Abstract

One of the most pervasive types of structural problems in aircraft industries is fatigue cracking that can potentially occur without anticipation with catastrophic failures and unexpected downtime. Acoustic emission (AE) is a passive structural health monitoring (SHM) technique, since it offers real time damage detection based on stress waves generated by cracking in the structure. Machine learning techniques have presented great success over the past few years with a large number of applications. This study assesses the progression of damage occurring on glass fiber reinforced polyester composite specimens using two approaches of machine learning, namely, Supervised and Unsupervised learning. A methodology for damage detection and characterization of composite is presented. The result shows that machine learning can predict damages in composite materials. All predictive models and their performance as well as AE parameters had a direct relationship with the applied stress values, suggesting that these correlations are reliable means of predicting fatigue life in a composite material.