(untitled) (Journal of Fiber Bioengineering and Informatics, 2012)
The purpose of this study is to investigate the damage mechanisms in UHMWPE/LDPE laminated by Acoustic Emission (AE) technique. Model specimens are fabricated to obtain expected damage mechanisms during tensile testing. Then, relationship among AE descriptors is studied by hierarchical cluster analysis, and AE signals are classified by k-means algorithm. Finally, an Artificial Neural Network (ANN) is created and trained by various optimal algorithms to identify damage mechanisms. The results reveal that typical damage mechanisms in PE self-reinforced composite can be classified in terms of the similarity between AE signals and identified by a well trained ANN.