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Research Papers

Data-Driven Damage Model Based on Nondestructive Evaluation

[+] Author and Article Information
Konstantinos P. Baxevanakis

Wolfson School of Mechanical,
Electrical and Manufacturing Engineering,
Loughborough University,
Loughborough LE11 3TU, UK

Brian Wisner, Sara Schlenker

Theoretical and Applied Mechanics Group,
Mechanical Engineering and Mechanics
Department,
Drexel University,
3141 Chestnut Street,
Philadelphia, PA 19104

Harsh Baid

AlphaSTAR Corporation,
5150 East Pacific Coast Highway,
Long Beach, CA 90804

Antonios Kontsos

Theoretical and Applied Mechanics Group,
Mechanical Engineering and Mechanics
Department,
Drexel University,
3141 Chestnut Street,
Philadelphia, PA 19104
e-mail: antonios.kontsos@drexel.edu

1Corresponding author.

Manuscript received November 7, 2017; final manuscript received April 13, 2018; published online May 14, 2018. Assoc. Editor: Andrei Zagrai.

ASME J Nondestructive Evaluation 1(3), 031007 (May 14, 2018) (12 pages) Paper No: NDE-17-1106; doi: 10.1115/1.4040040 History: Received November 07, 2017; Revised April 13, 2018

A computational damage model, which is driven by material, mechanical behavior, and nondestructive evaluation (NDE) data, is presented in this study. To collect material and mechanical behavior damage data, an aerospace grade precipitate-hardened aluminum alloy was mechanically loaded under monotonic conditions inside a scanning electron microscope, while acoustic and optical methods were used to track the damage accumulation process. In addition, to obtain experimental information about damage accumulation at the laboratory scale, a set of cyclic loading experiments was completed using three-point bending specimens made out of the same aluminum alloy and by employing the same nondestructive methods. The ensemble of recorded data for both cases was then used in a postprocessing scheme based on outlier analysis to form damage progression curves, which were subsequently used as custom damage laws in finite element (FE) simulations. Specifically, a plasticity model coupled with stiffness degradation triggered by the experimentally defined damage curves was used in custom subroutines. The results highlight the effect of the data-driven damage model on the simulated mechanical response of the geometries considered and provide an information workflow that is capable of coupling experiments with simulations that can be used for remaining useful life (RUL) estimations.

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Figures

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Fig. 1

Texture information: (a) inverse pole figure map showing predominantly equiaxed grains, (b) histogram showing grain size distribution, and (c) pole figure revealing a rolling texture

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Fig. 2

(a) Mechanical and nondestructive test setup in a FEI XL30 SEM, (b) GATAN stage inside the SEM showing the backscatter electron microscopy detector, (c) PICO sensor used for AE monitoring, and (d) PICO sensor frequency response

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Fig. 3

Hourglass-type specimen dimensions and placement of the acoustic emission sensors. The shaded area indicates the field of view for DIC measurements.

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Fig. 4

(a) Digital image correlation snapshots in the macroscopic stress–strain response curve and (b) strain evolution in the monitored by DIC region

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Fig. 5

Acoustic emission features plotted for the entire duration of the in situ tension test: (a) amplitude, (b) peak frequency, (c) partial power 3, and (d) counts, cumulative hits, and cumulative absolute energy

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Fig. 6

(a) Postmortem SEM image of the crack, microdamage identification near the crack (b), and away from the crack (c) in the forms of precipitate fracture (red circles), microcracks (orange boxes), and slip lines (green lines)

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Fig. 7

Cumulative MSD for monotonic Al 7075 specimens taken from the transverse direction during in situ experiments

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Fig. 8

Acoustic emission features plotted for the entire duration of the fatigue bending test: (a) amplitude; (b) peak frequency; (c) counts, hits, and absolute energy all overlaid with the maximum loading strain; and (d) the damage location and size at the end of the test

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Fig. 9

Normalized cumulative MSD for a three-point bending beam under cyclic loading

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Fig. 10

Pseudocode for implementation of the damage law in a finite element code

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Fig. 11

(a) Boundary conditions and loading assignment description for (a) the dogbone coupon and (b) the three-point bending simulation

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Fig. 12

Stress–strain response and damage parameter output for an undamaged and a damaged specimen following the MSD under (a) monotonic and (b) cyclic loading

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Fig. 13

Comparison of deformation modes between the (a) undamaged and (b) the damaged dogbone specimens under monotonic loading

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Fig. 14

Full field plots for displacement uy, von Mises stress, and strain εyy for monotonic loading in the case of (a) plasticity and (b) plasticity and damage

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Fig. 15

Damage parameter evolution for monotonic loading. The maximum damage levels are as follows: (a) 27%, (b) 52%, (c) 73%, and (d) 100%.

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Fig. 16

SN curve for Al 7075 under tensile cyclic conditions (R=0.1)

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Fig. 17

Full field plots for displacement uy, von Mises stress, and strain εyy for cyclic loading in the case of (a) plasticity and (b) plasticity and damage

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Fig. 18

(a) Damage evolution under cyclic loading and (b) variation of the deflection in the middle of the beam with respect to the number of fatigue cycles using both approaches

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