Abstract
Acoustic emission (AE) is used for damage monitoring and health diagnosis of materials. Several experimental investigations have shown the aptitude of AE to identify signatures of damage mechanisms. Nevertheless, there is a lack of numerical modelling or simulation to understand the link between the source and the AE signals. Since the interpretation of data of AE measurements mainly relies on empirical correlation between the signal and the mechanical source, a detailed description of the effects of the different stages of the acquisition chain is still lacking. Moreover, the geometry of the specimen can strongly influence the propagation modes. In this study, we propose to model AE with the Finite Element Method, in order to investigate the effect of the type of damage, the geometry of the specimen and the piezoelectric sensor on the waves and on the AE parameters. After validating the model with an experimental pencil lead break, we perform a modal analysis on the numerical signals. This consists of identifying the excited modes for several sources using a 2D Fast Fourier Transform. The last part is devoted to the identification of pertinent descriptors with a perfect point contact sensor and with a resonant sensor.
Highlights
When a material undergoes stress, it can release transient elastic sound waves named AcousticEmission (AE) events
This approach often uses classification algorithms to gather signals into classes as a function of parameter values measured on the signals: each class is associated with a specific damage mechanism
We showed how method to model wave propagation in the frequency band typically used in Acoustic emission (AE)
Summary
When a material undergoes stress, it can release transient elastic sound waves named AcousticEmission (AE) events. When a material undergoes stress, it can release transient elastic sound waves named Acoustic. AE, as a non-destructive testing technique, monitors damage in materials and structures, usually thanks to piezoelectric sensors directly applied on the sample surface in order to capture these elastic waves. The signals were coordinated to the load level during cyclic fatigue and attributed to a damage mechanism. This approach often uses classification algorithms to gather signals into classes as a function of parameter values measured on the signals: each class is associated with a specific damage mechanism. The attribution of each class to a specific damage mechanism is mainly based on empirical approaches, and the validation of this labelling remains difficult and is still a challenge. Extracting the coding parameters from signals outputted by the AE system as a whole
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