Milling exhibits forced vibrations at tooth passing frequency and its harmonics, as well as chatter vibrations close to one of the natural modes. In addition, there are sidebands, which are spread at the multiples of tooth passing frequency above and below the chatter frequency, and make the robust chatter detection difficult. This paper presents a novel on-line chatter detection method by monitoring the vibration energy. Forced vibrations are removed from the measurements in discrete time domain using a Kalman filter. After removing all periodic components, the amplitude and frequency of chatter are searched in between the two consecutive tooth passing frequency harmonics using a nonlinear energy operator (NEO). When the energy of any chatter component grows relative to the energy of forced vibrations, the presence of chatter is detected. The proposed method works in discrete real time intervals, and can detect the chatter earlier than frequency domain-based methods, which rely on fast Fourier Transforms. The method has been experimentally validated in several milling tests using both microphone and accelerometer measurements, as well as using spindle speed and current signals.

References

1.
Altintas
,
Y.
,
Stepan
,
G.
,
Merdol
,
D.
, and
Dombovari
,
Z.
,
2008
, “
Chatter Stability of Milling in Frequency and Discrete Time Domain
,”
CIRP J. Manuf. Sci. Technol.
,
1
(
1
), pp.
35
44
.
2.
Delio
,
T.
,
Tlusty
,
J.
, and
Smith
,
S.
,
1992
, “
Use of Audio Signals for Chatter Detection and Control
,”
ASME J. Eng. Ind.
,
114
(
2
), pp.
146
157
.
3.
Kuljanic
,
E.
,
Totis
,
G.
, and
Sortino
,
M.
,
2009
, “
Development of an Intelligent Multisensor Chatter Detection System in Milling
,”
Mech. Syst. Signal Process.
,
23
(
5
), pp.
1704
1718
.
4.
Soliman
,
E.
, and
Ismail
,
F.
,
1997
, “
Chatter Detection by Monitoring Spindle Drive Current
,”
Int. J. Adv. Manuf. Technol.
,
13
(
1
), pp.
27
34
.
5.
Lamraoui
,
M.
,
Thomas
,
M.
, and
El Badaoui
,
M.
,
2014
, “
Cyclostationarity Approach for Monitoring Chatter and Tool Wear in High Speed Milling
,”
Mech. Syst. Signal Process.
,
44
(
1–2
), pp.
177
198
.
6.
Choi
,
T.
, and
Shin
,
Y. C.
,
2003
, “
On-Line Chatter Detection Using Wavelet-Based Parameter Estimation
,”
ASME J. Manuf. Sci. Eng.
,
125
(
1
), pp.
21
28
.
7.
Tansel
,
I. N.
,
Wang
,
X.
,
Chen
,
P.
,
Yenilmez
,
A.
, and
Ozcelik
,
B.
,
2006
, “
Transformations in Machining—Part 2: Evaluation of Machining Quality and Detection of Chatter in Turning by Using s-Transformation
,”
Int. J. Mach. Tools Manuf.
,
46
(
1
), pp.
43
50
.
8.
Wang
,
L.
, and
Liang
,
M.
,
2009
, “
Chatter Detection Based on Probability Distribution of Wavelet Modulus Maxima
,”
Rob. Comput.-Integr. Manuf.
,
25
(
6
), pp.
989
998
.
9.
Cao
,
H.
,
Lei
,
Y.
, and
He
,
Z.
,
2013
, “
Chatter Identification in End Milling Process Using Wavelet Packets and Hilbert–Huang Transform
,”
Int. J. Mach. Tools Manuf.
,
69
, pp.
11
19
.
10.
Gradišek
,
J.
,
Govekar
,
E.
, and
Grabec
,
I.
,
1998
, “
Using Coarse-Grained Entropy Rate to Detect Chatter in Cutting
,”
J. Sound Vib.
,
214
(
5
), pp.
941
952
.
11.
Perez-Canales
,
D.
,
Vela-Martínez
,
L.
,
Jáuregui-Correa
,
J. C.
, and
Alvarez-Ramirez
,
J.
,
2012
, “
Analysis of the Entropy Randomness Index for Machining Chatter Detection
,”
Int. J. Mach. Tools Manuf.
,
62
, pp.
39
45
.
12.
Fu
,
Y.
,
Zhang
,
Y.
,
Zhou
,
H.
,
Li
,
D.
,
Liu
,
H.
,
Qiao
,
H.
, and
Wang
,
X.
,
2016
, “
Timely Online Chatter Detection in End Milling Process
,”
Mech. Syst. Signal Process.
,
75
, pp.
668
688
.
13.
Yongjian
,
J.
,
Xibin
,
W.
,
Zhibing
,
L.
,
Zhenghu
,
Y.
,
Li
,
J.
,
Dongqian
,
W.
, and
Junqing
,
W.
,
2017
, “
Eemd-Based Online Milling Chatter Detection by Fractal Dimension and Power Spectral Entropy
,”
Int. J. Adv. Manuf. Technol.
, pp.
1
16
.
14.
Cao
,
H.
,
Zhou
,
K.
, and
Chen
,
X.
,
2015
, “
Chatter Identification in End Milling Process Based on Eemd and Nonlinear Dimensionless Indicators
,”
Int. J. Mach. Tools Manuf.
,
92
, pp.
52
59
.
15.
Van Dijk
,
N. J. M.
,
Doppenberg
,
E. J. J.
,
Faassen
,
R. P. H.
,
Van De Wouw
,
N.
,
Oosterling
,
J. A. J.
, and
Nijmeijer
,
H.
,
2010
, “
Automatic in-Process Chatter Avoidance in the High-Speed Milling Process
,”
ASME J. Dyn. Syst. Meas. Control
,
132
(
3
), p.
031006
.
16.
Kuljanic
,
E.
,
Sortino
,
M.
, and
Totis
,
G.
,
2008
, “
Multisensor Approaches for Chatter Detection in Milling
,”
J. Sound Vib.
,
312
(
4–5
), pp.
672
693
.
17.
Li
,
X. Q.
,
Wong
,
Y. S.
, and
Nee
,
A. Y. C.
,
1997
, “
Tool Wear and Chatter Detection Using the Coherence Function of Two Crossed Accelerations
,”
Int. J. Mach. Tools Manuf.
,
37
(
4
), pp.
425
435
.
18.
Hynynen
,
K. M.
,
Ratava
,
J.
,
Lindh
,
T.
,
Rikkonen
,
M.
,
Ryynänen
,
V.
,
Lohtander
,
M.
, and
Varis
,
J.
,
2014
, “
Chatter Detection in Turning Processes Using Coherence of Acceleration and Audio Signals
,”
ASME J. Manuf. Sci. Eng.
,
136
(
4
), p.
044503
.
19.
Tangjitsitcharoen
,
S.
, and
Moriwaki
,
T.
,
2007
, “
Intelligent Identification of Turning Process Based on Pattern Recognition of Cutting States
,”
J. Mater. Process. Technol.
,
192–193
, pp.
491
496
.
20.
Schmitz
,
T. L.
,
2003
, “
Chatter Recognition by a Statistical Evaluation of the Synchronously Sampled Audio Signal
,”
J. Sound Vib.
,
262
(
3
), pp.
721
730
.
21.
Insperger
,
T.
,
Mann
,
B. P.
,
Surmann
,
T.
, and
Stépán
,
G.
,
2008
, “
On the Chatter Frequencies of Milling Processes With Runout
,”
Int. J. Mach. Tools Manuf.
,
48
(
10
), pp.
1081
1089
.
22.
Kakinuma
,
Y.
,
Sudo
,
Y.
, and
Aoyama
,
T.
,
2011
, “
Detection of Chatter Vibration in End Milling Applying Disturbance Observer
,”
CIRP Ann.-Manuf. Technol.
,
60
(
1
), pp.
109
112
.
23.
Al-Regib
,
E.
, and
Ni
,
J.
,
2010
, “
Chatter Detection in Machining Using Nonlinear Energy Operator
,”
ASME J. Dyn. Syst. Meas. Control
,
132
(
3
), p.
034502
.
24.
Altintas
,
Y.
, and
Chan
,
P. K.
,
1992
, “
In-Process Detection and Suppression of Chatter in Milling
,”
Int. J. Mach. Tools Manuf.
,
32
(
3
), pp.
329
347
.
25.
Girgis
,
A. A.
,
Bin Chang
,
W.
, and
Makram
,
E. B.
,
1991
, “
A Digital Recursive Measurement Scheme for Online Tracking of Power System Harmonics
,”
IEEE Trans. Power Delivery
,
6
(
3
), pp.
1153
1160
.
26.
Budak
,
E.
, and
Altintas
,
Y.
,
1998
, “
Analytical Prediction of Chatter Stability in Milling—Part I: General Formulation
,”
ASME J. Dyn. Syst. Meas. Control
,
120
(
1
), pp.
22
30
.
27.
Altintas
,
Y.
,
2012
,
Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design
,
Cambridge University Press
,
Cambridge, UK
.
28.
Eksioglu
,
C.
,
Kilic
,
Z. M.
, and
Altintas
,
Y.
,
2012
, “
Discrete-Time Prediction of Chatter Stability, Cutting Forces, and Surface Location Errors in Flexible Milling Systems
,”
ASME J. Manuf. Sci. Eng.
,
134
(
6
), p.
061006
.
29.
Insperger
,
T.
,
Stépán
,
G.
,
Bayly
,
P. V.
, and
Mann
,
B. P.
,
2003
, “
Multiple Chatter Frequencies in Milling Processes
,”
J. Sound Vib.
,
262
(
2
), pp.
333
345
.
30.
Brown
,
R. G.
, and
Hwang
,
P. Y. C.
,
2012
,
Introduction to Random Signals and Applied Kalman Filtering With Matlab
, 4th ed.,
Wiley
, Hoboken, NJ.
31.
Kaiser
,
J. F.
,
1990
, “
On a Simple Algorithm to Calculate The ‘Energy’of a Signal
,”
International Conference on Acoustics, Speech, and Signal Processing
(
ICASSP-90
), Albuquerque, NM, Apr. 3–6, pp.
381
384
.
32.
Maragos
,
P.
,
Kaiser
,
J. F.
, and
Quatieri
,
T. F.
,
1993
, “
Energy Separation in Signal Modulations With Application to Speech Analysis
,”
IEEE Trans. Signal Process.
,
41
(
10
), pp.
3024
3051
.
33.
Lin
,
W.
,
Hamilton
,
C.
, and
Chitrapu
,
P.
,
1995
, “
A Generalization to the Teager-Kaiser Energy Function and Application to Resolving Two Closely-Spaced Tones
,”
International Conference on Acoustics, Speech, and Signal Processing
(
ICASSP-95
), Detroit, MI, May 9–12, pp.
1637
1640
.
34.
Gerges
,
S. N. Y.
,
Sehrndt
,
G. A.
, and
Parthey
,
W.
,
2001
, “
Noise Sources
,”
Occupational Exposure to Noise: Evaluation, Prevention and Control
(Publication Series From the Federal Institute for Occupational Safety and Health),
World Health Organization
, Dortmund, Germany.
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