The modeling of tool wear effect on micro tool vibration and associated sound generation in the milling process was proposed and analyzed first for sound based micro tool monitoring. The LVQ based algorithms, as well as Fisher Linear Function, were also implemented for verify the sound signal capability for monitoring the tool wear condition in micro milling. Micro end-mills of 700 μm in diameter, a high speed spindle up to 60000 rpm, and sensors were installed to investigate the micro tool wear effect on the cutting system and provide the signals for modeling and system capability verification. Multi-sensor signals including the audible sound, cutting force and vibration were collected simultaneously during cutting processes. The simulation results were compared to experimental results and show good correlation to the collected sound signal. With the training and test sound signals collected in experiment, the classifier systems were established and the capability of sound based system were confirmed for detecting micro tool wear successfully.

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