Atlantis Press Paper Details: Feasibility Assessment of Support Vector Regression Models with Immune Algorithms in Predicting Fatigue Life of Composites

title:

Feasibility Assessment of Support Vector Regression Models with Immune Algorithms in Predicting Fatigue Life of Composites

publication:

JCIS-2006 Proceedings

part of series:

Advances in Intelligent Systems Research

ISBN:

978-90-78677-01-7

ISSN:

1951-6851

DOI:

doi:10.2991/jcis.2006.86 (how to use a DOI)

author(s):

Ping-Feng Pai, Wei-Chiang Hong, Feng-Min Lai, Jia-Hroung Wu, Shun-Lin Yang

corresponding author:

Ping-Feng Pai

publication date:

October 2006

keywords:

Support vector regression; immune algorithms; fatigue life prediction; composite materials

abstract:

Predicting fatigue life of composite materials is essential to increase reliability of manufacturing systems. The predicting techniques for fatigue life of composite materials are not widely investigated. The support vector regression (SVR) is an emerging forecasting technique and has been applied in many areas successfully. Therefore, this study attempts to examine the feasibility of SVR in predicting the fatigue life of composite materials. Additionally, immune algorithms (IA) are used to select three parameters of SVR models. An experimental data set from a laboratory was employed to depict the feasibility of develpoed SVRIA (support vector regression with immune algorithms) approach in predicting fatigue life of composite materials. Empirical results indicate that the SVRIA is a valid way in predicting fatigue life of composite materials.

© Atlantis Press. This article is distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.

full text:

JCIS06-FTT-55.pdf (62 K)

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title:		Feasibility Assessment of Support Vector Regression Models with Immune Algorithms in Predicting Fatigue Life of Composites
publication:		JCIS-2006 Proceedings
part of series:		Advances in Intelligent Systems Research
ISBN:		978-90-78677-01-7
ISSN:		1951-6851
DOI:		doi:10.2991/jcis.2006.86 (how to use a DOI)
author(s):		Ping-Feng Pai, Wei-Chiang Hong, Feng-Min Lai, Jia-Hroung Wu, Shun-Lin Yang
corresponding author:		Ping-Feng Pai
publication date:		October 2006
keywords:		Support vector regression; immune algorithms; fatigue life prediction; composite materials
abstract:		Predicting fatigue life of composite materials is essential to increase reliability of manufacturing systems. The predicting techniques for fatigue life of composite materials are not widely investigated. The support vector regression (SVR) is an emerging forecasting technique and has been applied in many areas successfully. Therefore, this study attempts to examine the feasibility of SVR in predicting the fatigue life of composite materials. Additionally, immune algorithms (IA) are used to select three parameters of SVR models. An experimental data set from a laboratory was employed to depict the feasibility of develpoed SVRIA (support vector regression with immune algorithms) approach in predicting fatigue life of composite materials. Empirical results indicate that the SVRIA is a valid way in predicting fatigue life of composite materials.
copyright:		© Atlantis Press. This article is distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.
full text:		JCIS06-FTT-55.pdf (62 K)