  We examined the use of three conventional anomaly detection methods and
assess their potential for on-line tool wear monitoring. Through efficient data
processing and transformation of the algorithm proposed here, in a real-time
environment, these methods were tested for fast evaluation of cutting tools on
CNC machines. The three-dimensional force data streams we used were extracted
from a turning experiment of 21 runs for which a tool was run until it
generally satisfied an end-of-life criterion. Our real-time anomaly detection
algorithm was scored and optimised according to how precisely it can predict
the progressive wear of the tool flank. Most of our tool wear predictions were
accurate and reliable as illustrated in our off-line simulation results.
Particularly when the multivariate analysis was applied, the algorithm we
develop was found to be very robust across different scenarios and against
parameter changes. It shall be reasonably easy to apply our approach elsewhere
for real-time tool wear analytics.
