Research Papers

Different Fatigue Dynamics Under Statistically and Spectrally Similar Deterministic and Stochastic Excitations

[+] Author and Article Information
David Chelidze

e-mail: chelidze@egr.uri.edu
Department of Mechanical,
Industrial and Systems Engineering,
University of Rhode Island,
Kingston, RI 02881

R = σmin/σmax, where σmin is the minimum peak stress and σmax is the maximum peak stress.

1Corresponding author.

Manuscript received May 1, 2013; final manuscript received July 10, 2013; accepted manuscript posted July 29, 2013; published online September 23, 2013. Assoc. Editor: John Lambros.

J. Appl. Mech 81(4), 041004 (Sep 23, 2013) (8 pages) Paper No: JAM-13-1182; doi: 10.1115/1.4025138 History: Received May 01, 2013; Revised July 10, 2013; Accepted July 29, 2013

Estimating and tracking crack growth dynamics is essential for fatigue failure prediction. A new experimental system—coupling structural and crack growth dynamics—was used to show fatigue damage accumulation is different under chaotic (i.e., deterministic) and stochastic (i.e., random) loading, even when both excitations possess the same spectral and statistical signatures. Furthermore, the conventional rain-flow counting method considerably overestimates damage in case of chaotic forcing. Important nonlinear loading characteristics, which can explain the observed discrepancies, are identified and suggested to be included as loading parameters in new macroscopic fatigue models.

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Fig. 1

(a): Experimental apparatus. (b): Schematic of the apparatus: 1. shaker; 2. granite base; 3. slip table; 4. linear bearings for the slip table; 5. back mass; 6. specimen supports; 7. pneumatic cylinder supports; 8. slip table rails; 9. front cylinder; 10. front mass; 11. the specimen; 12. linear bearings for the masses; 13. central rail for the masses; 14. back cylinder; and 15. flexible axial coupling.

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Fig. 2

Model of a specimen. The machined notch can be seen in the center, as well as the round hole on the left and the oblong slot on the right.

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Fig. 3

Histogram and power spectrum of the original chaotic (a) and stochastic (b) signals

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Fig. 4

Delay coordinate reconstruction of phase portraits for the stochastic (a) and original chaotic (b) signals

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Fig. 5

Histogram and power spectrum of the table acceleration data of the random (a) and chaotic (b) signals

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Fig. 6

Reconstructed phase portrait of the table acceleration data for the random (a) and chaotic (b) signals

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Fig. 7

Beam diagram with applied forces

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Fig. 8

Histograms of cycle amplitudes counted by the rainflow counting method in random (a) and chaotic (b) tests

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Fig. 9

Estimation of embedding dimension for the random (a) and chaotic (b) signals

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Fig. 10

Average trajectory divergence rates for all records in random loading (a) and chaotic loading (b)

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Fig. 11

Average trajectory divergence rates for all tests using random loading (a) and chaotic loading (b)

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Fig. 12

Correlation sum for all ten tests




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