Abstract
This paper presents a new method of signal processing for vibro-acoustic modulation (VAM) methods in order to detect damage accumulation in steel samples. Damage in the tested samples was produced by cycle loading, which, with a small amplitude, was used as a pump wave to modulate an ultrasonic probe wave. Multiple sideband peaks were observed, which were used to characterize the modulation effect. We propose the effectiveness sideband peak number (SPN) method as an indicator of any damage accumulation when the load cycle is applied. Moreover, after comparing the SPN with the previously used modulation index (MI), we concluded that, for some of the samples, the SPN provided better damage indication than the MI. The presented results can be explained by a simple model of bilinear crack nonlinearity. This model demonstrates that the amplitude dependences of the sideband components on the pump and the probe wave amplitudes are very different from the quadratic crack model that is usually used for MI test explanation.
Highlights
In many nondestructive evaluation (NDE) methods, nonlinear acoustics (NA) has a higher sensitivity for crack detection than linear acoustic methods
This paper investigated the vibro-acoustic modulation (VAM) application for delamination detection in glass-fiber-reinforced polymer laminates, and sideband peak amplitudes were used as features in a machine learning classification algorithm
The modulation index (MI) was computed according to the VAM technique described in [35] in order to evaluate the remaining fatigue life using VAM signals
Summary
In many nondestructive evaluation (NDE) methods, nonlinear acoustics (NA) has a higher sensitivity for crack detection than linear acoustic methods. Sideband Peak Count in a Vibro-Acoustic Modulation Method for Crack Detection. The majority of VAM techniques consider the level of sideband components with sum and difference frequencies as an indicator of nonlinearity.
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