Real-time monitoring and control of surface morphology variations in their incipient stages are vital for assuring nanometric range finish in the ultraprecision machining (UPM) process. A real-time monitoring approach, based on predicting and updating the process states from sensor signals (using advanced neural networks (NNs) and Bayesian analysis) is reported for detecting the incipient surface variations in UPM. An ultraprecision diamond turning machine is instrumented with three miniature accelerometers, a three-axis piezoelectric dynamometer, and an acoustic emission (AE) sensor for process monitoring. The machine tool is used for face-turning aluminum 6061 discs to a surface finish (Ra) in the range of 15–25 nm. While the sensor signals (especially the vibration signal in the feed direction) are sensitive to surface variations, the extraneous noise from the environment, machine elements, and sensing system prevents direct use of raw signal patterns for early detection of surface variations. Also, nonlinear and time-varying nature of the process dynamics does not lend conventional statistical process monitoring techniques suitable for characterizing UPM-machined surfaces. Consequently, instead of just monitoring the raw sensor signal patterns, the nonlinear process dynamics wherefrom the signal evolves are more effectively captured using a recurrent predictor neural network (RPNN). The parameters of the RPNN (weights and biases) serve as the surrogates of the process states, which are updated in real-time, based on measured sensor signals using a Bayesian particle filter (PF) technique. We show that the PF-updated RPNN can effectively capture the complex signal evolution patterns. We use a mean-shift statistic, estimated from the PF-estimated surrogate states, to detect surface variation-induced changes in the process dynamics. Experimental investigations show that variations in surface characteristics can be detected within 15 ms of their inception using the present approach, as opposed to 30 ms or higher with the conventional statistical change detection methods tested.

References

References
1.
Taniguchi
,
N.
,
1983
, “
Current Status in, and Future Trends of, Ultraprecision Machining and Ultrafine Materials Processing
,”
CIRP Ann.
,
32
(
2
), pp.
573
582
.10.1016/S0007-8506(07)60185-1
2.
Ikawa
,
N.
,
Donaldson
,
R.
,
Komanduri
,
R.
,
Konig
,
W.
,
Mckeown
,
P.
,
Moriwaki
,
T.
, and
Stowers
,
I.
,
1991
, “
Ultraprecision Metal Cutting—The Past, the Present and the Future
,”
CIRP Ann.
,
40
(
2
), pp.
587
594
.10.1016/S0007-8506(07)61134-2
3.
Dornfeld
,
D.
,
Min
,
S.
, and
Takeuchi
,
Y.
,
2006
, “
Recent Advances in Mechanical Micromachining
,”
CIRP Ann.
,
55
(
2
), pp.
745
768
.10.1016/j.cirp.2006.10.006
4.
Ikawa
,
N.
,
1992
, “
Minimum Thickness of Cut in Micromachining
,”
Nanotechnology
,
3
(
1
), p.
6
.10.1088/0957-4484/3/1/002
5.
Takasu
,
S.
,
Masuda
,
M.
,
Nishiguchi
,
T.
, and
Kobayashi
,
A.
,
1985
, “
Influence of Study Vibration With Small Amplitude Upon Surface Roughness in Diamond Machining
,”
CIRP Ann.
,
34
(
1
), pp.
463
467
.10.1016/S0007-8506(07)61812-5
6.
Moriwaki
,
T.
,
1990
, “
Effect of Cutting Heat on Machining Accuracy in Ultra-Precision Diamond Turning
,”
CIRP Ann.
,
39
(
1
), pp.
81
84
.10.1016/S0007-8506(07)61007-5
7.
Liu
,
X.
,
Devor
,
R. E.
,
Kapoor
,
S. G.
, and
Ehmann
,
K. F.
,
2004
, “
The Mechanics of Machining at the Microscale: Assessment of the Current State of the Science
,”
ASME J. Manuf. Sci. Eng.
,
126
(
4
), pp.
666
678
.10.1115/1.1813469
8.
Jasinevicius
,
R. G.
,
Duduch
,
J. G.
,
Montanari
,
L.
, and
Pizani
,
P. S.
,
2008
, “
Phase Transformation and Residual Stress Probed by Raman Spectroscopy in Diamond-Turned Single Crystal Silicon
,”
Proc. Inst. Mech. Eng., Part B
,
222
(
9
), pp.
1065
1073
.10.1243/09544054JEM1161
9.
Teti
,
R.
,
Jemielniak
,
K.
,
O'donnell
,
G.
, and
Dornfeld
,
D.
,
2010
, “
Advanced Monitoring of Machining Operations
,”
CIRP Ann.
,
59
(
2
), pp.
717
739
.10.1016/j.cirp.2010.05.010
10.
Cheung
,
C.
, and
Lee
,
W.
,
2000
, “
Modelling and Simulation of Surface Topography in Ultra-Precision Diamond Turning
,”
Proc. Inst. Mech. Eng.
, Part B,
214
(
6
), pp.
463
480
.10.1243/0954405001517775
11.
Cheung
,
C. F.
, and
Lee
,
W.
,
2003
, Surface Generation in Ultra-Precision Diamond Turning: Modelling and Practices, Engineering Research Series, Professional Engineering Publishing Limited, Suffolk, UK.
12.
Abouelatta
,
O.
, and
Madl
,
J.
,
2001
, “
Surface Roughness Prediction Based on Cutting Parameters and Tool Vibrations in Turning Operations
,”
J. Mater. Process. Technol.
,
118
(
1–3
), pp.
269
277
.10.1016/S0924-0136(01)00959-1
13.
Beggan
,
C.
,
Woulfe
,
M.
,
Young
,
P.
, and
Byrne
,
G.
,
1999
, “
Using Acoustic Emission to Predict Surface Quality
,”
Int. J. Adv. Manuf. Technol.
,
15
(
10
), pp.
737
742
.10.1007/s001700050126
14.
Hayashi
,
M.
,
Yoshioka
,
H.
, and
Shinno
,
H.
,
2008
, “
An Adaptive Control of Ultraprecision Machining With an In-Process Micro-Sensor
,”
J. Adv. Mech. Des., Syst., Manuf.
,
2
(
3
), pp.
322
331
.
15.
Yoshioka
,
H.
,
Matsumura
,
S.
,
Hashizume
,
H.
, and
Shinno
,
H.
,
2006
, “
Minimizing Thermal Deformation of Aerostatic Spindle System by Temperature Control of Supply Air
,”
JSME Int. J., Ser. C
,
49
(
2
), pp.
606
611
.10.1299/jsmec.49.606
16.
Shinno
,
H.
,
Hashizume
,
H.
, and
Sato
,
H.
,
1997
, “
In-Process Monitoring Method for Machining Environment Based on Simultaneous Multiphenomena Sensing
,”
CIRP Ann.
,
46
(
1
), pp.
53
56
.10.1016/S0007-8506(07)60774-4
17.
Lee
,
W. B.
,
Cheung
,
C. F.
, and
To
,
S.
,
2001
, “
Characteristics of Microcutting Force Variation in Ultraprecision Diamond Turning
,”
Mater. Manuf. Processes
,
16
(
2
), pp.
177
193
.10.1081/AMP-100104299
18.
Marsh
,
E. R.
, and
Schaut
,
A. J.
,
1998
, “
Measurement and Simulation of Regenerative Chatter in Diamond Turning
,”
Precis. Eng.
,
22
(
4
), pp.
252
257
.10.1016/S0141-6359(98)00020-8
19.
Jain
,
L. C.
, and
Medsker
,
L. R.
,
2000
,
Recurrent Neural Networks: Design and Applications
,
CRC Press
,
Boca Raton, FL
.
20.
Han
,
M.
,
Xi
,
J.
,
Xu
,
S.
, and
Yin
,
F. L.
,
2004
, “
Prediction of Chaotic Time Series Based on the Recurrent Predictor Neural Network
,”
IEEE Trans. Signal Process.
,
52
(
12
), pp.
3409
3416
.10.1109/TSP.2004.837418
21.
Doucet
,
A.
,
De Freitas
,
N.
, and
Gordon
,
N.
,
2001
,
Sequential Monte Carlo Methods in Practice
,
Springer Verlag
,
New York
.
22.
Moriwaki
,
T.
, and
Okuda
,
K.
,
1989
, “
Machinability of Copper in Ultra-Precision Micro Diamond Cutting
,”
CIRP Ann.
,
38
(
1
), pp.
115
118
.10.1016/S0007-8506(07)62664-X
23.
Lucca
,
D.
,
Rohrer
,
R. L.
, and
Komanduri
,
R.
,
1991
, “
Energy Dissipation in the Ultraprecision Machining of Copper
,”
CIRP Ann.
,
40
(
1
), pp.
69
72
.10.1016/S0007-8506(07)61936-2
24.
Prager
,
W.
,
1955
, “
The Theory of Plasticity: A Survey of Recent Achievements
,”
Proc. Inst. Mech. Eng.
,
169
(
1955
), pp.
41
57
.10.1243/PIME_PROC_1955_169_015_02
25.
Kong
,
M.
,
Lee
,
W.
,
Cheung
,
C.
, and
To
,
S.
,
2006
, “
A Study of Materials Swelling and Recovery in Single-Point Diamond Turning of Ductile Materials
,”
J. Mater. Process. Technol.
,
180
(
1–3
), pp.
210
215
.10.1016/j.jmatprotec.2006.06.006
26.
Lee
,
D. E.
,
Hwang
,
I.
,
Valente
,
C. M. O.
,
Oliveira
,
J. F. G.
, and
Dornfeld
,
D. A.
,
2006
, “
Precision Manufacturing Process Monitoring With Acoustic Emission
,”
Int. J. Mach. Tools Manuf.
,
46
(
2
), pp.
176
188
.10.1016/j.ijmachtools.2005.04.001
27.
Cheung
,
C. F.
, and
Lee
,
W. B.
,
2001
, “
A Framework of a Virtual Machining and Inspection System for Diamond Turning of Precision Optics
,”
J. Mater. Process. Technol.
,
119
(
1–3
), pp.
27
40
.10.1016/S0924-0136(01)00893-7
28.
Chan
,
K. C.
,
Cheung
,
C. F.
,
Ramesh
,
M. V.
,
Lee
,
W. B.
, and
To
,
S.
,
2001
, “
A Theoretical and Experimental Investigation of Surface Generation in Diamond Turning of an Al6061/Sicp Metal Matrix Composite
,”
Int. J. Mech. Sci.
,
43
(
9
), pp.
2047
2068
.10.1016/S0020-7403(01)00028-5
29.
Hocheng
,
H.
, and
Hsieh
,
M. L.
,
2004
, “
Signal Analysis of Surface Roughness in Diamond Turning of Lens Molds
,”
Int. J. Mach. Tools Manuf.
,
44
(
15
), pp.
1607
1618
.10.1016/j.ijmachtools.2004.06.003
30.
Bukkapatnam
,
S. T. S.
,
Lakhtakia
,
A.
, and
Kumara
,
S. R. T.
,
1995
, “
Analysis of Sensor Signals Shows Turning on a Lathe Exhibits Low-Dimensional Chaos
,”
Phys. Rev. E
,
52
(
3
), pp.
2375
2387
.10.1103/PhysRevE.52.2375
31.
Hamilton
,
J. D.
,
1994
,
Time Series Analysis
,
Princeton University Press
,
Princeton, NJ
.
32.
Dahlgren
,
R.
, and
Gerchman
,
M. C.
,
1987
, “
The Use of Aluminum Alloy Castings as Diamond Machining Substrates
,” retrieved on Nov. 9, 2011, http://www.precitech.com/pressroom/The%20Use%20of%20Aluminum%20Alloy%20Castings%20as%20Diamond%20Machining%20Substrates.pdf
33.
Li
,
L.
,
Collins
,
J. S. A.
, and
Yi
,
A. Y.
,
2010
, “
Optical Effects of Surface Finish by Ultraprecision Single Point Diamond Machining
,”
ASME J. Manuf. Sci. Eng.
,
132
(
2
), p.
021002
.10.1115/1.4001037
34.
Cheung
,
C. F.
,
Chan
,
K. C.
,
To
,
S.
, and
Lee
,
W. B.
,
2002
, “
Effect of Reinforcement in Ultra-Precision Machining of Al6061/Sic Metal Matrix Composites
,”
Scr. Mater.
,
47
(
2
), pp.
77
82
.10.1016/S1359-6462(02)00097-0
35.
Wang
,
H.
,
To
,
S.
,
Chan
,
C. Y.
,
Cheung
,
C. F.
, and
Lee
,
W. B.
,
2010
, “
A Theoretical and Experimental Investigation of the Tool-Tip Vibration and Its Influence Upon Surface Generation in Single-Point Diamond Turning
,”
Int. J. Mach. Tools Manuf.
,
50
(
3
), pp.
241
252
.10.1016/j.ijmachtools.2009.12.003
36.
Roblee
,
J.
, “
Factors Affecting Surface Finish in Diamond Turning
,” retrieved on Nov. 9, 2011, http://www.precitech.com/pressroom/FactorsAffectingSurfFinish.pdf
37.
Paul
,
E.
,
Evans
,
C. J.
,
Mangamelli
,
A.
,
Mcglauflin
,
M. L.
, and
Polvani
,
R. S.
,
1996
, “
Chemical Aspects of Tool Wear in Single Point Diamond Turning
,”
Precis. Eng.
,
18
(
1
), pp.
4
19
.10.1016/0141-6359(95)00019-4
38.
Aguirre
,
L. A.
, and
Billings
,
S.
,
1995
, “
Retrieving Dynamical Invariants From Chaotic Data Using Narmax Models
,”
Int. J. Bifurcation Chaos
,
5
(
2
), pp.
449
474
.10.1142/S0218127495000363
39.
Hagan
,
M. T.
,
Demuth
,
H. B.
, and
Beale
,
M.
,
1997
,
Neural Network Design
,
PWS Publishing
,
Boston, MA
.
40.
Dejesús
,
O.
, and
Hagan
,
M. T.
, eds.,
2001
,
Backpropagation Through Time for a General Class of Recurrent Network
,
Washington, DC
, pp.
2638
2642
.
41.
Kong
,
Z.
,
Oztekin
,
A.
,
Beyca
,
O. F.
,
Phatak
,
U.
,
Bukkapatnam
,
S. T. S.
, and
Komanduri
,
R.
,
2010
, “
Process Performance Prediction for Chemical Mechanical Planarization (CMP) by Integration of Nonlinear Bayesian Analysis and Statistical Modeling
,”
IEEE Trans. Semicond. Manuf.
,
23
(
2
), pp.
316
327
.10.1109/TSM.2010.2046110
42.
Comaniciu
,
D.
, and
Meer
,
P.
,
2002
, “
Mean Shift: A Robust Approach Toward Feature Space Analysis
,”
IEEE Trans. Pattern Anal. Mach. Intell.
,
24
(
5
), pp.
603
619
.10.1109/34.1000236
43.
Shaw
,
M. C.
,
1942
, “
The Chemico-Physical Role of the Cutting Fluid
,”
Metal Progress
,
42
, pp.
85
91
.
44.
Brinksmeier
,
E.
,
Lucca
,
D. A.
, and
Walter
,
A.
,
2004
, “
Chemical Aspects of Machining Processes
,”
CIRP Ann.
,
53
(
2
), pp.
685
699
.10.1016/S0007-8506(07)60035-3
45.
Burns
,
T. J.
, and
Davies
,
M. A.
,
1997
, “
Nonlinear Dynamics Model for Chip Segmentation in Machining
,”
Phys. Rev. Lett.
,
79
(
3
), pp.
447
450
.10.1103/PhysRevLett.79.447
46.
Kantz
,
H.
, and
Schreiber
,
T.
,
1997
,
Nonlinear Time Series Analysis
,
Cambridge University Press
,
Cambridge, UK
.
You do not currently have access to this content.