L. Atlas, M. Ostendorf, and G. Bernard,
``Hidden Markov models for monitoring machining tool-wear,''
Proceedings of the International Conference on Acoustics, Speech and Signal Processing, to appear.
As summarized by Atlas, Bernard and Narayanan (1996), the sensing of acoustic
vibrations can remotely estimate the state of wear at the tool edge. This
form of monitoring offers the potential to characterize, in real time, the
efficiency of metal removal processes such as drilling and milling. For
example, information about sudden increases in tool wear, if manifest
as a change in acoustic vibration, could be valuable to a machine operator.
The nature of this monitoring problem has some similarities to automatic
speech recognition. For example, there is significant tool-to-tool variation
in details of vibration and lifetime. Also, the easy adaptability of
monitoring systems across manufacturing processes is important. In this
work, we model the evolution of vibration signals with the same technique
which has shown to be successful in speech recognition: hidden Markov models
(HMMs). We focus on the monitoring of milling processes at three different
time scales and show how HMMs can give accurate wear prediction.
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