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

Author:

Carmi Rami1,Wisner Brian2,Vanniamparambil Prashanth A.3,Cuadra Jefferson4,Bussiba Arie5,Kontsos Antonios6

Affiliation:

1. Department of Materials, NRCN, Beer-Sheva P.O.B 9001, Israel e-mail:

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

3. Corning Inc., Corning, NY 14831 e-mail:

4. Lawrence Livermore National Laboratory, Livermore, CA 94550 e-mail:

5. Ben-Gurion University of the Negev, Beer Sheva 84105, Israel e-mail:

6. Department of Mechanical Engineering and Mechanics, College of Engineering, Drexel University, Philadelphia, PA 19104 e-mail:

Abstract

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.

Publisher

ASME International

Subject

Mechanics of Materials,Safety, Risk, Reliability and Quality,Civil and Structural Engineering

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