AN EXPERIMENTAL AND NUMERICAL INVESTIGATION IN TO DAMAGE MODES IN POLYPROPYLENE/POLYESTER COMPOSITES UNDER TENSILE LOADING USING AN UNSUPERVISED CLUSTERING METHOD OF ACOUSTIC EMISSION TRANSIENTS

The acoustic emission technique is a useful way for the investigation of local damage in materials. This study deals with the ability of Principal component analysis to classify recorded AE signals collected during tensile tests on cross-ply high modulus polypropylene/polyester composites in order to monitor the chronology of the damaging process. An unsupervised clustering analysis shows that AE signals are distributed into three clusters. In the present work, Principal component plot is applied as an unsupervised clustering method for the AE signals generated in cross-ply composite specimens during tensile tests. The input variables of the signal descriptors used in the clustering procedure are calculated from the signal waveforms. Outputs from this study revealed that the Principal component analysis is an effective tool for identifying damage modes such as matrix cracking, fiber/matrix debonding, fiber breakage and fiber pull-out in the composites. The presence of damage modes in composites was proven with scanning electron microscopy images

Keywords:

Acoustic emission , pripal component analysis , Damage mode , tensile loading , HMPP/polyester composite