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.

Issue Section:
Technical Briefs
Keywords:
injection moulding, pressure sensors, pressure measurement, signal processing, process monitoring, ultrasonic measurement, wavelet transforms, design engineering
Topics:
Cavities, Sensors, Trains, Wavelet transforms, Wavelets, Pressure measurement, Injection molding, Signals, Pressure sensors, Signal processing
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
.
Crossref
Search ADS
 
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
.
Crossref
Search ADS
 
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
.
Crossref
Search ADS
 
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
.
Crossref
Search ADS
 
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
.
Crossref
Search ADS
 
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
.
Crossref
Search ADS
 
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
.
Crossref
Search ADS
 
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
.
Crossref
Search ADS
PubMed
 
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
.
Crossref
Search ADS
 
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.
Copyright © 2006
 by American Society of Mechanical Engineers
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