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Tool Failure Detection Based on Statistical Analysis of Metal Cutting Acoustic Emission Signals

Authors: Othman Belgassim, Krzysztof Jemielniak

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.

Keywords: AE signal, skew, kurtosis, tool failure

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1057945

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References:


[1] Belgasim O., Jemielniak K., Tool condition monitoring, a review, Preceedings of Al Azhar engineering fourth international conference, December 1995
[2] Jemielniak k., Belgassim O., Characteristics of acoustic emission sensors employed for tool condition monitoring, preceedings of VII workshop on supervision and diagnostics of machining systems, Karpacz - Poland, (CIRP) March 1996
[3] Spiegel M., Theory and problems of probability and statistics, Schaum-s outline series, McGraw_Hill Inc. 1975
[4] Ndeeb C., Pflueg C., Real-time monitoring of chip form in turning processes with Acoustic Emission using thin film sensors, Transactions of NAMRI/SME Volume XXIV, 1996
[5] Kannatey-Asibu E., Investigation of the metal cutting process using acoustic emission signal analysis, Ph.D. Thesis, University of California, Berkeley. 1980
[6] Whitehouse D., Beta functions for surface typology, Ann. CIRP, 27 (1978) 491-497
[7] Kannatey-Asibu E, Dornfeld D, A study of tool wear using statistical analysis of metal cutting acoustic emission, Wear Journal, 76 (1982) 247-261
[8] Gabriel V., Matusky J., Pruśek A., żiżka J., Study of machining process by acoustic emission method, Proc. of IV int. conf. on monitoring & automatic supervision in manufacturing - Miedzeszyn- CIRP (1995) 143-148
[9] Jemielniak K., : Detection of Cutting Edge Breakage In Turning, Annals Of The CIRP 41/1: 97-100, 1992