Abstract
This paper presents the mechanical characterisation of sandwich composites. Different specimen configurations have been tested with a three-point bending load and their mechanical behavior has been discussed. In addition, the acoustic emission technique was used to detect the onset of damage mechanisms and to monitor their evolution. The proposed analysis is based on processing recorded acoustic emission bursts. An unsupervised classification approach, combining the k-means algorithm with Principal Component Analysis (PCA), is used to group the detected acoustic events. The cluster analysis of the acquired data allows for correlation with the damage mechanisms occurring in sandwich composites. In addition to the advantages of multivariate data analysis, the results highlight the influence of sensor placement on the analysis of damage mechanisms is investigated. A suitable sensor configuration is proposed to improve the detection of acoustic emission activity. The originality of this work lies in the combined mechanical–AE interpretation that provides new insight into the damage behaviour of both a synthetic and a bio-based sandwich material. The comparative analysis of these two types of materials, coupled with a dedicated evaluation of sensor placement effects on defect detection, offers a contribution not previously reported in the literature.