Abstract
In this paper, a micromilling type-2 fuzzy tool condition monitoring system based on multiple AE acoustic emission signal features is proposed. The type-2 fuzzy logic system is used as not only a powerful tool to model acoustic emission signal, but also a great estimator for the ambiguities and uncertainties associated with the signal itself. Using the results of root-mean-square error estimation and the variations in the results of type-2 fuzzy modeling of all signal features, the most reliable ones are selected and integrated into cutting tool life estimation models. The obtained results show that the type-2 fuzzy tool life estimation is in accordance with the cutting tool wear state during the micromilling process. The information about uncertainty prediction of tool life is of great importance for tool condition investigation and crucial when making decisions about maintaining the machining quality.
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