Abstract
The objective of present study is to classify and identify damage mechanisms in polyethylene(PE) self-reinforced composites by acoustic emission (AE) technique. Model specimens including LDPE resin, [90°]laminate, single fiber composite, fiber bundle composite, and [±45°] laminates are fabricated to obtain expected damage mechanisms during tensile testing. First, mechanical behaviors and corresponding AE response of model specimens are studied to validate damage mechanisms in UHMWPE/LDPE laminates. Second, relationship among AE descriptors is investigated by hierarchical cluster analysis, and AE signals are classified by k-means cluster analysis. Correlations between damage mechanisms and AE are established in terms of amplitude, duration, and peak frequency of AE signals. Finally, an artificial neural network 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 trained artificial neural network. POLYM. COMPOS., 2011. © 2011 Society of Plastics Engineers
Published Version
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