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Proceedings Paper

Comparative study of classification algorithms for damage classification in smart composite laminates
Author(s): Asif Khan; Chang-Kyung Ryoo; Heung Soo Kim
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Paper Abstract

This paper presents a comparative study of different classification algorithms for the classification of various types of inter-ply delaminations in smart composite laminates. Improved layerwise theory is used to model delamination at different interfaces along the thickness and longitudinal directions of the smart composite laminate. The input-output data obtained through surface bonded piezoelectric sensor and actuator is analyzed by the system identification algorithm to get the system parameters. The identified parameters for the healthy and delaminated structure are supplied as input data to the classification algorithms. The classification algorithms considered in this study are ZeroR, Classification via regression, Naïve Bayes, Multilayer Perceptron, Sequential Minimal Optimization, Multiclass-Classifier, and Decision tree (J48). The open source software of Waikato Environment for Knowledge Analysis (WEKA) is used to evaluate the classification performance of the classifiers mentioned above via 75-25 holdout and leave-one-sample-out cross-validation regarding classification accuracy, precision, recall, kappa statistic and ROC Area.

Paper Details

Date Published: 17 April 2017
PDF: 6 pages
Proc. SPIE 10167, Nanosensors, Biosensors, Info-Tech Sensors and 3D Systems 2017, 101671Q (17 April 2017); doi: 10.1117/12.2257296
Show Author Affiliations
Asif Khan, Dongguk Univ. (Korea, Republic of)
Chang-Kyung Ryoo, Inha Univ. (Korea, Republic of)
Heung Soo Kim, Dongguk Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 10167:
Nanosensors, Biosensors, Info-Tech Sensors and 3D Systems 2017
Vijay K. Varadan, Editor(s)

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