and is financially supported by FAPESP, CAPES, FINEP and CNPq (Brazil), and DFG (Germany).

Sem resumo em português.