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Research Papers

Development of an Inverse Algorithm for Resonance Inspection

[+] Author and Article Information
Canhai Lai1

Pacific Northwest National Laboratories, Richland, WA 99352kevin.lai@pnnl.gov

Wei Xu, Xin Sun

Pacific Northwest National Laboratories, Richland, WA 99352

1

Corresponding author. Present address: 902 Battelle Boulevard, P. O. Box 999, MSIN K7-90, Richland, WA 99352.

J. Vib. Acoust 134(5), 051017 (Sep 07, 2012) (10 pages) doi:10.1115/1.4006649 History: Received September 02, 2011; Revised March 02, 2012; Published September 07, 2012; Online September 07, 2012

Resonance inspection (RI), which employs the natural frequency spectra shift between the good and the anomalous part populations to detect defects, is a nondestructive evaluation (NDE) technique with many advantages, such as low inspection cost, high testing speed, and broad applicability to structures with complex geometry compared to other contemporary NDE methods. It has already been widely used in the automobile industry for quality inspections of safety critical parts. Unlike some conventionally used NDE methods, the current RI technology is unable to provide details, i.e., location, dimension, or types, of the flaws for the discrepant parts. Such limitation severely hinders its widespread applications and further development. In this study, an inverse RI algorithm based on maximum correlation function is proposed to quantify the location and size of flaws for a discrepant part. A dog-bone-shaped stainless steel sample with and without controlled flaws is used for algorithm development and validation. The results show that multiple flaws can be accurately pinpointed back, using the algorithms developed, and the prediction accuracy decreases with increasing flaw numbers and decreasing distance between flaws.

Copyright © 2012 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Connecting-rod meshed with 0.762-mm elements

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Figure 2

Two flaws at different locations

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Figure 3

Validation of linear superposition hypothesis for two distant flaws

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Figure 4

Two flaws in the vicinity of each other

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Figure 5

Validation of linear superposition hypothesis for two adjacent flaws

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Figure 6

Validation of linear superposition hypothesis for three adjacent flaws

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Figure 7

Validation of linear superposition hypothesis for three adjacent flaws

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Figure 8

Positions of possible flaws

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Figure 9

Correlation algorithm comparison for flaws spaced in one dimension. (a) C1 j calculated per different models. (b) C18j calculated per different models. (c) C22j calculated per different models. (d) C28j calculated per different models. (e) C35j calculated per different models.

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Figure 10

Positions of small flaw groups

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Figure 11

Correlation algorithm comparison for flaws spaced in two dimensions. (a) Contour of Cij of model 1. (b) Contour of Cij of model 2. (c) Contour of Cij of model 3.

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Figure 12

Maximum correlation approach for a single defect. (a) Resonance inversion for case 1. (b) Convergence check for case 1.

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Figure 13

Maximum correlation approach for two defects distant to each other. (a) Resonance inversion for case 2(a). (b) Convergence check for case 2(a).

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Figure 14

Maximum correlation approach for two defects adjacent to each other. (a) Resonance inversion for case 2(b). (b) Convergence check for case 2(b).

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Figure 15

Maximum correlation approach for three defects distant to each other. (a) Resonance inversion for case 3(a). (b) Convergence check for case 3(a).

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Figure 16

Maximum correlation approach for three defects close to each other. (a) Resonance inversion for case 3(b). (b) Convergence check for case 3(b).

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Figure 17

Maximum correlation approach for two partial defects. (a) Resonance inversion for case 4. (b) Convergence check for case 4.

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