In contrast to the well-established stability prediction tools, a robust real-time stability indicator is proposed for micromilling process, and it opens the possibility of online chatter avoidance based on successful detection. In this paper, a robust and easy-to-compute stability indicator is presented. This approach exploits the virtue of a stable milling process—the displacement of the vibrating tool repeats with a period of tooth passing. It has been observed that the standard deviation of the tool displacement sampled at once per tooth passing frequency is indicative of chatter, where a low standard deviation coincides with stable cutting. An increase in standard deviation is the direct consequence of an increase in asynchronous motion of the tool, coinciding with chatter. As it is also well known, this asynchronous vibration of the tool results in distinct marks on the workpiece surface. This paper presents the experimental validation of this real-time stability indicator. The ease of implementation makes the presented stability indicator a strong candidate for applications in chatter avoidance based on detection. The results are also verified against the standard stability prediction method.

References

1.
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
.
2.
Budak
,
E.
, and
Altintas
,
Y.
,
1998
, “
Analytical Prediction of Chatter Stability in Milling—Part II: Application of the General Formulation to Common Milling Systems
,”
ASME J. Dyn. Syst. Meas. Control
,
120
(
1
), pp.
31
36
.
3.
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
.
4.
Altintas
,
Y.
, and
Budak
,
E.
,
1995
, “
Analytical Prediction of Stability Lobes in Milling
,”
CIRP Ann.
,
44
(
1
), pp.
357
362
.
5.
Malekian
,
M.
,
Park
,
S. S.
, and
Jun
,
M. B. G.
,
2009
, “
Modeling of Dynamic Micro-Milling Cutting Forces
,”
Int. J. Mach. Tool Manuf.
,
49
(
7–8
), pp.
586
598
.
6.
Aggogeri
,
F.
,
Al-Bender
,
F.
,
Brunner
,
B.
,
Elsaid
,
M.
,
Mazzola
,
M.
,
Merlo
,
A.
,
Ricciardi
,
D.
,
de la O Rodriguez
,
M.
, and
Salvi
,
E.
,
2013
, “
Design of Piezo-Based AVC System for Machine Tool Applications
,”
Mech. Syst. Signal Process
,
36
(
1
), pp.
53
65
.
7.
Uhlmann
,
E.
,
Kushwaha
,
S.
,
Mewis
,
J.
, and
Richarz
,
S.
,
2017
, “
Automatic Design and Synthesis of Control for a Plug and Play Active Vibration Control Module
,”
J. Vib. Control
,
24
(
11
), pp.
1
13
.
8.
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
.
9.
Khalifa
,
O. O.
,
Densibali
,
A.
, and
Faris
,
W.
,
2006
, “
Image Processing for Chatter Identification in Machining Processes
,”
Int. J. Adv. Manuf. Technol.
,
31
(
5–6
), pp.
443
449
.
10.
Insperger
,
T.
,
Mann
,
B.
,
Surmann
,
T.
, and
Stepan
,
G.
,
2008
, “
On the Chatter Frequencies of Milling Processes With Runout
,”
Int. J. Mach. Tool Manuf.
,
48
(
10
), pp.
1081
1089
.
11.
Tsai
,
N. C.
,
Chen
,
D. C.
, and
Lee
,
R. M.
,
2010
, “
Chatter Prevention for Milling Process by Acoustic Signal Feedback
,”
Int. J. Adv. Manuf. Technol.
,
47
(
9–12
), pp.
1013
1021
.
12.
Singh
,
K. K.
,
Singh
,
R.
, and
Kartik
,
V.
,
2015
, “
Comparative Study of Chatter Detection Methods for High-Speed Micromilling of Ti6Al4V
,”
Proc. Manuf.
,
1
, pp.
593
606
.
13.
Cao
,
H.
,
Lei
,
Y.
, and
He
,
Z.
,
2013
, “
Chatter Identification in End Milling Process Using Wavelet Packets and Hilbert–Huang Transform
,”
Int. J. Mach. Tool Manuf.
,
69
, pp.
28
38
.
14.
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
.
15.
Honeycutt
,
A.
, and
Schmitz
,
T. L.
,
2016
, “
A New Metric for Automated Stability Identification in Time Domain Milling Simulation
,”
ASME J. Manuf. Sci. Eng.
,
138
(
7
), p.
074501
.
16.
Honeycutt
,
A.
, and
Schmitz
,
T. L.
,
2016
, “
Milling Stability Interrogation by Subharmonic Sampling
,”
ASME J. Manuf. Sci. Eng.
,
139
(
4
), pp.
1
9
.
17.
Smith
,
S.
, and
Tlusty
,
J.
,
1991
, “
An Overview of Modeling and Simulation of the Milling Process
,”
J. Eng. Ind.
,
113
(
2
), pp.
169
175
.
18.
Park
,
S. S.
,
Altintas
,
Y.
, and
Movahhedy
,
Y.
,
2003
, “
Receptance Coupling for End Mills
,”
Int. J. Mach. Tool Manuf.
,
43
(
9
), pp.
889
896
.
19.
Basuray
,
P. K.
,
Misra
,
B. K.
, and
Lal
,
G. K.
,
1977
, “
Transition From Ploughing to Cutting During Machining With Blunt Tools
,”
Wear
,
43
(
3
), pp.
341
349
.
20.
Marsh
,
E. R.
,
2009
,
Precision Spindle Metrology
,
Destech Publications
, Lancaster, PA.
21.
Afazov
,
S. M.
,
Ratchev
,
S. M.
,
Segal
,
J.
, and
Popov
,
A. A.
,
2012
, “
Chatter Modelling in Micro-Milling by Considering Process Nonlinearities
,”
Int. J. Mach. Tool Manuf.
,
56
, pp.
28
38
.
22.
Xuewei
,
Z.
,
Tianbiao
,
Y.
, and
Wanshan
,
W.
,
2016
, “
Chatter Stability of Micro End Milling by Considering Process Nonlinearities and Process Damping
,”
Int. J. Adv. Manuf. Technol.
,
87
(
9–12
), pp.
2785
2796
.
23.
Schützer
,
K.
,
da Silva de Luca Ramos
,
L. W.
,
Mewis
,
J.
,
Tamborlin
,
M. O.
, and
Baldo
,
C. R.
,
2018
, “
Simulation Tool for Prediction of Cutting Forces and Surface Quality of Micro-Milling Processes
,”
Int. J. Adv. Manuf. Technol.
,
99
(
1–4
), pp.
225
232
.
You do not currently have access to this content.