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Research Papers

Lamb Wave Interaction at Delamination and Debondings Due to Impact Damage in Complex Stiffened CFRP Structures

[+] Author and Article Information
Benjamin Eckstein

Airbus Group Innovations,
Airbus-Allee 1,
Bremen 28199, Germany
e-mail: Benjamin.Eckstein@airbus.com

Maria Moix Bonet

German Aerospace Center,
Lilienthalplatz 7,
Braunschweig 38108, Germany
e-mail: Maria.Moix-Bonet@dlr.de

Martin Bach

Airbus Group Innovations,
Airbus-Allee 1,
Bremen 28199, Germany
e-mail: Martin.Bach@airbus.com

Claus-Peter Fritzen

Department of Mechanical Engineering,
University of Siegen,
Paul-Bonatz-Str. 9-11,
Siegen 57076, Germany
e-mail: Claus-Peter.Fritzen@uni-siegen.de

1Corresponding author.

Manuscript received August 31, 2017; final manuscript received March 9, 2018; published online May 3, 2018. Assoc. Editor: Zhongqing Su.

ASME J Nondestructive Evaluation 1(3), 031003 (May 03, 2018) (10 pages) Paper No: NDE-17-1083; doi: 10.1115/1.4039692 History: Received August 31, 2017; Revised March 09, 2018

The increased usage of carbon fiber reinforced plastics (CFRP) for primary aerospace structures involves dealing with the susceptibility of composite laminates to impact loads as well as the occurrence of barely visible impact damages. One special case among impact sources is the so-called blunt impact, which may cause damage primarily to the internal structure. Therefore, the assessment of debonding of stiffening elements in CFRP structures poses an attractive application case for structural health monitoring by guided ultrasonic waves. Wave propagation phenomena at impact damages as well as the signal processing utilized to extract a damage related feature (i.e., damage index (DI)) contribute to the sensitivity, and thus, to the reliability of structural health monitoring (SHM) systems. This work is based on data from the EU-funded project SARISTU, where a generic CFRP door surrounding fuselage panel with an integrated sensor network has been built and tested by introducing a large number of impact damages. Wave interaction of delaminations and stringer debondings of different size and morphology in omega-stringer stiffened structures are examined to highlight the factors contributing to the sensitivity. Common damage indicator formulations for the use with imaging algorithms, such as the reconstruction algorithm for the probabilistic inspection of damage (RAPID), are applied on data from various damage cases. Furthermore, the difference in detectability of delaminations and debondings as well as the implications on imaging algorithms is examined.

Copyright © 2018 by ASME
Topics: Delamination , Damage
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References

Zhao, X. , Gao, H. , Zhang, G. , Ayhan, B. , Yan, F. , Kwan, C. , and Rose, J. L. , 2007, “ Active Health Monitoring of an Aircraft Wing With Embedded Piezoelectric Sensor/Actuator Network: I. Defect Detection, Localization and Growth Monitoring,” Smart Mater. Struct., 16(4), pp. 1208–1217. [CrossRef]
Moix Bonet, M. , Wierach, P. , Loendersloot, R. , and Bach, M. , 2016, “ Damage Assessment in Composite Structures Based on Acousto Ultrasonics—Evaluation of Performance,” Smart Intelligent Aircraft Structures (SARISTU): Proceedings of the Final Project Conference, Springer, Cham, Switzerland, pp. 617–629. [CrossRef]
Eckstein, B. , Moix Bonet, M. , Bach, M. , and Dobmann, N. , 2016, “ Reliability Aspects of Lamb Wave Interaction at Impact Damages in Complex Stiffened CFRP Structures,” Eighth European Workshop on Structural Health Monitoring, Bilbao, Spain, July 5–8. https://www.researchgate.net/publication/309557463_Reliability_Aspects_of_Lamb_Wave_Interaction_at_Impact_Damages_in_Complex_Stiffened_CFRP_Structures
Eckstein, B. , Moix Bonet, M. , Bach, M. , and Fritzen, C.-P. , 2017, “ Lamb Wave Interaction at Debondings Due to Impact Damage in Complex Stiffened CFRP Structures,” Proc. SPIE, 10170, p. 101701.
Eckstein, B. , Fritzen, C.-P. , and Bach, M. , 2012, “ Considerations on the Reliability of Guided Ultrasonic Wave-Based SHM Systems for CFRP Aerospace Structures,” Sixth European Workshop on Structural Health Monitoring, Dresden, Germany, July 3–6, pp. 957–964. http://www.ndt.net/article/ewshm2012/papers/tu3a1.pdf
Schmidt, D. , Kolbe, A. , Kaps, R. , Wierach, P. , Linke, S. , Steeger, S. , von Dungern, F. , Tauchner, J. , Breu, C. , and Newman, B. , 2016, “ Development of a Door Surround Structure With Integrated Structural Health Monitoring System,” Smart Intelligent Aircraft Structures (SARISTU): Final Project Conference, Springer, Cham, Switzerland, pp. 935–945. [CrossRef]
Bach, M. , Dobmann, N. , and Moix Bonet, M. , 2016, “ Damage Introduction, Detection, and Assessment at CFRP Door Surrounding Panel,” Smart Intelligent Aircraft Structures (SARISTU): Final Project Conference, Springer, Cham, Switzerland, pp. 947–957. [CrossRef]
Moix Bonet, M. , Eckstein, B. , Loendersloot, R. , and Wierach, P. , 2015, “ Identification of Barely Visible Impact Damages on a Stiffened Composite Panel With a Probability-Based Approach,” Tenth International Workshop on Structural Health Monitoring, Stanford, CA, Sept. 1–3, pp. 2334–2342.
Wu, Z. , Liu, K. , Wang, Y. , and Zheng, Y. , 2014, “ Validation and Evaluation of Damage Identification Using Probability-Based Diagnostic Imaging on a Stiffened Composite Panel,” J. Intell. Matls. Syst. Struct., 26(16), pp. 2181–2195. [CrossRef]
Loendersloot, R. , Buethe, I. , Michaelides, P. , Moix Bonet, M. , and Lampeas, G. , 2016, “ Damage Identification in Composite Panels—Methodologies and Visualisation,” Smart Intelligent Aircraft Structures (SARISTU): Final Project Conference, Springer, Cham, Switzerland, pp. 579–604. [CrossRef]
Su, Z. , and Lin, Y. , 2009, Identification of Damages Using Lamb Waves, Springer, Berlin, p. 64f. [CrossRef]

Figures

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Fig. 1

SARISTU door surrounding fuselage shell test structure [6]

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Fig. 2

Representative part of sensor grid with damage #060

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Fig. 3

C-scan with damage #060 at Omega-stringer foot

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Fig. 4

Illustration of elliptical distribution function

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Fig. 5

Example time signal of path 77–82 at 100 kHz

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Fig. 6

1 − ρCC versus excitation frequency for dataset 1

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Fig. 7

1 − ρSSSD versus excitation frequency for dataset 1

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Fig. 8

1 − ρSSSD versus excitation frequency for dataset 2

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Fig. 9

1 − ρCC for various damage sizes and paths of dataset 3 with α = 90 deg, R ≤ 1.10

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Fig. 10

1 − ρCC for various damage sizes and paths of dataset 3 with α = ±37 deg, R ≤ 1.10

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Fig. 11

1 − ρCC for various damage sizes and paths of dataset 3 with α = ±0 deg, R ≤ 1.10

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Fig. 12

1 − ρCC versus relative damage position for dataset 1, only paths with α = ±0 deg

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Fig. 13

1 − ρCC versus relative damage position for dataset 2

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Fig. 14

1 − ρCC versus relative damage position for damage cases of dataset 3

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Fig. 15

(1 − ρCC) versus relative damage position for dataset 3, only paths with α ± 90 deg

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Fig. 16

(1 − ρCC) versus R for damage cases of dataset 1

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Fig. 17

(1 − ρCC) versus R for damage cases of dataset 2

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Fig. 18

(1 − ρCC) versus R for damage cases of dataset 3

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Fig. 19

(1 − ρCC) versus R versus damage sizes AD for dataset 2

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