Paper Title:
Composite Materials Damage Characterization under Quasi-Static 3-Point Bending Test Using Fuzzy C-Means Clustering
  Abstract

In this study, acoustic emission (AE) monitoring with a Fuzzy C-Means (FCM) clustering is developed to detect the delamination process during quasi-static 3-point bending test on glass/epoxy composite materials. The main fracture mode that should be emphasized and has an effect on the residual strength of composite materials is delamination. The 3-point bending test simulates thrust force due to drilling process without backup plate. In this work, two types of specimen at different layups, woven [0,90] s and unidirectional [0] s, leading to different levels of damage evolution, were studied. Using acoustic emission monitoring can help to detect these fracture mechanisms. The obtained AE signals were classified using FCM. Dependency percentage of damages in each class is different in two specimens. Three parameters (Peak Amplitude, Count, and Average Frequency) were used to validate the FCM based classification. The results show that there is a good agreement with the FCM classification and microscopic observation by SEM.

  Info
Periodical
Chapter
Chapter 6: Composite Materials
Edited by
Wu Fan
Pages
1221-1228
DOI
10.4028/www.scientific.net/AMM.110-116.1221
Citation
F. Mohamad, H. Hossein, P. Farzad, M. Ahmadi Najaf Abadi, "Composite Materials Damage Characterization under Quasi-Static 3-Point Bending Test Using Fuzzy C-Means Clustering", Applied Mechanics and Materials, Vols. 110-116, pp. 1221-1228, 2012
Online since
October 2011
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Shan Wei, Chun Juan Ou Yang, Si Min Wei
Abstract:According to analyzing the different wavelet coefficients' transmission property of signals and noises under different scales of the wavelet...
569
Authors: Pan Fu, Wei Lin Li, Wei Qing Cao
Abstract:As one of the most common parts of various rolling mechanical equipments, rolling element bearing is vulnerable. Therefore, great attentions...
510
Authors: You Hang Zhou, Hui Guo, Yin Song Dong, Qi He
Abstract:To detect the quality of batch drilling quickly,a new approach based on Acoustic Emission signals is presented. The signals’ statistical...
877
Authors: Yin Sheng Zhang, Hui Lin Shan, Jia Qiang Li, Jie Zhou
Chapter 8: Nanomaterials and Nanomanufacturing
Abstract:The traditional K-means clustering algorithm prematurely plunges into a local optimum because of sensitive selection of the initial cluster...
1977
Authors: Yan Jun Cui, Yan Dong Ma, Jie Li, Zheng Zhao
Chapter 11: Artificial Intelligence
Abstract:A new algorithm for training radial basis function neural network (RBFNN) is presented in this paper. This algorithm is based on the dynamic...
1593