Uncertainty Prediction for Tool Wear Condition Using Type-2 TSK Fuzzy Approach

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.