Hidden Markov models for monitoring machining tool-wear

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 the how HMMs can give accurate wear prediction