Abstract: 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.
Abstract: the work contains the results of complex investigation
related to the evaluation of condition of working blades of gas turbine
engines during fatigue tests by applying the acoustic emission
method. It demonstrates the possibility of estimating the fatigue
damage of blades in the process of factory tests. The acoustic
emission criteria for detecting and testing the kinetics of fatigue crack
distribution were detected. It also shows the high effectiveness of the
method for non-destructive testing of condition of solid and cooled
working blades for high-temperature gas turbine engines.