%0 Journal Article
	%A Othman Belgassim and  Krzysztof Jemielniak
	%D 2011
	%J International Journal of Mechanical and Mechatronics Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 50, 2011
	%T Tool Failure Detection Based on Statistical Analysis of Metal Cutting Acoustic Emission Signals
	%U https://publications.waset.org/pdf/2812
	%V 50
	%X The analysis of Acoustic Emission (AE) signal
generated from metal cutting processes has often approached
statistically. This is due to the stochastic nature of the emission
signal as a result of factors effecting the signal from its generation
through transmission and sensing. Different techniques are applied in
this manner, each of which is suitable for certain processes. In metal
cutting where the emission generated by the deformation process is
rather continuous, an appropriate method for analysing the AE signal
based on the root mean square (RMS) of the signal is often used and
is suitable for use with the conventional signal processing systems.
The aim of this paper is to set a strategy in tool failure detection in
turning processes via the statistic analysis of the AE generated from
the cutting zone. The strategy is based on the investigation of the
distribution moments of the AE signal at predetermined sampling.
The skews and kurtosis of these distributions are the key elements in
the detection. A normal (Gaussian) distribution has first been
suggested then this was eliminated due to insufficiency. The so
called Beta distribution was then considered, this has been used with
an assumed β density function and has given promising results with
regard to chipping and tool breakage detection.
	%P 378 - 385