Evaluation of Welding Residual Stresses Using the Incremental Hole-Drilling Technique
p.768
p.768
Green and Sintered Properties of Consolidated Mixtures of Mechanically Alloyed and Elemental Al Powders
p.774
p.774
Assessment of Particle Clustering in MMCs by Quantitative Image Analysis
p.779
p.779
Wear Resistance of Powder Hard Metal Inserted Ductile Iron
p.784
p.784
Damage Mechanisms Identification in FRP Using Acoustic Emission and Artificial Neural Networks
p.789
p.789
Development of an Optic Fibre Sensor System for Acoustic Emission Sensing in FRP
p.794
p.794
On the Evaluation of the Mechanical Behaviour of Structural Glass Elements
p.799
p.799
A Multi-Level Low Cycle Fatigue Damage Model for Forging Die Material
p.804
p.804
Influence of the Interaction Potential Parameters on the Mechanical Response of Simulated Semi-Crystalline Polymeric Materials
p.810
p.810
Damage Mechanisms Identification in FRP Using Acoustic Emission and Artificial Neural Networks
Abstract:
In this study is proposed a procedure for damage discrimination based on acoustic emission signals clustering using artificial neural networks. An unsupervised methodology based on the self-organizing maps of Kohonen is developed considering the lack of a priori knowledge of the different signal classes. The methodology is described and applied to a cross-ply glassfibre/ polyester laminate submitted to a tensile test. In this case, six different AE waveforms were identified. The damage sequence could so be identified from the modal nature of those waves.
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Info:
Periodical:
Materials Science Forum (Volumes 514-516)
Pages:
789-793
Citation:
Online since:
May 2006
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