A new signal-processing technique based on analytic wavelet transform has been developed for detecting and differentiating temporally overlapped ultrasonic pulse trains that carry spatially distributed pressure information across an injection mold cavity. Compared to conventional wavelets that have a constant relative bandwidth at all the scales, the analytic wavelets investigated in this paper feature variable relative bandwidth, making it possible to simultaneously match the frequency characteristics of the ultrasonic pulse trains transmitted from the mold-embedded pressure sensors. As a result, more accurate detection and differentiation of the temporal and spectral information embedded within the ultrasonic pulse trains could be achieved. Theoretical framework for the analytic wavelet transform was established, and a multichannel ultrasonic pulse detector based on the complex Morlet wavelet was designed and experimentally investigated. The results have confirmed the effectiveness of the new signal-processing technique for on-line pressure sensing for injection molding process monitoring.

1.
Rawabdeh
,
I. A.
, and
Petersen
,
P. F.
, 1999, ”
In-Line Monitoring of Injection Molding Operations: A Literature Review
,”
J. Injection Molding Technol.
1533-905X,
3
, pp.
47
53
.
2.
Gao
,
R.
,
Kazmer
,
D.
,
Theurer.
,
C.
, and
Zhang
,
L.
, 2001, “
Fundamental Aspects for the Design of a Self-Energized Sensor for Injection Molding Process Monitoring
,”
Proc. NSF Design and Manufacturing Research Conference
, Tampa, FL.
3.
Zhang
,
L.
,
Theurer
,
C.
,
Gao
,
R.
, and
Kazmer
,
D.
, 2004, ”
A Self-Energized Sensor for Wireless Injection Mold Cavity Pressure Measurement: Design and Evaluation
,”
ASME J. Dyn. Syst., Meas., Control
0022-0434,
126
(
2
), pp.
309
318
.
4.
McGonnagle
,
W. J.
, 1966,
Nondestructive Testing
, 2nd Ed.,
Gordon and Breach
, New York, Chap. 8.
5.
Greguss
,
P.
, 1980,
Ultrasonic Imaging: Seeing by Sound: The Principles and Widespread Applications of Image Formation by Sonic, Ultrasonic, and Other Mechanical Waves
,
Focal Press
, New York.
6.
Lee
,
B. Y.
, and
Tarng
,
Y. S.
, 1999, ”
Application of the Discrete Wavelet Transform to the Monitoring of Tool Failure in End Milling Using the Spindle Motor Current
,”
Int. J. Adv. Manuf. Technol.
0268-3768,
15
(
4
), pp.
238
243
.
7.
Staszewski
,
W. J.
,
Ruotolo
,
R.
, and
Storer
,
D. M.
, 1999, “
Fault Detection in Ball-Bearings Using Wavelet Variance
,”
Proc. of International Modal Analysis Conference
,
2
, pp.
1335
1339
.
8.
Staszewski
,
W. J.
, 1998, “
Structural and Mechanical Damage Detection Using Wavelets
,”
Shock Vib. Dig.
0583-1024,
30
(
6
), pp.
457
472
.
9.
Pandit
,
S. M.
,
Joshi
,
G. A.
, and
Paul
,
D.
, 1993, “
Bearing Defect Detection Using DDS and Wavelet Methods
,”
ASME Symposium on Mechatronics
, DSC-ASME, New York,
50/PED-63
, pp.
285
293
.
10.
Mori
,
K.
,
Kasashima
,
N.
,
Yoshioka
,
T.
, and
Ueno
,
Y.
, 1996, “
Prediction of Spalling on a Ball Bearing by Applying the Discrete Wavelet Transform to Vibration Signals
,”
Wear
0043-1648,
195
(
1-2
), pp.
162
168
.
11.
Wang
,
W. J.
, and
McFadden
,
P. D.
, 1996, “
Application of Wavelets to Gearbox Vibration Signals for Fault Detection
,”
J. Sound Vib.
0022-460X,
192
(
5
), pp.
927
939
.
12.
Li
,
J.
, and
Ma
,
J.
, 1997, “
Wavelet Decomposition of Vibrations for Detection of Bearing-Localized Defects
,”
NDT & E Int.
0963-8695,
30
(
3
), pp.
143
149
.
13.
Gaouda
,
A. M.
,
Salama
,
M. M. A.
,
Sultan
,
M. R.
, and
Chikhani
,
A. Y.
, 1999, ”
Power Quality Detection and Classification Using Wavelet-Multiresolution Signal Decomposition
,”
IEEE Trans. Power Deliv.
0885-8977,
14
(
4
), pp.
1469
1476
.
14.
Mzaik
,
T.
, and
Jagadeesh
,
J. M.
, 1994, “
Wavelet-Based Detection of Transients in Biological Signals
,”
Proc. SPIE
0277-786X,
2303
, pp.
105
117
.
15.
Tuteur
,
F. B.
, 1988, “
Wavelet Transformations in Signal Detection
,”
Proceedings—ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
,
3
, pp.
1435
1438
.
16.
Mallat
,
S.
, 1999,
A Wavelet Tour of Signal Processing
, 2nd Ed.,
Academic Press
, San Diego, CA.
17.
Rioul
,
O.
, and
Vetterli
,
M.
, 1991, “
Wavelets and Signal Processing
,”
IEEE Signal Process. Mag.
1053-5888
8
(
4
), pp.
14
38
.
18.
Zhang
,
L.
,
Theurer
,
C.
,
Gao
,
R.
, and
Kazmer
,
D.
, 2005, ”
Design of Ultrasonic Transmitters With Defined Frequency Characteristics for Wireless Pressure Sensing in Injection Molding
,”
IEEE Trans. Ultrason. Ferroelectr. Freq. Control
0885-3010
52
(
8
), pp.
1360
1371
.
19.
Teolis
,
A.
1998,
Computational Signal Processing With Wavelets: Applied and Numerical Harmonic Analysis
,
AIMS
, Rockville, MD.
20.
Addison
,
P. S.
,
Watson
,
J. N.
, and
Feng
,
T.
, 2002, “
Low-Oscillation Complex Wavelets
,”
J. Sound Vib.
0022-460X,
254
(
4
), pp.
733
762
.
21.
Hahn
,
S. L.
, 1996,
Hilbert Transform in Signal Processing
,
Artech House
, Norwood, MA.
22.
Bendat
,
J. S.
, and
Piersol
,
A. G.
, 2000,
Random Data Analysis and Measurement Procedures
, 3rd Ed.,
Wiley
, New York.
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