Digital signal processing of acoustic emission signals using power spectral density and counts statistic applied to single-point dressing operation

• Published in: IET science, measurement & technology measurement & technology, 2017-08, Vol.11 (5), p.631-636
• Main Authors: Nascimento Lopes, Wenderson, Isaac Ferreira, Fabio, Aparecido Alexandre, Felipe, Santos Ribeiro, Danilo Marcus, Conceição Junior, Pedro de Oliveira, de Aguiar, Paulo Roberto, Bianchi, Eduardo Carlos
• Format: Article
• Language: English
• Subjects: acoustic emission; acoustic emission signal processing; AE monitoring system; AE signal processing; aluminium oxide grinding wheel; automatic control system; correlation methods; cutting; cutting tools; digital signal processing; filtering theory; grinding; grinding machines; grinding process; power spectral density; process automation; production engineering computing; production testing; Research Article; signal processing; single‐point dresser; single‐point dressing operation; spectral analysis; statistical analysis; tool cutting condition; wear; wheel tool reconditioning; wheels
• ISSN: 1751-8822; 1751-8830; 1751-8830
• DOI: 10.1049/iet-smt.2016.0317

Dressing is an important operation for the grinding process. Its goal is to recondition the wheel tool to re-establish its cutting characteristics, owing to the wear produced after successive passes. Monitoring systems that use acoustic emission (AE) have been studied to correlate the signals with several tool conditions. This study brings a new approach of processing AE signals with the purpose of identifying the correct moment to stop the dressing, which is essential in an automatic control system. From the AE signals collected in dressing tests with aluminium oxide grinding wheel and single-point dresser, spectral analysis was made through power spectral density, selecting frequencies bands that best characterise the process. The statistical parameter ‘counts’ was applied to the raw signal unfiltered and filtered in the selected bands in order to identify the tool condition and, in turn, towards a monitoring system implementation. Results showed an expressive relation between tool cutting conditions and processed signals in the selected bands. There was a great disparity of the filtered signals in the selected bands and signals unfiltered, reflecting that the filtered ones were more efficient in terms of process automation.