Mechanics & Industry
Volume 15, Number 6, 2014
|Page(s)||497 - 507|
|Published online||26 September 2014|
Application of the cyclostationairity for the cutting tool diagnosis
Groupe de Recherche en Science Pour l’Ingénieur, Université de
Reims Champagne Ardenne, UFR
Sciences Exactes et Naturelles, Moulin de la Housse, BP 1039, 51687
Reims Cedex 2,
2 Université de Lyon, Université Jean Monnet de Saint Etienne, Campus Roannais, Laboratoire d’Analyse des Signaux et des Processus Industriels (LASPI), 42300 Roanne, France
3 Faculté des sciences, Université Mohammed V-Agdal, 4 avenue Ibn Battouta, Rabat, Maroc
a Corresponding author:
Accepted: 15 May 2014
This work is interested to the analysis of the vibratory signals coming from a milling operation. The objective is the detection of cutting tool breakage using the cyclostationary tools. Initially, we will show that the vibration signals captured from the milling operation are cyclostationary. The proposed cyclostationary methods are the first and second order synchronous statistics and the spectral correlation. A test rig, composed of a milling machine (cutter with 5 teeth) and a workpiece, is used to extract the vibration signals that are angular sampled in the free fault case and one broken tooth case. This test rig is instrumented with three accelerometers, installed in the three directions, and an optical encoder that allows the angular sampling. Then we will see that the angular sampling of the signals captured from a milling operation is essential to preserve the cyclostationary properties destroyed, in the case of the temporal sampling, by speed fluctuations. The proposed method capacity to detect the broken tooth is shown. The synchronous statistics of order 1 and order 2 detect the broken tooth presence and its emplacement. The spectral correlation analysis distinguishes the broken tooth presence, but is not practical for the diagnosis. For that, an indicator based on the spectral correlation is proposed.
Key words: Vibration analysis / cyclostationarity / cutting tools / spectral correlation / synchronous statistics
© AFM, EDP Sciences 2014
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