Ensemble Feature Selection for Clustering Damage Modes in Carbon Fiber-Reinforced Polymer Sandwich Composites Using Acoustic Emission

Abstract

Acoustic Emission

In article number 2400317, Abdulkadir Gulsen and co-workers present a novel ensemble feature selection methodology to rank features relevant to damage modes on AE signals in CFRP sandwich composites. Subsequently, ranked features are utilized in unsupervised clustering models to identify damage modes. The comparative results demonstrate that, in addition to the commonly used features, other features, like partial powers, have a robust correlation with damage modes.