Feature representations for monitoring of tool wear

We address the general problem of reliable, real-time detection of faults in metal-removal processes in manufacturing. As has long been recognized by skilled machine operators, mechanical and acoustic vibrations can be reliable sources of cues for such monitoring. However, conventional dull-tool monitoring systems, which are generally based on stationary signal processing methods, are inadequate for real-time control of drilling procedure. Making use of a database from nine different drill bits, we (a) identify different features which seem to contain tool wear information, (b) document what we found to be superior signal processing tools to identify, extract and process these non-stationary features, and (c) stress the need for a fully annotated public-domain manufacturing signal database