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Research Papers

Acoustic Emission Monitoring for Ultrasonic Cavitation Based Dispersion Process

[+] Author and Article Information
Jianguo Wu

Department of Industrial and Systems Engineering,
University of Wisconsin-Madison,
3255 Mechanical Engineering,
1513 University Avenue,
Madison, WI 53706
e-mail: wu45@wisc.edu

Shiyu Zhou

Department of Industrial and Systems Engineering,
University of Wisconsin-Madison,
3270 Mechanical Engineering,
1513 University Avenue,
Madison, WI 53706
e-mail: szhou@engr.wisc.edu

Xiaochun Li

Department of Mechanical Engineering,
University of Wisconsin-Madison,
1035 Mechanical Engineering,
1513 University Avenue,
Madison, WI 53706
e-mail: xcli@engr.wisc.edu

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the Journal of Manufacturing Science and Engineering. Manuscript received July 18, 2012; final manuscript received February 12, 2013; published online May 24, 2013. Assoc. Editor: Robert Gao.

J. Manuf. Sci. Eng 135(3), 031015 (May 24, 2013) (12 pages) Paper No: MANU-12-1214; doi: 10.1115/1.4024041 History: Received July 18, 2012; Revised February 12, 2013

In the manufacturing of micro/nanocomposite materials, micro/nanoparticles need to be dispersed evenly into the base materials. However, due to their high surface-to-volume ratio and high surface energy, the micro/nanoparticles tend to agglomerate and cluster together. Ultrasonic cavitation is effective to disperse micro/nanoparticles. However, works on correlating the cavitation parameters with the micro/nanoparticle dispersion are limited. This paper presents a real-time acoustic monitoring method based on cavitation noises to monitor the micro/nanoparticle dispersion status. In this paper, two types of cavitation noise power indices computed based on the raw cavitation noise signals are used to monitor the cavitation status. Both off-line and on-line steady state detection algorithms are developed. These algorithms can be used to determine the critical process parameters including the power of the ultrasonic sound and the dispersion time. Extensive experiments have been conducted to illustrate the effectiveness of the developed methods.

Copyright © 2013 by ASME
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References

Figures

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Fig. 1

Experimental setup (a) and its schematic representation (b): (1) Misonix Sonicator 4000; (2) ultrasonic horn/probe; (3) standard 500 mL glass beaker; (4) titanium rod; (5) acoustic sensor; (6) Tektronix DPO7345 oscilloscope

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Fig. 2

Two representative cavitation noise waveforms with ultrasonic power 40 W for pure tap water and tap water with 20 g Al2O3 particles

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Fig. 3

Al2O3 suspension immediately after the ultrasonic treatment

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Fig. 4

Deposited Al2O3 particles (Al2O3 20 g, ultrasonic driving power 40 W, 26 hs after treatment)

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Fig. 5

The volume of the deposited Al2O3 particles as a function of the processing time

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Fig. 6

Cavitation noise spectrum for tap water with 10 g Al2O3 particles at the time of 40 s after the ultrasonic power is turned on: (a) 40 W, (b) 100 W, (c) 40 W, natural logarithmic scale, (d) 100 W, natural logarithmic scale

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Fig. 7

The influence of particle concentration and ultrasonic power on CNP-1: (a) CNP-1 as a function of time for different amounts of Al2O3 particles with ultrasonic power 40 W, (b) CNP-1 evolves with time for different ultrasonic power in tap water with 30 g Al2O3 particles

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Fig. 8

CNP-2 as a function of time for different ultrasonic power in tap water with 30 g Al2O3 particles

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Fig. 9

Illustration of MSER for CNP signals (power 40 W, Al2O3 30 g)

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Fig. 10

An example of MSER-EWMA on CNP signals (40 W, 30 g Al2O3)

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Fig. 11

MSER and MSER-EWMA detected transition times as functions of ultrasonic power for CNP-1 and CNP-2 (30 g Al2O3)

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Fig. 12

The influence of Al2O3 concentration on dispersion time estimated by MSER-EWMA on CNP-1 (40 W)

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Fig. 13

Mean CNP-1 in the transient state as a function of ultrasonic power (30 g Al2O3)

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Fig. 14

An example of generated signal (a = 0.01, T0 = 461)

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Fig. 15

The expected detection bias and false alarm rate as functions of detection threshold for NSDM and R-test (NSDM: m=50, D=2; R-test: λ1=0.05, λ2=0.05, λ3=0.08)

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Fig. 16

Illustration of NSDM (a) and R-test (b) for CNP signals (40 W, Al2O3 30 g)

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Fig. 17

Transition time detected by MSER-EWMA, NSDM and R-test (Al2O3 30 g)

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