Research Papers

Progressive Failure Monitoring of Fiber-Reinforced Metal Laminate Composites Using a Nondestructive Approach

[+] Author and Article Information
Rami Carmi

Department of Materials,
Beer-Sheva P.O.B 9001, Israel
e-mail: carmo_nm@netvision.net.il

Brian Wisner

Russ College of Engineering and Technology,
Ohio University,
Stocker Center 261,
Athens, OH 45701
e-mail: bwisner@ohio.edu

Prashanth A. Vanniamparambil

Corning Inc.,
Corning, NY 14831
e-mail: prashanth288@gmail.com

Jefferson Cuadra

Lawrence Livermore National Laboratory,
Livermore, CA 94550
e-mail: jcuadra9@gmail.com

Arie Bussiba

Ben-Gurion University of the Negev,
Beer Sheva 84105, Israel
e-mail: busarie@bezeqint.net

Antonios Kontsos

Department of Mechanical Engineering and Mechanics,
College of Engineering,
Drexel University,
Philadelphia, PA 19104
e-mail: antonios.kontsos@drexel.edu

1Corresponding author.

Manuscript received December 15, 2018; final manuscript received April 30, 2019; published online May 30, 2019. Assoc. Editor: Paul Fromme.

ASME J Nondestructive Evaluation 2(2), 021006 (May 30, 2019) (11 pages) Paper No: NDE-18-1050; doi: 10.1115/1.4043713 History: Received December 15, 2018; Accepted April 30, 2019

Fiber-reinforced metal laminate (FRML) composites are currently used as a structural material in the aerospace industry. A common FRML, glass layered aluminum reinforced epoxy (Glare), possesses a set of mechanical properties which was achieved by designing its layup structure to combine metal alloy and fiber-reinforced polymer phases. Beyond static and dynamic mechanical properties at the material characterization phase, however, the need exists to develop methods that could assess the evolving material state of Glare, especially in a progressive failure context. This paper presents a nondestructive approach to monitor the damage at the material scale and combine such information with characterization and postmortem evaluation methods, as well as data postprocessing to provide an assessment of the failure process during monotonic loading conditions. The approach is based on multiscale sensing using the acoustic emission (AE) method, which was augmented in this paper in two ways. First, by applying it to all material components separately in addition to actual Glare specimens. Second, by performing testing and evaluation at both the laboratory scale as well as at the scale defined inside the scanning electron microscopy. Such elaborate testing and nondestructive evaluation results provided the basis for the application of digital signal processing and machine learning methods which were capable to identify data trends that are shown to be correlated with the evolution of failure modes in Glare.

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Grahic Jump Location
Fig. 1

GLARE®1A: (a) the sketch of its structure showing the layers of Al alloy in green and the prepreg layers in brown, (b) cross-sectional views of the short face (red square), and (c) cross-sectional view of the side face parallel to fiber orientation (blue square)

Grahic Jump Location
Fig. 9

Wavelet analysis of AE signals taken at different stages of loading indication the different damage mechanisms: (a) plastic deformation, (b) matrix cracking, (c) fiber breakage, and (d) bundle of fibers breakage with interface cracking

Grahic Jump Location
Fig. 8

General view of GLARE®1A (a) before and (b) after the final fracture

Grahic Jump Location
Fig. 7

(a) Stress–strain curve of GLARE®1A combined with AE data where the light blue line indicates the achieved 1% strain; (b) time scale enlargement focusing on 1% strain value at 133.5 s; (c) wavelet analysis of AE signal captured at 133.5 s emphasizing several fibers breakage at 500 kHz; (df), SEM images taken before (123.5), during (133.5), and after (143.5) 1% of strain achieved

Grahic Jump Location
Fig. 6

(a) Stress–strain curve of GLARE®1A combined with AE data as recorded during the in situ tensile test and (b) WPF versus PP3 for AE data

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Fig. 5

(a) Stress–strain curve of GLARE®1A (black) combined with AE data, green line represents the linearity of the secondary modulus; (b) WPF versus PP3 for AE data; (c) cumulative counts combined with mechanical data; and (d) a typical wavelet analysis performed on an AE waveform recorded around the softening point (black circled in (a), red circle in (b))

Grahic Jump Location
Fig. 4

(a) Stress–strain curve of prepreg combined with AE data, green arrow represents the deflection from linearity together with fibers breakage in high PF; (b) WPF versus PP3 for AE data; (c and d) wavelet analysis of two representative AE signal indicated by the red circles in (b) corresponding to WPF of ∼100 kHz (left) and ∼200 kHz (right)

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Fig. 3

(a) Stress–strain curve of S2 fiberglass bundle combined with AE data; (b) WPF versus PP3 for AE data; (c and d) wavelet analysis of a representative AE signal indicated by the red circle

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Fig. 2

(a) Stress–strain curve of Al 7475 T76 combined with AE data; (b) WPF versus PP3 for AE data; (c and d) wavelet analysis of a particular AE signal shown in blue (left) and red circle (rights), respectively

Grahic Jump Location
Fig. 10

Clustering results including (a) GLARE specimen, (c) GLARE specimen in the SEM, (d) prepreg, (e) fibers, and (f) aluminum; (b) classification criteria for the identification of the number of classes and the evaluation of the classification quality

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Fig. 11

Visualization of identified class in Fig. 9 as probable to correspond to fiber fracture using the actual test data for GLARE specimens at the lab scale (left) and SEM (right)

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Fig. 12

Fundamental acoustic emission features used a representative waveform and its fast Fourier transform



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