ANNs were trained on a portion of the data acquired by the sensors and the ANNs were then validated with the remaining data. Several experiments were performed to determine the severity and location of the crack extensions in the structure.
Strain wave signals, measured by acoustic emission sensors, were further analyzed in post-processing by artificial neural networks (ANN). As the loading increased, designed cracks extended in length, releasing strain waves in the process.
Simple structural elements, consisting of flat aluminum plates of AL 2024-T3, were subjected to increasing static tensile loading. A novel method of implementing artificial neural networks and acoustic emission sensors to form a structural health monitoring (SHM) system for aerospace inspection routines was the focus of this research. Consequently, maintenance is an important issue. Aerospace systems are expected to remain in service well beyond their designed life.
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