Acoustic emission signal processing framework to identify fracture in aluminum alloys

• Published in: Engineering fracture mechanics Vol. 210; pp. 367 - 380
• Main Authors: Wisner, B., Mazur, K., Perumal, V., Baxevanakis, K.P., An, L., Feng, G., Kontsos, A.
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.04.2019; Elsevier BV
• Subjects: Acoustic emission; Acoustic emission testing; Acoustic noise; Aluminum alloys; Aluminum base alloys; Emission analysis; Fracture; Mechanical behavior; Microscopy; Nanoindentation; Nondestructive testing; Signal processing; Acoustic emission; Aluminum alloys; Mechanical behavior; Fracture; Microscopy
• ISSN: 0013-7944; 1873-7315
• DOI: 10.1016/j.engfracmech.2018.04.027

•Acoustic emission related to fracture in an aluminum alloy is reported.•In situ experiments inside the scanning electron microscope are combined with a novel AE signal processing framework.•Nanoindentation tests are used to validate acoustic emission information.•A Crystal Plasticity Finite Element Analysis coupled with a XFEM model is used to explain the targeted fracture events.•The framework is applied in both monotonic and cyclic tests. 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.