Investigation on Damage Mechanisms of PE Self-reinforced Composites by Acoustic Emission Technology
DOI:
10.3993/jfbi09201206
Journal of Fiber Bioengineering & Informatics, 5 (2012), pp. 281-287.
Published online: 2012-05
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@Article{JFBI-5-281,
author = {Xu Wang and Song-Mei Bi},
title = {Investigation on Damage Mechanisms of PE Self-reinforced Composites by Acoustic Emission Technology},
journal = {Journal of Fiber Bioengineering and Informatics},
year = {2012},
volume = {5},
number = {3},
pages = {281--287},
abstract = {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.},
issn = {2617-8699},
doi = {https://doi.org/10.3993/jfbi09201206},
url = {http://global-sci.org/intro/article_detail/jfbi/4882.html}
}
TY - JOUR
T1 - Investigation on Damage Mechanisms of PE Self-reinforced Composites by Acoustic Emission Technology
AU - Xu Wang & Song-Mei Bi
JO - Journal of Fiber Bioengineering and Informatics
VL - 3
SP - 281
EP - 287
PY - 2012
DA - 2012/05
SN - 5
DO - http://doi.org/10.3993/jfbi09201206
UR - https://global-sci.org/intro/article_detail/jfbi/4882.html
KW - Damage Mechanisms
KW - PE Self-reinforced Composite
KW - Acoustic Emission
KW - Clustering Analysis
KW - Artificial Neural Network
AB - 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.
Xu Wang and Song-Mei Bi. (2012). Investigation on Damage Mechanisms of PE Self-reinforced Composites by Acoustic Emission Technology.
Journal of Fiber Bioengineering and Informatics. 5 (3).
281-287.
doi:10.3993/jfbi09201206
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