Analysis of acoustic emission signals and monitoring of machining processes
Govekar, Edvard (Author), Gradišek, Janez (Author), Grabec, Igor (Author)

URLURL - Presentation file, Visit http://www.sciencedirect.com/science/journal/0041624X This link opens in a new window

Monitoring of a machining process on the basis of sensor signals requires a selection of informative inputs in order to reliably characterize and model the process. In this article, a system for selection of informative characteristics from signals of multiple sensors is presented. For signal analysis, methods of spectral analysis and methods of nonlinear time series analysis are used. With the aim of modeling relationships between signal characteristics and the corresponding process state, an adaptive empirical modeler is applied. The application of the system is demonstrated by characterization of different parameters defining the states of a turning machining process, such as: chip form, tool wear, and onset of chatter vibration. The results show that, in spite of the complexity of the turning process, the state of the process can be well characterized by just a few proper characteristics extracted from a representative sensor signal. The process characterization can be further improved by joining characteristics from multiple sensors and by application of chaotic characteristics.

Keywords:acoustic emission, AE, features selection, modeling, nonlinear analysis, process monitoring
Work type:Not categorized (r6)
Tipology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Number of pages:Str. 598-603
Numbering:Letn. 38, št. 1/8
ISSN on article:0041-624X
COBISS.SI-ID:3799323 Link is opened in a new window
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Record is a part of a journal

Shortened title:Ultrasonics
Publisher:Butterworth Scientific
COBISS.SI-ID:26569728 This link opens in a new window

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