Identification of damage mechanisms in self-reinforced polyethylene composites by using pattern recognition techniques on AE data

Acoustic emission (AE) signals collected from thermoplastic self-reinforced polyethylene composites ultra-high molecular weight polyethylene fibre reinforced low-density polyethylene (UHMWPE/LDPE) under quasi-static tensile load were clustered and identified by unsupervised pattern recognition (UPR) and supervised pattern recognition (SPR) techniques in order to clarify various damage modes in the composites. The purpose was to find an easy way to separate a set of data with a large number of unknown AE signals into several classes attributed to a specific damage mode each. UPR techniques were utilised first to classify the AE signals from simple lay-up laminate specimens automatically and mathematically. Different damage modes were identified and a physical validation was carried out by the scanning electron microscope (SEM) technique. Damage investigation of the specimen according to the clustering results showed reasonable results. Therefore, the labelling data set consisting of signals from different damage modes was used as the reference for a SPR system. A large number of AE signals from quasi-isotropic laminates were then identified by the supervised method. It showed good results, which were also supported by the SEM examination. A reliable and convenient procedure was established for a good identification of large number of unknown AE data for the UHMWPE/LDPE composites. This methodology is promising for any other fibre reinforced composites in the field of damage mechanisms analysis.