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

[+] Author and Article Information
Rami Carmi

P.O.B. 9001 Beer-Sheva, Israel P.O.B. 9001 Israel carmo_nm@netvision.net.il

Brian Wisner

3141 Chestnut Street Department of Mechanical Engineering and Mechanics Philadelphia, PA 19104 bjw63@drexel.edu

Prashanth A. Vanniamparambil

3141 Chestnut St Mechanical Engineering Department Philadelphia, PA 19104 prashanth288@gmail.com

Jefferson A. Cuadra

7000 East Ave PO Box 808, L229 Livermore, CA 94550 cuadra1@llnl.gov

Arie Bussiba

P.O.B. 9001 Beer-Sheva, P.O.B. 9001 Israel busarie@bezeqint.net

Antonios Kontsos

Department of Mechanical Engineering and Mechanics 3141 Chestnut St Philadelphia, PA 19 ak866@drexel.edu

1Corresponding author.

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

ASME 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, Glare (Glass Layered Aluminum Reinforced Epoxy), 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 manuscript presents a nondestructive approach to monitor damage at the material scale and combine such information with characterization and post mortem evaluation methods, as well as data post-processing 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 manuscript 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.

Copyright © 2019 by ASME
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