Structure identification of fuzzy model
Fuzzy Sets and Systems
Application of fuzzy logic techniques to the selection of cutting parameters in machining processes
Fuzzy Sets and Systems - Special issue on industrial applications
Sensitivity analysis for type-1 and type-2 TSK fuzzy models
MS '07 The 18th IASTED International Conference on Modelling and Simulation
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Modelling and Simulation in Engineering
Learning rule for TSK fuzzy logic systems using interval type-2 fuzzy subtractive clustering
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
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Because of the difficulty in understanding the physics of the machining process, several different intelligence methods, which employ cutting forces for estimation tool wear, have been developed in the past few years. Unfortunately, none of them can overcome the difficulty to estimate the errors of approximation during tool wear monitoring. This paper aimed at presenting a tool wear monitoring method using type-2 Takagi-Sugeno-Kang (TSK) fuzzy approach. This innovative method not only provides high reliability of the tool wear prediction over a wide range of cutting conditions, but also assesses the uncertainties associated with the modeling process and with the outcome of the model itself. The magnitude and direction of uncertainties in the machining process are described explicitly to increase the credibility of assessments.