Abstract
Feature extraction and decision-making is a matter of considerable interest for condition monitoring of complex phenomena with multiple sensors. In tool wear monitoring, the extraction of subtle aspects of signals from a range of transient and static events offers a special challenge for diagnostic and control systems due to the broad range of information in the signal. Features based on frequency spectra and statistical transformations of a number of sensor signals were studied in an attempt to obtain a reliable indication of the evolution of tool wear. Two neural networks and an expert system using Taylor's tool life equation were used to classify the tool wear state. Despite the complexity of the data and subsequent testing by the removal of two of the most clearly systematic features, a reproducible diagnosis of tool wear was obtained.
Published Version
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