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Research Papers

Microstructure Modeling and Ultrasonic Wave Propagation Simulation of A206–Al2O3 Metal Matrix Nanocomposites for Quality Inspection

[+] Author and Article Information
Yuhang Liu

Department of Industrial and Systems Engineering,
University of Wisconsin–Madison,
3255 Mechanical Engineering,
1513 University Avenue,
Madison, WI 53706
e-mail: liu427@wisc.edu

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 and Aerospace Engineering,
University of California,
Los Angeles, 48-121G Eng IV,
Los Angeles, CA 90095
e-mail: xcli@seas.ucla.edu

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received February 9, 2015; final manuscript received June 25, 2015; published online October 1, 2015. Assoc. Editor: Donggang Yao.

J. Manuf. Sci. Eng 138(3), 031008 (Oct 01, 2015) Paper No: MANU-15-1075; doi: 10.1115/1.4030981 History: Received February 09, 2015; Revised June 25, 2015

Ultrasonic testing is a promising alternative quality inspection technique to the expensive microscopic imaging to characterize metal matrix nanocomposites. However, due to the complexity of the wave–microstructure interaction, and the difficulty in fabricating nanocomposites of different microstructural features, it is very challenging to build reliable relationships between ultrasonic testing results and nanocomposites quality. In this research, we propose a microstructure modeling and wave propagation simulation method to simulate ultrasonic attenuation characteristic for A206–Al2O3 metal matrix nanocomposites (MMNCs). In particular, a modified Voronoi diagram is used to reproduce the microstructures and the numeric method elastodynamic finite integration technique (EFIT) is used to simulate the wave propagation through the generated microstructures. Linear mixed effects model (LME) is used to quantify the between-curve variation of ultrasonic attenuation from both experiment and simulation. Permutation test is employed to quantify the similarity of the quantified variation between experiment and simulation. This research supports the experimental results through the simulation approach and provides a better understanding of the relationship between attenuation curves and the microstructures.

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References

Figures

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Fig. 1

Illustration of the ultrasonic testing using ultrasonic attenuation curves [3]

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Fig. 2

Microstructures for pure A206 and A206–Al2O3 MMNCs. Left panel: experimental micrographs. Right panel: simulated microstructures.

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Fig. 3

Example of Voronoi diagram with 20 random points

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Fig. 4

The microstructure modeling process: (a) initial Vononoi diagram, (b) after edge dissolving step controlled by α and β, (c) after assigning random thickness to each edge, and (d) the random thickness assigning process

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Fig. 5

Microstructures generated using different parameters α, β, and N

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Fig. 6

Examples of input phantom, wave propagation snapshots, and transducer output by VEFIT

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Fig. 7

Simulation procedure using VEFIT and attenuation measurement

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Fig. 8

The comparison of experimental attenuation curves and the simulated attenuation curves with different simulation parameters (attenuation units: dB/mm, frequency unit: MHz)

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Fig. 9

The influence of α and β on the attenuation curves (N = 1200)

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Fig. 10

Histograms of the fitted random effects and residuals for the experimental measurements of A206-5 wt. % Al2O3 (top) and simulated attenuation curves shown in Fig. 8(c3) (bottom)

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Fig. 11

Illustration of permutation test on population means of two data sets

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Fig. 12

Illustration of the permutation test. (a) and (b): Figs. 8(c) versus 8(c3), p-value = 0.99; (c) and (d): Figs. 8(b) versus 8(c3), p-value = 0.06. The vertical dashed lines denote the observed test statistics.

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