Characterization of Failure modes in CPRP Composites - An ANN Approach

Bhat, Chandrashekhar and Bhat, M R and Murthy, C R L (2008) Characterization of Failure modes in CPRP Composites - An ANN Approach. Journal of Composite Materials, 42 (3). pp. 257-276.

[img] PDF
CB1-Journal of Composite Materials-Feb 2008.pdf - Published Version
Restricted to Registered users only

Download (19MB) | Request a copy


Carbon fiber reinforced polymer (CFRP) composite specimens with different thickness, geometry, and stacking sequences were subjected to fatigue spectrum loading in stages. Another set of specimens was subjected to static compression load. On-line acoustic Emission (AE) monitoring was carried out during these tests. Two artificial neural networks, Kohonen-self organizing feature map (KSOM), and multi-layer perceptron (MLP) have been developed for AE signal analysis. AE signals from specimens were clustered using the unsupervised learning KSOM. These clusters were correlated to the failure modes using available a priori information such as AE signal amplitude distributions, time of occurrence of signals, ultrasonic imaging, design of the laminates (stacking sequences, orientation of fibers), and AE parametric plots. Thereafter, AE signals generated from the rest of the specimens were classified by supervised learning MLP. The network developed is made suitable for on-line monitoring of AE signals in the presence of noise, which can be used for detection and identification of failure modes and their growth. The results indicate that the characteristics of AE signals from different failure modes in CFRP remain largely unaffected by the type of load, fiber orientation, and stacking sequences, they being representatives of the type of failure phenomena. The type of loading can have effect only on the extent of damage allowed before the specimens fail and hence on the number of AE signals during the test. The artificial neural networks (ANN) developed and the methods and procedures adopted show significant success in AE signal characterization under noisy environment (detection and identification of failure modes and their growth). KEY WORDS: polymer matrix composites, fatigue,

Item Type: Article
Subjects: Engineering > MIT Manipal > Mechanical and Manufacturing
Depositing User: MIT Library
Date Deposited: 06 Dec 2014 07:01
Last Modified: 06 Dec 2014 07:01

Actions (login required)

View Item View Item