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Artificial Neural Network Analysis for Acoustic Emission Signal and its significance in failure characterisation of composites

Bhat, Chandrashekhar and Bhat, M R and Murthy, C R L (2010) Artificial Neural Network Analysis for Acoustic Emission Signal and its significance in failure characterisation of composites. Journal of Non destructive Testing & Evaluation.

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Abstract

Effective use of the versatility of acoustic emission (AE) technique as a Non-Destructive Evaluation (NDE) tool still remains a challenge. Development of artificial neural network (ANN) analysis during the last decade and its successful application in many fields have attracted many NDE scientists. Researchers have tried to use ANN in different· NDE techniques including AE signal analysis. Significant success has been achieved in monitoring and analysis of AE signals for assessing tool wear and surface roughness generation while machining, failure mode detection in composite materials, assessment of machining of composites, detection of crack growth in metallic structures, monitoring of highway bridges. In this paper, concepts and characteristics of ANN and the result of a new approach developed by the authors for using ANN in characterizing AE signal generated by damage processes in composite materials is presented.

Item Type: Article
Uncontrolled Keywords: Acoustic Emission, Artificial Neural Network, Signal analysis, Signal Processing, Composites.
Subjects: Engineering > MIT Manipal > Mechanical and Manufacturing
Depositing User: MIT Library
Date Deposited: 06 Dec 2014 05:29
Last Modified: 06 Dec 2014 05:29
URI: http://eprints.manipal.edu/id/eprint/141215

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