DAMAGE MODES CLUSTERING IN COMPOSITES BY USING OF ACOUSTIC EMISSION DATA

AE can be used to separate the disparate kinds of damage bearing in a constrained composite. Although, the main problem associated with data analysis is the segmentation of different AE sources. The target of the cluster analysis is to classify a set of data into several classes that reflect the internal structure of the data. Indeed, clustering method is a vital tool for investigating and interpreting data. The objective of this study is to utilize a mathematical procedure for the evaluation of damage mechanisms in HMPP /epoxy composite specimen with different configuration based on the analysis of the AE signals of presented. PCA is powerful tool, which utilized for the classification of the monitored AE transients. We worked on epoxyL160 resin, HMPP fibre bundle and HMPP/epoxy unidirectional specimens, subjected to tensile loading. Using model specimens exhibiting a predominant damage mechanism, correlations were established between the observed damage growth mechanisms and the AE parameter variances result in PCA. Outputs from this study revealed that the PCA is an effective tool for identifying damage modes such as matrix cracking, fibre/matrix debonding, fibre breakage and fibre pull-out in the HMPP/epoxy composites. The presence of damage modes in HMPP/epoxy composites was proven with SEM images

DAMAGE MODES CLUSTERING IN COMPOSITES BY USING OF ACOUSTIC EMISSION DATA Keywords:

Acoustic emission , High modulus polypropylene fibre , Damage mode , Principal component analysis ,