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Acoustic emission signal processing framework to identify fracture in aluminum alloys

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journal contribution
posted on 10.07.2018, 13:08 by Brian Wisner, K. Mazur, V. Perumal, Konstantinos BaxevanakisKonstantinos Baxevanakis, L. An, G. Feng, A. Kontsos
Acoustic emission (AE) is a common nondestructive evaluation tool that has been used to monitor fracture in materials and structures. The direct connection between AE events and their source, however, is difficult because of material, geometry and sensor contributions to the recorded signals. Moreover, the recorded AE activity is affected by several noise sources which further complicate the identification process. This article uses a combination of in situ experiments inside the scanning electron microscope to observe fracture in an aluminum alloy at the time and scale it occurs and a novel AE signal processing framework to identify characteristics that correlate with fracture events. Specifically, a signal processing method is designed to cluster AE activity based on the selection of a subset of features objectively identified by examining their correlation and variance. The identified clusters are then compared to both mechanical and in situ observed microstructural damage. Results from a set of nanoindentation tests as well as a carefully designed computational model are also presented to validate the conclusions drawn from signal processing.


A. Kontsos would like to acknowledge the financial support received by the Office of Naval Research under the Young Investigator Program, Award #N00014-14-1-0571.



  • Mechanical, Electrical and Manufacturing Engineering

Published in

Engineering Fracture Mechanics


WISNER, B. al., 2019. Acoustic emission signal processing framework to identify fracture in aluminum alloys. Engineering Fracture Mechanics, 210, pp. 367-380.


© Elsevier


AM (Accepted Manuscript)

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This paper was accepted for publication in the journal Engineering Fracture Mechanics, and the definitive published version is available at