Abstract
In this work, an online acoustic emission (AE) monitoring system is developed, to investigate the effect of tool wear during the microturning of titanium alloy with a tungsten carbide insert of nose radius 0.1 mm. The AE signal parameters were analyzed in time domain, frequency domain and discrete wavelet transformation (DWT) techniques to correlate with the tool wear status. The root mean square (AERMS) and specific AE energies are also computed for the decomposed AE signals, using the DWT. The results demonstrated that dominant frequency and DWT techniques are found to be most suitable for online tool condition monitoring, using AE sensors in the microturning of titanium alloy.
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