Damage Assessment in Bonded Joints Through Wavelet Decomposition of Acoustic Signals and Classification with Artificial Inteligence Algorithms
Keywords:
Bonded joints, wavelet decomposition, machine learningAbstract
Structural adhesives are an alternative to traditional joints, but their integrity can be compromised by defects during application or curing. To ensure reliability, non-destructive testing (NDT) techniques are essential, with acoustic–ultrasonic methods proving particularly useful. This work proposes an approach based on wavelet decomposition of acoustic signals to extract features that, through artificial intelligence algorithms, enable the automatic detection of damage in adhesive joints. The methodology aims to improve accuracy in fault identification and provide an efficient tool for structural health monitoring.
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