Properties of Acoustic Emission Signals for Tool Condition Monitoring (TCM) Applications

Acoustic Emission data from mechanical machining with carbide cutting tools are analyzed, so as to reach a deeper understanding of the signal properties for Tool Condition Monitoring applications. They are gathered with a custom-built transducer capable of collecting signals close to the working point, thus allowing us to perform acquisitions in favorable conditions. By statistical analysis of the time series data and RMS values at various tool wear levels, we find that ageing features can be put into evidence in all cases. In particular, the histograms of raw data show that their distribution is power-law above a cross-over value, with larger events being more numerous in newer cutting tools when compared to more worn-out ones. For practical purposes, statistics based on root mean square values are more convenient, and are also capable of discriminating between tool wear levels. The assumption that experimental RMS histograms follow a Beta distribution has been tested, leading to the observation that direct comparison of the fitting Beta curves with experimental data shows relevant discrepancies, making the search of a more appropriate fitting function for the experimental distribution desiderable.