Abstract:
In this work, Tool Condition Monitoring (TCM) strategy is developed for micro-end milling of
titanium alloy and mild steel work-pieces. Full immersion slot milling experiments are
conducted using a solid tungsten carbide end mill for more than 1900 s to have reasonable
amount of tool wear. During the micro-end milling process, cutting force and vibration signals
are acquired using Kistler piezo-electric 3-component force dynamometer (9256C2) and
accelerometer (NI cDAQ-9188) respectively. The force components and the vibration signals
are processed using Discrete Wavelet Transformation (DWT) in both time and frequency
window. 5-level wavelet packet decomposition using Db-8 wavelet is carried out and the
detailed coefficients D1 to D5 for each of the signals are obtained. The results of the wavelet
transformation are correlated with the tool wear. In case of vibration signals, de-noising is done
for higher frequency components (D1) and force signals were de-noised for lower frequency
components (D5). Increasing value of MAD (Mean Absolute Deviation) of the detail
coefficients for successive channels depicted tool wear. The predictions of the tool wear are
confirmed from the actual wear observed in the SEM of the worn tool.
