Author/Authors :
Addin, O. Laboratory of Advanced Technology - Institute of Advanced Technology, Malaysia , Sapuan, S. M. Universiti Putra Malaysia - Faculty of Engineering - Department of Mechanical and Manufacturing Engineering, Malaysia , Othman, M. Universiti Putra Malaysia - Faculty of Food Science and Technology - Department of Food Service and Management, Malaysia
Abstract :
This paper intended to introduce the Bayesian network in general and the Naïve-bayes classifier in particular as one of the most successful intelligent systems to classify damages in composite materials. A method for feature subset selection has also been introduced. The method is based on mean and maximum values of the amplitudes of waves after dividing them into folds then grouping them by a clustering algorithm (e.g. k-means algorithm). The Naïve-bayes classifier and the feature subset selection method were analyzed and tested on sets of data. The data sets were conducted based on artificial damages created in quasi-isotopic laminates of the AS4/3501- 6 graphite/epoxy system and ball bearing of the type 6204 with a steel cage. The Naïve-bayes classifier and the proposed feature subset selection algorithm have been shown as very effective techniques for damage detection in composite materials
