Abstract
Nickel-based superalloys are widely used in the aircraft industry since they are exceptionally thermal resistant, retaining their mechanical properties at temperatures of up to 700 °C. On the other hand, since they are very difficult to machine, tool life is typically short and can finish abruptly. As catastrophic tool failure can destroy an expensive workpiece, automatic tool condition monitoring (TCM) has become particularly critical. This paper presents an application of the wavelet packet transform (WPT) for extracting useful TCM features from the cutting forces and acoustic emission (AE) signals during rough turning of Inconel 625. New, improved methods of signal feature (SF) relevancy evaluation were proposed based on determination and correlation coefficients. Out of several SFs calculated from bandpass signals, the most useful for TCM were automatically selected. The selected features were used for tool condition monitoring.